metadata: authors: 0: author: Anos Casero,Paloma
1: author: Udomsaph,Charles Chatree
count: Poland,Romania,Ukraine,Estonia,Bulgaria,Croatia,Czechia,Serbia
docna: 0: docna: What drives firm productivity growth ?
docty: Policy Research Working Paper
subtopic: Business Environment,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Employment and Unemployment,Business Cycles and Stabilization Policies
teratopic: Private Sector Development,Macroeconomics and Economic Growth,Governance,Social Protections and Labor,Finance and Financial Sector Development
abstracts: cdata!: This paper presents new evidence on the
            causal links between changes in the business environment and
            firm productivity growth. It contributes to the literature
            in three important aspects. First, it constructs a unique
            database merging information from two large firm-level
            databases. The samples of both databases are merged on four
            criteria-country, sub-national location, firm size, and
            year-producing a panel of 22,004 firms in eight economies of
            Eastern Europe and the former Soviet Union: Bulgaria,
            Croatia, Czech Republic, Estonia,, Poland, Romania, Serbia,
            and Ukraine. Second, the paper addresses shortcomings of
            earlier studies, namely reverse causation,
            multicollinearity, and unreliable productivity estimates.
            Firm productivity growth is estimated drawing on corporate
            financial data from manufacturing firms included in the
            AMADEUS database. Changes in the business environment are
            estimated from the World Bank Enterprise Surveys conducted
            in 2002 and 2005. Multicollinearity problems in the full
            model regression are mitigated by constructing a set of six
            aggregate indicators of the business environment (using
            principal component analysis). The paper finds that, over
            the period 2001 to 2004, an increase of one standard
            deviation in infrastructure quality, financial development,
            governance, labor market flexibility, labor quality, and
            market competition raises the total factor productivity of
            the average firm by 9.8, 7.8, 3.2, 3.4, 5.8, and 3 percent,
            respectively. Lastly, the paper decomposes firm productivity
            growth and ranks the relative impact of changes in these six
            aspects of the business environment by country, by firm
            size, and by industry.
geo_reg_key: 517208,82689,517190,517191
repnme: repnme: What drives firm productivity growth ?
display_title: What drives firm productivity growth ?
listing_relative_url: /research/2009/02/10278780/drives-firm-productivity-growth
topicv3: Business Environment Indicators m1326295 1279,Manufacturing Sectors m1326521 1281,Enterprise Surveys m1504072 2762,Labor Mobility m1582535 4713,Business Environment m1586411 2530
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Private Sector Development,Macroeconomics and Economic Growth,Governance,Social Protections and Labor,Finance and Financial Sector Development
no_of_pages: 54
author_keys: 0: author_key: 621633
1: author_key: 674084
model_extractions: WPS4841 Policy ReseaRch WoRking PaPeR 4841 What Drives Firm Productivity Growth? _Paloma Anos-Casero Charles Udomsaph_ The World Bank Eastern Europe and Central Asia Department Economic Policy Sector February 2009
Policy ReseaRch WoRking PaPeR 4841

# **Abstract**

This paper presents new evidence on the causal links between changes in the business environment and firm productivity growth. It contributes to the literature in three important aspects. First, it constructs a unique database merging information from two large firm-level databases. The samples of both databases are merged on four criteria—country, sub-national location, firm size, and year—producing a panel of 22,004 firms in eight economies of Eastern Europe and the former Soviet Union: Bulgaria, Croatia, Czech Republic, Estonia,, Poland, Romania, Serbia, and Ukraine. Second, the paper addresses shortcomings of earlier studies, namely reverse causation, multicollinearity, and unreliable productivity estimates. Firm productivity growth is estimated drawing on corporate financial data from manufacturing firms included in the AMADEUS database. Changes in the business environment are estimated from the World Bank Enterprise Surveys conducted in 2002 and 2005. Multicollinearity problems in the full model regression are mitigated by constructing a set of six aggregate indicators of the business environment (using principal component analysis). The paper finds that, over the period 2001 to 2004, an increase of one standard deviation in infrastructure quality, financial development, governance, labor market flexibility, labor quality, and market competition raises the total factor productivity of the average firm by 9.8, 7.8, 3.2, 3.4, 5.8, and 3 percent, respectively. Lastly, the paper decomposes firm productivity growth and ranks the relative impact of changes in these six aspects of the business environment by country, by firm size, and by industry.

