productivity differences in hungary and mechanisms of tfp

Written by Balaacutezs Murakoumlzy Maacuterta Bisztray and Balaacutezs Reizer (CERS HAS) 2018

Productivity differences in

Hungary and mechanisms of

TFP growth slowdown

EUROPEAN COMMISSION

Directorate-General for Internal Market Industry Entrepreneurship and SMEs

Directorate A mdash Competitiveness and European Semester Unit A2 mdash European Semester and Member Statesrsquo Competitiveness

Contact Tomas Braumlnnstroumlm

E-mail GROW-A2eceuropaeu

European Commission B-1049 Brussels

EUROPEAN COMMISSION


2018

PRODUCTIVITY DIFFERENCES

IN HUNGARY AND

MECHANISMS OF TFP GROWTH

SLOWDOWN

LEGAL NOTICE

This document has been prepared for the European Commission however it reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein

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Luxembourg Publications Office of the European Union 2018

ISBN 978-92-79-73462-5 doi 10287333213

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Productivity differences in Hungary and mechanisms of TFP growth slowdown

Table of contents

EXECUTIVE SUMMARY I

1 INTRODUCTION 1

2 DATA SOURCES 4

21 Cleaning the data and defining industry categories 5

22 Productivity estimation 6

23 Estimation sample 10

24 Firm-level variables 13

25 Industry categorization 16

3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON 18

31 Convergence 18

32 Within-industry heterogeneity 24

33 Firm dynamics 28

34 Conclusions 31

4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION 32

41 Context 32

42 Aggregate productivity and the self-employed 33

43 The evolution of productivity distribution in Hungary 36

44 Duality in productivity and productivity growth 47

45 Conclusions 56

5 ALLOCATIVE EFFICIENCY 58

51 Olley-Pakes efficiency 58

52 Product market and capital market distortions 62

53 Conclusions 70

6 REALLOCATION 73

61 Reallocation across industries 73

62 Reallocation across firms 76

63 Failure of reallocation Zombie firms 82

64 Conclusions 86

7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS 89

71 Productivity growth 89

72 Employment growth 95

73 Entry and exit 99

74 Conclusions 103

8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE 104

81 Context 104

82 Data 108

83 General trends 110

84 Allocative efficiency and reallocation 118

85 Trade 123

86 Policies Crisis taxes 131

87 Policies Mandatory Sunday closing 133

88 Conclusions 141

9 CONCLUSIONS 142

REFERENCES 144

APPENDIX 152

A3 Chapter 3 Internationally comparable data sources and methodology 152

A31 EU KLEMS amp OECD STAN 152

A32 OECD Structural and Demographic Business Statistics 152

A33 OECD Productivity Frontier 153

A4 Chapter 4 Evolution of the Productivity Distribution 154

A5 Chapter 5 Allocative Efficiency 160

A6 Chapter 6 Reallocation 171

A7 Chapter 7 Firm-level productivity growth and dynamics 175

A71 Productivity growth 175

A72 Employment growth 179

A73 Entry and exit 184

A8 Chapter 8 Retail 187


i

EXECUTIVE SUMMARY

Slow post-crisis total factor productivity (hereafter TFP) growth is a significant policy

challenge for many European countries in general and for Hungary in particular This

report aims at providing a comprehensive analysis of the processes behind productivity

growth slowdown in Hungary based on micro-data from administrative sources between

2001-2016

In particular the report aims to contribute to four ongoing debates First it attempts to

document the productivity growth slowdown in detail to uncover potential sources of

heterogeneity Besides documenting differences across industries it also makes an effort

to identify how the whole shape of the productivity distribution evolved along different

dimensions A focus on the whole distribution is motivated inter alia by recent findings

that in many countries productivity slowdown has resulted from a combination of healthy

productivity growth of frontier firms coupled with an increasing gap between frontier and

non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case

in Hungary (OECD 2016) where frontier firm productivity growth has actually been

similar to or slower than that of other firms Understanding the exact detail of this

phenomenon is of much interest given that slow frontier firm productivity growth

necessitates different policies from those that intend to close the gap between frontier and

non-frontier firms

The second overarching question related to frontier and non-frontier firms is the idea of

the so-called duality in Hungary The concept of duality emphasises the large differences in

terms of productivity and wages between globally oriented often foreign-owned large

firms and the rest of the economy Duality also refers to the lack of interconnectedness

between these two groups of firms in terms of supplier-buyer linkages and worker flows

which limits positive intergroup spillovers One version of the duality concept also asserts

that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a

number of methods and perspectives to provide evidence for the different dimensions of

this duality and investigate whether there is evidence for a narrowing gap Duality is an

important concept motivating many economic policy decisions therefore understanding its

causes and evolution is of considerable policy interest

The third group of questions relates to how efficiently resources are allocated across firms

Similarly to other countries within-industry productivity differences are at least a

magnitude larger than between-industry differences This implies that the efficiency of the

allocation of resources within an industry (ie whether more productive firms have access

to more labour and capital) matters much for aggregate productivity Two recent

developments might have affected allocative efficiency First the crisis put an immense

pressure on financial intermediation which could have distorted capital allocation decisions

(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools

some of which are size-dependent or target only a subset of firms within an industry

Finally the report is interested in the extent to which sectors and industries differ in terms

of productivity and firm dynamics One useful distinction here is between the traded and

non-traded sectors of the economy In traded sectors international competition can

provide powerful incentives for firms to invest into more productive technologies and

competitive pressure can also drive a more efficient allocation of resources by providing

opportunities for more efficient firms to grow and by forcing less efficient firms out of the

market Another operative distinction between industries is the role of knowledge in

production Knowledge-intensive sectors may exhibit different dynamics thanks to the

more significant role of technological differences and change

Executive Summary

ii

In the paper we use a number of different approaches to shed light on the various aspects

of these overarching research questions The basis of our research is a set of

administrative micro-data of all double-entry bookkeeping enterprises in Hungary We

introduce these data in Chapter 2 of this report in detail The database provides an

exhaustive picture of Hungarian businesses It is important to keep in mind though that it

omits two important parts of the economy the overwhelming majority of the non-market

sector (including public works) and the self-employed Given the number of people

employed in these two sectors their performance has a strong effect on macro numbers

The available albeit scarce data for the self-employed qualify the findings by suggesting

that the measured productivity level and growth of this group is considerably below than

that of double-entry bookkeeping firms ndash implying that within-industry productivity

dispersion may be even larger than what is indicated by the balance sheet data

Chapter 3 provides a context for our investigation by presenting internationally

comparable micro-data based information on different dimensions of productivity levels

growth dispersion and dynamics These comparisons primarily illustrate that Hungarian

productivity developments and patterns are well within the range found in similar

countries but in some respects ndash including the extent of productivity slowdown or the

relatively low entry and exit rates ndash they differ markedly from the averages of the

countryrsquos peer group OECD data also reveal that the level of productivity is relatively low

even at the top of the national distribution Hungarian frontier firms lag considerably

behind the global frontier

Chapter 4 analyses how the shape of productivity distribution evolved by reporting

productivity dynamics in the different deciles This analysis confirms that within-sector

productivity dispersion is indeed many times larger than across-industry differences The

analysis also reveals that in most industries frontier firmsrsquo productivity increased at

similar or lower rates than that of other deciles of the productivity distribution This makes

Hungary an exception from the general pattern of divergence between frontier and other

firms The report suggests that the main reason for this is that most of the Hungarian

frontier firms are far away from the global frontier

A similar approach reveals the importance of duality in terms of ownership About 50

percent of frontier firms are foreign-owned and on average they are 30 percent more

productive and pay 70 percent higher wages than domestically-owned firms The report

also finds little evidence for convergence This gap between averages however does not

imply a complete separation between the two groups many domestically-owned firms are

more productive than the typical foreign firm and vice versa The productivity distribution

of foreign firms is more dispersed than that of the domestically-owned showing more

technological heterogeneity within this group Regarding the distinction between private

and public enterprises ndash another possible dimension of duality ndash the report finds that

there are relatively few state-owned firms in Hungary and they are mainly concentrated in

utilities The performance of these firms lags behind privately-owned firms and the gap

has not been decreasing

The large productivity dispersion in the report motivates the analysis of allocative

efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent

to which more productive firms possess more resources and applies the Hsieh-Klenow

(2009) methodology to distinguish between product and capital market distortions Both

approaches suggest significantly higher efficiency in traded sectors Static allocative

efficiency varies substantially across industries but appears to be quite persistent with

little change during the period under study The strain on financial intermediation that

accompanied the crisis increased the misallocation of capital Not only did the implicit cost

of capital increase on average its rise was disproportionally larger for young firms

potentially constraining the reallocation process by the growth of new enterprises


iii

The static analysis of allocative efficiency is complemented by a dynamic approach to

productivity decomposition in Chapter 6 Reallocation across industries played a relatively

small role in aggregate productivity growth throughout the period under study most

productivity increase resulted from within-industry developments Pre-crisis within-

industry growth was dominated by reallocation but within-firm productivity growth was

also substantial During the crisis a large within-firm productivity decline was only partly

counterbalanced by reallocation across firms Post-crisis the contribution of the

reallocation process deteriorated significantly adding little to aggregate productivity

growth In particular globally integrated firms contributed a lot to productivity growth pre-

crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar

source for the failure of the reallocation process namely the survival of large

permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10

percent of all employees in most years even before the crisis One can however observe

some improvement in recent years in this respect

While the investigation of allocative efficiency and reallocation uses micro-data based

industry-level measures Chapter 7 examines these processes at the firm-level by relating

productivity to future productivity and employment growth as well as entry and exit This

approach can control for both industry- and firm-level heterogeneity Although these

dynamic processes are remarkably similar before and after the crisis the analysis reveals

characteristic differences between globally engaged and domestic-oriented firms relevant

for the duality debate In particular foreign firms near the Hungarian productivity frontier

seem to be able to increase their productivity further while similarly productive domestic

firms find such improvements much harder to achieve In terms of reallocation exporting

firms grow significantly faster than non-exporters (even of the same productivity)

suggesting reallocation to exporters

Besides presenting the trends in the full market economy a specific industry retail trade

is analysed in detail in Chapter 8 A key pattern observed in that industry is a

characteristic trend break around the crisis The pre-crisis period was characterized by

increasing concentration resulting from the expansion of large chains and foreign firms

These trends seem to have stopped or slowed down after the crisis In line with this

pattern the contribution of reallocation decreased post-crisis relative to earlier periods

While many factors can play a role in such a pattern it may be related to the different

size-dependent policies introduced after 2010 While smaller retail firms may benefit from

these developments consumers may face higher prices in the long run

The retail and wholesale sectors are also of interest as they play a large and increasing

role in mediating imports and exports for the market economy There was a large increase

in goods imported directly by retailers rather than indirectly via wholesalers This was

mainly driven by large foreign firms and may have benefited their consumers thanks to a

lower degree of double marginalisation and a wider choice Both the number of exporting

firms and the amount exported by wholesalers and retailers have increased most likely

benefitting from easy access to the Common Market and the opportunities provided by e-

commerce Exports by wholesalers and retailers can be an important channel for smaller

producers to reach foreign markets more easily

The results of this report confirm that Hungary is atypical because of the relatively poor

productivity performance of frontier firms Importantly contrary to a strong version of the

duality concept this is not a result of Hungarian frontier firms being on the global frontier

typically they are quite far away from it This robust pattern underlines that besides

helping non-frontier firms policy may also have to focus on the performance of the

frontier group A transparent environment with a strong rule of law complemented by a

well-educated workforce and a robust innovation system is key for providing incentives to

invest into the most advanced technologies

Executive Summary

iv

The analysis in this report reinforces the impression that there is a large productivity gap

between globally engaged or owned and other firms the gap being about 35 percent in

manufacturing and above 60 percent in services This gap seems to be roughly constant in

the period under study The firm-level analysis in Chapter 7 also reveals that one of the

mechanisms which conserves the gap is that foreign frontier firms are able to increase

their productivity more than their domestic counterparts even from frontier levels These

findings reinforce the importance of well-designed policies that are able to help domestic

firms to catch up with foreign firms A key precondition for domestic firms to build linkages

with foreign firms and to benefit more from their presence is a high level of absorptive

capacity High skills and an efficient innovation system can support this aim as well A

more specific conclusion is the importance of enabling high-productivity domestic firms to

improve their productivity levels even further

The large within-industry productivity dispersion the relatively low (though not extreme

in international comparison) allocative efficiency documented in some of the industries the

strong positive contribution of reallocation to total TFP growth before the crisis and the

relatively low entry rate imply that policies promoting reallocation have a potential to

increase aggregate productivity levels significantly These policies can include improving

the general framework conditions by cutting administrative costs reducing entry and exit

barriers and using a neutral regulation The fact that capital market distortions still appear

to be significantly above their pre-crisis levels impliesthat policies that reduce financial

frictions may help the reallocation process The fact that exporters tend to expand faster

relative to non-exporters suggests that access to EU and global markets generate a strong

and positive reallocation effect

Throughout our analysis we have found significant differences across sectors In general

traded and more knowledge-intensive sectors fared better both in terms of productivity

growth and allocative efficiency The difference between traded and non-traded sectors

points once again to the importance of global competition in promoting higher productivity

and more efficient allocation of resources This also implies that adopting policies that

focus on innovation or reallocation in services may be especially important given the large

number of people working in those sectors The better performance of and reallocation into

more knowledge-intensive sectors underline the importance of education policies aimed at

developing up-to-date and flexible skills and the significance of innovation policies that

help to improve the knowledge base and the functioning of the innovation system

Available evidence suggests a wide gap in the productivity level and earnings of people

working at firms with at least a few employees and those working in very small firms or as

self-employed The latter category represents 30-50 percent of the people engaged in

some important industries Inclusive policies may attempt to generate supportive

conditions for these people by providing knowledge and training as well as helping them

find jobs with wider perspectives or set up a well-operating firm The large share of these

unproductive economic entities holds back productivity growth even at the macro-level

The specific analysis of the retail sector has shown a marked difference between the pre-

crisis period characterised by strong reallocation mainly via the expansion of large

foreign-owned chains and the post-crisis period with a stagnating share of large chains

This break is likely to be linked to post-crisis policies favouring smaller firms While halting

further concentration in a country with already one of the highest share of multinationals

in this sector can have a number of benefits in the long run it is likely to lead to higher

prices and lower industry-level productivity growth Policies should balance carefully

between these trade-offs Another key pattern identified is the increasing role of retailers

(and wholesalers) in trade intermediation both on the import and export side

Policymakers should encourage these trends and design policies which provide capabilities

for such firms to enter international markets probably via e-commerce


1

1 INTRODUCTION

Slow post-crisis TFP growth is a significant policy challenge for many European countries in

general and for Hungary in particular This report aims at providing a comprehensive

analysis of the processes behind productivity growth slowdown in Hungary based on

micro-data from administrative sources between 2001-2016





dimensions The focus on the whole distribution is motivated inter alia by recent findings





similar to or slower than that of other firms Understanding the exact details of this



non-frontier firms






























Introduction

2




introduce these data in Chapter 2 of this report in detail The database provides a very

detailed and comprehensive picture of the Hungarian business economy It is important to

keep in mind though that it omits two important parts of the economy the overwhelming

majority of the non-market sector (including public works) and the self-employed Given

the number of people employed in these two sectors their performance has a strong effect

on macro numbers The available albeit scarce data for the self-employed qualify the

findings by suggesting that the measured productivity levels and growth of this group are

considerably below those of double-entry bookkeeping firms ndash implying that within-

industry productivity dispersion may even be larger than what is indicated by the balance

sheet data













analysis also reveals that in most industries the frontier firmsrsquo productivity increased at


























3

of capital increase on average its rise was disproportionately larger for young firms









reallocation process deteriorated significantly contributing little to aggregate productivity




















characteristic trend break around the crisis The pre-crisis period was characterised by












firms and the amount exported by wholesalers and retailers increased most likely




Data Sources

4

BOX 21 AMADEUS and the NAV balance sheet data

An alternative and frequently used source of balance sheet data is the AMADEUS dataset

In this box we compare the data about Hungary with the dataset used in this report

namely the administrative NAV panel

AMADEUS is a firm-level dataset collected and issued by Bureau Van Dijk a Moodyrsquos

Analytics Company It contains comprehensive financial information on around 21 million

companies across Europe with a focus on private company information It includes

information about company financials in a standard format (which makes it comparable

across countries) directors stock prices and detailed corporate ownership structures

(Global Ultimate Owners and subsidiaries) Financial information on firms consists of data

from balance sheets profit and loss statements and standard ratios Non-mandatory cells

are however often missing (eg employment) Therefore the drawbacks of this

database are that it is not representative and that not all firms provide enough

information to analyse issues such as productivity or TFP

Table B21 shows the coverage of AMADEUS (the number of firms as a share of the firms

in the administrative NAV data) by year and size category In earlier years the AMADEUS

sample consisted of mostly large firms but even the coverage of larger firms was

relatively low Recently the expanding coverage has made the AMADEUS sample more

representative While the smallest firms are still undersampled the coverage of firms with

more than 5 employees has reached nearly 100 (In some cases it is even above 100

because of slight differences in the number of employees reported)

The two databases also differ in terms of the variables they include The NAV data are

more detailed in terms of assets and liabilities AMADEUS in contrast provides more

information on ownership It defines the Global Ultimate Owner (GUO) for each company

and analyses their shareholding structure Ownership share is given in percentages and

in addition the degree of independence is also given

Our main aim in this report is to estimate productivity and its change reliably and

representatively for different types of firms small and large This requires a decent

coverage of all types of firms and reliable information on their finances for a number of

periods Because of this we prefer to use the NAV database with its large and universal

coverage and the rich information on firm inputs and outputs

2 DATA SOURCES

The main database we use in this project is the balance sheet panel of Hungarian firms

between 2000-2016 The balance sheet dataset is an administrative panel collected by the

National Tax Authority (NAV formerly APEH) from corporate tax declarations The

database includes the balance sheet and profit amp loss statements of all double-entry

bookkeeping Hungarian enterprises between 2000 and 2016 (see Section 42 for a brief

discussion of the size and the performance of the not double-entry bookkeeping sector of

the Hungarian economy) Besides key financial variables the database includes the

industry code of the firm the number of its employees its date of foundation the location

of its headquarters and whether it is domestically- or foreign-owned for each year


5

21 Cleaning the data and defining industry categories

We have taken a number of steps to clean the key variables in the balance sheet panel

First we impute missing observations for firms with more than 10 employees in the

preceding and following years For continuous variables we use the average of the

previous and following year values For categorical variables we use the value from the

previous year Similarly we impute missing data using lagged values for two of the largest

firms in year 2016

Then a baseline cleaning is applied to the values of all the financial variables to correct for

possible mistakes of reporting in HUF rather than 1000 HUF or for extremely small or big

values in the data Employment and sales are cleaned of extreme values and outliers

Suspiciously large jumps followed by another jump into the opposite direction are

smoothed by the average of the previous and following years Regarding capital stocks we

use the sum of tangible and intangible assets Whenever intangible assets are missing we

input a zero

We deflate the different variables with the appropriate price indices from the OECD STAN

which includes value added capital intermediate input and output price deflators at 2-

digit industry level1

Regarding industry codes the database in general includes the 2-digit industry code of a

firm in each year based on the actual industry classification system 4-digit industry codes

are only available between 2000 and 20052 We harmonize to NACE Rev 2 codes by using

1 A few industries are merged in the EU-KLEMS We will call this 64 category classification ldquo2-digitrdquo

industry in what follows

2 The database available in the CSO which we will use for Task 3 includes 4-digit codes for all years

BOX 21 Amadeus and the NAV database (cont)

Table B21 Coverage by employment categories (AmadeusNAV)

Year 1 emp 2-5 emp

6-10 emp

11-20 emp

21-49 emp

50-249 emp

250 lt emp

Total

2004 005 028 092 105 160 312 642 043

2005 010 050 169 288 483 1066 2227 108

2006 017 087 315 553 966 1935 3632 192

2007 2209 3006 4384 5249 5743 6082 7412 3135

2008 098 324 951 1692 2840 4868 7827 576

2009 5962 6070 7217 7428 7831 7798 9336 6301

2010 2142 4685 7034 7540 8424 8228 9634 4175

2011 2277 4736 7064 7753 8521 8657 9681 4220

2012 9397 8298 9305 9484 9507 9159 10121 8990

2013 7274 8140 9423 9981 9747 9445 10312 8044

Notes This table shows the number of observations in AMADEUS as a percentage of observations in the

NAV data for each year-size category cell

Data Sources

6

concordances from Eurostat3 We use these harmonized codes whenever we define deciles

and the frontier or within-industry variables so that NACE revisions should not affect the

results Finally we split those firms which switch from manufacturing to services or vice

versa adding separate firm identifiers for the two periods4

22 Productivity estimation

From many perspectives the most robust and convenient measure of productivity is

labour productivity We calculate this variable simply as the log of value added per

employee At the same time the key shortcoming of labour productivity is that it does not

reflect the differences in capital intensity across firms Total Factor Productivity (TFP) aims

to control for this issue We estimate TFP with the method of Ackerberg et al (2015) ndash we

refer to it as ACF ndash which can be regarded as the state of the art In the Appendix we

also provide robustness checks using different productivity measures

Technically firm-level TFP estimation involves estimating a production function

119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)

where i indexes firms t indexes years 119871119894119905 is the number of employees and 119870119894119905 is the capital

stock of firm i in year t In this specification the residual of the equation 휀119894119905 is the

estimated TFP for firm i in year t 120573119897 and 120573119896 are the output elasticities in the production

function reflecting the marginal product of labour and capital and the optimal capital

intensity

Estimating firm-level production functions involves several choices First it is usually

important to include year fixed effects in order to control for macro- or industry level

shocks Second industries may differ in their optimal capital intensity ie the coefficients

of the two variables To handle this we estimate the production function separately for

each 2-digit NACE industry Third financial data reported by small firms may not be very

accurate Including them into the sample on which the production function is estimated

may introduce bias into that regression Hence we estimate the production functions only

on the sample of firms with at least 5 employees but also predict the TFP for smaller firms

Fourth the Cobb-Douglas production function may be too restrictive in some cases but it is

possible to estimate more flexible functions (eg translog)

A key problem with firm-level TFP estimation is that input use (119871119894119905 and 119870119894119905) can be

correlated with the residual TFP Consequently OLS estimation may yield biased

coefficients The bias arises from attributing part of the productivity advantage to the

higher input use of more productive firms A simple and robust solution for this issue is to

estimate the production function with a fixed effects estimator This method controls for

endogeneity resulting from unobserved time-invariant firm characteristics

3 Because of the changes in the Hungarian industry classification in 2003 and 2008 industry code harmonization is required The Hungarian industry classification system (TEAOR) corresponds to NACE Rev 1 between 1998 and 2002 to NACE Rev11 from 2003 to 2007 and to NACE Rev 2 from 2008 onwards The conversion of industry codes in 2000-2002 to NACE Rev 11 is relatively straightforward and efficient thanks to the 4-digit codes The conversion from NACE Rev 11 to

NACE Rev 2 is less so as 4-digit codes are only available until 2005 Hence for each firm we assume that its 4-digit industry remained the same in the period of 2005-2007 and use this 4-digit industry for the conversion After these conversions we clean industry codes ignoring those changes when firms switch industries for 1-3 years and then switch back This process leads to a harmonized 2-digit NACE Rev 2 code for each year

4 After industry cleaning this can only happen either at the beginning or the end of the period when the firm is observed or if the firm switches industry for a period longer than 3 years


7

A second and related problem is that input use can also be correlated with time-variant

productivity shocks This type of endogeneity is not corrected by the fixed effects

estimator More specifically managers (unlike economists analysing the balance sheet)

may observe productivity shocks at the beginning of the year and adjust the flexible inputs

(labour in our case) accordingly As a result we may falsely ascribe a productivity

improvement to the increase in labour input The recent best practice of handling this

issue is the control function approach in which one controls for the productivity shock by

using a proxy for it in an initial step The proxy is another flexible input usually materials

or energy use As we have reliable data on materials we will use that variable

In this report we rely on the method of Ackerberg et al (2015)5 Importantly with this

method the production function coefficient estimates are close to what is expected6 and

the returns to scale are slightly above one (typically between 1 and 12 see Figure 21)7 8

After estimating the coefficients we simply calculate the estimated TFP for firm i in year t

by subtracting the product of inputs and the estimated elasticities

119879119865119875119894119905 = 119871119899 119881119860119894119905 minus 120573 lowast 119897119899 119871119894119905 minus 120573 lowast 119897119899 119870119894119905 (22)

In this way we calculate a TFP level (rather than its value relative to year and industry

fixed effects) which is important when calculating productivity changes Note that the

calculated productivity changes are very similar to the logic of the Solow residual

When interpreting productivity estimates it is important to remember that both the labour

productivity and TFP estimates are value added-based measures In other words in cross-

sectional comparisons they show how many forints or euros (rather than cars or apples)

are produced with a given amount of inputs Therefore value added based productivity

reflects both physical productivity and markups9

5 We have estimated all of these with the prodest (Rovigatti and Mollisi 2016) command in Stata

6 Reassuringly Ackerberg et al (2015) themselves report some production function estimates using data from Chile and their estimated coefficients are similar to what we get 08-09 for labour and about 02 for capital

7 We also control for attrition of firms from the sample but this does not affect the estimates significantly

8 The Levinsohn-Petrin (2003) and Wooldridge (2009) production function estimates are less attractive Most importantly the estimated returns to scale are well below 1 typically between 07 and 08 These implausibly low returns to scale imply an implausibly high TFP for larger firms with their TFP advantage being many times their labour productivity advantage even though they employ much more capital per worker The implausibly low returns to scale strongly affect our calculations In such a framework if a firm doubles all of its inputs and outputs its estimated TFP increases by about 30 percent even though it transforms inputs into outputs in the same way In

productivity decompositions for example size and growth are mechanically related to TFP leading to overestimating allocative efficiency

9 Recent literature has emphasized the difference between value added-based (revenue) and physical productivity and has also proposed a number of methods to distinguish between the two (see Foster et al 2008 Hsieh and Klenow 2009 Syverson 2011 Bellone et al 2014 De Loecker and Goldberg 2014) Hornok and Murakoumlzy (2018) also apply such methods to investigate the markup differences of Hungarian importers and exporters

Data Sources

8

Figure 21 ACF production function coefficients

A) Manufacturing

B) Services


9

We take some additional steps to clean our raw productivity estimates First we winsorize

productivity at the lowest and highest percentile of the 2-digit industry-year-specific

distribution of firms with at least 5 employees We fill out gaps of 1 or 2 years in the

productivity variable by using linear approximation Finally we clean the productivity of

firms with at least 5 employees based on changes We smooth large 1-year jumps10 and

disregard productivity values if there is a large jump after entry or before exit11

Table 21 presents the average labour productivity and TFP by 1-digit NACE categories in

2004 and 2016

Table 21 Average productivity measures by 1-digit industry in 2004 and 2016

unweighted

Labour productivity Total factor productivity

2004 2016 2004 2016

NACE Description Mean Stdev Mean Stdev Mean Stdev Mean Stdev

B Mining 797 088 867 086 408 087 441 065

C Manufacturing 777 087 806 079 581 079 598 077

D Electricity gas steam 929 106 953 138 629 091 634 132

E Water supply sewerage waste

812 085 830 089 604 091 593 094

F Construction 773 080 803 072 620 071 646 066

G Wholesale and retail trade 804 102 825 090 652 093 678 081

H Transportation and storage 841 071 837 072 625 067 623 072

I Accommodation 710 075 752 080 594 068 640 071

J ICT 834 094 862 090 631 101 669 098

M Professional scientific and technical activities

815 087 844 088 636 087 673 088

N Administrative and support services

763 098 792 094 640 107 662 113

Total 789 095 815 087 620 090 647 087

Notes The sample is our main sample (see Section 23) including observations with at least 5

employees and with an estimated TFP

10 We replace 119910119905 with 119910119905minus1+119910119905+1

2 if abs(119910119905 minus 119910119905minus1)gt1 abs(119910119905+1 minus 119910119905minus1)le 05 abs(119910119905minus1 minus 119910119905minus2)le 03 timesabs(119910119905 minus

119910119905minus1) abs(119910119905+2 minus 119910119905+1) le 03 timesabs(119910119905 minus 119910119905minus1) where 119910119905 denotes a productivity measure in logs of year

t Corresponding conditions are modified to abs(119910119905+1 minus 119910119905minus1) le 1 abs(119910119905+1 minus 119910119905minus1) le 03 times abs(119910119905 minus119910119905minus1) in the second observed year and in the year before the last observed one

11 abs(119910119905 minus 119910119905minus1)gt15

Data Sources

10

23 Estimation sample

Next we introduce some restrictions to define our baseline sample As our aim is to focus

on the market economy we constrain our sample based on industry and legal form We

keep only the market economy according to the OECD definition dropping observations in

agriculture and in non-market services (NACE Rev 2 categories 53 84-94 and 96-99)

We also drop financial and insurance activities12 as well as observations for which industry

is missing even after cleaning

We also drop firms which functioned as non-profit budgetary institutions or institutions

with technical codes at any time during the observed period We also drop firms which

never reported positive employment We refer to the remaining sample as the baseline

sample

Our main sample used for most of the calculations and for the estimations consists of

observations with at least 5 employees a non-missing total factor productivity value and

no remaining large productivity jumps13 We refer to the resulting sample as our main

sample Excluding the smallest firms has multiple advantages First exclusion of small

firms reduces measurement error as the smallest firms are the most likely to misreport

Additionally one-employee firms cannot be told apart from the self-employed who create

a firm for administrative reasons but clearly do not operate as an ordinary firm The

existence of such firms as well as their financial variables are likely to be strongly

determined by the differential in the tax treatment of personal versus corporate incomes

Because of these reasons both productivity levels and productivity changes may be

measured with an excessive amount of noise for the very small firms and therefore we

exclude them from our main analysis

Table 22 shows the distribution of firms by size category in our baseline sample Clearly

our sample expands strongly between 2000 and 2004 which is mainly a result of legal

changes requiring a larger group of firms to use double-entry bookkeeping While this

expansion is the strongest for the smallest firms it also affects a large number of firms

with up to 20 employees This artificial `entryrsquo of firms can bias estimates of productivity

growth (yielding a negative composition effect) and its decomposition (a negative entry

effect) For this reason in many cases we will start our analysis in 2004

Figure 22 investigates how much the exclusion of very small firms matters It shows that

while the share of 0 and 1 employee firms was between 50 and 60 percent their share in

terms of employment and sales was only around 5-6 percent hence even after their

exclusion our sample captures much of the national output We however report

robustness checks for our main results with all firms with a positive number of employees

in the Appendix

12 We decide to drop the financial sector because of conceptual and measurement problems of defining the productivity of financial firms especially during the crisis It might also distort the aggregate results Dropping these firms also corresponds to the usual practice (eg McGowan et al 2017) However including financial firms does not have a significant impact on our main results

13 We exclude firms that had a log productivity change higher than 15 in absolute value at any one time We also exclude firms switching between manufacturing and services more than twice


11

Table 22 Distribution of firm size by employment categories

Year 0 emp 1 emp 2-4 emp 5-9 emp 10-19 emp 20-49 emp 50-99 emp 100 lt emp Total

2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739

2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987

2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568

2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376

2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880

2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844

2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671

2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709

2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318

2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113

2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104

2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509

2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335

2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665

2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678

2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519

2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727

Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742

Notes The sample is our baseline sample (see Section 23) also including observations without an

estimated TFP

Figure 22 The share of 0 and 1 employee firms in the number of firms employees and

sales

Data Sources

12

Table 23 shows the number of observations lost because of missing values cleaning and

sample restrictions compared to the original data Dropping firms based on industry and

legal form as well as firms which never report positive number of employees does not

reduce the sample considerably The baseline data contains about 23 of the firms in the

original data The coverage in terms of total employment or value added is even higher

While the reduced sample of firms with at least 5 employees contains only about 20 of

the original number of firms the coverage of total employment and value added is still

above 70 We lose an additional 4 of firms which have no estimated TFP (negative

value added or missing capital) or which have large TFP jumps over time The

corresponding reduction in employment and value added coverage is about 20 and 15

percentage points respectively14 In the main sample we capture almost 23 of the total

employment and value added which we have in the original data

Table 23 Change in sample size and coverage after introducing restrictions

Number of

firms

Total

employment

Total value

added

Original data (after imputing observations) 1000 1000 1000

Drop agriculture and missing industry 952 954 984

Drop non-market services 845 895 948

Drop based on legal form 844 885 946

Drop firms which never had positive

employment

708 885 935

Keep only market economy according to OECD 667 859 912

Drop financial and insurance activities 652 830 790

Baseline sample 652 830 790

Keep observations with at least 5 employees 196 726 723

Keep firms which have no big TFP jump and

observations with non-missing TFP

157 600 647

Main sample 157 600 647

Table 24 shows the share of observations in the main sample by 1-digit NACE industry

The industry composition is quite stable over time Wholesale and retail trade has the

largest share close to 13 followed by manufacturing (21-31) construction (13-14)

and professional scientific and technical activities (7-9) The largest decline over time

was in manufacturing (from 31 to 21) Construction transport and storage

accommodation professional scientific and technical activities and administrative and

support services increased their share by more than one percentage point

14 While this cleaning certainly drops a large number of firms this is standard practice when the aim is to capture and decompose aggregate dynamics


13

Table 24 The share of observations by industry

NACE Description 2000 2004 2008 2012 2016

B Mining 029 025 024 021 018

C Manufacturing 3085 2636 2352 2280 2122

D Electricity gas steam 021 027 026 025 024


103 112 114 127 098

F Construction 1263 1439 1447 1263 1375

G Wholesale and retail trade 3207 3026 2954 3005 3034

H Transportation and storage 479 551 606 642 683

I Accommodation 477 617 650 719 783

J ICT 351 328 396 403 400

M Professional scientific and

technical activities 688 691 859 915 891


297 547 572 601 572

Total 100 100 100 100 100



24 Firm-level variables

For the present analysis we create firm groups based on different firm characteristics In

this subsection we explain these groupings and provide descriptive statistics

The database includes information on direct ownership Based on this one can identify

firms which are domestically-owned15 foreign-owned or state-owned (including municipal

ownership) We identify a firm as foreign-owned if the foreign share is above 10 percent

Similarly we classify a firm as state-owned if the state-owned share is above 50

percent16 Based on these definitions in 2016 nearly 10 percent of firms were foreign-

owned while the share of state-owned firms was about 1 percent (Table 25) Both foreign

and state ownership is more frequent in larger firms therefore foreign and state share is

higher in terms of employment 373 percent of employees work in foreign-owned firms

and 66 percent in state-owned ones Foreign ownership was concentrated in mining and

manufacturing electricity generation and distribution trade and ICT State ownership was

high in electricity generation and distribution and in utilities The fact that state-owned

firms are concentrated in these two industries limits the possibilities of how the effects of

state ownership and the effect of the peculiarities of these highly regulated industries can

be distinguished from each other Therefore in most cases we will not present results

separately for state-owned firms (except for Section 44)

15 For brevity we will mainly refer to domestically-owned private firms simply as domestically-owned

16 Only 15 of firms with more than 10 percent foreign share report a foreign share between 10 and 51 percent Re-classifing them as domestic does not affect our main results

Data Sources

14

Table 25 Share of state- and foreign-owned firms with at least 5 employees 2016

A) Number of firms

NACE Sector Domestic Foreign State Total

B Mining 8228 1772 000 100

C Manufacturing 8432 1522 046 100

D Electricity gas steam 5631 1942 2427 100

E Water supply sewerage waste 6351 450 3199 100

F Construction 9746 192 062 100

G Wholesale and retail 8957 1006 037 100

H Transportation 9005 890 105 100

I Accommodation 9411 467 121 100

J ICT 8314 1541 145 100


8982 915 102 100


8991 798 211 100

Total 8937 953 110 100



B) Employment


B Mining 725 275 00 100








J ICT 424 546 30 100


650 331 20 100


681 258 61 100

Total 560 373 66 100



The data include direct information on export sales and we classify a firm as an exporter

in a given year if its export sales are positive Table 26 shows the share of observations

both by ownership (foreign or private domestic) and exporter status The distribution of

firms across the four groups is stable over time Overall 65-75 of the firms are owned

domestically and supply only the domestic market The share of foreign firms decreased

from 143 in 2000 to 96 in 2016 After an initial decline the share of exporters

increased from 26 in 2000 to 315 by 2016 More than 23 of the foreign firms export

while the same ratio for domestic firms is less than 13


15

Table 26 Yearly share of observations by ownership and exporter status

Year Foreign

exporter

Foreign

non-

exporter

Domestic

exporter

Domestic

non-

exporter

2000 92 51 168 690

2001 89 46 172 693

2002 84 43 173 701

2003 79 40 164 717

2004 71 36 157 736

2005 70 34 160 736

2006 69 34 163 734

2007 73 32 180 715

2008 74 34 186 706

2009 78 36 195 692

2010 77 34 202 687

2011 78 32 215 675

2012 81 31 229 659

2013 79 30 236 655

2014 74 33 233 660

2015 71 31 238 659

2016 70 26 245 658

Total 76 35 196 692


employees and with an estimated TFP state-owned firms excluded

Table 27 presents some baseline descriptive statistics for the four firm groups created by

ownership and exporter status We define age using the year of foundation of the firm On

average foreign exporter firms are the largest and the most productive Within both

categories exporter firms are older larger and more productive in line with similar

patterns in other countries17 We will analyse differences further in Section 44

17 See for example Bernard-Jensen (1999)

Data Sources

16

Table 27 Average characteristics by ownership and exporter status in year 2004 and

2016

Foreign exporter

Foreign non-exporter

Domestic exporter

Domestic non-exporter

Year 2004

N of employees 1385 511 451 165

(5689) (2410) (1396) (404)

Labour productivity 877 825 830 769

(101) (120) (087) (086)

TFP ACF 666 660 634 611

(111) (112) (091) (084)

Age

101 85 99 85

(42) (46) (43) (43)

Year 2016

N of employees 1619 338 344 151

(6246) (1257) (1290) (405)


(088) (115) (075) (080)

TFP ACF 696 684 651 639

(113) (109) (086) (080)

Age 160 105 149 124

(82) (75) (76) (75)


employees and with an estimated TFP state-owned firms excluded Standard deviations are in

parentheses

25 Industry categorization

As we have mentioned already the main industry identifier is the 2-digit NACE Rev 2

industry classification These are hierarchically ordered into 1-digit categories

These categories however do not always lend themselves to easy interpretation On the

one hand one may want to distinguish between different types of manufacturing activities

Here a key question concerns the knowledge intensity or the high-techness of the activity

On the other hand sometimes it is useful to aggregate some of the service activities to

obtain more easily interpretable results

In order to do this we use Eurostatrsquos high-tech aggregation of manufacturing and services

by NACE Rev 2 which we will call industry type18 Note that these sets of industries

include only activities carried out in market industries (ie 10 to 82 NACE Rev 2 industry

codes) When using these categories we do not include firms in non-market sectors like

education (85) or arts entertainment and recreation (90 to 93) (See Table 28)

We would like to point out that while the Eurostat categories clearly reflect the global

technology and knowledge intensity of each industry the actual activity conducted in a

given country may differ from the technology category of the industry This issue is highly

relevant in Hungary where MNEs in high-tech industries operate affiliates conducting

assembly activities in Hungary without much RampD or innovation Still we find this

categorization a good way of aggregating data but still preserving some heterogeneity

18 Retrieved from httpeceuropaeueurostatcachemetadataAnnexeshtec_esms_an3pdf


17

Table 28 Industry categorization

Manufacturing NACE Rev 2 codes

High-technology manuf 21 26

Medium-high technology manuf 20 27 to 30

Medium-low technology manuf 19 22 to 25 33

Low technology manuf 10 to 18 31 to 32

Services

Knowledge-intensive services (KIS) 50 to 51 58 to 63 64 to 66 69 to 75 78 80

Less knowledge-intensive services (LKIS) 45 to 47 49 52 55 to 56 77 79 81 82

Utilities 35 to 39

Construction 41 to 43

Productivity Trends Hungary in International Comparison

18

3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON

The main aim of this chapter is to summarize existing evidence on Hungarian productivity

trends based on internationally comparable databases which include either industry-level

or micro-aggregated information The specificities and similarities of Hungary to

comparable countries will both guide and frame our analysis in the remaining chapters

which use Hungarian micro-data

31 Convergence

The fundamental question regarding the productivity evolution of Hungary or other less

developed EU member countries is whether productivity catches up with the most

developed countries at least in the medium or long run We investigate such medium- or

long-run trends in this subsection by analysing the evolution of relative productivity which

is defined as the level of labour productivity compared to one of the key economies of the

EU Germany (at ppp exchange rates) Figure 31 presents such a comparison of the

labour productivity levels of Hungary the Czech Republic Poland and Slovakia We use the

OECD STAN database for this exercise and present trends for as many years as possible to

reflect long-run developments

Figure 31 Relative labour productivity level (Germany=100)

Notes Labour productivity is defined as value added at constant prices per number of persons

engaged Source Own calculations based on OECD STAN and GGDC Productivity Level Database The

market economy excludes real estate For more details see Appendix A3

Let us start with the evolution of aggregate labour productivity According to Figure 31 all

of these countries seemed to be on the road to convergence to frontier countries in terms

of labour productivity before the financial crisis In particular labour productivity in

Hungary increased from 50 percent of the German level in 1998 to 65 percent in 2008 A

similar pre-crisis convergence can be observed in all three comparator countries19

19 Note that TFP is not available for Hungary in the EU KLEMS after 2008 Therefore we restrict this

international comparison to labour productivity


19

Note that the labour productivity decline during the crisis does not show up in the above

figure because it also affected the baseline country Post-crisis Hungarian labour

productivity (relative to Germany) remained flat stabilizing at around 65 percent While

this is similar to the productivity evolution of the Czech Republic it differs remarkably from

Poland and Slovakia which were able to close their productivity gap relative to Germany

by about 5 percentage points between 2009 and 2015 This slowdown of aggregate

productivity growth and the lack of further convergence from previous levels is actually

the main motivation for this study

A key question is whether the slowdown characterises the whole economy or it is

constrained to some of the sectors or types of enterprises The first dimension is to

distinguish between the state sector and the market economy According to OECD STAN

non-market sectors accounted for about 27 percent of all employment in 201520 The

second panel of Figure 31 restricts the sample to the lsquomarket economyrsquo21 Interestingly

productivity differences relative to Germany are larger in the market economy compared

to the whole economy suggesting that the productivity levels of the public sector in the

two countries appear to be closer to each other In Hungary the relative productivity of

the market economy follows a very similar trend to the whole economy with about 10 pp

relative productivity increase between 1998 and 2005 and stagnation post-crisis With the

exception of Slovakia post-crisis productivity growth is also flat in the comparator

countries

Figure 32 Relative labour productivity in manufacturing and business services

Germany=100


engaged Source Own calculations based on OECD STAN Business services excludes real estate For

more details see Appendix A3

20 According to the EU KLEMS this share has remained more or less constant since 2003

21 This includes NACE Rev 2 Codes 5-82 except real estate (68)


20

The market economy can be further disaggregated into manufacturing and business

services (Figure 32) There is strong evidence of catching up in manufacturing between

1995 and 2008 when relative productivity increased by more than 10 percentage points

Relative productivity fell immediately after the crisis with positive growth after 2011

reaching pre-crisis (relative) levels by 2015 Comparator countries which started from

much lower levels caught up faster pre-crisis and faced a much smaller fall around the

crisis years In other words comparator countries have caught up with Hungary in terms

of manufacturing productivity but there is no evidence for a sharp break in the trend post-

crisis

This contrasts sharply with business services where a period of catch-up until 2005 was

followed by a substantial decline in relative labour productivity This is also in strong

contrast with the comparator countries where relative productivity of business services

either increased (Czech Republic and Poland) or stagnated (in Slovakia) Business services

appear to be a key source of aggregate productivity slowdown

Figure 33 presents productivity dynamics in four specific industries to substantiate the

more aggregated picture with some more concrete examples The first two examples are

manufacturing industries namely the textiles and the automotive industry The relative

productivity level of textiles stagnated during the crisis at quite low levels fell during the

crisis followed by some growth from 2012 In motor vehicles relative productivity

increased by nearly 10 percentage points relative to Germany between 2001 and 2009

followed by a significant fall around the crisis and a strong recovery from 2012 The

picture is also varied in services In retail and wholesale there had been some productivity

improvement before the crisis followed by a declining trend post-crisis Both the level and

dynamics of relative productivity compares unfavourably to the comparator countries In

professional services relative labour productivity had grown quickly until 2011 followed

by a declining trend

Figure 33 Relative labour productivity evolution (Germany=100)


engaged Source Own calculations based on OECD STAN For more details see Appendix 3


21

Similar observations can be made when analysing the relative productivity of all types of

industries (Figure 34) The difference in productivity levels relative to Germany tends to

be larger in manufacturing than in services Light industries have especially low relative

productivity levels In terms of productivity growth we see mostly positive trends in most

manufacturing industries and a less clear picture in services with a decline or stagnation

in many service industries

Figure 34 Labour productivity of different industries relative to Germany 2005 and 2015


engaged Source Own calculations based on OECD STAN For more details see Appendix A3

Even in countries and industries with a relatively low level of average productivity it is

possible that a segment of the economy operates at world-class levels or shows fast

convergence to that This possibility may be especially relevant in economies where a

number of large and probably foreign-owned firms operate together with many smaller

domestically-owned firms which is certainly the case in Hungary One approach to

investigate this possibility was suggested and implemented by the OECD (Andrews et al

2017) This approach builds on cross-country micro-data to calculate the productivity of

the most productive firms in the world (global frontier) and compare it with the

productivity of the most productive firms in a country (national frontier)

Figure 35 shows these comparisons based on the OECDrsquos calculations22 In particular the

horizontal axis shows how productive Hungarian frontier firms are relative to the global

22 We would like to thank Peter Gal and his colleagues in the OECD for sharing these data with us In

this version global frontier is defined as the top 10 percent most productive firms worldwide

while the national frontier is the top 10 percent within the country according to ORBIS See

Appendix 3 and Box 41 for details on these data


22

frontier (100 is the global frontier) while the vertical axis compares Hungarian and global

non-frontier firms The figures suggest a number of conclusions To start with the frontier

productivity gap is strongly associated with the non-frontier productivity gap showing that

in industries where the typical firms are of relatively low productivity so are the frontier

firms Importantly the slope of the fitted line (06) is well below 1 suggesting that on

average there is a smaller gap between a top global and a top Hungarian firm than

between a typical (non-frontier) global firm and a typical Hungarian firm This is in line

with the duality hypothesis

That said one has to emphasise that the picture does not support a ldquostrong versionrdquo of the

duality hypothesis ie that the best Hungarian firms operate at world-class productivity

levels Even in manufacturing Hungarian frontier firms typically produce 40-60 percent

less value added per employee compared to the global frontier (good examples are

machinery (28) and motor vehicles (29)) The smallest gaps appear in a few relatively

low-tech service industries (trade and repair of vehicles (45) or warehousing (52)) where

frontier productivity is actually above the global frontier23

The observation that such large productivity differences exist between global frontier and

Hungarian frontier firms even within relatively narrowly defined industries suggests that

the low relative productivity of the Hungarian market economy is not a consequence of

industry composition ndash it mainly results from within-industry gaps Importantly these

main patterns are very similar and independent of how productivity is measured (labour

productivity or TFP) namely they are not a consequence of capital intensity differences

Finally by and large there is no evidence for convergence of frontier firms to the global

frontier between 2009 and 201324 If anything the gap between the global and the

Hungarian frontier widened in this period while the difference between the global and the

Hungarian frontier was 34 percent in the median industry in 2009 it widened to 38 by

2013

23 Naturally this is likely to be the case in other similar countries Still in different discussions it is often supposed implicitly that the best Hungarian firms are indistinguishable from the global frontier

24 Prior to 20082009 the coverage of ORBIS the source for the OECD calculations is fairly limited for

Hungary hence those calculations are less reliable


23

Figure 35 Productivity of Hungarian frontier and non-frontier firms relative to firms in

other countries (2013)

A) Labour productivity

B) TFP

Notes The industry codes are 2-digit NACE Rev 2 codes We have omitted industries with only few

observations (less than 5 Hungarian frontier firms) in the case of labour productivity outliers we

ignored those where the HU frontier was measured to be more productive than 125 percent of the

global frontier (ICT real estate and office administration services) Note that there are fewer

observations regarding TFP than labour productivity Source Data provided by the OECD calculated

from Andrews et al (2017) For more information see Appendix 3


24

We can draw a number of conclusions from these calculations First while Hungaryrsquos

labour productivity had been catching up similarly to other CEE countries to more

advanced economies before the crisis there was a trend break after the crisis especially

compared to Poland and Slovakia Only part of the productivity slowdown could be

explained by a slowdown in non-market sectors but there is also a pronounced slowdown

in the market economy This is not the result of having a combination of a few firms with

world-class productivity and many less efficient SMEs ndash actually the productivity of

frontier firms is only about 40-50 percent of global leaders even in industries where the

Hungarian frontier consists of many multinational firms There is no evidence that

Hungarian frontier firms were catching up with global leaders between 2009 and 2015

32 Within-industry heterogeneity

Since the beginning of the 2000s with the availability of detailed micro-data sets at the

firm-level it has become clear that within-industry heterogeneity in terms of productivity

is significantly larger than heterogeneity differences across industries (Bernard et al

2003 Bernard et al 2007 Bernard et al 2012 OECD 2017) Many factors have been

proposed which may generate and sustain the observed large productivity differences

including managerial practices different quality of labour capital and knowledge as well as

a number of external factors The exact role of different factors is an active area of

research (Syverson 2011) Recent research also hints at increasing dispersion within

sectors (Berlingieri et al 2017b)

In 2011 the level of the p90p10 ratio (90th and 10th percentile of productivity

distribution) was high in Hungary relative to other OECD countries taking a value of 279

in manufacturing and 329 in services (Table 31) These numbers are in logs representing

about 20-fold differences These numbers are similar to Chile and Indonesia A similar

pattern emerges with respect to TFP

Table 31 Productivity p90p10 ratio by country (2011)

Country

Year 2011

Log LP 90-10 ratio Log MFP 90-10 ratio

Manufacturing Services Manufacturing Services

Australia 187 205 190 212

Austria 196 242 - -

Belgium 160 174 180 178

Chile 300 353 307 387

Denmark 146 196 132 180

Finland 117 138 119 134

France 135 181 140 178

Hungary 279 329 254 286

Indonesia 311 - 341 -

Italy 166 201 160 188

Japan 126 138 117 138

Netherlands 200 298 227 227

New Zealand 184 209 192 208

sNorway 173 217 187 208

Portugal 188 265 188 275

Sweden 145 186 159 245

Notes This is a reproduction of Table 6 from Berlingieri et al (2017a) Note that the OECD uses the

term lsquoMFPrsquo (Multi-factor productivity) in the same sense as we use TFP in this report

Second as seen in Table 32 similarly to other OECD countries the overwhelming

majority of productivity differences results from within- rather than across-sector


25

differences The share of within-sector differences is 79 in manufacturing and 99 in

services The manufacturing share is close to the average of the countries in the sample

while the services share is at the high end

Table 32 Share of within-sector variance in total LP dispersion by country (2011)

Country

Year 2011

LP Dispersion

Manufacturing Services

Australia 98 99

Austria 86 90

Belgium 76 88

Chile 90 97

Denmark 84 63

Finland 65 76

France 63 85

Hungary 79 99

Indonesia 79 -

Italy 82 65

Japan 75 89

Netherlands 80 71

Norway 83 73

Portugal 62 76

Sweden 53 74

Notes This is a reproduction of Table 7 from Berlingieri et al (2017a)

These figures suggest that within-industry productivity dispersion is relatively high in

Hungary but it is not out of the range of countries at a similar level of development Still

these overall dispersion measures may not capture the duality between firms of different

sizes and ownership Internationally comparable data regarding productivity of firms in

different size classes is available from the OECD Structural and Demographic Business

Statistics (Figure 36) Size is strongly associated with productivity large firms are 45

times and 18 times as productive as very small firms in manufacturing and services

respectively However large these premia are not out of the range of similar countries in

services it is very similar to other CEE countries while in manufacturing it is at the high

end of the distribution but not extreme

Another relevant pattern in Figure 36 is that productivity differences by size are very

different between CEE countries and Western European countries This observation may

partly reflect the importance of large and productive multinational firms in CEE countries

but can also be a more or less automatic consequence of the fact that firm size distribution

significantly differs between the two groups of countries (Figure 37) Typically the share

of very small firms is larger in less developed economies leading to a more skewed firm

size distribution Such a distribution which is associated with a larger number of small

firms within size classes (the majority of firms with 1-9 employees in CEE employs only 1-

2 employees) leads to larger differences across size classes and larger within-industry

productivity dispersion The massive share of very small firms in these countries also

reflects that many of the lsquomicro-enterprisesrsquo (with only 1-2 employees) do not operate as

proper firms they behave more like individual entrepreneurs


26

Figure 36 Value added per person employed by size class (1-9 persons employed=100)

A) Manufacturing

B) Services of the business economy

Notes Value added per person employed defined as value added at factor costs divided by the

number of persons engaged in the reference period Economic sector lsquoManufacturingrsquo comprises

Divisions 10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business

economyrsquo comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities

of holding companies Source OECD SDBS For more details see Appendix 3 Main sample for 2015


27

Figure 37 Firm distribution by size class (2015)

A) Manufacturing


Notes Only enterprises with at least one employee are included lsquoManufacturingrsquo comprises Divisions

10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business economyrsquo

comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities of holding

companies Source OECD SDBS For more details see Appendix 3 Main sample


28

The main conclusion from investigating within-industry differences across firms is that both

the productivity dispersion and the productivity advantage of large firms is indeed

relatively large in international comparison but these numbers are not radically different

from similar countries Nevertheless differences in firm size distribution between more

and less developed countries go a long way towards explaining the differences between

