Labor reallocation and firm growth: benchmarking transition countries against mature market economies
© Mitra et al.; licensee Springer. 2014
Received: 10 March 2014
Accepted: 27 May 2014
Published: 6 August 2014
This paper uses firm-level survey data to study labor reallocation and firm growth in the transition countries over 1996–2005, including benchmarking against developed market economies. The data shows rapid growth of the new private sector and of the micro- and small-firm sectors, with the size distribution of firms moving towards the pattern observed in comparable surveys of developed market economies. Throughout, the regional patterns suggest greater convergence in the transition countries that joined the European Union in 2004 than in the other, lower-income transition economies. We also find evidence of Kuznet-Chenery type structural change across sectors.
L11, O12, P31.
KeywordsTransition Convergence Enterprise restructuring Labor reallocation
To use the terminology of classical growth theory, the period of socialism put the transition countries (TEs) off the path of “convergence” with, or “catching up” to, the mature or developed market economies (DMEs). The gap in per capita output between centrally planned economies and mature market economies stopped closing in the 1970s and 1980s. The underperformance of the centrally planned economies was a key factor that triggered the dissatisfaction with central planning and market socialism and the move from plan to market in Central and Eastern Europe and the former USSR. The expectation was that the return to the market would put these countries onto a growth path that would lead eventually to convergence with the mature market economies operating at the world technological frontier.
Importantly, such catching-up at the aggregate level may be decomposed into changes (or convergence) at lower levels, in particular, convergence in technology and productivity and convergence in economic structure. First, the inherent inefficiencies of central planning implied lags in innovation and diffusion of technology, resulting in a virtual stagnation in the 1980 and “equilibrium technological gap” (Gomulka 1986). Abandoning central planning would enable adoption of improved technology and management practices and enable the resumption of catching-up. Second, at the onset of the transition, the countries of Eastern Europe and Central Asia looked very different from the market economies at similar levels of income in terms of endowments and the structure of economic activity: large but low productivity industrial sectors, small agricultural sectors that would be more typical of richer, industrialized countries, and small services sectors. It was expected that the transition process would generate growth by reallocating factors away from the excessively-large industrial sector and into the market services that the central planners had repressed (Döhrn and Heilemann 1996; Raiser et al. 2004).
The process of economic transition, which in the long run manifests itself in improved macro indicators, should therefore be visible in more immediate indicators at the micro-level, which allow observing the “mechanics” of the process (Carlin et al. 2013). Indeed, productivity growth and reallocation at the firm level underlie both types of productivity-driven convergence in transition economies. Inefficient state-owned industrial firms were expected to downsize; new private firms would spring up, filling market niches that were neglected by the central planners; firms would adopt proven Western technology, production methods, and product standards; both new private firms and privatized state-owned firms would see efficiency improvements driven by the incentives brought by private ownership.
There is a large literature that deals with various aspects of reallocation at the firm level in the transition countries. The bulk of this literature focuses on labor reallocation as the most accurate and reliable indicator of changes at the micro-level. One of the first such studies is Konings et al. (1994), based on comprehensive data from manufacturing firms in Poland in the early transition. In that study, the authors find a large drop in net employment in state-owned firms driven by a jump in the job destruction rate and a disproportionate job creation in the private sector. In addition, they find that small firms appear to be considerably more dynamic than large firms.
Similar results indicating that small and new private firms contribute disproportionately to job creation while state-owned firms are responsible for most of the job destruction are reported for Bulgaria, Hungary and Romania (Bilsen and Konings 1996), the Czech Republic (Jurajda and Terrell 2002), and Estonia (Haltiwanger and Vodopivec 2002). Interestingly, the study by Jurajda and Terrell (2003), which contrasts the experience of Estonia and the Czech Republic, two countries that had quite dissimilar transition paths with a rapid destruction of pre-existing firms in the former and a gradual contraction of the old sector in the latter, finds that the growth of small start-up firms is similar in the two countries1. The importance of new private business (as compared to the restructuring of the existing firms) for a successful transition is also emphasized in McMillan and Woodruff (2002).
Another important conclusion from the literature is that the patterns of employment growth, job creation and job destruction vary over the transition period: job destruction dominates job creation in the early transition period, but the magnitude of the two processes converges at the later stages. In particular, already by 1995 the job reallocation rates in the CEE countries are similar to those in mature capitalist economies (about 20 percent) with roughly equal job creation and destruction rates (Davis and Haltiwanger 1999)2.
