european productivity, lagged adjustments and industry competition mary omahony, fei peng and...

European productivity, lagged adjustments and industry

competition

Mary O’Mahony, Fei Peng and Nikolai Zubanov

University of Birmingham

Presentation Overview

• Context• First Regression Results• Econometric Issues• Industry competition measures – company

accounts• Herfindahl indexes, France and UK• Industry dynamics• Linking the macro and micro

Context• Consider impact of technology shock on output growth

– specific lagged impact of ICT

• What factors can explain differences in speeds of adjustment and long run impacts

• Preliminary Regressions– production function ln(Q) on labour, non-ICT capital and ICT

capital, lagged up to five years – useful for stress testing – Dependent variable, MFP and ICT sole explanatory variable –

difficult to justify

• Panel regressions, fixed effects and time dummies included

• Panel is by country not conventional industry panel• But include some groups of like industries

Regression results (1) AtBindustry: AGRICULTURE, HUNTING, FORESTRY AND FISHINGvariable lag coeff. std. dev.ln(labour input) 0 0.319 0.059 ***ln(non-ICT capital) 0 0.093 0.049 *ln(ICT capital) 0 0.111 0.031 ***

1 -0.005 0.029 2 -0.017 0.028 3 0.012 0.028 4 -0.020 0.030 5 -0.039 0.021 *

other controls: time, industry and country dummiesobs. 301

Regression results (2) Cdependent variable: ln(value added, volume index)industry: MINING AND QUARRYINGvariable lag coeff. std. dev.ln(labour input) 0 0.491 0.135 ***ln(non-ICT capital) 0 1.527 0.202 ***ln(ICT capital) 0 0.008 0.096

1 -0.018 0.119 2 0.004 0.122 3 -0.016 0.122 4 0.035 0.122 5 0.111 0.083


Regression results (3) D1Consumer goods

dependent variable: ln(value added, volume index)industry: FOOD, BEVERAGES, TOBACCO, TEXTILES, LEATHER, FOOTWEAR, WOOD AND CORK, MANNUFACTURING NEC, RECYCLINGvariable lag coeff. std. dev.ln(labour input) 0 0.481 0.027 ***ln(non-ICT capital) 0 0.285 0.032 ***ln(ICT capital) 0 -0.001 0.022

1 -0.007 0.028 2 -0.003 0.024 3 -0.004 0.019 4 -0.027 0.017 5 0.037 0.011 ***


Regression results (4) D2Intermediate goods

dependent variable: ln(value added, volume index)industry: PULP, PAPER, PAPER, PRINTING AND PUBLISHING, CHEMICAL, RUBBER, PLASTICS AND FUEL, OTHER NON-METALLIC MINERALvariable lag coeff. std. dev.ln(labour input) 0 0.846 0.078 ***ln(non-ICT capital) 0 -0.016 0.073 ln(ICT capital) 0 0.021 0.089

1 -0.025 0.138 2 0.027 0.099 3 0.019 0.050 4 0.013 0.045 5 0.008 0.029


Regression results (5) D3Investment goods

dependent variable: ln(value added, volume index)industry: BASIC METALS AND FABRICATED METAL, MACHINERY NEC, ELECTRICAL AND OPTICAL EQUIPMENT, TRANSPORT EQvariable lag coeff. std. dev.ln(labour input) 0 0.775 0.049 ***ln(non-ICT capital) 0 0.621 0.043 ***ln(ICT capital) 0 0.154 0.068 **

1 -0.096 0.110 2 0.044 0.113 3 -0.001 0.110 4 -0.027 0.104 5 0.010 0.063


Regression results (6) E

industry: ELECTRICITY, GAS AND WATER SUPPLYvariable lag coeff. std. dev.ln(labour input) 0 -0.018 0.060 ln(non-ICT capital) 0 0.020 0.068 ln(ICT capital) 0 -0.072 0.062

1 0.117 0.102 2 -0.043 0.103 3 -0.004 0.102 4 -0.039 0.102 5 0.028 0.061


Regression results (7) F

dependent variable: ln(value added, volume index)industry: CONSTRUCTIONvariable lag coeff. std. dev.ln(labour input) 0 0.505 0.052 ***ln(non-ICT capital) 0 0.202 0.056 ***ln(ICT capital) 0 -0.023 0.057

1 -0.008 0.094 2 -0.002 0.092 3 0.025 0.066 4 -0.011 0.021 5 0.005 0.014


Regression results (8) Gdependent variable: ln(value added, volume index)industry: WHOLESALE AND RETAIL TRADEvariable lag coeff. std. dev.ln(labour input) 0 0.546 0.048 ***ln(non-ICT capital) 0 -0.059 0.032 *ln(ICT capital) 0 0.203 0.058 ***

1 -0.121 0.103 2 -0.040 0.103 3 0.015 0.099 4 0.025 0.089 5 -0.021 0.048


Regression results (9) H

dependent variable: ln(value added, volume index)industry: HOTELS AND RESTAURANTSvariable lag coeff. std. dev.ln(labour input) 0 0.518 0.064 ***ln(non-ICT capital) 0 -0.133 0.032 ***ln(ICT capital) 0 0.070 0.043