Western European and CEE countries

33 Firm dynamics

A potential reason for declining productivity growth may be weak dynamics including low

entry and exit rates as well as slower reallocation The OECD Structural and Demographic

Business Statistics database provides international comparisons of entry and exit rates and

their changes across countries (Figure 38 and Figure 39)

In general both exit and entry rates are higher in CEE countries relative to Western

European economies25 This stronger dynamism may reflect stronger growth but it is also

affected (in a mechanistic way) by the differences in firm size distribution Importantly in

a cross-section entry and exit rates are strongly correlated suggesting that they capture

the same general aspect of firm dynamics Services are more dynamic than

manufacturing once again partly because of the different size distributions

Within CEE countries entry and exit rates seem to be associated with productivity growth

(and level) Countries with stronger post-crisis productivity growth (Poland Slovakia and

Romania) exhibit significantly higher entry and exit rates while those with less dynamic

productivity growth (Hungary and the Czech Republic) have lower churning This provides

some evidence that lower entry and exit rates may be systematically related to the weaker

productivity performance of these countries We will take a more detailed look at the

relationship between entry and exit and productivity growth in Chapters 6 and 7

When comparing 2012 and 2015 the pictures provide evidence for increased entry and

decreased exit in parallel with recovery and better growth prospects Still entry rates

remain one of the lowest in CEE indicating that entry and dynamic young firms may

contribute less to productivity growth in Hungary compared to other CEE countries

25 Note that these OECD statistics include all enterprises (even those with no employees) hence

changes in the tax treatment of firms relative to individual entrepreneurs may affect measured

dynamics Also firm death is defined based on the rsquodeathrsquo of the legal entity which may happen

many years after stopping production For more information see Appendix 3


29

Figure 38 Birth rate of all enterprises

A) Manufacturing


Notes Birth rate is defined as the number of enterprise births divided by the number of enterprises

active in the reference period The population contains all enterprises including non-employers The

economic sector lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo

comprises Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry

classification Source OECD SDBS For more details see Appendix A3


30

Figure 39 Death rate of all enterprises

A) Manufacturing


Notes Death rate is defined as the number of enterprise deaths divided by the number of enterprises

active in the reference period The population contains all enterprises including non-employers

Poland has no available data for 2015 so the 2014 value is reported The economic sector

lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo comprises

Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry

classification Source OECD SDBS For more details see Appendix 3


31

34 Conclusions

In international comparison productivity slowdown after the crisis was especially severe in

Hungary both in manufacturing and services There are large productivity differences

within industries and also between small and large firms While these are at the high end

in international comparison they are not extreme compared to similar countries A

comparison to the global frontier suggests that even top Hungarian firms are significantly

behind top global firms in terms of productivity These facts provide a motivation for our

analysis of the evolution of the shape of the productivity distribution in Chapter 4

International comparison of firm dynamics suggests that ndash similarly to other CEE countries

ndash Hungarian industries are more dynamic than their Western European counterparts but

entry and exit rates in Hungary and the Czech Republic are below the average of CEE

countries This motivates our investigation of the contribution of entry and exit to

productivity growth in Chapters 6 and 7

Evolution of the Productivity Distribution

32

4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION

41 Context

The study of within-industry productivity differences is motivated by two concepts First

the OECD (2016) argues that one of the key issues of recent developments in productivity

growth is that there is a strong divergence between the productivity evolution of frontier

firms and other firms However this same publication reports that Hungary seems to be

an exception to this trend with slow productivity growth at the frontier and faster

productivity growth of less productive firms suggesting some within-industry catch-up

(Figure 41) We look into the particulars behind this phenomenon by following the

evolution of the average productivity of different deciles in the productivity distribution

Second as we have already mentioned a key concept of the Hungarian (and CEE) policy

debate is the lsquodualityrsquo of smalldomestically-owned and largeforeign-owned firms The

large gap between the two types of firms presents a challenge for policy but it also

indicates an opportunity for domestic firms to catch up with foreign firms which may use

more productive technology (still far in terms of productivity from the global frontier see

Chapter 31) The evolution of the productivity gap (or premium) between small and large

firms as well as domestic and foreign firms informs us about whether firms on the lsquowrong

sidersquo of the duality are able to catch up with the firms at the national frontier

The duality debate frames productivity differences partly as a consequence of the lsquomissingrsquo

medium-sized (domestic) firms Hsieh and Olken (2014) argue that in less productive

economies the full firm size distribution is shifted to the left because of the constraints on

the growth of small firms Thus according to this view the productivity difference is not a

result of too few medium sized firms but of too few firms which are not small

Figure 41 Divergence in labour productivity performance

A) Manufacturing


33

B) Non-financial Services

Notes This is a reproduction of Figure 16 from OECD (2016)

In this chapter we investigate how the shape of the productivity distribution evolved over

the years Section 42 contrasts the development of firms with other types of economic

entities Section 43 analyses how average productivity and productivity deciles evolved

while 44 investigates the duality based on size and ownership

42 Aggregate productivity and the self-employed

Before turning to the productivity distribution of firms it is worthwhile to describe how the

productivity level and evolution of firms ndash and in particular double-entry bookkeeping

enterprises ndash differ from other entities in particular the self-employed Given the large

number of people employed by those entities this exercise can reveal a lot both about

productivity dispersion and the drivers of aggregate productivity growth

Let us motivate this investigation by comparing aggregate statistics (derived from data

applicable to all people engaged in an industry) with patterns calculated from our NAV data

(which includes only double-entry bookkeeping firms) Figure 42 shows the labour

productivity growth reported by OECD STAN and the evolution of the average labour

productivity as calculated from the NAV data weighted by sales and employment (Figure

42) According to the Figure while these series co-move they do so with some

discrepancies While productivity dynamics in Manufacturing are very similar across all

samples the relationship is looser for services and for the market economy with the NAV

series notably exhibiting less pronounced post-crisis slowdown than the OECD STAN data


34

Figure 42 Cumulative labour productivity growth according to OECD STAN and the NAV

sample

There can be many reasons behind the differences between these series (see Biesebroeck

2008) but arguably one of the main factors is the discrepancy in the number of

employees in the two databases Firms in the full NAV database employed 24 million

people in 2015 compared with 286 million employed and 325 million lsquoengagedrsquo in the

market economy according to the OECD STAN One source of this difference may be that

while some unofficially employed workers report their true employment status in LFS

(Labour Force Survey) ndash which serves as the basis for our aggregate data ndash they do not

appear in any official registers and such the NAV data Benedek et al (2013) reaffirming

the statement compare LFS employment data with tax registers and show that 16-18

percent of jobs are not declared to the tax authorities

Even more importantly from our perspective the NAV data by definition includes no

information on the self-employed and typically small non-double-entry bookkeeping firms

operating under special taxation The distinct productivity dynamics of these two groups

along with changes in undeclared employment may explain another part of the difference

Obtaining direct information on this issue would be of great interest but acquiring it is far

from straightforward Some information on these entities is available from the Register of

Economic Organizations (Gazdasaacutegi Szervezetek Regisztere GSZR) which is available

between 2012 and 2015 Most importantly this database provides us with information on

the number of employees and sales updated annually This in and of itself does not allow

us to estimate productivity properly but with its help we can calculate a crude proxy

sales per employee for illustration

Table 41 reports26 the number of employees and the average sales per worker values for

three groups The first is the group of double-entry bookkeeping firms (ie the firms who

26 These tables were calculated as follows First we combined the GSZR and NAV databases for years 2012 and 2015 Observing that about 80 percent of the firms present in the NAV sample are also present in the GSZR register we restricted our sample to the entities who are listed in the GSZR so that our variables would be commensurable From this collection we selected those who


35

are present in the NAV data) the second is the category of the self-employed (ie those

who are registered as individual entrepreneurs) and the third category is that of lsquoother

firmsrsquo (ie entities who are registered as firms in the GFO (Gazdaacutelkodaacutesi Forma) coding

system but are not categorised as self-employed and are not following a double-entry

bookkeeping method) We distinguish between manufacturing and other industries of the

market economy27 We supply figures for the earliest and latest years for which data are

available The tables reveal two important observations

First according to the GSZR about 30 percent of reported employees in Manufacturing

and 50 percent of reported employees in other industries work outside the double-entry

bookkeeping group Importantly these numbers may be overestimates because the GSZR

may report the same person in multiple entities for example when they work part-time or

switch jobs within the year That said both the EU KLEMS and the GSZR suggest that a

large share of people work outside the double-entry bookkeeping group in the market

economy

Second while sales per worker is not drastically different between double-entry

bookkeeping firms and other firms the difference between firms and the self-employed is

between 6-10-fold This difference in sales per employee may represent 2-3-fold labour

productivity differences between people employed by firms and the self-employed on

average28

Third the dynamics of sales per worker differ markedly between double-entry

bookkeeping firms and other entities while it increased by 40 percent in the NAV sample

between 2012 and 2015 it stagnated for the self-employed This may results from a

number of factors ranging from composition effects changes in tax regulations or low

productivity growth Still the low measured productivity growth of this sector of the

economy may be an important factor behind the slower post-crisis aggregate productivity

growth in services compared to the NAV sample Table 41 illustrates this for the sales per

worker measure While it grew by 40 percent in the lsquoOtherrsquo category between 2012 and

2015 based on the NAV sample its lsquoaggregatersquo growth was only 6 percent during the same

period

Obviously one cannot draw far reaching conclusions from such statistics given the

immense measurement problems Still these patterns suggest that in a sense the duality

between firms and the self-employed may constitute a similarly deep divide to the one

belong to the lsquomarket economyrsquo (as defined in Chapter 2) and are registered as lsquofirmsrsquo according to GFO coding system (ie have 1-digit GFO codes 1 or 2) We tagged the firms present in the NAV sample as lsquodouble-entry bookkeeping firmsrsquo and marked those who have 2-digit GFO codes equalling to 23 as lsquoself-employedrsquo We categorised the rest of our sample as lsquoother firmsrsquo Further we distinguished between manufacturing and other market economy firms based on their NACE codes and then calculated for sales per worker measures on the level of each observation finally to compute for yearly aggregates for each group as indicated above

27 Notably in line with the definition in Chapter 2 these lsquoother industriesrsquo do not include agriculture

28 Needless to say this cannot be easily mapped into productivity differences given that firms using more intermediate inputs are more likely to choose double-entry bookkeping (and hence pay

taxes based on profits) rather than simplified taxes (and pay taxes based on sales) Still one can do the following back of the envelope calculation In the NAV sample the average ratio of material expenditure over sales was 066 both in 2012 and 2015 Therefore value added per employee (or labour productivity) could be about a third of the sales per employee variable If one conservativelly assumes that the self-employed have zero material costs their labour productivity is the same as their sales per employee index Based on this simple calculation the 6-10-fold difference in sales per employee map to at least 2-3-fold differences in labour productivity


36

that exist between globally integrated and domestic-oriented firms Consequently policies

can be formulated with an explicit focus on this group

Table 41 Number of employees and sales per employee for different entities

Number of employees

Manufacturing Other

2012 2015 2012 2015

Double-entry bookkeeping firm 621229 627391 1325299 1196332

Other firm 289636 296921 698326 771930

Self-employed 72674 74325 620699 638001

Total 983539 998637 2644324 2606263

Average sales per employee (HUF million)

Manufacturing Other

2012 2015 2012 2015


Other firm 151 146 196 201

Self-employed 25 25 29 28

Total 92 99 105 111

43 The evolution of productivity distribution in Hungary

Average productivity

Let us continue by investigating the evolution of average productivity Table 42 presents

the average labour productivity and TFP growth rates for the market economy

manufacturing and services as defined in Chapter 2 We report both unweighted and

labour-weighted productivity growth for each year

Let us start with the whole market economy Between 2004 and 2007 both labour

productivity and TFP was growing strongly by 7-8 percent on average as expected in a

catching up economy (as we have seen in Chapter 3) Importantly the weighted growth

rate was higher than the unweighted one suggesting that reallocation played a positive

role in aggregate productivity growth (see Section 62 for more details)

During the crisis we see a slight productivity decline in 2008 a sharp fall of about 8

percent in 2009 followed by a strong recovery in 2010 The 2010 productivity recovery

resulted from the productivity growth of large firms unweighted average productivity

growth was very slow This suggests an asymmetry in recovering from the crisis-related

productivity decline

Post-crisis all measures document a slowdown in productivity growth with typical growth

rates between 25-35 percent Notably weighted productivity growth measures were

similar to unweighted ones in the wake of the crisis suggesting deterioration in the

efficiency of the reallocation process The 2010-2013 and 2013-2016 periods seem to be

quite similar to each other


37

Importantly while labour productivity and TFP dynamics differ to some extent the overall

picture is very similar for the two productivity measures This is in line with the hypothesis

that any productivity slowdown is not merely a consequence of lower capital stock growth

The results are similar when using alternative TFP estimators (see Table A41 in the

Appendix)

Table 42 Labour productivity and (ACF) TFP growth in the sample

A) Market economy

Year LP TFP

unweighted emp w unweighted emp w

2005 20 58 19 74

2006 92 91 93 119

2007 53 60 39 56

2008 -10 -08 -10 -04

2009 -70 -81 -69 -82

2010 -05 44 11 80

2011 25 45 34 40

2012 25 22 21 01

2013 19 25 30 22

2014 39 45 40 59

2015 51 50 52 49

2016 36 19 20 03

Average

2004-2007 55 70 50 83

2007-2010 -28 -15 -23 -02

2010-2013 23 34 33 29

2013-2016 36 35 35 33

B) Manufacturing

Year LP TFP


2005 37 148 20 114

2006 124 163 114 149

2007 100 114 78 71

2008 25 -03 17 -17

2009 -115 -94 -133 -117

2010 82 161 80 173

2011 -02 34 04 18

2012 05 -46 -02 -58

2013 -14 31 -12 05

2014 11 48 -01 27

2015 38 37 30 14

2016 26 01 04 -23

Average

2004-2007 87 141 71 111

2007-2010 -02 22 -12 13

2010-2013 -04 17 04 -03

2013-2016 15 29 05 06


38

C) Market services

Year

LP TFP


2005 12 -04 10 32

2006 80 47 79 90

2007 39 25 24 48

2008 -22 -06 -21 -03

2009 -57 -68 -52 -71

2010 -29 -17 -11 26

2011 33 49 43 57

2012 31 60 30 48

2013 29 21 39 29

2014 46 45 46 78

2015 54 58 54 72

2016 39 30 25 20

Average

2004-2007 43 23 38 57

2007-2010 -36 -31 -28 -16

2010-2013 31 44 39 51

2013-2016 42 39 41 50

Notes This figure presents growth rates of labour productivity and aggregate TFP for lsquomarket

industriesrsquo (see section 25) The sample does not include agriculture mining and financial services

Services include construction and utilities Only firms with at least 5 employees

Comparing manufacturing and services shows a key dichotomy between the two large

sectors In Manufacturing productivity growth was strong before the crisis with above 10

percent average weighted growth rates This fell to very low levels after 2010 Similarly to

the whole market economy reallocation processes had been more efficient before 2008 In

contrast for services no clear structural break appears around the time of the crisis either

in terms of pre- and post-crisis growth rates or reallocation efficiency

Table 43 looks into industry differences in more detail The picture is similar for

manufacturing industries in the various technology categories with a very substantial

slowdown in productivity growth Productivity growth was fastest in high-tech both before

and after the crisis Services are a bit more heterogeneous High-tech services behaved

similarly to high-tech manufacturing with strong pre-crisis growth (around 10 percent on

average) followed by a slowdown to growth rates around 5 percent per year In less

knowledge-intensive services which represent the majority of business service

employment growth rates were similar before and after the crisis (around 5 percent)29

Lastly we see moderate growth rates and then some slowdown in construction and

utilities

29 Note however that this may not be the case for the self-employed as has been discussed in the previous chapter


39

Table 43 TFP growth by type of industry (employment-weighted ACF TFP)

A) Manufacturing

Year Low-tech Medium-low Medium-high High

2005 124 19 66 274 2006 240 137 39 33

2007 74 02 41 221

2008 -45 23 -15 59

2009 05 -191 -218 48

2010 135 111 264 168

2011 -45 18 34 100

2012 -15 -24 -83 -181

2013 -41 37 -22 125

2014 06 07 27 86

2015 65 01 -54 80

2016 -02 04 -27 -91

Average 2005-2007 146 53 49 176

2007-2010 32 -19 10 92

2010-2013 -34 07 -21 20

2013-2016 07 12 -19 50

B) Services

Year KIS LKIS Construction Utilities

2005 127 16 34 -48

2006 166 75 30 67

2007 13 58 42 29

2008 -16 14 -72 -26

2009 -63 -94 -04 25

2010 54 12 09 05

2011 97 46 65 29

2012 12 74 13 -22

2013 12 30 63 -07

2014 78 89 65 -81

2015 106 70 14 54

2016 16 31 -47 39

Average

2005-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

Notes This table shows the sales-weighted average ACF TFP growth rate by technology category (see

Section 25) Only firms with at least 5 employees The sample does not include agriculture mining

and financial services

In general patterns are similar for the unweighted measures (See Table A42 in the

Appendix) with weaker pre-crisis growth in manufacturing where reallocation seems to

have mattered most Labour productivity behaved similarly to TFP (See Table A43 in the

Appendix)


40

Frontier firms

The key motivation for this investigation is to understand better how productivity dynamics

of lsquofrontierrsquo firms differ from firms in other parts of the productivity distribution Defining

frontier firms is not a straightforward task (Andrews et al 2017) Inevitably all such

attempts have to face the trade-off between a narrow definition which may to a large

extent capture the behaviour of outliers and a broader definition which may include

many firms which are very far from the actual frontier

One can find a sensible compromise between the too narrow and the too broad definitions

by following the OECD practice (Andrews et al 2017) This solves the problem of

including small firms with potentially large noise by restricting the sample to firms with at

least 20 employees on average in the sample period Frontier is defined as the top 5

percent of such firms for each industry-year combination An additional issue is that the

number of observations may change across years This is solved by calculating the top 5

based on the median number of observations per year We will call these firms frontier

firms

An alternative definition is simply to define the top decile within the productivity

distribution in industry-year combination as frontier based on our main sample We will

employ this strategy as well for the sake of comparison

Table 44 investigates the prevalence of frontier firms in different groups30 The probability

of being frontier is not related strongly to size A foreign-owned firm is 3-4 times more

likely to be frontier than a domestically-owned private firm State-owned firms are similar

to privately owned domestic firms in this respect As a result about half of the frontier

firms are foreign-owned Finally frontier firms are substantially more prevalent in the

more developed regions of the country especially in Central Hungary These patterns are

quite stable throughout the years and they prevail in a multiple regression analysis The

top decile of the productivity distribution has a similar composition (see Table A44 in the

Appendix)31

Table 44 The share of frontier firms () among firms with at least 20 employees

A) By size

2004 2007 2010 2013 2016

20-49 emp 357 327 34 362 329

50-99 emp 401 468 542 486 555

100- emp 293 358 414 42 462

B) By ownership

2004 2007 2010 2013 2016

Domestic 213 194 236 272 289

Foreign 873 955 896 82 821

State 181 211 166 167 263

30 Note that we restrict the sample to firms with at least 20 employees because the definition of frontier requires to have at least 20 employees on average

31 When the definition is based on labour productivity the share of frontier firms increases with size The foreign advantage is also larger


41

C) By region

2004 2007 2010 2013 2016

Central HU 596 621 652 552 579

Northern Hungary 174 104 176 237 168

Northern Great Plain 152 195 199 38 268

Southern Great Plain 128 127 18 277 224

Central Transdanubia 296 27 32 359 322

Western Transdanubia 408 313 305 433 395

Southern

Transdanubia 131 081 188 159 211

Another key question is the extent to which frontier status is persistent Figure 43 shows

a transition matrix ie it considers frontier firms in year t and reports their status in t+3

Do they remain frontier or become a non-frontier firms or exit the market altogether

Overall the 3-year persistence of the frontier status is around 45 percent ndash nearly half of

frontier firms will also be frontier 3 years later This is a bit higher than what is found in

other countries Antildeoacuten Higoacuten et al (2017) for example report that about half of all

national frontier firms remain on the frontier for a year but only about 20 percent for 5

years The persistence of frontier status remained largely unchanged across the years

Frontier status is more persistent for foreign and exporter firms The transition matrix of

top decile firms is similar with slightly weaker persistence (Figure A41 in the Appendix)

Figure 43 Transition matrix for frontier firms

Notes This figure shows how many of the frontier firms in year 2010 were still frontier in 2013 how

many exited and how many continued as non-frontier Only firms with at least 20 employees The first

panel shows this transition matrix for various 3-year periods


42

Productivity evolution across deciles

The figures in this section compare the average productivity of frontier firms of the top

decile of the productivity distribution lsquohigh productivity firmsrsquo (8th and 9th deciles) lsquotypical

firmsrsquo (4th to 6th deciles) and lsquolow productivityrsquo firms (2nd and 3rd deciles) all of these

defined at the year-NACE 2 level This approach follows closely that of the OECD (2016)

Also we use the 8 lsquotechnologicalrsquo industry categories introduced in Section 25 to condense

information but still allow for heterogeneity across industries

Let us start with comparing TFP levels (Figure 44) and their cumulative changes (Figure

45) at the different parts of the productivity distribution (note that the vertical axes differ

across sectors) TFP levels are measured in natural logarithms For example in low-tech

manufacturing the difference between low-productivity firms and the frontier is about 2 log

points or more than 7-fold32 Within-industry productivity differentials are much larger

than across-industry differences or changes From a methodological point of view in most

industries frontier firms co-move with the top percentiles but there are a few exceptions

most prominently high-tech manufacturing

The overall productivity evolution is much in line with the averages reported in Table 42

There is strong pre-crisis growth in Manufacturing followed by a fall in 2009 and sluggish

growth afterwards High-tech manufacturing is a partial exception from this trend

Productivity growth actually accelerated after the crisis in services

Figure 44 TFP levels in various types of industries

A) Manufacturing

32 1198902 asymp 74


43

B) Services

Notes This figure shows the evolution of the (unweighted) average ACF TFP level of the different

deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo

are in the top 5 percentiles with at least 20 employees on average lsquotop decilersquo is the 10th decile lsquohighrsquo

is the 8-9th decile typical is the 4-6th deciles while `lowrsquo is 2-3rd deciles Main sample The industry

categories are described in Section 25 The sample includes the sectors of the market economy

except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less knowledge-

intensive services

Most importantly we do not find evidence for an increasing gap between frontier and other

firms (in line with OECD 2016) in any of the industries Within manufacturing there is

convergence between frontier and non-frontier firms in medium-low and high-tech

industries However this is not robust for the alternative definition of frontier (top decile)

which moves strongly together with other deciles Based on this one may say that there is

no robust evidence either for convergence or divergence in manufacturing There are some

signs of convergence pre-crisis in knowledge-intensive and less knowledge-intensive

services as well as in construction followed by stronger productivity growth in the highest

quartiles post-crisis Importantly any convergence or divergence appears to be small

relative to already existing differences


44

Figure 45 Cumulative TFP growth since 2004

A) Manufacturing

B) Services

Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various


are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is the 10th

decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main sample The

industry categories are described in Section 25 The sample includes the sectors of the market

economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less

knowledge-intensive services


45

The picture is somewhat different when labour productivity is considered (Figure 46) In

this case the difference in growth rates between frontier and other firms is more

pronounced than in the case of TFP One can plausibly claim that less productive deciles of

the distribution caught up somewhat with the most productive firms in high-tech

manufacturing in the two service sectors and also in construction This suggests that

capital deepening by less productive firms (or low investment by frontier firms) may lead

to some convergence in terms of labour productivity but less so in terms of TFP33

Figure 46 Cumulative labour productivity growth since 2004 for labour productivity

deciles

A) Manufacturing

33 Note that these figures are the most directly comparable ones to Figure 41 which also presents results for labour productivity In line with that figure we find evidence for convergence between the median firm and frontier firms We also find that low-productivity firms converge The most important reason for this is that we exclude firms with less than 5 employees from our sample


46

B) Services

Notes This figure shows the cumulative growth of the (unweighted) average labour productivity level

for various deciles of the productivity distribution within each 2-digit industry-year combination

lsquoFrontier firmsrsquo are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo

is the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main

sample The industry categories are described in Section 25 The sample includes the sectors of the

market economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo

Less knowledge-intensive services

Figure 47 zooms in to a few industries of interest which both confirm and qualify the

overall picture In textiles (a low-tech industry) frontier firms did not increase their

productivity in the period under study while lower productivity deciles experienced a

cumulative 40-50 percent productivity growth leading to an overall positive growth As

Section 61 discusses employment decline and firm exit were high in this industry

therefore the improvement of lower deciles may partly result from the exit of the lowest

productivity firms In machinery (a medium-high tech industry) all productivity deciles

had experienced strong TFP growth before the crisis and a significant fall during the crisis

followed by slow growth In this industry the full distribution has moved together

In retail (which is a member of the less knowledge-intensive services) TFP had grown to

some extent prior to the crisis followed by a large fall around the crisis and some growth

since 2012 Interestingly the fall was much larger and persistent for the most productive

firms while typical and low-productivity firms were able to maintain their pre-crisis

productivity levels The weak productivity performance of the top decile may have partly

resulted from regulatory changes and could have had large aggregate consequences given

the large employment share of retail (see Chapter 8) In lsquoComputer programming

consultancy and related activitiesrsquo there was a cumulative TFP increase of about 30 percent

since 2004 for all deciles without signs of convergence or divergence


47

Figure 47 Cumulative TFP growth since 2004 selected industries


deciles of the productivity distribution within each 2-digit industry-year combination in four industries

lsquoFrontier firmsrsquo are in the 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is

the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main

sample

44 Duality in productivity and productivity growth

Besides the evolution of the overall shape of productivity distribution it is important to

understand the lsquodualityrsquo of productivity with respect to ownership

As a starting point Figure 48 shows the distribution of TFP and the natural logarithm of

the average wage for our main sample34 We filter out 2-digit industry fixed effects from

the two variables to control for industry-level differences

Comparing private domestic and foreign-owned firms one can make a number of

observations The foreign-owned distribution clearly stochastically dominates the

productivity and wage distribution of domestically-owned firms On average foreign firms

have 40 percent higher TFP and pay 75 percent higher wages than domestically-owned

firms in the same industry That said the within-group heterogeneity is larger than the

across-group heterogeneity generating a substantial overlap between the two

distributions For example 30 percent of domestically-owned firms are more productive

than the median foreign firm The averages between the two groups differ substantially

but there are many productive domestically-owned firms and unproductive foreign ones

34 Result for other TFP measures are very similar


48

Another interesting difference between the distributions is that the foreign-owned

distribution is substantially more dispersed than the domestically-owned one (its standard

deviation is 23 percent larger) suggesting more technological heterogeneity within the

foreign-owned group This may suggest that this group includes both firms with world-

class technology and plants utilizing low-cost labour in a relatively unproductive way That

said the distribution is clearly not bi-modal there are no clearly distinguishable clusters of

high-tech and low-tech firms They operate along a continuum

Comparing state-owned firms to the other two groups shows that they are more similar to

the domestically-owned private firms with two interesting twists35 First the low-

productivity left tail of state-owned firms is much thicker than that of the privately owned

domestic firms Many state-owned firms operate with very low productivity levels (see also

Section 63) As a result the average productivity of these firms is 25 percent lower

compared to privately-owned domestic firms in the same industry

The second twist is that even though state-owned firms tend to be substantially less

productive than privately owned domestic firms they pay on average 25 percent higher

wages This may be a consequence of differences in worker composition but may also

suggest that these firms face soft budget constraints and their employees are able to

capture a larger slice from a smaller pie

Figure 48 Distribution of TFP and average wage by ownership (cleaned from industry-

year effects) 2016

Notes This figure shows the distribution of productivity and ln average wage after filtering out

industry-year fixed effects from it Domestically-owned is domestic privately-owned Main sample

35 Note that the sample of state owned firms is much smaller than the other two groups and operates in very specific indutries This may affect the distribution


49

Figure 49 shows the evolution of the productivity distributions across years Note that in

order to illustrate shifts in time industry-year fixed effects are not filtered out from this

figure Therefore comparing the distributions with Figure 47 shows how much industry

composition matters

Panel A) illustrates the productivity evolution of domestic private firms The shape of this

distribution remained remarkably similar across years There are clear rightward shifts

between 2004-2008 and 2012-2016 while the distribution did not change during the crisis

period Similar patterns can be observed regarding foreign-owned firms This distribution

was always more dispersed than the domestic one with little changes in its standard

deviation across years

The shape of the state-owned productivity distribution is more peculiar Most visibly it had

been bi-modal before the crisis This is mainly a consequence of industry composition the

low productivity part representing some utilities While the bi-modality disappeared post-

crisis the low-productivity tail of the distribution became thicker Finally we do not see

any rightward shift in this distribution there was little productivity improvement in this

small segment of the economy

Figure 49 Evolution of the distribution of TFP by ownership

A) Domestic private


50

B) Foreign

C) State

Notes This figure shows the distribution of TFP Domestically-owned is domestic privately-owned

Main sample


51

BOX 41 Duality between domestic and foreign-owned firms in an international context

We are not the first to document the substantial wage and productivity advantage of foreign firms Earle

and Telegdy (2008) by using NAV data between 1986-2003 show that foreign-owned firms were almost

twice as productive as domestic private firms (measured in terms of labour productivity) and also paid

40 higher wages when controlling for employee characteristics A substantial part of this premium

results from foreign owners acquiring more productive firms (mostly during the privatisation process)

but even after controlling for this selection process the foreign wage premium remains 14 Similar

results are found by Telegdy et al (2012) when using the longer period between 1986 and 2008

Foreign-owned firms tend to have positive productivity and wage premia in most countries developed or

emerging Among others Aitken et al (1996) show that foreign-owned firms have higher productivity

and wages in Mexico and Venezuela even after controlling for firm size skill mix and capital intensity

Conyon et al (2002) use acquisitions in the UK in 1989-1994 to find that foreign firms pay 34 higher

wages which can be fully attributed to their 13 higher productivity Girma et al (2002) have a similar

result showing that foreign firms in the UK have 8-15 higher productivity which leads to 4-5 higher

wages Using UK data from 1981-1994 Girma and Goumlrg (2007) find wage differentials of a similar

magnitude but heterogeneous with regard to the source country of the foreign investor Huttunen

(2007) looks at Finland and finds 26-37 wage premium of firms 3 years after being acquired by

foreign investors In the Central-Eastern-European region Djankov and Hoekman (2000) show that

foreign investment in the 90s increased the productivity of recipient firms in the Czech Republic

Governments aim to attract foreign direct investment (FDI) as it is assumed to have a positive impact

on the domestic economy From an economic point of view it is justifiable to provide incentives to

foreign investors if their investments have positive spillovers to domestic firms increasing their

productivity The higher productivity of foreign-owned firms which is documented in the previously

mentioned studies is a necessary condition for that At the same time if foreign firms establish no links

with domestic firms there is only limited opportunity for knowledge spillovers In this case the inflow of

foreign investments results in a dual structure of the economy

Evidence is rather mixed on FDI spillovers to domestic firms in the same industry because a negative

competition effect might dominate the positive technology or knowledge effect Haskel et al (2007) find

that a 10-percentage-point increase in the share of foreign ownership increases the TFP of domestic

firms in the same industry by 05 in the UK Konings (2001) finds negative spillovers for Bulgaria and

Romania and no spillovers for Poland Positive spillovers in vertically related industries are much more

general Using Lithuanian data Javorcik (2004) shows that one standard deviation increase in the foreign

share of an industry is associated with 15 increase in the output of domestic firms operating in the

supplier industry Similarly Kugler (2006) finds no within-industry spillovers but positive spillovers in

vertically related industries in Colombia


52

Let us turn to industry differences in duality The substantial difference between the average TFP of

domestic and foreign-owned firms is present in all kinds of industries (Figure 410 and 411) In

manufacturing the percentage difference is about 34 percent (a log difference of 03) while it is

around 65-100 percent in services Significantly the cumulative TFP growth of the two types of firms

was very similar by the end of the period There is no evidence for the catching-up of domestic firms

with foreign ones The duality in this respect does not seem to diminish substantially

The TFP gap between foreign and domestic firms is amplified by the much higher capital intensity of

foreign firms (Figure 412) In manufacturing foreign firms employed more than twice as much capital

per employee than domestic firms While the capital intensity of both domestic and foreign-owned

firms increased steadily during the period in that sector the gap remained constant showing little

catching-up of domestic firms in terms of capital deepening In a sharp contrast there was a decrease

in the capitallabour ratio in services and this phenomenon took place quicker in the case of foreign

firms

This picture is reinforced at the industry level (Figure 413) In textiles foreign firms invested more

than domestic ones leading to significant capital deepening for that group of firms In machinery both

groups of firms increased their capital intensity to a similar extent In retail foreign firms had invested

much before the crisis but cut their investments deeply after that while the capital intensity of

domestic firms remained mostly flat In programming capital intensity declined slightly following the

crisis


(cont)

Looking at Hungarian data several papers show the existence of positive FDI spillovers to domestic

firms Halpern and Murakoumlzy (2007) find significantly positive spillovers in the supplier industry but

no evidence for within-industry spillovers Beacutekeacutes et al (2009) find a negative effect on low-

productivity firms in the same industry while the spillover effect is positive for high-productivity

firms Iwasaki et al (2012) find positive spillovers even within the same industry conditional on the

proximity in product and technological space At the same time Bisztray (2016) shows that the

large-scale foreign direct investment of Audi did not increase the productivity of domestic firms in

the supplier industry

We know from the literature that the effect of FDI on domestic firms is highly heterogeneous even in

the supplier industry (see Smeets 2008 for a review) A crucial precondition of positive spillovers is

the absorptive capacity of the domestic firms (Crespo-Fontoura 2007) Using data from Bulgaria

Poland and Romania Nicolini and Resmini (2010) show that firm size matters as well Additionally

they find within-industry spillovers in labour-intensive sectors and cross-industry spillovers in high-

tech sectors Also the characteristics of the foreign investment play an important role in the

magnitude of the spillover effect Javorcik (2004) estimates a positive effect on the productivity of

domestic firms only in the case of shared foreign and domestic ownership but not for fully foreign-

owned firms Javorcik and Spatareanu (2011) show that the distance of the investorrsquos country is

also important as investors from far-away countries establish more links with local suppliers In line

with that they estimate positive vertical spillovers from US investors but not from European

investors in Romania Lin et al (2009) show that vertical FDI spillovers in China are weaker for

export-oriented FDI compared to domestic-oriented


53

Figure 410 TFP levels of foreign and domestic firms

A) Manufacturing

B) Services

Notes This figure shows the (unweighted) average ACF TFP level of foreign and domestically-owned firms Main

sample The industry categories are described in Section 25 The sample includes the sectors of the market

economy except agriculture mining and finance lsquoKISrsquo Knowledge- intensive services lsquoLKISrsquo Less knowledge-

intensive services


54

Figure 411 Cumulated TFP growth of foreign and domestic firms

A) Manufacturing

B) Services

Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level of foreign and

domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The

sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-

intensive services lsquoLKISrsquo Less knowledge-intensive services


55

Figure 412 Capital intensity of foreign and domestic firms

A) Manufacturing

B) Services

Notes This figure shows the average capital intensity (log tangible and intangible assetsemployee) of foreign- and





56

Figure 413 Cumulative change in capital intensity of foreign and domestic firms selected industries

Notes This figure shows the (unweighted) average capital intensity (log tangible and intangible assetsemployee)

of foreign and domestically-owned firms since 2004 in four industries Main sample

45 Conclusions

Our investigation of the evolution of productivity distribution has yielded a number of relevant

conclusions which will inform the work conducted in the remaining sections In line with international

evidence we have found that productivity dispersion within industries is many times larger than the

differences between industries Importantly Hungary seems to be an exception to the international

trend of frontier firms diverging from the rest of the economy ndash if anything there is evidence for the

low productivity growth of frontier firms and for some catching-up by others

OECD (2016 Figure 16) has found such a pattern only in Hungary and Italy with divergence in all the

other countries under study (Austria Belgium Canada Chile Denmark Finland France Japan

Norway and Sweden) We find two kinds of explanations plausible First in Hungary (unlike most other

countries in that sample) national frontier firms are quite far away from the global frontier As

Andrews et al (2015) argue the productivity divergence mainly arises between global frontier firms

and the rest If national frontier firms are far away from the global frontier they may find themselves

on the wrong side of global divergence Second it is possible that the policies and institutional

environment for national firms in Hungary is less conducive to adopt local frontier technologies A way

to learn more about the background of this result would be to use cross-country micro-data to study

the behaviour of frontier firms in even more countries including other CEE countries

The low productivity growth of Hungarian national frontier firms constrains productivity growth

directly Furthermore if national frontier firms do not adopt the most developed technology potential

spillovers to other firms will also remain limited Andrews et al (2015) have shown that good


57

framework conditions (most importantly good regulatory practices in upstream sectors) and innovation

related policies such as providing incentives for RampD and building a more robust national innovation

system are associated with a stronger catch-up of national frontier firms to the global frontier

The results reveal that duality especially between foreign and domestic firms is substantial and there

is no evidence for catching-up by domestic firms The gap is especially large in the service industries

That said the gap between the two groups can be bridged indeed the productivity differences

between the two groups are smaller than within them Duality while a sign of inefficiency also

provides an opportunity for domestic firms to tap into the knowledge base possessed by their foreign-

owned counterparts and to integrate into global value chains by relying on the links of foreign firms

While efficient strategies aiming at maximizing the benefits from FDI and global value chains may

differ across countries there are a few policy options which unambiguously help countries in benefiting

from the presence of multinational firms A robust result of the recent spillover literature is that

domestic firms need strong absorptive capacity including technological knowledge and a skilled

workforce to be able to benefit from the presence of foreign-owned firms (Girma 2005 Crespo and

Fontoura 2007 Zhang et al 2010) One dimension of absorptive capacity building is creating an

effective innovation system with a strong knowledge base and easy access to that knowledge Another

dimension is developing technological and management capabilities which enable firms to understand

and apply advanced knowledge Such capabilities are essential both for technological upgrading and for

integrating into global value chains (Taglioni and Winkler 2016)

An important caveat regarding these results is that they are limited to double-entry bookkeeping firms

We have emphasised that a large share of people work outside the double-entry bookkeeping entities

included in our sample While data are scarce about the productivity of these entities available

information suggests that both the levels and dynamics of productivity may differ radically between

double-entry bookkeeping firms and other entities If so inclusive policies could focus on providing

skills and opportunities for the self-employed

State-owned firms constitute a small part of the Hungarian market economy but such firms are

prevalent in some industries including utilities The productivity of some of these firms is very low

when compared to the productivity of privately-owned firms while they pay higher wages Both of

these phenomena hint at soft budget constraints and other inefficiencies Policies aiming at providing

better incentives either by improving corporate governance of state-owned firms (Arrobio et al 2014)

or by creating framework conditions more conducive to competition may help in in promoting

productivity growth in these important industries

Allocative efficiency

58

5 ALLOCATIVE EFFICIENCY

A key insight of recent productivity research is that differences in productivity levels across countries

largely result from the inefficient allocation of resources across firms rather than from differences in

the productivity of lsquotypical firmsrsquo both in cross-section (Hsieh and Klenow 2009 Restrucca and

Rogerson 2017) and in time-series (Gopintah et al 2017) Inefficient allocation refers to the

phenomenon that low-productivity firms possess a large amount of capital and labour (rather than

shrinking or exiting) or when firms with similar marginal products use a different amount or

composition of inputs

In this chapter we employ two strategies to quantify the extent of such distortions The first strategy

proposed by Olley and Pakes (1996) simply asks whether more productive firms are larger A more

positive covariance between productivity and employment suggests a better allocation of resources

across firms and higher industry level (labour-weighted) productivity (even when holding the

unweighted productivity level unchanged) The Olley-Pakes method is generally agnostic about the

specific nature of distortions but measures their results in an intuitive and robust way at the industry-

year level

Hsieh and Klenow (2009) attempt to identify the sources of distortions36 In particular they argue that

firms can face two main distortions product market distortion (modelled as an implicit sales tax and

identified from the wedge between labour costs and value added) and capital market distortion

(modelled as an implicit capital tax and identified from differences in the cost share of capital) These

variables can be measured at the firm-level Industry-level distortions can be quantified both as the

average of firm-level distortions and also as the dispersion of firm-level measures

This chapter describes these measures at the industry-year level Section 51 presents the Olley-Pakes

covariance terms while Section 52 implements the Hsieh-Klenow method

51 Olley-Pakes efficiency

The Olley-Pakes (also called static) approach of productivity decomposition consists of decomposing

the aggregated (industry-region-level) productivity which is the weighted average of firm-level

productivity levels into the unweighted average firm-level productivity and the covariance between

productivity and firm size (Olley and Pakes 1996) The latter term reflects how efficiently resources in

this case labour are allocated across firms A more positive covariance between size and productivity

reflects stronger allocative efficiency

Let us start with cross-country evidence from the OECD (Andrews and Criscuolo 2013) According to

this source in 2005 static allocative efficiency in Hungarian manufacturing (the covariance term) was

positive but slightly below the average of OECD countries similar to Portugal and Italy (Figure 51)37

Allocative efficiency in services was negative one of the lowest of the countries in the sample

(Andrews and Cingano 2014 Figure 10) showing that less productive firms tended to be larger in the

service sector Andrews and Cingano (2014) also show that the relatively low allocative efficiency in

Hungary is partly explained by policies including product market regulation and creditor protection

36 For an overview of the reallocation literature see Hoppenhayn (2014)

37 Note that these calculations use the ORBISAMADEUS database covering a relatively small fraction of larger

Hungarian firms in 2005 (about 3300 firms) see Box 21


59

Figure 51 Static allocative efficiency in Hungarian Manufacturing (2005)

Notes This figure is a reproduction of Figure 7 from Andrews and Criscuolo (2013)

Let us turn to our data The logic of the static decomposition is presented in Figure 52 for our main

sample by 2-digit industry38 The horizontal axis shows the unweighted average log labour productivity

of each industry while the vertical axis shows the productivity weighted by employment If all firms

were of equal size (or at least firm size was independent of productivity) weighted and unweighted

productivity would be equal ie all industries would be on the 45-degree line If size and productivity

were positively correlated the weighted productivity would be larger than the unweighted one The

difference between the weighted and unweighted average is the covariance between size and

productivity This measure of allocative efficiency is equal to the vertical distance between each point

and the 45-degree line Allocative efficiency contributes positively to industry productivity in industries

above the 45-degree line while it has a negative contribution for industries below the line

For example in the manufacture of machineries (28) the unweighted average productivity is 646

while the weighted average productivity is 665 Allocative efficiency resulting from more productive

machine manufacturers being larger contributes with 019 to the aggregate productivity of this

industry An industry with negative allocative efficiency is warehousing (52) where the lower

productivity of larger firms contributes negatively to aggregate productivity (the unweighted

productivity being 681 and the weighted only 585)

38 Appendix Table A51-Table A56 summarise the Olley-Pakes (1996) measures by industry


60

Importantly allocative efficiency is positive in most industries It is especially high in the most

knowledge-intensive services (scientific research (72) employment activities (78)) in service

industries with a few large firms (broadcasting (60) telecom (61)) and in key manufacturing

industries beverages (11) chemicals (20) machinery production (28) and vehicle production (29) In

a few industries low-productivity firms tend to be larger Prominent examples are professional

services advertising (69) and legal and accounting activities (73) services with many state-owned

firms transportation (39) waste management (49) and logistics (52) In line with OECD evidence

allocative efficiency tends to be more positive in manufacturing compared to services

Finally Figure A51 in the Appendix shows that allocative efficiency is significantly higher when labour

productivity is considered rather than TFP almost every industry has larger weighted labour

productivity than unweighted labour productivity This difference simply results from the positive

association between productivity and capital intensity

Figure 52 Weighted and unweighted TFP by 2-digit industry 2015 main sample

Notes All points represent a 2-digit industry The horizontal axis shows its unweighted TFP while the vertical axis

shows its weighted TFP in the same year We have omitted industries with less than 1000 observations TFP is

estimated using the method of Ackerberg et al (2015)

Another conclusion that can be drawn from Figure 52 is that allocative efficiency is higher in sectors

with higher unweighted productivity represented by the fitted line in the figure In other words high

firm-level efficiency seems to move together with higher allocative efficiency in the industry One

mechanism behind this relationship may be that incentives for technology upgrading are stronger

when the reallocation process is more effective (Restruccia and Rogerson 2017) but stronger

international competition can also affect positively both within-firm productivity dynamics and

reallocation across firms In Figure 53 we investigate whether this relationship changed between

years The figure shows that the positive relationship between unweighted productivity and allocative


61

efficiency did not change substantially over time This relationship is similar when labour productivity is

considered (see Figure A52 in the Appendix)

Figure 53 The relationship between weighted and unweighted TFP by year

Notes This figure shows the fitted lines from regressions between weighted and unweighted TFP levels run at the

2-digit industry level separately for 2005 2010 and 2016 TFP is estimated using the method of Ackerberg et al

(2015)

From the perspective of productivity slowdown a key question is whether allocative efficiency

deteriorated in some industries following the crisis Figure 54 shows the allocative efficiency of each

2-digit industry in 2010 and 2016 The axes here represent the distances from the 45-degree line in

Figure 52 If an industry is on the 45-degree line of this figure its allocative efficiency remained

unchanged in the period if an industry is above the line its allocative efficiency was better in 2016

compared to 2010 The first conclusion that can be drawn is that levels of allocative efficiency are

persistent industries cluster around the 45-degree line Also the fitted line shows that allocative

efficiency grew somewhat faster in industries where allocative efficiency was worse and this

relationship is statistically significant Therefore productivity growth decline is unlikely to be the result

of rapidly worsening allocative efficiency

One can however identify a couple of industries where substantial changes took place The machinery

industry (28) for example became more efficient partly because of the entry of new large foreign-

owned firms Office administration (82) and management activities (70) also increased their allocative

efficiency This is most likely due to the entry of large shared service providers Allocative efficiency

decreased in land transportation (39) waste management (49) and warehousing (52)

The evaluation of allocative efficiency in labour productivity shows similar patterns (Table A53 in the

Appendix)


62

Figure 54 The change in allocative efficiency by 2-digit industry

Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences

between the weighted and unweighted TFP) in 2010 while the vertical axis shows the same quantity in 2016 TFP is


52 Product market and capital market distortions

The Olley-Pakes static decomposition framework can quantify the overall allocative efficiency of sectors

but it is incapable of informing us about the nature of distortions In this section we implement the

methodology of Hsieh and Klenow (2009)39 to distinguish between product and capital market

distortions This distinction is of much interest given that the crisis and its aftermath ran parallel with

both financial market frictions and changes in product market regulation

The logic of the Hsieh and Klenow (2009) method is the following Under the assumptions of

monopolistic competition on product markets (similarly to Melitz 2003) and frictionless labour

markets the marginal product of labour and capital should be equalized across firms in the absence of

market distortions In turn if the production function is Cobb-Douglas the equality of marginal

products implies that the share of labour costs in value added and capital intensity (capitallabour)

should be equalized across firms Under product market distortions (modelled with a firm-specific

implicit lsquosales taxrsquo or a negative rent) the wedge between labour costs and value added will differ

across firms because firms facing lower implicit taxes charge higher markups The more heterogeneous

the lsquosales taxrsquo is the larger the dispersion of the wedge Capital market distortions are modelled as

39 The Hsieh-Klenow approach has been criticized recently by Haltiwanger et al (2018)


63

implicit firm-specific capital tax rates Firms facing different capital tax rates choose different capital

intensity levels and hence different capitallabour cost ratios Therefore the dispersion of capital

intensity (or more precisely the cost share of capital) reflects the dispersion of capital tax rates

Note that the implicit taxes proxy multiple sources of distortions from which differences in explicit

taxes represent only a small part The implicit lsquosales taxrsquo includes the cost of complying with different

types of regulations size-dependent regulation the effect of fixed costs and market power The

implicit lsquocapital taxrsquo includes for instance the full cost of accessing financing possible subsidies for

investment or differences in tax incentives to invest These implicit taxes provide a convenient way of

summarizing markup differences and differences in access to capital

As a result the dispersion of the wedge and capital intensity reflect how heterogeneous the two

implicit tax rates are More heterogeneity in implicit tax rates in turn implies more disperse total factor

productivity within industry40 and a less efficient allocation of resources In other words similarly

productive firms (having also similar marginal products of inputs) choose very different input quantities

and combinations

Product market distortions

We start our empirical investigation by calculating the rents (1-implicit sales tax rate) for every firm by

a proxy for markups41

1 minus 120591119884119904119894 =120590

120590minus1

120573119871119904+120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119881119860119904119894 (51)

where 120591119884119904119894 shows the size of the implicit lsquosales taxrsquo (or product market distortion) for firm i in sector s

120590 denotes the elasticity of substitution between firms by consumers and 120573119871119904 and 120573119870119904 are the

coefficients of labour and capital in the production function We follow the calibration of Hsieh and

Klenow (2009)42 and set 120590 = 3 while we plug in 120573119871119904 and 120573119870119904 using our production function estimation of

Section 22 119881119860119904119894 represents the real value added of the firm i in sector s while 119871119886119887119888119900119904119905119904119894 is labour

related expenses for firm i in sector s

The equation reflects the intuition that firms facing a lower implicit sales tax can charge higher

markups and as a result will pay a lower share of their value added to their employees Note that the

level of 120591119884119904119894 depends on a number of parameters and may be driven by differences in for example the

elasticity of substitution Therefore we will normalize the values of this estimate when comparing

typical distortions across industries

Figure 55 summarizes the implicit sales taxes by industry (120591119884119904119894) We standardise the values of 120591119884119904119894 by

subtracting the market level median from the firm-level implicit sales taxes and plot the median of

40 Appendix Table A57 summarises the dispersion of TFP within industry Note that dispersion in labour productivity

(log-value added per worker) is not necessarily related to product market distortions as firms with various

labour productivity may have the same TFP if the production function does not have the property of constant

return to scale

41 Hsieh and Klenow (2009) Equation 18

42 The predicted value of product market distortions crucially depends on the elasticity of substitution However the differences in 120591119884119904119894 across industries and years measures the changes in product market distortions even if the

elasticity of substitution is miscalibrated


64

these standardised values by industry If the standardised bar is positive (negative) than the median

firm in the industry faces a higher (lower) implicit sales tax than the median firm in the economy We

find that product market distortions tend to be larger in highly regulated industries (energy

transportation ICT) while they tend to be lower in less regulated ones with strong competition

including manufacturing accommodation and administrative services The difference between

industries is non-trivial the difference between highly regulated sectors and manufacturing is

equivalent to an extra 10-20 percentage `sales tax ratersquo

The ranking of the industries (with the exception of energy) remained similar between 2006 and 2016

but differences became somewhat larger with a relative decrease in implicit taxes in manufacturing

and administrative services and an increase in transportation and ICT43

Figure 55 Implicit sales taxes (120591119884119904119894) by industry

Notes The figure above shows the median size of product market rents in 2006 and 2016 Industries with positive

tax measures can achieve rents below the market average due to product market distortions

The previous exercise has investigated across-industry differences A further question is whether firms

face different tax rates even within industries because of for example size-dependent taxes This is a

key measure to examine whether resources are misallocated across firms within industries Our

measure for this is the standard deviation of ln(1 minus 120591119884119904119894) (Figure 56)44 This dispersion is substantial

43 We report these measures in more detail in Table A55 of the Appendix

44 Also note that this measure of dispersion is independent of the elasticity of substitution and the production function parameters


65

with the standard deviation equivalent to a 100 percent sales tax45 Within-industry differences in this

variable are similar across industries with a relatively small dispersion only in mining and energy

Figure 56 Standard deviation of implicit sales tax rates (ln(1 minus 120591119884119904119894)) by industry

Notes The figure shows the within industry product market distortions in 2006 and 2016 Resources are less

effectively distributed in industries with larger distortion measures

Capital market distortions

Distortions on the capital market are identified from how the ratio of expenses on labour and capital

(capital intensity in cost terms) differ from what is predicted by the production function with no capital

tax46

119877(1 + 120591119870119904119894) =120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119870119904119894 (52)

The left hand side of this equation represents the implicit cost of capital for firm i in sector s backed

out from the capital intensity of the firm If it is 01 the firm faces an implicit lsquointerest ratersquo of 10

percent if it is 02 the lsquointerest ratersquo is 20 This can be decomposed into 119877 the frictionless user

45 Similar differences have been found in other countries as well and they are in line with the vast degree of heterogeneity in terms of size and productivity within industries



66

costs47 of capital (having the same unit of measurement) multiplied by 1 plus the implicit lsquocapital tax

ratersquo 120591119870119904119894 which is firm-specific48

Similarly to the product market equation 120573119871119904 denotes the labour elasticity of the production function

120573119870119904 is the capital elasticity of the production function and 119871119886119887119888119900119904119905119904119894 is the total labour cost for firm i in

sector s The denominator consists the capital stock of the firm (119870119904119894)

It is not common in the literature to report 120591119870119904119894 because its absolute value depends crucially on the

calibration of the rental rate of capital This is an issue because it is hard to obtain reliable information

on the frictionless rate of capital which most likely changed substantially between the pre-crisis

disinflationary period and the wake of the crisis Besides 120591119870119904119894 takes extremely large values for firms

with low level of capital (eg if the firm rents its capital instead of owning it) Note that the levels of

this variable are identified from the difference between the observed capital intensity (in cost terms)

and the optimal one implied from the production function Therefore we prefer to report the more

easily interpretable implicit median cost of capital 119877(1 + 120591119870119904119894) by industry49

