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Symposium Paper

Total Factor Productivity Growth,Structural Change and Convergence in the

New Members of the European Union

EL-HADJ M BAH1 & JOSEF C BRADA2,3

1Department of Economics, University of Auckland, Private Bag 92019 Auckland,

New Zealand.2Department of Economics, Arizona State University, Box 873806, Tempe, AZ,852870-3806, USA.

3Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2, 1000 Skopje,Republic of Macedonia.

We estimate total factor productivity (TFP) growth in agriculture, industry and

services in new European Union member countries and show how structural change

contributes to growth. Because of the difficulties in measuring the capital stock of transition economies, we develop a model that estimates sectoral TFPs from data on

sectoral employment and GDP per capita. Compared to Austria, new EU members have

lower TFP levels, but their TFP growth is largely higher. Inter-sectoral movements of

labour do not play a large role in aggregate TFP growth, and capital accumulation is

an important component of convergence to EU levels of per capita GDP.

Comparative Economic Studies (2009) 51, 421–446. doi:10.1057/ces.2009.8;

published online 4 June 2009

Keywords: European Union, total factor productivity, structural change,

economic convergence, economic growth, capital accumulation, productivity

JEL Classifications: D24, E22, O11, O14, O41, O47

INTRODUCTION

A key task for the transition economies that have recently joined theEuropean Union (EU) is real convergence, the catching up with the per capita

i l l f h ld d d l d b l h h

Comparative Economic Studies, 2009, 51, (421–446)r 2009 ACES. All rights reserved. 0888-7233/09

www.palgrave-journals.com/ces/


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in physical and human capital (Blanchard, 1997, Buiter, 2000), the growth

accounting literature suggests that these are not likely to be the decisive

forces leading to convergence. This literature, from Solow (1957) to Prescott(1998) and Hall and Jones (1999), stresses that economic growth as well as

inter-county differences in per capita income are largely because of changes,

or differences, in total factor productivity, with the accumulation of physical

and human capital playing only a subsidiary role.1 The EU’s own experts

(European Union, 2006, p. 35) project that the new member countries’ per

capita incomes will grow at about 4% per annum while those of the older and

richer members will grow at 2%, leading to a convergence of per capita

incomes in 2040. Moreover, the same estimates assume that over 50% of the

growth in per capita incomes in both old and new members will be the result

of total factor productivity growth rather than of factor accumulation,

although, of course, total factor productivity (TFP) growth in the new member

countries would thus be about twice as fast as in the old member countries.

The ability of the new EU members to generate and sustain significant

gains in TFP should not be taken for granted. The USSR and the countries of

East Europe saw gains in TFP came to a virtual halt in the early 1980s, if not

before then, a situation unprecedented among countries at such a level of

development. The first to note the slowdown in TFP growth in the USSR wasKaplan (1968) who showed that, for any plausible parameter values of a

Cobb-Douglas production function, Soviet TFP growth was falling towards

zero.2 The results of similar research for East Europe by researchers in both

the West and in the communist countries themselves came to more or less

that same conclusion, namely that, by the start of the 1980s, the only sources

1 For example, Hall and Jones deconstruct the ‘the 35-fold difference in output per worker

between the United States and Niger. Different capital intensities in the two countries contributed a

factor of 1.5 to the income differences, while different levels of educational attainment contributed afactor of 3.1. The remaining difference – a factor of 7.7 – remains as the productivity residual’ (Hall

and Jones, 1999, p. 83). Prescott (1998) reaches a similar conclusion without assuming a specific

form of the production function. See also, Klenow and Rodriguez-Clare (1997), Hendricks (2002),

Parente and Prescott (1994,2000) and Caselli (2005).2 The literature on the decline in Soviet productivity growth took a unique direction because of

Weitzman (1970), who attributed the output slowdown to a low elasticity of substitution between

capital and labour, a finding that, of course, improved estimated TFP growth given the slow growth

of the labour force and the rapid growth of the capital stock in the communist countries. Easterly

and Fisher (1995) continued this insistence on the low elasticity of substitution explanation for

Soviet growth retardation, but, in the face of the critique of their empirical work by Beare (2008),

they were forced to accept the conclusion that ‘the rate of technical progress declined over thecourse of the history of the former Soviet Union’ (Easterly and Fisher, 2008, p. 147). Efforts to find

similarly low elasticities of substitution between capital and labour in East Europe during the

EM Bah & JC BradaTFP Growth and Structural Change

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of growth in East Europe were increases in capital and hours worked, with

TFP growth non-existent or negative.3 It is possible that the forces that

retarded TFP growth so severely during the late communist era have not beenentirely eliminated by the process of transition. If the sources of TFP growth

cannot be greatly influenced by short-term changes in policies, institutions or

economic systems, then the transition economies would be consigned to

being second-class members of the EU for a long time.4

Alternatively, if TFP does respond quickly to changes in policies,

institutions and economic system, then the recent upsurge in TFP growth in

the new EU members’ countries could be because of temporary factors, whose

effects will soon wear off, a scenario developed in some detail by Van Ark

(1999). Hall and Jones (1999) attribute high TFP levels to better institutions,

but the new EU members have already undertaken many of the reforms needed

to create functioning market economies and to meet the institutional and legal

standards of EU membership. Thus, while some institutional improvements

may still be possible, their pace, and thus that of TFP growth, may be much

slower than it was in the past decade. In contrast to Hall and Jones, Frankel and

Romer (1999) stress the role of openness to trade as the driver of TFP, but the

new EU member countries have already undertaken as much opening up to

international trade and investment as is likely to be feasible and further rapidgrowth of trade to GDP ratios does not seem likely.

