ISSN: 1439-2305
Number 189 – February 2014
TFP ESTIMATION AND PRODUCTIVITY
DRIVERS IN THE EUROPEAN UNION
Agnieszka Gehringer, Inmaculada Martínez-Zarzoso, Felicitas
Nowak-Lehmann Danzinger
1
TFP ESTIMATION AND PRODUCTIVITY DRIVERS IN THE EUROPEAN UNION
by
Agnieszka Gehringer a,*
Inmaculada Martínez-Zarzoso a,b
Felicitas Nowak-Lehmann Danzinger a
________________________________________________
a Georg-August-University of Göttingen, Germany
b Universidad Jaume I, Spain
* corresponding author: Platz der Göttinger Sieben 3; 37073 Göttingen; Phone: +40-(0)551 39
3932; E-Mail: [email protected]
2
TFP estimation and productivity drivers in the European Union
Abstract
This paper examines the development and drivers of total factor productivity (TFP) in the
manufacturing sector for a panel of 17 EU countries over the period of 1995-2007. Recent
panel data estimation techniques are used in a twofold approach. First, we estimate
aggregated and sectoral TFP for 17 EU countries by means of the augmented mean group
estimator to control for endogeneity, cross-section dependence and heterogeneous production
technology. Second, we investigate the relative importance of the drivers of predicted TFP,
namely Foreign Direct Investment (FDI), investment in Information and Communication
Technologies (ICT), human capital, R&D, trade openness and rationalization efforts. The
results confirm that rationalization, human capital and ICT are the main drivers of TFP.
Keywords: sectoral TFP, heterogeneous production functions, common dynamic process,
European Union
JEL: C26; F43; O47
1. Introduction
Productivity improvements based on technological progress and human capital
accumulation play an important role in fostering economic growth. The innovation-based
endogenous growth models by Romer (1990), Rivera-Batiz and Romer (1991), Grossman and
Helpman (1991), and Aghion and Howitt (1992, 1998) build upon the contribution of R&D-
based innovative efforts in leading an economic system to the path of long-term development.
The role of human capital in fostering economic growth has also been analyzed within the
framework of the endogenous growth theory by Romer (1986) and Lucas (1998). Human
capital can have a direct effect on growth since it enters the production function, but can also
have an indirect effect. Indeed, thanks to specific creative skills and abilities, human capital
3
facilitates the generation of innovation, and through this channel it contributes to output
growth. More precisely, human capital is strictly complementary with R&D activities in
spurring productivity growth (Autor et al. 1998; Berman et al. 1998; Borensztein et al. 1998;
Redding 1996).
More recently, additional factors, namely, Foreign Direct Investment (FDI), Information
and Communication Technologies (ICT) and trade have been identified as additional drivers
of productivity, and in turn output growth. However, such factors have only rarely been
analyzed in a unified framework (Bengoa-Calvo and Perez 2011; Biatour and Dumont 2011;
Cameron et al. 2005; Marrocu et al. 2012).
We depart from this literature by estimating TFP based on heterogeneous production
functions1 and by focusing on a rich set of TFP drivers suggested in the previous literature.
Additionally, to our knowledge, there has been no attempt to assess the determinants of TFP
at the industry level in the EU as a whole.
The main contribution of this paper to the existing literature is twofold. First, we obtain
estimates of TFP across countries and sectors in the European Union (EU) using
heterogeneous production functions with common trends. To this end, we use sectoral data for
a panel of 17 EU countries over the period 1995 to 2007. Based on our estimations, we
analyze the evolution of TFP over time for each country and sector. Second, we use the
predicted TFP to investigate the drivers of TFP in the EU context. In this context, we consider
the usual factors, namely, human capital, R&D, FDI and ICT, but additionally we explicitly
account for the ongoing global and European integration process and the corresponding
pressure to reduce costs. Regarding the latter, it constitutes a novel factor with which we
enrich this strand of the literature. We label this factor as rationalization efforts and measure
1 It is highly preferable to estimate TFP taking country heterogeneity into account than doing some TFP
accounting based on standard labor and capital input shares (with the conventionally assumed elasticities of 2/3 and 1/3 for labor and capital, respectively) given that we are interested in country-specific TFP differences.
4
them in terms of factor cost savings over time. We conjecture that rationalization efforts
eventually lead to efficiency gains in the production process through which TFP is enhanced.
It is worth noting that our first contribution is also relevant from a methodological point of
view, in using a novel approach to estimate the production functions proposed by Eberhardt
and Teal (2010). This method allows us to account simultaneously for country-specific
factors, as well as for cross-sectional dependence and endogeneity caused by time-varying
factors that are common to all countries.
The main results from the first part of our analysis indicate that there are non negligible
TFP differences between countries and sectors so that the assumption of heterogeneous
production functions is reasonable. Moreover, common factors influencing all countries are
relevant and this generates cross-sectional dependence. The estimation of the determinant of
TFP shows that that rationalization, human capital and ICT are the main drivers of TFP.
The rest of the paper is structured as follows. Section 2 outlines the methodology
employed to estimate sector-level TFP for 17 EU countries. In Section 3, we present the
evolution over time of country-level TFPs for the aggregated manufacturing sector and for 13
sub-sectors, using the estimated time series obtained in Section 2. In Section 4, we first
theoretically discuss and subsequently empirically analyze the determinants of TFP. Finally,
Section 5 provides some concluding remarks and economic policy lessons.
2. Estimating TFP based on heterogeneous production functions
From a methodological point of view, we are interested in measuring productivity,
expressed in terms of increases in TFP. TFP is often called a measure of ignorance, as little is
known about the non-input - often unobservable - determinants of economic growth.2 TFP
could also be defined in a narrow or wide sense (Eberhardt and Teal 2010). TFP can either
stand for output growth due to technological and efficiency improvements (narrow definition)
2 The first to label TFP as a “measure of our ignorance” was Moses Abramovitz (1956: 11) when analysing the causes of economic growth.
5
or due to all sorts of factors, such as a more favorable (i.e. more efficient) resource
endowment, a better investment climate, better functioning institutions or less corruption
(wide definition).3
Our methodology accommodates the wide definition. Indeed, we obtain TFP as a residual
from the estimation of a heterogeneous production function, where we do not account for the
influence on output of factors other than the standard production inputs. We first outline the
general approach (2.1) and later introduce an estimation method that allows for country-
specific parameters and common factors that influence TFP (Section 2.2).
2.1 Estimating TFP based on the value added approach
We follow the value added approach to estimate sector-specific TFP levels over time and
across countries. According to this approach, TFP is obtained as a residual from the valued
added-based Cobb-Douglas production function, in which real value added is used as the
target measure. 4 An alternative would be to use the output-based approach, according to
which TFP is obtained as a residual from the output-based production function and where, in
addition to capital and labor, intermediate inputs (such as raw materials, energy and
intermediate goods and services) are also included as additional determinants of output in the
estimation of the production function. It has been argued that this approach is theoretically
more appropriate, as it permits the explicit consideration of intermediate production factors in
the technologically-driven sector-level growth (Jorgenson and Stiroh 2000). However, some
practical reasons (Hall et al. 2009) and the lack of data on deflated intermediate inputs make
the value added approach more reliable in our particular context.
3 TFP can be further decomposed into technical progress, changes in technical efficiency, changes in allocative efficiency, and scale effects (Kim and Han, 2001). 4 Alternatively, it is possible to calculate TFP as an index in log-levels, given as the difference between the log values of real output/value added and the weighted factors (capital and labor) contributions. There are dedicated studies that investigate the performance of estimation-based versus calculated measures of TFP (for instance, Van Biesebroeck (2007)). This remains outside of the scope of our analysis.
6
It is also worth noting that TFP can be computed at different aggregation levels.5 Most of
the past contributions in the growth accounting framework focused on country-level TFP.
Only recently, an increasing number of studies have been dealing with sector- or firm-level
productivity.6 There are at least two advantages of adopting a more disaggregated approach.