More recently, Earle (2012) revisits the magnitude and determinants of the labor market flows associated with the fall in Romanian industrial employment in the early transition. On the background of a large drop in aggregate industry employment, with declines in each of the disaggregated two-digit sectors, substantial gross flows in both directions are documented. Workers from the private sector appear to have a greater probability of exiting their industry, as well as higher probabilities of finding jobs in services, as compared with workers in state-owned companies.
Jackson and Mach (2009) study reallocation of labor in Poland over the 1988–1998 period. They document job losses in state-owned firms and job creation in new private firms as dominant trends in employment dynamics, other than exit from the labor force. A significant share of this involves a spell of unemployment or exit from the labor force before obtaining a new job in the private sector. Dong and Xu (2009) examine the patterns and determinants of the labor restructuring process in China between 1998 and 2002. They document substantial labor retrenchment in the public sector. In contrast to transition countries of Central and Eastern Europe, China has experienced a more synchronized pace of job creation and destruction and higher rates of excess job reallocation, although even there a large chunk of the displaced workers had extremely long spells of unemployment or permanently left the labor force3.
Despite these numerous contributions, the evidence on labor reallocation and firm growth – defined as either employment or sales growth – in the transition countries remains fragmented and incomplete. In particular, there are lacunas in terms of both country coverage and time coverage, and more importantly in terms of comparing different transition countries with each other as well as with mature market economies. While several studies have noted convergence of the transition countries with mature market economies, e.g. in terms of the size distribution of firms (Jurajda and Terrell 2003) and rates of job destruction and creation (Faggio and Konings 2003), the evidence of such convergence remains scarce.
In this paper we use cross-country firm-level data to analyze the convergence process in the transition economies. We ask several research questions. What are the patterns of labor reallocation and firm growth in the transition countries? To what extent are the differences in the employment growth rates observed across regions due to the differences in the endowments of the respective economies –specifically, ownership, sectoral distribution, and size of firms? Or do they stem from different relationships between these characteristics and firm growth across the regions? For example, we expect that “traditional” (state-owned and privatized) firms will contribute less to employment growth than the new private sector. The sectoral distribution of employment across traditional and new private firms is different in the EU8 countries4 and in the CIS countries. How much of the difference in the employment growth rates between the two groups of economies should we attribute to having different employment shares of traditional firms? And how much should we attribute to the fact that the employment growth rates of traditional firms are different in these two groups of economies, reflecting different progress in enterprise restructuring?
To address these issues, we take advantage of the data from the Business Environment and Enterprise Performance Surveys (BEEPS) which are large-scale surveys of firms that have been implemented in the transition countries since the late 1990s. In particular, we use a series of three snapshots of virtually all transition economies in 1999, 2002 and 2005. The first year of the BEEPS surveys is 1999, and it happens also to be the first post-financial crisis year, when the transformational recession is more or less over, and growth starts across the region. We are able to analyze six years of change, convergence and growth. In addition, we have BEEPS data from developed and cohesion Europe (Germany, Spain, Greece, Ireland, Portugal) collected in 2004/05. This allows us to benchmark the TEs against established market economies in 2004/05, before the disruption of the world financial crisis that started in 2008.
The major strength of our study is a comprehensive analysis of firm restructuring in virtually all transition countries and over a long period of time is. In addition, we are the first, to our best knowledge, to benchmark the patterns of labor reallocation and firm growth in transition countries against developed economies using compatible data. The use of decomposition techniques in the analysis of labor reallocation and firm growth is another distinct feature of our analysis.
The paper is organized as follows. Section 2 describes the survey and data as well as introduces the country classification we use. Section 3 describes the methodological approach chosen. Section 4 presents evidence on labor reallocation and firm growth from the BEEPS surveys. Section 5 concludes.
2. The survey and data
2.1. The survey
BEEPS composition by country (number of firms sampled)
Bosnia & Herzegovina
Serbia & Montenegro
The BEEPS were originally planned as a repeated cross-sectional survey. However, starting from the third wave (2005), some of the previously sampled firms were re-interviewed, which results in a (restricted) panel sub-set of the data6. Although the BEEPS survey instrument has been modified each time it was implemented, the range of questions that remained consistent across surveys is substantial, especially in 1999, 2002, and 2005. In 2008, there was a significant change in the survey instrument which makes more recent data not fully comparable with the data from the earlier waves.
