Total factor productivity growth, structural change and convergence in the new members of the European Union.
Bah, Hadj El- M. ; Brada, Josef C.
INTRODUCTION
A key task for the transition economies that have recently joined
the European Union (EU) is real convergence, the catching up with the
per capita income levels of the older and more developed members.
Although some observers have stressed that this process would require
extensive investment 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 was Kaplan (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 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
been entirely 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 rapid growth 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 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, and they 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 characteristics could
only be introduced into newly built and staffed factories bur 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 in the
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
convergence. 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 be found 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 favour of services at the expense of agriculture and industry occurs
with rising per capita incomes in nearly all market economies is
unclear. In either case, structural change is more rapid in the new EU
member countries so it remains 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 accounting
exercises for the transition economies at the sectoral level, thus
measuring TFP levels and their evolution over rime, bur in reality we
face a fundamental problem in estimating the stock of capital. The
transition from socialism to capitalism effectively destroyed a large,
bur 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 driving the 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)
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 plausible official 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 used by 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 transition
economies. With a few exceptions, in all sectors Austrian TFP exceeds
that of 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-up with 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 TFP growth
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 rime O 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:
(1) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [A.sub.t] is the agricultural good and [[PHI].sub.t] is a
composite consumption good defined as a CES aggregate of the industrial
good ([M.sub.t]) and the services ([S.sub.t]).
[[PHI].sub.t] = [([lambda][M.sup.[epsilon] - 1]/[epsilon].sub.t] +
(1 - [lambda]) [S.sup.[epsilon] -
1/[epsilon].sub.t]).sup.[epsilon]/[epsilon] - 1] (2)
Lifetime utility is given by:
[[delta].summation over (t=0)] [[beta].sup.t]U([[PHI].sub.t],
[A.sub.t]) (3)
where [beta] is the discount factor.
This specification of preferences implies that the economy
specialises in agriculture until the subsistence level [bar.A] is
reached. Moreover, the economy will never produce more of the
agricultural good than [bar.A].
Technologies
All three sectors use Cobb-Douglas production functions. The inputs
for agriculture 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:
[A.sub.t] = [A.sub.at] [N.sup.[alpha].sub.at]
[L.sup.1-[alpha].sub.t] (4)
where
[A.sub.at] = [A.sub.a] [(1 + [[gamma].sub.at]).sup.t] (5)
The TFP parameters [A.sub.a], [[gamma].sub.at] are assumed to be
country specific. (11)
The industrial sector's output is used for consumption
([M.sub.t]) in the composite good and investment ([X.sub.t]). The
industry sector resource constraint is:
[M.sub.t] + [X.sub.t] = [A.sub.mt] [K.sup.[theta].sub.mt]
[N.sup.1-[theta].sub.mt] (6)
where
[A.sub.mt] = [A.sub.m] [(1 + [[gamma].sub.mt]).sup.t] (7)
The law of motion of the aggregate capital stock ([K.sub.t]) in the
economy is given by
[K.sub.t+1] = (1 - [delta])[K.sub.t] + [X.sub.t] (8)
where [delta] is the depreciation rate.
The output of the service sector is only used for consumption
through the composite good. Therefore, the service sector resource
constraint is given by
[S.sub.t] = [A.sub.st] [K.sup.[theta].sub.st]
[N.sup.1-[theta].sub.st] (9)
where
[A.sub.st] = [A.sub.s] [(1 + [[gamma].sub.st]).sup.t] (10)
In the equations above, the TFP parameters ([A.sub.m],
[[gamma].sub.mt], [A.sub.s] and [[gamma].sub.st]) 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 [A.sub.a][(1 + [[gamma].sub.a]).sup.t] <
[bar.A]. Once [A.sub.a][(1 + [[gamma].sub.at]).sup.t] [greater than or
equal to] [bar.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 the equilibrium allocations from the social planner's
problem.