Finally, the transition economies, including the new EU members, may

have been the beneficiaries of what we may call temporary Prescott–Granick

effects that led to rapid, but short-lived, gains in TFP. Prescott (1998) and

Parente and Prescott (2000) argue that TFP growth and levels are inversely

related to the ability of incumbent workers to prevent the adoption new

technologies, work rules and ways of organising production in order to

protect the rents that they can earn using older technologies or ways of

organising their work. In the case of the transition economies, the pattern of TFP growth can thus be understood in terms of David Granick’s (1989)

description of the Soviet economy as a ‘job-rights economy’ in which workers

had an explicit right to a particular job at a particular location. At the start of

the Soviet experiment, unskilled workers were brought into factories from

3 See Brada (1989) for estimates of TFP and Ofer (1987) for a survey of the literature on this

topic.4 The literature on the causes of TFP differentials is quite unsettled in this respect. Some

authors, including Prescott (1998), take the view that a country’s TFP levels are subject to ratherrapid change because of institutional and policy changes, while others, such as Hall and Jones

(1999) and Frankel and Romer (1999) suggest that rather immutable factors such a legal origins,


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agriculture; they had no rents to preserve and no understanding of their job

rights. As workers gained tenure at their places of work, they were also

increasingly able to earn rents from operating the existing technology, andthey thus had both the ability and the incentives to block the efficient

introduction of new technologies and ways of working. As a result, new

technology and ways of fully exploiting its productivity-enhancing character-

istics could only be introduced into newly built and staffed factories but not

into existing ones, thereby sharply reducing TFP growth.

In the course of the transition, these job rights disappeared because open

unemployment reduced workers’ bargaining power and because socialist-era

laws providing these job rights were swept away, and the rents earned by the

old industrial elite of the work force disappeared. Thus, in the Prescott–

Grancik view, the current accelerated pace of TFP growth in the new EU

member countries will continue only so long as workers continue to be

unorganised and unable to exert pressure to slow changes in work rules and

the fully effective introduction of new technologies. Because the work-place

inflexibilities that the Prescott–Granick view considers important barriers to

TFP growth are alleged to be the cause of slow productivity growth in the

older EU member countries, fears that they will also spread to the new

members are not unfounded. Consequently, the future pace of TFP growth inthe new EU member countries is both uncertain and of great importance to

their future well being.

A second and related aspect of real convergence is structural conver-

gence. The communist regimes in East Europe had followed a development

strategy that favoured industry and agriculture at the expense of services.

Thus, these countries entered the transition, and EU membership, with

employment shares in industry and agriculture that were much larger than

those found in market economies at similar levels of development and with

service sectors that had much lower shares of employment than were to befound in comparable market economies (Gregory, 1970; Ofer, 1976). These

disparities in employment carried over to the shares of these sectors in

aggregate output as well. As these economies turned to the market to allocate

resources, the service sector expanded dramatically while agriculture and

industry lost employment share (European Union, 2006), although all of

these economies continue to exhibit higher labour shares in agriculture and

industry and lower shares in services than are to be found in the older EU

member counties. Whether this structural difference is a legacy of communist

policies or whether it simply reflects the fact that structural change in favourof services at the expense of agriculture and industry occurs with rising per

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to be seen whether this faster and ongoing shift of resources between sectors

is an important contributor to, or retardant of, aggregate TFP growth.5

In principle, it should be possible to undertake growth accountingexercises for the transition economies at the sectoral level, thus measuring

TFP levels and their evolution over time, but in reality we face a fundamental

problem in estimating the stock of capital. The transition from socialism to

capitalism effectively destroyed a large, but unknown, part of the capital

stock. Part of the destruction was physical; factories were abandoned and

equipment was scrapped or thrown away. Another part of the destruction was

what might be called ‘moral’, meaning that the huge changes in the structure

of demand and the wholesale acquisition of new and more productive

technologies from the West that occurred in the course of transition devalued,

or accelerated the depreciation of, much of the communist-era capital stock

(Campos and Coricelli, 2002). Studies of this phenomenon have produced

estimates of surprisingly large declines in Russian and East European capital

stock over the course of the transition. For example, Deliktas and Balcilar

(2005) estimate that up to 50% of the communist-era capital stock was

destroyed in the early transition.6 The various estimates of the excess

destruction of capital stock differ in their magnitude as well as in the

methodologies utilised to estimate the losses and in the assumptions drivingthe estimates. Moreover, given the logic of the argument for the destruction of

capital, the amount destroyed in each country should depend, inter alia, on

the communist-era openness of the country, on its industrial structure, and on

the degree of its integration into the CMEA or Soviet economy.7

5 Stephan (2002) investigates the extent to which structural differences between the old and

new EU members lead to differences in per capita incomes, but his analysis focuses on labour

productivity differences rather than on differences in TFP.6

See McKinsey Report (1999), Kushnirsky (2001) and Darvas and Simon (2000) for other, butsimilar, estimates. Izumov and Vahaly (2006, 2008) provide a survey of the issues and the literature

on the transition-era capital stock as well as a methodologically consistent set of estimates of the

‘adjusted’ capital stocks of the Russian and former CIS economies. While we do not examine these

economies in our paper, the gaps between official and adjusted estimates of the capital stock and

their implications for TFP estimates shown by these studies are instructive.7 A referee suggested that there was also destruction of communist-era human capital and that

the value of East European human capital as measured by years of schooling may have been

overstated. Thus, for example, Steffen and Stephan (2008) attribute much of the productivity

differential between East and West to a human capital deficit, and Földvári and Van Leeuwen (2009)

claim that human capital accumulation had a positive effect on Hungarian GDP growth in the 1990s.