Ex-ante, when estimating TFP it is crucial to avoid a potential aggregation bias which arises
from disregarding heterogeneity across sectors/firms. Ex-post, disaggregated TFP data allows
us to detect how different determinants of TFP might work differently in particular
sectors/firms. Consequently, such differences should be taken into account in formulating the
right policy implications.
Let us consider the standard Cobb-Douglas production function:
M
m imktiktiktiktiktmKL XKLTFPY
1
(1)
where TFPikt measures the contribution of technology to output (Y) in sector i and country k
at time t, coefficients refer to estimated output elasticity of each production factor (L, K and
all the remaining factors, X, with ) Transforming the production function into a log-log model, we obtain:
imktmktiktiiktKiktLiktikt XXXKLTFPY ln...lnlnlnlnlnln 2211 (2)
Finally, the productivity level can be derived as the residual, )(ln iktTFP , by subtracting the
observable input contributions weighted by their corresponding output elasticities from
output:
iktikt YTFP lnln imktmktiktiiktKiktL XXXKL ln...lnlnlnln 2211 (3)
5 Bartelsman (2010) offers an overview of the productivity growth analysis at different levels of aggregation. 6 For a sector-level investigation of the determinants of TFP, see Biatour and Dumont (2011) and for a firm-level analysis, see Brynjolfsson and Hitt (2003).
7
Applying the value added approach and considering that value added (VA) is defined as
value of gross output (Y) minus value generated by inputs (X1 … Xm):
itit YVA lnln mm XXX ln..lnln 2211 (4)
TFP can be derived as residual from an analogous log-log estimation model as in (3):
iktKiktLiktikt KLVAPFT lnˆlnˆlnˆln (5)
Thus, TFP growth )ˆlnˆ(ln 1 iktikt PFTPFT is defined as the value added growth not caused by
an increase in inputs (labor and capital).
In what follows, for the reasons already mentioned, we will follow the value added
approach in order to estimate the labor )( L and capital )( K coefficients – that we assume to
be common at the sector level – and, finally, obtain the logarithmic values of TFP over time
for each country-sector pair included in our analysis.
2.2 Special estimation methodology allowing for heterogeneity
Most econometric estimations of TFP based on production functions consider
homogeneous production functions for all countries, which is an unrealistic and rather
restrictive assumption (Eberhardt and Teal 2010). Given that production technology is
localized and thus geographically heterogeneous, we will include production heterogeneity in
our model to estimate the following equation:
iktiktiKiktiLiikt vKβLβαVA lnlnln (6)
where tiiktikt cfv and where all coefficients ( iKiLi ,, ) are country specific.
We obtain TFP following the estimation approach suggested by Eberhardt and Teal (2010)
called the augmented mean group estimator (AMG). This estimator was developed to provide
a valid alternative to the common correlated mean group estimator (CCEMG) proposed by
8
Pesaran (2006) and Pesaran et al. (2010), which was used to account for unobservable
common factors. 7 Indeed, in the CCEMG approach these factors are treated as disturbance,
without placing any particular interest in their interpretation. Alternatively, in constructing the
AMG estimator, Eberhardt and Teal (2010) account simultaneously for country-specific
factors, as well as for factors common to all countries ( tcf ) that are responsible for
endogeneity and generate cross-section dependence. More precisely, tcf measures common
factors that have an impact on all countries but do so in a country-specific way, i.e. each
country reacts differently to the common factors (measured by elasticity i ). The AMG
procedure is implemented in three steps. First, based on equation (6), a regression model with
year dummies is estimated in first differences and the coefficients of the (differenced) year
dummies are collected. The first stage estimations are performed for each sector separately
based on the following equation:
(7)
where iktu is a well behaved error term with iid N(0; 2
u ), cft representing an estimated cross-
group average of the evolution of unobservable TFP over time, named “common dynamic
process”. The rest of variables have been described above.
Second, the regression model (Equation 6, above) is then augmented with this estimated
common process and with country- specific time trends. This second stage regression is given
by:
iktitikiktiKiktiLkikt wcftrendKLVA lnlnln (8)
7 CCEMG estimator solves the problem of correlation between inputs with unobserved productivity shocks by augmenting the group-specific regression equation with the cross-sectional averages of the dependent and independent variables. Consequently, these averages can account for the unobserved common factor and provide estimates for the heterogeneous impact.
ktt
T
ttktKktLkt uDUMcfKbLbVA
2
lnlnln
9
From the above specification it follows that the production function is influenced by both
country-specific time effects (trendk) and by the dynamic processes that are common for all
countries (such as financial crises or EU enlargement) but affect each country differently.
This differential impact is measured by )( i . Finally, the estimated residual, wikt, is our sector,
country and time specific TFP.
3. TFP development across sectors and EU countries: stylized facts
On a sectoral basis, TFP developed quite smoothly in the period 1995 to 2007 (see Figures
A.1-A.13 in the Appendix A.1). In many sectors, such as chemicals, electrical equipment,
machinery, manufacturing, metal products, non-metallic products, paper, and rubber; TFP
showed on average an upward trend.8 However, such a positive development was interrupted
in a few countries: in chemicals, Greece, Spain, the Czech Republic and Hungary faced
decreases in TFP; in electrical equipment, TFP took a dip in Greece, Italy and Spain after
2002; in machinery, all countries studied did well except for Estonia, Slovakia and Slovenia
which had low TFP levels; in manufacturing n.e.c., we could detect slight drops in TFP in
2002 in all countries, but huge drops in France, Greece, Italy and Spain; in metal products, all
countries did well except for Spain and in non-metallic products the only under-performer
was Belgium.
Sectors with mixed TFP experiences were: food where we observed drops in TFP for
Germany, Greece, Denmark and Hungary; textiles where downward dips in Italy, Spain,
Denmark and Hungary become apparent; transport equipment in which Greece, Italy, Spain
and Denmark experienced problems; wood where Greece, the Netherlands, Spain and
Hungary saw some decreases in TFP.
With regard to the production of coke, we observed a continuous downward trend for all
countries under study. In summary, the countries that quite often experienced TFP decreases
8 The list of sectors with their full names is included in Appendix A.2.
10
were: Greece, Italy, Spain and Hungary; whereas countries for which TFP increased were:
Austria, Belgium, Finland, Germany, the Netherlands, Sweden and the UK. This outcome
confirms that it might be a reasonable in our forthcoming empirical investigation to account
for this differential performance of the core EU countries as opposed to the other more
peripheral EU members. It also makes sense to take this heterogeneity into account due to
obvious differences in the historical experiences of Southern and Eastern EU countries. Thus,
for the purposes of our empirical investigation, we determine three distinct groups of EU
members: core (Austria, Belgium, Denmark, Finland, France, Germany, the Netherlands,
Sweden, the UK), South (Greece, Italy, Portugal, Spain) and East (Czech Republic, Estonia,
Slovakia and Slovenia).
Figure 1, 2 and 3 show the estimated average TFP (in logarithmic terms) for the three
country groups: EU core (Figure 1), EU South (Figure 2) and EU East (Figure 3).
Figure 1. Evolution of estimated lnTFP over the period 1995 to 2007 for EU core
Source: Own estimations.
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
1995 1997 1999 2001 2003 2005 2007
EU core
Austria
Belgium
Denmark
Finland
France
Germany
Netherlands
Sweden
UK
11
Figure 2. Evolution of estimated lnTFP over the period 1995 to 2007 for EU South
Source: Own estimations.
A clear pattern seems to emerge in both EU core and EU South and also when comparing
these two country groups (Figures 1 and 2). Aggregate TFP was moderately increasing in
almost all countries (in Germany, there was a short period between 1997 and 1999 with
decreasing TFP levels) through the entire period under consideration. Regarding the relative
positions, the best performing economies are the core EU countries: Sweden, Denmark,
Germany and France. Conversely, the Southern European countries were lying below the EU-
12 averages, with Greece performing the worst in terms of TFP levels. An exception here was
Italy, which maintained its position among the best performers in the entire period. Also
exceptional, though in the opposite direction, was the below-EU-average performance of
Finland - at least in terms of TFP levels.
In all Eastern European countries, a clear growth pattern could be observed (Figure 3).