Labour in agriculture is given by:
[N.sub.at] = [([bar.A]/[A.sub.at]).sup.1/[alpha]] (11)
Let [N.sub.t] = 1 - [N.sub.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:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (12)
Once capital is known, the quantity of labour used in the service
sector is given by:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (13)
where [C.sub.t] is the non-agriculture aggregate expenditure and is
given by
[C.sub.t] = [A.sub.mt] [([K.sub.t]/[N.sub.t]).sup.[theta]]
[N.sub.t] + (1 - [delta]) [K.sub.t] - [K.sub.t+1] (14)
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
[A.sub.i(i=a,m,s)] are normalised to 1 in 1950. This corresponds to
choosing units. Following the literature, labour's share in
agriculture ([alpha]) is set to 0.7 and capital's share in industry
and services ([theta]) is set to 0.3.
The TFP growth rates for industry and services ([[gamma].sub.m],
[[gamma].sub.s])the discount rate [beta]] and [delta] 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 ([[gamma].sub.at]) is chosen
such that the model matches 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
([epsilon]) and the weight of the industrial good in the production of
the composite good ([lambda]). These two parameters determine the labour
reallocation between the industrial and service sectors. We choose
values of [epsilon] and [lambda] 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.
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 for Austria 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 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 the United
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 anal sectoral TFP. The first panel shows
per capita GDP, with 1995 normalised to one. The simulated and actual
data for GDP per capita are dose to each other as are the sectoral
employment shares reported in Panel 2. (15) The shares of agriculture
and industry in employment have 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 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.
[FIGURE 1 OMITTED]
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 false 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 industry to services can be seen as a
drag on growth in the sense that resources were moved from sectors with
high rates of productivity growth to 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 5 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 the transition 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 actual changes 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 lower share 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 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 the
service sector are also quite consistent, but poor. Services is never
the best sector relative to US 1950 TFP, and most often it is the sector
that lags all others relative to US TFP. The long pre-transition neglect
of services, the clear 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 internationalisation 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 sector because 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 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 structural change 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 GDP convergence 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 grew faster than did Austria's. As a result,
by the end of our sample period, Czech TFP in agriculture was nearly 20%
higher than Austria's. This is not a surprising result because,
while the two countries share a similar continental 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 labour
from 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.
[FIGURE 2 OMITTED]
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 poor agricultural TFP showing. In these countries,
lagging agrarian productivity may require additional structural support
from the EU if the CAP is not to be burdened with inefficient farm
sectors.
[FIGURE 3 OMITTED]
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 marches that of Austria.
Nevertheless, four transition countries, Estonia, Latvia, Lithuania and
Hungary showed large gains in TFP over the sample 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
transition economies 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 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 problems in 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.
[FIGURE 4 OMITTED]
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. We therefore use Equation 12 to
compute the aggregate capital stock for our sample of transition
economies. If there were a major reduction in the starting capital stock
of transition economies for the reasons discussed at the 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 Czech Republic) 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
the capital stock affects our perception of TFP growth of the transition
economies is difficult to judge because of a lack of other estimates
that are strictly comparable in terms of both time period covered and
measurement of inputs.
Perhaps the closest in comparability are estimates of the share of
TFP in real GDP growth provided by Rapacki and Prochniak (2009) who
calculated the annual contribution of TFP growth to real GDP growth for
transition economies using total employment and perpetual inventory
capital stock estimates based on gross investment for the period
1990-2003. We averaged Rapacki and Prochniak'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 that have 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, bur
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 the largest and increasing share of the
labour force. This structural change is not necessary favourable for the
transition economies in the sense that the TFP gap between themselves
and Austria is the smallest in agriculture and larger 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 hall of the transition
economies 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.
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 Groningen 10-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.
Acknowledgements
We thank Athanasios Vamvakidis, Paul Wachtel and annonymous
referees for helpful comments.
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(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 a factor 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 the
course 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
socialist era were generally not successful (see Brada, 1985) and
evidence for the more recent period seems to confirm the general
usefulness of the Cobb-Douglas specification (Gollin, 2002).