Nevertheless, we refer the reader to the sources cited in Footnote 1 for compelling arguments whyhuman capital accumulation is not likely to be a key driver of TFP growth. In this paper, we ignore

any explicit accounting for human capital, and, in our results, inter-country differences in human


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Needless to say, wide divergences in these unofficial estimates of the

capital stock lead to wide divergences in estimates of TFP growth and levels

in the course of transition (Burda and Severgnini, 2008a). Absent plausibleofficial estimates of sectoral and even aggregate capital stocks and the wide

divergence in the unofficial estimates, as well as the lack of sectoral capital

stock data, we propose to measure sectoral TFPs without recourse to capital

stock data.8 Effectively, our approach substitutes readily available data on

sectoral employment and aggregate GDP, the constraints on the interrelations

between macroeconomic variables derived from a widely used model of

economic growth and structural change, and model parameters obtained

through calibration for generally unreliable or unavailable data on sectoral

capital stocks in the transition economies. This substitution of easily

obtainable data and a model and parameters that have proven their value

in other applications seems to us to be a useful way to approach the questions

that lie at the heart of this paper. A similar data limitation for developing

countries has led researchers to develop indirect methods for estimating

sectoral TFPs by making use of cross-section prices in a multi-sector growth

model similar to the one we use to infer sectoral relative TFPs.9 In this paper

we use a three-sector model developed by Bah (2008) to infer sectoral TFP

time series for the new EU members. This kind of model also has been usedby Rogerson (2008) to analyse labour market outcomes in Europe.10

The rest of the paper is organised as follows. Section ‘A three-sector

model of structural transformation’ describes the model, characterises the

competitive equilibrium and calibrates the model to the US economy. Section

‘Estimates of sectoral TFP in Austria and transition economics’ applies the

model to Austria and to a sample of transition countries to demonstrate

differences in sectoral TFP levels and their change relative to Austria, an EU

member with a per capita output close to the (old member) EU average and

with some similarities in size and location to a number of the transitioneconomies. With a few exceptions, in all sectors Austrian TFP exceeds that of

years of schooling in 2000, while Poland, for example, had 12.0 years in 2001, and the Czech

Republic 10.4 in 2001.8 Using a different methodology, Burda and Severgnini (2008b). also choose to estimate

aggregate TFP in the transition economies without recourse to official capital stock data. They show

that attempting to construct capital stock data using perpetual inventory methods leads to highly

unreliable estimates of both stocks and TFP.9

See Herrendorf and Valentinyi (2006) and Hsieh and Klenow (2007).10The decomposition of the economy into three sectors, agriculture, services and industry, and

the emphasis on the growth effects of reallocating labor and capital among these sectors also links


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the new member countries, but there are important sectoral differences in

TFP between the new EU member countries and Austria. Moreover, not all

members’ TFPs are progressing in all sectors in a way that promotes catch-upwith Austrian per capita income. Although structural change does not appear

to be a serious barrier to growth in the new EU member countries, some

of them are found to rely heavily on input growth rather than TFP

improvements for their GDP growth. The last section draws out some policy

implications of our findings.

A THREE-SECTOR MODEL OF STRUCTURAL TRANSFORMATION

Below, we present a model developed by Bah (2008). The model is a closed

economy growth model with three sectors: agriculture, industry and services.

The key features that drive labour reallocation across sectors are as follows.

First, preferences are non-homothetic. As income rises, the household shifts

its demand from agricultural goods to industrial goods and services. Because

the household produces just enough of the agricultural good for subsistence,

resources are shifted away from that sector as its productivity rises. Second,

the elasticity of substitution between manufacturing and services and the TFPgrowth differential determine labour reallocation between those two sectors.

The model thus generates a process of structural transformation that was first

described by Kuznets (1966).

The model

Preferences and endowments

There is a representative household who lives forever, and we normalise its

size to 1 for simplicity. In each period the household is endowed with one

unit of time, and it is also endowed with initial capital stock at time 0 and the

total land for the economy which we normalise to 1.

The household supplies labour inelastically to the three sectors. The

instantaneous utility is given by:

U ðFt ; At Þ ¼ At if At o A

logðFt Þ þ A if At A

ð1Þ

where At is the agricultural good and Ft is a composite consumption good

defined as a CES aggregate of the industrial good ( M t ) and the services (St ).

e


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Lifetime utility is given by:

X1

t ¼0

bt U ðFt ; At Þ ð3Þ

where b is the discount factor.

This specification of preferences implies that the economy specialises in

agriculture until the subsistence level Ā is reached. Moreover, the economy

will never produce more of the agricultural good than Ā .

Technologies

All three sectors use Cobb-Douglas production functions. The inputs foragriculture are labour ( N ) and land ( L) while industry and services use labour

and capital. The agricultural good is only used for consumption so the

resource constraint is given by:

At ¼ Aat N aat L

1at ð4Þ

where

Aat ¼ Aað1 þ gat Þt ð5Þ

The TFP parameters Aa, gat are assumed to be country specific.11

The industrial sector’s output is used for consumption ( M t ) in the

composite good and investment ( X t ). The industry sector resource constraint is:

M t þ X t ¼ Amt K ymt N

1ymt ð6Þ

where

Amt ¼ Amð1 þ gmt Þt ð7Þ

The law of motion of the aggregate capital stock ( K t ) in the economy is

given by

K t þ1 ¼ ð1 dÞ K t þ X t ð8Þ

where d is the depreciation rate.