BEEPS composition by firm size
An important sampling restriction in the BEEPS is that only firms that have been in business for at least three full years are considered. For example, the 2005 wave excludes firms that were established in 2002 or later. This restriction allows to include in the questionnaires a set of 3-year retrospective questions that help in tracing changes in firm dynamics8. Its main disadvantage is that the survey may sample “better” firms, those able to survive for at least three years. Such a survivor bias may be particularly important for small startups in the private sector, among which the exit rate is usually high.
In each firm, face-to-face interviews were conducted with the “person who normally represents the company for official purposes, that is who normally deals with banks or government agencies/institutions”. In small firms, interviews usually involved one respondent. In larger enterprises, the principle respondent often had to consult with accountants and personnel managers, especially in the case when detailed data about the firm (such as sales and employment) were requested9.
The main strength of the survey, from the point of view of this paper, are the use of a consistent survey instrument across virtually all transition countries and range of market economy comparators, and, for the transition countries, over a substantial period of time. The main weakness of the BEEPS is the consequence of the wide coverage and finite budgets: the sample sizes for individual countries are relatively small. Even in the biggest BEEPS round in 2005, most country samples have fewer than 400 firms. In the first BEEPS surveys in 1999, a typical country sample had about 150 firms.
2.2. The data
Due to the change in the survey instrument in 2008, we have decided to use the first three waves of the survey – 1999, 2002 and 2005. The retrospective questions allow us to cover almost a decade of the transition, from 1996 to 2005, in almost all the transition countries of Central and Eastern Europe and the former Soviet Union. In addition, we use data from developed market economies of Western Europe (Germany, Greece, Ireland, Portugal, and Spain) collected in a special wave of the survey in 2004/200510. Table 1 shows the composition of the BEEPS by country and year of implementation.
Small sample sizes for individual countries makes analysis at the country level a problematic endeavor. Indeed, too great a degree of disaggregation in the analysis would results in systematic differences across countries and over time being swamped by noise in the data. We therefore aggregate across countries in much of our analysis.
Cohesion countries (Greece, Ireland, Portugal, and Spain)
EU8 (new members as of May 2004) (the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, the Slovak Republic, and Slovenia)
Lower middle income transition countries (Albania, Bosnia and Herzegovina, Bulgaria, Macedonia, Romania, Serbia and Montenegro, Belarus, Kazakhstan, the Russian Federation, and Ukraine)
SEE (Albania, Bosnia and Herzegovina, Bulgaria, Macedonia, Romania, and Serbia and Montenegro)
Middle income CIS (Belarus, Kazakhstan, the Russian Federation, and Ukraine)
Low income CIS (Armenia, Azerbaijan, Georgia, Kyrgyzstan, Moldova, Tajikistan, Turkmenistan, and Uzbekistan)
We will sometimes refer to Groups I and II as the “pre-2001 EU” or “developed market economies” (DMEs), and groups III-V as “transition economies” (TEs).
GNI per capita in the BEEPS countries
GNI per capita 1999, Atlas method, current USD
GNI per capita 2004, Atlas method, current USD
Rank in 1999
Rank in 2004
Bosnia & Herzegovina
Serbia & Montenegro
In this study, we rely on two principal methods of analysis. First, we use descriptive analysis of the data to produce a general picture of labor reallocation and firm growth in the transition and comparator countries. The descriptive analysis is instrumental not only for highlighting the differences between countries and groups of countries, but also for identifying the time trends in the key variables of interest.
where R denotes the raw differential between the means of the dependent variable y measured for two groups of observations, x is the row vector of the means of the explanatory variables x1,…,xk, and β1 and β2 the column vectors of the coefficients for the two groups obtained in the regression analysis12. In the final part of the expression, E = Endowments, C = Coefficient, and CE = Interaction of C & E. The question that usually comes up is how to allocate CE. In the Oaxaca-Blinder decomposition, it is allocated along with coefficients, so that Explained = Endowments and Unexplained = Coefficients + Interaction. However, CE can also be allocated to E, or even divided between E and C. In what follows we allocate the interaction effect along with the coefficient effect. Our decomposition analysis is based on Ben Jann’s (2005) “decompose” addin for Stata.
We consider 3 sources of differences in growth: ownership, sectors, and size. The decompositions are performed for the following groups of countries:
Cohesion group versus EU8 member states
EU8 member states versus SEE group
EU8 member states versus CIS group
SEE countries versus CIS economies.