(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 rather rapid
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, geography and so on are
important determinants of TFP levels, which means that sustained rapid
TFP growth in the East European countries would be difficult if not
impossible.
(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, but similar, 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 Foldvari 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 why human capital accumulation is not likely to be
a key driver of TFP growth. In this paper, we ignore any explicit
accounting for human capital, anal, in our results, inter-country
differences in human capital are consequently reflected in differences
in TFP. If we measure human capital by years of schooling, then the
transition economies are only mildly behind the United States, which had
12.8 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).
(10) The 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
our model to the work of Dowrick (1989) and Dowrick and Nguyen (1989) on
growth in OECD countries.
(11) Because of the way in which [A.sub.at] 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
values, although the interim values are not. Thus, in Table 4 we present
only the predicted values of agriculture's share because the actual
values are the same.
(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 variables needed 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 that we are able to replicate the time
paths of sectoral employment and per capita income in all these
countries using the US-based parameters. The data sources are explained
in Appendix.
(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 some of these
countries adjust for the informal sector, and, in any event, Austria,
has a non-trivial informal sector as well.
(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
transition countries 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
countries as well. If the model could not match the sample
countries' dynamics, it would mean that the parameters obtained
from the US-based parameterization were inapplicable.
(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 as well.
(17) A number of countries show a small reversal in that, at the
end of our period of observation, industry slightly gains labor share at
the expense of services. This may be related to large inflows of FDI and
the emergence of East Europe as a sourcing point for manufactured goods
exports to the EU.
(18) It is important to note that, in the model, labour
reallocation across sectors results from differences in sectoral TFP
growth rates and the preference specifications.
(19) The divergence in industrial TFP performance in the transition
economies is somewhat surprising. All received massive inflows of FDI,
investment rates have not differed much, and general reform measures
have been quite similar as well.
EL-HADJ M BAH [1] & JOSEF C BRADA [2,3]
[1] Department of Economics, University of Auckland, Private Bag
92019 Auckland, New Zealand.
[2] Department of Economics, Arizona State University, Box 873806,
Tempe, AZ, 852870-3806, USA.
[3] Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2,
1000 Skopje, Republic of Macedonia.
Table 1: Calibrated Parameters
Parameter [A.sub.a] [A.sub.m] [A.sub.s] [bar.A] [alpha]
Value 1 1 1 0.24 0.7
Parameter [beta] [delta] [epsilon] [[gamma].sub.m]
Value 0.975 0.05 0.30 0.019
Parameter [[gamma].sub.s] [lambda] [theta]
Value 0.009 0.02 0.3
Table 2: Per Capita Incomes as Percentage of EU-15 Average
Country 1997 2005
Austria 112.9 113.3
Czech Republic 61.9 67.8
Estonia 35.0 51.7
Latvia 29.8 43.1
Lithuania 33.3 47.1
Hungary 45.5 57.2
Poland 40.1 46.0
Slovak Republic 42.3 50.1
Slovenia 64.5 75.0
Source: EU (2006)
Table 3: Actual and Predicted per capita GDP Growth 1995-2005
Country Model Actual
Austria 19.71 20.22
Bulgaria 49.32 49.01
Czech Republic 27.46 27.44
Estonia 107.00 106.95
Hungary 57.73 57.71
Latvia 116.59 116.66
Lithuania 89.15 88.98
Poland 48.74 48.77
Slovak Republic 41.56 41.34
Slovenia 45.51 45.46
Note: The numbers are in percent.