This paper is a product of the Economic Policy Division, Eastern Europe and Central Asia Department. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at panoscasero@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **What Drives Firm Productivity Growth?**

Paloma Anos-Casero* and Charles Udomsaph** World Bank Keywords: total factor productivity, business environment, transition economies JEL Codes: D24, O12, P27

*Paloma Anos-Casero is Senior Economist at the World Bank (email: <u>panoscasero@worldbank.org). Charles Udomsaph is consultant (STC) at the World Bank (email:</u> cudompsaph@worldbank.org)
# **1. Introduction**

This paper addresses a central question in the recent literature on the microeconomics of growth: What is the impact of changes in the business environment on firm productivity? Institutions and policies determine the business environment within which individuals accumulate skills and firms accumulate capital and produce output. Regulations and laws exist to protect against diversion, but are often instruments of predation in an economy. A good business environment reduces rent seeking activities, supports productive activities, and encourages skill acquisition, capital accumulation, and innovation.

This paper builds upon the recent research by Dollar, Hallward-Driemeier, and Mengistae (2003), Bastos and Nasir (2004), and Escribano and Guasch (2005) in using data from recent World Bank Enterprise Surveys to link indicators of the business environment to firm-level productivity. These studies have done much to overcome the many shortcomings of the macroeconomic literature on this topic (Knack and Keefer, 1995; Hall and Jones, 1999; Acemoglu, Johnson, and Robinson, 2001).<sup>1</sup> However, these earlier papers that were the first to use data from the World Bank Enterprise Surveys suffer from two major estimation problems. First, the countries covered were surveyed just once and only a single year of business environment indicators is available. Given the cross-sectional nature of the data, regressions potentially suffer from a problem of reverse causality—some business environment indicators whose effect on firm productivity is estimated may themselves be affected by firm productivity. For example, financing from foreign banks may have a positive effect on firm productivity, but concurrently, financing from foreign banks may be influenced by firm productivity (i.e., foreign banks are more willing to lend to only the most productive domestic firms). Second, while multiple years of data are collected for production function variables in some of these countries, measurement error and non-response plague the recall data collected by these surveys.

The paper contributes to the literature in two important respects. First, a unique dataset is constructed by merging information from two large databases of European firms—the Business

> 1 As Dollar, Hallward-Driemeier, and Mengistae (2003) note, the literature that examines the links between the business environment and productivity at the macroeconomic level suffers from three major shortcomings: (i) few countries have good data on the business environment that are necessary to derive robust statistical results (Levine and Renelt, 1992; Rodriguez and Rodrik, 2000); (ii) the proxies used as explanatory variables provide minimal guidance about what governments need to do to improve their business environment; and (iii) using national-level data assumes that the business environment is the same across locations within a country, but interesting variation may exist based on heterogeneous local governments and institutions.

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Environment and Enterprise Performance Survey (BEEPS) and AMADEUS—in order to address the aforementioned shortcomings of the earlier studies. For the measurement of the business environment, the analysis draws on firm-level data from the BEEPS, which was conducted by the World Bank in conjunction with the European Bank for Reconstruction and Development (EBRD) and covers all countries of Central and Eastern Europe, the former Soviet Union, and Turkey. In an effort to track changes of evolving business environments and benchmark the effects of reforms, BEEPS was conducted in 2002 and again in 2005, asking an identical core set of questions (covering 367 variables) in both rounds to ensure comparability across countries and years. For the estimation of firm productivity, the analysis uses data from the May 2006 edition of the AMADEUS database, a comprehensive, pan-European commercial database compiled by Bureau van Dijk. For each firm, the database includes up to ten years of accounting data. The manufacturing sector of these two large databases are merged on four criteria—country, subnational location, firm size, and year—producing a large 4-year panel of 22,004 manufacturing firms in 8 countries, Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine. This unique dataset enables us to measure the effect of changes in the business environment on firm-level productivity growth over the period 2001 to 2004.