While we find differences and changes in the implicit cost of capital informative it is not a direct

measure of capital market distortions because it can also reflect differences in the user cost of capital

across industries and years However the ratio (or log difference) of the implicit cost of capital

between two firms measures the difference between their respective implicit capital tax rates (or more

precisely between their 1 + 120591119870119904119894) As a result the standard deviation of the log implicit cost of capital

provides a pure measure of the dispersion of implicit capital taxes independently from the exact value

of 119877 Its interpretation is the relative standard deviation of the user cost of capital which is identified

from the dispersion of capital intensities

Figure 57 summarizes the median size of implicit cost of capital across industries50 Administrative and

professional services and ICT seem to pay the highest implicit cost for capital it is above 40 percent in

these industries As opposed to these utilities accommodation and food services face implicit costs of

capital below 20 percent The large differences in access to capital across industries are likely to result

mainly from differences in the size and age distribution of firms as well as from the different share of

tangible capital in different industries Moreover the median implicit cost of capital rose practically in

all service industries but decreased slightly in manufacturing

47 The rental price of capital covers the interest rate and the depreciation of capital stock

48 If one is willing to assume a specific value for the frictionless user cost of capital it is easy to back out 120591119870119904119894 For

example if the implicit cost of capital for firm 119894 (the left hand side) is 02 and (following Hsieh and Klenow 2009) one sets R = 01 then 120591119870119904119894 = 1 meaning that firm 119894 can obtain capital at a 10 percentage points higher interest rate

relative to the frictionless rate

49 The median of 119877(1 minus 120591119870119904119894) is less dependent on the extreme values of the distribution than the average so it is a

more precise measure of capital market distortions a typical firm faces than the average of it

50 We can validate our implicit capital cost measure by comparing our results to Kaacutetay and Wolf (2004) According to their estimates (using a different methodology) the median user cost of capital was 189 percent between 1993 and 2002 Our results have similar magnitude as the median implicit cost of capital was 255 percent in 2006 and 287 percent in 2016


67

Figure 57 Median implicit cost of capital by industry

Notes The figure shows the average size of capital market distortions in 2006 and 2016 Industries with larger

distortion measures are more constrained in accessing capital due to capital market distortions

Again the differences in typical capital costs across industries are much smaller than differences across

firms within an industry (see Figure 58) In industries where median implicit capital costs are lower

the dispersion of those costs also tends to be smaller the estimated cost of accessing capital is

significantly more unequal in the retail sector and administrative services relative to manufacturing

The notable exemption is the energy sector which has the lowest median and the largest dispersion in

the implicit cost of capital reflecting a relatively low level of capital costs resulting from predictable

tangible capital intensive activities


68

Figure 58 The standard deviation of the estimated implicit cost of capital by industry

Notes The figure shows the standard deviations of capital market distortions log (119877(1 + 120591119870119904119894)) in 2006 and 2016

Most importantly capital market distortions increased within nearly all industries both in terms of

their levels and dispersion Hungary is not an exception in this respect This trend has been

documented in other countries where FDI played important role in economic growth A key study on

this topic is Gopinath et al (2017) who show that large capital inflows and credit market constraints

of small firms jointly increased capital market distortions in Spain This evidence suggests that the

crisis led to similar developments in Hungary making capital costs more unequal by generating

financial frictions This inefficiency seems to have resulted in the misallocation of capital in all types of

industries

A key question from a policy perspective is whether one can identify types of firms which faced a

systematically large increase in their cost of capital We follow the approach of Gorodnichenko et al

(2018) who quantified the misallocation of capital at the firm-level and found that small and young

firms faced an exceptionally high cost of capital We follow this strategy to identify observables which

are likely to be related to the level and change of capital costs

Figure 59 plots the relationship between firm age firm size and the estimated implicit cost of capital

119877(1 + 120591119870119904119894) The figure sorts the firms into twenty equally-sized bins by age and size and plots the

median implicit cost of capital separately for 2006 and 2016 Panel (a) highlights that the implicit cost

of capital was decreasing with firm age even before the crisis with young firms facing about 25

percentage points higher capital costs compared to firms older than 10 years This function became

dramatically steeper by 2016 when the median `oldrsquo firm (more than 10 years old) faced an implicit

capital cost of 25 percent a median 5-year old firm paid 50 percent and a very young firm faced more

than a 100 percent implicit cost of capital This figure suggests that capital market frictions generate

important constraints for entry and the growth of small firms hindering reallocation and innovation


69

Panel (b) of Figure 59 visualizes the relationship between employment and the implicit cost of capital

We find that firms with more than 20 employees faced an implicit cost of capital below the median of

the whole sample both in 2006 and 2016 As opposed to this small firms faced above the median

implicit cost of capital in 2006 and suffered from a disproportionally large increase in the next decade

This again constrains the growth of small firms relative to their larger peers

Figure 59 The evolution of the implicit cost of capital by age and firm size

A) Age of firms

B) Size of firms

Notes The figure shows the median implicit cost of capital 119877(1 + 120591119870119904119894) by age and size categories


70

The results presented above have shown two patterns an increasing dispersion of the implicit cost of

capital on the one hand and a steeper gradient between observables (age and size) and capital taxes

on the other A natural question is whether increased financial friction led to larger differences in

access to capital along observables One explanation for this could be that banks have become more

wary about allocating capital to say firms operating in industries with much intangible capital The

alternative is that the increased variance in capital access reflects mainly differences along unobserved

dimensions by for example more scrutiny of managers when deciding about firm loans These two

possibilities can have different policy implications In the former case for example policymakers may

promote access to capital for specific groups of firms

Table 51 presents regressions with the implicit capital cost as a dependent variable and key firm-level

characteristics as explanatory variables Our first conclusion is that the regressions explain only a

relatively small part (less than 20 percent) of the variation in the implicit cost of capital the

overwhelming majority of the variation arises from unobservables In this sense policies targeting

specific types of firms may have a limited effect

That said the explanatory power of observables increased by around a third between 2006 and 2016

While the explanatory power of industry dummies slightly decreased that of age increased

substantially from 2 percent to 57 percent The explanatory power of size was much smaller in both

periods suggesting that its correlation with the implicit cost of capital may be confounded by its

correlation with age and industry This evidence together with Figure 59 suggests that indeed

capital access by young firms deteriorated substantially after the crisis

Table 51 Variance decomposition of implicit cost of capital

Variance in 2006 Variance in 2016

Variance

component

Share

of total

Variance

component

Share

of total

Total Variance of log-implicit

cost of capital

2126 100 2443 100

Components of Variance

Variance of age 0042 20 0140 57

Variance of size 0006 03 0002 01

Variance of ownership 0012 06 0022 09

Variance of region 0012 06 0025 10

Variance of industry 0202 95 0180 74

Residual 1830 861 1995 817

Notes Control variables are dummies for age ownership (private foreign or state-owned) region (7 NUTS2

region) and 2 digit industry

53 Conclusions

This section summarises the static measures of allocative efficiency by industry types (Table 52) A

key pattern that emerges is that resources are allocated more efficiently in the manufacturing sectors

First on average the OP covariance is strongly positive within manufacturing while it is very close to

zero in less knowledge-intensive services The Hsieh-Klenow (2009) efficiency measures suggest that

product market distortions are similar across sectors but capital market distortions are significantly

lower in manufacturing These findings are in line with the disciplinary effect of international

competition in the traded sector


71

Table 52 Allocative efficiency within industries sectors (2016)

Industry type TFP

level

in

2016

TFP

growth

between

2011 and

2016

Olley-

Pakes

allocative

efficiency

Dispersion

of implicit

sales taxes

Dispersion

of implicit

cost of

capital

Low-tech mfg 5694 0027 0197 111 146

Medium-low tech mfg 6081 0027 0017 102 142

Medium-high tech mfg 6129 -0093 0119 111 131

High-tech mfg 6708 0276 0072 107 145

Total manufacturing 5942 0021 0242 107 143

Knowledge-intensive serv 6706 0225 0403 106 166

Less knowledge-intensive serv 6566 021 -0081 108 159

Construction 6411 0082 0023 109 148

Utilities 5949 -0138 0801 093 155

Total services 6598 0212 0055 108 160

Notes The table summarises the allocative efficiency measures by broad industry categories The dispersion of

implicit sales taxes is measured by the standard deviation of 119897119899(1 minus 120591119884119904119894) while the dispersion of the implicit cost of

capital is measured by the standard deviation of ln (119877(1 + 120591119870119904119894))

Capital market distortions became more severe in the wake of the financial crisis while there was no

such trend in terms of product market distortions This finding is in line with results for Southern

Europe (Gamberoni et al 2016a Gopinath et al 2017) and CEE countries in general (Gamberoni et

al 2016b) (see Figure 510) This suggests that the financial intermediation system is still less

effective relative to its pre-crisis performance

Investigating at the firm-level we found that the deterioration of the financial conditions did not hit all

firms equally In particular young firms were hit especially hard by ever increasing capital costs even

though many policy tools were introduced to help such firms including the subsidized access to capital

by the Central Bank (eg the NHP program) Deteriorating access to capital by young firms can be

especially harmful for reallocation often driven by dynamic young firms Policies aimed at promoting

equal and efficient access to capital especially for young firms may help to reduce these inefficiencies

Given the magnitude of the still existing allocative inefficiency policies which support reallocation could

have a significant positive effect on aggregate productivity A key conclusion of recent research is that

firm-specific distortions which may result from discretionary policies or non-transparent regulations

have a quantitatively significant effect on aggregate productivity (Hsieh and Klenow 2009 Bartelsman

et al 2013 Restuccia and Rogerson 2017) In particular size-dependent taxes and regulations

(Garicano et al 2016) ineffective labour and product market regulations and FDI barriers (Andrews

and Cingano 2014) have been shown to be negatively associated with allocative efficiency and its

improvement Gamberoni et al (2016b) also demonstrate that higher corruption levels slow down the

improvement of allocative efficiency Chapter 8 will investigate the effects of such policies in more

detail using the example of the retail industry

The specific pattern showing that capital distortions are relatively high and have increased in Hungary

(similarly to other CEE and Southern European countries) suggests that policies which facilitate the

reduction of financial frictions and provide symmetric access to capital for all firms could improve

allocative efficiency to a large degree Specifically policies should attempt to facilitate capital flows to


72

more efficient firms even if young rather than to firms with a higher net worth or more tangible

assets (Gopinath et al 2017)

Figure 510 Capital and labour misallocation in CEE countries country-specific weighted average

across sectors

Notes This is a reproduction of Figure 1 from Gamberoni et al (2016b)


73

6 REALLOCATION

After investigating the level of allocative efficiency in Chapter 5 namely a lsquostaticrsquo approach we now

turn to a dynamic view focusing on how much reallocation across industries (Section 61) and firms

(Section 62) contributed to aggregate and sectoral productivity growth

61 Reallocation across industries

An important channel behind the relationship between economic development and productivity is the

structural change of the economy first from agriculture to manufacturing and then from

manufacturing to services (Herrendorf et al 2014 McMillan et al 2017) But at higher levels of

development economic growth is also associated with reallocation across industries within these broad

sectors primarily from more traditional to more knowledge-intensive ones (Hausmann and Rodrik

2003 Hausmann et al 2007) Kuunk et al (2017) demonstrate that in terms of its contribution to

productivity growth across-industry reallocation within sectors dominated reallocation across sectors

in CEE countries In this subsection we take a brief look at the importance of this process in Hungary

by quantifying the reallocation of employees across and within 2-digit industries

Table 61 shows how the employment share of different industries in our main sample changed over

time51 The most important pattern is a pronounced shift from manufacturing to services until 2010

and near-constant sectoral shares after that In particular the share of manufacturing decreased by

nearly a quarter from 38 percent to 32 percent between 2004 and 2010 but this number remained

unchanged in the years following the crisis The crisis seems to have constituted a structural break in

this process

A more detailed look at the composition of industries shows that ndash in net terms ndash this structural

change was driven by a transition of employment from low-tech manufacturing52 to both knowledge-

intensive and less knowledge-intensive services while the employment share of the more high-tech

manufacturing industries remained practically unchanged After 2010 the structure of manufacturing

remained mainly unchanged in this aspect with no further shift away from low-tech manufacturing

activities Within services we see a continuous increase in the share of knowledge-intensive services

both before and after the crisis In the 12 years under study the employment share of knowledge-

intensive services increased by 6 percentage points or nearly 60 percent

51 Note that these calculations in line with other parts of this report apply to the firm sector of the Hungarian

economy ie ignore the self-employed (see Section 42) When taking into account the self-employed the share of

services and sectoral share follow somewhat different dynamics

52 One factor behind this process might have been the almost doubling of the minimum wage in 2000 and 2001

(Koumlllő 2010 Harasztosi and Lindner 2017) and a growing import competition in the light industries (David et al

2013)

Reallocation

74

Table 61 Employment in different sectors (main sample)

2004 2007 2010 2013 2016

Low-tech mfg 152 117 107 105 100


Medium-high tech mfg 94 96 82 89 93

High-tech mfg 49 49 44 39 35



Less knowledge-intensive serv 382 395 410 405 397

Construction 86 88 83 75 75

Utilities 40 34 34 33 34

Total services 616 645 676 671 673

Notes This table shows employment shares by industry type (see Section 25) for the full sample

To provide a more detailed picture Figure 61 illustrates how employment growth in different 2-digit

industries is associated with their initial productivity level (Figure 61) In particular if more productive

sectors increase their employment share faster aggregate productivity should grow

Figure 61 Employment change as a function of initial TFP

A) Manufacturing


75

B) Services

Notes Industries are 2-digit NACE Rev 2 industries The fitted line is weighted with initial employment Main

sample

To quantify whether across-industry reallocation matters we decompose the aggregate productivity

growth observed in our sample into the contributions of cross-industry reallocation and within-industry

productivity growth We divide our sample into three-year periods and calculate the average yearly

productivity growth by periods

∆119905119891119905 = sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119901119894119905minus3)119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905

+ sum 119905119891119901119894119905minus3 lowast (119904ℎ119886119903119890119894119905 minus 119904ℎ119886119903119890119894119905minus3)119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905

(61)

where the left hand side variable is the change in aggregate TFP between years 119905 minus 3 and 119905 119904ℎ119886119903119890119894119905 is

the share of the (2-digit) industry i in year t in the total employment and 119905119891119901119894119905 is average TFP of the

industry The first term on the right side is the within-industry TFP growth weighted by initial market

shares and the second term is the between effect capturing whether more productive industries have

increased their employment shares53

The decomposition in Figure 62 presents the result of this reallocation exercise for annualized growth

rates Its interpretation is the following between 2004 and 2007 average annual productivity growth

was nearly 8 percent in the total economy Around 7 percentage points from it is explained by within-

industry developments and only about 1 percentage point by reallocation across industries

53 This decomposition gives a comprehensive measure of the reallocation between industries but it is unable to

show the importance of firm exits and entries We investigate this in the next section

Reallocation

76

In general the figure shows that within-industry reallocation rather than cross-industry

developments played the key role in aggregate productivity growth Furthermore in line with Table

61 the contribution of between-industry reallocation was effectively zero post-crisis During the crisis

cross-industry productivity growth contributed positively to aggregate productivity growth while within

industry reallocation dramatically lowered aggregate productivity

This overall picture suggests that the flow of resources from light industries to other manufacturing

the growing share of services and especially knowledge-intensive services were a detectable though

not dominant driver of productivity growth only before 2010 Within-industry developments were

quantitatively more important throughout the whole period under study

This latter finding hints at a deterioration in the environment determining the reallocation process

post-crisis This seems to be the case for the whole economy but the negative contribution of

reallocation is more pronounced in manufacturing

Figure 62 Across and within industry productivity growth annualized log

Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth main

sample

62 Reallocation across firms

In this subsection we take a look at the role of reallocation from a different perspective Rather than

focusing on whether the resources flow across industries we take a firm-level focus and decompose

TFP growth to within and across firm components The usefulness of this approach lies in the fact that

it sheds more light on the flexibility and efficiency of the process determining resource flows across

firms and also allows us to distinguish between resource flows across continuing firms on the one hand

and entry and exit on the other


77

There are two general methods of measuring the reallocation of resources from less efficient to more

efficient firms The first method quantifies the labour and capital gains of more efficient firms directly

(Harasztosi 2011 Petrin et al 2011 Petrin and Levinson 2012) The second method is based on

product-market developments allocation of resources improves if the market share of high

productivity firms increases (Baily et al 1992 Griliches and Regev 1995 Brown and Earle 2008)

We adopt this second method as it can quantify directly the TFP contribution of firm entries and exits

To begin with we decompose the aggregate TFP growth between years t and t-3 based on the method

of Foster et al (2001) and Foster et al (2008)

∆119905119891119905 = sum 119904ℎ119886119903119890119894119905minus3 lowast ∆119905119891119901119894119905minus3119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905

+ sum (119905119891119901119894119905minus3 minus 119905119891119905minus3 + ∆119905119891119901119894119905) lowast ∆119904ℎ119886119903119890119894119905minus3119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905

+

sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119905minus3)119894isin119873⏟ 119890119899119905119903119910 119890119891119891119890119888119905

+sum 119904ℎ119886119903119890119894119905minus3 lowast (119905119891119901119894119905minus3 minus 119905119891119905minus3)119894isin119873⏟ 119890119909119894119905 119890119891119891119890119888119905

where the left hand side variable is the average annual aggregate TFP growth between years t-3 and t

and 119905119891119905 is the employment weighted average aggregate TFP while 119905119891119901119894119905 is the TFP of firm i in year t

119904ℎ119886119903119890119894119905 denotes the employment share of firm i in year t The first and second sum contain every firm

while the third sum consists of only firms which enter between years t-3 and t and the fourth sum

consists firms which leave the market between years t-3 and t

Each element of this decomposition has an intuitive economic interpretation In order of inclusion

these are i) within-firm TFP growth weighted by initial market shares ii) between effect capturing

whether initially more productive firms have raised their market shares and whether firms with

increasing productivity also expand (cross effect) and the iii) entry effect and iv) exit effect We pull

the last two terms together and interpret it as net entry effect which captures whether more

productive firms entered than exited54

Figure 63 summarizes the results for the market economy Before the crisis all three components

contributed positively to aggregate productivity growth Reallocation both across continuing firms and

on the margin of entry and exit was an important driver of productivity growth Productivity growth

was negative during the crisis as we have seen in Section 43 This was a result of strong negative

within-firm growth partly counterbalanced by positive reallocation Within-firm growth was still

sluggish immediately after the crisis but reallocation was relatively intensive and efficient Within-firm

growth recovered after 2013 and the importance of reallocation decreased Still the contribution of all

three components is substantially smaller relative to pre-crisis suggesting that the productivity

slowdown results from a combination of low within-firm growth and less effective reallocation

54 Note that these quantities cannot be easily linked to the withinacross industry decomposition of the previous

section Across firm reallocation and the entry effect can take place both across and within sectors

(62)

Reallocation

78

Figure 63 Dynamic decomposition annualized log main sample

Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth by 3-

year periods main sample

Figure 64 repeats the decomposition exercise for each industry type For ease of interpretation (and

to get more stable results) we aggregate the three non-high-tech manufacturing sectors for these

calculations

As we have seen in Section 43 productivity dynamics differed markedly across these sectors Still

there are some common patterns First the strong pre-crisis productivity growth resulted from a

combination of strong within-firm productivity growth and efficient reallocation The sectors differ in

terms of the weights of these forces reallocation (especially entry) was most important in non-high-

tech manufacturing while within-firm growth dominated in high-tech manufacturing In services the

two components were of roughly equal importance

As we have seen productivity increased even during the crisis in high-tech manufacturing as a

combination of within and across productivity growth In other industries productivity growth was

negative during the crisis In non-high-tech manufacturing a strongly negative within growth was

somewhat counterbalanced by positive reallocation In contrast we find evidence for a negative

reallocation effect in services during the crisis

Immediately following the crisis (2010-2013) within growth remained sluggish but reallocation

resulting from firm entry and exit intensified especially in non-high-tech manufacturing and high-tech

services By 2013-2016 within growth recovered and the effect of reallocation became smaller


79

Figure 64 Dynamic decomposition by sector

A) High-tech Manufacturing

B) Non-high-tech Manufacturing

Reallocation

80

C) Knowledge-intensive services (KIS)

D) Not knowledge-intensive services (NKIS)

Notes This figure shows the Foster et al (2008) type dynamic decomposition of the productivity growth in our

sample for 3 periods by broad sectors as defined by the EurostatOECD (httpeceuropaeueurostatstatistics-

explainedindexphpGlossaryHigh-tech)


81

One of the main messages of our analysis in Section 44 has been the large and persistent duality

between globally oriented and other firms This motivates our investigation of the extent to which

exporters and foreign-owned firms contributed to productivity growth and also whether reallocation

via the expansion of the more productive group contributed to aggregate productivity growth In order

to investigate these questions we decompose aggregate productivity growth into three parts the

within contribution of exporters (in the starting period) the within-contribution of non-exporters and

the reallocation between the two groups (which mainly reflects the change in the market share of

exporters) We conduct a similar analysis between foreign and domestically-owned firms

Table 62 shows the decomposition by export status Pre-crisis exporters contributed substantially

more to productivity growth than non-exporters both in manufacturing and services The reallocation

of resources to exporters mattered little Exporters were still capable of improving their productivity

levels during the crisis though it was not enough at the aggregate to counterbalance the falling

productivity of non-exporters Post-crisis the productivity growth of exporters slowed down and

aggregate growth was mainly driven by productivity changes within the non-exporter group

Productivity growth became much less exporter-driven post-crisis

Table 62 TFP growth decomposition by exporter status annualized log

2004-2007

Total Exporter Non-exporter Across

Market economy 793 577 226 -009

Manufacturing 1053 877 164 011

Market services 606 387 222 -003

2007-2010


Market economy -045 091 -136 000


Market services -178 129 -305 -002

2010-2013


Market economy 206 033 144 029

Manufacturing -122 -129 -012 019

Market services 471 077 289 106

2013-2016



Manufacturing 057 049 011 -003


Notes This table decomposes the sales-weighted productivity growth into within-exporter within-non-exporter

contributions and the contribution of the reallocation between the two groups main sample

Table 63 decomposes productivity growth by ownership The picture is similar to the exporter

decomposition with a key contribution of foreign-owned firms to productivity growth pre-crisis and a

much smaller contribution after that Again reallocation of resources to foreign-owned firms played a

limited role in productivity growth

Reallocation

82

Table 63 TFP growth decomposition by ownership status annualized log

2004-2007

Total Foreign Domestic Across




2007-2010



Manufacturing 054 081 -034 007

Market services -178 -131 -120 073

2010-2013



Manufacturing -122 -152 027 003


2010-2016





Notes This table decomposes the sales-weighted productivity growth into within-foreign within-domestic

contributions and the contribution of the reallocation between the two groups Main sample

63 Failure of reallocation Zombie firms

Following the crisis it was suggested that weak productivity performance could be linked to the

survival of unprofitable and ineffective firms The presence of many such firms limits the access of

better-managed firms to capital and generates congestion in the product markets which limits entry

(Caballero et al 2008) McGowan et al (2017) have argued and provided evidence that the share of

such ldquozombie firmsrdquo has risen since the middle of the 2000s and that the higher share of such firms is

associated with lower productivity growth and investment at the industry level

Given the productivity slowdown in Hungary and the extent to which the financial crisis has affected

bank lending it is of interest to see whether the prevalence of ldquozombie firmsrdquo increased

disproportionately after the crisis

Figure 65 shows the share of ldquozombie firmsrdquo in 9 OECD countries from McGowan et al (2017) The

share of such firms in the full sample increased from just below 3 percent in 2003 to 5 percent in

2013 The rise was especially noticeable in Spain and Italy where in 2013 the share of firms reached

11 and 5 percent respectively Even more importantly the employment share of ldquozombie firmsrdquo rose

above 15 percent in both countries by 2013 possibly generating significant effects for other firms


83

Figure 65 Share of ldquozombie firmsrdquo in some OECD countries

Notes This is a reproduction of Figure 5A from McGowan et al (2017) Country codes should be interpreted as

follows BEL ndash Belgium ESP ndash Spain FIN ndash Finland FRA ndash France GBR ndash Great Britain ITA ndash Italy KOR ndash South

Korea SWE ndash Sweden SVN ndash Slovenia

Our basic definition of ldquozombie firmsrdquo follows McGowan et al (2017) for comparability We define a

firm as a zombie if it is at least 10 years old and its interest coverage ratio (the ratio of operating

income to interest expenses) has been below one for the last three years A limitation of this definition

is that interest expenses are not reported (or missing) for many smaller firms which only submit a

less detailed financial statement (or have no bank financing) To overcome this problem we also

categorize firms as zombies if their operating profit is negative for three subsequent years In such

cases the coverage ratio is not defined but the firmrsquos income is clearly not enough to cover its interest

expenses Note that this is a very conservative definition ndash one could also input interest expenses for

external capital for firms with missing interest expenditures (Figure 66)55

55 In actual fact 95 percent of zombies defined in this manner have negative profits

Reallocation

84

Figure 66 Share of ldquozombie firmsrdquo in Hungary

Notes Main sample

The patterns are the following First the share of ldquozombie firmsrdquo among firms with at least 5

employees was relatively high even before the crisis reaching about 8 percent by 2006 This increased

slightly in the wake of the crisis but started to decline after that falling to 3 percent in 2016 ldquoZombie

employmentrdquo fluctuated around 12-15 percent in most years with a steep decline after 2014

Put in an international context it is clear that the existence of ldquozombie firmsrdquo is a relatively big issue in

Hungary with their employment share at the highest end of the distribution of the OECD countries

examined by McGowan et al (2017) The prevalence of such firms however had been relatively high

even before the crisis with a relatively moderate growth between 2009 and 2011 followed by a

significant fall of the share of these firms Therefore ldquozombie firmsrdquo may have constrained productivity

growth in Hungary in the whole period but it is unlikely that an increase in zombie share is a key

explanation for productivity slowdown following the crisis

Table 64 shows the employment share of zombie firms in different dimensions One can see a U-

shaped relationship in terms of size with the largest zombie share among the smallest and the largest

firms The somewhat larger share of zombies among small firms may be explained by the tendency of

such firms to report losses in order to evade business taxes Large firms may be able to operate

persistently under losses either because of their accumulated savings or even more likely because of

the deep pockets of their owners This is also suggested by part B) which shows that a firm is more


85

likely to be a zombie if owned by the state56 or by foreigners In the latter case profit-shifting motives

may also play a role in reporting losses for sustained periods in Hungary Finally zombies are more

prevalent in services compared to manufacturing and in low-tech industries compared to high-tech

Table 64 Zombie employment by size ownership and industry

A) By size

2004 2007 2010 2013 2016

5-9 emp 62 751 862 802 411

10-19 emp 618 626 679 652 297

20-49 emp 607 554 698 648 296

50-99 emp 711 685 792 829 438

100- emp 2005 1839 1559 1489 456

Total 1548 1367 1229 1194 417

B) By ownership

2004 2007 2010 2013 2016

Domestic 66 671 769 614 253

Foreign 989 1011 1139 1577 585

State 6289 5954 4127 2792 7

Total 155 1369 1228 1194 417

C) By type of industry

2004 2007 2010 2013 2016

Low-tech mfg 1236 1255 1149 906 371



High-tech mfg 427 1392 429 268 1

KIS 2098 737 875 1408 592

LKIS 282 2327 187 1867 365

Construction 258 394 339 515 352

Utilities 297 1031 705 593 756

Total 1553 1372 1229 1194 418

Notes Main sample

Importantly all these patterns persist in multiple regressions when one includes all these variables at

the same time together with other controls (ie larger firms are more likely to be zombies even when

controlling for ownership) In such regressions (lag) productivity is the strongest predictor of not

56 Obviously the extreme employment share of state-owned zombie firms partly results from the massive size of some large utilities including the national railways and the Hungarian Post

Reallocation

86

becoming a zombie firm later one standard deviation higher productivity is associated with a 5

percentage point lower probability of becoming a zombie in the next period Note however that

productivity is actually a close measure of profitability hence this finding mostly reflects a mechanical

relationship of high profitability firms being less likely to become low profitability firms in the future

Figure 67 shows a 3-year transition matrix for zombie firms ie the share of year t zombie firms

which ldquorecoverrdquo remain zombies or exit from the market by year t+3 One cannot see radical changes

across years with somewhat more firms recovering and less exiting in later periods In line with the

argument about deeper pockets larger firms are more likely to remain zombies and less likely to exit

than smaller ones This is related to ownership foreign (and to a smaller extent state-owned) firms

are more likely to remain zombies There also seems to be a characteristic difference between

manufacturing and services manufacturing firms seem to be less likely to lsquorecoverrsquo and more likely to

exit suggesting more persistence of low performance in that sector

Figure 67 What happens to zombie firms within 3 years (2010)

Notes Main sample

64 Conclusions

In line with the immense within-industry productivity heterogeneity documented in Chapter 4 and 5

we find that while there was some reallocation across sectors in the economy the overwhelming

majority of productivity growth took place within industries This emphasizes the usefulness of policies

which promote productivity growth in a sector-neutral way rather than prioritizing some sectors of the

economy

In line with the lower efficiency of capital allocation post-crisis we have found that by and large both

within-firm productivity growth and reallocation across firms and industries became less efficient post-

crisis relative to its pre-crisis level This may reflect the presence of policies which promote specific

sectors or inhibit the growth and entry of more productive firms


87

In terms of the participation of global networks we have found that at least pre-crisis exporters and

foreign-owned firms contributed significantly to productivity growth Post-crisis the productivity

contribution of internationalized firms became much less substantial Adopting policies that create an

environment which is favourable for innovative investments and does not hamper the expansion of

globally oriented firms may contribute substantially to strengthening productivity growth

The presence of firms which are loss making for an extended period of time suggests a serious failure

of the reallocation process The share of such firms was relatively high in Hungary employing well

above 10 percent of the employees in our sample in most years This level was already high pre-crisis

and increased further during the crisis but there has been substantial improvement in recent years

The problem is more severe for larger firms and state owned firms Improving the corporate

governance of these firms and the effectiveness of the banking system may help in further alleviating

the problem

Andrews et al (2017) argue that the presence of zombie firms ndash and other barriers to firm dynamics ndash

is heavily related to the efficiency of insolvency regimes and the effectiveness of the banking system

Figure 68 shows an insolvency regime index developed by the OECD (the higher the index value the

slower the restructuring) Hungary is one of the countries with the weakest insolvency systems with

all sub-measures taking high values This coupled with the presence of weak banks can be one of the

reasons for the permanently high zombie firm share as well as the increasingly inefficient capital

allocation across firms Therefore insolvency reform complemented with policies aimed at improving

bank forbearance can help to reduce the presence of zombie firms The presence of zombie firms may

also be related to the large share of bank financing Promoting market-based financing including bond

and venture capital markets may also help to diminish the problem

Figure 68 Insolvency regimes across countries

Notes This chart is a reproduction of Andrews et al (2017)rsquos original except for being restricted to European

states only The stacked bars represent the 3 main components of a countrys insolvency index for the year 2016

while the diamond figure indicates these measures aggregate for the year 2010 The authors constructed these

figures with the help of an OECD questionnaire Each measure is associated with a factor that in the long term is

thought to reduce a countrys business dynamism and consequently hamper its proclivity for productivity growth

The first one Personal costs of insolvency stands for environmental factors which could curb a failed

entrepreneurs ability to start new businesses in the future The second measure Lack of prevention and

streamlining indicates whether there are sufficient practices in place for the early detection and resolution of

Reallocation

88

financial distress Thirdly Barriers to restructuring shows how easy it is for a firm suffering from short-term

financial troubles to restructure its debt Country codes should be interpreted as follows GBR ndash Great Britian FRA

ndash France DNK ndash Denmark DEU ndash Germany ESP ndash Spain FIN ndash Finland IRL ndash Ireland SVN ndash Slovenia PRT ndash

Portugal AUT ndash Austria GRC ndash Greece SVK ndash Slovakia ITA ndash Italy LVA ndash Latvia POL ndash Poland NOR ndash Norway

SWE ndash Sweden LTU ndash Lithuania BEL ndash Belgium CZE ndash Czech Republic MLD ndash Moldova HUN ndash Hungary EST ndash

Estonia


89

7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS

The main aim of this section is to investigate the micro-level processes which underlie the patterns

documented at the industry level in the previous chapters (especially in Chapter 6) by presenting a few

descriptive relationships between firm characteristics and firm dynamics More specifically we would

like to understand how various firm characteristics are associated with the observed patterns of

productivity and employment growth to illustrate the micro-level processes behind within-firm

productivity growth and reallocation Additionally we look at which types of firms enter and exit in

order to shed light on how they contribute to changes in the average productivity level

We seek to answer three main questions First was there a structural break either in the productivity

growth or in the reallocation process after the crisis which may have contributed to the productivity

growth slowdown Second do we see a structural difference in these processes along the main

dimensions of the lsquodualityrsquo of the Hungarian economy eg the characteristic differences between

globally involved large firms and their domestic market oriented peers Third can we find peculiar

patterns which may explain the unusual evolution of productivity quintiles namely the slow

productivity growth of frontier firms relative to less productive firms (as documented in detail in

Section 44)

In terms of firm characteristics we focus on variables which are likely to be related to the duality (see

Section 44) ownership size age and exporter status We do firm-level regression analyses which

allows us to use a rich set of controls and fixed effects Additionally we look at the interaction of the

different characteristics to get an even more precise picture about the main factors driving productivity

growth and reallocation

The structure of this chapter follows closely the logic of the dynamic productivity decomposition

exercise in Chapter 6 In Section 71 we investigate the determinants of within-firm productivity

growth In Section 72 we explore how firm characteristics are related to future employment growth ndash

ie to between firm reallocation ndash followed by the analysis of entry and exit in Section 73

71 Productivity growth

Questions and descriptive patterns

A key relationship of interest is how future productivity growth is related to current productivity levels

Our main motivation to study this question is that it can shed light on the extent of convergence to

more productive firms within the industry If there is a tendency for low-productivity firms to catch up

the productivity growth of such firms will be higher We analyse this relationship for the whole

economy and will also split the sample along different dimensions We are particularly interested in

three questions First is there a difference between the productivity growth rates of firms along some

dimensions even when controlling for productivity We think that this question is highly relevant but

will also qualify the findings of for example Section 32 where we compared firms with different

ownership structures and of different sizes with each other unconditionally which may mask the

different composition of the two groups in terms of initial productivity levels Second we are interested

in whether the slope of the relationship between the initial productivity level and subsequent

productivity growth differ along observable dimensions Is it the case for example that domestically-

owned firms face a productivity ceiling beyond which they cannot improve their efficiency any further

while foreign firms are better able to push forward even starting from very high productivity levels

Third we would like to find out whether there are structural changes in this relationship which may be

associated with the productivity slowdown following the crisis

Firm-level Productivity growth and dynamics

90

While the main mechanism behind this relationship is likely to result from a process of convergence

between firms the measured relationship can also partly arise from a mechanical negative relationship

coming from regression to the mean A large positive measurement error in productivity in year t

automatically generates a large negative growth rate from t As we are interested in the convergence

process rather than the mechanics of the regression to the mean we look at the relationship between

lagged productivity levels and 3-year productivity growth We assume that regression to the mean

resulting from measurement errors is less likely to show up when the productivity level is lagged An

additional limitation of this exercise is survivorship bias because lower productivity firms are more

likely to exit if they are unable to improve their productivity level We will analyse exit and entry

separately in Section 73

First to see the overall patterns we present the relationship between initial productivity levels and

productivity growth in the following 3 years in a non-parametric way (see Figure 71) To do so we

classify firms within each industry into 20 quantiles based on productivity in the previous year For

example we show how productivity growth between 2012 and 2015 is related to productivity levels in

2011 For each quantile we calculate average growth after partialling out 2-digit industry fixed

effects We show this relationship for different years to see whether there is a structural change in the

within-firm productivity growth process57 We demean lagged productivity levels by 2-digit industry

and year so zero on the horizontal axis corresponds to the mean productivity level We take four

periods pre-crisis (2003-2006) crisis (2006-2009) post-crisis (2009-2012) and recent (2012-

2015)58

Figure 71 shows that the relationship between previous productivity levels (on the horizontal axis) and

subsequent 3-year growth (on the vertical axis) can be well approximated with a linear relationship

We see a pronounced negative relationship in all periods reflecting that (surviving) lower productivity

firms increase their productivity faster than more productive firms generating some within-firm

convergence in the sample of continuing firms The slope of the relationship ie the productivity

growth premium of less productive firms is quite stable across non-crisis years but differs markedly in

the crisis showing that the crisis-related productivity decline was more severe for more productive

firms probably because these firms had been hit the hardest by the collapse of global trade59 Note

that this is much in line with the slow productivity growth of frontier firms in the same period

documented in Section 43 Figure 44 In normal times macro conditions seem to shift the whole line

up or down rather than rotate it The average 3-year productivity growth rate is the lowest during the

crisis and is still low in the post-crisis period but there is no difference between the pre-crisis and the

recent periods60

57 As in the previous chapters we use our main sample (see Chapter 2) in which we only consider firms with at

least 5 employees and measure productivity with the method of Ackerberg Caves and Frazer (2015)

58 Note that to measure subsequent growth we need three years following the base year when the level of

productivity is measured Consequently the last year we include is 2012 ndash and follow what happens to firms

between 2012 and 2015

59 More exit of low-productivity firms during the crisis may have also introduced a survivorship bias but as the

patterns in Figure A71 of the Appendix show this seems not to be the case

60 Table A71 of the Appendix shows the same patterns from a regression


91

Figure 71 The relationship between lagged productivity levels and subsequent productivity growth

over time

Notes This figure shows how the log of productivity in t-1 (on the horizontal axis demeaned by 2-digit industry

and year) is related to productivity growth between t and t+3 Each dot represents one of 20 quantiles of the

productivity level distribution and the average 3-year growth rate of firms within that quantile including 2-digit

industry fixed effects

Estimation

After establishing a linear relationship between lagged productivity level and subsequent growth we

look at the role of firm characteristics in productivity growth We do it in two steps First we look at

cross-sectional patterns taking the most recent period (2012-2015) We ask if there is a difference

between firm groups in productivity growth for the average firm (ie a firm having industry-average

productivity) and if there is a difference in the convergence pattern These two aspects correspond to

differences in the level and the slope of the line We estimate the following regressions

1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905

119896

119870

119896=1

+ 1205732(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1) +sum1205733119896(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1)119866119894119905

119896

119870

119896=1

+ 119883119894119905 + 120572119895(119894) + 휀119894119905

We denote productivity of firm i in year t with 119905119891119901119894119905 1198893_119905119891119901119894119905 stands for 3-year productivity growth

119905119891119901 119895(119894)119905minus1 is the year-specific average lagged productivity in industry j of firm i G is a firm characteristic

(eg ownership or size) which contains K categories (eg one ownership group foreign or three size

categories) 119883119894119905 is a set of additional firm-level controls (these can be size age ownership or exporter

status) 120572119895(119894) is industry or industry-region fixed effects and 휀119894119905 is the error term Then 1205731119896 measures the

productivity-growth difference for average-productivity firms in firm group 119866119896 (eg foreign) compared

to average-productivity firms in the baseline category (eg domestic) 1205733119896 measures the difference in

the convergence patterns between firm group 119866119896 and the baseline category

(71)


92

Second we also check dynamic patterns to see how the role of these firm characteristics changed over

time taking the same periods as in Figure 71 The baseline regression for comparing productivity

dynamics across years is as follows

1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905

119897

119897=200320062009

+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1

119897 )119863119905119897

119897=2003200620092012

+ 119883119894119905 + 120572119895(119894) + 휀119894119905

As before 1198893_119905119891119901119894119905 is the 3-year productivity growth of firm i from year t to t+3 and 119905119891119901119894119905minus1 denotes the

productivity level of firm i in t-1 119905119891119901 119895(119894)119905minus1119897 denotes the year-specific average lagged productivity in

industry j which firm i belongs to 119863119905119897 is an indicator for year l 119883119894119905 is a set of firm-specific time-variant

controls and 120572119895(119894) is industry or industry-region fixed effects as in the previous specification 1205731119897

measures the difference between the productivity growth of firms with industry-average productivity in

year l and in year 2012 The difference comes from two sources industry-level average productivity

levels could change over time and productivity growth for firms with the same productivity level could

also vary As we are interested in how the productivity growth of the average firm changed over time

we will not separate these two effects 1205732119897 measures the slope of the relationship between lagged

productivity levels and subsequent productivity growth in year l Comparing the different 1205732119897 coefficients

shows how the process of convergence between low- and high-productivity firms changed over time

We take a similar approach when we compare group-specific productivity dynamics over time We

interact group indicators demeaned productivity levels and the interaction of the two from the static

regression with a full set of year dummies and include year dummies separately as well

1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905

119896119863119905119897

119870

119896=1119897=2003200620092012


119897 )119863119905119897

119897=2003200620092012

+ sum sum1205733119896119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1

119897 )119866119894119905119896

119870

119896=1

119863119905119897

119897=2003200620092012

+ sum 1205734119897119863119905

119897

119897=200320062009

+ 119883119894119905 + 120572119895(119894) + 휀119894119905

Comparing 1205731119896119897 coefficients for different l-s shows how the difference between average-productivity

firms in the baseline category and in group k changed over time Similarly comparing 1205733119896119897 coefficients

with different l-s shows how convergence differences between the baseline category and group k firms

evolved over time These specifications allow us to add industry-year fixed effects so we can also

control for industry-specific trends

Results

Figure 72 shows the non-parametric relationships by firm characteristics creating scatter plots which

show productivity quantiles separately by firm groups These figures hint at the fact that on average

foreign-owned and exporter firms experience higher productivity growth conditional on initial

productivity levels In addition the relationship between the initial productivity level and subsequent

growth is weaker for foreign-owned firms suggesting that even highly productive foreign firms are

able to raise their productivity further while similar domestic firms have a harder time doing so Size

groups and age groups are similar to each other though the smallest firms have stronger convergence

patterns than the largest

We can discover the same scenarios using regression analysis in which we can control for the

abovementioned firm characteristics and fixed effects (Table 71) The most important conclusion is

that average-productivity foreign-owned firms raise their productivity faster relative to similar

domestic firms by about 10 percentage points Average exporters also have a TFP growth advantage

(72)

(73)


93

relative to non-exporters but this premium disappears when we control for ownership We find some

evidence for a positive interaction between productivity levels and foreign ownership in line with lower

constraints for further TFP growth in the case of foreign frontier firms The same pattern applies to

exporter firms

Figure 72 The relationship between lagged productivity levels and subsequent productivity growth by

firm group

By ownership By exporter status

By size By age

Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to log productivity growth

between t and t+3 Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-

year growth rate of firms within that quantile including 2-digit industry fixed effects

The similar results for exporters and foreign-owned firms ndash and the strong correlation between foreign

ownership and exporter status ndash raise the question does this difference arise from foreign ownership

or exporting or do both variables have an independent effect Table A73 in the Appendix examines

this question only to find that the foreign premium in average productivity growth unconditional on

the productivity level is there both for exporters and non-exporters and is higher for younger and

smaller firms When we look at how the relationship between lagged productivity level and subsequent

growth differs by both characteristics at the same time we find that both foreign ownership and

exporter status matter but for different aspects of the relationship The difference in the slope of the

relationship comes both from the foreign-owned and from exporters compared to low-productivity

firms of the same category high-productivity firms grow relatively faster if they are foreign-owned The

same is true for comparing exporters and non-exporters At the same time the average difference in


94

productivity growth comes from foreign ownership firms with industry-average productivity levels

have a higher productivity growth if they are foreign There is no significant additional effect for foreign

exporters on top of adding up foreign and exporter premia either in average productivity growth or in

convergence61

The TFP growth advantage of foreign-owned firms even when compared to domestically-owned firms

with the same productivity level points at a mechanism that reinforces the already existing duality

when domestic firms reach frontier productivity levels their TFP growth slows down much more than

that of foreign firms This self-reinforcing mechanism may be behind the non-convergence between

foreign and domestic firms (Section 44) With regard to size and age we find that high-productivity

firms have a relatively greater chance to increase their productivity if they are larger or older

compared to their smaller and younger counterparts (see Table A72 in the Appendix)

Table 71 The relationship between lagged productivity levels and subsequent productivity growth by

ownership and exporter status

Dep var TFP growth from t to t+3 (t=2012)

Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190

(000556) (000568) (000622) (000629) (000656) (000663)

TFP in t-1 Foreign 00475 00418 00433 00423

(00140) (00142) (00252) (00253)

TFP in t-1 Exporter 00477 00287 00216 00224

(00104) (00106) (00125) (00126)

TFP in t-1 Foreign exporter -000697 -00151

(00312) (00314)

Foreign 0117 0109 0120 0104

(00121) (00132) (00223) (00226)

Exporter 00240 000260 000401 0000919

(000791) (000845) (000851) (000881)

Foreign exporter -000670 000871

(00267) (00272)

Industry FE YES YES YES

Industry-region FE YES YES YES

Firm-level controls YES YES YES

Observations 29717 29717 30135 30062 29717 29717

R-squared 0060 0072 0056 0073 0060 0072

61 Looking at the same patterns over time (Table A74 in the Appendix) suggests that higher average productivity

growth is a rather stable feature of foreign firms The only exception was the crisis period when it disappeared

Splitting the sample by broad sectors shows that foreign firms have higher average productivity growth both in

manufacturing and services The difference in within-group convergence patterns stayed the same for the

foreign The same is true for exporters except for the pre-crisis period when the coefficient is not significant


95

72 Employment growth

Question and descriptive patterns

The relationship between initial productivity levels and subsequent employment growth shows the

reallocation of continuing firms Between-firm reallocation results from more productive firms growing

faster In this subsection we ask how between-firm reallocation changed over time and how

reallocation patterns vary by different firm characteristics

To measure reallocation we use a similar approach to that in the previous subsection but the

lsquodependentrsquo variable will be 3-year employment growth in log terms rather than productivity growth

The slope of the estimated relationship reflects the employment growth advantage of more productive

firms or the strength of ldquocreative destructionrdquo among surviving firms Shifts in the level show changes

in the average growth rate

We calculate the 3-year employment growth using the formula119871119905+3minus119871119905

(119871119905+3+119871119905)2 where 119871119905 is the number of

employees in year t This formula shows the percentage increase in employment from year t to t+3

compared to the average size in year t and t+3 This measure performs better for smaller firms than a

simple log difference in employment as it does not result in extremely high numbers with a low initial

employment level62 In all the regressions of this subsection we control for exact firm size using the

logarithm of the number of employees

Figure 73 Reallocation by year

Notes The figure shows how the log of productivity in t-1 (horizontal axis) is related to employment growth

between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)

Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment

growth rate of firms within that quantile including 2-digit industry fixed effects

62 Additionally while the baseline estimates are only for continuing firms this measure allows us to include firms

exiting in the period (t+1t+3) as well in some robustness checks In these cases we take Lt+3 = 0


96

Figure 73 illustrates the patterns in the data non-parametrically The relationship between previous-

year productivity levels and subsequent employment growth is positive in all years This shows that in

line with the creative destruction hypothesis more productive firms are more likely to grow in the

subsequent three years The figure doesnrsquot show characteristic changes in the reallocation process

across years the slope of the curves being similar to each other Our regression estimates presented

in the Appendix (Tables A75 and A76) support that reallocation patterns are stable over time63 The

average growth rate of typical firms naturally follows the macro cycle strongly ndash aggregate changes

seem to shift the line up or down but do not seem to rotate it In other words with this approach we

do not find evidence for a structural change in the reallocation process therefore it is unlikely that

such a change should explain satisfactorily the productivity slowdown

We create similar figures for the most recent period (2012-2015) by different firm characteristics

(Figure 74) The most important result is that exporters grow significantly faster than non-exporters

when controlling for their initial productivity This leads to reallocation from non-exporters to

exporters Given that the productivity advantage of exporters is in the order of 30-100 percent in the

different industries (see Section 43) this reallocation process can yield enormous productivity gains

The slope of the curve is also less steep for exporters suggesting that their expansion is less

dependent on their productivity level relative to domestic firms in other words reallocation within the

exporter group is weaker relative to non-exporters

63 As before the relationship between lagged productivity levels and subsequent employment growth can be

properly approximated by a linear function


97

Figure 74 Reallocation by firm groups


By size By age

Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to employment growth





98

Estimation results

Table 72 Reallocation by ownership and exporter status

Dep var employment growth from t to t+3 (t=2012)

Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 0105 0102 0105 0107 0107 0108

(000484) (000493) (000539) (000546) (000570) (000575)

TFP in t-1 Foreign

-00369 -00328 -00252 -00224

(00123) (00124) (00217) (00217)

TFP in t-1 Exporter

-00344 -00298 -00250 -00249

(000913) (000932) (00109) (00110)

TFP in t-1 Foreign exporter

-0000806 000194

(00271) (00272)

Foreign 000105 -000863 -000786 -0000106

(00112) (00116) (00194) (00196)

Exporter 00586 00635 00653 00672

(000738) (000754) (000777) (000786)

Foreign exporter

-000647 -00123

(00234) (00238)


Industry-region FE

YES YES YES

Firm-level controls

YES YES YES

Log of employees

YES YES YES YES YES YES

Observations 31662 31662 32124 32043 31662 31662

R-squared 0035 0049 0038 0051 0037 0049

Looking at the regression results (Table 72) confirms our previous findings even after controlling for

fixed effects Exporters with an average productivity level grow about 6 percentage points faster than

non-exporters hinting at strong positive reallocation between the two groups with slightly weaker

reallocation within the exporter group64 At the same time average-productivity foreign-owned firms

do not have higher employment growth than domestic ones Similarly to productivity growth we find

no extra premium for foreign exporters65 66 Overall these results emphasise that participation in

64 We define exporters based on their export activity in year t so the group of exporters also includes those firms

which export in t but not any more afterwards This means that a worse subsequent performance ndash lower

growth and exiting from exporting ndash has no effect on our exporter classification

65 The main patterns concerning employment growth of average-productivity firms are robust to modifying the

employment growth measure in such a way that it includes exits as a full employment decline (See Table A77

in the Appendix) In this version employment growth of foreign firms is significantly lower overall but this is

counterbalanced by the significantly positive coefficient of the foreign exporter indicator

66 We show in Table A78 of the Appendix that the higher average growth of exporters is present in all size (except

for the largest) age and ownership groups Dynamic patterns suggest (in Table A79 of the Appendix) that the

higher growth rate of average-productivity exporters is robust over time This result is also robust to splitting

the sample into manufacturing and services


99

international markets is an important driver of industry and aggregate productivity growth in Hungary

by providing opportunities for exporters to expand as Section 62 has documented

As Table 73 shows competitive pressure also seems to affect more the growth prospects of smaller

firms the relationship between initial TFP levels and employment growth is significantly stronger for

smaller firms Between-firm reallocation appears to be much stronger for smaller firms while less

productive large firms are unlikely to contract even if they are inefficient conditional on survival

There are no clear patterns by age groups

Table 73 Reallocation by size and age group


Firm categories by size age

VARIABLES (1) (2) (3) (4)

TFP in t-1 0112 0110 00875 00863

(000486) (000502) (00133) (00133)

TFP in t-1 Group 2 -00455 -00431 00445 00384

(00128) (00129) (00182) (00183)

TFP in t-1 Group 3 -00745 -00748 000733 000822

(00194) (00196) (00141) (00142)

TFP in t-1 Group 4 -00953 -00957

(00218) (00220)

Group 2 -000596 -000810 -00188 -00212

(00122) (00123) (00141) (00141)

Group 3 -000928 -00157 -00115 -00169

(00199) (00201) (00114) (00115)

Group 4 00328 00280

(00276) (00278)

Industry FE YES YES

Industry-region FE YES YES

Firm-level controls YES YES

Log of employees YES YES YES YES

Observations 32124 32043 32124 32043

R-squared 0038 0052 0037 0051

Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees and size group 4 is 100+

employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5 years age group 3 is

firms older than 5 The baseline category is firms of 2-3 years

73 Entry and exit

Questions

This subsection aims at investigating which firms enter and exit and in particular how productive

those firms are relative to continuing firms This corresponds to the micro-level equivalent of the net

entry effect (see Chapter 6) The motivation for the micro-level investigation is that in this manner we


100

can study which firm-level factors determine the type of firms that enter and exit and control for

industry heterogeneity

Our approach is similar to the previous section with the main difference being that this time the

dependent variable is productivity while the variables of interest are entry and exit dummies Their

coefficients show the productivity lsquopremiarsquo (often negative) of new entrants and exiting firms relative

to continuing firms These premia are especially useful to answer two kinds of questions First their

magnitude and size inform us about how entry and exit contribute to productivity growth Second

changes in these premia are also indicative of the changes in the costs of entry and the survival of

low-productivity firms

Estimation

To use a symmetric approach we define entrants and exiting firms using a 3-year interval An entrant

is a firm that has entered in the previous 3 years67 This means we look at the productivity of firms in

year t and compare it between incumbents (ie firms older than 4 years) and entrants (ie firms being

2-4 years old) In a similar way we compare the productivity in year t of firms exiting in the following

3 years (ie the last time the firm reports positive employment is in the period (t t+2) and non-

exiting firms (firms still reporting positive employment in year t+3)

As before we start with a static approach looking at the productivity premium of entrants and exiting

firms in the most recent period (taking year 2015 for 2012-2014 entrants and 2012 for firms exiting in

2013-2015) Then as a dynamic approach we take all four time periods as before and interact the

premia with year dummies The static regression we estimate is as follows

119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905

119896119873119864119894119905119870119896=1 + 1205732119866119894119905

0119864119894119905 + sum 1205733119896119866119894119905

119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)

119905119891119901119894119905 is the productivity of firm i in year t (measured in logarithm) 119866119894119905119896 is the k-th category (eg size

category 5 with more than 100 employees) in a grouping according to firm characteristics G (eg

size) and 1198661198941199050 is the baseline category (eg firms with 5-49 employees) 119864119894119905 stands for entrant or

exiting firm dummy in the different specifications and 119873119864119894119905 are incumbent or continuing firms

accordingly Then 1205732 measures the entry or exit premium for firms in the baseline category and 1205733119896

measures the same premium for firms in category k of grouping G Both premia are calculated

compared to incumbentscontinuing firms in the baseline category 1198661198941199050 1205731