11 Because of the way in which Aat is calculated, the beginning and starting values of

agriculture’s share of employment predicted by the model are by construction the same as the actual


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The output of the service sector is only used for consumption through the

composite good. Therefore, the service sector resource constraint is given by

St ¼ Ast K yst N

1yst ð9Þ

where

Ast ¼ Asð1 þ gst Þt ð10Þ

In the equations above, the TFP parameters ( Am, gmt , As and gst ) are also

assumed to be country specific. We may expect that a country’s institutions

and policies affect the productivity in each of these economic sectors.

Equilibrium

Given that there are no distortions in this economy, the competitive equilibrium

allocations can be obtained by solving a social planner’s problem. The economy

specialises in the production of agriculture as long as Aað1 þ gaÞt o A. Once

Aað1 þ gat Þt A, the economy begins the production of industrial goods and

services. This corresponds to the start of structural transformation, and we will

solve for the competitive equilibrium from this point on.

We sketch the solution of the model and refer the reader to Bah (2008) for

the details. Below, we present the key equations that determine theequilibrium allocations from the social planner’s problem.

Labour in agriculture is given by:

N at ¼ A

Aat

1a

ð11Þ

Let N t ¼ 1 N at be the total time that can be allocated between the

industry and service sectors. The aggregate capital stock is given by the

following dynamic equation:

K t þ1 ¼ Amt K t

N t

y N t þ ð1 dÞ K t b 1 d þ y Amt

K t

N t

y1" #

Amt 1 K t 1

N t 1

y N t 1 þ ð1 dÞ K t 1 K t

" # ð12Þ

Once capital is known, the quantity of labour used in the service sector is

given by:

N st ¼ C t y 1e ð13Þ


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where C t is the non-agriculture aggregate expenditure and is given by

C t ¼ Amt K t

N t

y N t þ ð1 dÞ K t K t þ1 ð14Þ

The other equilibrium allocations can be easily derived.

Calibration to the US Economy

The model is calibrated to match the US economy from 1950 to 2000.12 There

are 13 parameters to calibrate. The productivity levels Ai(i¼a,m, s) arenormalised to 1 in 1950. This corresponds to choosing units. Following the

literature, labour’s share in agriculture (a) is set to 0.7 and capital’s share in

industry and services (y) is set to 0.3.

The TFP growth rates for industry and services (gm, gs)the discount rate b

and d are jointly calibrated to match four averages in the data from 1950 to

2000: average growth rate of GDP per capita, average growth rate of the price

of the service good relative to the industrial good, average investment-to-

output ratio and average capital-to-output ratio.

The growth rate of agricultural TFP (gat ) is chosen such that the modelmatches the agricultural shares of hours worked in the United States. We

assume that the growth rate varies each decade starting in 1950. The

agricultural subsistence level is equal to the agricultural production in every

period after the start of structural transformation. Given that the agricultural

TFP is normalised to 1 in 1950, the subsistence level can be easily computed

using the shares of hours in agriculture. The last two parameters to calibrate

are the elasticity of substitution between the industrial good and services (e)

and the weight of the industrial good in the production of the composite good

(l). These two parameters determine the labour reallocation between theindustrial and service sectors. We choose values of e and l to minimise the

quadratic norm of the difference between the predicted and actual industrial

employment shares from 1950 to 2000. Table 1 summarises the calibrated

parameter values.

12 For details, see Bah (2008). Because we will implicitly test the appropriateness of the

US-derived parameters for our sample of countries later in the paper, we ask the reader to defer

concerns about the validity of the United States as a benchmark. A compelling reason for using the

United States for the calibration exercise is the longer and more stable time series on variablesneeded for calibration. Whether the United States is the most appropriate country for deriving

parameters for our sample of countries cannot be answered in any definitive way, but we do note


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ESTIMATES OF SECTORAL TFP IN AUSTRIA AND SELECTED

TRANSITION ECONOMIES

We use the parameters derived from our calibration exercise and data on

sectoral labour shares and GDP per capita to estimate the sectoral TFPs forAustria and for nine transition economies: Bulgaria, the Czech Republic,

Estonia, Hungary, Latvia, Lithuania, Poland, the Slovak Republic and

Slovenia, that have joined the EU.13 Because of unavailability of consistent

data on hours worked by sector, our analysis covers only the period

1995–2005. Although our model is one of sectoral change, which is a long-

term phenomenon, as we shall see, the sectoral changes in employment in the

transition economies were quite significant even over this shorter period. We

use Austria as a standard for comparison because in terms of population and

land area it falls within the range of the transition economies in our sample,and it also is close geographically to a number of them. Moreover, it can be

seen as a typical old EU member country in terms of its per capita income

(Table 2).

Austria’s per capita income in 1997 and 2005 exceeded that of the

transition countries by a palpable amount.14 Austria is also slightly above the

average per capita GDP of the old EU member countries, but its position

relative to other ‘old’ EU members changed very little between 1995 and

2005. The transition economies have gained appreciably in their standing vis

a vis the EU 15 average, although the gains differ considerably across

countries.

In the application of the calibrated model to Austria and the transition

countries, we assume all the parameters are the same across countries except

the series for sectoral TFP. We use the model to find sectoral TFP series such

that, within the framework of the model, we can best replicate the paths of

Table 1: Calibrated Parameters

Parameter Aa Am A s A¯

a b d e gm g s l y

Value 1 1 1 0.24 0.7 0.975 0.05 0.30 0.019 0.009 0.02 0.3

13 The sectoral employment shares for Romania proved somewhat problematic, and thus we

dropped that country from our analysis even though it, too, is now an EU member.14 The new member countries may have larger informal sectors, which would somewhat close

the gap between their per capita incomes and that of Austria. However, official estimates of GDP in


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GDP per capita and the sectoral employment shares. For agricultural TFP, we

use the fact the subsistence level is assumed to be the same in every country.