Nevertheless, the picture emerging for this group of countries is less homogenous than for the
EU-15. Whereas for Estonia, Slovenia and Slovakia the estimates confirm the expectations
that the productivity levels in the Eastern countries would lie under the levels in the more
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
1995 1997 1999 2001 2003 2005 2007
EU South
Greece
Italy
Spain
12
developed rest of the EU, the estimates for the Czech Republic and Hungary seem to be at
odds with our expectations. A deeper investigation of the sources of these anomalies
confirmed that the reason for the two outliers was most probably statistical (precisely, the
measurement error). More precisely, the ratio between employment and value added was
disproportionally lower for both countries than for the rest of the sample.9
Figure 3. Evolution of estimated lnTFP over the period 1995 to 2007 for EU East
Source: Own estimations.
4. Determinants of TFP
4.1 Theory and evidence on the main transmission channels
The endogenous growth theory suggests R&D activities and human capital as two crucial
factors that determine TFP. These are the most theoretically and empirically investigated
factors that are supposed to determine productivity and, finally, growth.
According to innovation-based growth models (Aghion and Howitt 1992 and 1998; Grossman
and Helpman 1991; Rivera-Batiz and Romer 1991; Romer 1990) R&D activities can produce
9 This ratio was lying on average at about 0.00003 for all the other countries, whereas it assumed values of 0.000003 and 0.0000004 for the Czech Republic and Hungary, respectively.
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
1995 1997 1999 2001 2003 2005 2007
EU East
Czech Rep.
Estonia
Hungary
Slovakia
Slovenia
13
innovations, and consequently, increase output. Such positive productivity increases are likely
to materialize, given the important role played by knowledge externalities, in a knowledge
generation process (Griliches 1979). R&D spillovers can be domestic or international in
nature.10 Cross-border knowledge externalities are mainly transferred through international
trade and FDI (for instance, Coe and Helpman (1995) and Nadiri and Kim (1996)).
Additionally, other channels are also possible, as for instance direct R&D collaboration
between foreign and domestic enterprises, independently of whether related trade or FDI
flows take place. Regarding the R&D activities internal to the firm or to a sector, their
beneficial role in contributing to TFP has been questioned. In particular, the empirical
investigations by Jones and Williams (1998), Comin (2004) and more recently by Gehringer
(2011 and 2012) as well as by Antonelli and Gehringer (2012) suggest that there is no
significant direct channel through which in-house R&D efforts would contribute to innovative
outcomes. The relevant channels seem to be indirect and precisely going through pecuniary
knowledge externalities, stemming from the inter-sectoral and knowledge-intensive relations
(Gehringer, 2011). The effects of the internal R&D activity are hard to capture as the effects
are not only indirect but lagged as innovation reacts with unknown but most probably large
lags to investment in R&D.
Another strand of endogenous growth models emphasizes the role of human capital in
fostering productivity (Lucas 1998; Romer 1986). More precisely, skilled human capital
possesses necessary abilities, not only to become familiar with and efficient in the use of
existing innovations, but also to contribute to the generation of brand new innovations.
Nevertheless, the empirical evidence on the role of human capital on productivity (growth) is
10 To give an example of an empirically confirmed positive relationship between R&D and TFP, Guellec and van Pottelsberghe de la Potterie (2001) investigate the aggregate level data related to 16 OECD countries between 1980 and 1998.
14
inconclusive.11 Merging the two aforementioned approaches, some authors have recognized
the complementary character between R&D activities and human capital in spurring
productivity growth and, thus, indirectly benefitting the general process of growth.12
A careful investigation of the related literature suggests that there might be other factors
influencing TFP. Among them, following the past literature, we consider ICT capital services,
FDI and the trade channel. More precisely, the role of ICT consists in offering a platform, on
which network externalities can operate (Schreyer 2000), and consequently, spur TFP. At the
country-level, O’Mahony and Van Ark (2003) and Basu et al. (2004) for a sample of OECD
countries and Gordon (2000) for the US find that ICT has a significant and positive effect on
aggregate productivity. The role of FDI, both in the host and the home economy, as a source
of TFP has been investigated by a number of authors.13 Regarding the host country, Griffith
et al. (2003) identify two main mechanisms through which inward FDI can generate a positive
productivity impact. First, the entrance of foreign firms into the domestic market may increase
the degree of competition both from inside and from international markets. If such
competition is incentive-increasing, this might spur domestic innovative outcomes. Second,
technology transfer occurring with inward FDI is expected to benefit the receiving economy,
in particular if knowledge externalities positively influence inter-sectoral relations (Keller,
2004). Such a positive productivity-increasing effect is potentially more important for less
developed economies given their larger distance to the international technological frontier and
higher capital returns. However, some authors argue that the developing countries might be
too weak in terms of their absorptive capacity to profit from inward FDI (Aitken and Harrison
1999). Analogously, also the source country might expect to benefit from positive
11 Mankiw et al. (1992), Barro and Lee (2001), and Sianesi and van Reenen (2003) report a positive influence of human capital on productivity growth. On the contrary, Prichett (2001) finds strongly significant but negative influence of education capital growth on TFP. 12 See, for instance, Redding (1996), Autor et al. (1998), Berman et al. (1998), and Borensztein et al. (1998). 13 Aitken and Harrison (1999) and more recently Keller (2009) find positive productivity effects from FDI.
15
productivity effects in case knowledge-base synergies are strong enough to flow from the host
to the source country (Branstetter 2006).
Concerning measurement aspects, both FDI flows and stocks are suitable to investigate
knowledge spillovers. Indeed, whereas the former are supposed to relate to the knowledge
generated and moving between economies in a given year (or other time interval under
consideration), the latter refer more to the progressive accumulation of knowledge over time.
From a methodological point of view, the flows show more fluctuations that prevent us from
capturing the true relationship, whereas the stocks of FDI measure the cumulative amount of
foreign capital, comprising both brand new - potentially novel investment - and older vintages
of capital.14
Also the degree of openness in international trade has been argued to have a positive
effect on productivity (Alcalá and Ciccone 2004; Coe and Helpman 1995; Greenaway and
Kneller 2007; Wagner 2007). As in the case of FDI, trade flows of goods and services might
induce domestic sectors to innovate in order to maintain their competitive position at home
and abroad. The usual way to measure trade openness is in terms of the sum of nominal
imports and exports relative to nominal GDP.15
We include the above described determinants of TFP in our main estimation framework.
Additionally, we analyze the role played by rationalization efforts measuring the failure to
save labor costs. Indeed, TFP might be significantly influenced by a sensible pressure on
factor costs due to increased international competition. Such rationalization efforts are a
driving force of efficiency gains that are due to increased exploitation of production factors.
14 There is a tendency followed in the literature investigating the effects of financial liberalization on growth to prefer the stock rather than flow measures, relating to the general de facto measures of financial liberalization, but also when considered in terms of single indicators, like for instance FDI (Kose et al. 2009). For a recent discussion on the issue, see Gehringer (2013). 15 Alcalá and Ciccone (2004) proposed the use of real openness, defined as the sum of $ exchanged imports and exports relative to GDP in PPP US $. This measure, on the contrary to the traditional measure of trade openness should avoid distortions due to cross-country differences in the relative price of non-tradable goods. Similarly to the studies applying the usual measure of openness (ex. Frankel and Romer (1999) and Alesina et al. (2000)), they arrive at statistically significant and positive influence of trade on aggregate productivity.
16
But next to the beneficial effects of rationalization, there are clearly losers in the process and
thus the net welfare outcome is ambiguous.
Still, the contributions investigating the role played by the aforementioned factors in a
unified framework are scarce. Moreover, little attention has been dedicated so far to the EU
case. The only exception here has been provided by Marrocu et al. (2012) who, nevertheless,
focus exclusively on the role played by agglomeration economies in the EU regions. Some
other authors, instead, were aiming to disentangle the forces determining TFP growth in
single European countries. In particular, Biatour and Dumont (2011) analyze the standard
determinants of industry-level TFP in Belgium for the period 1988-2007 and find that R&D
significantly influences TFP dynamics. Bengoa and Perez (2011) focus on the Spanish regions
and also find a positive impact of R&D activities, but dependant on the nature of funding
(private versus public). Finally, Cameron et al. (2005) investigates the case of the UK and
detects a significant and positive link between R&D expenditures, human capital and the
levels of imports on productivity growth.