Second, in order to mitigate the problems of multicollinearity in the full model regression, a new set of robust indicators is constructed, using principal component analysis on quantitative variables from the BEEPS manufacturing dataset, that summarizes the following five distinct aspects of the business environment: (a) infrastructure quality, (b) financial development, (c) governance, (d) labor market flexibility, and (e) labor quality. Variable selection for PCA is guided by the preference for quantitative over qualitative indicators for two reasons. First, quantitative responses link directly to objective, actionable policy actions, as opposed to firm perceptions. Second, there are numerous statistical and measurement problems associated with the use of perception-based data, such as Likert-scale survey responses.<sup>2</sup> The construction of each indicator meets the three variables per component minimum threshold recommended for exploratory factor analysis (Thurstone, 1935; Kim and Mueller, 1978b). Furthermore, all synthetic indicators are given by the first principal component of their

> 2 For example, based on data from Enterprise Surveys in 33 African and Latin American countries that used instruments similar to those in the BEEPS, González, López-Córdova, and Valladares (2007) show that perceptions adjust slowly to firms’ experience with corrupt officials and hence are an imperfect proxy for the true incidence of graft.

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respective set of underlying BEEPS variables, and three separate tests—the Guttman-Kaiser criterion, Cattell’s scree test, and Humphrey-Ilgen parallel analysis—confirm the decision to retain only the first principal component.

The estimation strategy follows a two-step approach and exploits cross-cell (defined by country, sub-national location, and firm size) variation in the changes across time of the five synthetic business environment indicators, as well as a sixth measuring the level of competition (based on the four-firm concentration ratio for each 4-digit NACE industry), to determine their effect on firm-level productivity.<sup>3</sup> First, a production function equation whose residuals measure total factor productivity (TFP) is estimated using the methodology of Levinsohn and Petrin (2003), which corrects for the crucial simultaneity bias arising from the fact that firms make input choices with knowledge of their productivity. Second, a first-differenced equation in firm characteristics, whose dependent variable is the two-year change in log TFP and whose main regressors of interest are the lagged two-year changes in six different business environment indicators, is estimated using ordinary least squares with White correction for heteroskedasticity. The availability of four years of production function data from the AMADEUS database allows the model specification to control for lagged productivity in this second step. This feature is particularly important for consistency given the assumption in Levinsohn and Petrin (2003) of a Markov process for productivity (Fernandes, 2007).

The results of the regression analysis confirm that firm-level productivity growth is directly linked to important factors in the business environment and strongly support the presence of large TFP gains from successful efforts to improve these microeconomic foundations of economic development, even after controlling for unobserved firm, industry, sub-national location, and country heterogeneity. The main findings of the paper are as follows. Over the period 2001 to 2004, (i) a one standard deviation increase in the _infrastructure_ indicator raises TFP of the average firm by 9.8 percent; (ii) a one standard deviation increase in the _financial development_ indicator raises TFP of the average firm by 7.8 percent; (iii) a one standard deviation increase in the _governance_ indicator raises TFP of the average firm by 3.2 percent; (iv) a one standard deviation increase in the _labor market flexibility_ indicator raises TFP of the

> 3 NACE Rev.1 ( _Nomenclature générale des activités économiques dans les Communautés européennes_ ), the standard industrial classification of economic activities within the European Communities, is identical to the United Nations Statistical Division’s International Standard Industrial Classification of All Economic Activities (ISIC Rev. 3) at the one- and two-digit levels.

3
average firm by 3.4 percent; (v) a one standard deviation increase in the _labor quality_ indicator raises TFP of the average firm by 5.8 percent; and (vi) a one standard deviation increase in the _competition_ indicator raises TFP of the average firm by 3 percent.