119896 measures the productivity

advantage or disadvantage of incumbentcontinuing firms in category k of grouping G also compared

to the average productivity level in the baseline group As before 119883119894119905 includes additional firm-level

characteristics and 120572119895(119894) is industry or industry-region fixed effects In those versions where we include

industry fixed effects we identify from within-industry differences This means that 1205733119896 measures the

same entry or exit premium for firms in category k of grouping G compared to incumbentscontinuing

firms in the same category As before we create the dynamic version of the above regression by

interacting 119866119894119905119896119873119864119894119905 119866119894119905

0119864119894119905 and 119866119894119905119896119864119894119905 with year dummies and including year dummies separately as well

67 We consider firms changing industry from manufacturing to services or vice versa as exitors and new entrants at the same time (see Chapter 2)


101

Results

Table 74 shows how the productivity premium of entrants and exiting firms changed over time In

these specifications we compare the yearly average productivity of incumbents and entering or exiting

firms separately in each year The point estimates suggest that entering firms were about 2-4 percent

more productive than incumbents except for the 5 percent productivity disadvantage in the pre-crisis

period while exiting firms were 10-20 percent less productive than the continuing firms The

productivity advantage of entrants and the disadvantage of exiting firms did not change radically

during our time period This difference constitutes a potential for positive net entry effects in terms of

reallocation The exact value of the net entry effect also depends on the share of employees affected

by entry and exit While the premia of entering and exiting firms remained roughly the same in the

different periods exit and entry rates changed (see Section 33) which results in positive net entry

effects before the crisis and negative effects after that (see Section 62)

Table 74 Productivity premium of entering and exiting firms over time

Dep var TFP in year t

Firm group Entry Exit

(1) (2) (3) (4) (5) (6)

EntrantExitorPeriod 2003-2006

-00532 -00535 -00465 -0214 -0199 -0200

(000906) (000873) (000879) (00102) (000987) (000989)


00269 00212 00232 -0135 -0117 -0119

(000967) (000931) (000936) (000897) (000865) (000865)


00369 00444 00360 -0133 -0114 -0121

(000960) (000926) (000931) (000920) (000889) (000889)

EntrantExitorPeriod

2012-2015

00374 00324 00252 -0171 -0150 -0157

(000971) (000936) (000944) (00107) (00103) (00103)

Period 2003-2006 -0115 -00927 -00711 -00445

(000519) (000501) (000556) (000537)

Period 2006-2009 -0175 -0169 -000972 00122

(000522) (000503) (000537) (000518)

Period 2009-2012 -0116 -0117 -00580 -00528

(000528) (000508) (000543) (000522)

Year FE YES YES YES YES

Industry FE YES YES


Firm-level controls YES YES YES YES

Industry-year FE YES YES

Observations 166607 166168 166168 158143 157711 157711

R-squared 0327 0380 0380 0332 0385 0387


102

Next we focus on the most recent period and look at the productivity differences of entrants and

exiting firms by different firm groups

Figure 75 Productivity premium for entering and exiting firms by ownership

Figure 75 presents the premia of domestic and foreign entering and exiting firms relative to domestic

incumbents As we saw in Section 44 foreign firms are on average more productive than domestic

ones68 foreign incumbents have on average a premium of 669 Compared to domestic incumbents

foreign entrants have 513 higher productivity There is also a positive productivity premium of 29

for exiting foreign firms Similarly the productivity of exiting exporters is 186 higher than that of

continuing non-exporters69 This means that domestic incumbent firms can survive longer even with a

lower level of productivity Consequently having many foreign entrants has a positive effect on

average productivity while on average foreign exits do not affect average productivity70 71

68 Table A711 shows that the productivity advantage of foreign-owned firms is present in all size and age groups as well as both within the exporter and non-exporter firm groups

69 Table A710 of the Appendix shows the estimation results with standard errors

70 As Table A712 of the Appendix shows foreign entrant premium and the premium of continuing or exiting

foreign and exporter firms seem fairly stable over time The positive premium of entering and exiting foreign

firms is also robust for splitting the sample into manufacturing and services

71 As Table A713 of the Appendix shows there is no considerable difference in the productivity disadvantage of

exiting firms by size or age group


103

74 Conclusions

One contribution of this chapter is that we have documented that one of the factors behind the

sustained duality in productivity between foreign and domestically-owned firms is that foreign-owned

firms tend to be more capable of upgrading their productivity even from already high productivity

levels Similar patterns apply to the more globally oriented exporters This mechanism underlines the

importance of policies that promote absorptive capacity-building (see Section 45) a strong knowledge

base easy access to external knowledge and flexible and advanced skills are especially important

when upgrading productivity beyond already high levels

We have also found strong reallocation from non-exporters to exporters Given the high productivity

premia of exporters in Hungary (Beacutekeacutes et al 2011) and in general (Wagner 2007) such a

reallocation can lead to substantial improvement in aggregate productivity (and as we have seen in

Section 62 it did to some extent before the crisis) These results emphasise that participation in


because it provides valuable opportunities for exporters to expand Note that this reallocation effect of

international openness has been in the focus of the recent literature on international trade (Melitz

2003 Bernard et al 2006 Amiti and Konings 2007 Topalova and Khandelwal 2011 De Loecker

2011) Note also that the asymmetric expansion possibilities of exporters and domestic firms also

amplify the duality between the two groups

The analysis of entry and exit has revealed that entrants are somewhat more productive than

incumbents even a few years after entry Exiting firms are significantly less productive This on the

one hand implies that exit and entry is a substantial source of reallocation (as Section 62 has shown)

On the other hand the low productivity of exiting firms also suggests that domestic firms can survive

long even with relatively low productivity levels maybe because of inefficiencies in the capital

allocation process including the insolvency regime

Productivity evolution and reallocation in retail trade

104

8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE

The previous chapters have presented a number of results on the productivity and growth in different

sectors of the economy The aim of this chapter is to look deeper into one of the key sectors of the

economy namely retail trade for more detailed insights

Two main reasons have motivated us to choose the retail sector First retail is a key sector of the

economy which provides jobs for a great many people and influences what consumers can buy and at

what prices Retail (and wholesale) does not only interact with consumers it is a key supplier of inputs

while beig a buyer of outputs for all other firms in the market economy72 The degree to which it is

capable of supplying a large variety of intermediate inputs at reasonable prices is an important

determinant of the productivity of firms relying on these sources Its market structure also affects

fundamentally the incentives that producers experience73

The second reason is that there have been a number of regulatory changes in the retail sector in

Hungary in recent years While these policies had multiple motivations one of their common features

is that they are size-dependent either explicitly or implicitly As such they have a potential to increase

the costs of larger firms and influence the reallocation process in favour of smaller mostly

domestically-owned firms This may matter as international evidence has shown that much of retail

productivity growth in recent decades has resulted from the expansion of large store chains (Foster et

al 2006) Exactly because of the strong links between retail and other industries regulatory

restrictions in retail represent nearly a third of all service-related restrictions which are carried over to

other sectors of the economy74

The structure of this chapter is the following Section 81 describes the policy context of Hungarian

retailing Section 82 introduces the available datasets Section 83 describes the major developments

in retail productivity Section 84 describes trends in reallocation The last three sections describe three

specific questions Section 85 analyses the role of retailers and wholesalers in importing and

exporting Section 86 provides a few illustrative statistics on how size-dependent taxes could have

affected reallocation and prices Finally Section 87 evaluates a specific policy namely the mandatory

Sunday closing of larger shops Section 88 concludes

81 Context

The retail industry is an important employer in all EU member states and Hungary is not an exception

Its employment share in our sample has been around 12 percent (Figure 81) Similarly to the EU as a

whole retail productivity is below the average of the market economy therefore its GDP share is

below its employment share Still it represented 6-7 percent of total value added in our sample

72 See EC (2018) for the importance of the retail industry in Europe

73 See Smith (2016) for a review of this literature

74 EC (2018) p 5


105

Figure 81 The share of retail and wholesale firms in market economy value added and employment

Notes Full sample with at least 1 employee in any of the years

The largest sub-industry within retail is groceries (NACE 4711) Its share of the total turnover around

40 percent is at the lower end of the EU distribution75 Given its importance (and the large sample size

within it) we will often study only groceries in our empirical analyses

Measuring the restrictiveness of different regulations in any sector of the economy is not an easy task

The European Commission has designed a ldquoRetail Restrictiveness Indicatorrdquo to quantify the potential

effect of these regulations in force at the end of 2017 (see Figure 82) The higher values of the

indicator indicate more restrictive regulations76 According to this indicator the restrictiveness of retail

regulation in Hungary is slightly below the EU average and similar to other CEE countries

The indicator distinguishes between regulations related to the establishment of shops on the one hand

and those related to their operation on the other In Hungary there are few operations restrictions

(mainly restrictions on distribution channels) while entry is regulated more heavily mostly by size-

related restrictions and requirements for economic data

75 EC (2018) p 4

76 There is ample empirical evidence that entry barriers planning regulations and operating restrictions are related to productivity and prices in retail Some examples are Bertrand and Kramarz (2002) Viviano (2008) Haskel and Sadun (2012) Sadun (2015) Daveri et al (2016)


106

Figure 82 Retail Restrictiveness Indicator

Notes This is a reproduction of Figure 8 from EC (2018)

While regulation in Hungary is not especially restrictive a number of new measures were introduced

following the crisis (see Box 81) While these have various motivations a common feature of most of

them is that they are size-dependent As such they may distort competition and constrain reallocation

to larger firms

One type of size-dependent policies is size-dependent taxes Crisis taxes introduced right after the

crisis (and phased out in 2013) were highly progressive in sales volume Local business taxes have

been similarly progressive in total sales at the firm-level since 2013 Other size-dependent policies are

restrictions on the establishment of shops or their operation The Plaza Stop law constrained the

establishment of malls larger than 300 m2 Another peculiar policy was requiring larger shops to close

on Sundays between March 2015 and April 2016

Quantifying the effect of such policies is not an easy task In some cases it is not possible with the

data at hand to identify the shops and firms which were affected by the different types of taxes For

example without knowing the exact location of the establishment it is not possible to identify which

firms operate in malls and hence could have been affected by the Plaza Stop law As we discuss in

Section 85 the highest bracket of the crisis tax only affected 6 firms and thus it is hard to run

statistical tests with an appropriate power In contrast some of the effects of the mandatory Sunday

closing policy can be very effectively estimated based on shop-level data

Therefore we will apply two complementary strategies The first is to investigate whether there are

trend breaks in the reallocation process following the crisis when many of the new policies were


107

introduced While we find suggestive changes around the crisis one cannot make casual statements

based on this strategy given the number of other changes in the economy The second strategy is to

examine specific policies where a credible differences-in-differences identification is possible

Unfortunately this strategy is basically limited to Sunday closing

BOX 81 Size-dependent taxes and regulations in the retail sector

This box describes a number of size-dependent taxes and regulations which could be linked to the retail data and investigated during this exercise The list is only indicative and will be appended by desk research and possibly interviews

2010-2013 crisis taxes

Crisis taxes were introduced in 2010 and were in force (mostly) until 2013 They affected the energy telecom and retail sector as their base was operating profits resulting from these activities The tax rate was strongly progressive for retail

Below 500m HUF 0

Between 500m and 30bn HUF 01

Between 30bn and 100bn HUF 04

Above 100bn HUF 25

Between March 15 2015 and April 23 2016 Sunday closing for larger and non-employee owned retail stores

The 2014 CII law which came into force on March 15 2015 banned shops with a retail space of more than 400 square meters to open on Sundays with some exceptions most notably the new tobacco shops Smaller shops could only open if their workers had at least a 20 stake in the

business or if they were close relatives of the owner The law was repealed in 2016

2013-today Progressive local business tax

The base of local business tax is the ldquoadjustedrdquo revenue of firms This usually means revenue minus material expenditures but regulation stipulating the exact method of calculation has changed a number of times since the introduction of this type of tax In 2013 a progressive

element was introduced by making the definition of the cost of purchased goods size-dependent In particular smaller firms can now deduct more of their expenditures than larger ones The deductible part is

Below 500m HUF of net sales 100



Above 80bn HUF 70 of the cost of goods is eligible


108

82 Data

We rely on two main data sources in this chapter The first one is the NAV balance sheet data

described in detail in Chapter 2 Based on the industry code identifier we restrict the sample to firms

in industry 47 retail There are a few firms which switch to this category from other industries (mainly

wholesale of food manufacturing) We keep the whole history of these firms throughout the analysis

Second we use a retail-specific survey conducted by the Hungarian Central Statistical Office which

samples firms and collects data for all shops of the sampled firm77 Firms included in the sample are

compelled by law to submit monthly reports on their turnover and 4-digit industry-codes plus for all

of their stores information about these entitiesrsquo location (municipality) identification number 4-digit

77 httpswwwkshhudocshuninfo02osap2018kerdoivk181045pdf

BOX 81 Size-dependent taxes and regulations in the retail sector (cont)

2013-today Licensing of tobacco wholesale and retail

On 22 April 2013 in line with Act CXXXIV ldquoon reducing smoking prevalence among young people

and the retail of tobacco productsrdquo (adopted by the Hungarian Parliament on 11 September

2012) the National Tobacco Trading Non-profit Company (a 100 government-owned joint-stock

company controlled by the relevant minister under the mandate of this law) was established

From then on only special ldquonational tobacco shopsrdquo licensed by the state have been allowed to

sell tobacco products These shops enjoy a number of benefits compared to other shops

Exempted from the Sunday closing for retail shops

National tobacco shops are exempted from the ban on selling alcohol after 10pm rarr in effect

tobacco shops do not come under the ruling of the commercial law Local municipalities can

otherwise regulate shops based on that law

2011- today ldquoPlaza Stoprdquo Law

The so-called Plaza Stop Law (the 2011 CLXVI Law) came into force in January 2012 It

prohibits the construction of new retail facilities or the expansion of any already existing one with

a leasable area of more than 300 msup2 Exemptions could be granted to certain developments by a

committee of ministry officials and with the approval of the Minister of National Economy

In 2013 the law was extended to include building conversions In February 2015 a new

amendment was ratified which basically renewed the effect of the 2011 law and introduced some

modifications to it Now retail facilities with a floor space of less than 400m2 can be built without

any special procedure Furthermore the right to grant exemptions was given to a special

administrative department which is supplemented by a committee made up of delegated

members of different ministries


109

industry-code sales and the monthly number of days spent open The sample consists of all larger

retail firms78 and a representative sample of other firms re-sampled on an annual basis

An important consequence of this design is that we observe each of the shops of the sampled firms

This is valuable in two respects First with this information it is possible to calculate the number of

shops and average shop size at the firm-level Second one can identify new and exiting shops for

firms which are in the sample continuously ie larger firms Further with the help of the firmsrsquo

identification number we are also able to link this information to data from the NAV database for

qualified analysis

Two caveats may be mentioned here First the re-sampling of the representative part of the sample

prevents us from following small firms through the entire sampling frame Second in the beginning of

2012 there was a switchover in the coding of shop-level identification numbers which prevents us

from linking shops before and after

As mentioned above the database also includes information on the industry classification of the shop

In most of our exercises we restrict the sample to grocery stores more formally bdquoRetail sale in non-

specialised stores with food beverages or tobacco predominatingrdquo (NACE 4711) Table 81 shows the

sample size of the merged database for groceries We have classified firms according to the number of

shops they have and report their number and their storesrsquo number according to these categories

Table 81 The number of firms and the number of shops in different size categories in Groceries

1 shop 2-4 shops 5-9 shops 10-49 shops gt50 shops year firm shop firm shop firm shop firm shop firm shop

2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446

2006 573 573 51 131 59 430 111 2008 30 1548

2007 546 546 53 125 60 429 110 1987 33 1634

2008 628 628 45 102 50 350 104 1823 21 1574

2009 527 527 33 72 41 290 99 1879 24 1754

2010 472 472 22 49 32 238 94 1793 22 1968

2011 537 537 14 30 29 212 92 1758 22 2027

2012 374 374 30 68 49 335 88 1643 23 2107

2013 503 503 48 121 42 277 88 1622 25 2094

2014 410 410 106 239 48 320 81 1530 24 2054

2015 512 512 135 311 42 292 80 1544 30 2090

2016 518 518 120 292 37 271 77 1457 23 2022

A key distinction in this merged database is the one between shops and firms Sales employment

ownership is observable only at the firm-year level so these variables are the same for each of the

shops of a firm for a calendar year Shops are only observable for sampled firms but we observe

sales and the number of days they were open at a monthly regularity As a result even if one runs

regressions at the shop-month-level productivity and employment can only vary at the firm-year

level For this reason we always cluster the standard errors at the firm or firm-year level

78 Larger firms are defined as having more than 7 stores in operation or with a number of employees of more than 50 and at least 6 stores or with a significantly large store in a product category


110

While balance sheet data includes information on exports it does not inform us about imports In

Section 84 we use detailed trade data to analyse importing by wholesale and retail firms This is

reported at the importer firm-product (8 digit Harmonized System)-country of origin level Most

importantly we can link this information to the balance sheet of the firm This is collected by a survey

following the European Unionrsquos practice79 We aggregate these data to the firm-year level but

distinguish between consumer goods capital goods and intermediate inputs used in further production

by relying on the correspondence table of the Eurostat between the Harmonized System and Broad

Economic Category classifications

83 General trends

Let us start with describing the firm size distribution across years (see Table 82) for firms with at least

one employee Similarly to other EU countries the majority of firms in retail are very small in

different years between 70-75 percent of retail firms employed less than 5 people80 The share of firms

with more than 50 employees fluctuated at around 1 percent

As one would expect larger firms have a significantly larger weight in terms of employment and sales

The top 05 percent of firms employed more than 30 percent of all employees in each year The

employment-share of these top firms increased nearly monotonically from 33 percent in 2004 to more

than 38 percent in 2011 when it reached its peak This was followed by a slightly declining trend to

357 percent in 2016 This time path represents the gradual expansion of large chains both organically

and via the acquisition of stores81 up to the crisis when this trend seems to have ended

The market share of large firms is even larger reaching 45 percent in 2016 The difference between

the employment share and sales share shows that large retail firms are substantially more efficient ndash

at least in terms of sales over employees ndash than the average firm At the other extreme the smallest

retail firms generate only 126 percent of sales with 20 percent of employees suggesting that in 2016

each of their employees sold only about half of the average The patterns are similar in other years

Efficiency differences are large in this sector though not larger than in most other sectors of the

economy (see Chapter 4)

79 See httpeceuropaeueurostatstatistics-explainedindexphpInternational_trade_statistics_-_background An important limitation of these data is that firms only report transactions above a specific size This may bias estimates of firm-level importing downward for small firms

80 As we discussed in Section 42 the NAV sample includes only double-entry bookeeping firms while the unemployed and people working in firms with simplified accounting are omitted from these data These people are likely to work in small economic units with low productivity levels

81 The increasing share of large retailers is a general trend globally see Ellickson (2016)


111

Table 82 Share of firms in different size categories (at least 1 employee)

A) Number of firms

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100

2006 7333 1613 687 252 065 049 100

2007 7369 1597 674 244 067 050 100

2008 7410 1571 667 236 067 048 100

2009 7522 1535 614 221 059 048 100

2010 7551 1508 640 201 057 044 100

2011 7591 1493 623 194 057 041 100

2012 7625 1506 568 204 055 042 100

2013 7526 1596 577 207 052 042 100

2014 7380 1684 628 210 056 042 100

2015 7239 1771 661 232 056 041 100

2016 7163 1804 676 252 063 043 100

B) Employment

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000

2006 2034 1475 1269 1025 679 3525 10000

2007 2026 1443 1241 984 669 3644 10000

2008 2020 1391 1138 910 579 3980 10000

2009 2002 1427 1244 870 570 3789 10000

2010 2099 1443 1243 862 577 3731 10000

2011 2144 1458 1119 895 555 3830 10000

2012 2144 1541 1131 906 541 3713 10000

2013 2168 1591 1204 896 555 3650 10000

2014 2104 1644 1236 970 548 3538 10000

2015 2064 1620 1216 1006 588 3570 10000

2016 1999 1620 1216 1006 588 3570 10000

C) Sales

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000

2006 1114 1278 1587 1025 776 4226 10000

2007 1110 1266 1543 956 855 4268 10000

2008 1112 1524 1045 891 531 4906 10000

2009 1104 1641 1073 856 718 4607 10000

2010 1104 1521 1103 930 693 4594 10000

2011 1160 1577 1004 934 624 4714 10000

2012 1146 1647 1003 913 629 4578 10000

2013 1229 1411 1090 945 709 4360 10000

2014 1486 1472 1093 1001 752 4389 10000

2015 1293 1458 1118 985 660 4512 10000

2016 1266 1458 1118 985 660 4512 10000


112

Figure 83 presents C5 concentration measures82 for the full retail sector and for some of its

subsectors83 The share of the top 5 retail firms was around 30 percent of total retail sales

Concentration was increasing pre-crisis from 30 percent in 2003 to 35 percent in 2009 Concentration

decreased and returned to its 2003 value by 2014 The latter trend as we will discuss in Section 85

may be associated with size-dependent policies

The various sub-industries exhibit different patterns in terms of concentration Let us start with

groceries Pre-crisis the dynamics in this subsector was driven mainly by the expansion of large

chains Consequently concentration was strongly increasing with C5 growing from 50 percent in 2003

to more than 60 percent by 2008 Concentration in this subsector was rising further post-crisis but at

a somewhat slower pace We observe a similar pattern of increasing concentration with a trend break

around the crisis in sales of books and clothes in specialized stores In these sub-industries

establishment regulations like the Plaza Stop law could have played a more important role in the trend

break than taxes Specialized cosmetics retailing was already very highly concentrated at the

beginning of the period and remained largely unchanged

Figure 83 Concentration in retail and various sub-industries

This observation motivates a more detailed look at different measures of efficiency and prices Panel A)

of Table 83 calculates the average TFP levels84 both for different size categories and for the

aggregate Note that TFP calculated from balance sheet data is revenue productivity measuring the

82 Calculated as the sales share of the 5 firms with the largest sales

83 We rely on a slighly different version of the NAV data for this exercise which includes 4-digit identifiers but only runs until 2014

84 See Section 22 on details of TFP estimation


113

amount of revenue produced by an input bundle Consequently it does not only measure physical

productivity (units sold per unit of input) but also markups This distinction is especially important in

retail85

Let us start with the two aggregate series one unweighted and the other weighted by employment

The employment-weighted series has higher values because more productive firms tend to be larger

(see Section 51) The two series follow a parallel trend suggesting that the correlation between size

and productivity did not change radically TFP has increased by about 15 percent from 2004 to 2006

remained constant until 2008 fallen by around 10 percent in 2009 and then started to grow by 4-5

percent each year from 2011

Note that this productivity evolution is similar to what is reported by OECD STAN before and during the

crisis but the post-crisis recovery in our data is much more pronounced (Figure 84) As we have

discussed in detail in Section 42 this is most likely a result of the large number of self-employed and

the distinct productivity level and evolution of that group86 Productivity has been definitely increasing

since 2011 in our sample

Interestingly TFP is not increasing monotonically with firm size There is a clear 25-30 percentage

point difference between the smallest firms (1-4 employees) and firms in other size categories which

have a similar TFP to each other Besides differences in efficiency this may also be partly explained by

the tax avoiding behaviour of the smallest firms ie under-reporting sales or over-reporting costs

Panel B) of Table 83 investigates gross margins These are calculated as

119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905

which is the margin that retailer 119894 realises in year 119905 on the cost it pays for the sold goods in

percentage A value of 20 shows that the price the consumer pays is 20 percent higher than what the

retailer paid for the goods87 Note that gross margins reflect a combination of two factors `physical

productivityrsquo (how much capital and labour is needed for a given amount of sales) and markups Still

gross margins are of interest because they are the closest proxy available in financial statements of

prices paid by customers

We can make two key observations First on average (weighted) margins increased from about 155

percent to 19 percent during the period under study with a fall during the crisis Second margins

were about 5 percentage points higher in the smallest retail firms compared to larger ones

Interestingly during and immediately after the crisis (between 2009 and 2012) the margins of the

largest firms were substantially lower than those of other firms This is because the margins of the

largest firms actually fell during this period while that of smaller firms remained roughly constant

85 Measurement of productivity in retail raises a number of conceptual and measurement issues (Ratchford 2016) Two main problems are the measurement of output (conceptually retail services) and of the inputs used (for example shop area) In practice however such detailed data are not available and it is standard to use TFP

86 The figures for retail are similar to those for the whole of the service industry About a third of all people engaged are self-employed operating at a significantly lower productivity level than retail firms The productivity of the self-employed did not grow between 2012 and 2015

87 We winsorise it at the 5th and 95th percentiles Note that the cost of goods sold would be preferable to material costs but that is often missing from the data especially for small firms


114

Most likely larger firms were able to cut markups while smaller firms with already lower markups

were not able to do so

As we have mentioned above margins reflect a combination of cost factors and market power The

gross operating rate88 attempts to control for labour costs and shows margins after personal cost

119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905

with value added calculated as discussed in Chapter 2 In international comparison gross operating

rates are relatively low in Hungary89 These rates show a clear downward trend with time Gross

operating margins are clearly decreasing with firm size showing that larger firms operate with large

scale and low margins Similarly to the gross margin we see a fall during the crisis

Figure 84 Productivity evolution in the NAV sample and the OECD STAN

88 httpeceuropaeueurostatstatistics-explainedindexphpGlossaryGross_operating_rate_-_SBS

89 As shown by EC (2018) Figure 2 Our weighted estimates are similar to what is reported there based on Eurostat data


115

Table 83 Performance and margins (at least 1 employee)

A) TFP

B) Gross Margin

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 1769 1548 1793 1883 1584 1495 1735 1554

2005 1839 1571 1858 1878 1504 1666 1794 1584

2006 2040 1658 1842 1879 1599 1660 1956 1653

2007 2220 1720 1875 1940 1664 1702 2104 1712

2008 2192 1792 1872 1992 1522 1739 2096 1728

2009 2084 1719 1864 1967 1599 1527 2006 1630

2010 2096 1749 1867 2102 1664 1535 2024 1682

2011 2172 1765 1862 2003 1759 1558 2084 1728

2012 2203 1740 1828 1913 1969 1541 2102 1733

2013 2243 1734 1806 2018 1999 1794 2128 1790

2014 2363 1789 1817 2149 2022 1869 2223 1855

2015 2390 1803 1885 2165 2215 1987 2245 1928

2016 2379 1799 1911 2172 2403 2170 2237 1948

C) Gross operating rate

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

unweighted weighted

2004 651 701 663 677 479 480 658 611

2005 827 757 777 712 526 560 806 644

2006 908 773 777 753 579 797 870 706

2007 807 635 653 704 500 580 764 606

2008 665 588 577 610 422 478 643 525

2009 618 535 538 522 351 339 595 456

2010 669 587 629 626 421 341 650 510

2011 698 617 591 637 410 393 676 542

2012 770 643 599 624 485 404 735 577

2013 735 594 555 631 478 369 697 530

2014 745 578 568 612 446 398 700 531

2015 766 609 625 646 547 452 724 579

2016 759 597 621 632 609 487 715 588

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 599 633 637 628 612 623 610 626

2005 610 635 643 628 616 626 619 631

2006 623 646 651 640 630 642 631 643

2007 624 643 650 641 627 643 631 642

2008 625 645 647 640 630 640 631 642

2009 619 641 642 631 619 630 626 630

2010 617 641 643 634 622 631 625 631

2011 621 643 645 637 629 639 628 639

2012 624 646 644 637 629 639 631 644

2013 626 647 642 640 637 642 632 645

2014 628 648 650 645 640 648 634 648

2015 638 658 658 655 652 659 644 659

2016 643 661 665 659 655 661 649 665


116

As Section 44 has shown for the market economy in general the Hungarian economy can be

characterised by a strong duality between foreign and domestically-owned firms Retail is one of the

sectors where this is the most transparent with many small domestic firms operating alongside large

multinational super- and hypermarket chains90 Figure 83 shows the share of foreign-owned firms in

terms of number employment and market share Foreign-owned retail firms are substantially larger

than domestic ones between 5-7 percent of firms are foreign-owned but they employ around 30

percent of employees and realise around 40 percent of sales This also implies that the salesworker

share is also larger in foreign firms than in domestic ones This results from the larger typical size of

foreign firms when controlling for size salesworker is not higher for foreign firms The market share

of foreign-owned firms is at the top of the distribution in EU countries with a larger foreign share only

in Latvia and Poland91

Figure 85 shows an inverted U-shaped pattern with an increasing market share of foreign firms until

2009 followed by a fall of nearly 5 percentage points between 2013 and 2016 This fall in foreign share

ran parallel with the introduction of policies favouring smaller firms in various ways

Figure 85 Share of foreign firms with at least 1 employee

There is much variation behind the overall pattern as Figure 86 illustrates plotting the market share

of foreign firms across sub-industries In groceries foreign share fluctuated around 70 percent It was

90 There is limited literature on the spillover effects generated by multinational retailers See for example Atkin et al (2018)

91 See EC (2018) Figure 2


117

rising slightly pre-crisis in parallel with the increasing concentration of the industry The increase of

the market share of foreign firms was the strongest in clothes reflecting the expansion of different

multinational chains mainly in plazas The increasing trend observable for the category seems to have

broken around 2012 which coincides with the introduction of the Plaza Stop regulation Foreign

market share was always high in the highly concentrated cosmetics sector A few foreign chains were

dominant in this sector throughout the period Foreign share actually decreased sharply in books and

newspapers

Figure 86 Foreign share in sub-industries

A key question when evaluating the expansion of foreign firms is their performance Foreign retail

firms are substantially more productive than domestic ones (Figure 87) With the exception of the

crisis years labour productivity advantage was between 60-80 percent while the TFP advantage was

between 20-40 percent The TFP advantage is smaller because of the larger capital intensity of foreign

firms These productivity premia are not purely a consequence of the larger size of foreign firms this

pattern is robust to controlling for firm size There is no clear trend in the premia they were declining

before the crisis (suggesting that domestically-owned firms were catching up) and rising after it The

figure also shows a large decline in the premia in the crisis years This is likely to be a consequence of

more pro-cyclical margins of foreign firms which are captured by revenue productivity measures


118

Figure 87 Productivity premia of foreign firms labour weighted

The main message of this section is that similarly to other industries large productivity differences

persist in retail These differences are primarily associated with size larger firms are more productive

and charge lower margins The performance of very small shops and the self-employed looks

especially weak The pre-crisis period was characterised by an expansion of large and foreign firms

but this growth stopped after 2010

84 Allocative efficiency and reallocation

In this section we follow the approach of Chapters 5 and 6 in analysing allocative efficiency and

reallocation with a focus on the retail industry

Chapter 5 showed that an important metric of allocative efficiency at any point in time is the degree of

co-variance of productivity and size which is directly related to aggregate productivity Figure 88

shows the elasticity of the number of employees with respect to labour productivity and TFP A more

positive relationship represents a more efficient allocation of labour across firms92 The figure shows

these relationships both for the full sample (of firms with at least 1 employee) and the main sample

(firms with at least 5 employees)

The elasticity depends crucially both on the sample and the productivity measure We find that the

correlations are much stronger when the full sample is considered rather than the base sample This

reflects our findings in Table 83 namely that the smallest firms differ substantially from other firms

92 These are coefficients from separate yearly univariate regressions with ln number of employees on the left hand side and productivity as the explanatory variable


119

while firms with at least 5 employees are quite similar to each other The labour productivity premium

of larger firms is greater than their TFP premium reflecting their higher capital intensity

The key insight from Table 83 is that most of the Olley-Pakes correlation or measured allocative

efficiency results from the fact that very small firms are of very low productivity Within the group of

firms with at least 5 employees the correlation between TFP and size is practically zero There is a

positive although small correlation within the group between employment and labour productivity93

There is also no key trend in this measure of allocative efficiency some measures show improvement

while others a deterioration94

Figure 88 The elasticity of employment with respect to productivity main sample

Figure 89 performs the dynamic (Foster-type) productivity decomposition for the retail industry The

picture is not very different from the patterns found for services in general (see Figure 64) Pre-crisis

parallel with the strong growth of large chains growth was mainly driven by reallocation primarily in

the form of firm entry The crisis was accompanied by an annual 5 percent fall in productivity driven

by within-firm productivity decline As we have seen in Table 83 this was most likely the results of

margin-cutting by large firms Between 2010-2013 within-firm productivity growth and net entry

contributed similarly to the (relatively low) productivity growth Productivity growth sped up between

2013-2016 mainly driven by the within-firm component with little reallocation The trend break in the

growth of large chains is clearly reflected in this decomposition

93 As we have discussed in Chaper 5 this is not exceptional ndash actually similar correlations are found in services in other European contries

94 These low levels of allocative efficiency are in line with international evidence In fact these correlations have been negative in the majority of EU member states (EC 2018 p 7)


120

Figure 89 Dynamic decomposition of productivity growth in retail

While these results are informative about reallocation at the firm-level the shop-level data enable us

to investigate reallocation at a more detailed level These data enable us to investigate whether key

firm or shop-level variables are related to opening new shops closing shops or the growth of the shops

of continuing firms We investigate these questions in the paragraphs that follow

The simplest way to explore the shop-extensive margin or the change in the number of shops is to

aggregate the shop-level data to the firm-level In particular we calculate the change in the number of

shops the number of new shops and the number of old shops for each firm 119894 and year 119905 Denoting

these variables which show changes between year 119905 and 119905 + 1 by 119910119894119905 we run the following firm-level

regressions

119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905

where 119883119894119905 is a vector of firm-level variables These proxy productivity (by ln labour productivity) and

size (by the number of shops of the firm and the average sales per shop) 120575119905 is a full set of year

dummies

When estimating these equations one has to make a number of compromises Most importantly one

can only observe the change in shop numbers when the firm is present in the sample both in year 119905

and 119905 + 1 Otherwise one cannot be sure whether all the shops were closed or simply not sampled in

119905 + 1 Unfortunately this is a serious restriction for two reasons First one cannot observe the exit or


121

entry only survival for single-shop firms95 Second we also miss when a multi-shop firm exits with all

its shops

One also has to make a number of further methodological choices We restrict our sample to groceries

which is a relatively homogeneous group with many observations Another choice is that even though

we observe shops on a monthly basis we consider only year-to-year changes between May and the

following May Running the regressions on the monthly data would inflate artificially the number of

observations and introduce important methodological problems including seasonality

Table 84 presents the results In column (1) the dependent variable is the (net) change in the

number of shops The results suggest that productivity is of limited importance as a determinant of

change in shop numbers but size matters Firms with more and larger shops were more likely to

expand in terms of opening new shops Foreign firms expand faster because they are larger

conditional on size ownership does not matter Size is correlated both with shop opening and closing

firms with a larger average shop size are more likely to open new shops while chains with more shops

are less likely to close existing ones

Table 84 Determinants of the change in the number of shops at the firm-level groceries

(1) (2) (3)

Dependent Change in

number of

shops

New

shops

Closed

shops

Labour productivity 0001 0025 -0011

(0024) (0014) (0018)

Foreign-owned -0087 -0042 0019

(0064) (0034) (0045)

ln( (average

salesshop)

0056 0034 -0017

(0016) (0010) (0014)

5-9 shops 0176 0001 -0126

(0070) (0036) (0054)

10-49 shops 0231 -0009 -0167

(0067) (0034) (0052)

more than 50 shops 0194 0002 -0109

(0071) (0038) (0055)

Year FE yes yes yes

Observations 815 815 815

R-squared 0105 0093 0084

Notes One observation is a firm-year Standard errors are clustered at the firm-level

One may get a more detailed picture by investigating at the shop-level Here we can straightforwardly

estimate both the exit part of the extensive margin (did a specific shop close) and the intensive

margin (did the shop extend its sales)

95 For this reason we drop single-shop firms altogether from the analysis


122

In particular we run regressions of the following form

119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905

where 119894 denotes firms 119895 shops and 119905 years The outcome variable 119910119894119895119905 is either a dummy showing

that the shop closed96 between 119905 and 119905 + 1 or represents the growth of (log) sales of the shop 119883119894119905 are

firm-level variables such as productivity while 119885119894119895119905 are shop-level variables such as shop-level sales

The same restrictions apply as in the previous case

Table 85 reports basic regressions We run both the exit and sales growth regressions for three

subperiods 2004-2007 2008-2010 and 2012-2015 Our main question is whether one can identify

any changes in the relocation process across these subperiods

Let us start with the exit regressions Similarly to the firm-level results we find that productivity and

ownership are not associated with the probability of exit Shop size is significantly related to closing a

shop twice as large sales are associated with 5 percentage points lower probability of the event

occurring This relationship became stronger by the third period The number of shops of the firm is

also negatively associated with the probability of closing the shops and this effect only became

significant post-crisis In addition the explanatory power of the regression is also higher by nearly 50

percent in this last period compared to the earlier ones To sum up we find that the size of the shop

and the turned out to be more important post-crisis making such shops less likely to close

In contrast to the exit equation we do not find significant effects in the growth regressions Neither

size nor productivity seem to be related to growth at the shop-level

To sum up the level of allocative efficiency in retail is relatively low ndash similarly to other European

countries ndash and one cannot see a significant change in this respect Pre-crisis when large chains

expanded rapidly reallocation played a significant role in aggregate productivity growth while within-

firm growth became dominant after the crisis Shop-level data suggest that the expansion in terms of

number of shops is mainly determined by firm size rather than productivity and ownership Sales

growth of existing shops does not seem to be related to size ownership or productivity The lack of

evidence for a relationship between opening new shops or the growth of existing shops is much in line

with the low measured allocative efficiency in the industry

96 We run linear probability models for shop exits Probit models yield similar results


123

Table 85 Probability of closing a shop and growth regression NACE 4711

(1) (2) (3) (4) (5) (6)

Dependent Closing the shop Growth

Period 2004-

2007

2008-

2010

2012-

2015

2004-

2007

2008-

2010

2012-

2015

labour productivity 0005 -0021 0102 0034 -0012 0097

(0009) (0015) (0087) (0018) (0022) (0063)

foreign-owned -0005 0063 0056 0016 -0014 -0035

(0023) (0045) (0057) (0023) (0055) (0066)

ln sales -0026 -0029 -0057 -0018 -0010 0010

(0006) (0007) (0021) (0007) (0017) (0005)

5-9 shops -0014 -0066 -0111 0061 -0035 -0055

(0026) (0051) (0043) (0046) (0044) (0026)

10-49 shops -0045 -0114 -0149 0046 -0038 -0023

(0024) (0048) (0039) (0044) (0043) (0017)

more than 50

shops

0001 -0077 -0134 0061 -0027 -0016

(0029) (0048) (0040) (0045) (0044) (0022)

Observations 15374 10946 15038 14025 10120 13458

R-squared 0030 0053 0073 0023 0041 0121

Notes OLS regressions run at the shop-year level only for firms present both in t and t+1 In columns (1)-(3) the

dependent variable is a dummy indicating whether the shop closes between t and t+1 while in columns (4)-(6) it is

the growth rate of sales between t and t+1 The explanatory variables are measured at year t The number of

shops variables are dummies representing the number of shops of the firm County and year fixed effects are

included Period 1 2004-2007 Period 2 2008-2010 period 3 2012-2015 Standard errors are clustered at the

firm-level

85 Trade

In small open economies a very important function of the wholesale and retail sector is the

intermediation of international trade for consumers and firms The operation and efficiency of these

industries can have a strong impact on aggregate welfare and productivity by determining both the

cost and variety of imported goods available as well as the cost of exporting products (Raff and

Schmitt 2016)

Many interesting questions emerge in this framework One of the key issues is the problem of double

marginalisation In the case of consumers (and consumer goods) one dimension of this question is

whether retailers import products directly or via wholesalers If retailers find it very hard to import

directly (because of say large fixed costs) double marginalisation can raise prices for consumers

Through this channel lower trade cost of retailers can benefit consumers As a result the share of

consumer goods imported directly by retailers may be an important proxy for the lower prevalence of

double marginalisation

In the case of intermediate inputs manufacturing firms face the choice of importing the product

directly (and paying the fixed costs of doing so) or relying on an intermediary Again reduced fixed

cost may make imported goods cheaper contributing positively to productivity growth Access to

imported intermediate inputs has been shown to be strongly correlated with the productivity of

Hungarian manufacturing firms (Halpern et al 2015)


124

The question of duality is also highly relevant in this context Multinational retailers can easily rely on

producers abroad hence their expansion can have important effects on Hungarian producers

Domestic chains on the other hand may find it hard to import a large variety of foreign products

which may result in a reduced choice set for consumers

Ultimately it is the questions above that motivate our investigation of importing and exporting by

wholesalers and retailers Our data are exceptionally suitable for this exercise Given that firm balance

sheets can be linked to detailed export and import data one can quantify the amount of products

imported and exported from different product categories by wholesalers and retailers

An important methodological note is that we only observe direct imports in the trade data The most

important consequence of this limitation is that while in actual fact the share of imported goods on a

retailerrsquos shelf is a combination of goods imported directly by the retailer and those imported by a

wholesaler and sold to the retailer with the data we are only able to observe the former (Basker and

Van 2010) Also note that in contrast to imports exports are reported in the balance sheet

Therefore we will use this source of information when analysing exporting

Importing

To start with Figure 810 shows the share of retailers and wholesalers from the total Hungarian

imports of different types of goods In terms of all imports the share of these two groups of firms

fluctuated around 25 percent with a slightly decreasing trend The bulk of the imports were conducted

by manufacturing firms with an especially large share by multinational affiliates strongly integrated

into global value chains for example in the automotive industry Overall wholesalersrsquo imports were

about 5 times larger than those of retailers97

Naturally wholesalers and retailers dominate the importing of consumer goods by a share of around

70 percent A key trend here is the increasing share of retailers In 2004 21 percent of intermediated

trade (imports of wholesalers and retailers) were imported by retailers which increased gradually to

33 by 2015 This is a significant shift which reflects in part the expansion of multinational retail

chains but probably also easier access to imports by retailers

The share of intermediated trade was around 20 percent both for intermediate inputs and capital

goods dominated by wholesalers This reflects that in aggregate terms the overwhelming majority of

goods used by firms in production are imported directly The share of intermediated trade decreased

strongly following the crisis from 20 percent in 2010 to 13 percent in 2015 Given the skewed size

distribution of manufacturing firms this does not mean that most firms import their inputs directly

many smaller firms rely strongly on trade intermediaries when purchasing their inputs

Figure 811 looks into the trends behind consumer goods imports in more detail The left hand side

figure shows the share of imports compared to the total cost of goods sold (COGS) by wholesalers and

retailers98 We find that this ratio is roughly constant for wholesalers namely around 10 percent99

97 This can be compared to the results of Bernard et al (2010) who report that retailers and firms active both in retail and wholesale represent 14 percent of importing firms and 9 percent of imports in the US

98 In particular we calculate total consumer goods imports for wholesale and retail firms and divide it with the sum cost of goods sold across all retailers

99 Needless to say wholesalers also import other type of goods which are part of their cost of goods sold This ratio was 36 percent in 2015 showing that more than a third of their sales was imported


125

This contrasts sharply with retailers where the share of directly imported goods nearly doubled

between 2005 and 2015 from 6 percent to 11 percent100 This corresponds to a substantial increase in

the share of imported goods offered to consumers by retailers and an increasing share of this volume

is imported directly by the retailer presumably with a smaller degree of double marginalisation

One can also decompose the increasing direct import share of retailers to its different margins One

possibility is that - probably thanks to the declining fixed costs of importing - more and more retailers

started to import (an extensive margin effect) The right panel of Figure 811 shows that this is not the

case the share of directly importing retailers stagnated at about 8 percent of firms (with at least 5

employees) in the whole period Instead the rise of direct imports was driven by the intensive margin

or the average direct import per retailer Other regressions (not reported) suggest that this does result

mainly from the increased imports of large retailers

Figure 810 Share of wholesale retail and other firmsrsquo imports relative to total imports across

product categories

100 Again considering all goods the importcost of goods sold ratio increased from 11 to 18 percent for retailers


126

Figure 811 The share of consumer goods imports relative to the cost of goods sold and the share of

direct consumer goods importers by industry

Notes Firms with at least 5 employees

Figure 812 distinguishes between foreign and domestically-owned retail firms Both the share of

importers and their intensive margins are much higher for foreign-owned firms in the industry The

share of consumer goods imports in foreign firms in terms of cost nearly tripled between 2005 and

2015 from 7 to 21 percent101 compared to the 2-5 percent increase for domestically-owned firms

The increase in imports by retailers hence was mainly driven by multinationals

101 A similar increase from 18 percent in 2005 to 32 percent in 2015 can be observed when non- consumer goods are considered


127

Figure 812 The share of consumer goods imports relative to cost of goods sold and the share of

direct consumer goods importers by ownership


Table 86 presents the cross-sectional linear regressions in order to investigate the premia of importers

among retailers along several dimensions In these regressions the dependent variable is a dummy

which shows whether a firm imports at least 1 percent of its cost of goods sold102 We find substantial

and highly significant premia in terms of size productivity and ownership 100 percent higher

productivity translates into about 5 percentage points higher probability of importing This premium

was increasing significantly between 2005 and 2015 showing a stronger self-selection of more

productive retailers into direct importing Foreign retailers are 20-25 percentage points more likely to

import on average A doubling of employees is associated with around 9 percentage points higher

probability of importing103

102 These are linear probability models but probit specifications yield similar marginal effects

103 Similar premia are found for importers in most industries and are mainly explained by the fixed costs of importing (Vogel and Wagner 2010)


128

Table 86 Determinants of importing linear probability models Retailers

(1) (2) (3) (4) (5)

Year 2005 2008 2010 2012 2015

Dependent Imports at least 1 percent of purchases

Labour productivity 0050 0054 0047 0059 0065

(0003) (0003) (0003) (0003) (0003)

Foreign-owned 0249 0196 0224 0238 0217

(0014) (0011) (0012) (0012) (0012)

Ln employees 0082 0082 0075 0073 0088

(0004) (0004) (0004) (0004) (0004)

Constant -0470 -0536 -0464 -0551 -0637

(0027) (0026) (0027) (0028) (0027)

Observations 7467 7977 7400 7122 8308

R-squared 0116 0130 0127 0140 0143

Notes Firms with at least 5 employees These are cross-sectional regressions where the dependent variable is

dummy representing whether the firm imports at least 1 percent of its cost of goods sold

Exporting

Wholesalers and retailers can also play a significant role as export intermediaries Extended export

activities of these firms can be an important source of growth for these firms but can also benefit

many smaller producers who would not find it profitable to export directly (Ahn et al 2011)

Figure 813 shows that 85-90 percent of exporting was conducted directly by producers rather than by

wholesalers or retailers The share of intermediated exports was constant pre-crisis but started to fall

after 2012


129

Figure 813 Share of wholesale retail and other firmsrsquo exports relative to total exports of firms

Many wholesalers and retailers started to export in the period under study (Figure 814) The share of

exporters in wholesale firms increased from 25 percent in 2005 to 35 percent in 2015 while the share

of exporting retailers doubled in this period The share of exports in the turnover of these firms also

increased substantially

Figure 814 Share of exports relative to turnover and share of exporters by industry

While foreign-owned firms are about 4 times more likely to export than domestic ones entry into

exporting was not limited to foreign-owned firms (Figure 815) the share of exporters among

domestically-owned firms doubled between 2005 and 2015 This was paralleled with an increase in the

share of exports relative to total turnover


130

Figure 815 Share of exports relative to turnover and share of exporters by ownership for the retail

sector

Table 87 reports linear probability models with export status as the dependent variable More

productive larger and foreign-owned firms are more likely to export In general both the size and

labour productivity premia increased between 2005 and 2015 once again suggesting stronger self-

selection based on these variables

Table 87 Determinants of exporting linear probability models retail

(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015

Dependent Exports at least 1 percent of total revenue

Labour

productivity

0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)

Foreign-owned 0083 0137 0141 0119 0107

(0009) (0011) (0013) (0013) (0012)

Ln employees 0019 0029 0028 0031 0034

(0003) (0004) (0004) (0004) (0004)

Constant -0159 -0277 -0271 -0321 -0317

(0018) (0026) (0027) (0029) (0028)

Observations 7622 7976 7663 7384 8730

R-squared 0028 0045 0041 0041 0036

This section has shown that the role of retailers in international trade is becoming more and more

important in Hungary This can have many benefits from providing a larger variety of potentially lower

priced goods to consumers to letting smaller producers reach foreign markets Increasing exports

mostly reflect opportunities provided by European integration and the internet but policies can also

help firms to become more adapt at utilising these opportunities


131

86 Policies Crisis taxes

As we have described briefly in Section 81 some of the new policies introduced after the crisis were

size-dependent either explicitly or implicitly The crisis taxes and the local business tax104 were based

on explicitly taxing large firms at higher rates Such policies can have substantial effects at the sectoral

level (Guner et al 2008)

Evaluating the effects of these taxes is not a straightforward task A possible approach was followed in

Section 84 where we have investigated the reallocation process in detail While such an approach is

not capable of identifying the causal effects of specific policies it may provide a broad picture The

results most importantly Figure 88 suggest that the importance of the reallocation process declined

relative to within-firm productivity growth Still this could have resulted from many reasons other than

policy changes

A more direct approach is to identify specific firms which were affected by a policy and to compare

their behaviour to similar firms not affected by the policy Such a diff-in-diff approach may be an

effective policy evaluation tool when there are sharp breakpoints in the tax schedule with enough

`treatedrsquo and control firms in the two groups

As for the crisis taxes the only sharp discontinuity was at the top rate when the tax rate increased

from 04 to 25 percent of profits The cutoff was at HUF 100bn and according to our data altogether

6 retail firms qualified for inclusion in this group This sample size does not allow for a statistically

powerful test

Still a few graphs may illustrate the processes First the market share of these large mainly

multinational firms were expanding quickly before 2010 and stagnated afterwards (Figure 816)

Second we can illustrate some of the key performance measures discussed in Section 83 Figure 817

compares the treated firms to a control group consisting of firms with at least 100 employees We find

that the premium of the treated group in terms of both productivity measures and margins were

higher between 2010 and 2013 than before or after105 As we have discussed earlier at least in the

short term these revenue-based measures are likely to reflect changes in prices Hence this figure

hints at increased prices in the treated group relative to the control group suggesting that treated

firms passed on the tax to consumers Note that these differences are not statistically significant and

to reiterate may have resulted from many other factors rather than just the effects of this specific

policy

104 The effect of the local business tax is much harder to test given its more continuous nature

105 Note that the margin premia are in fact negative in line with the lower margins charged by the largest firms


132

Figure 816 Sales and employment share of firms in the top bracket of the crisis tax

Notes Full sample

Figure 817 Margin TFP and labour productivity advantage of firms in the top bracket of the crisis tax

firms with more than 100 employees


133

87 Policies Mandatory Sunday closing

One of the most characteristic non-tax based size-dependent policies was mandatory Sunday closing of

larger shops introduced in March 2015 and reversed in April 2016 While the policy had multiple aims

it was partly motivated by supporting smaller and family-owned shops In this section we investigate

two outcomes related to this policy First we aim at understanding its reallocation effects ie the

extent to which the market share of treated shops lost market share Second we are interested in the

extent to which consumption was reallocated to other days of the week

The shop-level data is ideal to investigate the effects of this policy First the policy was defined at the

shop- rather than the firm-level We can identify the affected shops precisely based on the number of

days they were open Second many shops have been affected by this policy making the test

powerful Third the policy has a clearly defined beginning and end making a difference in differences

strategy feasible

Our empirical approach starts with restricting the sample to comparable firms First we investigate

mainly grocery shops where we have sizable treated and control groups106 In the sample we include

only shops which were continuously in the sample between January 2015 and October 2016 An issue

is that the treated and the control group may be very different We attempt to guarantee that the

common support condition is satisfied by excluding very small and very large shops107 For similar

reasons we also exclude shops which were not open even on Saturdays either before or during the

policy108

An important part of the analysis is the definition of the treated group As we do not observe directly

the area and the ownership of the shop we rely on the change in the number of days open We

consider a shop treated if it was open for at least 30 days per month before the policy (in median) and

it was open for less than 26 days after the policy was introduced (again in median)109 The control

group consists of other firms in the sample

Taking a look at the number of days open for the two groups reveals that compliance was very high

More than 95 percent of the shops that had been open on Sundays before the policy were closed on

Sundays during the whole policy period More than 95 percent of shops in the control group were

closed on Sundays both before and after the policy There are few firms which deviated from this

pattern by for example opening on Sundays when the policy started110

106 In other 4-digit sectors either there are too few firms or nearly all of them are treated (clothes shoes etc) or none of them (fuel)

107 Based on the 5th and 95th percentiles of the median sales distribution based on sales before the policy Unfortunately we do not have other measures of shop size

108 More precisely we exclude shops for which the median monthly days open was below 21 days either before or during the policy

109 A potential worry with this approach is that some shops may have closed voluntarily when the policy was introduced We cannot exclude this possibility but this may not be that important for the relatively large shops in the sample One can expect that voluntary Sunday closure would not start exactly at the beginning of the policy but rather after a period of gathering information about consumer demand on Sunday By checking the monthly distribution of the number of days open we find only few firms which changed their behaviour in this respect during the policy

110 Note that many small shops remained open on Sundays but most of them are missing from our restricted sample because of small median sales


134

Figure 815 reports descriptive statistics of the key variables Panel A) compares the evolution of

average sales of the treated and the control group before during and after the introduction of the

policy The dynamics of sales growth was remarkably similar before the policy was introduced

suggesting that the parallel trend assumption was satisfied Average sales in the control group are

somewhat higher during the policy suggesting some reallocation of market share to that group After

the policy the treated group seems to slightly overperform the control group

Part B) of Figure 818 shows the evolution of average sales per day open Again the pre-policy trends

are similar for the two groups Sales per day increases significantly for both groups during the policy

consumers did their Sunday shopping on other days The increase is substantially larger for the treated

group showing that most of the former Sunday shopping took place in the same shop but on other

days of the week The fact that there is an increase in the control group shows that part of the former

Sunday shopping was reallocated to these shops Interestingly the sales per day advantage of the

treated group remained even after the policy was abandoned As we will see the main reason for this

is that after abandoning the policy some of the shops remained closed

Figure 818 The evolution of key variables in the treated group and the control group groceries