Therefore, for any other country, we can use the US employment shares and

calculated agricultural TFP to deduce that country’s agricultural TFP. We

calculate the agricultural TFPs in 1995, 2000 and 2005 and then assume

constant growth between those dates. For the TFP series in industry and

services and the initial capital stock, we match GDP per capita relative to theUnited States in 1995, the average GDP per capita growth and labour

reallocation from industry between 1995 and 2005.

Sectoral TFP in Austria

Figure 1 shows the results of our simulation of Austrian per capita GDP

growth, sectoral employment and sectoral TFP. The first panel shows per

capita GDP, with 1995 normalised to one. The simulated and actual data for

GDP per capita are close to each other as are the sectoral employment shares

reported in Panel 2.15 The shares of agriculture and industry in employmenthave fallen while services employment’s share has increased. Overall,

structural change in Austria has been relatively slow over the period

analysed. The last panel in Figure 1 shows that Austrian total factor

productivity in industry relative to US industrial TFP in 1950 was around 2.1

in 1995 and close to 2.4 in 2005. Agriculture and services TFPs in Austria in

Table 2: Per Capita Incomes as Percentage of EU-15 Average

Country 1997 2005

Austria 112.9 113.3Czech Republic 61.9 67.8Estonia 35.0 51.7Latvia 29.8 43.1Lithuania 33.3 47.1Hungary 45.5 57.2Poland 40.1 46.0Slovak Republic 42.3 50.1Slovenia 64.5 75.0

Source: EU (2006)

15 That the model is able to match Austrian sectoral employment and per capita GDP over the

sample period is a strong, but not absolute, verification of the validity of the US-based parameters for

Austria. Because of space constraints we do not provide analogous Figures for the transitioncountries but summarize the results in tabular form. These Figures are available from the authors.

Moreover, the model fits the actual GDP per capita and sectoral employment shares of the transition


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1995 were between 1.4 and 1.5 times the corresponding 1950 level in the

United States. TFP growth in Austrian industry and agriculture was relatively

high, but TFP growth in the Austrian services sector was very slow.

The ratios of Austrian TFPs to US TFPs should not be taken as indications

of the relative productivity in the three sectors of the Austrian economy.

While Austrian industry’s TFP relative to the 1950 US level is higher than

the ratio of Austria’s agricultural TFP to the US level, many studies of US productivity suggest that, in 1950, TFP in US agriculture was higher than

was TFP in industry. Thus, Austria’s greater gains in industrial TFP vis a vis

the United States may not have offset the 1950 advantage of US agricultural

TFP over US industry’s TFP. As a result we are not able to infer from Figure 1

whether the expansion of one of Austria’s three sectors at the expense of

the other two tends to raise or lower aggregate TFP growth. By observing the

growth rates of TFP in the three sectors, we can, however, determine whether

such inter-sectoral shifts in resources promote aggregate TFP growth by

moving more resources into sectors that enjoy faster TFP growth over time.In the case of Austria, the movement of resources from agriculture and

d b d h h h

Figure 1: Actual and model predictions of per capita GDP, employment shares and sectoral TFP for Austria.


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one with low TFP growth.16 Given the slow pace of labour reallocation in

Austria, the effect on aggregate growth is likely to be negligible.

Sectoral TFP in transition economies

In Tables 3 and 4 we summarise the simulation results for the transition

economies over the period 1995–2005. We first briefly discuss cross-country

similarities and differences in the results and then discuss how sectoral TFP

levels and trends influence the convergence of the transition economies to EU

levels of per capita GDP. Next we provide an international comparison of

sectoral TFPs to investigate whether the aggregate TFP lag implied by thetransition economies’ lower per capita GDP is because of lower TFPs in all

sectors of the economy or whether their lower aggregate TFPs are the result of

particularly poor productivity in particular sectors of their economies.

Table 3 shows that the model was able to generate GDP per capita growth

rates that closely reflect actual GDP per capita growth in the transition

countries. Table 4 shows the sectoral employment shares projected by the

model for the transition economies as well as their actual employment shares.

We thus note that the model is able to generate both per capita GDP growth

rates and changes in sectoral employment that closely approximate the actualchanges experienced by these countries. This, like the close tracking of

Austrian growth and structural change, suggests the validity of our

parameterisation on the basis of US data.

All the transition countries underwent a similar change in structure that

involved the movement of labour out of agriculture and industry and into

services.17 In this sense, structural change in the new EU members mirrors

that taking place in the old EU members, even if, looking at the current

sectoral distribution of labour, the new members lag behind the older ones by

having higher shares of employment in agriculture and industry and a lowershare of services employment. On the other hand, the pace of structural

change is faster in the transition countries than it is in Austria.

There are significant differences among the nine transition economies in

terms of the TFPs of their sectors relative to 1950 United States sectoral TFP

levels. Table 5 ranks the sectors of the new EU member countries relative to

16 A referee noted that the low growth of productivity in services may be an artifact of problems

in measuring the output of services and that the services sector may be a catalyst for productivity

growth in the other two sectors. This, of course, would apply to the new members and to Austria aswell.