1 -0.019 0.069 2 0.000 0.066 3 -0.059 0.041 4 0.004 0.013 5 0.018 0.010 *


Regression results (9) Idependent variable: ln(value added, volume index)industry: TRANSPORT AND STORAGE, POST AND TELECOMvariable lag coeff. std. dev.ln(labour input) 0 -0.030 0.054 ln(non-ICT capital) 0 0.487 0.050 ***ln(ICT capital) 0 0.106 0.098

1 -0.116 0.172 2 0.047 0.178 3 -0.039 0.171 4 -0.066 0.159 5 0.125 0.086


Regression results (10) Jdependent variable: ln(value added, volume index)industry: FINANCIAL INTERMEDIATIONvariable lag coeff. std. dev.ln(labour input) 0 0.709 0.119 ***ln(non-ICT capital) 0 -0.250 0.052 ***ln(ICT capital) 0 0.029 0.104

1 0.007 0.198 2 0.044 0.204 3 -0.017 0.195 4 -0.237 0.184 5 0.345 0.094 ***


Regression results (11) Kdependent variable: ln(value added, volume index)industry: RENTING OF MACHINERY&EQ, OTHR BUS. ACTIVITIESvariable lag coeff. std. dev.ln(labour input) 0 0.623 0.038 ***ln(non-ICT capital) 0 -0.003 0.013 ln(ICT capital) 0 0.152 0.077 **

1 -0.090 0.144 2 -0.024 0.144 3 0.030 0.143 4 -0.168 0.135 5 0.192 0.071 ***


Regression results (12) O

dependent variable: ln(value added, volume index)industry: OTHER COMMUNITY, SOCIAL AND PERSONAL SERVICESvariable lag coeff. std. dev.ln(labour input) 0 0.831 0.090 ***ln(non-ICT capital) 0 -0.088 0.035 **ln(ICT capital) 0 0.007 0.080

1 -0.001 0.134 2 -0.035 0.136 3 -0.003 0.130 4 0.004 0.124 5 0.045 0.072


Summary – long run impact ICTMostly positive, some very large e.g. J &KBut warning – not well specified production

functions

AGRICULTURE, HUNTING, FORESTRY AND FISHING 0.040 MINING AND QUARRYING 0.124CONSUMER GOODS -0.006INTERMEDIATE GOODS 0.063INVESTMENT GOODS 0.082 ELECTRICITY, GAS AND WATER SUPPLY -0.013 CONSTRUCTION -0.014 WHOLESALE AND RETAIL TRADE 0.063 HOTELS AND RESTAURANTS 0.014 TRANSPORT AND STORAGE, POST AND TELECOM 0.057 FINANCIAL INTERMEDIATION 0.170RENTING OF MACHINERY&EQ, OTHR BUS. ACTIVITIES 0.092 OTHER COMMUNITY, SOCIAL AND PERSONAL SERVICES 0.017

Summary – long run impact ICTDependent variable = MFP

Not highly correlated with previous estimatesSome wild swings in lag structure

AGRICULTURE, HUNTING, FORESTRY AND FISHING -0.015 MINING AND QUARRYING -0.684CONSUMER GOODS 0.113INTERMEDIATE GOODS 0.240INVESTMENT GOODS 0.010 ELECTRICITY, GAS AND WATER SUPPLY 0.066 CONSTRUCTION 0.038 WHOLESALE AND RETAIL TRADE 0.028 HOTELS AND RESTAURANTS -0.011 TRANSPORT AND STORAGE, POST AND TELECOM 0.080 FINANCIAL INTERMEDIATION 0.066RENTING OF MACHINERY&EQ, OTHR BUS. ACTIVITIES -0.061 OTHER COMMUNITY, SOCIAL AND PERSONAL SERVICES 0.000

Econometric estimation issues• The impact of ICT capital on output is distributed across

many years• We need to allow for dynamic adjustment of output to

inputs• Estimation options available: • Short-run vs. long-run coefficient estimation (like Engle-

Granger two-stage procedure)• One-stage autoregressive distributed lag estimation• estimators available: OLS, fixed-effects, pooled or

weighted mean group• other options: instrumental variables, GMM or system

estimators

Industry competition and dynamics• need to integrate country and industry context into the

model, in particular, market concentration and firm dynamics

• Using AMADEUS database, 250,000 large to medium size companies – very small unincorporated companies not available

• Data from 1996-2005, all EU countries included • estimate concentration rates by country and industry• Cannot use for entry/exit but know when company was

incorporated• So can derive variables such as average age of

companies (sales weighted)• Shares of ‘new’ companies in turnover • First results 133 three digit industries, all manufacturing,