Table 4: Actual and Predicted Sectoral Shares in Employment
Predicted by the Model
Agriculture Industry Services
1995 2005 1995 2005 1995 2005
Austria 7.25 5.08 30.68 30.34 62.07 64.58
Bulgaria 25.14 23.73 30.53 28.15 44.33 48.12
Czech Rep 6.37 4.00 40.79 38.42 52.84 57.58
Estonia 10.16 5.28 33.58 33.59 56.26 61.13
Hungary 8.38 4.96 34.61 34.62 57.01 60.42
Latvia 19.32 12.44 27.69 26.88 52.99 60.68
Lithuania 23.48 14.71 27.93 28.27 48.59 57.02
Poland 21.77 17.40 32.67 29.71 45.56 52.89
Slovak Rep 9.49 4.64 39.55 38.00 50.96 57.36
Slovenia 11.19 8.84 40.91 36.32 47.90 54.84
Actual Data
Industry Services
1995 2005 1995 2005
Austria 31.57 27.87 61.04 66.87
Bulgaria 33.31 24.68 41.59 51.58
Czech Rep 41.64 39.03 51.89 56.95
Estonia 33.48 33.41 56.35 61.31
Hungary 33.13 33.16 58.39 61.87
Latvia 26.81 26.31 53.75 61.10
Lithuania 27.83 28.26 48.69 57.01
Poland 32.41 28.52 45.78 54.07
Slovak Rep 39.14 38.40 51.36 56.81
Slovenia 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.
Table 5: Rankings of Sectoral TFPs Relative to the US--1950
Country Sector Ranking
Bulgaria Industry>Services>Agriculture
Czech Republic Agriculture>Industry>Services
Estonia Agriculture>Industry>Services
Hungary Agriculture>Industry>Services
Latvia Industry>Agriculture>Services
Lithuania Industry>Services>Agriculture
Poland Industry>Services>Agriculture
Slovak Republic Agriculture>Industry>Services
Slovenia Industry>Services>Agriculture
Table 6: Loss of GDP Per Capita Due to Structural Transformation
(as a percent of 1995 GDP Per Capita)
Country Percentage Loss
Austria 1.28
Bulgaria 0.95
Czech Republic 2.29
Estonia 3.83
Hungary 2.31
Latvia 4.47
Lithuania 6.55
Poland 2.74
Slovak Republic 4.44
Slovenia 3.16
Table 7: Estimates of Aggregate Capital Stock, 1995=100
1996 1997 1998 1999 2000
Austria 103 107 110 114 117
Bulgaria 109 118 127 136 145
Czech Republic 108 115 122 129 135
Estonia 113 126 139 154 169
Hungary 107 115 122 130 138
Latvia 115 131 148 166 185
Lithuania 113 126 140 155 170
Poland 113 126 138 151 163
Slovak Republic 109 119 127 136 144
Slovenia 113 126 138 150 161
2001 2002 2003 2004 2005
Austria 120 124 127 130 134
Bulgaria 153 162 171 181 191
Czech Republic 141 147 153 159 164
Estonia 185 202 220 239 260
Hungary 146 154 163 172 181
Latvia 205 226 250 275 302
Lithuania 186 202 220 238 258
Poland 175 187 200 212 224
Slovak Republic 152 160 168 175 182
Slovenia 172 182 192 201 210
Table 8: Contributions of Capital Accumulation and TFP Growth to GDP
per capita growth 1995-2005
Growth 1995-2000 Austria Bulgaria Czech Estonia Hungary
(percent) Rep
GDP 19.71 49.32 27.46 107.00 57.73
Capital 33.66 90.68 64.01 159.65 80.79
TFP 9.62 22.12 8.26 59.10 33.50
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
Rapacki and Prochniak
(2009) 1995-2003 68.5 103.7 112.5 43.0
Growth 1995-2000 Latvia Lithuania Poland Slovak Slovenia
(percent) Rep
GDP 116.59 89.15 48.74 41.56 45.51
Capital 202.09 157.75 124.01 82.45 110.42
TFP 55.96 41.82 11.54 16.83 12.39
Contributions of TFP growth to GDP growth as percentage of total growth
Our Estimate 1995-2005 48.0 46.9 23.8 40.8 27.2
Rapacki and Prochniak
(2009) 1995-2003 67.5 125.7 86.7 84.0 69.9