4.2 Empirical model
The estimation technique employed to investigate the determinants of TFP (see equation (9))
is the Dynamic Ordinary Least Squares (DOLS) technique that allows us to control for
endogeneity (Stock and Watson 1993; Wooldridge 2009). Endogeneity in the form of
feedback effects or reverse causality between the left hand side and the right hand side
variables creates concerns that have to be dealt with. More precisely, higher TFP might affect
at least some right hand side variables, e.g. higher TFP might attract more FDI, enable more
R&D and promote human capital formation.
The model is specified as:
ktiikt6it6ikt5
ikt4ikt3ikt 2t ik1ikt
EF+HCopen_EXTln
+open_EUln + D&Rln +ICTln +FDIln +=TFPln
w
TF itiik
(9)
17
where the dependent variable is the estimated total factor productivity level for industry i,
country k and at time t (as described in Section 2 above). R&D refers to research and
development expenditure as a percentage of GDP. As proxies for Open we use the percentage
share of the sum of imports and exports over GDP. Here we distinguish between the internal
(within EU) trade openness and external trade openness (with respect to non-EU countries).
HC stands for the human capital that in our case is proxied by the percentage share of persons
with secondary education. EF measures the efforts to improve efficiency and is obtained as
the difference between the current labor cost share (in the period 1995-2007) from the average
labor cost share16 in the period 1988-1994. If the term is positive, this means that the sector
has failed to improve efficiency, whereas the negative sign of the term would indicate
successful efficiency efforts. Thus we expect the negative sign of the estimated coefficient to
enhance sector-level TFP.
Ft denotes year dummies and T is a country-specific time trend. The error term (wikt) is
assumed to be well behaved.
The DOLS procedure controls for endogeneity of all explanatory variables by inserting
leads and lags of the changes of all right hand side variables. DOLS turns out to be a very
powerful estimation technique according to Saikkonen (1991) and Stock and Watson (1993).
Within this estimation framework, standard errors are corrected for heteroscedasticity and
cross-section correlation. It can be shown that by decomposing the error term and inserting
the leads and lags of the right-hand side variables in first differences, the explanatory
variables become (super-) exogenous and the regression results thus become unbiased
(Wooldridge, 2009). The baseline regression, which does not control for endogeneity and
which reflects a situation whereby all adjustments have come to an end, is given by equation
(9) above. Within equation (9) wikt is the iid-N error term with the properties of the classical
16
The average labor cost share is computed for each country and each sector within a country. Thus, it indicates
rationalization efforts that prevailed in a certain sector in a specific country.
18
linear regression model. Controlling for endogeneity requires the decomposition of the error
term wikt into the endogenous changes of the right-hand side variables, which are correlated
with wikt (the changes in the variables) and the exogenous part of the error term ikt ;
with
iktpikt
pp
pikt
pppikt
pppikt
pp
pikt
pppikt
pppikt
ppikt
EFb
HCbEXTopenbEUopenb
DRbICTbFDIb
7
654
321
_ln_ln
&lnlnln w
(10)
Inserting equation (10) into equation (9) leads to the following equation (11) in which all
explanatory variables from the baseline model can be considered as exogenous:
kti7
6543
21ikt6it6ikt5
ikt4ikt3ikt 2 tik1ikt
_ln_ln&ln
lnlnEF+HCopen_EXT ln
+open_EU ln+ D&R ln+ICT ln +FDI ln+=TFP ln
pikt
p
p
pikt
p
ppikt
p
ppikt
p
ppikt
p
p
pikt
p
ppikt
p
p
tik
EFb
HCbEXTopenbEUopenbDRb
ICTbFDIb
TF
(11)
with ik representing country and sector fixed effects and indicating that the variables are
in first differences; the error term ikt should fulfill the requirements of the classical linear
regression model. LnICT; LnFDI, LnR&D, LnOPEN; HC and EF become exogenous and the
coefficients 1 , 2 , 3 ,
4 , 5 and 6 follow a t-distribution. This property allows us to
draw statistical inferences on the impact of these variables on TFP.17
Omitted variables
Application of the DOLS procedure requires that the series are non-stationary and have to
stand in a long-run relationship, i.e. they have to be systematically related over time. This
second characteristic is called ‘co-integration’. In Appendix A3 and A4 we provide evidence
that both requirements have been fulfilled. Having found cointegration (see Table A4 in the
17
Coefficients b1, b2, b3, b4, b5, and b6 belong to the endogenous part of the explanatory variables and do not
follow a t-distribution. Since we are not interested in the influence of these ‘differenced variables’ on TFP, their coefficients will not be reported.
19
Appendix), we can be sure that we do not estimate spurious relationships and that omitted
variables (which are lumped together in the error term) do not systematically influence the
long-run relationship between TFP and the right hand side variables. Omitted variables could
be all sorts of variables, such as specific policies (for instance, industrial policies that are
present in certain countries and in certain sectors, structural policies at a national and at EU
level) but also additional human capital variables (e.g. on the job training, dual education etc.
or the number of engineers per 1000 inhabitants). A characteristic of cointegration is
stationarity of the error term that becomes I(0). An I(0) variable which oscillates around a
constant mean is statistically not able to systematically influence the non-stationary(I(I)) TFP
variable (see Appendix figures A1-A13) and therefore it can be concluded that omitted
variables do not affect and bias our results. In short, the residuals that capture the omitted
variables and TFP are not correlated and therefore, it suggests that the omitted variables
cannot impact TFP. Nonetheless, the error term might still contain some unexpected
events/shocks.18 In the traditional panel data literature, it has become very common to work
with time fixed effects (time dummies). They are intended to proxy these unquantifiable
events, which are assumed to be identical for all countries in the sample but change over time.
4.3 Empirical results and interpretation
The main determinants of TFP are estimated according to specification (11) using sectoral
data over the period 1995 to 2007 for the above mentioned 17 countries and their 13 sectors.
As the explanatory variables are in different dimensions, we compute standardized beta
coefficients for the determinants that turn out to be significant. This way we can identify the
most important drivers of TFP and make statements concerning their relative importance.
The main results are presented in Table 1. The first column shows the estimates obtained
using country and sector fixed effects and year dummies. The second column uses country
18 Our findings from the cointegration test tell us that these shocks are only of a temporary nature.
20
and sector fixed effects and a time trend. The results are quite similar so that a joint
interpretation of the results is possible.
Table 1. Determinants of estimated TFP
DOLS with country and sector fixed effects and year dummies
DOLS with country and sector fixed effects and a time trend
Standardized beta coefficients
lnFDI -0.02 -0.01 ---
(-0.87) (-1.03)
lnICT 0.06 0.07 0.07***
(2.40) (3.19)
lnR&D 0.02 0.02 ---
(0.51) (0.42)
lnOPEN_EU 0.18 0.21 ---
(0.66) (0.90)
lnOPEN_EXT -0.56 -0.37 -0.25**
(-2.44) (-1.68)
HC 0.05 0.05 0.69***
(2.90) (2.73)
EF (lack of efficiency efforts) -0.006 -0.006 -0.11***
(-3.38) (-3.37)
Constant 2.83 -171.25
(3.52) (-4.69)
Country and sector fixed effects Yes Yes
Time fixed effects Yes No
Time trend No Yes
N 451 451
R-squared 0.99 0.99
R-squared adjusted 0.98 0.98
Note: t-values in parentheses. The coefficients of the variables in first differences (unlagged, lagged, and the
leads) are not reported in this table, nor are the country and sector fixed effects, the time fixed effects or the time
trend.
Focusing on the specific average effect of the explanatory variables, human capital has a
positive and significant impact on TFP and is most influential. Its standardized beta
coefficient of 0.69 indicates that a one standard deviation increase in human capital leads to a
0.69 increase in lnTFP’s standard deviation. This underlines the importance of secondary
education as a minimum requirement for increased productivity. It would have been nice to
empirically test for the role of dual education and on the job training. However, lack of data
21
makes this sort of analysis unfeasible and the findings of cointegration furthermore show that
these omitted variables would not change the results obtained.