Lastly, to complement the productivity analysis that is based on regression analysis, productivity growth over the period 2002 to 2004 is decomposed following Olley and Pakes (1996) as a way to measure and rank the relative impact of these six aspects of the business environment on a country-by-country basis. In Bulgaria, relative to the total impact of changes in all six business environment indicators, improvements in infrastructure quality contributed 15 percent to log TFP growth over the period 2002 to 2004, whereas a decrease in the level of competition accounted for a 33 percent negative impact. In Croatia, the increases in the infrastructure quality and governance indicators led to relative contributions of 36 and 28 percent, respectively, while the decrease in the labor quality indicator accounted for a −12 percent impact. In the Czech Republic, the increases in the infrastructure quality and financial development indicators led to relative contributions of 25 and 17 percent, and conversely, the decline in the labor market flexibility indicator resulted in a −23 percent relative contribution. In Estonia, the increases in the labor quality and infrastructure quality indicators led to relative contributions of 30 and 27 percent, while the decrease in the labor market flexibility indicator resulted in a relative contribution of −27 percent. In Poland, the increase in the labor market flexibility indicator led to a relative contribution of 34 percent to log TFP growth, whereas the decline in the financial development indicator resulted in a relative contribution of −39 percent. In Romania, all aspects of the business environment improved over the period 2001 to 2003, with the change in the governance indicator accounting for 42 percent of the total positive impact on log TFP. In Serbia, the increase in the infrastructure quality indicator led to a relative contribution of 53 percent to log TFP growth, and conversely, the decline in the financial development indicator resulted in a relative contribution of −17 percent. In Ukraine, the increase in the infrastructure quality indicator led to a relative contribution of 85 percent to log TFP growth and dominated the relative contributions of the other five aspects of the business environment.

The paper proceeds as follows. Section 2 describes the data. Section 3 presents the empirical methodology. Section 4 discusses results. Section 5 concludes. The annex presents descriptive statistics and main results.

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# **2. Data**

The empirical analysis in the paper merges information from two large databases of European firms: the Business Environment and Enterprise Performance Survey (BEEPS) and AMADEUS databases.

## **_2.1 Business Environment and Enterprise Productivity Survey (BEEPS_** _)_

For the measurement of the business environment, the analysis draws on firm-level data from the BEEPS, which was conducted by the World Bank in conjunction with the European Bank for Reconstruction and Development (EBRD). BEEPS covers establishments of all sizes in many industries and provides a wide array of qualitative and quantitative information regarding the business environment in all countries of Central and Eastern Europe, the former Soviet Union, and Turkey. Topics covered in the BEEPS include the obstacles to doing business, infrastructure, finance, corruption and red tape, legal and judicial issues, labor market regulations, and the skills and education of available workers. Taken together, the qualitative and quantitative data capture all aspects of the business environment within countries that affect firm productivity and performance.

In an effort to track changes of evolving business environments and benchmark the effects of reforms, the survey was conducted in 2002 and again in 2005. An identical core set of questions (covering 367 variables) was asked in all countries in both rounds to ensure comparability across countries and years, and all questionnaires in every country in both rounds of the BEEPS were implemented through face-to-face interviews with managers and owners. In each country, the sectoral composition of the sample in terms of industry (ISIC codes 10-14, 1537, 45) versus services (ISIC codes 50-52, 55, 60-64, 70-74) was determined by their relative contribution to GDP. Furthermore, the sampling design in both rounds included quotas for a set of firm characteristics to ensure sufficient numbers for statistical analysis, specifically, city/town (i.e., large, medium, small), firm size (i.e., small=2-49, medium=50-249, large=250-9,999), ownership (i.e., domestic, foreign, state), and exporters/non-exporters. The sampling approach was the same in both rounds of the BEEPS and was implemented nationwide.<sup>4</sup>

> 4 The BEEPS 2002 and 2005 datasets in Stata and CSV format as well as documentation on sampling and implementation are available for download from the following World Bank website:

5
# **_2.2. AMADEUS Database_**

For the estimation of firm productivity, the analysis uses data from the May 2006 edition of the AMADEUS database, a comprehensive, pan-European commercial database compiled by Bureau van Dijk. For each firm, AMADEUS provides accounting data in standardized financial format for 24 balance sheet items, 25 profit and loss account items, 26 financial ratios, and additional information including trade description and activity codes. The database includes up to ten years of information per firm through 2004, although coverage varies by country. AMADEUS is created by collecting standardized data received from 50 vendors across Europe, where the local source for these data is generally the office of the Registrar of Companies.<sup>5</sup> The accounts for each firm are transformed into a universal format to allow for comparison across countries. All accounting data is converted into U.S. dollars using period average exchange rates, based on monthly series from the International Monetary Fund, nearest to the end date of each respective financial account. Nominal values are deflated using countrylevel GDP deflators to express values in 2001 US dollars. In addition, all firms are categorized by industry according to NACE Rev.1., and for the analysis, industry dummy variables are coded based on the 4-digit activity code following NACE Rev.1 that AMADEUS assigns to each firm.