In these comparisons, the first group plays the role of a benchmark (leaders) while the second group embraces the countries that, according to the transition literature, can be regarded as convergers or followers.
There are almost no privatized and state firms in our sample of cohesion countries, and we therefore drop any remaining privatized and state firms from the Cohesion group. The TE groups retain these. The benchmark category (excluded from the decomposition regressions) is new private firms. The regressions contain 2 dummy variables for the remaining ownership categories, privatized and state-owned firms. They also include 6 dummy variables for sectors. For simplicity of estimation and interpretation, we do not interact sector and ownership dummies thus assuming that the sector growth patterns do not vary by ownership. In contrast, size effects in our specifications can vary by ownership, since we want to separate size effects from ownership effects (for example, new private firms can grow fast because they are small and/or because they are entrepreneurial). Therefore, we interact size (average employment over 2002–05 measured in thousands) and ownership to get size-ownership effects for the TEs.
4. Empirical analysis
4.1. Descriptive analysis
Balance between growing and shrinking firms (share of sample) by country group
I. W. Germany
IVb. Mid inc CIS
V. Low inc CIS
nb: E Germany
The picture in terms of employment is more muted. The number of firms with expanding employment in the EU8 has barely exceeded the number of downsizing firms from the very start of the BEEPS surveys. This share is, moreover, low compared to that in the cohesion countries. Here we see the first indication of a possible failure of convergence: evidence of possible stagnating job growth in the new EU members. We will return to this point below. The pattern in the other regions is quite different: in SEE, firms expanding employment have markedly outnumbered firms shedding labor since 1996–99; and in the middle income CIS and low income countries, stagnation in 1996–99 is replaced by large-scale expansion in more recent years.
Balance between growing and shrinking firms (share of sample) by ownership type, TEs only
Job reallocation rate (JRR), job creation rate (JCR), job destruction rate (JDR) and job growth rate (JGR), by country group
Developed market economies (2004/2005):
Transition economies (2005):
Mid inc CIS
Low inc CIS
Transition economies (2002):
Mid inc CIS
Low inc CIS
Transition economies (1999):
Mid inc CIS
Low inc CIS
How do job creation, destruction and reallocation compare in transition and developed market economies? Previous such comparisons have been made by Konings et al. (1994); Davis and Haltiwanger (1999); Haltiwanger and Vodopivec (2002), and the papers in the symposium edited by Haltiwanger et al. (2003). These studies typically show that during the socialist period and in the early years of transition, gross job creation rates in state-owned manufacturing did not change hugely and were similar to those in the OECD, while job destruction rates in the state-owned sector following the start of transition increased dramatically and then decline. New private sector firms, by contrast, show high rates of job creation, job destruction, with the former predominant especially in the early phase of transition. It should be noted that such comparisons need to be interpreted with caution as they were hampered by lack of full compatibility of samples; in particular, studies for TEs have typically used firm-level data, whereas studies of JC/JD in Western economies have used establishment-level data. In this respect, the BEEPS data offer a better opportunity for such comparison. The data presented in Table 6 show that for the later transition period, the job reallocation rate is actually no higher in the TEs than in the cohesion countries – about 20% – and has been very steady in the TEs.
4.2. Decomposition analysis
The decompositions come in two forms: (a) decomposition of aggregate or total employment growth, which is obtained by using regressions weighted by average employment; (b) decomposition of average or firm employment growth, which is obtained by using unweighted regressions. The former shows aggregate employment effects and is comparable to the analysis of job creation and destruction presented above (Table 6). The latter is comparable to much of literature on the growth of firms, and to our analysis above (Tables 4 and 5). The definitions of growth are the same as those used for the job creation/job destruction growth rate definitions.
In what follows, we discuss the results of our decomposition analysis using graphical representation. The Additional file 1 contains additional details regarding the decomposition analysis for the pair of cohesion and EU-8 countries. Full results for all groups of countries are available in Mitra et al. (2008).