17 A number of countries show a small reversal in that, at the end of our period of observation,


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US 1950 levels. The situation of industry is least ambiguous; it ranks as best

or second best vis a vis the United States in all transition countries, and it is

never the last sector in rank. For a sector that produces tradables and thus

faces international competition, a sector receiving large amounts of FDI, and

a sector where technology transfer by multinational firms is routine and

relatively easy, such high rankings are not surprising. The rankings for theservice sector are also quite consistent, but poor. Services is never the best

l d f h h l ll

Table 3: Actual and Predicted per capita GDP Growth 1995–2005

Country Model Actual

Austria 19.71 20.22Bulgaria 49.32 49.01Czech Republic 27.46 27.44Estonia 107.00 106.95Hungary 57.73 57.71Latvia 116.59 116.66Lithuania 89.15 88.98Poland 48.74 48.77Slovak Republic 41.56 41.34Slovenia 45.51 45.46

Note: The numbers are in percent.

Table 4: Actual and Predicted Sectoral Shares in Employment

Predicted by the Model Actual Data

Agriculture Industry Services Industry Services

1995 2005 1995 2005 1995 2005 1995 2005 1995 2005

Austria 7.25 5.08 30.68 30.34 62.07 64.58 31.57 27.87 61.04 66.87Bulgaria 25.14 23.73 30.53 28.15 44.33 48.12 33.31 24.68 41.59 51.58Czech Rep 6.37 4.00 40.79 38.42 52.84 57.58 41.64 39.03 51.89 56.95Estonia 10.16 5.28 33.58 33.59 56.26 61.13 33.48 33.41 56.35 61.31Hungary 8.38 4.96 34.61 34.62 57.01 60.42 33.13 33.16 58.39 61.87Latvia 19.32 12.44 27.69 26.88 52.99 60.68 26.81 26.31 53.75 61.10Lithuania 23.48 14.71 27.93 28.27 48.59 57.02 27.83 28.26 48.69 57.01Poland 21.77 17.40 32.67 29.71 45.56 52.89 32.41 28.52 45.78 54.07Slovak Rep 9.49 4.64 39.55 38.00 50.96 57.36 39.14 38.40 51.36 56.81Slovenia 11.19 8.84 40.91 36.32 47.90 54.84 42.15 36.04 46.52 54.17

Note: Actual and predicted labor shares for agriculture are the same for the beginning and ending year,although not for other years in the simulation.


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shortages that existed in the provision of retail and other such ‘low

productivity’ services after the fall of communism and the subsequent rapid

expansion of those sectors, and slowness in developing a modern services

sector help to account for this poor productivity picture. A weak internatio-

nalisation of the services sector may also be a factor. The relative position of

agriculture is the most variable of the three sectors. In some countries it comes

closest to US TFP levels, but in other counties it shows the biggest gap. This

may reflect cross-country differences in the productivity of the agrarian sectorbecause of variations in the effectiveness and extent of agricultural reforms, to

the dissolution of collective agriculture, to the effectiveness of land distribution

and reform and so on. Clearly, the poor productivity performance of services,

the sector that shows the greatest employment gains, should be a policy

concern for transition-economy governments.

Structural change and aggregate TFP growth

In this subsection we estimate the loss in GDP that results from the structural

transformation process. This question is motivated by the fact that in all of our countries labour moves among sectors at a faster pace than it does in the

old EU member countries. We note that the process of structural change is a

key feature accompanying development.

To determine whether structural change, either by moving labour from

low to high TFP sectors or vice versa has an important impact on aggregate

growth, we use the model to compute GDP per capita using the capital stock

and TFP series estimated in the foregoing section. However, instead of using

the corresponding labour shares from the model, we use the sectoral

employment shares given by the data for 1995.18 Table 6 summarises the loss

Table 5: Rankings of Sectoral TFPs Relative to the US -1950

Country Sector Ranking

Bulgaria Industry>Services>AgricultureCzech Republic Agriculture>Industry>ServicesEstonia Agriculture>Industry>ServicesHungary Agriculture>Industry>ServicesLatvia Industry>Agriculture>ServicesLithuania Industry>Services>AgriculturePoland Industry>Services>AgricultureSlovak Republic Agriculture>Industry>ServicesSlovenia Industry>Services>Agriculture


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of GDP per capita growth from 1995. The largest loss in potential per capita

GDP was in Lithuania, whose per capita GDP in 2005 was 6.55% of 1995

per capita GDP less that it would have been with no structural change. This

means that annual growth of per capita GDP was around a half a percent

slower than it would have been with no structural change. This is not a trivial

amount, even when judged against the almost doubling of per capita GDP

between 1995 and 2005, but for the other countries the effect of structuralchange on growth is negligible. Consequently, we can conclude that past and

future structural change, even at the accelerated pace seen in the transition

economies over the past decade, has a relatively minor impact on the new EU

members’ ability to catch up with the older EU countries in terms of per

capita GDP.

Sectoral TFP in comparative perspective

Our results show that aggregate TFPs have risen in all of the transition

economies over the sample period, 1995–2005. However, per capita GDPconvergence between the transition economies and the older EU member

countries will require the sectoral TFPs of the transition economies to grow

closer to the levels of the old member countries. To the extent that some

transition economies’ sectors lag behind in TFP growth, their other sectors

will have to achieve even faster TFP growth to assure convergence.