A) Sales


135

B) Sales per day

While these patterns are suggestive the data allow us to conduct a more precise econometric event

study exercise We do so by creating a number of quarterly event study dummies to capture the

differential dynamics of the treated and control groups We define the variable lsquoevent timersquo which

shows the number of months since the policy started (it is zero in March 2015) This variable takes

negative values before that date We define quarterly dummies based on the event time variable For

example the first treatment quarter dummy is one when event time is 0 1 or 2 and the firm is in the

treated group The first pre-treatment dummy takes the value of 1 when event time is -1 -2 or -3 and

the firm is in the treated group

We run the following regression to estimate these trends

119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591

120591 + 120583119894119895 + 120575119905 + 휀119894119895119905

In this regression the dependent variable is days open ln(monthly sales) and ln(salesdays open) 119894

denotes firms 119895 shops and 119905 time measured in month while 120591 is event time in quarters The variables

of interest are the full set of event study dummies The base category will be the second pre-trend

dummy (event time -4 -5 or -6) The motivation for this choice is that the policy was announced in

this period (December 2014) hence the first pre-trend period the beginning of 2015 may include

preparation for the policy 120583119894119895 are shop fixed effects to control for shop heterogeneity 120575119905 are time

(monthly) fixed effects which control both for seasonality and macro shocks When we run the

regression by pooling different 4-digit industries we allow these dummies to vary across industries In

a more demanding specification we also include firm-time fixed effects and identify from the

differences across the treated and non-treated shops of the same firm in the same month We cluster

standard errors at the shop-level

Figure 819 summarizes the main results for the whole retail sector while the regressions are reported

in Table A71 in the Appendix Panel A) shows the results for days open with the right-hand panel

including firm-time fixed effects We see that on average treated firms cut the number of days open

by 2-3 days relative to the control group ndash the effect is more pronounced with firm fixed effects There


136

is practically no pre-trend and the timing of the reduction of days open is strongly in line with the

introduction of the policy The number of days open increases sharply after the end of the policy but

only to below pre-policy levels This suggests that some shops did not re-open on Sundays after the

policy probably because they learned that their sales did not suffer much

Panel B) shows the behaviour of average monthly sales Again there is no evidence for a pre-trend

During the policy treated firms experienced a 2-3 percent lower sales growth relative to the control

group This shows how much of sales was re-allocated to other shops Post-policy variables suggest

full recovery to pre-policy levels

Panel C) of the same figure shows the effect of the policy on sales per day open This variable

increased by 5-10 percent in the treated group relative to the control group The bulk of consumers

seem to have remained loyal to their familiar shops and simply made their shopping on other days

This may have also been helped by longer opening hours on other days of the week and further efforts

made by shops to retain their customers Sales per day remain higher even after the end of the policy

most likely because some shops did not re-open on Sundays but probably also because of

organizational changes during the policy

Figure 819 Event study results for the whole retail sector

A) Days


137

B) Sales

C) Sales per day

Notes This figure presents point estimates and 95 confidence intervals from the event study regression showing

the evolution of number of days open sales and sales per day of the treated group compared to the control group

as described in the text All specifications include shop fixed effects The left panel regressions also include 4-digit

industry-time fixed effects while the right side panels include firm-time dummies


138

Figure 820 re-estimates the same regressions for groceries where the policy was most relevant The

regression results are reported in Table A72 in the Appendix We find very similar results to the whole

retail sector The only exception is that the evolution of post-policy behaviour of sales is less clear

Figure 820 Event study results for NACE 4711

A) Days

B) Sales


139

C) Sales per day

Notes The figure above presents point estimates and 95 confidence intervals from the event study regression

showing the evolution of number of days open sales and sales per day of the treated group compared to the

control group as described in the text All specifications include shop fixed effects The left panel regressions also

include 4-digit industry-time fixed effects while the right side panels include firm-time dummies

A possible concern with these estimates is that the increase in sales per day may result from a simple

composition effect If sales are usually very small on Sundays anyway then closing on Sundays may

mechanically increase average daily sales We check for this possibility by estimating sales on different

days of the week from the pre-policy period While we do not observe the sales on each day of the

week we observe sales in different months with a different combination of days We rely on this

variation to estimate a regression of the following form

ln 119904119886119897119890119904119894119895119905 = 120573 lowast 119883119905 + 120574 lowast 119889119886119905119890119905 + 120583119894119895 + 휀119894119895119905

where 119883119905 is a vector of variables containing the number of Mondays Tuesdays etc in month 119905 We

also control for the number of holidays in the month We control for seasonality by including dummies

for December January and summer months The regression also includes firm fixed effects and is

estimated on the period 2009-2014 120574 lowast 119889119886119905119890119905 is a linear trend The estimated results are reported in

Table A73 in the Appendix

The regression shows that sales on Sundays were not that small namely similar to a typical Monday

or Wednesday Thus the composition effect is unlikely to affect the results much To check for the

relevance of these composition effects Figure 821 A) reports sales predicted from the above

regression for the treated group (by setting the number of Sundays to be zero during the policy)

Therefore the `predictedrsquo line shows what would have happened if sales had remained the same on


140

non-Sundays during the policy The actual line is clearly above the predicted one suggesting that sales

on other days have increased

Panel B) of Figure 821 shows how sales per day would have evolved based on a similar regression

Note that predicted sales per day are slightly larger during the policy than beforehand thanks to the

mechanical composition effect resulting from the slightly lower sales on Sundays Actual sales per day

however are substantially higher than this simple prediction showing again that sales per day

increased on other days of the week

Figure 821 The evolution of the variables versus prediction

A) Sales

B) Sales per day


141

All in all the mandatory Sunday closing of shops was effective in terms of compliance It did not have

strong reallocative effects with a 2-3 percent fall in sales in the treated group Consumers seem to

have remained mostly loyal to the shop they had frequented and made their shopping on other days

of the week at the same shop Interestingly some of the shops seem to have learned that it is optimal

to remain closed on Sundays even after the policy was cancelled

88 Conclusions

In line with the main message of other parts of this study there are huge productivity differences

across firms within the retail sector There is a strong duality between small and large firms both in

terms of productivity and margins Consumers are likely to pay significantly lower prices in the shops

of large firms Many of the large firms are multinationals which had expanded rapidly before the crisis

At the other end of the range the exceptionally low performance of very small firms seems to be a

significant issue Many technologies applied by the most productive retailers could be adapted

relatively easily by some of the less productive firms Increasing absorptive capacity and effective

financing could help in promoting this Still many of the low-productivity very small shops may not be

viable in the long run

A key pattern observed is the increasing concentration of the retail sector pre-crisis resulting from the

expansion of large chains and foreign firms These trends seem to have stopped or slowed down after

the crisis In line with this pattern the contribution of reallocation decreased post-crisis relative to

earlier periods While many factors can play a role in this pattern it may be related to the different

size-dependent policies introduced after 2010 While these developments may help smaller retail firms

consumers may face higher prices in the long run

Not all the policies introduced can be properly evaluated based on the data at hand especially because

multiple policies were introduced at the same time with some of them affecting only few firms We

were able to analyse precisely the effects of mandatory Sunday closing based on store level data We

found that a relatively small share of the demand was lost by the treated shops and the majority of

consumers simply switched to shopping at the same place on other days Interestingly some of the

treated shops found it optimal not to re-open on Sundays even when the policy was reversed

Additionally retailers and wholesalers also play a large and increasing role in mediating imports and

exports We found a large increase in goods imported directly by retailers rather than indirectly via

wholesalers This was mainly driven by large foreign firms and may have benefited their consumers

thanks to a lower degree of double marginalisation Both the number of exporting firms and the

amount exported by wholesalers and retailers increased most likely benefitting from easy access to

markets of other EU member states and probably from the opportunities provided by e-commerce

This can benefit both the exporting firms and the Hungarian producers who can more easily reach

foreign markets with the help of these intermediaries Policies may help retailers to internationalise by

making international sales especially on the internet even easier

Conclusions

142

9 CONCLUSIONS

The results of this report confirm that Hungary is atypical because of the relatively poor productivity

performance of frontier firms Importantly contrary to a strong version of the duality concept this is

not a result of Hungarian frontier firms being on the global frontier typically they are quite far away

from it This robust pattern underlines that besides helping non-frontier firms policies may also have

to focus on the performance of the frontier group A transparent environment with a strong rule of law

complemented by a well-educated workforce and a strong innovation system is key for providing

incentives to invest into the most advanced technologies

The analysis in this report reinforces the impression that there is a large productivity gap between

globally engaged or owned and other firms the gap being about 35 percent in manufacturing and

above 60 percent in services This gap seems to be roughly constant in the period under study The

firm-level analysis in Chapter 7 also reveals that one of the mechanisms which conserves the gap is

that foreign frontier firms are able to increase their productivity more than their domestic counterparts

even from frontier levels These findings reinforce the importance of well-designed policies that are

able to help domestic firms to catch up with foreign firms A key precondition for domestic firms to

build linkages with foreign firms and to benefit more from their presence is a high level of absorptive

capacity High skills and an efficient innovation system can support this aim as well A more specific

conclusion is the importance of enabling high-productivity domestic firms to improve their productivity

levels even further

The large within-industry productivity dispersion the relatively low (though not extreme in

international comparison) allocative efficiency documented in some of the industries the strong

positive contribution of reallocation to total TFP growth before the crisis and the relatively low entry

rate imply that policies promoting reallocation have a potential to increase aggregate productivity

levels significantly These policies can include improving general framework conditions by cutting

administrative costs reducing entry and exit barriers and using a neutral regulation The fact that

capital market distortions still appear to be significantly above their pre-crisis levels implies that

policies that reduce financial frictions may help the reallocation process The fact that exporters tend to

expand faster relative to non-exporters indicates that access to EU and global markets generates a

strong and positive reallocation effect

Throughout our analysis we have found significant differences across sectors In general traded and

more knowledge-intensive sectors fared better both in terms of productivity growth and allocative

efficiency The difference between traded and non-traded sectors points again to the importance of

global competition in promoting higher productivity and more efficient allocation of resources This also

implies that adopting policies that focus on innovation or reallocation in services may be especially

important given the large number of people working in those sectors The better performance of and

reallocation into more knowledge-intensive sectors underlines the importance of education policies

aimed at developing up-to-date and flexible skills and innovation policies that help improve the

knowledge base and the functioning of the innovation system

Available evidence suggests a wide gap in the productivity level and earnings of people working at

firms with at least a few employees and those working in very small firms or self-employed The latter

category represents 30-50 percent of people engaged in some important industries Inclusive policies

may attempt to generate supportive conditions for these people by providing knowledge and training

as well as helping them to find jobs with wider perspectives or to set up well-operating firms The large

share of these unproductive economic entities holds back productivity growth even at the macro-level

The specific analysis of the retail sector has shown a characteristic difference between the pre-crisis

period characterised by strong reallocation mainly via the expansion of large foreign-owned chains


143

and the post-crisis period with a stagnating share of large chains This break is likely to be linked to

post-crisis policies favouring smaller firms While halting further concentration in a country with

already one of the highest share of multinationals in this sector can have a number of benefits it is

likely to lead to higher prices and lower industry-level productivity growth in the long run Policies

should balance carefully between these trade-offs Another key pattern identified is the increasing role

of retailers (and wholesalers) in trade intermediation both on the import and export side Policymakers

should encourage these trends and design policies which provide capabilities for such firms to enter

international markets probably via e-commerce

References

144

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Ahn J Khandelwal A K and Wei S J (2011) ldquoThe role of intermediaries in facilitating traderdquo Journal of International Economics 84(1) 73-85

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Andrews D McGowan M A and Millot V (2017) ldquoConfronting the zombies Policies for productivity

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Antildeoacuten Higoacuten D Mantildeez J A Rochina-Barrachina M E Sanchis A and Sanchis-Llopis J A (2017)

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Arrobbio A Barros A C H Beauchard R F Berg A S Brumby J Fortin H Garrido J Kikeri

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Atkin D Faber B and Gonzalez-Navarro M (2018) ldquoRetail globalization and household welfare

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Baily M N Hulten C Campbell D Bresnahan T and Caves R (1992) ldquoProductivity dynamics in

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Basker E and Van P H (2010) ldquoImports lsquoЯrsquo us Retail chains as platforms for developing country importsrdquo American Economic Review 100(2) 414-18

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Beacutekeacutes G Murakoumlzy B and Harasztosi P (2011) ldquoFirms and products in international trade

Evidence from Hungaryrdquo Economic Systems 35(1) 4-24

Bellone F Musso P Nesta L and Warzynski F (2014) ldquoInternational trade and firm-level

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Benedek D Elek P and Koumlllő J (2013) ldquoTax avoidance tax evasion black and grey employmentrdquo

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Biesebroeck J V (2008) ldquoAggregating and decomposing productivityrdquo Review of Business and

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Bisztray M (2016) ldquoThe effect of FDI on local suppliers Evidence from Audi in Hungaryrdquo IEHAS

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Girma S and Goumlrg H (2007) ldquoEvaluating the foreign ownership wage premium using a difference-

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Gopinath G Kalemli-Ozcan S Karabarbounis L and Villegas-Sanchez C (2017) ldquoCapital allocation

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Gorodnichenko Y Revoltella D Svejnar J and Weiss C T (2018) ldquoResource misallocation in

European firms The role of constraints firm characteristics and managerial decisionsrdquo NBER Working

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Econometrics 65(1) 175ndash203

Guner N Ventura G and Xu Y (2008) ldquoMacroeconomic implications of size-dependent policiesrdquo Review of Economic Dynamics 11(4) 721-744

Halpern L Koren M and Szeidl A (2015) ldquoImported inputs and productivityrdquo American Economic

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Harasztosi P and Lindner A (2017) ldquoWho Pays for the Minimum Wagerdquo Mimeo

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Haskel J E Pereira S C and Slaughter M J (2007) ldquoDoes inward foreign direct investment boost

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Hausmann R Hwang J and Rodrik D (2007) ldquoWhat you export mattersrdquo Journal of Economic

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Herrendorf B Rogerson R and Valentinyi A (2014) ldquoGrowth and structural transformationrdquo

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Hopenhayn H A (2014) ldquoFirms misallocation and aggregate productivity A reviewrdquo Annual Review

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Hornok C and Murakoumlzy B (2018) ldquoMarkups of exporters and importers Evidence from Hungaryrdquo

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Melitz J (2003) ldquoThe impact of trade on intra-industry reallocations and aggregate industry

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Nicolini M and Resmini L (2010) ldquoFDI spillovers in new EU member statesrdquo Economics of


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Ratchford B T (2016) ldquoRetail productivityrdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 54-72

Restuccia D and Rogerson R (2017) ldquoThe causes and costs of misallocationrdquo Journal of Economic

Perspectives 31(3) 151-74

Rovigatti G and Mollisi V (2016) ldquoPRODEST Stata module for production function estimation based

on the control function approachrdquo Statistical Software Components S458239 Boston College

Department of Economics Revised 12 Jun 2017 Accessed October 26 2017

httpsideasrepecorgcbocbocodes458239html

Sadun R (2015) ldquoDoes planning regulation protect independent retailersrdquo Review of Economics and Statistics 97(5) 983-1001

Scarpetta S Hemmings P Tressel T and Woo J (2002) ldquoThe role of policy and institutions for

productivity and firm dynamics Evidence from micro and industry datardquo OECD Economics Department

Working Papers (No 329) Available at SSRN httpsssrncomabstract=308680 or

httpdxdoiorg102139ssrn308680

Smith H (2016) ldquoThe economics of retailer-supplier pricing relationships Theory and evidencerdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 97-136

Smeets R (2008) ldquoCollecting the pieces of the FDI knowledge spillovers puzzlerdquo The World Bank

Research Observer 23(2) 107-138

Syverson C (2011) ldquoWhat determines productivityrdquo Journal of Economic Literature 49(2) 326-65

Taglioni D and Winkler D (2016) ldquoMaking global value chains work for developmentrdquo The World

Bank Issue 143 1-10

Topalova P and Khandelwal A (2011) ldquoTrade liberalization and firm productivity The case of

Indiardquo Review of Economics and Statistics 93(3) 995-1009

Viviano E (2008) ldquoEntry regulations and labour market outcomes Evidence from the Italian retail trade sectorrdquo Labour Economics 15(6) 1200-1222

Vogel A and Wagner J (2010) ldquoHigher productivity in importing German manufacturing firms Self-selection learning from importing or bothrdquo Review of World Economics 145(4) 641-665

Wagner J (2007) ldquoExports and productivity A survey of the evidence from firm‐level datardquo The

World Economy 30(1) 60-82


151

Wooldridge J M (2009) ldquoOn estimating firm-level production functions using proxy variables to

control for unobservablesrdquo Economics Letters 104(3) 112-114

Zhang Y Li H Li Y and Zhou L A (2010) ldquoFDI spillovers in an emerging market The role of

foreign firms country origin diversity and domestic firms absorptive capacityrdquo Strategic Management

Journal 31(9) 969-989

Appendix

152

APPENDIX

A3 Chapter 3 Internationally comparable data sources and methodology

A31 EU KLEMS amp OECD STAN

The EU KLEMS project aimed at creating a database on measures of economic growth productivity

employment creation capital formation and technological change at the industry level for all European

Union member states from 1970 onwards The database provides an important input to policy

evaluation in particular for the assessment of the goals concerning competitiveness and economic

growth potential as established by the Lisbon and Barcelona summit goals

The input measures include various categories of capital labour energy material and service inputs

Productivity measures have also been developed in particular with growth accounting techniques

Several measures on knowledge creation have also been constructed

The basic data of the EU KLEMS is also available in the OECD STAN database sometimes in a more up

to date version We have downloaded the following variables from there

- EMPE Number of employees

- EMPN Number of persons engaged ndash total employment

- SELF Number of self-employed

- VALU Value added current prices (millions of national currency)

- VALK Value added volumes (current price of the reference year 2010 millions)

- VALP Value added deflators (reference year 2010 = 100))

Labour productivity is defined as gross value added at constant prices divided by the number of

persons engaged In order to create comparative labour productivity levels we used the 2005

benchmark from the GGDC Productivity Level Database111 This project provides productivity levels

relative to the USA that can be used together with EU KLEMS growth accounts to create comparable

productivity level extrapolations (Inklaar and Timmer 2008 Inklaar and Timmer 2009)

A32 OECD Structural and Demographic Business Statistics

The OECD Structural and Demographic Business Statistics (SDBS) consists of two databases the

OECD Business Demography Indicators (BDI) and the OECD Structural Business Statistics (SBS)

The OECD Business Demography Indicators (BDI) database contains data on births and deaths of

enterprises their life expectancy and the important role they play in economic growth and

productivity The OECD Structural Business Statistics (SBS) database features the data collection

of the Statistics Directorate relating to a number of key variables such as for example value added

operating surplus employment and the number of business units broken down by ISIC Rev 4

industry groups referred to as the Structural Statistics on Industry and Services (SSIS) database and

by economic sector and enterprise size class referred to as the Business Statistics by Size Class (BSC)

database For most countries the main sources of information used in the compilation of structural

business statistics are business surveys economic censuses and business registers

111 More information can be found on the homepage of GGDC Production Level Database

httpswwwrugnlggdcproductivitypldearlier-release


153

The statistical population is composed of enterprises (or establishments when no data on enterprises

are available) In the case of BDI database the population contains all enterprises including non-

employers ie enterprises with no employees while the population of SBS contains only the employer

enterprises ie firms with at least one employee

Birth rate of all enterprises is the ratio of the number of enterprise births and the number of

enterprises active in the reference period Births do not include entries into the population due to

mergers break-ups the split-off or restructuring of a set of enterprises It does not include entries

into a sub-population resulting only from a change of activity (Source BDI)

Death rate of all enterprises is the ratio of the number of enterprise deaths and the number of

enterprises active in the reference period Deaths do not include exits from the population due to

mergers take-overs break-ups or the restructuring of a set of enterprises It does not include exits

from a sub-population resulting only from a change of activity An enterprise is included in the count of

deaths only if it is not reactivated within two years Equally a reactivation within two years is not

counted as a birth (Source BDI)

Number of enterprises is a count of the number of enterprises active during at least a part of the

reference period (Source SBS)

A33 OECD Productivity Frontier

The OECD productivity frontier dataset is based on AMADEUSORBIS and calculates comparable labour

productivity and TFP (MFP) measures across countries The project aims at defining the most

productive (frontier) enterprises both globally and for every country at the 2-digit industry level

(Andrews et al 2016)

Here we use data kindly provided by the OECD for the global and the Hungarian national productivity

frontier Two types of productivity measures are presented labour productivity and Wooldridge MFP

Both frontier series are defined as the average of log-productivity of the top 10 within each 2-digit

industry and year To make this measure less sensitive to expanding coverage over time the 10 is

chosen based on the median number of observations within a 2-digit industry The median for each 2

digit industry is calculated over all the years retained in the analysis

A key issue with AMADEUSORBIS with regard to Hungary is its changing coverage (see Box in Chapter

2) This makes these comparisons meaningful only from 20082009 onwards The underlying sample

includes all firms that over their observed lifespan had at least 20 employees on average

To arrive at internationally comparable real series 2-digit country specific industry value added and

investment deflators were used (2005 = 1) and the monetary values were converted to 2005 USDs

using industry level PPPs from the Groningen Growth and Development Centrersquos Productivity Level

Database112

112 For more information visit the Centrersquos homepage httpswwwrugnlggdcproductivitypld

Appendix

154

A4 Chapter 4 Evolution of the Productivity Distribution

Table A41 Average TFP growth with alternative TFP measures

A) Market economy

Year ACF translog Fixed effects


2005 19 74 16 60

2006 93 119 95 97

2007 39 56 49 65

2008 -10 -04 -06 01

2009 -69 -82 -65 -63

2010 11 80 05 60

2011 34 40 31 45

2012 21 01 24 18

2013 30 22 22 22

2014 40 59 36 48

2015 52 49 50 43

2016 20 03 25 12

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Manufacturing



2005 20 114 24 127

2006 114 149 118 137

2007 78 71 86 98

2008 17 -17 32 -11

2009 -133 -117 -120 -87

2010 80 173 85 178

2011 04 18 01 25

2012 -02 -58 07 -38

2013 -12 05 -15 16

2014 -01 27 01 34

2015 30 14 34 19

2016 04 -23 14 -05

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01


155

C) Market services



2005 10 32 06 01

2006 79 90 82 64

2007 24 48 35 44

2008 -21 -03 -19 05

2009 -52 -71 -51 -54

2010 -11 26 -19 -05

2011 43 57 40 57

2012 30 48 31 57

2013 39 29 31 25

2014 46 78 39 55

2015 54 72 52 58

2016 25 20 29 23

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

Notes This table presents growth rates of TFP estimated with the translog ACF estimator and the Fixed Effects

estimator for lsquomarket industriesrsquo (see Section 25) The sample does not include agriculture mining and financial

services Services include construction and utilities

Appendix

156

Table A42 Unweighted TFP growth for different industry types

A) Manufacturing


2005 21 -02 -09 144

2006 118 143 58 47

2007 59 43 90 348

2008 -09 79 17 111

2009 -53 -191 -197 -139

2010 80 76 85 130

2011 -22 17 10 153

2012 01 14 -57 -06

2013 -38 20 -38 54

2014 -03 -05 08 33

2015 61 04 -19 132

2016 09 -10 12 91

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Market Services


2005 127 16 -01 -46

2006 166 75 94 66

2007 13 58 60 16

2008 -16 14 -37 -28

2009 -63 -94 -15 44

2010 54 12 -08 23

2011 97 46 77 -29

2012 12 74 06 -57

2013 12 30 60 -71

2014 78 89 65 -31

2015 106 70 22 12

2016 16 31 -47 37

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

Notes This table shows the unweighted average ACF TFP growth rate by technology category (see Section 25)

Only firms with at least 5 employees The sample does not include agriculture and financial services


157

Table A43 Employment-weighted labour productivity growth for different industry types

A) Manufacturing


2005 172 32 73 300

2006 266 114 54 10

2007 121 52 69 243

2008 -25 -17 -03 126

2009 31 -151 -186 35

2010 135 114 199 207

2011 -33 -10 96 96

2012 03 -34 -32 -226

2013 -35 22 26 253

2014 33 19 53 94

2015 82 -04 -06 102

2016 34 18 08 -110

Average

2004-2007 186 66 65 184

2007-2010 47 -18 03 123

2010-2013 -21 02 24 35

2013-2016 28 14 20 85

B) Services


2005 127 -05 41 -31

2006 166 75 21 54

2007 13 11 25 -36

2008 -16 -19 05 -02

2009 -63 -117 09 04

2010 54 -01 -05 13

2011 97 47 54 13

2012 12 62 19 -47

2013 12 21 62 -44

2014 78 55 64 -39

2015 106 54 07 65

2016 16 49 -60 43

Average

2004-2007 102 27 29 -04

2007-2010 -08 -46 03 05

2010-2013 40 48 24 -01

2013-2016 53 45 18 06

Notes This table shows the employment-weighted average LP growth rate by technology category (see Section

25) Only firms with at least 5 employees The sample does not include agriculture and financial services

Appendix

158

Table A44 The share of firms in the top decile ()

A) By size

2004 2007 2010 2013 2016

5-9 emp 1049 1051 1043 1096 1045

10-19 emp 954 962 92 904 92

20-49 emp 994 903 939 856 998

50-99 emp 896 1024 1188 1009 1096

100- emp 721 81 839 748 728

B) By ownership

2004 2007 2010 2013 2016

Domestic 833 818 814 824 837

Foreign 2344 2499 2422 2384 2488

State 554 728 81 575 695

C) By region

2004 2007 2010 2013 2016

Central HU 567 568 59 56 549

Northern

Hungary 195 116 19 208 224

Northern

Great Plain 161 178 239 23 249

Southern

Great Plain 137 118 17 258 179

Central

Transdanubia 276 33 332 369 332

Western

Transdanubia 311 283 244 361 444

Southern

Transdanubia 184 201 235 143 181

Notes Main sample


159

Figure A41 Persistence of top decile status

Notes This figure shows how many of top decile firms in year 2010 were frontier in 2013 how many exited and

how many continued as non-frontier The first panel shows this transition matrix for different 3-year periods

Appendix

160

A5 Chapter 5 Allocative Efficiency

Table A51 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees

Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP

covariance unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168

C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209

D - Electricity gas

steam and AC

6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743

E - Water supply

sewerage waste

6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872

F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023

G - Wholesale and

retail trade

6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303

H - Transportation

and storage

6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985

I - Accommodation

food service

6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250

J - Information and

Communication

6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046

M - Professional

Scientific and Tech Act

6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075

N - Administrative and support service

6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026

Notes Total factor productivity is measured by the method of Ackerberg et al (2015)


161

Table A52 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries full sample

Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692

C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109

D - Electricity

gas steam and

AC

6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291

E - Water supply

sewerage waste

6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916

F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436

G - Wholesale and

retail trade

6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260

H - Transportation

and storage

6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776

I -

Accommodation

food service

6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899

J - Information

and

Communication

6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800

M - Professional

Scientific and

Tech Act

6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242

N - Administrative

and support service

6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039


Appendix

162

Table A53 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees

Year 2001 2005 2010 2015

Industry unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covar

iance

unweight

ed LP

weighted

LP

covar

iance

B - Mining and quarrying 7509 8072 0564 8038 8583 0546 8378 9440 1063 8609 9028 0419

C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796

D - Electricity gas steam and

AC

9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197

E - Water supply sewerage waste

8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703

F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096

G - Wholesale and retail trade 7955 8252 0297 8036 8452 0415 7955 8307 0352 8197 8589 0392

H - Transportation and

storage

8364 8475 0110 8300 8525 0224 8194 7698 -

0496

8292 7289 -

1003

I - Accommodation food

service

7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651

J - Information and Communication

8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988

M - Professional Scientific and Tech Act

8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406

N - Administrative and

support service

7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238


163

Table A54 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries full sample

Year 2001 2005 2010 2015

Industry unweighted

labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity

covariance

B - Mining and

quarrying

7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358

C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960

D - Electricity gas

steam and AC

9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289

E - Water supply

sewerage waste

8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064

F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159

G - Wholesale and

retail trade

7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037

H - Transportation

and storage

8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022

I - Accommodation

food service

7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363

J - Information and

Communication

7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263

M - Professional

Scientific and Tech

Act

7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431

N - Administrative

and support service

7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216

Appendix

164

Table A55 Allocative efficiency based on Hsieh-Klenow (2009) ndash 1 digit industries

Distortions in 2001 Productivity Productivity dispersion

Median implicit sales taxes

Dispersion of implicit sales taxes

Median implicit cost of capital

Dispersion of implicit cost of

capital

B - Mining and quarrying 4802 1540 0299 0803 19127 1152

C - Manufacturing 5620 1300 0425 0818 12807 1008

D - Electricity gas steam and AC 6760 0503 0591 0456 6171 0784

E - Water supply sewerage waste 6629 0599 0103 1127 6245 1248

F - Construction 6706 0818 0280 0954 21186 1227

G - Wholesale and retail trade 7225 1088 0395 1007 21997 1211

H - Transportation and storage 6073 0984 -0154 1647 15193 1144

I - Accommodation food service 6201 0684 -0025 0919 7951 1263

J - Information and Communication 5499 1273 0549 0603 5387 1265

M - Professional Scientific and Tech Act 6961 0920 0253 1062 45052 1293

N - Administrative and support service 6778 1237 0084 1020 42372 1546


165

Table A55- continuedhellip






capital


C - Manufacturing 5919 1173 0497 0890 13439 0998



F - Construction 6794 0744 0155 0947 20440 1099


H - Transportation and storage 6305 1063 0017 1205 11362 1232

I - Accommodation food service 6085 0660 0098 1287 5680 1239



N - Administrative and support service 6904 1206 -0004 1055 47387 1649

Appendix

166




Dispersion of implicit sales

taxes



capital

B - Mining and quarrying 4219 0669 -0104 0759 11170 1012

C - Manufacturing 6024 1201 0523 0740 12732 1001



F - Construction 6621 0775 0200 1075 30395 1437







167



Median implicit sales

taxes


taxes

Median implicit cost

of capital


capital


C - Manufacturing 6022 1110 0514 0971 11130 1074



F - Construction 6938 0809 0298 0868 28761 1453







Notes Total factor productivity is measured by the method of Ackerberg et al (2015) See Chapter 52 for details

Appendix

168

Appendix Figure 51 Weighted and unweighted labour productivity by 2-digit industry 2016 firms with at least 5 employees

Notes All points represent a 2-digit industry The horizontal axis shows its unweighted log labour productivity in 2016 while the horizontal axis shows its

weighted log labour productivity in the same year We have omitted industries with less than 1000 observations


169

Appendix Figure 52 The relationship between weighted and unweighted labour productivity by year

Notes This figure shows the fitted lines from regressions between weighted and unweighted labour productivity levels run at the 2-digit industry level

separately for 2005 2010 and 2016

Appendix

170

Appendix Figure 53 the change in allocative efficiency by 2-digit industry

Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences between the weighted and unweighted

labour productivity) in 2010 while the vertical axis shows the same quantity in 2016


171

A6 Chapter 6 Reallocation

Table A61 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries full sample

2004-2007 2007-2010

teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry

B Mining and quarrying -93 -38 10 -65 -02 -10 50 -43

C Manufacturing 108 23 48 36 -02 -11 03 05

D Electricity gas 08 07 05 -04 26 -06 22 10

E Water supply sewerage 17 -17 31 03 08 -09 09 09

F Construction 26 04 08 13 -14 -02 -19 07

G Wholesale and retail trade 30 03 11 16 -55 -08 -65 18

H Transportation and storage -21 14 -43 08 -39 10 -57 08

I Accommodation 68 -07 53 22 -44 00 -37 -07

J ICT 96 10 63 23 29 -24 35 18

M Professional scientific 39 -13 35 17 -38 -04 -26 -08

N Administrative and support 104 11 37 56 -49 -02 -04 -43

2010-2013 2013-2016


B Mining and quarrying 08 11 09 -12 41 60 -30 10

C Manufacturing -18 07 -30 05 10 -08 22 -04

D Electricity gas -26 26 -70 18 74 07 31 36

E Water supply sewerage -08 15 -05 -18 -04 05 00 -09

F Construction 42 03 26 13 01 06 -16 11


H Transportation and storage 89 14 51 25 21 -30 06 45

I Accommodation 85 -05 59 32 51 -03 46 08

J ICT 19 -02 11 10 47 -02 38 11

M Professional scientific 69 05 12 53 30 -04 18 16

N Administrative and support 50 00 36 14 106 01 87 18


Appendix

172

Table A62 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries main sample

2004-2007 2007-2010


B Mining and quarrying -253 -59 -19 -175 73 -02 39 36

C Manufacturing 105 20 51 34 06 -14 03 16


E Water supply sewerage 21 -12 32 02 -06 -09 00 03

F Construction 35 07 12 16 -23 -03 -24 04

G Wholesale and retail trade 27 06 06 16 -39 03 -59 17


I Accommodation 67 -09 50 26 -42 03 -39 -05

J ICT 85 14 39 32 27 -14 21 19


N Administrative and support 122 29 28 65 -49 00 -09 -40

2010-2013 2013-2016


B Mining and quarrying -08 11 07 -26 24 -03 06 22

C Manufacturing -12 06 -25 07 06 -04 11 -01

D Electricity gas 16 16 -15 15 30 00 25 06

E Water supply sewerage -07 15 -02 -20 -14 -01 02 -15

F Construction 45 03 22 20 10 02 -05 12



I Accommodation 81 -04 54 30 51 -02 42 11

J ICT 13 00 00 13 49 10 33 06

M Professional scientific 64 08 11 45 32 00 14 18




173

Table A63 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries full sample

2004-2007 2007-2010

LP growth Within Between Net entry LP growth Within Between Net entry

B Mining and quarrying 93 24 44 26 105 12 59 34

C Manufacturing 132 34 54 44 08 19 -12 01

D Electricity gas 13 -04 09 08 41 02 25 14

E Water supply sewerage 45 -02 37 09 -08 -09 04 -03

F Construction 24 07 10 07 -01 07 -09 01


H Transportation and storage -25 06 -28 -04 -47 03 -56 06

I Accommodation 59 -03 56 07 -74 -12 -41 -21

J ICT 58 19 80 -40 20 -21 40 00

M Professional scientific 61 11 34 16 -67 02 -26 -43

N Administrative and support 61 -20 38 43 -63 -24 -11 -29

2010-2013 2013-2016


B Mining and quarrying 26 04 -01 24 -29 17 -28 -18

C Manufacturing 00 14 -21 07 33 16 19 -01


E Water supply sewerage -20 -07 -08 -05 04 -03 06 01

F Construction 40 05 26 10 -03 05 -05 -03

G Wholesale and retail trade 49 05 31 13 68 13 57 -01


I Accommodation 74 -07 55 26 47 -08 54 02

J ICT 16 -07 11 12 22 -25 42 05


N Administrative and support 61 08 31 21 81 -11 76 15

Appendix

174

Table A64 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries main sample

2004-2007 2007-2010

LP growth Within Between Net entry LP gtowth Within Between Net entry

B Mining and quarrying 48 15 -01 34 70 17 53 00

C Manufacturing 132 32 56 45 16 14 -03 05

D Electricity gas 14 -03 05 11 35 -07 30 12

E Water supply sewerage 48 00 39 09 -10 -06 03 -07

F Construction 28 10 14 04 03 06 -14 11



I Accommodation 62 -03 52 12 -65 -09 -43 -13

J ICT 00 -15 49 -34 03 -22 14 12



2010-2013 2013-2016


B Mining and quarrying 33 -11 04 40 50 28 05 17

C Manufacturing 06 13 -15 07 28 12 16 00



F Construction 44 05 26 14 03 02 07 -07



I Accommodation 70 -07 52 25 44 -07 51 01

J ICT 26 07 04 16 17 -20 37 00




175

A7 Chapter 7 Firm-level productivity growth and dynamics

A71 Productivity growth

Table A71 Relationship between lagged productivity level and subsequent productivity

growth over time


VARIABLES (1) (2) (3)

TFP in t-1 Year 2003 -0188 -0203 -0203

(000550) (000558) (000551)

TFP in t-1 Year 2006 -0222 -0238 -0235

(000518) (000525) (000519)

TFP in t-1 Year 2009 -0143 -0159 -0155

(000570) (000579) (000572)

TFP in t-1 Year 2012 -0156 -0172 -0171

(000516) (000524) (000517)

Year 2003 -00313 -00297

(000507) (000510)

Year 2006 -0184 -0183

(000489) (000491)

Year 2009 -00766 -00762

(000492) (000493)

Year FE YES YES

Industry FE YES

Industry-region FE YES


Region FE YES

Industry-year FE YES

Observations 114200 113900 113900

R-squared 0061 0067 0084

Appendix

176

Table A72 Relationship between lagged productivity levels and subsequent productivity

growth by size and age



VARIABLES (1) (2) (3) (4)

TFP in t-1 -0170 -0186 -0213 -0223

(000561) (000578) (00155) (00155)

TFP in t-1 Group 2 00397 00243 -000502 -000776

(00146) (00147) (00213) (00213)

TFP in t-1 Group 3 00793 00652 00725 00600

(00221) (00222) (00164) (00165)

TFP in t-1 Group 4 00753 00666

(00244) (00247)

Group 2 00227 000593 -0000410 0000118

(000940) (000963) (00162) (00162)

Group 3 00216 -000934 00235 00220

(00150) (00154) (00131) (00132)

Group 4 00235 -00351

(00157) (00169)

Industry FE YES YES



Observations 30135 30062 30135 30062

R-squared 0056 0073 0056 0073

Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees size group 4 is

100+ employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5

years age group 3 is firms older than 5 The baseline category is firms of 2-3 years


177

Table A73 Differences in productivity growth by ownership group within different firm

groups


VARIABLES (1) (2) (3) (4)

Foreign 00476

(00114)

Foreign Non-exporter 00573

(00213)

Foreign Exporter 00610

(00139)

Foreign Size group 1 00295

(00162)


(00243)


(00340)


(00318)

Foreign Age group 1 0119

(00381)

Foreign Age group 2 -00117

(00363)


(00124)

Industry-region FE YES YES YES YES

Firm group indicators YES YES YES

Observations 31642 31642 31642 31274

R-squared 0032 0033 0033 0033


50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is

firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter

dummy in column 2 and size and age group dummies in columns 3 and 4 respectively

Appendix

178


growth by ownership and exporter status over time


Firm categories by

foreign ownership exporter status

VARIABLES (1) (2) (3) (4)

TFP in t-1 Firm group Year 2003

00577 00607 00141 00214

(00151) (00151) (00124) (00124)


00703 0101 00361 00558

(00152) (00152) (00118) (00118)


00338 00306 00450 00406

(00153) (00153) (00122) (00121)


00758 00436 00474 00321

(00146) (00146) (00109) (00109)

Firm group Year 2003

00978 00756 00286 000961

(00128) (00130) (000912) (000977)


-00290 -00145 -00592 -00411

(00133) (00135) (000871) (000932)


0114 0116 00502 00457

(00124) (00127) (000824) (000881)


0120 0120 00234 00155

(00126) (00129) (000782) (000835)

Year FE YES YES

Industry FE YES YES

Firm-level controls

YES YES YES YES

Region FE YES YES

Industry-year FE

YES YES

Observations 112374 112374 113900 113900

R-squared 0066 0085 0065 0085

Notes Firm group refers to foreign ownership in columns (1) and (2) and to exporter status in

columns (3) and (4)


179

A72 Employment growth

Table A75 Relationship between lagged productivity levels and subsequent employment

growth over time



TFP in t-1 Year 2003 0113 0113 0113

(000472) (000478) (000475)

TFP in t-1 Year 2006 0120 0120 0119

(000434) (000439) (000437)

TFP in t-1 Year 2009 0109 0109 0107

(000479) (000485) (000482)

TFP in t-1 Year 2012 00982 00958 00956

(000442) (000448) (000445)

Year 2003 -00171 -00125

(000441) (000444)

Year 2006 -0134 -0128

(000422) (000423)

Year 2009 -00899 -00873

(000425) (000426)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 123900 123574 123574

R-squared 0042 0049 0054

Appendix

180


growth over time with alternative employment growth measures including exiting firms

Dep var employment growth from t to t+3 (including exiting firms (t=2003200620092012)


TFP in t-1 Year 2003 0156 0147 0148

(000641) (000647) (000644)

TFP in t-1 Year 2006 0134 0127 0128

(000581) (000587) (000584)

TFP in t-1 Year 2009 0139 0132 0134

(000648) (000655) (000651)

TFP in t-1 Year 2012 0132 0126 0127

(000618) (000624) (000621)

Year 2003 -00765 -00618

(000617) (000619)

Year 2006 -0220 -0211

(000586) (000587)

Year 2009 -0177 -0173

(000591) (000590)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 143011 142638 142638

R-squared 0037 0047 0051


181


growth by ownership and exporter status with alternative employment growth measures

including exiting firms

Dep var employment growth from t to t+3 (including exiting firms t=2012)

VARIABLES (1) (2) (3) (4) (5) (6)

TFP in t-1 0134 0130 0134 0137 0134 0136

(000651) (000660) (000722) (000729) (000764) (000767)

TFP in t-1 Foreign -00109 -00138 00116 000347

(00166) (00167) (00289) (00289)

TFP in t-1 Exporter -00371 -00256 -00304 -00226

(00124) (00126) (00148) (00148)


(00364) (00365)

Foreign -00254 -00351 -0102 -00739

(00151) (00156) (00254) (00256)

Exporter 00998 00982 00940 00889

(00100) (00102) (00106) (00107)

Foreign exporter 00855 00605

(00312) (00315)




Observations 34980 34980 35564 35473 34980 34980

R-squared 0031 0051 0037 0054 0034 0052

Appendix

182

Table A78 Differences in employment growth by exporter status within different firm

groups


VARIABLES (1) (2) (3) (4)

Exporter 00876

(000741)

Exporter Domestic 00893

(000788)

Exporter Foreign 00703

(00207)

Exporter Size group 1 00858

(000850)


(00159)


(00276)


(00329)

Exporter Age group 1 00968

(00230)


(00212)


(000801)

industry-region FE YES YES YES YES

Firm-group indicators YES YES YES

Observations 34418 33909 34418 33989

R-squared 0034 0034 0034 0036




dummy in column (2) and size and age group dummies in columns (3) and (4) respectively


183



Dep var Employment growth from t to t+3 (t=2003200620092012)

Firm categories by foreign ownership exporter status

VARIABLES (1) (2) (3) (4)

TFP in t-1 Firm group Year 2003 000927 00131 00178 00190

(00129) (00130) (00107) (00107)


(00126) (00127) (00101) (00101)

TFP in t-1 Firm group Year 2009 -00778 -00676 -00498 -00426

(00129) (00130) (00104) (00104)


(00126) (00126) (000942) (000942)

Firm group Year 2003 -00601 -00332 000244 00299

(00110) (00113) (000795) (000856)

Firm group Year 2006 -00159 -000559 00640 00786

(00112) (00115) (000752) (000807)

Firm group Year 2009 00404 00249 0111 00882

(00106) (00109) (000714) (000767)

Firm group Year 2012 -00102 -00116 00747 00607

(00110) (00112) (000684) (000735)

Year FE YES YES

Industry FE YES YES


Region FE YES YES


Observations 121954 121954 123574 123574

R-squared 0046 0055 0045 0055

Notes Firm group refers to foreign ownership in columns (1) and (2) and exporter status in columns

(3) and (4)

Appendix

184

A73 Entry and exit

Table A710 Entry and exit premium by ownership and exporter status

Depvar TFP in year t (t=2015 for entry and t=2012 for exit)

VARIABLES (1) (2) (3) (4) (5) (6)

Entry Domestic 00363 00433 Exit Domestic -0165 -0161 Exit Non-exporter

-0172 -0186

(00103) (00102) (00112) (00112) (00122) (00121)

Entry Foreign 0414 0354 Exit Foreign 0255 0203 Exit Exporter

0171 0126

(00284) (00281) (00311) (00309) (00213) (00211)

Incumbent Foreign

0512 0461 Continuing Foreign

0465 0411 Continuing Exporter

0279 0232

(00122) (00129) (00123) (00131) (000887) (000926)

Industry FE YES Industry FE YES Industry FE YES

Industry-region FE YES Industry-region FE

YES Industry-region FE

YES

Firm-level controls YES Firm-level controls

YES Firm-level controls

YES

Observations 44231 44231 Observations 38367 38367 Observations 39020 38916

R-squared 0355 0383 R-squared 0339 0369 R-squared 0331 0370

Table A711 Differences in productivity levels by ownership group within different firm

groups

Depvar TFP in year t (t=2012)

VARIABLES (1) (2) (3) (4)

Foreign 0429

(00118)


(00206)


(00146)


(00162)


(00254)


(00363)


(00341)


(00352)


(00356)


(00131)



Observations 38367 38367 38367 37822

R-squared 0350 0361 0353 0356






185

Table A712 Entry and exit premium by ownership and exporter status over time


VARIABLES (1) (2) VARIABLES (3) (4) VARIABLES (5) (6)

Entry Domestic Year 2006

-00510 -00403 Exit Domestic 2003 -0187 -0188 Exit Non-exporter 2003 -0197 -0198

(000924) (000923) (00107) (00106) (00114) (00113)


00244 00230 Exit Domestic 2006 -00996 -0101 Exit Non-exporter 2006 -0114 -0118

(000999) (000996) (000917) (000911) (000977) (000971)



(000985) (000983) (000942) (000937) (00101) (00101)



(000998) (000999) (00111) (00110) (00119) (00119)

Entry Foreign Year 2006

0374 0313 Exit Foreign 2003 0116 00940 Exit Exporter 2003 00659 00517

(00265) (00264) (00264) (00263) (00196) (00197)



(00257) (00257) (00267) (00265) (00183) (00183)



(00279) (00278) (00278) (00277) (00185) (00185)



(00276) (00275) (00307) (00305) (00208) (00208)

Incumbent Foreign Year 2006

0485 0428 Continuing Foreign 2003 0416 0386 Continuing Exporter 2003 0278 0257

(00122) (00124) (00124) (00126) (000943) (000994)



(00120) (00122) (00122) (00124) (000895) (000943)



(00122) (00124) (00119) (00122) (000867) (000915)



(00118) (00120) (00120) (00122) (000827) (000876)

Year FE YES Year FE YES Year FE YES


Firm-level controls YES YES Firm-level controls YES YES Firm-level controls YES YES

Industry-year FE YES Industry-year FE YES Industry-year FE YES

Region FE YES Region FE YES Region FE YES



Table A713 Entry and exit premium by size and age



VARIABLES (1) (2) VARIABLES (3) (4) (5) (6)

Entry Group 1 00233 00151 Exit Group 1 -0170 -0171 -0214 -0210

(00108) (00105) (00121) (00118) (00250) (00241)


(00298) (00289) (00280) (00272) (00271) (00261)


(00574) (00556) (00479) (00464) (00179) (00173)

Entry Group 4 0123 -00552 Exit Group 4 -0291 -0453

(00720) (00697) (00532) (00517)

Incumbent Group 2 00137 -00620 Continuing Group 2 -00108 -00902 -00277 -00256

(00104) (00101) (00111) (00109) (00170) (00164)

Incumbent Group 3 00163 -0130 Continuing Group 3 000582 -0148 -00759 -00758

(00170) (00168) (00179) (00176) (00131) (00127)

Incumbent Group 4 00150 -0268 Continuing Group 4 -00159 -0293

(00181) (00185) (00188) (00192)

Industry FE YES Industry FE YES YES

Industry-region FE YES Industry-region FE YES YES

Firm-level controls YES Firm-level controls YES YES

Observations 46160 46034 39020 38916 38459 38357

R-squared 0296 0355 0311 0369 0315 0374

Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is 50-99

employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is firms of 4-5

years age group 3 is firms older than 5 years

Figure A71 Share of exiting firms in the subsequent 3 years by lagged productivity levels in

different periods

A8 Chapter 8 Retail

Appendix Table A81 Event study regression for the whole retail industry

(1) (2) (3) (4) (5) (6)

Dependent lsales lsales sales_day sales_day days_open days_open

pre_trend_treated1 0005 0014 0012 0030 -0230 -0511 (0006) (0005) (0005) (0005) (0030) (0049)

pre_trend_treated3 -0010 -0003 -0008 -0003 -0037 -0031 (0006) (0008) (0006) (0008) (0030) (0056)

pre_trend_treated4 -0020 -0010 -0014 0000 -0209 -0299 (0006) (0007) (0006) (0007) (0029) (0051)


pre_trend_treated6 -0008 0001 -0004 0008 -0129 -0222 (0007) (0008) (0006) (0008) (0035) (0065)

pre_trend_treated7 0001 -0001 0016 0017 -0365 -0496 (0010) (0013) (0010) (0012) (0053) (0072)

trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)

trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)

trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)

trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)

post_trend_treated1 -0039 -0006 -0007 0044 -0885 -1394 (0012) (0012) (0012) (0011) (0061) (0096)

post_trend_treated2 0022 0003 0044 0048 -0665 -1273 (0012) (0012) (0011) (0011) (0068) (0100)

post_trend_treated3 -0001 0004 0035 0058 -0993 -1531 (0012) (0012) (0012) (0012) (0058) (0092)

Shop FE yes yes yes yes yes yes

Firm-year FE no yes no yes no yes

Observations 225866 209604 225860 209598 225908 209647

R-squared 0958 0978 0961 0980 0684 0809

Appendix

188

Appendix Table A82 Event study regression for NACE 4711

(1) (2) (3) (4) (5) (6)







pre_trend_treated7 -0016 -0032 0002 -0007 -0433 -0640 (0010) (0016) (0009) (0015) (0068) (0091)

trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)

trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)

trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)

trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)

post_trend_treated1 -0009 -0032 0033 0036 -1163 -1875 (0008) (0012) (0008) (0011) (0084) (0118)

post_trend_treated2 0057 -0024 0087 0041 -0888 -1810 (0014) (0013) (0012) (0012) (0097) (0121)




Observations 94740 87533 94737 87530 94740 87533

R-squared 0968 0982 0973 0985 0642 0809

Appendix Table A83 Sales and the number of different days in a month

(1)

Dependent ln sales

Sunday 0049 (0001)

Saturday 0059 (0001)

Friday 0054 (0001)

Thursday 0050 (0001)

Wednesday 0053 (0001)

Tuesday 0060 (0001)

Monday 0048 (0001)

holiday 0008 (0000)

Jan -0169 (0002)

Dec 0138 (0003)

summer 0032 (0002)

date -0000 (0000)

Observations 463345

R-squared 0970

Appendix

190

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EUROPEAN COMMISSION


Directorate A mdash Competitiveness and European Semester Unit A2 mdash European Semester and Member Statesrsquo Competitiveness

Contact Tomas Braumlnnstroumlm

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ISBN 978-92-79-73462-5 doi 10287333213





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Table of contents

EXECUTIVE SUMMARY I

1 INTRODUCTION 1

2 DATA SOURCES 4







31 Convergence 18


33 Firm dynamics 28

34 Conclusions 31


41 Context 32




45 Conclusions 56




53 Conclusions 70

6 REALLOCATION 73




64 Conclusions 86





74 Conclusions 103


81 Context 104

82 Data 108



85 Trade 123



88 Conclusions 141

9 CONCLUSIONS 142

REFERENCES 144

APPENDIX 152














i

EXECUTIVE SUMMARY





2001-2016













non-frontier firms






























Executive Summary

ii





















































iii




















































Executive Summary

iv





















































1

1 INTRODUCTION

















non-frontier firms






























Introduction

2













sheet data







































3














































Data Sources

4
































2 DATA SOURCES











5







firms in year 2016







input a zero












Year 1 emp 2-5 emp

6-10 emp

11-20 emp

21-49 emp

50-249 emp

250 lt emp

Total

2004 005 028 092 105 160 312 642 043

2005 010 050 169 288 483 1066 2227 108

2006 017 087 315 553 966 1935 3632 192

2007 2209 3006 4384 5249 5743 6082 7412 3135

2008 098 324 951 1692 2840 4868 7827 576

2009 5962 6070 7217 7428 7831 7798 9336 6301

2010 2142 4685 7034 7540 8424 8228 9634 4175

2011 2277 4736 7064 7753 8521 8657 9681 4220

2012 9397 8298 9305 9484 9507 9159 10121 8990

2013 7274 8140 9423 9981 9747 9445 10312 8044



Data Sources

6














119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)





intensity





















7






























Data Sources

8


A) Manufacturing

B) Services


9








2004 and 2016


unweighted


2004 2016 2004 2016


B Mining 797 088 867 086 408 087 441 065

C Manufacturing 777 087 806 079 581 079 598 077



812 085 830 089 604 091 593 094

F Construction 773 080 803 072 620 071 646 066



I Accommodation 710 075 752 080 594 068 640 071

J ICT 834 094 862 090 631 101 669 098


815 087 844 088 636 087 673 088


763 098 792 094 640 107 662 113

Total 789 095 815 087 620 090 647 087








Data Sources

10











sample

























in the Appendix




11



2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739

2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987

2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568

2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376

2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880

2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844

2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671

2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709

2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318

2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113

2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104

2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509

2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335

2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665

2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678

2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519

2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727

Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742


estimated TFP


sales

Data Sources

12














Number of

firms

Total

employment

Total value

added






employment

708 885 935







157 600 647











13



B Mining 029 025 024 021 018

C Manufacturing 3085 2636 2352 2280 2122



103 112 114 127 098

F Construction 1263 1439 1447 1263 1375




J ICT 351 328 396 403 400




297 547 572 601 572

Total 100 100 100 100 100























Data Sources

14


A) Number of firms


B Mining 8228 1772 000 100








J ICT 8314 1541 145 100


8982 915 102 100


8991 798 211 100

Total 8937 953 110 100



B) Employment


B Mining 725 275 00 100








J ICT 424 546 30 100


650 331 20 100


681 258 61 100

Total 560 373 66 100












15


Year Foreign

exporter

Foreign

non-

exporter

Domestic

exporter

Domestic

non-

exporter

2000 92 51 168 690

2001 89 46 172 693

2002 84 43 173 701

2003 79 40 164 717

2004 71 36 157 736

2005 70 34 160 736

2006 69 34 163 734

2007 73 32 180 715

2008 74 34 186 706

2009 78 36 195 692

2010 77 34 202 687

2011 78 32 215 675

2012 81 31 229 659

2013 79 30 236 655

2014 74 33 233 660

2015 71 31 238 659

2016 70 26 245 658

Total 76 35 196 692









Data Sources

16


2016

Foreign exporter


Domestic exporter


Year 2004

N of employees 1385 511 451 165

(5689) (2410) (1396) (404)