As noted already, another test of the convergence hypothesis is to use data on reallocation across industrial sectors in the course of transition, where we would expect to see a Kuznets-Chenery-type pattern19. Raiser et al. (2004), in a study of 20-odd transition countries, divide total employment into broad sectors (agriculture, industry, markets services and nonmarket services), and show that employment shares during the transition have generally moved towards benchmarks calculated from a sample of market economy comparators: in particular, the share of industry has fallen and the share of market services has risen in all TEs. These patterns are also evidenced in the relative growth rates of firm employment and jobs in the BEEPS surveys, but with a twist. The employment growth regressions for 1999, 2002 and 2005 show that employment in trade and services firms has grown consistently faster than in manufacturing firms in the EU8 countries20. The twist is that, for the lower-income TE country groups (SEE and CIS), the differential switches size and manufacturing firms grow as fast as services firms in 1996–99 and then faster than services firms in 1999–02 and 2002–05. When we look at aggregate employment growth (i.e., job growth), however, the pattern is different for TEs as a whole – net job growth is slower in manufacturing throughout the period – which is consistent with the findings by Raiser et al. (2004)21. In short, we have evidence at the firm level of two different Kuznets-Chenery-type patterns. In the higher-income TEs, the lower rate of employment growth in manufacturing relative to services reflects primarily a convergence to market economy benchmarks driven by industrial sectors that were “too large” at the start of transition, and market services sectors that were “too small”. In the lower-income TEs, the observed pattern of relatively higher rates of employment growth in manufacturing relative to services is consistent with a bigger impact of the standard Kuznets-Chenery-type pattern in which, as a country develops and productivity grows, employment in manufacturing first increases and then decreases.
In sum, the picture painted by the BEEPS data is broadly consistent with both the basic macroeconomic trends in the region, and with previous sectoral and firm-level studies: following the “transformational recession” (Kornai 1994) of the mid-1990s, TEs have been growing, and at a faster rate than that observed in the developed market economies – convergence is under way. The pattern of growth at the country, sectoral and firm level show more rapid growth in the private and especially new private sectors, movement in the size distribution of firms towards the pattern of large numbers of small firms as seen in developed market economies, more evidence of convergence in the new EU members than in the poorer TEs, and evidence as well of Kuznet-Chenery type structural change across sectors.
The move from plan to market in Central and Eastern Europe and the former USSR was to a large extent driven by the expectation that the return to the market would put these countries onto a growth path that would lead eventually to convergence with the developed market economies operating at the world technological frontier. More than two decades after the start of the transition process, it is the right time to ask if such convergence has indeed been taking place. Most existing studies of convergence focus either on the macro aspect (convergence in terms of per capita GDP) or the micro aspect (convergence in firm productivity). Our study belongs to the second group and focuses on labor reallocation and firm growth.
We use data from several waves of the BEEPS exercise; due to a number of unique features, these data are particularly appropriate for studying the process of convergence in the transition economies. The BEEPS consist of a series of 3 snapshots of virtually all transition economies in 1999, 2002 and 2005 and covers random and representative samples from these countries. In addition, the 2005 survey contains firm level data from a number of developed market economies, which makes it possible to directly benchmark TEs against these economies.
Our analysis of firm growth, sectoral changes and changes in size distribution of firms provides a clear picture of the convergence process. Overall, the BEEPS data show a faster growth of firms in the TEs compared with the developed market economies. The pattern of growth at the country, sectoral and firm level shows more rapid growth in the private and especially new private sectors, movement in the size distribution of firms towards the pattern of large numbers of small firms as seen in developed market economies, more evidence of convergence in the new EU members than in the poorer TEs, as well as evidence of Kuznet-Chenery type structural change across sectors.
2A thorough survey of these and other earlier studies analyzing employment growth, job creation and destruction in the transition economies is provided in Haltiwanger et al. (2003).
3We thank the reviewer for pointing this out. See also Giles et al. (2006).
4The Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, the Slovak Republic, and Slovenia.
5Details about the survey are available at http://ebrd-beeps.com/ (as accessed on April 20, 2014).
6For example, the two-wave panel covering the years of 2002 and 2005 contains about 1,400 observations. Due to the small number of observations in the panel, we ignore this feature of the BEEPS in the analysis that follows.
7A small number of firms in agriculture, fishery, forestry as well as in power generation in the 1999 BEEPS survey are classified in this paper as manufacturing.
8The respondents are usually asked to give two values, as of the time of the interview and 36 months ago. This is, for example, the case of employment. In some cases, they are asked to provide the growth rate (in real terms) over the last 36 months. This is the case of sales and other financial variables, which, if recorded in local currencies, would be very hard to deal with in a cross-country study.
9See, e.g., the 2005 report on survey implementation available online at http://ebrd-beeps.com/wp-content/uploads/2013/09/beeps2005r1.pdf (as accessed on April 20, 2014).
