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: agnieszka.gehringer@wiwi.uni-goettingen.de

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.

References

Abramovitz M (1956) Resource and output trends in the United States since 1870. Am Econ

Rev 46:5-23

Aghion P, Howitt P (1992) A model of growth through creative destruction. Econometrica

60:323-351

Aghion P, Howitt P (1998) Endogenous Growth Theory. MIT Press, Cambridge MA.

Aitken B, Harrison A (1999) Do domestic firm benefit from direct foreign investment?

Evidence from Venezuela. Am Econ Rev 89:605-618

24

Alcalá F, Ciccone A (2004) Trade and productivity. Q J Econ 119:613-646

Alesina A, Spolaore E, Wacziarg, R (2000) Economic integration and political disintegration.

Am Econ Rev 90:1276-1296.

Antonelli C, Gehringer A (2012) Knowledge externalities and demand pull: The European

evidence. BRICK&LEI Working Paper No. 14

Autor DH, Katz LF, Kreuger AB (1998) Computing inequality: have computers changed the

labour market? Q J Econ 63:1169-1213

Barro R, Lee J (2001) International data on educational attainment: Updates and implications.

Oxf Eco Pap 53:541-563

Bartelsman EJ (2010) Searching for the sources of productivity from macro to micro and

back. Ind Corp Change 19:1891-1917

Basu S, Fernald J, Oulton N, Srinivasan S (2004) The case of the missing productivity

growth: or, does information technology explain why productivity accelerated in the

United States but not in the United Kingdom? In: Gertler M, Rogoff K (eds) NBER

Macroeconomics Annual 2003. The MIT Press, pp 9-63

Bengoa-Calvo M, Perez P (2011) Determinants of total factor productivity in the Spanish

regions. Social Science Research Network. http://ssrn.com/abstract=1965740. Accessed 15

February 2013

Berman E, Bound J, Machin S (1998) Implications of skilled-biased technological change:

international evidence. Q J Econ 113:1245-1279

Biatour B, Dumont M (2011) The determinants of industry-level total factor productivity in

Belgium. Federal Planning Bureau Working Paper 7-11

25

Borensztein E, De Gregorio J, Lee JW (1998) How does FDI affect economic growth? J Int

Econ 45:115-135

Branstetter L (2007) Is foreign direct investment a channel of knowledge spillovers? Evidence

from Japan’s FDI in the United States. J Int Econ 68:325-344

Brynjolfsson E, Hitt LM (2003). Computing productivity: Firm-level evidence. Rev Econ Stat

85:793-808

Cameron G, Proudman J, Redding S (2005) Technological convergence, R&D, trade and

productivity growth. Eur Econ Rev 49:775-807

Comin D (2004) R&D: a small contribution to productivity growth. NBER Working Paper

No. 10625

Coe DT, Helpman E (1995) International R&D spillovers. Eur Econ Rev 39:859-887

Eberhardt M, Teal F (2010) Productivity analysis in global manufacturing production.

Economics Series Working Papers 515. University of Oxford, Department of Economics

Frankel J, Romer D (1999) Does trade cause growth? Am Econ Rev 89:437-466

Gehringer A (2011) Pecuniary Knowledge Externalities and Innovation: Intersectoral

Linkages and their Effects beyond Technological Spillovers. Econ Innov New Technol

20:495-515

Gehringer A (2012) A New Taxonomy Based on Pecuniary Knowledge Externalities:

Knowledge Interactions in a Vertically Integrated System, Econ Syst Res 24(1):35-55

Gehringer A (2013) Financial liberalization, growth, productivity and capital accumulation:

The case of European integration, Int Rev Econ Financ 25:291-309

26

Greenaway D, Kneller R (2007) Firm heterogeneity, exporting and foreign direct investment:

A survey. Econ J 117:F134-F161

Griffith R, Redding S, Simpson H (2003) Productivity convergence and foreign ownership at

the establishment level. Discussion Paper No. 57. Centre for Economic Performance,