(101) (120) (087) (086)

TFP ACF 666 660 634 611

(111) (112) (091) (084)

Age

101 85 99 85

(42) (46) (43) (43)

Year 2016

N of employees 1619 338 344 151

(6246) (1257) (1290) (405)


(088) (115) (075) (080)

TFP ACF 696 684 651 639

(113) (109) (086) (080)

Age 160 105 149 124

(82) (75) (76) (75)



parentheses






















17







Services



Utilities 35 to 39



18







31 Convergence






















19




















countries


Germany=100







20









crisis






















21


























22


























2013





23




B) TFP








24



























Country

Year 2011



Australia 187 205 190 212

Austria 196 242 - -

Belgium 160 174 180 178

Chile 300 353 307 387

Denmark 146 196 132 180

Finland 117 138 119 134

France 135 181 140 178

Hungary 279 329 254 286


Italy 166 201 160 188

Japan 126 138 117 138


New Zealand 184 209 192 208

sNorway 173 217 187 208

Portugal 188 265 188 275

Sweden 145 186 159 245






25





Country

Year 2011

LP Dispersion


Australia 98 99

Austria 86 90

Belgium 76 88

Chile 90 97

Denmark 84 63

Finland 65 76

France 63 85

Hungary 79 99

Indonesia 79 -

Italy 82 65

Japan 75 89

Netherlands 80 71

Norway 83 73

Portugal 62 76

Sweden 53 74

























26


A) Manufacturing








27


A) Manufacturing







28







33 Firm dynamics



























29


A) Manufacturing








30


A) Manufacturing









31

34 Conclusions














32


41 Context























A) Manufacturing


33























34


sample


























35














economy





average28










period









36




Number of employees

Manufacturing Other

2012 2015 2012 2015


Other firm 289636 296921 698326 771930

Self-employed 72674 74325 620699 638001

Total 983539 998637 2644324 2606263


Manufacturing Other

2012 2015 2012 2015


Other firm 151 146 196 201


Total 92 99 105 111























37





Appendix)


A) Market economy

Year LP TFP


2005 20 58 19 74

2006 92 91 93 119

2007 53 60 39 56

2008 -10 -08 -10 -04

2009 -70 -81 -69 -82

2010 -05 44 11 80

2011 25 45 34 40

2012 25 22 21 01

2013 19 25 30 22

2014 39 45 40 59

2015 51 50 52 49

2016 36 19 20 03

Average

2004-2007 55 70 50 83

2007-2010 -28 -15 -23 -02

2010-2013 23 34 33 29

2013-2016 36 35 35 33

B) Manufacturing

Year LP TFP


2005 37 148 20 114

2006 124 163 114 149

2007 100 114 78 71

2008 25 -03 17 -17

2009 -115 -94 -133 -117

2010 82 161 80 173

2011 -02 34 04 18

2012 05 -46 -02 -58

2013 -14 31 -12 05

2014 11 48 -01 27

2015 38 37 30 14

2016 26 01 04 -23

Average

2004-2007 87 141 71 111

2007-2010 -02 22 -12 13

2010-2013 -04 17 04 -03

2013-2016 15 29 05 06


38

C) Market services

Year

LP TFP


2005 12 -04 10 32

2006 80 47 79 90

2007 39 25 24 48

2008 -22 -06 -21 -03

2009 -57 -68 -52 -71

2010 -29 -17 -11 26

2011 33 49 43 57

2012 31 60 30 48

2013 29 21 39 29

2014 46 45 46 78

2015 54 58 54 72

2016 39 30 25 20

Average

2004-2007 43 23 38 57

2007-2010 -36 -31 -28 -16

2010-2013 31 44 39 51

2013-2016 42 39 41 50



















utilities



39


A) Manufacturing


2005 124 19 66 274 2006 240 137 39 33

2007 74 02 41 221

2008 -45 23 -15 59

2009 05 -191 -218 48

2010 135 111 264 168

2011 -45 18 34 100

2012 -15 -24 -83 -181

2013 -41 37 -22 125

2014 06 07 27 86

2015 65 01 -54 80

2016 -02 04 -27 -91

Average 2005-2007 146 53 49 176

2007-2010 32 -19 10 92

2010-2013 -34 07 -21 20

2013-2016 07 12 -19 50

B) Services


2005 127 16 34 -48

2006 166 75 30 67

2007 13 58 42 29

2008 -16 14 -72 -26

2009 -63 -94 -04 25

2010 54 12 09 05

2011 97 46 65 29

2012 12 74 13 -22

2013 12 30 63 -07

2014 78 89 65 -81

2015 106 70 14 54

2016 16 31 -47 39

Average

2005-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01







Appendix)


40

Frontier firms














firms












Appendix)31


A) By size

2004 2007 2010 2013 2016

20-49 emp 357 327 34 362 329

50-99 emp 401 468 542 486 555

100- emp 293 358 414 42 462

B) By ownership

2004 2007 2010 2013 2016

Domestic 213 194 236 272 289

Foreign 873 955 896 82 821

State 181 211 166 167 263




41

C) By region

2004 2007 2010 2013 2016

Central HU 596 621 652 552 579






Southern

Transdanubia 131 081 188 159 211
















42





















A) Manufacturing

32 1198902 asymp 74


43

B) Services







intensive services












44


A) Manufacturing

B) Services









45









deciles

A) Manufacturing



46

B) Services



























47






sample


















48




















year effects) 2016





49




composition matters














A) Domestic private


50

B) Foreign

C) State


Main sample


51





































52













firms






crisis


(cont)























53


A) Manufacturing

B) Services




intensive services


54


A) Manufacturing

B) Services






55


A) Manufacturing

B) Services






56




45 Conclusions





















57


































58















year level



























59





















60


























61






(2015)

















Appendix)


62






















63














and combinations




1 minus 120591119884119904119894 =120590

120590minus1

120573119871119904+120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119881119860119904119894 (51)

















return to scale





64





















65









tax46

119877(1 + 120591119870119904119894) =120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119870119904119894 (52)







66





































67












68










industries
















69







A) Age of firms

B) Size of firms



70























Variance

component

Share

of total

Variance

component

Share

of total


cost of capital

2126 100 2443 100







Residual 1830 861 1995 817



53 Conclusions









71


Industry type TFP

level

in

2016

TFP

growth

between

2011 and

2016

Olley-

Pakes

allocative

efficiency

Dispersion

of implicit

sales taxes

Dispersion

of implicit

cost of

capital

Low-tech mfg 5694 0027 0197 111 146



High-tech mfg 6708 0276 0072 107 145




Construction 6411 0082 0023 109 148

Utilities 5949 -0138 0801 093 155

Total services 6598 0212 0055 108 160






























72




across sectors



73

6 REALLOCATION



















this process














2013)

Reallocation

74


2004 2007 2010 2013 2016

Low-tech mfg 152 117 107 105 100



High-tech mfg 49 49 44 39 35





Utilities 40 34 34 33 34

Total services 616 645 676 671 673






A) Manufacturing


75

B) Services


sample







(61)












Reallocation

76















sample









77











+

























(62)

Reallocation

78






calculations
















79




Reallocation

80







81

















2004-2007





2007-2010





2010-2013





2013-2016











Reallocation

82


2004-2007





2007-2010





2010-2013





2010-2016























83















Reallocation

84


Notes Main sample



















85





A) By size

2004 2007 2010 2013 2016

5-9 emp 62 751 862 802 411

10-19 emp 618 626 679 652 297

20-49 emp 607 554 698 648 296

50-99 emp 711 685 792 829 438

100- emp 2005 1839 1559 1489 456

Total 1548 1367 1229 1194 417

B) By ownership

2004 2007 2010 2013 2016

Domestic 66 671 769 614 253

Foreign 989 1011 1139 1577 585

State 6289 5954 4127 2792 7

Total 155 1369 1228 1194 417


2004 2007 2010 2013 2016

Low-tech mfg 1236 1255 1149 906 371



High-tech mfg 427 1392 429 268 1

KIS 2098 737 875 1408 592

LKIS 282 2327 187 1867 365

Construction 258 394 339 515 352

Utilities 297 1031 705 593 756

Total 1553 1372 1229 1194 418

Notes Main sample





Reallocation

86














Notes Main sample

64 Conclusions





economy






87












the problem




















Reallocation

88






Estonia


89
















Section 44)





























90




















2015)58













recent periods60










91


over time





Estimation







1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905

119896

119870

119896=1


119896

119870

119896=1

+ 119883119894119905 + 120572119895(119894) + 휀119894119905









(71)


92




1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905

119897

119897=200320062009


119897 )119863119905119897

119897=2003200620092012

+ 119883119894119905 + 120572119895(119894) + 휀119894119905















1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905

119896119863119905119897

119870

119896=1119897=2003200620092012


119897 )119863119905119897

119897=2003200620092012


119897 )119866119894119905119896

119870

119896=1

119863119905119897

119897=2003200620092012

+ sum 1205734119897119863119905

119897

119897=200320062009

+ 119883119894119905 + 120572119895(119894) + 휀119894119905






Results













(72)

(73)


93




exporter firms


firm group


By size By age
















94




convergence61











Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190

(000556) (000568) (000622) (000629) (000656) (000663)

TFP in t-1 Foreign 00475 00418 00433 00423

(00140) (00142) (00252) (00253)

TFP in t-1 Exporter 00477 00287 00216 00224

(00104) (00106) (00125) (00126)


(00312) (00314)

Foreign 0117 0109 0120 0104

(00121) (00132) (00223) (00226)

Exporter 00240 000260 000401 0000919

(000791) (000845) (000851) (000881)


(00267) (00272)




Observations 29717 29717 30135 30062 29717 29717

R-squared 0060 0072 0056 0073 0060 0072







95



























96






















97



By size By age






98

Estimation results



Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 0105 0102 0105 0107 0107 0108

(000484) (000493) (000539) (000546) (000570) (000575)

TFP in t-1 Foreign

-00369 -00328 -00252 -00224

(00123) (00124) (00217) (00217)

TFP in t-1 Exporter

-00344 -00298 -00250 -00249

(000913) (000932) (00109) (00110)


-0000806 000194

(00271) (00272)

Foreign 000105 -000863 -000786 -0000106

(00112) (00116) (00194) (00196)

Exporter 00586 00635 00653 00672

(000738) (000754) (000777) (000786)

Foreign exporter

-000647 -00123

(00234) (00238)


Industry-region FE

YES YES YES

Firm-level controls

YES YES YES

Log of employees


Observations 31662 31662 32124 32043 31662 31662

R-squared 0035 0049 0038 0051 0037 0049



















99











VARIABLES (1) (2) (3) (4)

TFP in t-1 0112 0110 00875 00863

(000486) (000502) (00133) (00133)

TFP in t-1 Group 2 -00455 -00431 00445 00384

(00128) (00129) (00182) (00183)

TFP in t-1 Group 3 -00745 -00748 000733 000822

(00194) (00196) (00141) (00142)

TFP in t-1 Group 4 -00953 -00957

(00218) (00220)

Group 2 -000596 -000810 -00188 -00212

(00122) (00123) (00141) (00141)

Group 3 -000928 -00157 -00115 -00169

(00199) (00201) (00114) (00115)

Group 4 00328 00280

(00276) (00278)

Industry FE YES YES




Observations 32124 32043 32124 32043

R-squared 0038 0052 0037 0051




73 Entry and exit

Questions





100










Estimation











119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905

119896119873119864119894119905119870119896=1 + 1205732119866119894119905

0119864119894119905 + sum 1205733119896119866119894119905

119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)















interacting 119866119894119905119896119873119864119894119905 119866119894119905




101

Results















(1) (2) (3) (4) (5) (6)


-00532 -00535 -00465 -0214 -0199 -0200

(000906) (000873) (000879) (00102) (000987) (000989)


00269 00212 00232 -0135 -0117 -0119

(000967) (000931) (000936) (000897) (000865) (000865)


00369 00444 00360 -0133 -0114 -0121

(000960) (000926) (000931) (000920) (000889) (000889)

EntrantExitorPeriod

2012-2015

00374 00324 00252 -0171 -0150 -0157

(000971) (000936) (000944) (00107) (00103) (00103)

Period 2003-2006 -0115 -00927 -00711 -00445

(000519) (000501) (000556) (000537)

Period 2006-2009 -0175 -0169 -000972 00122

(000522) (000503) (000537) (000518)

Period 2009-2012 -0116 -0117 -00580 -00528

(000528) (000508) (000543) (000522)


Industry FE YES YES




Observations 166607 166168 166168 158143 157711 157711

R-squared 0327 0380 0380 0332 0385 0387


102




















103

74 Conclusions

























104




























81 Context







74 EC (2018) p 5


105















75 EC (2018) p 4



106






to larger firms

















107









Below 500m HUF 0



Above 100bn HUF 25












108

82 Data


































109








qualified analysis












2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446

2006 573 573 51 131 59 430 111 2008 30 1548

2007 546 546 53 125 60 429 110 1987 33 1634

2008 628 628 45 102 50 350 104 1823 21 1574

2009 527 527 33 72 41 290 99 1879 24 1754

2010 472 472 22 49 32 238 94 1793 22 1968

2011 537 537 14 30 29 212 92 1758 22 2027

2012 374 374 30 68 49 335 88 1643 23 2107

2013 503 503 48 121 42 277 88 1622 25 2094

2014 410 410 106 239 48 320 81 1530 24 2054

2015 512 512 135 311 42 292 80 1544 30 2090

2016 518 518 120 292 37 271 77 1457 23 2022









110









83 General trends






















111


A) Number of firms

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100

2006 7333 1613 687 252 065 049 100

2007 7369 1597 674 244 067 050 100

2008 7410 1571 667 236 067 048 100

2009 7522 1535 614 221 059 048 100

2010 7551 1508 640 201 057 044 100

2011 7591 1493 623 194 057 041 100

2012 7625 1506 568 204 055 042 100

2013 7526 1596 577 207 052 042 100

2014 7380 1684 628 210 056 042 100

2015 7239 1771 661 232 056 041 100

2016 7163 1804 676 252 063 043 100

B) Employment

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000

2006 2034 1475 1269 1025 679 3525 10000

2007 2026 1443 1241 984 669 3644 10000

2008 2020 1391 1138 910 579 3980 10000

2009 2002 1427 1244 870 570 3789 10000

2010 2099 1443 1243 862 577 3731 10000

2011 2144 1458 1119 895 555 3830 10000

2012 2144 1541 1131 906 541 3713 10000

2013 2168 1591 1204 896 555 3650 10000

2014 2104 1644 1236 970 548 3538 10000

2015 2064 1620 1216 1006 588 3570 10000

2016 1999 1620 1216 1006 588 3570 10000

C) Sales

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000

2006 1114 1278 1587 1025 776 4226 10000

2007 1110 1266 1543 956 855 4268 10000

2008 1112 1524 1045 891 531 4906 10000

2009 1104 1641 1073 856 718 4607 10000

2010 1104 1521 1103 930 693 4594 10000

2011 1160 1577 1004 934 624 4714 10000

2012 1146 1647 1003 913 629 4578 10000

2013 1229 1411 1090 945 709 4360 10000

2014 1486 1472 1093 1001 752 4389 10000

2015 1293 1458 1118 985 660 4512 10000

2016 1266 1458 1118 985 660 4512 10000


112























113



retail85

















119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905

















114





119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905









115


A) TFP

B) Gross Margin

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 1769 1548 1793 1883 1584 1495 1735 1554

2005 1839 1571 1858 1878 1504 1666 1794 1584

2006 2040 1658 1842 1879 1599 1660 1956 1653

2007 2220 1720 1875 1940 1664 1702 2104 1712

2008 2192 1792 1872 1992 1522 1739 2096 1728

2009 2084 1719 1864 1967 1599 1527 2006 1630

2010 2096 1749 1867 2102 1664 1535 2024 1682

2011 2172 1765 1862 2003 1759 1558 2084 1728

2012 2203 1740 1828 1913 1969 1541 2102 1733

2013 2243 1734 1806 2018 1999 1794 2128 1790

2014 2363 1789 1817 2149 2022 1869 2223 1855

2015 2390 1803 1885 2165 2215 1987 2245 1928

2016 2379 1799 1911 2172 2403 2170 2237 1948


Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

unweighted weighted

2004 651 701 663 677 479 480 658 611

2005 827 757 777 712 526 560 806 644

2006 908 773 777 753 579 797 870 706

2007 807 635 653 704 500 580 764 606

2008 665 588 577 610 422 478 643 525

2009 618 535 538 522 351 339 595 456

2010 669 587 629 626 421 341 650 510

2011 698 617 591 637 410 393 676 542

2012 770 643 599 624 485 404 735 577

2013 735 594 555 631 478 369 697 530

2014 745 578 568 612 446 398 700 531

2015 766 609 625 646 547 452 724 579

2016 759 597 621 632 609 487 715 588

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 599 633 637 628 612 623 610 626

2005 610 635 643 628 616 626 619 631

2006 623 646 651 640 630 642 631 643

2007 624 643 650 641 627 643 631 642

2008 625 645 647 640 630 640 631 642

2009 619 641 642 631 619 630 626 630

2010 617 641 643 634 622 631 625 631

2011 621 643 645 637 629 639 628 639

2012 624 646 644 637 629 639 631 644

2013 626 647 642 640 637 642 632 645

2014 628 648 650 645 640 648 634 648

2015 638 658 658 655 652 659 644 659

2016 643 661 665 659 655 661 649 665


116





















117







newspapers












118





















119






















120










regressions

119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905



dummies






121


its shops














(1) (2) (3)

Dependent Change in

number of

shops

New

shops

Closed

shops


(0024) (0014) (0018)


(0064) (0034) (0045)

ln( (average

salesshop)

0056 0034 -0017

(0016) (0010) (0014)

5-9 shops 0176 0001 -0126

(0070) (0036) (0054)

10-49 shops 0231 -0009 -0167

(0067) (0034) (0052)

more than 50 shops 0194 0002 -0109

(0071) (0038) (0055)

Year FE yes yes yes


R-squared 0105 0093 0084







122


119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905




























123


(1) (2) (3) (4) (5) (6)


Period 2004-

2007

2008-

2010

2012-

2015

2004-

2007

2008-

2010

2012-

2015


(0009) (0015) (0087) (0018) (0022) (0063)

foreign-owned -0005 0063 0056 0016 -0014 -0035

(0023) (0045) (0057) (0023) (0055) (0066)

ln sales -0026 -0029 -0057 -0018 -0010 0010

(0006) (0007) (0021) (0007) (0017) (0005)

5-9 shops -0014 -0066 -0111 0061 -0035 -0055

(0026) (0051) (0043) (0046) (0044) (0026)

10-49 shops -0045 -0114 -0149 0046 -0038 -0023

(0024) (0048) (0039) (0044) (0043) (0017)

more than 50

shops

0001 -0077 -0134 0061 -0027 -0016

(0029) (0048) (0040) (0045) (0044) (0022)

Observations 15374 10946 15038 14025 10120 13458

R-squared 0030 0053 0073 0023 0041 0121






firm-level

85 Trade





Schmitt 2016)














124















Importing

























125













product categories



126











127
















128


(1) (2) (3) (4) (5)

Year 2005 2008 2010 2012 2015



(0003) (0003) (0003) (0003) (0003)

Foreign-owned 0249 0196 0224 0238 0217

(0014) (0011) (0012) (0012) (0012)

Ln employees 0082 0082 0075 0073 0088

(0004) (0004) (0004) (0004) (0004)

Constant -0470 -0536 -0464 -0551 -0637

(0027) (0026) (0027) (0028) (0027)

Observations 7467 7977 7400 7122 8308

R-squared 0116 0130 0127 0140 0143



Exporting






after 2012


129












130


sector






(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015


Labour

productivity

0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)

Foreign-owned 0083 0137 0141 0119 0107

(0009) (0011) (0013) (0013) (0012)

Ln employees 0019 0029 0028 0031 0034

(0003) (0004) (0004) (0004) (0004)

Constant -0159 -0277 -0271 -0321 -0317

(0018) (0026) (0027) (0029) (0028)

Observations 7622 7976 7663 7384 8730

R-squared 0028 0045 0041 0041 0036







131











policy changes








powerful test











policy




132


Notes Full sample




133












strategy feasible







policy108

















134
















A) Sales


135

B) Sales per day










119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591

120591 + 120583119894119895 + 120575119905 + 휀119894119895119905

















136

















A) Days


137

B) Sales

C) Sales per day






138





A) Days

B) Sales


139

C) Sales per day























140









A) Sales

B) Sales per day


141






88 Conclusions































Conclusions

142

9 CONCLUSIONS


















levels even further





























143









References

144

REFERENCES




































145



































References

146



































147


Academy of Sciences































Review 105(12) 3660-3703


15(4) 781-805

References

148













Growth 12(1) 1-25















673-709




149



126-138
















50(1) 21-43


















References

150

























Bank Issue 143 1-10








151





Journal 31(9) 969-989

Appendix

152

APPENDIX







































153


































Database112


Appendix

154



A) Market economy



2005 19 74 16 60

2006 93 119 95 97

2007 39 56 49 65

2008 -10 -04 -06 01

2009 -69 -82 -65 -63

2010 11 80 05 60

2011 34 40 31 45

2012 21 01 24 18

2013 30 22 22 22

2014 40 59 36 48

2015 52 49 50 43

2016 20 03 25 12

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Manufacturing



2005 20 114 24 127

2006 114 149 118 137

2007 78 71 86 98

2008 17 -17 32 -11

2009 -133 -117 -120 -87

2010 80 173 85 178

2011 04 18 01 25

2012 -02 -58 07 -38

2013 -12 05 -15 16

2014 -01 27 01 34

2015 30 14 34 19

2016 04 -23 14 -05

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01


155

C) Market services



2005 10 32 06 01

2006 79 90 82 64

2007 24 48 35 44

2008 -21 -03 -19 05

2009 -52 -71 -51 -54

2010 -11 26 -19 -05

2011 43 57 40 57

2012 30 48 31 57

2013 39 29 31 25

2014 46 78 39 55

2015 54 72 52 58

2016 25 20 29 23

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01




Appendix

156


A) Manufacturing


2005 21 -02 -09 144

2006 118 143 58 47

2007 59 43 90 348

2008 -09 79 17 111

2009 -53 -191 -197 -139

2010 80 76 85 130

2011 -22 17 10 153

2012 01 14 -57 -06

2013 -38 20 -38 54

2014 -03 -05 08 33

2015 61 04 -19 132

2016 09 -10 12 91

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Market Services


2005 127 16 -01 -46

2006 166 75 94 66

2007 13 58 60 16

2008 -16 14 -37 -28

2009 -63 -94 -15 44

2010 54 12 -08 23

2011 97 46 77 -29

2012 12 74 06 -57

2013 12 30 60 -71

2014 78 89 65 -31

2015 106 70 22 12

2016 16 31 -47 37

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01




157


A) Manufacturing


2005 172 32 73 300

2006 266 114 54 10

2007 121 52 69 243

2008 -25 -17 -03 126

2009 31 -151 -186 35

2010 135 114 199 207

2011 -33 -10 96 96

2012 03 -34 -32 -226

2013 -35 22 26 253

2014 33 19 53 94

2015 82 -04 -06 102

2016 34 18 08 -110

Average

2004-2007 186 66 65 184

2007-2010 47 -18 03 123

2010-2013 -21 02 24 35

2013-2016 28 14 20 85

B) Services


2005 127 -05 41 -31

2006 166 75 21 54

2007 13 11 25 -36

2008 -16 -19 05 -02

2009 -63 -117 09 04

2010 54 -01 -05 13

2011 97 47 54 13

2012 12 62 19 -47

2013 12 21 62 -44

2014 78 55 64 -39

2015 106 54 07 65

2016 16 49 -60 43

Average

2004-2007 102 27 29 -04

2007-2010 -08 -46 03 05

2010-2013 40 48 24 -01

2013-2016 53 45 18 06



Appendix

158


A) By size

2004 2007 2010 2013 2016

5-9 emp 1049 1051 1043 1096 1045

10-19 emp 954 962 92 904 92

20-49 emp 994 903 939 856 998

50-99 emp 896 1024 1188 1009 1096

100- emp 721 81 839 748 728

B) By ownership

2004 2007 2010 2013 2016

Domestic 833 818 814 824 837

Foreign 2344 2499 2422 2384 2488

State 554 728 81 575 695

C) By region

2004 2007 2010 2013 2016

Central HU 567 568 59 56 549

Northern

Hungary 195 116 19 208 224

Northern

Great Plain 161 178 239 23 249

Southern

Great Plain 137 118 17 258 179

Central

Transdanubia 276 33 332 369 332

Western

Transdanubia 311 283 244 361 444

Southern

Transdanubia 184 201 235 143 181

Notes Main sample


159




Appendix

160



Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168

C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209

D - Electricity gas

steam and AC

6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743

E - Water supply

sewerage waste

6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872

F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023

G - Wholesale and

retail trade

6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303

H - Transportation

and storage

6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985

I - Accommodation

food service

6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250

J - Information and

Communication

6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046

M - Professional


6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075


6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026



161


Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692

C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109

D - Electricity

gas steam and

AC

6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291

E - Water supply

sewerage waste

6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916

F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436

G - Wholesale and

retail trade

6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260

H - Transportation

and storage

6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776

I -

Accommodation

food service

6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899

J - Information

and

Communication

6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800

M - Professional

Scientific and

Tech Act

6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242

N - Administrative

and support service

6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039


Appendix

162


Year 2001 2005 2010 2015

Industry unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covar

iance

unweight

ed LP

weighted

LP

covar

iance


C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796


AC

9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197


8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703

F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096



storage

8364 8475 0110 8300 8525 0224 8194 7698 -

0496

8292 7289 -

1003


service

7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651


8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988


8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406


support service

7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238


163


Year 2001 2005 2010 2015

Industry unweighted

labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity

covariance

B - Mining and

quarrying

7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358

C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960

D - Electricity gas

steam and AC

9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289

E - Water supply

sewerage waste

8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064

F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159

G - Wholesale and

retail trade

7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037

H - Transportation

and storage

8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022

I - Accommodation

food service

7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363

J - Information and

Communication

7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263

M - Professional

Scientific and Tech

Act

7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431

N - Administrative

and support service

7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216

Appendix

164







capital


C - Manufacturing 5620 1300 0425 0818 12807 1008



F - Construction 6706 0818 0280 0954 21186 1227








165







capital


C - Manufacturing 5919 1173 0497 0890 13439 0998



F - Construction 6794 0744 0155 0947 20440 1099







Appendix

166





taxes



capital


C - Manufacturing 6024 1201 0523 0740 12732 1001



F - Construction 6621 0775 0200 1075 30395 1437







167




taxes


taxes


of capital


capital


C - Manufacturing 6022 1110 0514 0971 11130 1074



F - Construction 6938 0809 0298 0868 28761 1453








Appendix

168





169




Appendix

170





171



2004-2007 2007-2010



C Manufacturing 108 23 48 36 -02 -11 03 05



F Construction 26 04 08 13 -14 -02 -19 07



I Accommodation 68 -07 53 22 -44 00 -37 -07

J ICT 96 10 63 23 29 -24 35 18



2010-2013 2013-2016



C Manufacturing -18 07 -30 05 10 -08 22 -04



F Construction 42 03 26 13 01 06 -16 11



I Accommodation 85 -05 59 32 51 -03 46 08

J ICT 19 -02 11 10 47 -02 38 11




Appendix

172


2004-2007 2007-2010



C Manufacturing 105 20 51 34 06 -14 03 16



F Construction 35 07 12 16 -23 -03 -24 04



I Accommodation 67 -09 50 26 -42 03 -39 -05

J ICT 85 14 39 32 27 -14 21 19



2010-2013 2013-2016



C Manufacturing -12 06 -25 07 06 -04 11 -01



F Construction 45 03 22 20 10 02 -05 12



I Accommodation 81 -04 54 30 51 -02 42 11

J ICT 13 00 00 13 49 10 33 06





173


2004-2007 2007-2010



C Manufacturing 132 34 54 44 08 19 -12 01



F Construction 24 07 10 07 -01 07 -09 01



I Accommodation 59 -03 56 07 -74 -12 -41 -21

J ICT 58 19 80 -40 20 -21 40 00



2010-2013 2013-2016



C Manufacturing 00 14 -21 07 33 16 19 -01



F Construction 40 05 26 10 -03 05 -05 -03



I Accommodation 74 -07 55 26 47 -08 54 02

J ICT 16 -07 11 12 22 -25 42 05



Appendix

174


2004-2007 2007-2010



C Manufacturing 132 32 56 45 16 14 -03 05



F Construction 28 10 14 04 03 06 -14 11



I Accommodation 62 -03 52 12 -65 -09 -43 -13

J ICT 00 -15 49 -34 03 -22 14 12



2010-2013 2013-2016



C Manufacturing 06 13 -15 07 28 12 16 00



F Construction 44 05 26 14 03 02 07 -07



I Accommodation 70 -07 52 25 44 -07 51 01

J ICT 26 07 04 16 17 -20 37 00




175




growth over time



TFP in t-1 Year 2003 -0188 -0203 -0203

(000550) (000558) (000551)

TFP in t-1 Year 2006 -0222 -0238 -0235

(000518) (000525) (000519)

TFP in t-1 Year 2009 -0143 -0159 -0155

(000570) (000579) (000572)

TFP in t-1 Year 2012 -0156 -0172 -0171

(000516) (000524) (000517)

Year 2003 -00313 -00297

(000507) (000510)

Year 2006 -0184 -0183

(000489) (000491)

Year 2009 -00766 -00762

(000492) (000493)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 114200 113900 113900

R-squared 0061 0067 0084

Appendix

176





VARIABLES (1) (2) (3) (4)

TFP in t-1 -0170 -0186 -0213 -0223

(000561) (000578) (00155) (00155)

TFP in t-1 Group 2 00397 00243 -000502 -000776

(00146) (00147) (00213) (00213)

TFP in t-1 Group 3 00793 00652 00725 00600

(00221) (00222) (00164) (00165)

TFP in t-1 Group 4 00753 00666

(00244) (00247)

Group 2 00227 000593 -0000410 0000118

(000940) (000963) (00162) (00162)

Group 3 00216 -000934 00235 00220

(00150) (00154) (00131) (00132)

Group 4 00235 -00351

(00157) (00169)

Industry FE YES YES



Observations 30135 30062 30135 30062

R-squared 0056 0073 0056 0073





177


groups


VARIABLES (1) (2) (3) (4)

Foreign 00476

(00114)


(00213)


(00139)


(00162)


(00243)


(00340)


(00318)


(00381)


(00363)


(00124)



Observations 31642 31642 31642 31274

R-squared 0032 0033 0033 0033





Appendix

178




Firm categories by


VARIABLES (1) (2) (3) (4)


00577 00607 00141 00214

(00151) (00151) (00124) (00124)


00703 0101 00361 00558

(00152) (00152) (00118) (00118)


00338 00306 00450 00406

(00153) (00153) (00122) (00121)


00758 00436 00474 00321

(00146) (00146) (00109) (00109)


00978 00756 00286 000961

(00128) (00130) (000912) (000977)


-00290 -00145 -00592 -00411

(00133) (00135) (000871) (000932)


0114 0116 00502 00457

(00124) (00127) (000824) (000881)


0120 0120 00234 00155

(00126) (00129) (000782) (000835)

Year FE YES YES

Industry FE YES YES

Firm-level controls

YES YES YES YES

Region FE YES YES

Industry-year FE

YES YES

Observations 112374 112374 113900 113900

R-squared 0066 0085 0065 0085


columns (3) and (4)


179



growth over time



TFP in t-1 Year 2003 0113 0113 0113

(000472) (000478) (000475)

TFP in t-1 Year 2006 0120 0120 0119

(000434) (000439) (000437)

TFP in t-1 Year 2009 0109 0109 0107

(000479) (000485) (000482)

TFP in t-1 Year 2012 00982 00958 00956

(000442) (000448) (000445)

Year 2003 -00171 -00125

(000441) (000444)

Year 2006 -0134 -0128

(000422) (000423)

Year 2009 -00899 -00873

(000425) (000426)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 123900 123574 123574

R-squared 0042 0049 0054

Appendix

180





TFP in t-1 Year 2003 0156 0147 0148

(000641) (000647) (000644)

TFP in t-1 Year 2006 0134 0127 0128

(000581) (000587) (000584)

TFP in t-1 Year 2009 0139 0132 0134

(000648) (000655) (000651)

TFP in t-1 Year 2012 0132 0126 0127

(000618) (000624) (000621)

Year 2003 -00765 -00618

(000617) (000619)

Year 2006 -0220 -0211

(000586) (000587)

Year 2009 -0177 -0173

(000591) (000590)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 143011 142638 142638

R-squared 0037 0047 0051


181





VARIABLES (1) (2) (3) (4) (5) (6)

TFP in t-1 0134 0130 0134 0137 0134 0136

(000651) (000660) (000722) (000729) (000764) (000767)

TFP in t-1 Foreign -00109 -00138 00116 000347

(00166) (00167) (00289) (00289)

TFP in t-1 Exporter -00371 -00256 -00304 -00226

(00124) (00126) (00148) (00148)


(00364) (00365)

Foreign -00254 -00351 -0102 -00739

(00151) (00156) (00254) (00256)

Exporter 00998 00982 00940 00889

(00100) (00102) (00106) (00107)


(00312) (00315)




Observations 34980 34980 35564 35473 34980 34980

R-squared 0031 0051 0037 0054 0034 0052

Appendix

182


groups


VARIABLES (1) (2) (3) (4)

Exporter 00876

(000741)


(000788)


(00207)


(000850)


(00159)


(00276)


(00329)


(00230)


(00212)


(000801)



Observations 34418 33909 34418 33989

R-squared 0034 0034 0034 0036






183





VARIABLES (1) (2) (3) (4)


(00129) (00130) (00107) (00107)


(00126) (00127) (00101) (00101)


(00129) (00130) (00104) (00104)


(00126) (00126) (000942) (000942)

Firm group Year 2003 -00601 -00332 000244 00299

(00110) (00113) (000795) (000856)

Firm group Year 2006 -00159 -000559 00640 00786

(00112) (00115) (000752) (000807)

Firm group Year 2009 00404 00249 0111 00882

(00106) (00109) (000714) (000767)

Firm group Year 2012 -00102 -00116 00747 00607

(00110) (00112) (000684) (000735)

Year FE YES YES

Industry FE YES YES


Region FE YES YES


Observations 121954 121954 123574 123574

R-squared 0046 0055 0045 0055


(3) and (4)

Appendix

184

A73 Entry and exit



VARIABLES (1) (2) (3) (4) (5) (6)


-0172 -0186

(00103) (00102) (00112) (00112) (00122) (00121)


0171 0126

(00284) (00281) (00311) (00309) (00213) (00211)

Incumbent Foreign



0279 0232

(00122) (00129) (00123) (00131) (000887) (000926)




YES



YES




groups


VARIABLES (1) (2) (3) (4)

Foreign 0429

(00118)


(00206)


(00146)


(00162)


(00254)


(00363)


(00341)


(00352)


(00356)


(00131)



Observations 38367 38367 38367 37822

R-squared 0350 0361 0353 0356






185






(000924) (000923) (00107) (00106) (00114) (00113)



(000999) (000996) (000917) (000911) (000977) (000971)



(000985) (000983) (000942) (000937) (00101) (00101)



(000998) (000999) (00111) (00110) (00119) (00119)



(00265) (00264) (00264) (00263) (00196) (00197)



(00257) (00257) (00267) (00265) (00183) (00183)



(00279) (00278) (00278) (00277) (00185) (00185)



(00276) (00275) (00307) (00305) (00208) (00208)



(00122) (00124) (00124) (00126) (000943) (000994)



(00120) (00122) (00122) (00124) (000895) (000943)



(00122) (00124) (00119) (00122) (000867) (000915)



(00118) (00120) (00120) (00122) (000827) (000876)













(00108) (00105) (00121) (00118) (00250) (00241)


(00298) (00289) (00280) (00272) (00271) (00261)


(00574) (00556) (00479) (00464) (00179) (00173)


(00720) (00697) (00532) (00517)


(00104) (00101) (00111) (00109) (00170) (00164)


(00170) (00168) (00179) (00176) (00131) (00127)


(00181) (00185) (00188) (00192)




Observations 46160 46034 39020 38916 38459 38357

R-squared 0296 0355 0311 0369 0315 0374





different periods

A8 Chapter 8 Retail


(1) (2) (3) (4) (5) (6)








trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)

trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)

trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)

trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)






Observations 225866 209604 225860 209598 225908 209647

R-squared 0958 0978 0961 0980 0684 0809

Appendix

188


(1) (2) (3) (4) (5) (6)








trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)

trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)

trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)

trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)






Observations 94740 87533 94737 87530 94740 87533

R-squared 0968 0982 0973 0985 0642 0809


(1)

Dependent ln sales

Sunday 0049 (0001)


Friday 0054 (0001)



Tuesday 0060 (0001)

Monday 0048 (0001)

holiday 0008 (0000)

Jan -0169 (0002)

Dec 0138 (0003)

summer 0032 (0002)

date -0000 (0000)

Observations 463345

R-squared 0970

Appendix

190


Free publications

bull one copy








Priced publications





doi 10287333213

ET-0

4-1

7-8

33-E

N-N

EUROPEAN COMMISSION


2018


IN HUNGARY AND


SLOWDOWN

LEGAL NOTICE




ISBN 978-92-79-73462-5 doi 10287333213





Freephone number ()

00 800 6 7 8 9 10 11



Table of contents

EXECUTIVE SUMMARY I

1 INTRODUCTION 1

2 DATA SOURCES 4







31 Convergence 18


33 Firm dynamics 28

34 Conclusions 31


41 Context 32




45 Conclusions 56




53 Conclusions 70

6 REALLOCATION 73




64 Conclusions 86





74 Conclusions 103


81 Context 104

82 Data 108



85 Trade 123



88 Conclusions 141

9 CONCLUSIONS 142

REFERENCES 144

APPENDIX 152














i

EXECUTIVE SUMMARY





2001-2016













non-frontier firms






























Executive Summary

ii





















































iii




















































Executive Summary

iv





















































1

1 INTRODUCTION

















non-frontier firms






























Introduction

2













sheet data







































3














































Data Sources

4
































2 DATA SOURCES











5







firms in year 2016







input a zero












Year 1 emp 2-5 emp

6-10 emp

11-20 emp

21-49 emp

50-249 emp

250 lt emp

Total

2004 005 028 092 105 160 312 642 043

2005 010 050 169 288 483 1066 2227 108

2006 017 087 315 553 966 1935 3632 192

2007 2209 3006 4384 5249 5743 6082 7412 3135

2008 098 324 951 1692 2840 4868 7827 576

2009 5962 6070 7217 7428 7831 7798 9336 6301

2010 2142 4685 7034 7540 8424 8228 9634 4175

2011 2277 4736 7064 7753 8521 8657 9681 4220

2012 9397 8298 9305 9484 9507 9159 10121 8990

2013 7274 8140 9423 9981 9747 9445 10312 8044



Data Sources

6














119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)





intensity





















7






























Data Sources

8


A) Manufacturing

B) Services


9








2004 and 2016


unweighted


2004 2016 2004 2016


B Mining 797 088 867 086 408 087 441 065

C Manufacturing 777 087 806 079 581 079 598 077



812 085 830 089 604 091 593 094

F Construction 773 080 803 072 620 071 646 066



I Accommodation 710 075 752 080 594 068 640 071

J ICT 834 094 862 090 631 101 669 098


815 087 844 088 636 087 673 088


763 098 792 094 640 107 662 113

Total 789 095 815 087 620 090 647 087








Data Sources

10











sample

























in the Appendix




11



2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739

2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987

2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568

2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376

2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880

2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844

2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671

2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709

2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318

2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113

2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104

2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509

2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335

2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665

2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678

2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519

2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727

Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742


estimated TFP


sales

Data Sources

12














Number of

firms

Total

employment

Total value

added






employment

708 885 935







157 600 647











13



B Mining 029 025 024 021 018

C Manufacturing 3085 2636 2352 2280 2122



103 112 114 127 098

F Construction 1263 1439 1447 1263 1375




J ICT 351 328 396 403 400




297 547 572 601 572

Total 100 100 100 100 100























Data Sources

14


A) Number of firms


B Mining 8228 1772 000 100








J ICT 8314 1541 145 100


8982 915 102 100


8991 798 211 100

Total 8937 953 110 100



B) Employment


B Mining 725 275 00 100








J ICT 424 546 30 100


650 331 20 100


681 258 61 100

Total 560 373 66 100












15


Year Foreign

exporter

Foreign

non-

exporter

Domestic

exporter

Domestic

non-

exporter

2000 92 51 168 690

2001 89 46 172 693

2002 84 43 173 701

2003 79 40 164 717

2004 71 36 157 736

2005 70 34 160 736

2006 69 34 163 734

2007 73 32 180 715

2008 74 34 186 706

2009 78 36 195 692

2010 77 34 202 687

2011 78 32 215 675

2012 81 31 229 659

2013 79 30 236 655

2014 74 33 233 660

2015 71 31 238 659

2016 70 26 245 658

Total 76 35 196 692









Data Sources

16


2016

Foreign exporter


Domestic exporter


Year 2004

N of employees 1385 511 451 165

(5689) (2410) (1396) (404)


(101) (120) (087) (086)

TFP ACF 666 660 634 611

(111) (112) (091) (084)

Age

101 85 99 85

(42) (46) (43) (43)

Year 2016

N of employees 1619 338 344 151

(6246) (1257) (1290) (405)


(088) (115) (075) (080)

TFP ACF 696 684 651 639

(113) (109) (086) (080)

Age 160 105 149 124

(82) (75) (76) (75)



parentheses






















17







Services



Utilities 35 to 39



18







31 Convergence






















19




















countries


Germany=100







20









crisis






















21


























22


























2013





23




B) TFP








24



























Country

Year 2011



Australia 187 205 190 212

Austria 196 242 - -

Belgium 160 174 180 178

Chile 300 353 307 387

Denmark 146 196 132 180

Finland 117 138 119 134

France 135 181 140 178

Hungary 279 329 254 286


Italy 166 201 160 188

Japan 126 138 117 138


New Zealand 184 209 192 208

sNorway 173 217 187 208

Portugal 188 265 188 275

Sweden 145 186 159 245






25





Country

Year 2011

LP Dispersion


Australia 98 99

Austria 86 90

Belgium 76 88

Chile 90 97

Denmark 84 63

Finland 65 76

France 63 85

Hungary 79 99

Indonesia 79 -

Italy 82 65

Japan 75 89

Netherlands 80 71

Norway 83 73

Portugal 62 76

Sweden 53 74

























26


A) Manufacturing








27


A) Manufacturing







28







33 Firm dynamics



























29


A) Manufacturing








30


A) Manufacturing









31

34 Conclusions














32


41 Context























A) Manufacturing


33























34


sample


























35














economy





average28










period









36




Number of employees

Manufacturing Other

2012 2015 2012 2015


Other firm 289636 296921 698326 771930

Self-employed 72674 74325 620699 638001

Total 983539 998637 2644324 2606263


Manufacturing Other

2012 2015 2012 2015


Other firm 151 146 196 201


Total 92 99 105 111























37





Appendix)


A) Market economy

Year LP TFP


2005 20 58 19 74

2006 92 91 93 119

2007 53 60 39 56

2008 -10 -08 -10 -04

2009 -70 -81 -69 -82

2010 -05 44 11 80

2011 25 45 34 40

2012 25 22 21 01

2013 19 25 30 22

2014 39 45 40 59

2015 51 50 52 49

2016 36 19 20 03

Average

2004-2007 55 70 50 83

2007-2010 -28 -15 -23 -02

2010-2013 23 34 33 29

2013-2016 36 35 35 33

B) Manufacturing

Year LP TFP


2005 37 148 20 114

2006 124 163 114 149

2007 100 114 78 71

2008 25 -03 17 -17

2009 -115 -94 -133 -117

2010 82 161 80 173

2011 -02 34 04 18

2012 05 -46 -02 -58

2013 -14 31 -12 05

2014 11 48 -01 27

2015 38 37 30 14

2016 26 01 04 -23

Average

2004-2007 87 141 71 111

2007-2010 -02 22 -12 13

2010-2013 -04 17 04 -03

2013-2016 15 29 05 06


38

C) Market services

Year

LP TFP


2005 12 -04 10 32

2006 80 47 79 90

2007 39 25 24 48

2008 -22 -06 -21 -03

2009 -57 -68 -52 -71

2010 -29 -17 -11 26

2011 33 49 43 57

2012 31 60 30 48

2013 29 21 39 29

2014 46 45 46 78

2015 54 58 54 72

2016 39 30 25 20

Average

2004-2007 43 23 38 57

2007-2010 -36 -31 -28 -16

2010-2013 31 44 39 51

2013-2016 42 39 41 50



















utilities



39


A) Manufacturing


2005 124 19 66 274 2006 240 137 39 33

2007 74 02 41 221

2008 -45 23 -15 59

2009 05 -191 -218 48

2010 135 111 264 168

2011 -45 18 34 100

2012 -15 -24 -83 -181

2013 -41 37 -22 125

2014 06 07 27 86

2015 65 01 -54 80

2016 -02 04 -27 -91

Average 2005-2007 146 53 49 176

2007-2010 32 -19 10 92

2010-2013 -34 07 -21 20

2013-2016 07 12 -19 50

B) Services


2005 127 16 34 -48

2006 166 75 30 67

2007 13 58 42 29

2008 -16 14 -72 -26

2009 -63 -94 -04 25

2010 54 12 09 05

2011 97 46 65 29

2012 12 74 13 -22

2013 12 30 63 -07

2014 78 89 65 -81

2015 106 70 14 54

2016 16 31 -47 39

Average

2005-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01







Appendix)


40

Frontier firms














firms












Appendix)31


A) By size

2004 2007 2010 2013 2016

20-49 emp 357 327 34 362 329

50-99 emp 401 468 542 486 555

100- emp 293 358 414 42 462

B) By ownership

2004 2007 2010 2013 2016

Domestic 213 194 236 272 289

Foreign 873 955 896 82 821

State 181 211 166 167 263




41

C) By region

2004 2007 2010 2013 2016

Central HU 596 621 652 552 579






Southern

Transdanubia 131 081 188 159 211
















42





















A) Manufacturing

32 1198902 asymp 74


43

B) Services







intensive services












44


A) Manufacturing

B) Services









45









deciles

A) Manufacturing



46

B) Services



























47






sample


















48




















year effects) 2016





49




composition matters














A) Domestic private


50

B) Foreign

C) State


Main sample


51





































52













firms






crisis


(cont)























53


A) Manufacturing

B) Services




intensive services


54


A) Manufacturing

B) Services






55


A) Manufacturing

B) Services






56




45 Conclusions





















57


































58















year level



























59





















60


























61






(2015)

















Appendix)


62






















63














and combinations




1 minus 120591119884119904119894 =120590

120590minus1

120573119871119904+120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119881119860119904119894 (51)

















return to scale





64





















65









tax46

119877(1 + 120591119870119904119894) =120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119870119904119894 (52)







66





































67












68










industries
















69







A) Age of firms

B) Size of firms



70























Variance

component

Share

of total

Variance

component

Share

of total


cost of capital

2126 100 2443 100







Residual 1830 861 1995 817



53 Conclusions









71


Industry type TFP

level

in

2016

TFP

growth

between

2011 and

2016

Olley-

Pakes

allocative

efficiency

Dispersion

of implicit

sales taxes

Dispersion

of implicit

cost of

capital

Low-tech mfg 5694 0027 0197 111 146



High-tech mfg 6708 0276 0072 107 145




Construction 6411 0082 0023 109 148

Utilities 5949 -0138 0801 093 155

Total services 6598 0212 0055 108 160






























72




across sectors



73

6 REALLOCATION



















this process














2013)

Reallocation

74


2004 2007 2010 2013 2016

Low-tech mfg 152 117 107 105 100



High-tech mfg 49 49 44 39 35





Utilities 40 34 34 33 34

Total services 616 645 676 671 673






A) Manufacturing


75

B) Services


sample







(61)












Reallocation

76















sample









77











+

























(62)

Reallocation

78






calculations
















79




Reallocation

80







81

















2004-2007





2007-2010





2010-2013





2013-2016











Reallocation

82


2004-2007





2007-2010





2010-2013





2010-2016























83















Reallocation

84


Notes Main sample



















85





A) By size

2004 2007 2010 2013 2016

5-9 emp 62 751 862 802 411

10-19 emp 618 626 679 652 297

20-49 emp 607 554 698 648 296

50-99 emp 711 685 792 829 438

100- emp 2005 1839 1559 1489 456

Total 1548 1367 1229 1194 417

B) By ownership

2004 2007 2010 2013 2016

Domestic 66 671 769 614 253

Foreign 989 1011 1139 1577 585

State 6289 5954 4127 2792 7

Total 155 1369 1228 1194 417


2004 2007 2010 2013 2016

Low-tech mfg 1236 1255 1149 906 371



High-tech mfg 427 1392 429 268 1

KIS 2098 737 875 1408 592

LKIS 282 2327 187 1867 365

Construction 258 394 339 515 352

Utilities 297 1031 705 593 756

Total 1553 1372 1229 1194 418

Notes Main sample





Reallocation

86














Notes Main sample

64 Conclusions





economy






87












the problem




















Reallocation

88






Estonia


89
















Section 44)





























90




















2015)58













recent periods60










91


over time





Estimation







1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905

119896

119870

119896=1


119896

119870

119896=1

+ 119883119894119905 + 120572119895(119894) + 휀119894119905









(71)


92




1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905

119897

119897=200320062009


119897 )119863119905119897

119897=2003200620092012

+ 119883119894119905 + 120572119895(119894) + 휀119894119905















1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905

119896119863119905119897

119870

119896=1119897=2003200620092012


119897 )119863119905119897

119897=2003200620092012


119897 )119866119894119905119896

119870

119896=1

119863119905119897

119897=2003200620092012

+ sum 1205734119897119863119905

119897

119897=200320062009

+ 119883119894119905 + 120572119895(119894) + 휀119894119905






Results













(72)

(73)


93




exporter firms


firm group


By size By age
















94




convergence61











Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190

(000556) (000568) (000622) (000629) (000656) (000663)

TFP in t-1 Foreign 00475 00418 00433 00423

(00140) (00142) (00252) (00253)

TFP in t-1 Exporter 00477 00287 00216 00224

(00104) (00106) (00125) (00126)


(00312) (00314)

Foreign 0117 0109 0120 0104

(00121) (00132) (00223) (00226)

Exporter 00240 000260 000401 0000919

(000791) (000845) (000851) (000881)


(00267) (00272)




Observations 29717 29717 30135 30062 29717 29717

R-squared 0060 0072 0056 0073 0060 0072







95



























96






















97



By size By age






98

Estimation results



Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 0105 0102 0105 0107 0107 0108

(000484) (000493) (000539) (000546) (000570) (000575)

TFP in t-1 Foreign

-00369 -00328 -00252 -00224

(00123) (00124) (00217) (00217)

TFP in t-1 Exporter

-00344 -00298 -00250 -00249

(000913) (000932) (00109) (00110)


-0000806 000194

(00271) (00272)

Foreign 000105 -000863 -000786 -0000106

(00112) (00116) (00194) (00196)

Exporter 00586 00635 00653 00672

(000738) (000754) (000777) (000786)

Foreign exporter

-000647 -00123

(00234) (00238)


Industry-region FE

YES YES YES

Firm-level controls

YES YES YES

Log of employees


Observations 31662 31662 32124 32043 31662 31662

R-squared 0035 0049 0038 0051 0037 0049



















99











VARIABLES (1) (2) (3) (4)

TFP in t-1 0112 0110 00875 00863

(000486) (000502) (00133) (00133)

TFP in t-1 Group 2 -00455 -00431 00445 00384

(00128) (00129) (00182) (00183)

TFP in t-1 Group 3 -00745 -00748 000733 000822

(00194) (00196) (00141) (00142)

TFP in t-1 Group 4 -00953 -00957

(00218) (00220)

Group 2 -000596 -000810 -00188 -00212

(00122) (00123) (00141) (00141)

Group 3 -000928 -00157 -00115 -00169

(00199) (00201) (00114) (00115)

Group 4 00328 00280

(00276) (00278)

Industry FE YES YES




Observations 32124 32043 32124 32043

R-squared 0038 0052 0037 0051




73 Entry and exit

Questions





100










Estimation











119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905

119896119873119864119894119905119870119896=1 + 1205732119866119894119905

0119864119894119905 + sum 1205733119896119866119894119905

119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)















interacting 119866119894119905119896119873119864119894119905 119866119894119905




101

Results















(1) (2) (3) (4) (5) (6)


-00532 -00535 -00465 -0214 -0199 -0200

(000906) (000873) (000879) (00102) (000987) (000989)


00269 00212 00232 -0135 -0117 -0119

(000967) (000931) (000936) (000897) (000865) (000865)


00369 00444 00360 -0133 -0114 -0121

(000960) (000926) (000931) (000920) (000889) (000889)

EntrantExitorPeriod

2012-2015

00374 00324 00252 -0171 -0150 -0157

(000971) (000936) (000944) (00107) (00103) (00103)

Period 2003-2006 -0115 -00927 -00711 -00445

(000519) (000501) (000556) (000537)