# **_2.3. Sample Selection_**

The econometric analysis of firm-level TFP growth and changes in the business environment uses a first-differenced equation in firm characteristics with two-year changes and requires a panel of manufacturing firms from the AMADEUS database with complete information on production function variables for the years 2001 through 2004, the period that correspond to the 2002 and 2005 rounds of the BEEPS. Specifically, output, labor, material inputs, and capital are given by the operating revenues, number of employees, material costs, and tangible fixed assets of firms in the AMADEUS database. Consequently, observations that are missing values in just one of these four production function variables must be dropped from the sample. Given these data requirements, sufficient information exists in the AMADEUS database

> http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/EXTECAREGTOPANTCOR/0,,content MDK:21303980~pagePK:34004173~piPK:34003707~theSitePK:704666,00.html.

> 5 Further details about the AMADEUS database can be found on the product page of Bureau van Dijk’s website: http://www.bvdep.com/en/AMADEUS.html.

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to estimate TFP for manufacturing firms in eight countries: Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine.

A number of additional restrictions are imposed to reduce sample bias in the panel of AMADEUS firms with complete data on production function variables. First, observations that are “inactive”, “dissolved”, “in bankruptcy”, or “in liquidation” are dropped from the panel. Bureau van Dijk removes firms from the AMADEUS database only when there is no reporting for at least five years; specifically a “not available/missing” is reported for four years following the last included filing. Second, observations with data sourced from consolidated statements are dropped from the panel in order to avoid the double-counting of firms and subsidiaries or operations abroad. For most firms in the AMADEUS database, unconsolidated statements are reported and consolidated statements are provided when available. Third, observations with a positive number of subsidiaries are also dropped from the panel to reduce double-counting. Fourth, observations with less than two employees are dropped from the sample. This criterion helps to exclude any dummy (phantom) firms established for tax or other purposes.

Fifth, certain manufacturing industries are excluded when the activity is country-specific. Observations in the manufacture of tobacco products (NACE code 16) are dropped from panel because there are no such observations from Croatia, the Czech Republic, Estonia, Serbia, and Ukraine in the AMADEUS database. Similarly, observations in the manufacture of coke, refined petroleum products and nuclear fuel (NACE code 23) are dropped from panel because there are no such observations from Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Serbia, and Ukraine in the AMADEUS database. Lastly, observations in recycling (NACE code 37) are dropped from the panel because there are no such observations from Bulgaria with complete information on production function variables in the AMADEUS database.

A number of additional criteria are imposed on the set of four production function variables to reduce measurement error. First, observations with negative tangible fixed assets and material costs are dropped from the sample. Second, observations with material costsoperating revenues and cost of employees-operating revenues ratios greater than one are dropped from the sample. Lastly, observations with operating revenues-number of employees, tangible fixed assets-number of employees, material costs-number of employees, material costsoperating revenues, and cost of employees-operating revenues ratios that are greater (less) than 7
three times the standard deviation from the upper (lower) quartile in the corresponding two-digit NACE industry, country, and year are considered outliers and dropped from the sample.