10Note that the retrospective questions only allow us to generate three-annual growth rates of key variables such as sales and employment. This can lead to potential underestimation of the dynamic adjustment in a given country or sector. For example, a small fall in employment over three years in a particular firm may be caused by a large fall in year one, a large rise in year two and a relatively small fall in year three. We believe, however, that such profiles are very unlikely and the bias from a wider than usual time horizon is not very strong. Importantly, this bias should be in the same direction for both transition and developed economies and is therefore of lesser importance in comparative studies such as ours.
11We admit that the aggregation scheme may mask some important differences between countries from the same region. Most strikingly is this in the case of SEE, where we have “basket cases” like Bosnia and Herzegovina (which relied heavily on EU funds) and Macedonia (which in the 1990s had a completely immobile labor market with extremely small job creation, but also with tiny flows between labor market states – see, e.g., Lehmann (2010)) and countries like Romania and Bulgaria. However, as already noted, the small sample sizes make it virtually impossible to conduct analysis at a more disaggregate level.
12In the regression analysis, the dependent variable is the growth rate of employment, and the regressors are a set of sector dummy variables, a size variable (log employment), dummy variables for state and privatized firms, and the interaction of the state and privatized dummies with the size variable.
13Job creation rate (JCR) is defined as the sum of all employment gains in the expanding firms in the economy divided by total employment, job destruction rate (JDR) is the sum of all employment losses in the contracting firms divided by total employment, job reallocation rate (JRR) is the sum of the two (JCR + JDR) and job growth rate (JGR) is the difference between JCR and JDR.
14This is particularly true of SEE countries. Although the number of firms that expanded employment in this region in 1996–1999 was far larger than the number of firms that downsized, the JDR was considerably larger than the JCR.
15These numbers are the predicted values from the regression of employment growth in the Cohesion countries on sectoral dummies and firm size variable (the ownership variables are missing because the Cohesion sub-sample only includes new private firms).
16These numbers are the predicted values from the regression of employment growth in the new UE members on sectoral and ownership dummies, firm size variable, and its interaction with ownership variables.
17State and privatized firms are particularly large in the TEs with the result that their slower growth has a more deleterious impact on aggregate rather than average employment growth.
18What is driving the smaller negative contributions of the state and privatized sectors in the decomposition of average as opposed to aggregate growth is that these sectors consist of firms which are relatively large and make a bigger negative contribution to aggregates than to means. We can see this by comparing the values for “State” and “Privatized” in the “Mean” columns in the weighted and unweighted results in the Additional file 1. In the weighted results, these are the values of aggregate employment in the sample, i.e., in the New EU sample, SOE + Privatized = 0.360 + 0.255 = 61.5% of employment; in the SEE/CIS sample, SOE + Privatized = 0.318 + 0.366 = 68.4% of employment. In the unweighted results, SOE + Privatized = 0.078 + 0.092 = 17.0% of firms in the New EU sample, and = 0.086 + 0.129 = 21.5% of firms in the SEE/CIS sample.
19See Kuznets (1955, 1965) and Chenery and Taylor (1968), Chenery and Syrquin (1975). As market economies develop, their structure changes in various ways. In particular, the share of agriculture in GDP and employment falls and the shares of manufacturing and services increase. The sources of these changes in the size of sectors have been modeled by Rowthorn and Ramaswamy (1997) amongst others as driven by (exogenous) differences in productivity growth across sectors. Convergence by the former socialist economies in this context would generate growth by reallocating factors away from the excessively-large industrial sector and into the market services that the central planners had repressed (Döhrn and Heilemann 1996; Raiser et al. 2004).
20These and other results discussed in this paragraph are not shown in the paper, but are available on request from the authors.
21The explanation for this contrast is as follows: Raiser et al. (2004) and other studies that have looked at structural change in this framework use shares of total employment, whereas the faster growth of manufacturing firms relative to services firms in the lower-income TEs that we report is based on firm level data, and as already noted, the changes in employment in smaller firms play a larger role in the latter because of the growth of the small firm sector in TEs.
The first version of this paper was written when Mitra was Chief Economist, Europe and Central Asia Region, World Bank and appeared as “Convergence in institutions and market outcomes: cross-country and time-series evidence from the BEEPS surveys in transition economies” (World Bank Policy Research Working Paper No. 4819). Views expressed are the authors’ and do not necessarily reflect those of the World Bank.
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