Period 2006-2009 -0175 -0169 -000972 00122

(000522) (000503) (000537) (000518)

Period 2009-2012 -0116 -0117 -00580 -00528

(000528) (000508) (000543) (000522)


Industry FE YES YES




Observations 166607 166168 166168 158143 157711 157711

R-squared 0327 0380 0380 0332 0385 0387


102




















103

74 Conclusions

























104




























81 Context







74 EC (2018) p 5


105















75 EC (2018) p 4



106






to larger firms

















107









Below 500m HUF 0



Above 100bn HUF 25












108

82 Data


































109








qualified analysis












2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446

2006 573 573 51 131 59 430 111 2008 30 1548

2007 546 546 53 125 60 429 110 1987 33 1634

2008 628 628 45 102 50 350 104 1823 21 1574

2009 527 527 33 72 41 290 99 1879 24 1754

2010 472 472 22 49 32 238 94 1793 22 1968

2011 537 537 14 30 29 212 92 1758 22 2027

2012 374 374 30 68 49 335 88 1643 23 2107

2013 503 503 48 121 42 277 88 1622 25 2094

2014 410 410 106 239 48 320 81 1530 24 2054

2015 512 512 135 311 42 292 80 1544 30 2090

2016 518 518 120 292 37 271 77 1457 23 2022









110









83 General trends






















111


A) Number of firms

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100

2006 7333 1613 687 252 065 049 100

2007 7369 1597 674 244 067 050 100

2008 7410 1571 667 236 067 048 100

2009 7522 1535 614 221 059 048 100

2010 7551 1508 640 201 057 044 100

2011 7591 1493 623 194 057 041 100

2012 7625 1506 568 204 055 042 100

2013 7526 1596 577 207 052 042 100

2014 7380 1684 628 210 056 042 100

2015 7239 1771 661 232 056 041 100

2016 7163 1804 676 252 063 043 100

B) Employment

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000

2006 2034 1475 1269 1025 679 3525 10000

2007 2026 1443 1241 984 669 3644 10000

2008 2020 1391 1138 910 579 3980 10000

2009 2002 1427 1244 870 570 3789 10000

2010 2099 1443 1243 862 577 3731 10000

2011 2144 1458 1119 895 555 3830 10000

2012 2144 1541 1131 906 541 3713 10000

2013 2168 1591 1204 896 555 3650 10000

2014 2104 1644 1236 970 548 3538 10000

2015 2064 1620 1216 1006 588 3570 10000

2016 1999 1620 1216 1006 588 3570 10000

C) Sales

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000

2006 1114 1278 1587 1025 776 4226 10000

2007 1110 1266 1543 956 855 4268 10000

2008 1112 1524 1045 891 531 4906 10000

2009 1104 1641 1073 856 718 4607 10000

2010 1104 1521 1103 930 693 4594 10000

2011 1160 1577 1004 934 624 4714 10000

2012 1146 1647 1003 913 629 4578 10000

2013 1229 1411 1090 945 709 4360 10000

2014 1486 1472 1093 1001 752 4389 10000

2015 1293 1458 1118 985 660 4512 10000

2016 1266 1458 1118 985 660 4512 10000


112























113



retail85

















119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905

















114





119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905









115


A) TFP

B) Gross Margin

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 1769 1548 1793 1883 1584 1495 1735 1554

2005 1839 1571 1858 1878 1504 1666 1794 1584

2006 2040 1658 1842 1879 1599 1660 1956 1653

2007 2220 1720 1875 1940 1664 1702 2104 1712

2008 2192 1792 1872 1992 1522 1739 2096 1728

2009 2084 1719 1864 1967 1599 1527 2006 1630

2010 2096 1749 1867 2102 1664 1535 2024 1682

2011 2172 1765 1862 2003 1759 1558 2084 1728

2012 2203 1740 1828 1913 1969 1541 2102 1733

2013 2243 1734 1806 2018 1999 1794 2128 1790

2014 2363 1789 1817 2149 2022 1869 2223 1855

2015 2390 1803 1885 2165 2215 1987 2245 1928

2016 2379 1799 1911 2172 2403 2170 2237 1948


Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

unweighted weighted

2004 651 701 663 677 479 480 658 611

2005 827 757 777 712 526 560 806 644

2006 908 773 777 753 579 797 870 706

2007 807 635 653 704 500 580 764 606

2008 665 588 577 610 422 478 643 525

2009 618 535 538 522 351 339 595 456

2010 669 587 629 626 421 341 650 510

2011 698 617 591 637 410 393 676 542

2012 770 643 599 624 485 404 735 577

2013 735 594 555 631 478 369 697 530

2014 745 578 568 612 446 398 700 531

2015 766 609 625 646 547 452 724 579

2016 759 597 621 632 609 487 715 588

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 599 633 637 628 612 623 610 626

2005 610 635 643 628 616 626 619 631

2006 623 646 651 640 630 642 631 643

2007 624 643 650 641 627 643 631 642

2008 625 645 647 640 630 640 631 642

2009 619 641 642 631 619 630 626 630

2010 617 641 643 634 622 631 625 631

2011 621 643 645 637 629 639 628 639

2012 624 646 644 637 629 639 631 644

2013 626 647 642 640 637 642 632 645

2014 628 648 650 645 640 648 634 648

2015 638 658 658 655 652 659 644 659

2016 643 661 665 659 655 661 649 665


116





















117







newspapers












118





















119






















120










regressions

119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905



dummies






121


its shops














(1) (2) (3)

Dependent Change in

number of

shops

New

shops

Closed

shops


(0024) (0014) (0018)


(0064) (0034) (0045)

ln( (average

salesshop)

0056 0034 -0017

(0016) (0010) (0014)

5-9 shops 0176 0001 -0126

(0070) (0036) (0054)

10-49 shops 0231 -0009 -0167

(0067) (0034) (0052)

more than 50 shops 0194 0002 -0109

(0071) (0038) (0055)

Year FE yes yes yes


R-squared 0105 0093 0084







122


119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905




























123


(1) (2) (3) (4) (5) (6)


Period 2004-

2007

2008-

2010

2012-

2015

2004-

2007

2008-

2010

2012-

2015


(0009) (0015) (0087) (0018) (0022) (0063)

foreign-owned -0005 0063 0056 0016 -0014 -0035

(0023) (0045) (0057) (0023) (0055) (0066)

ln sales -0026 -0029 -0057 -0018 -0010 0010

(0006) (0007) (0021) (0007) (0017) (0005)

5-9 shops -0014 -0066 -0111 0061 -0035 -0055

(0026) (0051) (0043) (0046) (0044) (0026)

10-49 shops -0045 -0114 -0149 0046 -0038 -0023

(0024) (0048) (0039) (0044) (0043) (0017)

more than 50

shops

0001 -0077 -0134 0061 -0027 -0016

(0029) (0048) (0040) (0045) (0044) (0022)

Observations 15374 10946 15038 14025 10120 13458

R-squared 0030 0053 0073 0023 0041 0121






firm-level

85 Trade





Schmitt 2016)














124















Importing

























125













product categories



126











127
















128


(1) (2) (3) (4) (5)

Year 2005 2008 2010 2012 2015



(0003) (0003) (0003) (0003) (0003)

Foreign-owned 0249 0196 0224 0238 0217

(0014) (0011) (0012) (0012) (0012)

Ln employees 0082 0082 0075 0073 0088

(0004) (0004) (0004) (0004) (0004)

Constant -0470 -0536 -0464 -0551 -0637

(0027) (0026) (0027) (0028) (0027)

Observations 7467 7977 7400 7122 8308

R-squared 0116 0130 0127 0140 0143



Exporting






after 2012


129












130


sector






(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015


Labour

productivity

0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)

Foreign-owned 0083 0137 0141 0119 0107

(0009) (0011) (0013) (0013) (0012)

Ln employees 0019 0029 0028 0031 0034

(0003) (0004) (0004) (0004) (0004)

Constant -0159 -0277 -0271 -0321 -0317

(0018) (0026) (0027) (0029) (0028)

Observations 7622 7976 7663 7384 8730

R-squared 0028 0045 0041 0041 0036







131











policy changes








powerful test











policy




132


Notes Full sample




133












strategy feasible







policy108

















134
















A) Sales


135

B) Sales per day










119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591

120591 + 120583119894119895 + 120575119905 + 휀119894119895119905

















136

















A) Days


137

B) Sales

C) Sales per day






138





A) Days

B) Sales


139

C) Sales per day























140









A) Sales

B) Sales per day


141






88 Conclusions































Conclusions

142

9 CONCLUSIONS


















levels even further





























143









References

144

REFERENCES




































145



































References

146



































147


Academy of Sciences































Review 105(12) 3660-3703


15(4) 781-805

References

148













Growth 12(1) 1-25















673-709




149



126-138
















50(1) 21-43


















References

150

























Bank Issue 143 1-10








151





Journal 31(9) 969-989

Appendix

152

APPENDIX







































153


































Database112


Appendix

154



A) Market economy



2005 19 74 16 60

2006 93 119 95 97

2007 39 56 49 65

2008 -10 -04 -06 01

2009 -69 -82 -65 -63

2010 11 80 05 60

2011 34 40 31 45

2012 21 01 24 18

2013 30 22 22 22

2014 40 59 36 48

2015 52 49 50 43

2016 20 03 25 12

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Manufacturing



2005 20 114 24 127

2006 114 149 118 137

2007 78 71 86 98

2008 17 -17 32 -11

2009 -133 -117 -120 -87

2010 80 173 85 178

2011 04 18 01 25

2012 -02 -58 07 -38

2013 -12 05 -15 16

2014 -01 27 01 34

2015 30 14 34 19

2016 04 -23 14 -05

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01


155

C) Market services



2005 10 32 06 01

2006 79 90 82 64

2007 24 48 35 44

2008 -21 -03 -19 05

2009 -52 -71 -51 -54

2010 -11 26 -19 -05

2011 43 57 40 57

2012 30 48 31 57

2013 39 29 31 25

2014 46 78 39 55

2015 54 72 52 58

2016 25 20 29 23

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01




Appendix

156


A) Manufacturing


2005 21 -02 -09 144

2006 118 143 58 47

2007 59 43 90 348

2008 -09 79 17 111

2009 -53 -191 -197 -139

2010 80 76 85 130

2011 -22 17 10 153

2012 01 14 -57 -06

2013 -38 20 -38 54

2014 -03 -05 08 33

2015 61 04 -19 132

2016 09 -10 12 91

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Market Services


2005 127 16 -01 -46

2006 166 75 94 66

2007 13 58 60 16

2008 -16 14 -37 -28

2009 -63 -94 -15 44

2010 54 12 -08 23

2011 97 46 77 -29

2012 12 74 06 -57

2013 12 30 60 -71

2014 78 89 65 -31

2015 106 70 22 12

2016 16 31 -47 37

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01




157


A) Manufacturing


2005 172 32 73 300

2006 266 114 54 10

2007 121 52 69 243

2008 -25 -17 -03 126

2009 31 -151 -186 35

2010 135 114 199 207

2011 -33 -10 96 96

2012 03 -34 -32 -226

2013 -35 22 26 253

2014 33 19 53 94

2015 82 -04 -06 102

2016 34 18 08 -110

Average

2004-2007 186 66 65 184

2007-2010 47 -18 03 123

2010-2013 -21 02 24 35

2013-2016 28 14 20 85

B) Services


2005 127 -05 41 -31

2006 166 75 21 54

2007 13 11 25 -36

2008 -16 -19 05 -02

2009 -63 -117 09 04

2010 54 -01 -05 13

2011 97 47 54 13

2012 12 62 19 -47

2013 12 21 62 -44

2014 78 55 64 -39

2015 106 54 07 65

2016 16 49 -60 43

Average

2004-2007 102 27 29 -04

2007-2010 -08 -46 03 05

2010-2013 40 48 24 -01

2013-2016 53 45 18 06



Appendix

158


A) By size

2004 2007 2010 2013 2016

5-9 emp 1049 1051 1043 1096 1045

10-19 emp 954 962 92 904 92

20-49 emp 994 903 939 856 998

50-99 emp 896 1024 1188 1009 1096

100- emp 721 81 839 748 728

B) By ownership

2004 2007 2010 2013 2016

Domestic 833 818 814 824 837

Foreign 2344 2499 2422 2384 2488

State 554 728 81 575 695

C) By region

2004 2007 2010 2013 2016

Central HU 567 568 59 56 549

Northern

Hungary 195 116 19 208 224

Northern

Great Plain 161 178 239 23 249

Southern

Great Plain 137 118 17 258 179

Central

Transdanubia 276 33 332 369 332

Western

Transdanubia 311 283 244 361 444

Southern

Transdanubia 184 201 235 143 181

Notes Main sample


159




Appendix

160



Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168

C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209

D - Electricity gas

steam and AC

6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743

E - Water supply

sewerage waste

6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872

F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023

G - Wholesale and

retail trade

6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303

H - Transportation

and storage

6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985

I - Accommodation

food service

6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250

J - Information and

Communication

6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046

M - Professional


6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075


6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026



161


Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692

C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109

D - Electricity

gas steam and

AC

6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291

E - Water supply

sewerage waste

6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916

F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436

G - Wholesale and

retail trade

6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260

H - Transportation

and storage

6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776

I -

Accommodation

food service

6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899

J - Information

and

Communication

6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800

M - Professional

Scientific and

Tech Act

6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242

N - Administrative

and support service

6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039


Appendix

162


Year 2001 2005 2010 2015

Industry unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covar

iance

unweight

ed LP

weighted

LP

covar

iance


C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796


AC

9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197


8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703

F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096



storage

8364 8475 0110 8300 8525 0224 8194 7698 -

0496

8292 7289 -

1003


service

7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651


8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988


8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406


support service

7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238


163


Year 2001 2005 2010 2015

Industry unweighted

labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity

covariance

B - Mining and

quarrying

7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358

C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960

D - Electricity gas

steam and AC

9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289

E - Water supply

sewerage waste

8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064

F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159

G - Wholesale and

retail trade

7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037

H - Transportation

and storage

8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022

I - Accommodation

food service

7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363

J - Information and

Communication

7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263

M - Professional

Scientific and Tech

Act

7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431

N - Administrative

and support service

7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216

Appendix

164







capital


C - Manufacturing 5620 1300 0425 0818 12807 1008



F - Construction 6706 0818 0280 0954 21186 1227








165







capital


C - Manufacturing 5919 1173 0497 0890 13439 0998



F - Construction 6794 0744 0155 0947 20440 1099







Appendix

166





taxes



capital


C - Manufacturing 6024 1201 0523 0740 12732 1001



F - Construction 6621 0775 0200 1075 30395 1437







167




taxes


taxes


of capital


capital


C - Manufacturing 6022 1110 0514 0971 11130 1074



F - Construction 6938 0809 0298 0868 28761 1453








Appendix

168





169




Appendix

170





171



2004-2007 2007-2010



C Manufacturing 108 23 48 36 -02 -11 03 05



F Construction 26 04 08 13 -14 -02 -19 07



I Accommodation 68 -07 53 22 -44 00 -37 -07

J ICT 96 10 63 23 29 -24 35 18



2010-2013 2013-2016



C Manufacturing -18 07 -30 05 10 -08 22 -04



F Construction 42 03 26 13 01 06 -16 11



I Accommodation 85 -05 59 32 51 -03 46 08

J ICT 19 -02 11 10 47 -02 38 11




Appendix

172


2004-2007 2007-2010



C Manufacturing 105 20 51 34 06 -14 03 16



F Construction 35 07 12 16 -23 -03 -24 04



I Accommodation 67 -09 50 26 -42 03 -39 -05

J ICT 85 14 39 32 27 -14 21 19



2010-2013 2013-2016



C Manufacturing -12 06 -25 07 06 -04 11 -01



F Construction 45 03 22 20 10 02 -05 12



I Accommodation 81 -04 54 30 51 -02 42 11

J ICT 13 00 00 13 49 10 33 06





173


2004-2007 2007-2010



C Manufacturing 132 34 54 44 08 19 -12 01



F Construction 24 07 10 07 -01 07 -09 01



I Accommodation 59 -03 56 07 -74 -12 -41 -21

J ICT 58 19 80 -40 20 -21 40 00



2010-2013 2013-2016



C Manufacturing 00 14 -21 07 33 16 19 -01



F Construction 40 05 26 10 -03 05 -05 -03



I Accommodation 74 -07 55 26 47 -08 54 02

J ICT 16 -07 11 12 22 -25 42 05



Appendix

174


2004-2007 2007-2010



C Manufacturing 132 32 56 45 16 14 -03 05



F Construction 28 10 14 04 03 06 -14 11



I Accommodation 62 -03 52 12 -65 -09 -43 -13

J ICT 00 -15 49 -34 03 -22 14 12



2010-2013 2013-2016



C Manufacturing 06 13 -15 07 28 12 16 00



F Construction 44 05 26 14 03 02 07 -07



I Accommodation 70 -07 52 25 44 -07 51 01

J ICT 26 07 04 16 17 -20 37 00




175




growth over time



TFP in t-1 Year 2003 -0188 -0203 -0203

(000550) (000558) (000551)

TFP in t-1 Year 2006 -0222 -0238 -0235

(000518) (000525) (000519)

TFP in t-1 Year 2009 -0143 -0159 -0155

(000570) (000579) (000572)

TFP in t-1 Year 2012 -0156 -0172 -0171

(000516) (000524) (000517)

Year 2003 -00313 -00297

(000507) (000510)

Year 2006 -0184 -0183

(000489) (000491)

Year 2009 -00766 -00762

(000492) (000493)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 114200 113900 113900

R-squared 0061 0067 0084

Appendix

176





VARIABLES (1) (2) (3) (4)

TFP in t-1 -0170 -0186 -0213 -0223

(000561) (000578) (00155) (00155)

TFP in t-1 Group 2 00397 00243 -000502 -000776

(00146) (00147) (00213) (00213)

TFP in t-1 Group 3 00793 00652 00725 00600

(00221) (00222) (00164) (00165)

TFP in t-1 Group 4 00753 00666

(00244) (00247)

Group 2 00227 000593 -0000410 0000118

(000940) (000963) (00162) (00162)

Group 3 00216 -000934 00235 00220

(00150) (00154) (00131) (00132)

Group 4 00235 -00351

(00157) (00169)

Industry FE YES YES



Observations 30135 30062 30135 30062

R-squared 0056 0073 0056 0073





177


groups


VARIABLES (1) (2) (3) (4)

Foreign 00476

(00114)


(00213)


(00139)


(00162)


(00243)


(00340)


(00318)


(00381)


(00363)


(00124)



Observations 31642 31642 31642 31274

R-squared 0032 0033 0033 0033





Appendix

178




Firm categories by


VARIABLES (1) (2) (3) (4)


00577 00607 00141 00214

(00151) (00151) (00124) (00124)


00703 0101 00361 00558

(00152) (00152) (00118) (00118)


00338 00306 00450 00406

(00153) (00153) (00122) (00121)


00758 00436 00474 00321

(00146) (00146) (00109) (00109)


00978 00756 00286 000961

(00128) (00130) (000912) (000977)


-00290 -00145 -00592 -00411

(00133) (00135) (000871) (000932)


0114 0116 00502 00457

(00124) (00127) (000824) (000881)


0120 0120 00234 00155

(00126) (00129) (000782) (000835)

Year FE YES YES

Industry FE YES YES

Firm-level controls

YES YES YES YES

Region FE YES YES

Industry-year FE

YES YES

Observations 112374 112374 113900 113900

R-squared 0066 0085 0065 0085


columns (3) and (4)


179



growth over time



TFP in t-1 Year 2003 0113 0113 0113

(000472) (000478) (000475)

TFP in t-1 Year 2006 0120 0120 0119

(000434) (000439) (000437)

TFP in t-1 Year 2009 0109 0109 0107

(000479) (000485) (000482)

TFP in t-1 Year 2012 00982 00958 00956

(000442) (000448) (000445)

Year 2003 -00171 -00125

(000441) (000444)

Year 2006 -0134 -0128

(000422) (000423)

Year 2009 -00899 -00873

(000425) (000426)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 123900 123574 123574

R-squared 0042 0049 0054

Appendix

180





TFP in t-1 Year 2003 0156 0147 0148

(000641) (000647) (000644)

TFP in t-1 Year 2006 0134 0127 0128

(000581) (000587) (000584)

TFP in t-1 Year 2009 0139 0132 0134

(000648) (000655) (000651)

TFP in t-1 Year 2012 0132 0126 0127

(000618) (000624) (000621)

Year 2003 -00765 -00618

(000617) (000619)

Year 2006 -0220 -0211

(000586) (000587)

Year 2009 -0177 -0173

(000591) (000590)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 143011 142638 142638

R-squared 0037 0047 0051


181





VARIABLES (1) (2) (3) (4) (5) (6)

TFP in t-1 0134 0130 0134 0137 0134 0136

(000651) (000660) (000722) (000729) (000764) (000767)

TFP in t-1 Foreign -00109 -00138 00116 000347

(00166) (00167) (00289) (00289)

TFP in t-1 Exporter -00371 -00256 -00304 -00226

(00124) (00126) (00148) (00148)


(00364) (00365)

Foreign -00254 -00351 -0102 -00739

(00151) (00156) (00254) (00256)

Exporter 00998 00982 00940 00889

(00100) (00102) (00106) (00107)


(00312) (00315)




Observations 34980 34980 35564 35473 34980 34980

R-squared 0031 0051 0037 0054 0034 0052

Appendix

182


groups


VARIABLES (1) (2) (3) (4)

Exporter 00876

(000741)


(000788)


(00207)


(000850)


(00159)


(00276)


(00329)


(00230)


(00212)


(000801)



Observations 34418 33909 34418 33989

R-squared 0034 0034 0034 0036






183





VARIABLES (1) (2) (3) (4)


(00129) (00130) (00107) (00107)


(00126) (00127) (00101) (00101)


(00129) (00130) (00104) (00104)


(00126) (00126) (000942) (000942)

Firm group Year 2003 -00601 -00332 000244 00299

(00110) (00113) (000795) (000856)

Firm group Year 2006 -00159 -000559 00640 00786

(00112) (00115) (000752) (000807)

Firm group Year 2009 00404 00249 0111 00882

(00106) (00109) (000714) (000767)

Firm group Year 2012 -00102 -00116 00747 00607

(00110) (00112) (000684) (000735)

Year FE YES YES

Industry FE YES YES


Region FE YES YES


Observations 121954 121954 123574 123574

R-squared 0046 0055 0045 0055


(3) and (4)

Appendix

184

A73 Entry and exit



VARIABLES (1) (2) (3) (4) (5) (6)


-0172 -0186

(00103) (00102) (00112) (00112) (00122) (00121)


0171 0126

(00284) (00281) (00311) (00309) (00213) (00211)

Incumbent Foreign



0279 0232

(00122) (00129) (00123) (00131) (000887) (000926)




YES



YES




groups


VARIABLES (1) (2) (3) (4)

Foreign 0429

(00118)


(00206)


(00146)


(00162)


(00254)


(00363)


(00341)


(00352)


(00356)


(00131)



Observations 38367 38367 38367 37822

R-squared 0350 0361 0353 0356






185






(000924) (000923) (00107) (00106) (00114) (00113)



(000999) (000996) (000917) (000911) (000977) (000971)



(000985) (000983) (000942) (000937) (00101) (00101)



(000998) (000999) (00111) (00110) (00119) (00119)



(00265) (00264) (00264) (00263) (00196) (00197)



(00257) (00257) (00267) (00265) (00183) (00183)



(00279) (00278) (00278) (00277) (00185) (00185)



(00276) (00275) (00307) (00305) (00208) (00208)



(00122) (00124) (00124) (00126) (000943) (000994)



(00120) (00122) (00122) (00124) (000895) (000943)



(00122) (00124) (00119) (00122) (000867) (000915)



(00118) (00120) (00120) (00122) (000827) (000876)













(00108) (00105) (00121) (00118) (00250) (00241)


(00298) (00289) (00280) (00272) (00271) (00261)


(00574) (00556) (00479) (00464) (00179) (00173)


(00720) (00697) (00532) (00517)


(00104) (00101) (00111) (00109) (00170) (00164)


(00170) (00168) (00179) (00176) (00131) (00127)


(00181) (00185) (00188) (00192)




Observations 46160 46034 39020 38916 38459 38357

R-squared 0296 0355 0311 0369 0315 0374





different periods

A8 Chapter 8 Retail


(1) (2) (3) (4) (5) (6)








trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)

trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)

trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)

trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)






Observations 225866 209604 225860 209598 225908 209647

R-squared 0958 0978 0961 0980 0684 0809

Appendix

188


(1) (2) (3) (4) (5) (6)








trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)

trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)

trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)

trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)






Observations 94740 87533 94737 87530 94740 87533

R-squared 0968 0982 0973 0985 0642 0809


(1)

Dependent ln sales

Sunday 0049 (0001)


Friday 0054 (0001)



Tuesday 0060 (0001)

Monday 0048 (0001)

holiday 0008 (0000)

Jan -0169 (0002)

Dec 0138 (0003)

summer 0032 (0002)

date -0000 (0000)

Observations 463345

R-squared 0970

Appendix

190


Free publications

bull one copy








Priced publications





doi 10287333213

ET-0

4-1

7-8

33-E

N-N

LEGAL NOTICE




ISBN 978-92-79-73462-5 doi 10287333213





Freephone number ()

00 800 6 7 8 9 10 11



Table of contents

EXECUTIVE SUMMARY I

1 INTRODUCTION 1

2 DATA SOURCES 4







31 Convergence 18


33 Firm dynamics 28

34 Conclusions 31


41 Context 32




45 Conclusions 56




53 Conclusions 70

6 REALLOCATION 73




64 Conclusions 86





74 Conclusions 103


81 Context 104

82 Data 108



85 Trade 123



88 Conclusions 141

9 CONCLUSIONS 142

REFERENCES 144

APPENDIX 152














i

EXECUTIVE SUMMARY





2001-2016













non-frontier firms






























Executive Summary

ii





















































iii




















































Executive Summary

iv





















































1

1 INTRODUCTION

















non-frontier firms






























Introduction

2













sheet data







































3














































Data Sources

4
































2 DATA SOURCES











5







firms in year 2016







input a zero












Year 1 emp 2-5 emp

6-10 emp

11-20 emp

21-49 emp

50-249 emp

250 lt emp

Total

2004 005 028 092 105 160 312 642 043

2005 010 050 169 288 483 1066 2227 108

2006 017 087 315 553 966 1935 3632 192

2007 2209 3006 4384 5249 5743 6082 7412 3135

2008 098 324 951 1692 2840 4868 7827 576

2009 5962 6070 7217 7428 7831 7798 9336 6301

2010 2142 4685 7034 7540 8424 8228 9634 4175

2011 2277 4736 7064 7753 8521 8657 9681 4220

2012 9397 8298 9305 9484 9507 9159 10121 8990

2013 7274 8140 9423 9981 9747 9445 10312 8044



Data Sources

6














119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)





intensity





















7






























Data Sources

8


A) Manufacturing

B) Services


9








2004 and 2016


unweighted


2004 2016 2004 2016


B Mining 797 088 867 086 408 087 441 065

C Manufacturing 777 087 806 079 581 079 598 077



812 085 830 089 604 091 593 094

F Construction 773 080 803 072 620 071 646 066



I Accommodation 710 075 752 080 594 068 640 071

J ICT 834 094 862 090 631 101 669 098


815 087 844 088 636 087 673 088


763 098 792 094 640 107 662 113

Total 789 095 815 087 620 090 647 087








Data Sources

10











sample

























in the Appendix




11



2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739

2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987

2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568

2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376

2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880

2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844

2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671

2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709

2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318

2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113

2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104

2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509

2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335

2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665

2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678

2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519

2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727

Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742


estimated TFP


sales

Data Sources

12














Number of

firms

Total

employment

Total value

added






employment

708 885 935







157 600 647











13



B Mining 029 025 024 021 018

C Manufacturing 3085 2636 2352 2280 2122



103 112 114 127 098

F Construction 1263 1439 1447 1263 1375




J ICT 351 328 396 403 400




297 547 572 601 572

Total 100 100 100 100 100























Data Sources

14


A) Number of firms


B Mining 8228 1772 000 100








J ICT 8314 1541 145 100


8982 915 102 100


8991 798 211 100

Total 8937 953 110 100



B) Employment


B Mining 725 275 00 100








J ICT 424 546 30 100


650 331 20 100


681 258 61 100

Total 560 373 66 100












15


Year Foreign

exporter

Foreign

non-

exporter

Domestic

exporter

Domestic

non-

exporter

2000 92 51 168 690

2001 89 46 172 693

2002 84 43 173 701

2003 79 40 164 717

2004 71 36 157 736

2005 70 34 160 736

2006 69 34 163 734

2007 73 32 180 715

2008 74 34 186 706

2009 78 36 195 692

2010 77 34 202 687

2011 78 32 215 675

2012 81 31 229 659

2013 79 30 236 655

2014 74 33 233 660

2015 71 31 238 659

2016 70 26 245 658

Total 76 35 196 692









Data Sources

16


2016

Foreign exporter


Domestic exporter


Year 2004

N of employees 1385 511 451 165

(5689) (2410) (1396) (404)


(101) (120) (087) (086)

TFP ACF 666 660 634 611

(111) (112) (091) (084)

Age

101 85 99 85

(42) (46) (43) (43)

Year 2016

N of employees 1619 338 344 151

(6246) (1257) (1290) (405)


(088) (115) (075) (080)

TFP ACF 696 684 651 639

(113) (109) (086) (080)

Age 160 105 149 124

(82) (75) (76) (75)



parentheses






















17







Services



Utilities 35 to 39



18







31 Convergence






















19




















countries


Germany=100







20









crisis






















21


























22


























2013





23




B) TFP








24



























Country

Year 2011



Australia 187 205 190 212

Austria 196 242 - -

Belgium 160 174 180 178

Chile 300 353 307 387

Denmark 146 196 132 180

Finland 117 138 119 134

France 135 181 140 178

Hungary 279 329 254 286


Italy 166 201 160 188

Japan 126 138 117 138


New Zealand 184 209 192 208

sNorway 173 217 187 208

Portugal 188 265 188 275

Sweden 145 186 159 245






25





Country

Year 2011

LP Dispersion


Australia 98 99

Austria 86 90

Belgium 76 88

Chile 90 97

Denmark 84 63

Finland 65 76

France 63 85

Hungary 79 99

Indonesia 79 -

Italy 82 65

Japan 75 89

Netherlands 80 71

Norway 83 73

Portugal 62 76

Sweden 53 74

























26


A) Manufacturing








27


A) Manufacturing







28







33 Firm dynamics



























29


A) Manufacturing








30


A) Manufacturing









31

34 Conclusions














32


41 Context























A) Manufacturing


33























34


sample


























35














economy





average28










period









36




Number of employees

Manufacturing Other

2012 2015 2012 2015


Other firm 289636 296921 698326 771930

Self-employed 72674 74325 620699 638001

Total 983539 998637 2644324 2606263


Manufacturing Other

2012 2015 2012 2015


Other firm 151 146 196 201


Total 92 99 105 111























37





Appendix)


A) Market economy

Year LP TFP


2005 20 58 19 74

2006 92 91 93 119

2007 53 60 39 56

2008 -10 -08 -10 -04

2009 -70 -81 -69 -82

2010 -05 44 11 80

2011 25 45 34 40

2012 25 22 21 01

2013 19 25 30 22

2014 39 45 40 59

2015 51 50 52 49

2016 36 19 20 03

Average

2004-2007 55 70 50 83

2007-2010 -28 -15 -23 -02

2010-2013 23 34 33 29

2013-2016 36 35 35 33

B) Manufacturing

Year LP TFP


2005 37 148 20 114

2006 124 163 114 149

2007 100 114 78 71

2008 25 -03 17 -17

2009 -115 -94 -133 -117

2010 82 161 80 173

2011 -02 34 04 18

2012 05 -46 -02 -58

2013 -14 31 -12 05

2014 11 48 -01 27

2015 38 37 30 14

2016 26 01 04 -23

Average

2004-2007 87 141 71 111

2007-2010 -02 22 -12 13

2010-2013 -04 17 04 -03

2013-2016 15 29 05 06


38

C) Market services

Year

LP TFP


2005 12 -04 10 32

2006 80 47 79 90

2007 39 25 24 48

2008 -22 -06 -21 -03

2009 -57 -68 -52 -71

2010 -29 -17 -11 26

2011 33 49 43 57

2012 31 60 30 48

2013 29 21 39 29

2014 46 45 46 78

2015 54 58 54 72

2016 39 30 25 20

Average

2004-2007 43 23 38 57

2007-2010 -36 -31 -28 -16

2010-2013 31 44 39 51

2013-2016 42 39 41 50



















utilities



39


A) Manufacturing


2005 124 19 66 274 2006 240 137 39 33

2007 74 02 41 221

2008 -45 23 -15 59

2009 05 -191 -218 48

2010 135 111 264 168

2011 -45 18 34 100

2012 -15 -24 -83 -181

2013 -41 37 -22 125

2014 06 07 27 86

2015 65 01 -54 80

2016 -02 04 -27 -91

Average 2005-2007 146 53 49 176

2007-2010 32 -19 10 92

2010-2013 -34 07 -21 20

2013-2016 07 12 -19 50

B) Services


2005 127 16 34 -48

2006 166 75 30 67

2007 13 58 42 29

2008 -16 14 -72 -26

2009 -63 -94 -04 25

2010 54 12 09 05

2011 97 46 65 29

2012 12 74 13 -22

2013 12 30 63 -07

2014 78 89 65 -81

2015 106 70 14 54

2016 16 31 -47 39

Average

2005-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01







Appendix)


40

Frontier firms














firms












Appendix)31


A) By size

2004 2007 2010 2013 2016

20-49 emp 357 327 34 362 329

50-99 emp 401 468 542 486 555

100- emp 293 358 414 42 462

B) By ownership

2004 2007 2010 2013 2016

Domestic 213 194 236 272 289

Foreign 873 955 896 82 821

State 181 211 166 167 263




41

C) By region

2004 2007 2010 2013 2016

Central HU 596 621 652 552 579






Southern

Transdanubia 131 081 188 159 211
















42





















A) Manufacturing

32 1198902 asymp 74


43

B) Services







intensive services












44


A) Manufacturing

B) Services









45









deciles

A) Manufacturing



46

B) Services



























47






sample


















48




















year effects) 2016





49




composition matters














A) Domestic private


50

B) Foreign

C) State


Main sample


51





































52













firms






crisis


(cont)























53


A) Manufacturing

B) Services




intensive services


54


A) Manufacturing

B) Services






55


A) Manufacturing

B) Services






56




45 Conclusions





















57


































58















year level



























59





















60


























61






(2015)

















Appendix)


62






















63














and combinations




1 minus 120591119884119904119894 =120590

120590minus1

120573119871119904+120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119881119860119904119894 (51)

















return to scale





64





















65









tax46

119877(1 + 120591119870119904119894) =120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119870119904119894 (52)







66





































67












68










industries
















69







A) Age of firms

B) Size of firms



70























Variance

component

Share

of total

Variance

component

Share

of total


cost of capital

2126 100 2443 100







Residual 1830 861 1995 817



53 Conclusions









71


Industry type TFP

level

in

2016

TFP

growth

between

2011 and

2016

Olley-

Pakes

allocative

efficiency

Dispersion

of implicit

sales taxes

Dispersion

of implicit

cost of

capital

Low-tech mfg 5694 0027 0197 111 146



High-tech mfg 6708 0276 0072 107 145




Construction 6411 0082 0023 109 148

Utilities 5949 -0138 0801 093 155

Total services 6598 0212 0055 108 160






























72




across sectors



73

6 REALLOCATION



















this process














2013)

Reallocation

74


2004 2007 2010 2013 2016

Low-tech mfg 152 117 107 105 100



High-tech mfg 49 49 44 39 35





Utilities 40 34 34 33 34

Total services 616 645 676 671 673






A) Manufacturing


75

B) Services


sample







(61)












Reallocation

76















sample









77











+

























(62)

Reallocation

78






calculations
















79




Reallocation

80







81

















2004-2007





2007-2010





2010-2013





2013-2016











Reallocation

82


2004-2007





2007-2010





2010-2013





2010-2016























83















Reallocation

84


Notes Main sample



















85





A) By size

2004 2007 2010 2013 2016

5-9 emp 62 751 862 802 411

10-19 emp 618 626 679 652 297

20-49 emp 607 554 698 648 296

50-99 emp 711 685 792 829 438

100- emp 2005 1839 1559 1489 456

Total 1548 1367 1229 1194 417

B) By ownership

2004 2007 2010 2013 2016

Domestic 66 671 769 614 253

Foreign 989 1011 1139 1577 585

State 6289 5954 4127 2792 7

Total 155 1369 1228 1194 417


2004 2007 2010 2013 2016

Low-tech mfg 1236 1255 1149 906 371



High-tech mfg 427 1392 429 268 1

KIS 2098 737 875 1408 592

LKIS 282 2327 187 1867 365

Construction 258 394 339 515 352

Utilities 297 1031 705 593 756

Total 1553 1372 1229 1194 418

Notes Main sample





Reallocation

86














Notes Main sample

64 Conclusions





economy






87












the problem




















Reallocation

88






Estonia


89
















Section 44)





























90




















2015)58













recent periods60










91


over time





Estimation







1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905

119896

119870

119896=1


119896

119870

119896=1

+ 119883119894119905 + 120572119895(119894) + 휀119894119905









(71)


92




1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905

119897

119897=200320062009


119897 )119863119905119897

119897=2003200620092012

+ 119883119894119905 + 120572119895(119894) + 휀119894119905















1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905

119896119863119905119897

119870

119896=1119897=2003200620092012


119897 )119863119905119897

119897=2003200620092012


119897 )119866119894119905119896

119870

119896=1

119863119905119897

119897=2003200620092012

+ sum 1205734119897119863119905

119897

119897=200320062009

+ 119883119894119905 + 120572119895(119894) + 휀119894119905






Results













(72)

(73)


93




exporter firms


firm group


By size By age
















94




convergence61











Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190

(000556) (000568) (000622) (000629) (000656) (000663)

TFP in t-1 Foreign 00475 00418 00433 00423

(00140) (00142) (00252) (00253)

TFP in t-1 Exporter 00477 00287 00216 00224

(00104) (00106) (00125) (00126)


(00312) (00314)

Foreign 0117 0109 0120 0104

(00121) (00132) (00223) (00226)

Exporter 00240 000260 000401 0000919

(000791) (000845) (000851) (000881)


(00267) (00272)




Observations 29717 29717 30135 30062 29717 29717

R-squared 0060 0072 0056 0073 0060 0072







95



























96






















97



By size By age






98

Estimation results



Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 0105 0102 0105 0107 0107 0108

(000484) (000493) (000539) (000546) (000570) (000575)

TFP in t-1 Foreign

-00369 -00328 -00252 -00224

(00123) (00124) (00217) (00217)

TFP in t-1 Exporter

-00344 -00298 -00250 -00249

(000913) (000932) (00109) (00110)


-0000806 000194

(00271) (00272)

Foreign 000105 -000863 -000786 -0000106

(00112) (00116) (00194) (00196)

Exporter 00586 00635 00653 00672

(000738) (000754) (000777) (000786)

Foreign exporter

-000647 -00123

(00234) (00238)


Industry-region FE

YES YES YES

Firm-level controls

YES YES YES

Log of employees


Observations 31662 31662 32124 32043 31662 31662

R-squared 0035 0049 0038 0051 0037 0049



















99











VARIABLES (1) (2) (3) (4)

TFP in t-1 0112 0110 00875 00863

(000486) (000502) (00133) (00133)

TFP in t-1 Group 2 -00455 -00431 00445 00384

(00128) (00129) (00182) (00183)

TFP in t-1 Group 3 -00745 -00748 000733 000822

(00194) (00196) (00141) (00142)

TFP in t-1 Group 4 -00953 -00957

(00218) (00220)

Group 2 -000596 -000810 -00188 -00212

(00122) (00123) (00141) (00141)

Group 3 -000928 -00157 -00115 -00169

(00199) (00201) (00114) (00115)

Group 4 00328 00280

(00276) (00278)

Industry FE YES YES




Observations 32124 32043 32124 32043

R-squared 0038 0052 0037 0051




73 Entry and exit

Questions





100










Estimation











119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905

119896119873119864119894119905119870119896=1 + 1205732119866119894119905

0119864119894119905 + sum 1205733119896119866119894119905

119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)















interacting 119866119894119905119896119873119864119894119905 119866119894119905




101

Results















(1) (2) (3) (4) (5) (6)


-00532 -00535 -00465 -0214 -0199 -0200

(000906) (000873) (000879) (00102) (000987) (000989)


00269 00212 00232 -0135 -0117 -0119

(000967) (000931) (000936) (000897) (000865) (000865)


00369 00444 00360 -0133 -0114 -0121

(000960) (000926) (000931) (000920) (000889) (000889)

EntrantExitorPeriod

2012-2015

00374 00324 00252 -0171 -0150 -0157

(000971) (000936) (000944) (00107) (00103) (00103)

Period 2003-2006 -0115 -00927 -00711 -00445

(000519) (000501) (000556) (000537)

Period 2006-2009 -0175 -0169 -000972 00122

(000522) (000503) (000537) (000518)

Period 2009-2012 -0116 -0117 -00580 -00528

(000528) (000508) (000543) (000522)


Industry FE YES YES




Observations 166607 166168 166168 158143 157711 157711

R-squared 0327 0380 0380 0332 0385 0387


102




















103

74 Conclusions

























104




























81 Context







74 EC (2018) p 5


105















75 EC (2018) p 4



106






to larger firms

















107









Below 500m HUF 0



Above 100bn HUF 25












108

82 Data


































109








qualified analysis












2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446

2006 573 573 51 131 59 430 111 2008 30 1548

2007 546 546 53 125 60 429 110 1987 33 1634

2008 628 628 45 102 50 350 104 1823 21 1574

2009 527 527 33 72 41 290 99 1879 24 1754

2010 472 472 22 49 32 238 94 1793 22 1968

2011 537 537 14 30 29 212 92 1758 22 2027

2012 374 374 30 68 49 335 88 1643 23 2107

2013 503 503 48 121 42 277 88 1622 25 2094

2014 410 410 106 239 48 320 81 1530 24 2054

2015 512 512 135 311 42 292 80 1544 30 2090

2016 518 518 120 292 37 271 77 1457 23 2022









110









83 General trends






















111


A) Number of firms

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100

2006 7333 1613 687 252 065 049 100

2007 7369 1597 674 244 067 050 100

2008 7410 1571 667 236 067 048 100

2009 7522 1535 614 221 059 048 100

2010 7551 1508 640 201 057 044 100

2011 7591 1493 623 194 057 041 100

2012 7625 1506 568 204 055 042 100

2013 7526 1596 577 207 052 042 100

2014 7380 1684 628 210 056 042 100

2015 7239 1771 661 232 056 041 100

2016 7163 1804 676 252 063 043 100

B) Employment

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000

2006 2034 1475 1269 1025 679 3525 10000

2007 2026 1443 1241 984 669 3644 10000

2008 2020 1391 1138 910 579 3980 10000

2009 2002 1427 1244 870 570 3789 10000

2010 2099 1443 1243 862 577 3731 10000

2011 2144 1458 1119 895 555 3830 10000

2012 2144 1541 1131 906 541 3713 10000

2013 2168 1591 1204 896 555 3650 10000

2014 2104 1644 1236 970 548 3538 10000

2015 2064 1620 1216 1006 588 3570 10000

2016 1999 1620 1216 1006 588 3570 10000

C) Sales

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000

2006 1114 1278 1587 1025 776 4226 10000

2007 1110 1266 1543 956 855 4268 10000

2008 1112 1524 1045 891 531 4906 10000

2009 1104 1641 1073 856 718 4607 10000

2010 1104 1521 1103 930 693 4594 10000

2011 1160 1577 1004 934 624 4714 10000

2012 1146 1647 1003 913 629 4578 10000

2013 1229 1411 1090 945 709 4360 10000

2014 1486 1472 1093 1001 752 4389 10000

2015 1293 1458 1118 985 660 4512 10000

2016 1266 1458 1118 985 660 4512 10000


112























113



retail85

















119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905

















114





119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905









115


A) TFP

B) Gross Margin

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 1769 1548 1793 1883 1584 1495 1735 1554

2005 1839 1571 1858 1878 1504 1666 1794 1584

2006 2040 1658 1842 1879 1599 1660 1956 1653

2007 2220 1720 1875 1940 1664 1702 2104 1712

2008 2192 1792 1872 1992 1522 1739 2096 1728

2009 2084 1719 1864 1967 1599 1527 2006 1630

2010 2096 1749 1867 2102 1664 1535 2024 1682

2011 2172 1765 1862 2003 1759 1558 2084 1728

2012 2203 1740 1828 1913 1969 1541 2102 1733

2013 2243 1734 1806 2018 1999 1794 2128 1790

2014 2363 1789 1817 2149 2022 1869 2223 1855

2015 2390 1803 1885 2165 2215 1987 2245 1928

2016 2379 1799 1911 2172 2403 2170 2237 1948


Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

unweighted weighted

2004 651 701 663 677 479 480 658 611

2005 827 757 777 712 526 560 806 644

2006 908 773 777 753 579 797 870 706

2007 807 635 653 704 500 580 764 606

2008 665 588 577 610 422 478 643 525

2009 618 535 538 522 351 339 595 456

2010 669 587 629 626 421 341 650 510

2011 698 617 591 637 410 393 676 542

2012 770 643 599 624 485 404 735 577

2013 735 594 555 631 478 369 697 530

2014 745 578 568 612 446 398 700 531

2015 766 609 625 646 547 452 724 579

2016 759 597 621 632 609 487 715 588

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 599 633 637 628 612 623 610 626

2005 610 635 643 628 616 626 619 631

2006 623 646 651 640 630 642 631 643

2007 624 643 650 641 627 643 631 642

2008 625 645 647 640 630 640 631 642

2009 619 641 642 631 619 630 626 630

2010 617 641 643 634 622 631 625 631

2011 621 643 645 637 629 639 628 639

2012 624 646 644 637 629 639 631 644

2013 626 647 642 640 637 642 632 645

2014 628 648 650 645 640 648 634 648

2015 638 658 658 655 652 659 644 659

2016 643 661 665 659 655 661 649 665


116





















117







newspapers












118





















119






















120










regressions

119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905



dummies






121


its shops














(1) (2) (3)

Dependent Change in

number of

shops

New

shops

Closed

shops


(0024) (0014) (0018)


(0064) (0034) (0045)

ln( (average

salesshop)

0056 0034 -0017

(0016) (0010) (0014)

5-9 shops 0176 0001 -0126

(0070) (0036) (0054)

10-49 shops 0231 -0009 -0167

(0067) (0034) (0052)

more than 50 shops 0194 0002 -0109

(0071) (0038) (0055)

Year FE yes yes yes


R-squared 0105 0093 0084







122


119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905




























123


(1) (2) (3) (4) (5) (6)


Period 2004-

2007

2008-

2010

2012-

2015

2004-

2007

2008-

2010

2012-

2015


(0009) (0015) (0087) (0018) (0022) (0063)

foreign-owned -0005 0063 0056 0016 -0014 -0035

(0023) (0045) (0057) (0023) (0055) (0066)

ln sales -0026 -0029 -0057 -0018 -0010 0010

(0006) (0007) (0021) (0007) (0017) (0005)

5-9 shops -0014 -0066 -0111 0061 -0035 -0055

(0026) (0051) (0043) (0046) (0044) (0026)

10-49 shops -0045 -0114 -0149 0046 -0038 -0023

(0024) (0048) (0039) (0044) (0043) (0017)

more than 50

shops

0001 -0077 -0134 0061 -0027 -0016

(0029) (0048) (0040) (0045) (0044) (0022)

Observations 15374 10946 15038 14025 10120 13458

R-squared 0030 0053 0073 0023 0041 0121






firm-level

85 Trade





Schmitt 2016)














124















Importing

























125













product categories



126











127
















128


(1) (2) (3) (4) (5)

Year 2005 2008 2010 2012 2015



(0003) (0003) (0003) (0003) (0003)

Foreign-owned 0249 0196 0224 0238 0217

(0014) (0011) (0012) (0012) (0012)

Ln employees 0082 0082 0075 0073 0088

(0004) (0004) (0004) (0004) (0004)

Constant -0470 -0536 -0464 -0551 -0637

(0027) (0026) (0027) (0028) (0027)

Observations 7467 7977 7400 7122 8308

R-squared 0116 0130 0127 0140 0143



Exporting






after 2012


129












130


sector






(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015


Labour

productivity

0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)

Foreign-owned 0083 0137 0141 0119 0107

(0009) (0011) (0013) (0013) (0012)

Ln employees 0019 0029 0028 0031 0034

(0003) (0004) (0004) (0004) (0004)

Constant -0159 -0277 -0271 -0321 -0317

(0018) (0026) (0027) (0029) (0028)

Observations 7622 7976 7663 7384 8730

R-squared 0028 0045 0041 0041 0036







131











policy changes








powerful test











policy




132


Notes Full sample




133












strategy feasible







policy108

















134
















A) Sales


135

B) Sales per day










119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591

120591 + 120583119894119895 + 120575119905 + 휀119894119895119905

















136

















A) Days


137

B) Sales

C) Sales per day






138





A) Days

B) Sales


139

C) Sales per day























140









A) Sales

B) Sales per day


141






88 Conclusions































Conclusions

142

9 CONCLUSIONS


















levels even further





























143









References

144

REFERENCES




































145



































References

146



































147


Academy of Sciences































Review 105(12) 3660-3703


15(4) 781-805

References

148













Growth 12(1) 1-25















673-709




149



126-138
















50(1) 21-43


















References

150

























Bank Issue 143 1-10








151





Journal 31(9) 969-989

Appendix

152

APPENDIX







































153


































Database112


Appendix

154



A) Market economy



2005 19 74 16 60

2006 93 119 95 97

2007 39 56 49 65

2008 -10 -04 -06 01

2009 -69 -82 -65 -63

2010 11 80 05 60

2011 34 40 31 45

2012 21 01 24 18

2013 30 22 22 22

2014 40 59 36 48

2015 52 49 50 43

2016 20 03 25 12

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Manufacturing



2005 20 114 24 127

2006 114 149 118 137

2007 78 71 86 98

2008 17 -17 32 -11

2009 -133 -117 -120 -87

2010 80 173 85 178

2011 04 18 01 25

2012 -02 -58 07 -38

2013 -12 05 -15 16

2014 -01 27 01 34

2015 30 14 34 19

2016 04 -23 14 -05

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01


155

C) Market services



2005 10 32 06 01

2006 79 90 82 64

2007 24 48 35 44

2008 -21 -03 -19 05

2009 -52 -71 -51 -54

2010 -11 26 -19 -05

2011 43 57 40 57

2012 30 48 31 57

2013 39 29 31 25

2014 46 78 39 55

2015 54 72 52 58

2016 25 20 29 23

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01




Appendix

156


A) Manufacturing


2005 21 -02 -09 144

2006 118 143 58 47

2007 59 43 90 348

2008 -09 79 17 111

2009 -53 -191 -197 -139

2010 80 76 85 130

2011 -22 17 10 153

2012 01 14 -57 -06

2013 -38 20 -38 54

2014 -03 -05 08 33

2015 61 04 -19 132

2016 09 -10 12 91

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Market Services


2005 127 16 -01 -46

2006 166 75 94 66

2007 13 58 60 16

2008 -16 14 -37 -28

2009 -63 -94 -15 44

2010 54 12 -08 23

2011 97 46 77 -29

2012 12 74 06 -57

2013 12 30 60 -71

2014 78 89 65 -31

2015 106 70 22 12

2016 16 31 -47 37

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01




157


A) Manufacturing


2005 172 32 73 300

2006 266 114 54 10

2007 121 52 69 243

2008 -25 -17 -03 126

2009 31 -151 -186 35

2010 135 114 199 207

2011 -33 -10 96 96

2012 03 -34 -32 -226

2013 -35 22 26 253

2014 33 19 53 94

2015 82 -04 -06 102

2016 34 18 08 -110

Average

2004-2007 186 66 65 184

2007-2010 47 -18 03 123

2010-2013 -21 02 24 35

2013-2016 28 14 20 85

B) Services


2005 127 -05 41 -31

2006 166 75 21 54

2007 13 11 25 -36

2008 -16 -19 05 -02

2009 -63 -117 09 04

2010 54 -01 -05 13

2011 97 47 54 13

2012 12 62 19 -47

2013 12 21 62 -44

2014 78 55 64 -39

2015 106 54 07 65

2016 16 49 -60 43

Average

2004-2007 102 27 29 -04

2007-2010 -08 -46 03 05

2010-2013 40 48 24 -01

2013-2016 53 45 18 06



Appendix

158


A) By size

2004 2007 2010 2013 2016

5-9 emp 1049 1051 1043 1096 1045

10-19 emp 954 962 92 904 92

20-49 emp 994 903 939 856 998

50-99 emp 896 1024 1188 1009 1096

100- emp 721 81 839 748 728

B) By ownership

2004 2007 2010 2013 2016

Domestic 833 818 814 824 837

Foreign 2344 2499 2422 2384 2488

State 554 728 81 575 695

C) By region

2004 2007 2010 2013 2016

Central HU 567 568 59 56 549

Northern

Hungary 195 116 19 208 224

Northern

Great Plain 161 178 239 23 249

Southern

Great Plain 137 118 17 258 179

Central

Transdanubia 276 33 332 369 332

Western

Transdanubia 311 283 244 361 444

Southern

Transdanubia 184 201 235 143 181

Notes Main sample


159




Appendix

160



Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168

C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209

D - Electricity gas

steam and AC

6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743

E - Water supply

sewerage waste

6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872

F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023

G - Wholesale and

retail trade

6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303

H - Transportation

and storage

6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985

I - Accommodation

food service

6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250

J - Information and

Communication

6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046

M - Professional


6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075


6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026



161


Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692

C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109

D - Electricity

gas steam and

AC

6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291

E - Water supply

sewerage waste

6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916

F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436

G - Wholesale and

retail trade

6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260

H - Transportation

and storage

6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776

I -

Accommodation

food service

6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899

J - Information

and

Communication

6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800

M - Professional

Scientific and

Tech Act

6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242

N - Administrative

and support service

6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039


Appendix

162


Year 2001 2005 2010 2015

Industry unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covar

iance

unweight

ed LP

weighted

LP

covar

iance


C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796


AC

9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197


8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703

F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096



storage

8364 8475 0110 8300 8525 0224 8194 7698 -

0496

8292 7289 -

1003


service

7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651


8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988


8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406


support service

7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238


163


Year 2001 2005 2010 2015

Industry unweighted

labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity

covariance

B - Mining and

quarrying

7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358

C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960

D - Electricity gas

steam and AC

9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289

E - Water supply

sewerage waste

8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064

F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159

G - Wholesale and

retail trade

7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037

H - Transportation

and storage

8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022

I - Accommodation

food service

7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363

J - Information and

Communication

7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263

M - Professional

Scientific and Tech

Act

7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431

N - Administrative

and support service

7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216

Appendix

164







capital


C - Manufacturing 5620 1300 0425 0818 12807 1008



F - Construction 6706 0818 0280 0954 21186 1227








165







capital


C - Manufacturing 5919 1173 0497 0890 13439 0998



F - Construction 6794 0744 0155 0947 20440 1099







Appendix

166





taxes



capital


C - Manufacturing 6024 1201 0523 0740 12732 1001



F - Construction 6621 0775 0200 1075 30395 1437







167




taxes


taxes


of capital


capital


C - Manufacturing 6022 1110 0514 0971 11130 1074



F - Construction 6938 0809 0298 0868 28761 1453








Appendix

168





169




Appendix

170





171



2004-2007 2007-2010



C Manufacturing 108 23 48 36 -02 -11 03 05



F Construction 26 04 08 13 -14 -02 -19 07



I Accommodation 68 -07 53 22 -44 00 -37 -07

J ICT 96 10 63 23 29 -24 35 18



2010-2013 2013-2016



C Manufacturing -18 07 -30 05 10 -08 22 -04



F Construction 42 03 26 13 01 06 -16 11



I Accommodation 85 -05 59 32 51 -03 46 08

J ICT 19 -02 11 10 47 -02 38 11




Appendix

172


2004-2007 2007-2010



C Manufacturing 105 20 51 34 06 -14 03 16



F Construction 35 07 12 16 -23 -03 -24 04



I Accommodation 67 -09 50 26 -42 03 -39 -05

J ICT 85 14 39 32 27 -14 21 19



2010-2013 2013-2016



C Manufacturing -12 06 -25 07 06 -04 11 -01



F Construction 45 03 22 20 10 02 -05 12



I Accommodation 81 -04 54 30 51 -02 42 11

J ICT 13 00 00 13 49 10 33 06





173


2004-2007 2007-2010



C Manufacturing 132 34 54 44 08 19 -12 01



F Construction 24 07 10 07 -01 07 -09 01



I Accommodation 59 -03 56 07 -74 -12 -41 -21

J ICT 58 19 80 -40 20 -21 40 00



2010-2013 2013-2016



C Manufacturing 00 14 -21 07 33 16 19 -01



F Construction 40 05 26 10 -03 05 -05 -03



I Accommodation 74 -07 55 26 47 -08 54 02

J ICT 16 -07 11 12 22 -25 42 05



Appendix

174


2004-2007 2007-2010



C Manufacturing 132 32 56 45 16 14 -03 05



F Construction 28 10 14 04 03 06 -14 11



I Accommodation 62 -03 52 12 -65 -09 -43 -13

J ICT 00 -15 49 -34 03 -22 14 12



2010-2013 2013-2016



C Manufacturing 06 13 -15 07 28 12 16 00



F Construction 44 05 26 14 03 02 07 -07



I Accommodation 70 -07 52 25 44 -07 51 01

J ICT 26 07 04 16 17 -20 37 00




175




growth over time



TFP in t-1 Year 2003 -0188 -0203 -0203

(000550) (000558) (000551)