Given that respondents to the BEEPS were asked to answer questions with respect to business operations occurring in the previous year, BEEPS 2002 and 2005 data are assumed to capture the characteristics of the business environment in 2001 and 2003, respectively, in order to fit the first-difference model with two-year changes in firm productivity regressed on lagged two-year changes in the business environment. BEEPS 2002 and 2005, therefore, is match merged with Amadeus 2002 and 2004 observations, respectively, on country, sector, subnational location, and firm size. Specifically, averages of variables from the BEEPS manufacturing dataset are first calculated for groups defined by country, sub-national location, and firm size in each respective year using only the responses of manufacturing firms (NACE codes 15-36). There are three sub-national location categories: capital city, large city (defined as a non-capital city having a population of 250,000 or greater), and small city (defined as a noncapital city having a population less than 250,000); and two firm size categories: small (defined as employing 2 to 49 full-time workers) and large (defined as employing 50 or more full-time workers). These country-location-size-year averages of BEEPS variables for the manufacturing sector are then match merged to each AMADEUS observation on this identical set of variables. To illustrate, the average number of days in 2001 that large-sized manufacturing firms located in small cities in Bulgaria experienced power outages or surges from the public grid is first calculated from the BEEPS 2002 database, and then this value is assigned to all observations in the 2002 AMADEUS sample that operate in the manufacturing sector, employ 50 or more fulltime workers, and are located in cities with populations less than 250,000 in Bulgaria.

The final sample that will be used for the econometric analysis of the effect of changes in the business environment on firm-level TFP growth over the period 2001 to 2004 consists of 22,004 manufacturing firms in 8 countries: Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine. The distribution of the merged AMADEUS-BEEPS balanced panel dataset by countries is as follows: Bulgaria 221; Croatia 1,780; Czech Republic 964; Estonia 1,253; Poland 1,133; Romania 12,576; Serbia 2,237; and Ukraine 1,840. The above inclusion criteria create the most comparable sample of firms across countries. Note, however, that strong conclusions at the international level cannot be derived from direct cross-country 8
comparisons because data requirements for the estimation of log TFP result in varying sample attrition across countries, leading to non-representative country samples.

# **3. Estimation Methodology**

To estimate the impact of the business environment on firm performance, the two-year change in log TFP of manufacturing firms is regressed on lagged two-year changes in several aspects of the business environment as measured by a wide array of BEEPS variables.

## **_3.1. Estimation of TFP in the Presence of Simultaneity_**

Total factor productivity is measured as the residual from the estimation of a log-linear three factor Cobb-Douglas production function. For the analysis, the production function of firm _i_ in NACE 2-digit manufacturing industry (15-36) _j_ at time _t_ is assumed to have the following form: where _Y_ is a measure of output, and _L_ , _M_ , and _K_ are the usage of labor, material inputs, and capital with output shares _λ_ , _μ_ , and _κ_ , respectively. Drawing on the AMADEUS database, _Y_ is measured by operating revenues (thousands of 2001 U.S. dollars), _L_ is measured by the number of employees, _M_ is measured by material cost (thousands of 2001 U.S. dollars), and _K_ is measured by the value of tangible fixed assets (thousands of 2001 U.S. dollars). _Aijt_ represents TFP and increases the marginal product of all factors simultaneously. Transforming equation (1) into logarithms allows for linear estimation of TFP with the equation for the general form written as: where industry-specific coefficients— _λj_ , _μj_ , and _κj_ — are given by the estimation of the production function.

A simultaneity problem, however, arises when there is contemporaneous correlation between the factors of production and the errors, often thought as Hicks neutral productivity shocks. The firm, for example, may observe productivity shocks early enough to allow for a change in factor input decisions. In the context of the Cobb-Douglas production function, the error term is therefore assumed to be additively separable in two distinct components: 9
where _y_ is the logarithm of output; _l_ and _m_ are the logarithm of the freely variable inputs of labor and materials; _k_ is the logarithm of the state variable capital; _ω_ is the part of the error term that is observed by the firm when decisions on optimal factor input choices are being made, and thus, are correlated with the inputs, _l_ , _m_ , and _k_ ; and _η_ is a true error term uncorrelated with factor input choices that may contain both unobserved shocks (i.e., unpredictable zero-mean shocks realized after inputs are chosen) and measurement errors. As pointed out by Griliches and Mareisse (1998), profit-maximizing firms immediately adjust their inputs each time a productivity shock is observed, resulting in input levels correlated with _ω_ in the regression. This simultaneity violates the OLS conditions for unbiased and consistent estimation.