Surprisingly, openness with respect to non-EU countries has a negative and significant
impact on TFP, being -0.25. To put it differently, fewer imports incoming from these
countries and a smaller amount of exports directed into these countries might be responsible
for higher levels of TFP. In other words, we would expect that higher openness within the EU
(more sectoral trade in the EU) could have a positive impact on TFP. And indeed, openness
within the EU pushes up productivity, although its impact is not significant.
As expected, the efficiency variable (lack of efficiency) reports a negative and significant
sign of the coefficient, with the standardized beta coefficient being -0.11. This means that
failure to rationalize the production process (by not reducing the labor cost share compared to
the reference period)19 leads to a reduction of TFP. These labor cost savings are indicative of
the competitive pressure the manufacturing industry is exposed to. The pressure to reduce
labor costs forces companies to increase not only their cost efficiency but also their
organizational and its managerial efficiency. So, one can think of this rationalization term as a
catch-all variable for efforts to increase efficiency.
Larger use of information and communication technologies leads to an improvement in
TFP. The standardized beta coefficient is 0.07. This shows that ICT is a business-related
service for the manufacturing production and other activities which explains, at least partly,
the observed increases in productivity in the last two decades.
All other variables (FDI, R&D and openness with respect to EU countries) do not have a
significant impact on TFP. This is somewhat unexpected as theory indicated several channels
through which FDI, R&D and within EU openness could operate and increase productivity.
As the role of FDI, R&D and openness might vary strongly from sector to sector and from
country to country, their influence in the sample we are looking at does not come out. The
19 The reference period is 1988 to 1994.
22
significant impact of FDI, R&D and openness in some sectors and countries is cancelled out
by the insignificant impact of these factors in other sectors. E.g. R&D is much more important
in certain sectors, such as coke and petroleum refinery, electrical equipment, transport
equipment and machinery; than in other sectors, such as food, paper, rubber, wood, and
textiles.20 As for FDI, it is more important in the Eastern European countries and in small
countries, such as Austria, Belgium, the Netherlands; than in large countries, such as
Germany, France and Italy.21 The same applies to the role of openness which is much more
relevant in small economies.22
5. Economic policy lessons and conclusions
Differences in the TFP performance are common not only across countries but also
between sectors. In this paper, we have presented new estimates of TFP using a value added
approach and sectoral data for 17 EU countries over recent years. In an econometric
estimation setting, we have also searched for indicators affecting our estimated TFP. When
estimating TFP, we have used a recently proposed estimation technique, namely the
augmented mean group estimator, which considers common dynamic factors and specific
time-varying factors as important components of value added and TFP. When searching for
the determinants of TFP, we have used Dynamic Ordinary Least Squares (DOLS), a technique
whose specific strengths are the elimination of endogeneity and its ability to control for
heteroscedasticity, autocorrelation and cross-section correlation by means of robust standard
errors.
20 Sectoral R&D expenditures in percentage of the sectoral output range between the maximum of 6.9% and 6.4% in the sector of coke and petroleum refinery and in electrical equipment, respectively, and the minimum of 0.08% in the wood sector. 21 FDI in percentage of GDP amounted for an average of 5.5% in the Eastern EU members, 12.2% in Belgium, 8.8% in Austria, 7.2% in the Netherlands, 2.7% in France and Germany and 1% in Italy. 22 The most open to international trade is Belgium, with trade openness of 153%, followed by small Eastern EU countries, namely, Slovakia (133%), Estonia (121%) and Hungary (120%). The least open is Greece (31%), Italy, Spain and the UK (each with the international trade exposure of 41%).
23
The main results show that TFP varies across sectors and countries and over time and that
these variations are mainly explained by factors common to all countries: human capital,
trade openness, rationalization efforts, and the use of information and communication
technologies.
The main policy recommendation is that countries aiming at improving TFP and therefore,
their economic performance in the global economy, should favor specific policies that
enhance human capital formation, widen the use of ICTs and control labor costs.
Consequently, all policies that promote competitiveness go hand in hand with improvements
in TFP.
Due to data constraints regarding our time and country dimensions, we leave for further
research the study of the failure or success of related policies, namely EU regional policies
and also the analysis of other unexplored determinants of TFP in the EU. In particular, the
potential impact of between sector-reallocations on TFP levels - as a factor permitting the
transference of productive resources from one sector to another – as well as specific measures
of sectoral competitiveness, such as industrial policy, which could be expected to exercise an
important impact on TFP.
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Appendix A.1:
Sectoral TFP (1995-2007)
Figure A.1: TFP development in the chemicals sector
Note: The country codes valid also for figures A.1 to A.13 : 1_Austria; 2_Belgium;