Figure 2 shows the agricultural TFPs of the transition economies relative

to Austria’s agricultural TFP, which is normalised to one in each year. One

transition economy, the Czech Republic, has higher TFP in agriculture than

does Austria for the entire sample period and its agricultural TFP also grewfaster than did Austria’s. As a result, by the end of our sample period, Czech

l l % h h h h

Table 6: Loss of GDP Per Capita Due to Structural Transformation (as a percnt of 1995 GDP Per Capita)

Country Percentage Loss

Austria 1.28Bulgaria 0.95Czech Republic 2.29Estonia 3.83Hungary 2.31Latvia 4.47Lithuania 6.55Poland 2.74Slovak Republic 4.44Slovenia 3.16


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climate and grow similar crops using similar technologies, the quality of

Czech land is higher because of a more favourable topography. Moreover, the

Czech Republic has experienced a drastic dismantling of socialist-era

collectives and the outflow of part-time and low-productivity labourfrom agriculture. Three other transition economies, the Slovak Republic,

Hungary and Estonia also show rapid convergence to Austrian TFP levels,

with the former two countries surpassing Austria by the end of the sample

period and Estonia’s TFP is nearly equal to that of Austria. These four

countries’ agrarian sectors thus already operate at productivity levels that are

comparable to that of an old EU member with an above (old EU) average per

capita income. This suggests that concerns that CAP funding would be

required to support relatively inefficient agrarian sectors in these countries

are exaggerated.For the other transition economies the picture is less favourable. Slovenia

started the period with TFP in agriculture at about three-fourths of Austria’s,

but its TFP growth failed to match Austria’s over the sample period so that the

TFP gap between the two countries widened. The two Baltic Republics, Latvia

and Lithuania have TFP about one half of Austria’s and they made only

modest progress in closing this productivity gap between 1995 and 2005.

Poland and Bulgaria fell farther behind Austria, and both have TFPs in

agriculture that are less than one-half of Austria’s. For Poland, its many small

and inefficient private farms are a likely source of that country’s pooragricultural TFP showing. In these countries, lagging agrarian productivity

dd l l f h f h b

Figure 2: Agricultural TFP Relative to Austria.


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Figure 3 provides similar comparisons of the transition countries’ TFPs in

industry to that of Austria. None of the transition economies has an industrial

TFP that matches that of Austria. Nevertheless, four transition countries,

Estonia, Latvia, Lithuania and Hungary showed large gains in TFP over thesample period, ending the period with TFPs that are from two-thirds to three-

fourths of Austria’s. Poland experienced a more gradual convergence to

Austria’s TFP levels, while the Czech Republic and Slovenia had relatively

high levels of TFP, but failed to keep up with TFP growth in Austria over the

sample period. The Slovak Republic and Bulgaria had TFPs in industry that

were about one-half of Austria’s.19 Because industry continues to be a major

part of the new members’ economies, poor performance vis a vis Austria is a

significant hindrance to catch-up.

The TFPs for services are reported in Figure 4. None of the transitioneconomies matches Austria’s services TFP although Slovenia, the Czech

Republic and the Slovak Republic are near, but closing the gap only very

slowly. The three Baltic Republics and Hungary, while staring at relatively

low TFP levels, all made significant improvements in service sector TFP over

the sample period. In contrast, Poland and Bulgaria had low TFPs and failed

to make much headway in catching up with the other economies.

This analysis of relative TFP performance yields several conclusions. The

first is that TFP in agriculture is not a major barrier to catch-up for many new

Figure 3: Industrial TFP Relative to Austria.

19 The divergence in industrial TFP performance in the transition economies is somewhat


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members because they already have achieved relatively high productivity

levels. Moreover, agriculture plays a diminishing role in aggregate economic

activity. Second, some countries, such as Bulgaria, have significant problemsin achieving acceptable rates of TFP growth in industry and services, and,

given the growing share of these sectors in aggregate output, this poor

performance is a real barrier to these countries’ efforts to catch up to the

average EU per capita income. Conversely, some of the transition economies,

such as the Baltic States and Hungary, are making good progress in raising

TFP levels in both industry and services, and these countries thus should also

experience high aggregate TFP growth that will facilitate their convergence to

EU living standards.

Sources of economic growth in new EU member countries

So far, we have shown that the new EU member countries have not had their

growth severely hampered by structural change, and that they differ

considerably among themselves with respect to which of their sectors’ TFP

levels are closest to those of Austria and which are contributing the most to

aggregate TFP growth. In this section we return to the question of aggregate

TFP growth and catch-up for the new EU member countries. In the

introduction, we noted that the greatest obstacle to measuring aggregate

TFP growth in the transition countries lies in estimating the capital stock. Wetherefore use Equation 12 to compute the aggregate capital stock for our

l f f h d h

Figure 4: Service TFP Relative to Austria.


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beginning of this paper, then the modelled stock of capita would exhibit faster

growth from this lower starting point than would the official capital stock

data. Moreover, if this difference in growth rates were significant, then a

growth accounting exercise based on our estimates of the capital stock would

show a faster growth rate of the capital stock and a correspondingly lower

growth of TFP than would be obtained by undertaking the same exercise

using capital stock series that did not adjust for the excess destruction of capital.

Table 7 provides the aggregate capital stocks estimated from Equation 12

for each country for the period 1995–2005. All the transition economies

experienced faster capital stock growth than did Austria, but there were also

important differences among the transition countries themselves, with the

Baltic Republics noteworthy for the rapid growth of their capital stocks.

Table 8 then sets out the results of the growth accounting exercise based on

the growth of GDP and of our estimates of the capital stock in the transition

countries. As can be seen, GDP, the capital stock and TFP (except in the CzechRepublic) grew more rapidly in the new member states than they did in

Austria over our sample period. However, only in Estonia and Hungary did

TFP growth contribute more than 50 percent of GDP growth, and only in

these two countries did the contribution of TFP growth to GDP growth exceed

that of Austria, while in Latvia and Lithuania, TFP growth accounted for

about the same percentage of aggregate growth as it did in Austria. In the

other transition economies, capital accumulation was the main driver of

aggregate growth.