Olley and Pakes (1996) and Levinsohn and Petrin (2003) have developed two similar semi-parametric estimation procedures to overcome the simultaneity problem when estimating production functions. Olley and Pakes include the investment decision of the firm in the estimation equation to proxy for unobserved productivity shocks. Derived from a structural model of the optimizing firm, the proxy controls for the part of the error correlated with inputs, _ω_ , by “annihilating” any variation that is possibly related to the productivity term. The method suggested by Olley and Pakes, however, generates consistent and unbiased estimates if and only if there is a strictly monotonous relationship between the proxy and output. Consequently, firms that make only intermittent investments will have their zero-investment observations truncated from the estimation routine because the monotonicity condition does not hold for these observations. For AMADEUS, this is a large portion of the data.<sup>6</sup> Given the considerable attrition in the AMADEUS sample when using the Olley and Pakes approach, the paper adopts the method developed by Levinsohn and Petrin (2003) to estimate production functions. Levinsohn and Petrin offer an estimation technique that is very close in spirit to the Olley and Pakes approach but uses intermediate inputs in lieu of investment as a proxy for unobserved productivity shocks. Nearly all firms in the AMADEUS database almost always report positive material costs. Therefore, the Levinsohn-Petrin intermediate input proxy estimator is the optimal choice for the AMADEUS sample.

> 6 Calculating investment as the year-to-year change in the real value of tangible fixed assets, only 1,947 (8.5 percent) of the 22,004 manufacturing firms in the final sample used for the econometric analysis in this paper have strictly positive investment in years 2001 through 2004.

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Given differences in production technologies across industries, the analysis estimates heterogeneous, industry-specific (2-digit NACE) production functions using the Levinsohn and Petrin technique to obtain consistent and unbiased estimates of _λj_ , _μj_ , and _κj_ for the derivation of log TFP estimates according to equation (3), which takes two step.<sup>7</sup> In the first step, the coefficient on labor is obtained using semi-parametric techniques. Assuming that the firm’s demand for material inputs increases monotonically with its productivity conditional on its capital, the inverse demand function for material inputs then depends only on observable materials usage and capital and its nonparametric estimate can be used to control for unobservable productivity, thus removing the simultaneity bias.<sup>8</sup> In the second step, the coefficients for material inputs and capital are obtained using generalized method of moments techniques. The identification assumption is that capital adjusts with a lag to productivity, specifically productivity is assumed to follow a Markov process,  _ijt_  _E_ [  _ijt_ |  _ijt_  1<sup>]</sup>  _ijt_ , where  _ijt_ is the unexpected part of current productivity to which capital does not adjust. The estimates of firm log TFP are given by the residuals from equation (3), _TFPijt_   _ijt_   _ijt_ , and capture the efficiency in transforming inputs into outputs and may include changes in factor utilization.<sup>9</sup>

Table 1 presents descriptive statistics of log TFP estimates, calculated according to the technique of Levinsohn and Petrin (2003), for firms in the manufacturing sector (NACE 1500 to 3663) of Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine. For years 2001 through 2004, means are provided for the whole sample, by country, and also by groups defined by country, sub-national location, and firm size upon which the BEEPS manufacturing dataset is merged. On average, firms experienced an overall increase in log TFP

> 7 Alternatively, a fixed effects model can be used to address the simultaneity problem if the part of the error that influences input factor decision, _ωi_ , is assumed to be a firm-specific attribute and time invariant (e.g., managerial skills, organizational efficiency, etc.).  In this case, unobserved firm heterogeneity that remains constant over time can be removed (for example, by subtracting the means from each variable for each observation) before estimating the production function so that _l_ , _m_ , and _k_ are no longer correlated with the error term.

> However, evidence from BEEPS suggests that managerial skills and organizational structure have changed significantly over time, and therefore, preclude the adoption of fixed effect methods for the analysis in the paper. Among the 1,416 firms from Bulgaria, Croatia, Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine that comprise the BEEPS 2005 sample, 22 percent had “some reallocation of responsibility and resources between departments”, 11 percent had “major reallocations of responsibility and resources between departments”, and 5 percent had a “completely new organisational structure” over the last three years.

8 Making mild assumptions about the firm’s production technology, Levinsohn and Petrin (2003) show that the demand function is monotonically increasing in  _ijt._

> 9 ˆ ˆ The estimated <sup>ˆ</sup> _j_ ,  _j_ , and  _j_ show the importance of the simultaneity bias when compared to OLS.  The production function parameters are available from the authors upon request.