3_Finland; 4_France; 5_Germany; 6_Greece; 7_Italy; 8_Netherlands; 9_Spain; 10_Denmark;
11_Sweden; 12_UK.
12.8
13.0
13.2
13.4
13.6
13.8
1996 1998 2000 2002 2004 2006
austria
13.25
13.30
13.35
13.40
13.45
13.50
1996 1998 2000 2002 2004 2006
belgium
12.8
12.9
13.0
13.1
13.2
13.3
1996 1998 2000 2002 2004 2006
finland
13.5
13.6
13.7
13.8
13.9
14.0
1996 1998 2000 2002 2004 2006
france
13.0
13.1
13.2
13.3
13.4
13.5
13.6
1996 1998 2000 2002 2004 2006
germany
12.4
12.6
12.8
13.0
13.2
1996 1998 2000 2002 2004 2006
greece
12.92
12.94
12.96
12.98
13.00
13.02
13.04
1996 1998 2000 2002 2004 2006
italy
13.2
13.4
13.6
13.8
14.0
1996 1998 2000 2002 2004 2006
netherlands
12.96
12.98
13.00
13.02
13.04
13.06
1996 1998 2000 2002 2004 2006
spain
14.2
14.4
14.6
14.8
15.0
1996 1998 2000 2002 2004 2006
denmark
15.0
15.2
15.4
15.6
15.8
1996 1998 2000 2002 2004 2006
sweden
12.7
12.8
12.9
13.0
13.1
13.2
13.3
1996 1998 2000 2002 2004 2006
uk
14.4
14.6
14.8
15.0
15.2
1996 1998 2000 2002 2004 2006
czech
11.6
11.8
12.0
12.2
12.4
12.6
1996 1998 2000 2002 2004 2006
estonia
16.0
16.1
16.2
16.3
16.4
16.5
1996 1998 2000 2002 2004 2006
hungary
11.8
12.0
12.2
12.4
12.6
12.8
13.0
1996 1998 2000 2002 2004 2006
slovak
12.2
12.4
12.6
12.8
13.0
13.2
1996 1998 2000 2002 2004 2006
slovenia
LTFP_CHEM
30
Figure A.2: TFP development in the coke sector
Figure A.3: TFP development in the electrical equipment sector
-17.4
-17.2
-17.0
-16.8
-16.6
1996 1998 2000 2002 2004 2006
austria
-19.0
-18.8
-18.6
-18.4
-18.2
-18.0
-17.8
1996 1998 2000 2002 2004 2006
belgium
-19.00
-18.75
-18.50
-18.25
-18.00
-17.75
-17.50
1996 1998 2000 2002 2004 2006
finland
-19.8
-19.7
-19.6
-19.5
-19.4
-19.3
-19.2
1996 1998 2000 2002 2004 2006
france
-21.5
-21.0
-20.5
-20.0
-19.5
-19.0
-18.5
1996 1998 2000 2002 2004 2006
germany
-20
-18
-16
-14
-12
-10
-8
1996 1998 2000 2002 2004 2006
greece
-21.5
-21.0
-20.5
-20.0
-19.5
-19.0
1996 1998 2000 2002 2004 2006
i taly
-19.4
-19.2
-19.0
-18.8
-18.6
-18.4
1996 1998 2000 2002 2004 2006
netherlands
-20.2
-20.0
-19.8
-19.6
-19.4
-19.2
1996 1998 2000 2002 2004 2006
spain
-17.8
-17.6
-17.4
-17.2
-17.0
-16.8
-16.6
1996 1998 2000 2002 2004 2006
sweden
-21.2
-21.0
-20.8
-20.6
-20.4
1996 1998 2000 2002 2004 2006
uk
-23
-22
-21
-20
-19
-18
1996 1998 2000 2002 2004 2006
czech
-17
-16
-15
-14
-13
-12
1996 1998 2000 2002 2004 2006
estonia
-24.0
-23.6
-23.2
-22.8
-22.4
1996 1998 2000 2002 2004 2006
hungary
-19.6
-19.2
-18.8
-18.4
-18.0
-17.6
1996 1998 2000 2002 2004 2006
s lovak
-19
-18
-17
-16
-15
-14
1996 1998 2000 2002 2004 2006
s lovenia
LTFP_COKE
10.3
10.4
10.5
10.6
10.7
10.8
1996 1998 2000 2002 2004 2006
austria
9.9
10.0
10.1
10.2
10.3
10.4
1996 1998 2000 2002 2004 2006
belgium
10.4
10.8
11.2
11.6
12.0
12.4
1996 1998 2000 2002 2004 2006
finland
9.8
10.0
10.2
10.4
10.6
10.8
1996 1998 2000 2002 2004 2006
france
9.8
10.0
10.2
10.4
10.6
10.8
11.0
1996 1998 2000 2002 2004 2006
germany
9.6
9.8
10.0
10.2
10.4
1996 1998 2000 2002 2004 2006
greece
9.94
9.96
9.98
10.00
10.02
10.04
10.06
1996 1998 2000 2002 2004 2006
italy
9.9
10.0
10.1
10.2
10.3
10.4
1996 1998 2000 2002 2004 2006
netherlands
9.90
9.95
10.00
10.05
10.10
1996 1998 2000 2002 2004 2006
spain
11.6
11.7
11.8
11.9
12.0
12.1
1996 1998 2000 2002 2004 2006
denmark
10.8
11.2
11.6
12.0
12.4
12.8
13.2
13.6
1996 1998 2000 2002 2004 2006
sweden
9.8
9.9
10.0
10.1
10.2
10.3
1996 1998 2000 2002 2004 2006
uk
11.0
11.2
11.4
11.6
11.8
12.0
1996 1998 2000 2002 2004 2006
czech
8.8
9.2
9.6
10.0
10.4
1996 1998 2000 2002 2004 2006
estonia
11.0
11.5
12.0
12.5
13.0
13.5
1996 1998 2000 2002 2004 2006
hungary
8.8
9.2
9.6
10.0
10.4
1996 1998 2000 2002 2004 2006
slovak
9.6
9.8
10.0
10.2
10.4
1996 1998 2000 2002 2004 2006
slovenia
LTFP_ELEC
31
Figure A.4: TFP development in the food sector
Figure A.5: TFP development in the machinery sector
34.8
34.9
35.0
35.1
35.2
1996 1998 2000 2002 2004 2006
austria
35.55
35.60
35.65
35.70
35.75
35.80
35.85
1996 1998 2000 2002 2004 2006
belgium
33.8
33.9
34.0
34.1
34.2
34.3
1996 1998 2000 2002 2004 2006
finland
38.16
38.20
38.24
38.28
38.32
1996 1998 2000 2002 2004 2006
france
38.55
38.60
38.65
38.70
38.75
1996 1998 2000 2002 2004 2006
germany
34.1
34.2
34.3
34.4
34.5
34.6
1996 1998 2000 2002 2004 2006
greece
37.80
37.85
37.90
37.95
38.00
38.05
38.10
1996 1998 2000 2002 2004 2006
italy
36.10
36.15
36.20
36.25
36.30
1996 1998 2000 2002 2004 2006
netherlands
37.1
37.2
37.3
37.4
37.5
37.6
1996 1998 2000 2002 2004 2006
spain
37.85
37.90
37.95
38.00
38.05
38.10
1996 1998 2000 2002 2004 2006
denmark
37.55
37.60
37.65
37.70
37.75
37.80
1996 1998 2000 2002 2004 2006
sweden
37.52
37.54
37.56
37.58
37.60
37.62
37.64
1996 1998 2000 2002 2004 2006
uk
39.2
39.3
39.4
39.5
39.6
39.7
1996 1998 2000 2002 2004 2006
czech
29.8
29.9
30.0
30.1
30.2
30.3
30.4
1996 1998 2000 2002 2004 2006
estonia
41.6
41.8
42.0
42.2
42.4
1996 1998 2000 2002 2004 2006
hungary
32.4
32.6
32.8
33.0
33.2
33.4
1996 1998 2000 2002 2004 2006
slovak
30.8
30.9
31.0
31.1
31.2
1996 1998 2000 2002 2004 2006
slovenia
LTFP_FOOD
27.2
27.4
27.6
27.8
28.0
1996 1998 2000 2002 2004 2006
austria
27.3
27.4
27.5
27.6
27.7
27.8
1996 1998 2000 2002 2004 2006
belgium
27.1
27.2
27.3
27.4
27.5
27.6
27.7
1996 1998 2000 2002 2004 2006
finland
27.4
27.6
27.8
28.0
28.2
1996 1998 2000 2002 2004 2006
france
28.70
28.75
28.80
28.85
28.90
1996 1998 2000 2002 2004 2006
germany
24.8
25.2
25.6
26.0
26.4
1996 1998 2000 2002 2004 2006
greece
28.56
28.60
28.64
28.68
28.72
1996 1998 2000 2002 2004 2006
italy
26.8
27.0
27.2
27.4
27.6
27.8
1996 1998 2000 2002 2004 2006
netherlands
27.1
27.2
27.3
27.4
27.5
1996 1998 2000 2002 2004 2006
spain
30.1
30.2
30.3
30.4
30.5
1996 1998 2000 2002 2004 2006
denmark
30.2
30.4
30.6
30.8
31.0
1996 1998 2000 2002 2004 2006
sweden
27.6
27.7
27.8
27.9
28.0
1996 1998 2000 2002 2004 2006
uk
30.0
30.4
30.8
31.2
31.6