TFP in t-1 Year 2006 -0222 -0238 -0235

(000518) (000525) (000519)

TFP in t-1 Year 2009 -0143 -0159 -0155

(000570) (000579) (000572)

TFP in t-1 Year 2012 -0156 -0172 -0171

(000516) (000524) (000517)

Year 2003 -00313 -00297

(000507) (000510)

Year 2006 -0184 -0183

(000489) (000491)

Year 2009 -00766 -00762

(000492) (000493)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 114200 113900 113900

R-squared 0061 0067 0084

Appendix

176





VARIABLES (1) (2) (3) (4)

TFP in t-1 -0170 -0186 -0213 -0223

(000561) (000578) (00155) (00155)

TFP in t-1 Group 2 00397 00243 -000502 -000776

(00146) (00147) (00213) (00213)

TFP in t-1 Group 3 00793 00652 00725 00600

(00221) (00222) (00164) (00165)

TFP in t-1 Group 4 00753 00666

(00244) (00247)

Group 2 00227 000593 -0000410 0000118

(000940) (000963) (00162) (00162)

Group 3 00216 -000934 00235 00220

(00150) (00154) (00131) (00132)

Group 4 00235 -00351

(00157) (00169)

Industry FE YES YES



Observations 30135 30062 30135 30062

R-squared 0056 0073 0056 0073





177


groups


VARIABLES (1) (2) (3) (4)

Foreign 00476

(00114)


(00213)


(00139)


(00162)


(00243)


(00340)


(00318)


(00381)


(00363)


(00124)



Observations 31642 31642 31642 31274

R-squared 0032 0033 0033 0033





Appendix

178




Firm categories by


VARIABLES (1) (2) (3) (4)


00577 00607 00141 00214

(00151) (00151) (00124) (00124)


00703 0101 00361 00558

(00152) (00152) (00118) (00118)


00338 00306 00450 00406

(00153) (00153) (00122) (00121)


00758 00436 00474 00321

(00146) (00146) (00109) (00109)


00978 00756 00286 000961

(00128) (00130) (000912) (000977)


-00290 -00145 -00592 -00411

(00133) (00135) (000871) (000932)


0114 0116 00502 00457

(00124) (00127) (000824) (000881)


0120 0120 00234 00155

(00126) (00129) (000782) (000835)

Year FE YES YES

Industry FE YES YES

Firm-level controls

YES YES YES YES

Region FE YES YES

Industry-year FE

YES YES

Observations 112374 112374 113900 113900

R-squared 0066 0085 0065 0085


columns (3) and (4)


179



growth over time



TFP in t-1 Year 2003 0113 0113 0113

(000472) (000478) (000475)

TFP in t-1 Year 2006 0120 0120 0119

(000434) (000439) (000437)

TFP in t-1 Year 2009 0109 0109 0107

(000479) (000485) (000482)

TFP in t-1 Year 2012 00982 00958 00956

(000442) (000448) (000445)

Year 2003 -00171 -00125

(000441) (000444)

Year 2006 -0134 -0128

(000422) (000423)

Year 2009 -00899 -00873

(000425) (000426)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 123900 123574 123574

R-squared 0042 0049 0054

Appendix

180





TFP in t-1 Year 2003 0156 0147 0148

(000641) (000647) (000644)

TFP in t-1 Year 2006 0134 0127 0128

(000581) (000587) (000584)

TFP in t-1 Year 2009 0139 0132 0134

(000648) (000655) (000651)

TFP in t-1 Year 2012 0132 0126 0127

(000618) (000624) (000621)

Year 2003 -00765 -00618

(000617) (000619)

Year 2006 -0220 -0211

(000586) (000587)

Year 2009 -0177 -0173

(000591) (000590)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 143011 142638 142638

R-squared 0037 0047 0051


181





VARIABLES (1) (2) (3) (4) (5) (6)

TFP in t-1 0134 0130 0134 0137 0134 0136

(000651) (000660) (000722) (000729) (000764) (000767)

TFP in t-1 Foreign -00109 -00138 00116 000347

(00166) (00167) (00289) (00289)

TFP in t-1 Exporter -00371 -00256 -00304 -00226

(00124) (00126) (00148) (00148)


(00364) (00365)

Foreign -00254 -00351 -0102 -00739

(00151) (00156) (00254) (00256)

Exporter 00998 00982 00940 00889

(00100) (00102) (00106) (00107)


(00312) (00315)




Observations 34980 34980 35564 35473 34980 34980

R-squared 0031 0051 0037 0054 0034 0052

Appendix

182


groups


VARIABLES (1) (2) (3) (4)

Exporter 00876

(000741)


(000788)


(00207)


(000850)


(00159)


(00276)


(00329)


(00230)


(00212)


(000801)



Observations 34418 33909 34418 33989

R-squared 0034 0034 0034 0036






183





VARIABLES (1) (2) (3) (4)


(00129) (00130) (00107) (00107)


(00126) (00127) (00101) (00101)


(00129) (00130) (00104) (00104)


(00126) (00126) (000942) (000942)

Firm group Year 2003 -00601 -00332 000244 00299

(00110) (00113) (000795) (000856)

Firm group Year 2006 -00159 -000559 00640 00786

(00112) (00115) (000752) (000807)

Firm group Year 2009 00404 00249 0111 00882

(00106) (00109) (000714) (000767)

Firm group Year 2012 -00102 -00116 00747 00607

(00110) (00112) (000684) (000735)

Year FE YES YES

Industry FE YES YES


Region FE YES YES


Observations 121954 121954 123574 123574

R-squared 0046 0055 0045 0055


(3) and (4)

Appendix

184

A73 Entry and exit



VARIABLES (1) (2) (3) (4) (5) (6)


-0172 -0186

(00103) (00102) (00112) (00112) (00122) (00121)


0171 0126

(00284) (00281) (00311) (00309) (00213) (00211)

Incumbent Foreign



0279 0232

(00122) (00129) (00123) (00131) (000887) (000926)




YES



YES




groups


VARIABLES (1) (2) (3) (4)

Foreign 0429

(00118)


(00206)


(00146)


(00162)


(00254)


(00363)


(00341)


(00352)


(00356)


(00131)



Observations 38367 38367 38367 37822

R-squared 0350 0361 0353 0356






185






(000924) (000923) (00107) (00106) (00114) (00113)



(000999) (000996) (000917) (000911) (000977) (000971)



(000985) (000983) (000942) (000937) (00101) (00101)



(000998) (000999) (00111) (00110) (00119) (00119)



(00265) (00264) (00264) (00263) (00196) (00197)



(00257) (00257) (00267) (00265) (00183) (00183)



(00279) (00278) (00278) (00277) (00185) (00185)



(00276) (00275) (00307) (00305) (00208) (00208)



(00122) (00124) (00124) (00126) (000943) (000994)



(00120) (00122) (00122) (00124) (000895) (000943)



(00122) (00124) (00119) (00122) (000867) (000915)



(00118) (00120) (00120) (00122) (000827) (000876)













(00108) (00105) (00121) (00118) (00250) (00241)


(00298) (00289) (00280) (00272) (00271) (00261)


(00574) (00556) (00479) (00464) (00179) (00173)


(00720) (00697) (00532) (00517)


(00104) (00101) (00111) (00109) (00170) (00164)


(00170) (00168) (00179) (00176) (00131) (00127)


(00181) (00185) (00188) (00192)




Observations 46160 46034 39020 38916 38459 38357

R-squared 0296 0355 0311 0369 0315 0374





different periods

A8 Chapter 8 Retail


(1) (2) (3) (4) (5) (6)








trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)

trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)

trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)

trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)






Observations 225866 209604 225860 209598 225908 209647

R-squared 0958 0978 0961 0980 0684 0809

Appendix

188


(1) (2) (3) (4) (5) (6)








trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)

trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)

trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)

trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)






Observations 94740 87533 94737 87530 94740 87533

R-squared 0968 0982 0973 0985 0642 0809


(1)

Dependent ln sales

Sunday 0049 (0001)


Friday 0054 (0001)



Tuesday 0060 (0001)

Monday 0048 (0001)

holiday 0008 (0000)

Jan -0169 (0002)

Dec 0138 (0003)

summer 0032 (0002)

date -0000 (0000)

Observations 463345

R-squared 0970

Appendix

190


Free publications

bull one copy








Priced publications





doi 10287333213

ET-0

4-1

7-8

33-E

N-N


Table of contents

EXECUTIVE SUMMARY I

1 INTRODUCTION 1

2 DATA SOURCES 4







31 Convergence 18


33 Firm dynamics 28

34 Conclusions 31


41 Context 32




45 Conclusions 56




53 Conclusions 70

6 REALLOCATION 73




64 Conclusions 86





74 Conclusions 103


81 Context 104

82 Data 108



85 Trade 123



88 Conclusions 141

9 CONCLUSIONS 142

REFERENCES 144

APPENDIX 152














i

EXECUTIVE SUMMARY





2001-2016













non-frontier firms






























Executive Summary

ii





















































iii




















































Executive Summary

iv





















































1

1 INTRODUCTION

















non-frontier firms






























Introduction

2













sheet data







































3














































Data Sources

4
































2 DATA SOURCES











5







firms in year 2016







input a zero












Year 1 emp 2-5 emp

6-10 emp

11-20 emp

21-49 emp

50-249 emp

250 lt emp

Total

2004 005 028 092 105 160 312 642 043

2005 010 050 169 288 483 1066 2227 108

2006 017 087 315 553 966 1935 3632 192

2007 2209 3006 4384 5249 5743 6082 7412 3135

2008 098 324 951 1692 2840 4868 7827 576

2009 5962 6070 7217 7428 7831 7798 9336 6301

2010 2142 4685 7034 7540 8424 8228 9634 4175

2011 2277 4736 7064 7753 8521 8657 9681 4220

2012 9397 8298 9305 9484 9507 9159 10121 8990

2013 7274 8140 9423 9981 9747 9445 10312 8044



Data Sources

6














119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)





intensity





















7






























Data Sources

8


A) Manufacturing

B) Services


9








2004 and 2016


unweighted


2004 2016 2004 2016


B Mining 797 088 867 086 408 087 441 065

C Manufacturing 777 087 806 079 581 079 598 077



812 085 830 089 604 091 593 094

F Construction 773 080 803 072 620 071 646 066



I Accommodation 710 075 752 080 594 068 640 071

J ICT 834 094 862 090 631 101 669 098


815 087 844 088 636 087 673 088


763 098 792 094 640 107 662 113

Total 789 095 815 087 620 090 647 087








Data Sources

10











sample

























in the Appendix




11



2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739

2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987

2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568

2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376

2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880

2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844

2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671

2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709

2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318

2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113

2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104

2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509

2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335

2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665

2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678

2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519

2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727

Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742


estimated TFP


sales

Data Sources

12














Number of

firms

Total

employment

Total value

added






employment

708 885 935







157 600 647











13



B Mining 029 025 024 021 018

C Manufacturing 3085 2636 2352 2280 2122



103 112 114 127 098

F Construction 1263 1439 1447 1263 1375




J ICT 351 328 396 403 400




297 547 572 601 572

Total 100 100 100 100 100























Data Sources

14


A) Number of firms


B Mining 8228 1772 000 100








J ICT 8314 1541 145 100


8982 915 102 100


8991 798 211 100

Total 8937 953 110 100



B) Employment


B Mining 725 275 00 100








J ICT 424 546 30 100


650 331 20 100


681 258 61 100

Total 560 373 66 100












15


Year Foreign

exporter

Foreign

non-

exporter

Domestic

exporter

Domestic

non-

exporter

2000 92 51 168 690

2001 89 46 172 693

2002 84 43 173 701

2003 79 40 164 717

2004 71 36 157 736

2005 70 34 160 736

2006 69 34 163 734

2007 73 32 180 715

2008 74 34 186 706

2009 78 36 195 692

2010 77 34 202 687

2011 78 32 215 675

2012 81 31 229 659

2013 79 30 236 655

2014 74 33 233 660

2015 71 31 238 659

2016 70 26 245 658

Total 76 35 196 692









Data Sources

16


2016

Foreign exporter


Domestic exporter


Year 2004

N of employees 1385 511 451 165

(5689) (2410) (1396) (404)


(101) (120) (087) (086)

TFP ACF 666 660 634 611

(111) (112) (091) (084)

Age

101 85 99 85

(42) (46) (43) (43)

Year 2016

N of employees 1619 338 344 151

(6246) (1257) (1290) (405)


(088) (115) (075) (080)

TFP ACF 696 684 651 639

(113) (109) (086) (080)

Age 160 105 149 124

(82) (75) (76) (75)



parentheses






















17







Services



Utilities 35 to 39



18







31 Convergence






















19




















countries


Germany=100







20









crisis






















21


























22


























2013





23




B) TFP








24



























Country

Year 2011



Australia 187 205 190 212

Austria 196 242 - -

Belgium 160 174 180 178

Chile 300 353 307 387

Denmark 146 196 132 180

Finland 117 138 119 134

France 135 181 140 178

Hungary 279 329 254 286


Italy 166 201 160 188

Japan 126 138 117 138


New Zealand 184 209 192 208

sNorway 173 217 187 208

Portugal 188 265 188 275

Sweden 145 186 159 245






25





Country

Year 2011

LP Dispersion


Australia 98 99

Austria 86 90

Belgium 76 88

Chile 90 97

Denmark 84 63

Finland 65 76

France 63 85

Hungary 79 99

Indonesia 79 -

Italy 82 65

Japan 75 89

Netherlands 80 71

Norway 83 73

Portugal 62 76

Sweden 53 74

























26


A) Manufacturing








27


A) Manufacturing







28







33 Firm dynamics



























29


A) Manufacturing








30


A) Manufacturing









31

34 Conclusions














32


41 Context























A) Manufacturing


33























34


sample


























35














economy





average28










period









36




Number of employees

Manufacturing Other

2012 2015 2012 2015


Other firm 289636 296921 698326 771930

Self-employed 72674 74325 620699 638001

Total 983539 998637 2644324 2606263


Manufacturing Other

2012 2015 2012 2015


Other firm 151 146 196 201


Total 92 99 105 111























37





Appendix)


A) Market economy

Year LP TFP


2005 20 58 19 74

2006 92 91 93 119

2007 53 60 39 56

2008 -10 -08 -10 -04

2009 -70 -81 -69 -82

2010 -05 44 11 80

2011 25 45 34 40

2012 25 22 21 01

2013 19 25 30 22

2014 39 45 40 59

2015 51 50 52 49

2016 36 19 20 03

Average

2004-2007 55 70 50 83

2007-2010 -28 -15 -23 -02

2010-2013 23 34 33 29

2013-2016 36 35 35 33

B) Manufacturing

Year LP TFP


2005 37 148 20 114

2006 124 163 114 149

2007 100 114 78 71

2008 25 -03 17 -17

2009 -115 -94 -133 -117

2010 82 161 80 173

2011 -02 34 04 18

2012 05 -46 -02 -58

2013 -14 31 -12 05

2014 11 48 -01 27

2015 38 37 30 14

2016 26 01 04 -23

Average

2004-2007 87 141 71 111

2007-2010 -02 22 -12 13

2010-2013 -04 17 04 -03

2013-2016 15 29 05 06


38

C) Market services

Year

LP TFP


2005 12 -04 10 32

2006 80 47 79 90

2007 39 25 24 48

2008 -22 -06 -21 -03

2009 -57 -68 -52 -71

2010 -29 -17 -11 26

2011 33 49 43 57

2012 31 60 30 48

2013 29 21 39 29

2014 46 45 46 78

2015 54 58 54 72

2016 39 30 25 20

Average

2004-2007 43 23 38 57

2007-2010 -36 -31 -28 -16

2010-2013 31 44 39 51

2013-2016 42 39 41 50



















utilities



39


A) Manufacturing


2005 124 19 66 274 2006 240 137 39 33

2007 74 02 41 221

2008 -45 23 -15 59

2009 05 -191 -218 48

2010 135 111 264 168

2011 -45 18 34 100

2012 -15 -24 -83 -181

2013 -41 37 -22 125

2014 06 07 27 86

2015 65 01 -54 80

2016 -02 04 -27 -91

Average 2005-2007 146 53 49 176

2007-2010 32 -19 10 92

2010-2013 -34 07 -21 20

2013-2016 07 12 -19 50

B) Services


2005 127 16 34 -48

2006 166 75 30 67

2007 13 58 42 29

2008 -16 14 -72 -26

2009 -63 -94 -04 25

2010 54 12 09 05

2011 97 46 65 29

2012 12 74 13 -22

2013 12 30 63 -07

2014 78 89 65 -81

2015 106 70 14 54

2016 16 31 -47 39

Average

2005-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01







Appendix)


40

Frontier firms














firms












Appendix)31


A) By size

2004 2007 2010 2013 2016

20-49 emp 357 327 34 362 329

50-99 emp 401 468 542 486 555

100- emp 293 358 414 42 462

B) By ownership

2004 2007 2010 2013 2016

Domestic 213 194 236 272 289

Foreign 873 955 896 82 821

State 181 211 166 167 263




41

C) By region

2004 2007 2010 2013 2016

Central HU 596 621 652 552 579






Southern

Transdanubia 131 081 188 159 211
















42





















A) Manufacturing

32 1198902 asymp 74


43

B) Services







intensive services












44


A) Manufacturing

B) Services









45









deciles

A) Manufacturing



46

B) Services



























47






sample


















48




















year effects) 2016





49




composition matters














A) Domestic private


50

B) Foreign

C) State


Main sample


51





































52













firms






crisis


(cont)























53


A) Manufacturing

B) Services




intensive services


54


A) Manufacturing

B) Services






55


A) Manufacturing

B) Services






56




45 Conclusions





















57


































58















year level



























59





















60


























61






(2015)

















Appendix)


62






















63














and combinations




1 minus 120591119884119904119894 =120590

120590minus1

120573119871119904+120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119881119860119904119894 (51)

















return to scale





64





















65









tax46

119877(1 + 120591119870119904119894) =120573119870119904

120573119871119904

119871119886119887119888119900119904119905119904119894

119870119904119894 (52)







66





































67












68










industries
















69







A) Age of firms

B) Size of firms



70























Variance

component

Share

of total

Variance

component

Share

of total


cost of capital

2126 100 2443 100







Residual 1830 861 1995 817



53 Conclusions









71


Industry type TFP

level

in

2016

TFP

growth

between

2011 and

2016

Olley-

Pakes

allocative

efficiency

Dispersion

of implicit

sales taxes

Dispersion

of implicit

cost of

capital

Low-tech mfg 5694 0027 0197 111 146



High-tech mfg 6708 0276 0072 107 145




Construction 6411 0082 0023 109 148

Utilities 5949 -0138 0801 093 155

Total services 6598 0212 0055 108 160






























72




across sectors



73

6 REALLOCATION



















this process














2013)

Reallocation

74


2004 2007 2010 2013 2016

Low-tech mfg 152 117 107 105 100



High-tech mfg 49 49 44 39 35





Utilities 40 34 34 33 34

Total services 616 645 676 671 673






A) Manufacturing


75

B) Services


sample







(61)












Reallocation

76















sample









77











+

























(62)

Reallocation

78






calculations
















79




Reallocation

80







81

















2004-2007





2007-2010





2010-2013





2013-2016











Reallocation

82


2004-2007





2007-2010





2010-2013





2010-2016























83















Reallocation

84


Notes Main sample



















85





A) By size

2004 2007 2010 2013 2016

5-9 emp 62 751 862 802 411

10-19 emp 618 626 679 652 297

20-49 emp 607 554 698 648 296

50-99 emp 711 685 792 829 438

100- emp 2005 1839 1559 1489 456

Total 1548 1367 1229 1194 417

B) By ownership

2004 2007 2010 2013 2016

Domestic 66 671 769 614 253

Foreign 989 1011 1139 1577 585

State 6289 5954 4127 2792 7

Total 155 1369 1228 1194 417


2004 2007 2010 2013 2016

Low-tech mfg 1236 1255 1149 906 371



High-tech mfg 427 1392 429 268 1

KIS 2098 737 875 1408 592

LKIS 282 2327 187 1867 365

Construction 258 394 339 515 352

Utilities 297 1031 705 593 756

Total 1553 1372 1229 1194 418

Notes Main sample





Reallocation

86














Notes Main sample

64 Conclusions





economy






87












the problem




















Reallocation

88






Estonia


89
















Section 44)





























90




















2015)58













recent periods60










91


over time





Estimation







1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905

119896

119870

119896=1


119896

119870

119896=1

+ 119883119894119905 + 120572119895(119894) + 휀119894119905









(71)


92




1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905

119897

119897=200320062009


119897 )119863119905119897

119897=2003200620092012

+ 119883119894119905 + 120572119895(119894) + 휀119894119905















1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905

119896119863119905119897

119870

119896=1119897=2003200620092012


119897 )119863119905119897

119897=2003200620092012


119897 )119866119894119905119896

119870

119896=1

119863119905119897

119897=2003200620092012

+ sum 1205734119897119863119905

119897

119897=200320062009

+ 119883119894119905 + 120572119895(119894) + 휀119894119905






Results













(72)

(73)


93




exporter firms


firm group


By size By age
















94




convergence61











Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190

(000556) (000568) (000622) (000629) (000656) (000663)

TFP in t-1 Foreign 00475 00418 00433 00423

(00140) (00142) (00252) (00253)

TFP in t-1 Exporter 00477 00287 00216 00224

(00104) (00106) (00125) (00126)


(00312) (00314)

Foreign 0117 0109 0120 0104

(00121) (00132) (00223) (00226)

Exporter 00240 000260 000401 0000919

(000791) (000845) (000851) (000881)


(00267) (00272)




Observations 29717 29717 30135 30062 29717 29717

R-squared 0060 0072 0056 0073 0060 0072







95



























96






















97



By size By age






98

Estimation results



Variables (1) (2) (3) (4) (5) (6)

TFP in t-1 0105 0102 0105 0107 0107 0108

(000484) (000493) (000539) (000546) (000570) (000575)

TFP in t-1 Foreign

-00369 -00328 -00252 -00224

(00123) (00124) (00217) (00217)

TFP in t-1 Exporter

-00344 -00298 -00250 -00249

(000913) (000932) (00109) (00110)


-0000806 000194

(00271) (00272)

Foreign 000105 -000863 -000786 -0000106

(00112) (00116) (00194) (00196)

Exporter 00586 00635 00653 00672

(000738) (000754) (000777) (000786)

Foreign exporter

-000647 -00123

(00234) (00238)


Industry-region FE

YES YES YES

Firm-level controls

YES YES YES

Log of employees


Observations 31662 31662 32124 32043 31662 31662

R-squared 0035 0049 0038 0051 0037 0049



















99











VARIABLES (1) (2) (3) (4)

TFP in t-1 0112 0110 00875 00863

(000486) (000502) (00133) (00133)

TFP in t-1 Group 2 -00455 -00431 00445 00384

(00128) (00129) (00182) (00183)

TFP in t-1 Group 3 -00745 -00748 000733 000822

(00194) (00196) (00141) (00142)

TFP in t-1 Group 4 -00953 -00957

(00218) (00220)

Group 2 -000596 -000810 -00188 -00212

(00122) (00123) (00141) (00141)

Group 3 -000928 -00157 -00115 -00169

(00199) (00201) (00114) (00115)

Group 4 00328 00280

(00276) (00278)

Industry FE YES YES




Observations 32124 32043 32124 32043

R-squared 0038 0052 0037 0051




73 Entry and exit

Questions





100










Estimation











119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905

119896119873119864119894119905119870119896=1 + 1205732119866119894119905

0119864119894119905 + sum 1205733119896119866119894119905

119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)















interacting 119866119894119905119896119873119864119894119905 119866119894119905




101

Results















(1) (2) (3) (4) (5) (6)


-00532 -00535 -00465 -0214 -0199 -0200

(000906) (000873) (000879) (00102) (000987) (000989)


00269 00212 00232 -0135 -0117 -0119

(000967) (000931) (000936) (000897) (000865) (000865)


00369 00444 00360 -0133 -0114 -0121

(000960) (000926) (000931) (000920) (000889) (000889)

EntrantExitorPeriod

2012-2015

00374 00324 00252 -0171 -0150 -0157

(000971) (000936) (000944) (00107) (00103) (00103)

Period 2003-2006 -0115 -00927 -00711 -00445

(000519) (000501) (000556) (000537)

Period 2006-2009 -0175 -0169 -000972 00122

(000522) (000503) (000537) (000518)

Period 2009-2012 -0116 -0117 -00580 -00528

(000528) (000508) (000543) (000522)


Industry FE YES YES




Observations 166607 166168 166168 158143 157711 157711

R-squared 0327 0380 0380 0332 0385 0387


102




















103

74 Conclusions

























104




























81 Context







74 EC (2018) p 5


105















75 EC (2018) p 4



106






to larger firms

















107









Below 500m HUF 0



Above 100bn HUF 25












108

82 Data


































109








qualified analysis












2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446

2006 573 573 51 131 59 430 111 2008 30 1548

2007 546 546 53 125 60 429 110 1987 33 1634

2008 628 628 45 102 50 350 104 1823 21 1574

2009 527 527 33 72 41 290 99 1879 24 1754

2010 472 472 22 49 32 238 94 1793 22 1968

2011 537 537 14 30 29 212 92 1758 22 2027

2012 374 374 30 68 49 335 88 1643 23 2107

2013 503 503 48 121 42 277 88 1622 25 2094

2014 410 410 106 239 48 320 81 1530 24 2054

2015 512 512 135 311 42 292 80 1544 30 2090

2016 518 518 120 292 37 271 77 1457 23 2022









110









83 General trends






















111


A) Number of firms

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100

2006 7333 1613 687 252 065 049 100

2007 7369 1597 674 244 067 050 100

2008 7410 1571 667 236 067 048 100

2009 7522 1535 614 221 059 048 100

2010 7551 1508 640 201 057 044 100

2011 7591 1493 623 194 057 041 100

2012 7625 1506 568 204 055 042 100

2013 7526 1596 577 207 052 042 100

2014 7380 1684 628 210 056 042 100

2015 7239 1771 661 232 056 041 100

2016 7163 1804 676 252 063 043 100

B) Employment

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000

2006 2034 1475 1269 1025 679 3525 10000

2007 2026 1443 1241 984 669 3644 10000

2008 2020 1391 1138 910 579 3980 10000

2009 2002 1427 1244 870 570 3789 10000

2010 2099 1443 1243 862 577 3731 10000

2011 2144 1458 1119 895 555 3830 10000

2012 2144 1541 1131 906 541 3713 10000

2013 2168 1591 1204 896 555 3650 10000

2014 2104 1644 1236 970 548 3538 10000

2015 2064 1620 1216 1006 588 3570 10000

2016 1999 1620 1216 1006 588 3570 10000

C) Sales

Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

total

2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000

2006 1114 1278 1587 1025 776 4226 10000

2007 1110 1266 1543 956 855 4268 10000

2008 1112 1524 1045 891 531 4906 10000

2009 1104 1641 1073 856 718 4607 10000

2010 1104 1521 1103 930 693 4594 10000

2011 1160 1577 1004 934 624 4714 10000

2012 1146 1647 1003 913 629 4578 10000

2013 1229 1411 1090 945 709 4360 10000

2014 1486 1472 1093 1001 752 4389 10000

2015 1293 1458 1118 985 660 4512 10000

2016 1266 1458 1118 985 660 4512 10000


112























113



retail85

















119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905

















114





119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905

119904119886119897119890119904119894119905









115


A) TFP

B) Gross Margin

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 1769 1548 1793 1883 1584 1495 1735 1554

2005 1839 1571 1858 1878 1504 1666 1794 1584

2006 2040 1658 1842 1879 1599 1660 1956 1653

2007 2220 1720 1875 1940 1664 1702 2104 1712

2008 2192 1792 1872 1992 1522 1739 2096 1728

2009 2084 1719 1864 1967 1599 1527 2006 1630

2010 2096 1749 1867 2102 1664 1535 2024 1682

2011 2172 1765 1862 2003 1759 1558 2084 1728

2012 2203 1740 1828 1913 1969 1541 2102 1733

2013 2243 1734 1806 2018 1999 1794 2128 1790

2014 2363 1789 1817 2149 2022 1869 2223 1855

2015 2390 1803 1885 2165 2215 1987 2245 1928

2016 2379 1799 1911 2172 2403 2170 2237 1948


Size 1-4

emp

5-9

emp

10-19

emp

20-49

emp

50-99

emp

100+

emp

unweighted weighted

2004 651 701 663 677 479 480 658 611

2005 827 757 777 712 526 560 806 644

2006 908 773 777 753 579 797 870 706

2007 807 635 653 704 500 580 764 606

2008 665 588 577 610 422 478 643 525

2009 618 535 538 522 351 339 595 456

2010 669 587 629 626 421 341 650 510

2011 698 617 591 637 410 393 676 542

2012 770 643 599 624 485 404 735 577

2013 735 594 555 631 478 369 697 530

2014 745 578 568 612 446 398 700 531

2015 766 609 625 646 547 452 724 579

2016 759 597 621 632 609 487 715 588

Size 1-4 emp

5-9 emp

10-19 emp

20-49 emp

50-99 emp

100+ emp

unweighted weighted

2004 599 633 637 628 612 623 610 626

2005 610 635 643 628 616 626 619 631

2006 623 646 651 640 630 642 631 643

2007 624 643 650 641 627 643 631 642

2008 625 645 647 640 630 640 631 642

2009 619 641 642 631 619 630 626 630

2010 617 641 643 634 622 631 625 631

2011 621 643 645 637 629 639 628 639

2012 624 646 644 637 629 639 631 644

2013 626 647 642 640 637 642 632 645

2014 628 648 650 645 640 648 634 648

2015 638 658 658 655 652 659 644 659

2016 643 661 665 659 655 661 649 665


116





















117







newspapers












118





















119






















120










regressions

119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905



dummies






121


its shops














(1) (2) (3)

Dependent Change in

number of

shops

New

shops

Closed

shops


(0024) (0014) (0018)


(0064) (0034) (0045)

ln( (average

salesshop)

0056 0034 -0017

(0016) (0010) (0014)

5-9 shops 0176 0001 -0126

(0070) (0036) (0054)

10-49 shops 0231 -0009 -0167

(0067) (0034) (0052)

more than 50 shops 0194 0002 -0109

(0071) (0038) (0055)

Year FE yes yes yes


R-squared 0105 0093 0084







122


119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905




























123


(1) (2) (3) (4) (5) (6)


Period 2004-

2007

2008-

2010

2012-

2015

2004-

2007

2008-

2010

2012-

2015


(0009) (0015) (0087) (0018) (0022) (0063)

foreign-owned -0005 0063 0056 0016 -0014 -0035

(0023) (0045) (0057) (0023) (0055) (0066)

ln sales -0026 -0029 -0057 -0018 -0010 0010

(0006) (0007) (0021) (0007) (0017) (0005)

5-9 shops -0014 -0066 -0111 0061 -0035 -0055

(0026) (0051) (0043) (0046) (0044) (0026)

10-49 shops -0045 -0114 -0149 0046 -0038 -0023

(0024) (0048) (0039) (0044) (0043) (0017)

more than 50

shops

0001 -0077 -0134 0061 -0027 -0016

(0029) (0048) (0040) (0045) (0044) (0022)

Observations 15374 10946 15038 14025 10120 13458

R-squared 0030 0053 0073 0023 0041 0121






firm-level

85 Trade





Schmitt 2016)














124















Importing

























125













product categories



126











127
















128


(1) (2) (3) (4) (5)

Year 2005 2008 2010 2012 2015



(0003) (0003) (0003) (0003) (0003)

Foreign-owned 0249 0196 0224 0238 0217

(0014) (0011) (0012) (0012) (0012)

Ln employees 0082 0082 0075 0073 0088

(0004) (0004) (0004) (0004) (0004)

Constant -0470 -0536 -0464 -0551 -0637

(0027) (0026) (0027) (0028) (0027)

Observations 7467 7977 7400 7122 8308

R-squared 0116 0130 0127 0140 0143



Exporting






after 2012


129












130


sector






(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015


Labour

productivity

0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)

Foreign-owned 0083 0137 0141 0119 0107

(0009) (0011) (0013) (0013) (0012)

Ln employees 0019 0029 0028 0031 0034

(0003) (0004) (0004) (0004) (0004)

Constant -0159 -0277 -0271 -0321 -0317

(0018) (0026) (0027) (0029) (0028)

Observations 7622 7976 7663 7384 8730

R-squared 0028 0045 0041 0041 0036







131











policy changes








powerful test











policy




132


Notes Full sample




133












strategy feasible







policy108

















134
















A) Sales


135

B) Sales per day










119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591

120591 + 120583119894119895 + 120575119905 + 휀119894119895119905

















136

















A) Days


137

B) Sales

C) Sales per day






138





A) Days

B) Sales


139

C) Sales per day























140









A) Sales

B) Sales per day


141






88 Conclusions































Conclusions

142

9 CONCLUSIONS


















levels even further





























143









References

144

REFERENCES




































145



































References

146



































147


Academy of Sciences































Review 105(12) 3660-3703


15(4) 781-805

References

148













Growth 12(1) 1-25















673-709




149



126-138
















50(1) 21-43


















References

150

























Bank Issue 143 1-10








151





Journal 31(9) 969-989

Appendix

152

APPENDIX







































153


































Database112


Appendix

154



A) Market economy



2005 19 74 16 60

2006 93 119 95 97

2007 39 56 49 65

2008 -10 -04 -06 01

2009 -69 -82 -65 -63

2010 11 80 05 60

2011 34 40 31 45

2012 21 01 24 18

2013 30 22 22 22

2014 40 59 36 48

2015 52 49 50 43

2016 20 03 25 12

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Manufacturing



2005 20 114 24 127

2006 114 149 118 137

2007 78 71 86 98

2008 17 -17 32 -11

2009 -133 -117 -120 -87

2010 80 173 85 178

2011 04 18 01 25

2012 -02 -58 07 -38

2013 -12 05 -15 16

2014 -01 27 01 34

2015 30 14 34 19

2016 04 -23 14 -05

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01


155

C) Market services



2005 10 32 06 01

2006 79 90 82 64

2007 24 48 35 44

2008 -21 -03 -19 05

2009 -52 -71 -51 -54

2010 -11 26 -19 -05

2011 43 57 40 57

2012 30 48 31 57

2013 39 29 31 25

2014 46 78 39 55

2015 54 72 52 58

2016 25 20 29 23

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01




Appendix

156


A) Manufacturing


2005 21 -02 -09 144

2006 118 143 58 47

2007 59 43 90 348

2008 -09 79 17 111

2009 -53 -191 -197 -139

2010 80 76 85 130

2011 -22 17 10 153

2012 01 14 -57 -06

2013 -38 20 -38 54

2014 -03 -05 08 33

2015 61 04 -19 132

2016 09 -10 12 91

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01

B) Market Services


2005 127 16 -01 -46

2006 166 75 94 66

2007 13 58 60 16

2008 -16 14 -37 -28

2009 -63 -94 -15 44

2010 54 12 -08 23

2011 97 46 77 -29

2012 12 74 06 -57

2013 12 30 60 -71

2014 78 89 65 -31

2015 106 70 22 12

2016 16 31 -47 37

Average

2004-2007 102 50 35 16

2007-2010 -08 -23 -22 02

2010-2013 40 57 29 02

2013-2016 53 55 24 01




157


A) Manufacturing


2005 172 32 73 300

2006 266 114 54 10

2007 121 52 69 243

2008 -25 -17 -03 126

2009 31 -151 -186 35

2010 135 114 199 207

2011 -33 -10 96 96

2012 03 -34 -32 -226

2013 -35 22 26 253

2014 33 19 53 94

2015 82 -04 -06 102

2016 34 18 08 -110

Average

2004-2007 186 66 65 184

2007-2010 47 -18 03 123

2010-2013 -21 02 24 35

2013-2016 28 14 20 85

B) Services


2005 127 -05 41 -31

2006 166 75 21 54

2007 13 11 25 -36

2008 -16 -19 05 -02

2009 -63 -117 09 04

2010 54 -01 -05 13

2011 97 47 54 13

2012 12 62 19 -47

2013 12 21 62 -44

2014 78 55 64 -39

2015 106 54 07 65

2016 16 49 -60 43

Average

2004-2007 102 27 29 -04

2007-2010 -08 -46 03 05

2010-2013 40 48 24 -01

2013-2016 53 45 18 06



Appendix

158


A) By size

2004 2007 2010 2013 2016

5-9 emp 1049 1051 1043 1096 1045

10-19 emp 954 962 92 904 92

20-49 emp 994 903 939 856 998

50-99 emp 896 1024 1188 1009 1096

100- emp 721 81 839 748 728

B) By ownership

2004 2007 2010 2013 2016

Domestic 833 818 814 824 837

Foreign 2344 2499 2422 2384 2488

State 554 728 81 575 695

C) By region

2004 2007 2010 2013 2016

Central HU 567 568 59 56 549

Northern

Hungary 195 116 19 208 224

Northern

Great Plain 161 178 239 23 249

Southern

Great Plain 137 118 17 258 179

Central

Transdanubia 276 33 332 369 332

Western

Transdanubia 311 283 244 361 444

Southern

Transdanubia 184 201 235 143 181

Notes Main sample


159




Appendix

160



Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168

C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209

D - Electricity gas

steam and AC

6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743

E - Water supply

sewerage waste

6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872

F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023

G - Wholesale and

retail trade

6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303

H - Transportation

and storage

6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985

I - Accommodation

food service

6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250

J - Information and

Communication

6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046

M - Professional


6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075


6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026



161


Year 2001 2005 2010 2015

Industry unweighted

TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP


TFP

weighted

TFP

covariance

B - Mining and

quarrying

3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692

C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109

D - Electricity

gas steam and

AC

6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291

E - Water supply

sewerage waste

6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916

F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436

G - Wholesale and

retail trade

6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260

H - Transportation

and storage

6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776

I -

Accommodation

food service

6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899

J - Information

and

Communication

6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800

M - Professional

Scientific and

Tech Act

6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242

N - Administrative

and support service

6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039


Appendix

162


Year 2001 2005 2010 2015

Industry unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covari

ance

unweigh

ted LP

weighted

LP

covar

iance

unweight

ed LP

weighted

LP

covar

iance


C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796


AC

9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197


8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703

F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096



storage

8364 8475 0110 8300 8525 0224 8194 7698 -

0496

8292 7289 -

1003


service

7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651


8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988


8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406


support service

7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238


163


Year 2001 2005 2010 2015

Industry unweighted

labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity


labour productivity

weighted

labour productivity

covariance

B - Mining and

quarrying

7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358

C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960

D - Electricity gas

steam and AC

9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289

E - Water supply

sewerage waste

8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064

F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159

G - Wholesale and

retail trade

7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037

H - Transportation

and storage

8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022

I - Accommodation

food service

7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363

J - Information and

Communication

7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263

M - Professional

Scientific and Tech

Act

7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431

N - Administrative

and support service

7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216

Appendix

164







capital


C - Manufacturing 5620 1300 0425 0818 12807 1008



F - Construction 6706 0818 0280 0954 21186 1227








165







capital


C - Manufacturing 5919 1173 0497 0890 13439 0998



F - Construction 6794 0744 0155 0947 20440 1099







Appendix

166





taxes



capital


C - Manufacturing 6024 1201 0523 0740 12732 1001



F - Construction 6621 0775 0200 1075 30395 1437







167




taxes


taxes


of capital


capital


C - Manufacturing 6022 1110 0514 0971 11130 1074



F - Construction 6938 0809 0298 0868 28761 1453








Appendix

168





169




Appendix

170





171



2004-2007 2007-2010



C Manufacturing 108 23 48 36 -02 -11 03 05



F Construction 26 04 08 13 -14 -02 -19 07



I Accommodation 68 -07 53 22 -44 00 -37 -07

J ICT 96 10 63 23 29 -24 35 18



2010-2013 2013-2016



C Manufacturing -18 07 -30 05 10 -08 22 -04



F Construction 42 03 26 13 01 06 -16 11



I Accommodation 85 -05 59 32 51 -03 46 08

J ICT 19 -02 11 10 47 -02 38 11




Appendix

172


2004-2007 2007-2010



C Manufacturing 105 20 51 34 06 -14 03 16



F Construction 35 07 12 16 -23 -03 -24 04



I Accommodation 67 -09 50 26 -42 03 -39 -05

J ICT 85 14 39 32 27 -14 21 19



2010-2013 2013-2016



C Manufacturing -12 06 -25 07 06 -04 11 -01



F Construction 45 03 22 20 10 02 -05 12



I Accommodation 81 -04 54 30 51 -02 42 11

J ICT 13 00 00 13 49 10 33 06





173


2004-2007 2007-2010



C Manufacturing 132 34 54 44 08 19 -12 01



F Construction 24 07 10 07 -01 07 -09 01



I Accommodation 59 -03 56 07 -74 -12 -41 -21

J ICT 58 19 80 -40 20 -21 40 00



2010-2013 2013-2016



C Manufacturing 00 14 -21 07 33 16 19 -01



F Construction 40 05 26 10 -03 05 -05 -03



I Accommodation 74 -07 55 26 47 -08 54 02

J ICT 16 -07 11 12 22 -25 42 05



Appendix

174


2004-2007 2007-2010



C Manufacturing 132 32 56 45 16 14 -03 05



F Construction 28 10 14 04 03 06 -14 11



I Accommodation 62 -03 52 12 -65 -09 -43 -13

J ICT 00 -15 49 -34 03 -22 14 12



2010-2013 2013-2016



C Manufacturing 06 13 -15 07 28 12 16 00



F Construction 44 05 26 14 03 02 07 -07



I Accommodation 70 -07 52 25 44 -07 51 01

J ICT 26 07 04 16 17 -20 37 00




175




growth over time



TFP in t-1 Year 2003 -0188 -0203 -0203

(000550) (000558) (000551)

TFP in t-1 Year 2006 -0222 -0238 -0235

(000518) (000525) (000519)

TFP in t-1 Year 2009 -0143 -0159 -0155

(000570) (000579) (000572)

TFP in t-1 Year 2012 -0156 -0172 -0171

(000516) (000524) (000517)

Year 2003 -00313 -00297

(000507) (000510)

Year 2006 -0184 -0183

(000489) (000491)

Year 2009 -00766 -00762

(000492) (000493)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 114200 113900 113900

R-squared 0061 0067 0084

Appendix

176





VARIABLES (1) (2) (3) (4)

TFP in t-1 -0170 -0186 -0213 -0223

(000561) (000578) (00155) (00155)

TFP in t-1 Group 2 00397 00243 -000502 -000776

(00146) (00147) (00213) (00213)

TFP in t-1 Group 3 00793 00652 00725 00600

(00221) (00222) (00164) (00165)

TFP in t-1 Group 4 00753 00666

(00244) (00247)

Group 2 00227 000593 -0000410 0000118

(000940) (000963) (00162) (00162)

Group 3 00216 -000934 00235 00220

(00150) (00154) (00131) (00132)

Group 4 00235 -00351

(00157) (00169)

Industry FE YES YES



Observations 30135 30062 30135 30062

R-squared 0056 0073 0056 0073





177


groups


VARIABLES (1) (2) (3) (4)

Foreign 00476

(00114)


(00213)


(00139)


(00162)


(00243)


(00340)


(00318)


(00381)


(00363)


(00124)



Observations 31642 31642 31642 31274

R-squared 0032 0033 0033 0033





Appendix

178




Firm categories by


VARIABLES (1) (2) (3) (4)


00577 00607 00141 00214

(00151) (00151) (00124) (00124)


00703 0101 00361 00558

(00152) (00152) (00118) (00118)


00338 00306 00450 00406

(00153) (00153) (00122) (00121)


00758 00436 00474 00321

(00146) (00146) (00109) (00109)


00978 00756 00286 000961

(00128) (00130) (000912) (000977)


-00290 -00145 -00592 -00411

(00133) (00135) (000871) (000932)


0114 0116 00502 00457

(00124) (00127) (000824) (000881)


0120 0120 00234 00155

(00126) (00129) (000782) (000835)

Year FE YES YES

Industry FE YES YES

Firm-level controls

YES YES YES YES

Region FE YES YES

Industry-year FE

YES YES

Observations 112374 112374 113900 113900

R-squared 0066 0085 0065 0085


columns (3) and (4)


179



growth over time



TFP in t-1 Year 2003 0113 0113 0113

(000472) (000478) (000475)

TFP in t-1 Year 2006 0120 0120 0119

(000434) (000439) (000437)

TFP in t-1 Year 2009 0109 0109 0107

(000479) (000485) (000482)

TFP in t-1 Year 2012 00982 00958 00956

(000442) (000448) (000445)

Year 2003 -00171 -00125

(000441) (000444)

Year 2006 -0134 -0128

(000422) (000423)

Year 2009 -00899 -00873

(000425) (000426)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 123900 123574 123574

R-squared 0042 0049 0054

Appendix

180





TFP in t-1 Year 2003 0156 0147 0148

(000641) (000647) (000644)

TFP in t-1 Year 2006 0134 0127 0128

(000581) (000587) (000584)

TFP in t-1 Year 2009 0139 0132 0134

(000648) (000655) (000651)

TFP in t-1 Year 2012 0132 0126 0127

(000618) (000624) (000621)

Year 2003 -00765 -00618

(000617) (000619)

Year 2006 -0220 -0211

(000586) (000587)

Year 2009 -0177 -0173

(000591) (000590)

Year FE YES YES

Industry FE YES



Region FE YES


Observations 143011 142638 142638

R-squared 0037 0047 0051


181





VARIABLES (1) (2) (3) (4) (5) (6)

TFP in t-1 0134 0130 0134 0137 0134 0136

(000651) (000660) (000722) (000729) (000764) (000767)

TFP in t-1 Foreign -00109 -00138 00116 000347

(00166) (00167) (00289) (00289)

TFP in t-1 Exporter -00371 -00256 -00304 -00226

(00124) (00126) (00148) (00148)


(00364) (00365)

Foreign -00254 -00351 -0102 -00739

(00151) (00156) (00254) (00256)

Exporter 00998 00982 00940 00889

(00100) (00102) (00106) (00107)


(00312) (00315)




Observations 34980 34980 35564 35473 34980 34980

R-squared 0031 0051 0037 0054 0034 0052

Appendix

182


groups


VARIABLES (1) (2) (3) (4)

Exporter 00876

(000741)


(000788)


(00207)


(000850)


(00159)


(00276)


(00329)


(00230)


(00212)


(000801)



Observations 34418 33909 34418 33989

R-squared 0034 0034 0034 0036






183





VARIABLES (1) (2) (3) (4)


(00129) (00130) (00107) (00107)


(00126) (00127) (00101) (00101)


(00129) (00130) (00104) (00104)


(00126) (00126) (000942) (000942)

Firm group Year 2003 -00601 -00332 000244 00299

(00110) (00113) (000795) (000856)

Firm group Year 2006 -00159 -000559 00640 00786

(00112) (00115) (000752) (000807)

Firm group Year 2009 00404 00249 0111 00882

(00106) (00109) (000714) (000767)

Firm group Year 2012 -00102 -00116 00747 00607

(00110) (00112) (000684) (000735)

Year FE YES YES

Industry FE YES YES


Region FE YES YES


Observations 121954 121954 123574 123574

R-squared 0046 0055 0045 0055


(3) and (4)

Appendix

184

A73 Entry and exit



VARIABLES (1) (2) (3) (4) (5) (6)


-0172 -0186

(00103) (00102) (00112) (00112) (00122) (00121)


0171 0126

(00284) (00281) (00311) (00309) (00213) (00211)

Incumbent Foreign



0279 0232

(00122) (00129) (00123) (00131) (000887) (000926)




YES



YES




groups


VARIABLES (1) (2) (3) (4)

Foreign 0429

(00118)


(00206)


(00146)


(00162)


(00254)


(00363)


(00341)


(00352)


(00356)


(00131)



Observations 38367 38367 38367 37822

R-squared 0350 0361 0353 0356






185






(000924) (000923) (00107) (00106) (00114) (00113)



(000999) (000996) (000917) (000911) (000977) (000971)



(000985) (000983) (000942) (000937) (00101) (00101)



(000998) (000999) (00111) (00110) (00119) (00119)



(00265) (00264) (00264) (00263) (00196) (00197)



(00257) (00257) (00267) (00265) (00183) (00183)



(00279) (00278) (00278) (00277) (00185) (00185)



(00276) (00275) (00307) (00305) (00208) (00208)



(00122) (00124) (00124) (00126) (000943) (000994)



(00120) (00122) (00122) (00124) (000895) (000943)



(00122) (00124) (00119) (00122) (000867) (000915)



(00118) (00120) (00120) (00122) (000827) (000876)













(00108) (00105) (00121) (00118) (00250) (00241)


(00298) (00289) (00280) (00272) (00271) (00261)


(00574) (00556) (00479) (00464) (00179) (00173)


(00720) (00697) (00532) (00517)


(00104) (00101) (00111) (00109) (00170) (00164)


(00170) (00168) (00179) (00176) (00131) (00127)


(00181) (00185) (00188) (00192)




Observations 46160 46034 39020 38916 38459 38357

R-squared 0296 0355 0311 0369 0315 0374





different periods

A8 Chapter 8 Retail


(1) (2) (3) (4) (5) (6)








trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)

trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)

trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)

trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)






Observations 225866 209604 225860 209598 225908 209647

R-squared 0958 0978 0961 0980 0684 0809

Appendix

188


(1) (2) (3) (4) (5) (6)








trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)

trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)

trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)

trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)






Observations 94740 87533 94737 87530 94740 87533

R-squared 0968 0982 0973 0985 0642 0809


(1)

Dependent ln sales

Sunday 0049 (0001)


Friday 0054 (0001)



Tuesday 0060 (0001)

Monday 0048 (0001)

holiday 0008 (0000)

Jan -0169 (0002)

Dec 0138 (0003)

summer 0032 (0002)

date -0000 (0000)

Observations 463345

R-squared 0970

Appendix

190


Free publications

bull one copy








Priced publications





doi 10287333213

ET-0

4-1

7-8

33-E

N-N

productivity differences in hungary and mechanisms of tfp

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