50-52,55,63, 71-74

2.4.2 Market structure – firm Competitiveness and

concentration: Amadeus

UK

1%

2-5%

6-10%

11-20%

21-30%

31%+

2.4.2 Market structure – firm data Competitiveness and concentration: Amadeuspercentage of industries by concentration range

1%

2-5%

6-10%

11-20%

21-30%

31%+

FRANCE

23 18.04.23

Market structure – firm data Competitiveness and concentration:

Amadeus Three digit and two digit industry Herfindahl

indices generated for 2004, calculated as:

The closer this is to 1, the more concentrated the industry

Also can calculate a normalised Herfindahl – useful if comparing companies across countries where there is a possibly of different reporting rates

H* = (H-1/N) / (1- 1/N)

Shown in charts for UK and France

i iSH 2)(

24 18.04.23


Amadeus

0

0.2

0.4

0.6

0.8

1

1.2

0 20 40 60 80 100 120 140

UK

France

Normalised Herfindahl Index, Company accounts, 2004

25 18.04.23


Amadeus

Shows wide variance in index in both countries but again suggests UK more concentrated.

Correlation H (UK, France) = 0.31

Examples - UK highest 283 (manufacture of boilers), only ranked 44 in France

France highest 323 (TV and radio transmitters) – ranked 45 in UK

Both countries show greater concentration in manufacturing

26 18.04.23


Amadeus

NACE UK France Diff NACE UK France Diff151 132 137 -5 201 126 77 49152 52 84 -32 202 5 80 -75153 97 88 9 203 103 29 74154 24 5 19 204 4 124 -120155 80 87 -7 205 105 69 36156 34 70 -36 211 39 96 -57157 19 115 -96 212 106 114 -8158 76 104 -28 221 116 128 -12159 66 107 -41 222 70 111 -41171 43 64 -21 223 49 93 -44172 60 63 -3 232 3 2 1173 71 82 -11 241 65 52 13174 58 133 -75 242 41 59 -18175 98 134 -36 243 9 119 -110176 123 141 -18 244 30 101 -71177 31 22 9 245 7 18 -11182 113 132 -19 246 101 99 2191 111 129 -18 247 95 78 17192 32 10 22 251 82 21 61193 16 83 -67 252 138 56 82

27 18.04.23

2.4.2 Market structure – firm data Competitiveness and concentration:

Amadeus

NACE UK France Diff NACE UK France Diff261 23 76 -53 293 21 38 -17262 48 73 -25 294 78 86 -8263 90 39 51 295 91 116 -25264 12 14 -2 296 28 7 21265 6 20 -14 297 47 71 -24266 94 110 -16 300 55 49 6268 100 31 69 311 85 79 6271 20 53 -33 312 26 40 -14272 15 36 -21 313 29 35 -6273 93 41 52 314 141 66 75274 74 51 23 315 109 48 61275 50 103 -53 316 133 65 68281 86 138 -52 321 40 54 -14282 2 68 -66 322 8 30 -22283 1 33 -32 323 45 1 44284 89 112 -23 331 121 45 76285 75 109 -34 332 99 11 88286 115 81 34 333 11 60 -49291 62 120 -58 334 35 3 32292 124 131 -7 335 140 61 79

28 18.04.23


Amadeus

NACE UK France Diff NACE UK France Diff341 61 25 36 505 54 9 45342 127 126 1 511 42 106 -64343 53 121 -68 512 87 122 -35351 81 13 68 513 136 136 0352 18 6 12 514 112 118 -6353 72 15 57 515 38 139 -101354 10 26 -16 518 119 127 -8355 107 37 70 519 134 46 88361 69 91 -22 521 59 67 -8362 73 44 29 522 22 98 -76363 63 140 -77 523 51 42 9364 46 17 29 524 120 125 -5365 68 27 41 525 117 23 94366 64 12 52 526 77 75 2371 33 92 -59 527 17 24 -7372 25 135 -110 551 27 4 23501 137 113 24 552 56 8 48502 118 43 75 553 92 95 -3503 108 94 14 555 13 58 -45504 14 16 -2

29 18.04.23


Amadeus

NACE UK France Diff NACE UK France Diff631 122 57 65 724 84 108 -24632 102 32 70 725 125 85 40633 110 100 10 731 57 28 29634 129 130 -1 741 139 105 34711 44 62 -18 742 131 123 8712 67 72 -5 743 130 19 111713 104 55 49 744 114 89 25714 37 50 -13 745 135 34 101721 36 47 -11 746 83 90 -7722 128 117 11 747 96 102 -6723 79 97 -18 748 88 74 14

Industry competition and dynamics• Reporting rates may vary across country – can

cross check with industry data, e.g. UK Amadeus covers over 90% employment

• Econometric difficulty is that market concentration data are available for a short period of time

• can regress the industry fixed effects on market concentration or/and

• introduce cross-products of production factors and market concentration

european productivity, lagged adjustments and industry competition mary omahony, fei peng and...

Documents

j slide

c slide

f slide

g slide

atb slide

h slide

micro slide

lag structure slide