1996 1998 2000 2002 2004 2006
czech
21
22
23
24
25
1996 1998 2000 2002 2004 2006
estonia
32.0
32.5
33.0
33.5
34.0
1996 1998 2000 2002 2004 2006
hungary
24.4
24.8
25.2
25.6
26.0
26.4
1996 1998 2000 2002 2004 2006
slovak
24.0
24.4
24.8
25.2
25.6
26.0
1996 1998 2000 2002 2004 2006
slovenia
LTFP_MACH
32
Figure A.6: TFP development in the manufacturing n.e.c. sector
Figure A.7: TFP development in the metal sector
14.8
14.9
15.0
15.1
15.2
15.3
15.4
1996 1998 2000 2002 2004 2006
austria
14.8
14.9
15.0
15.1
15.2
15.3
1996 1998 2000 2002 2004 2006
belgium
15.0
15.1
15.2
15.3
15.4
1996 1998 2000 2002 2004 2006
finland
14.70
14.75
14.80
14.85
14.90
1996 1998 2000 2002 2004 2006
france
14.50
14.55
14.60
14.65
14.70
14.75
1996 1998 2000 2002 2004 2006
germany
13.9
14.0
14.1
14.2
14.3
14.4
14.5
1996 1998 2000 2002 2004 2006
greece
14.40
14.44
14.48
14.52
14.56
1996 1998 2000 2002 2004 2006
italy
14.48
14.52
14.56
14.60
14.64
14.68
1996 1998 2000 2002 2004 2006
netherlands
14.24
14.26
14.28
14.30
14.32
14.34
1996 1998 2000 2002 2004 2006
spain
17.0
17.1
17.2
17.3
17.4
1996 1998 2000 2002 2004 2006
denmark
16.4
16.6
16.8
17.0
17.2
1996 1998 2000 2002 2004 2006
sweden
14.3
14.4
14.5
14.6
14.7
1996 1998 2000 2002 2004 2006
uk
16.2
16.4
16.6
16.8
17.0
1996 1998 2000 2002 2004 2006
czech
12.8
13.2
13.6
14.0
14.4
1996 1998 2000 2002 2004 2006
estonia
18.1
18.2
18.3
18.4
18.5
18.6
18.7
1996 1998 2000 2002 2004 2006
hungary
13.5
14.0
14.5
15.0
15.5
1996 1998 2000 2002 2004 2006
slovak
13.8
14.0
14.2
14.4
14.6
14.8
1996 1998 2000 2002 2004 2006
slovenia
LTFP_MANU
20.1
20.2
20.3
20.4
20.5
1996 1998 2000 2002 2004 2006
austria
20.2
20.3
20.4
20.5
20.6
1996 1998 2000 2002 2004 2006
belgium
20.2
20.3
20.4
20.5
20.6
20.7
1996 1998 2000 2002 2004 2006
finland
20.20
20.24
20.28
20.32
20.36
1996 1998 2000 2002 2004 2006
france
20.0
20.1
20.2
20.3
20.4
1996 1998 2000 2002 2004 2006
germany
19.4
19.6
19.8
20.0
20.2
20.4
1996 1998 2000 2002 2004 2006
greece
20.06
20.08
20.10
20.12
20.14
20.16
1996 1998 2000 2002 2004 2006
italy
20.1
20.2
20.3
20.4
20.5
20.6
1996 1998 2000 2002 2004 2006
netherlands
20.00
20.04
20.08
20.12
20.16
1996 1998 2000 2002 2004 2006
spain
22.10
22.15
22.20
22.25
22.30
1996 1998 2000 2002 2004 2006
denmark
22.45
22.50
22.55
22.60
22.65
22.70
22.75
1996 1998 2000 2002 2004 2006
sweden
19.5
19.6
19.7
19.8
19.9
20.0
20.1
1996 1998 2000 2002 2004 2006
uk
22.12
22.16
22.20
22.24
22.28
22.32
1996 1998 2000 2002 2004 2006
czech
18.0
18.2
18.4
18.6
18.8
19.0
1996 1998 2000 2002 2004 2006
estonia
23.8
24.0
24.2
24.4
24.6
1996 1998 2000 2002 2004 2006
hungary
18.4
18.8
19.2
19.6
20.0
1996 1998 2000 2002 2004 2006
slovak
18.6
18.8
19.0
19.2
19.4
19.6
1996 1998 2000 2002 2004 2006
slovenia
LTFP_MET
33
Figure A.8: TFP development in the other non-metallic minerals sector
Figure A.9: TFP development in the paper & pulp sector
17.05
17.10
17.15
17.20
17.25
17.30
17.35
1996 1998 2000 2002 2004 2006
austria
17.20
17.22
17.24
17.26
17.28
17.30
1996 1998 2000 2002 2004 2006
belgium
16.8
16.9
17.0
17.1
17.2
17.3
1996 1998 2000 2002 2004 2006
finland
17.25
17.30
17.35
17.40
17.45
17.50
1996 1998 2000 2002 2004 2006
france
17.3
17.4
17.5
17.6
17.7
1996 1998 2000 2002 2004 2006
germany
16.4
16.5
16.6
16.7
16.8
16.9
17.0
1996 1998 2000 2002 2004 2006
greece
17.16
17.20
17.24
17.28
17.32
1996 1998 2000 2002 2004 2006
italy
17.0
17.1
17.2
17.3
17.4
17.5
1996 1998 2000 2002 2004 2006
netherlands
17.00
17.05
17.10
17.15
17.20
17.25
1996 1998 2000 2002 2004 2006
spain
18.7
18.8
18.9
19.0
19.1
1996 1998 2000 2002 2004 2006
denmark
18.6
18.8
19.0
19.2
19.4
1996 1998 2000 2002 2004 2006
sweden
16.7
16.8
16.9
17.0
17.1
17.2
17.3
1996 1998 2000 2002 2004 2006
uk
18.8
19.0
19.2
19.4
19.6
19.8
1996 1998 2000 2002 2004 2006
czech
14.4
14.8
15.2
15.6
16.0
16.4
1996 1998 2000 2002 2004 2006
estonia
20.6
20.8
21.0
21.2
21.4
21.6
1996 1998 2000 2002 2004 2006
hungary
15.2
15.6
16.0
16.4
16.8
1996 1998 2000 2002 2004 2006
slovak
15.4
15.6
15.8
16.0
16.2
16.4
1996 1998 2000 2002 2004 2006
slovenia
LTFP_ONM
29.4
29.6
29.8
30.0
30.2
1996 1998 2000 2002 2004 2006
austria
29.7
29.8
29.9
30.0
30.1
30.2
1996 1998 2000 2002 2004 2006
belgium
30.5
30.6
30.7
30.8
30.9
1996 1998 2000 2002 2004 2006
finland
30.90
30.95
31.00
31.05
31.10
31.15
1996 1998 2000 2002 2004 2006
france
31.76
31.80
31.84
31.88
31.92
31.96
1996 1998 2000 2002 2004 2006
germany
27.6
27.8
28.0
28.2
28.4
1996 1998 2000 2002 2004 2006
greece
31.00
31.05
31.10
31.15
31.20
31.25
31.30
1996 1998 2000 2002 2004 2006
italy
30.00
30.05
30.10
30.15
30.20
30.25
1996 1998 2000 2002 2004 2006
netherlands
30.3
30.4
30.5
30.6
30.7
30.8
30.9
1996 1998 2000 2002 2004 2006
spain
31.95
32.00
32.05
32.10
32.15
32.20
1996 1998 2000 2002 2004 2006
denmark
33.2
33.3
33.4
33.5
33.6
1996 1998 2000 2002 2004 2006
sweden
31.18
31.20
31.22
31.24
31.26
31.28
1996 1998 2000 2002 2004 2006
uk
32.2
32.4
32.6
32.8
33.0
33.2
33.4
1996 1998 2000 2002 2004 2006
czech
24.4
24.8
25.2
25.6
26.0
1996 1998 2000 2002 2004 2006
estonia
34.0
34.4
34.8
35.2
35.6
1996 1998 2000 2002 2004 2006
hungary
27.0
27.2
27.4
27.6
27.8
28.0
1996 1998 2000 2002 2004 2006
slovak
26.4
26.6
26.8
27.0
27.2
1996 1998 2000 2002 2004 2006
slovenia
LTFP_PAP
34
Figure A.10: TFP development of the rubber sector
Figure A.11: TFP development in the textiles sector
44.4
44.6
44.8
45.0
45.2
1996 1998 2000 2002 2004 2006
austria
45.8
46.0
46.2
46.4
46.6
1996 1998 2000 2002 2004 2006
belgium
43.6
43.8
44.0
44.2
44.4
44.6
1996 1998 2000 2002 2004 2006
finland
47.8
47.9
48.0
48.1
48.2
48.3
48.4
1996 1998 2000 2002 2004 2006
france
48.7
48.8
48.9
49.0
49.1
49.2
1996 1998 2000 2002 2004 2006
germany
40
41
42
43
44
45
1996 1998 2000 2002 2004 2006
greece
48.5
48.6
48.7
48.8
48.9
49.0
1996 1998 2000 2002 2004 2006
italy
44.8
44.9
45.0
45.1
45.2
45.3
45.4
1996 1998 2000 2002 2004 2006