The extent to which the transition-induced excess depreciation of thecapital stock affects our perception of TFP growth of the transition economies

d ff l d b f l k f h h l

Table 7: Estimates of Aggregate Capital Stock, 1995=100

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Austria 103 107 110 114 117 120 124 127 130 134Bulgaria 109 118 127 136 145 153 162 171 181 191Czech Republic 108 115 122 129 135 141 147 153 159 164Estonia 113 126 139 154 169 185 202 220 239 260Hungary 107 115 122 130 138 146 154 163 172 181Latvia 115 131 148 166 185 205 226 250 275 302Lithuania 113 126 140 155 170 186 202 220 238 258Poland 113 126 138 151 163 175 187 200 212 224Slovak Republic 109 119 127 136 144 152 160 168 175 182Slovenia 113 126 138 150 161 172 182 192 201 210


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Table 8: Contributions of Capital Accumulation and TFP Growth to GDP per capita growth 1995–2005

Growth 1995–2000 (percent) Austria Bulgaria Czech Rep Estonia Hungary Lat

GDP 19.71 49.32 27.46 107.00 57.73 11

Capital 33.66 90.68 64.01 159.65 80.79 20

TFP 9.62 22.12 8.26 59.10 33.50 5

Contributions of TFP growth to GDP growth as percentage of total growth

Our Estimate 1995–2005 48.8 44.8 30.1 55.2 58.0 4

Rapacki and Próchniak (2009) 1995–2003 68.5 103.7 112.5 43.0 6

C om p ar a t i v e E c on omi c S t u d i e s


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Perhaps the closest in comparability are estimates of the share of TFP in real

GDP growth provided by Rapacki and Próchniak (2009) who calculated the

annual contribution of TFP growth to real GDP growth for transitioneconomies using total employment and perpetual inventory capital stock

estimates based on gross investment for the period 1990–2003. We averaged

Rapacki and Próchniak’s annual estimates over the 1995–2003 period, and

these are also reported in Table 8. In their estimates, which do not account

for excess depreciation of capital, TFP growth accounts for a significantly

higher share of GDP growth, in some cases over 100 percent of it. Some of

the difference between their estimates and ours may be the result of

differences in measuring the labour input, but, as suggested at the start of this

section, our estimates attribute a larger role to capital accumulation in

the convergence process than do estimates that do not account for the

transition-induced destruction of capital and the subsequent faster growth of

the capital stock.

CONCLUSIONS AND POLICY IMPLICATIONS

In this paper we have estimated the TFPs of the transition economies thathave joined the EU and of a roughly comparable ‘old’ EU member, Austria,

which has a per capita income that is higher than that of any of the new

members. These differences in per capita income mirror differences in

aggregate TFP, as well as in differences between sectoral TFPs, which on

average are also lower than those of Austria. However, the TFP gaps between

Austria and the new members we observed are not uniform across sectors of

the economy. The TFP gap appears smallest in agriculture and greatest in

industry or services depending on the country. Moreover, the new member

countries differ in the relative TFP levels of the three sectors vis a vis Austria.A second finding is that the transition economies themselves should not

be seen as a homogeneous group. There are great TFP differences between

them, and perhaps more troubling, some of them are not improving

productivity in industry or services or both, suggesting that catching up with

the EU average may prove an impossible task for them. The structural

changes taking place in the transition economies mirror, but appear to be

faster than, those taking place in Austria, and, indeed, in virtually all EU

member countries. The proportion of the labour force employed in agriculture

is falling, as is that of those employed in industry, while services employ thelargest and increasing share of the labour force. This structural change is not

f bl f h h h h


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in services and particularly in industry. The gap between Austria and the

transition economies in services TFP is not large for some of the transition

countries, but more troubling is that about one half of the transitioneconomies are not catching up with Austria in this key sector. Thus, if there is

to be convergence in per capita GDP between the old and the new members of

the EU, then measures to improve productivity in services and, perhaps as

well, in industry will be required.

Also noteworthy is that the aggregate TFP growth of each of the transition

economies is based on strikingly different TFP dynamics and levels in the

three sectors. There is a tendency when discussing the growth and income

levels of the transition economies to assume that reforms, liberalisation, and

greater reliance on the market, as captured by broad indicators of economic

reform, such as the EBRD reform index, rankings of ‘competitiveness’, etc

influence the performance of all sectors of the economy in more or less the

same way. However, our results suggest that this may not be the case.

Countries with broadly similar reform policies and reform ‘scores’ have

different sectoral TFP levels and dynamics. This suggests that our measures

of reforms may be too broad or that more specific policies that often go

unnoticed play a more important role in determining TFP levels and growth

than is commonly thought.Finally, our results suggest that the new members do not differ from the

old EU members in terms of the contribution of capital accumulation and TFP

growth to the growth of GDP. Our estimates, which account for the transition-

related destruction of capital in Eastern Europe, indicate that capital

accumulation has played an important role in per capita income convergence,

and thus future convergence also depends on continued high rates of capital

accumulation.

Acknowledgements

We thank Athanasios Vamvakidis, Paul Wachtel and annonymous referees for

helpful comments.

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APPENDIX: DATA SOURCES

For the United States, the data for GDP per capita, expressed in 1990

international Geary-Khamis dollars, is from the Historical Statistics for the

World Economy: 1-2003 AD by Maddison. The shares of sectoral hours

worked and the price of services relative to industry are from the Groningen10-sector industry database. We obtained average capital-to-output ratio and

average investment-to-output ratio from the NIPA tables. The price of services

relative to industry is calculated using data from the Gronigen 10-sector

industry database. The database shows the value-added of each sector in

constant and current prices. The price of a sector is obtained by dividing the

value-added in current prices by the value-added in constant prices. For

Austria and the transition countries, GDP per capita in constant 2000 PPP

dollars and the sectoral employment shares are from World Development

Indicators Online Database.


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