London

Gordon R (2000) Does the “New Economy” measure up to the great inventions of the past? J

Econ Perspect 14:49-74

Griliches Z (1979) Issues in assessing the contribution of research and development to

productivity growth. Bell J Econ 10:92-116

Grossman G, Helpman E (1991) Innovation and Growth in the Global Economy. MIT Press,

Cambridge MA and London UK

Guellec D, van Pottelsberghe de la Potterie B (2001) R&D and productivity growth: panel

data analysis of 16 OECD countries. OECD Economic Studies No. 33

Hall BH, Mairesse J, Mohnen P (2009) Measuring the returns to R&D. NBER Working Paper

15622

Jones C, Williams J (1998) Measuring the social return to R&D. Q J Econ 113:1119-1138.

Jorgenson DW, Stiroh KJ (2000) U.S. economic growth at the industry level. Am Econ Rev

90:161-167

Kose MA, Prasad ES, Terrones ME (2009) Does openness to international financial flows

raise productivity growth? J Int Money Financ 28:554-558

Keller W (2004) International technology diffusion. J Econ Lit 42:752-82

27

Keller W (2009) International trade, foreign direct investment, and technology spillovers.

NBER Working Paper No. 15442

Kim S, Han G (2001) A Decomposition of total factor productivity growth in Korean

manufacturing industries: A stochastic frontier approach. J Prod Anal 16:269-281

Lucas R (1998) On the mechanisms of economic development. J Monet Econ 22:3-42

Mankiw G, Romer D, Weil D (1992) A contribution to the empirics of economic growth. Q J

Econ 107:407-437

Marrocu E, Paci R, Usai S (2012) Productivity growth in the old and new Europe: the role of

agglomeration economies. J Reg Sci. doi: 10.1111/jors.12000

Nadiri MI, Kim S (1996) International R&D spillovers, trade and productivity in major

OECD countries. NBER Working Paper 5801

O’Mahony M, Van Ark B (2003) EU productivity and competitiveness: An industry

perspective. Can Europe resume the catching up process? European

Commission/Enterprise Publication: Luxembourg

O’Mahony M, Roncon-Aznar A, Robinson R (2010) Productivity growth in Europe and the

US: A sectoral study. Rev Econ Inst 1:1-27

Pesaran MH (2006) Estimation and inference in large heterogeneous panels with a multifactor

error structure. Econometrica 74(4):967-1012

Pesaran MH, Tosetti E (2010) Large panels with common factors and spatial correlations.

Unpublished working paper December 2010. Cambridge University

Prichett L (2001) Where has all the education gone? World Bank Econ Rev 15:367-391

28

Rivera-Batiz LA, Romer PM (1991) Economic integration and economic growth. Q J Econ

106:531-556

Redding S. (1996) The low-skill, low-quality trap: strategic complementarities between

human capital and R&D. Econ J 106:458-470

Romer PM (1986) Increasing returns and long-run growth. J Polit Econ 94:1002-1037

Romer PM (1990) Endogenous technological change. J Polit Econ 98:71-102

Saikkonen P (1991) Asymptotically efficient estimation of cointegration regression. Econom

Theory 7:1-21

Schreyer P (2000) The contribution of information and communication technology to output

growth: A study of the G7 countries. OECD Science, Technology and Industry Working

Papers 2000/2

Sianesi B, van Reenen J (2003) The returns to education: Macroeconomics. J Econ Surv

17:157-200

Stock JH, Watson MW (1993) A simple estimator of cointegrating vectors in higher order

integrated systems. Econometrica 61(4):783-820

Van Biesebroeck J (2007) Robustness of productivity estimates. J Ind Econ 55(3):529-569

Wagner J (2007) Exports and productivity: A survey of the evidence from firm-level data.

World Econ 30(1):60-82

Wooldridge J (2009) Introductory econometrics: A modern approach. South-Western, Ohio

29

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.