netherlands
46.4
46.6
46.8
47.0
47.2
47.4
1996 1998 2000 2002 2004 2006
spain
48.8
49.0
49.2
49.4
49.6
49.8
1996 1998 2000 2002 2004 2006
denmark
48.4
48.6
48.8
49.0
49.2
49.4
1996 1998 2000 2002 2004 2006
sweden
47.04
47.08
47.12
47.16
47.20
1996 1998 2000 2002 2004 2006
uk
49
50
51
52
53
1996 1998 2000 2002 2004 2006
czech
37.0
37.5
38.0
38.5
39.0
39.5
1996 1998 2000 2002 2004 2006
estonia
53.0
53.5
54.0
54.5
55.0
55.5
1996 1998 2000 2002 2004 2006
hungary
41.5
42.0
42.5
43.0
43.5
44.0
1996 1998 2000 2002 2004 2006
slovak
40.0
40.5
41.0
41.5
42.0
1996 1998 2000 2002 2004 2006
slovenia
LTFP_RUB
12.1
12.2
12.3
12.4
12.5
12.6
1996 1998 2000 2002 2004 2006
austria
11.9
12.0
12.1
12.2
12.3
12.4
12.5
1996 1998 2000 2002 2004 2006
belgium
11.9
12.0
12.1
12.2
12.3
12.4
1996 1998 2000 2002 2004 2006
finland
12.1
12.2
12.3
12.4
12.5
1996 1998 2000 2002 2004 2006
france
11.9
12.0
12.1
12.2
12.3
12.4
1996 1998 2000 2002 2004 2006
germany
11.6
11.7
11.8
11.9
12.0
12.1
1996 1998 2000 2002 2004 2006
greece
11.80
11.84
11.88
11.92
11.96
1996 1998 2000 2002 2004 2006
italy
12.3
12.4
12.5
12.6
12.7
12.8
1996 1998 2000 2002 2004 2006
netherlands
11.70
11.72
11.74
11.76
11.78
11.80
1996 1998 2000 2002 2004 2006
spain
13.60
13.65
13.70
13.75
13.80
13.85
13.90
1996 1998 2000 2002 2004 2006
denmark
13.9
14.0
14.1
14.2
14.3
1996 1998 2000 2002 2004 2006
sweden
11.7
11.8
11.9
12.0
12.1
12.2
1996 1998 2000 2002 2004 2006
uk
12.8
13.0
13.2
13.4
13.6
13.8
14.0
1996 1998 2000 2002 2004 2006
czech
10.7
10.8
10.9
11.0
11.1
11.2
11.3
1996 1998 2000 2002 2004 2006
estonia
14.75
14.80
14.85
14.90
14.95
15.00
15.05
1996 1998 2000 2002 2004 2006
hungary
11.0
11.2
11.4
11.6
11.8
1996 1998 2000 2002 2004 2006
slovak
11.2
11.3
11.4
11.5
11.6
11.7
11.8
1996 1998 2000 2002 2004 2006
slovenia
LTFP_TEX
35
Figure A.12: TFP development in the transport equipment sector
Figure A.13: TFP development in the wood sector
11.8
12.0
12.2
12.4
12.6
1996 1998 2000 2002 2004 2006
austria
11.96
12.00
12.04
12.08
12.12
12.16
12.20
1996 1998 2000 2002 2004 2006
belgium
11.35
11.40
11.45
11.50
11.55
11.60
11.65
1996 1998 2000 2002 2004 2006
finland
12.2
12.3
12.4
12.5
12.6
1996 1998 2000 2002 2004 2006
france
12.6
12.7
12.8
12.9
13.0
13.1
13.2
1996 1998 2000 2002 2004 2006
germany
11.2
11.4
11.6
11.8
12.0
12.2
1996 1998 2000 2002 2004 2006
greece
11.85
11.90
11.95
12.00
12.05
1996 1998 2000 2002 2004 2006
italy
11.8
12.0
12.2
12.4
12.6
1996 1998 2000 2002 2004 2006
netherlands
12.18
12.20
12.22
12.24
12.26
12.28
1996 1998 2000 2002 2004 2006
spain
12.9
13.0
13.1
13.2
13.3
13.4
1996 1998 2000 2002 2004 2006
denmark
13.6
13.8
14.0
14.2
14.4
1996 1998 2000 2002 2004 2006
sweden
12.1
12.2
12.3
12.4
12.5
1996 1998 2000 2002 2004 2006
uk
12.8
13.2
13.6
14.0
14.4
1996 1998 2000 2002 2004 2006
czech
9.6
10.0
10.4
10.8
11.2
1996 1998 2000 2002 2004 2006
estonia
14.0
14.4
14.8
15.2
15.6
1996 1998 2000 2002 2004 2006
hungary
10.5
11.0
11.5
12.0
12.5
1996 1998 2000 2002 2004 2006
slovak
10.6
10.8
11.0
11.2
11.4
1996 1998 2000 2002 2004 2006
slovenia
LTFP_TREQ
12.8
12.9
13.0
13.1
13.2
1996 1998 2000 2002 2004 2006
austria
12.6
12.7
12.8
12.9
13.0
13.1
13.2
1996 1998 2000 2002 2004 2006
belgium
12.6
12.7
12.8
12.9
13.0
13.1
13.2
1996 1998 2000 2002 2004 2006
finland
12.6
12.7
12.8
12.9
13.0
13.1
13.2
1996 1998 2000 2002 2004 2006
france
12.5
12.6
12.7
12.8
12.9
13.0
1996 1998 2000 2002 2004 2006
germany
11.4
11.6
11.8
12.0
12.2
12.4
1996 1998 2000 2002 2004 2006
greece
12.12
12.16
12.20
12.24
12.28
12.32
12.36
1996 1998 2000 2002 2004 2006
italy
13.00
13.05
13.10
13.15
13.20
1996 1998 2000 2002 2004 2006
netherlands
12.25
12.30
12.35
12.40
12.45
1996 1998 2000 2002 2004 2006
spain
14.4
14.5
14.6
14.7
14.8
14.9
1996 1998 2000 2002 2004 2006
denmark
14.4
14.5
14.6
14.7
14.8
14.9
15.0
1996 1998 2000 2002 2004 2006
sweden
12.30
12.35
12.40
12.45
12.50
12.55
1996 1998 2000 2002 2004 2006
uk
13.8
14.0
14.2
14.4
14.6
1996 1998 2000 2002 2004 2006
czech
11.0
11.2
11.4
11.6
11.8
12.0
1996 1998 2000 2002 2004 2006
estonia
15.4
15.5
15.6
15.7
15.8
1996 1998 2000 2002 2004 2006
hungary
11.2
11.6
12.0
12.4
12.8
1996 1998 2000 2002 2004 2006
slovak
11.8
12.0
12.2
12.4
12.6
1996 1998 2000 2002 2004 2006
slovenia
LTFP_WOOD
36
Appendix A.2. List of sectors and their names
Sector’s short name NACE code Full name of the sector
Food 15 to 16 Food product, beverages and tobacco
Textiles 17 to 19 Textiles, textile products, leather and footwear
Wood 20 Wood and products of wood and cork
Paper 21 to 22 Pulp, paper, paper products, printing and publishing
Chemicals 23 Chemicals and chemical products
Coke 24 Coke, refined petroleum products and nuclear fuel
Rubber 25 Rubber and plastic products
Non-metallic products 26 Other non-metallic mineral products
Metal products 27 to 28 Basic metals and fabricated metal products
Machinery 29 Machinery and equipment
Electrical equipment 30 to 33 Electrical equipment
Transport equipment 34 to 35 Transport equipment
Manufacturing n.e.c. 36 to 37 Manufacturing (non otherwise classified)
Appendix A.3. Non-stationarity of the series. Results from the ADF unit root test
Series Property Order of integration p-value of unit root test
LFDI stationary I(0) 0.00
LICT Non-stationary I(1) 1.00
LR&D Non-stationary I(1) 0.29
LOPEN_EU Non-stationary I(1) 1.00
LOPEN_EXT Non-stationary I(1) 1.00
HC Non-stationary I(1) 1.00
FAIL_RAT Non-stationary I(1) 0.52
Note: Null hypothesis: Unit root (individual unit root process); number of lags: 3; All series (except LFDI) are
non-stationary and integrated of order 1.
37
Appendix A4. Cointegration of the series
Kao Residual cointegration test LTFP, LICT, LR&D, LOPEN_EU, LOPEN_EXT,
HC, FAIL_RAT
ADF t-statistic 8.18
ADF p-value 0.00
Note: Null hypothesis: No cointegration; Trend assumption: No deterministic trend.