11
of 0.062 log points from 2002 to 2004, but performance varied greatly among firms—the standard deviation of sample is 0.386. Log TFP of the average firm in Serbia grew the fastest, increasing by 0.219 log points, whereas log TFP of the average firm in Romania grew the slowest, increasing by only 0.019 log points.

Figure 1 through 3 present kernel density estimations of log TFP for several different cuts of the sample using estimates in all years 2001 through 2004 for the panel of 22,004 firms, resulting in a total of 88,016 observations. An adaptive kernel density estimation method using a varying, rather than fixed, bandwidth is used to draw the distributions. The fixed bandwidth tends to oversmooth the middle of the log TFP distribution. On the contrary, the adaptive kernel estimate is smoother in the tails (especially in the higher tail).<sup>10</sup> All estimations use the Epanechnikov kernel function, start with an oversmoothed global bandwidth of 0.3, and specify 3,000 equally spaced grid points.

Figure 1 presents the kernel density estimation for the sample as a whole. Figure 2a shows the kernel density estimation of log TFP by firm size. Not surprisingly, the distribution for large firms (250 or more employees) is higher than that for small firms (less than 250 employees). Figure 2b presents the kernel density estimation of log TFP by sub-national location. The order of the distributions from highest to lower are also as expected: firms located in capital cities, firms located in large cities (population greater or equal to 250,000), and lastly, firms located in small cities (populations less than 250,000). Figure 2c shows the kernel density estimation of log TFP by the average industry factor intensity. The distribution for firms in capital-intensity industries (i.e., 4-digit NACE industries with average tangible fixed assets per employee in the top two quintiles, specifically greater than or equal to $8,837.43) is higher than that for firms in labor-intensive industries (i.e., 4-digit NACE industries with average tangible fixed assets per employee in the bottom two quintiles, specifically less than or equal to $6,785.85). Figure 3 shows the kernel density estimation of log TFP by the country.

> 10 The advantages of varying or local bandwidths is widely acknowledged in the estimation of long-tailed density functions with kernel methods, when a fixed or global bandwidth approach may result in undersmoothing in areas with sparse observations, while oversmoothing in areas with abundant observations. Varying the bandwidth along the support of the sample data gives flexibility to reduce the variance of the estimates in areas with few observations and can reduce the bias of the estimates in areas with many observations.

> An adaptive kernel approach adapts to the sparseness of the data by varying the bandwidth inversely with the density using an iterative procedure.  An initial (fixed bandwidth) density estimate is computed to get an approximation of the density at each of the specified grid points.  Subsequently, this pilot estimate is used to adapt the size of the bandwidth over the data points when computing a new kernel density estimate.  For a discussion, see Silverman (1986), Bowman and Azzalini (1997), and Van Kerm (2003).

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The separations observed in the kernel density estimates presented in Figures 2 and 3 confirm the necessity to match merge the BEEPS data with the Amadeus observations on country, sector, sub-national location, and firm size. However, it is important to reiterate here that strong conclusions at the international level cannot be derived from direct cross-country comparisons because of data requirements and varying sample attrition across countries. For example, given the limited data sources available for Serbia, firms that have the prerequisite data on production function variables in all four years of the panel exhibit very high log TFP levels, resulting in a distribution much higher than those of the other countries. Nonetheless, even though sample biases may exist between countries, the basic test in this paper examines withinindustry differences across countries and will not be affected unless there are systematic biases in sub-national location-size-year groups within industries in each country.

# **_3.2. Identification Strategy_**

The analysis exploits cross-cell (defined by country, sub-national location, and firm size) variation in the changes of the business environment variables across time to determine their effect on firm-level productivity. Estimated using ordinary least squares (OLS) with White correction for heteroskedasticity, the full regression model is a first-differenced equation in firm characteristics with two-year changes whose main regressors of interest are lagged two-year changes in business environment indicators and is formally specified as follows:  ln _TFPit_     1  _INFRASTRUCTUREts_  ,1 _l_ , _c_   2  _FINANCEts_  ,1 _l_ , _c_   3  _GOVERNANCEts_  ,1 _l_ , _c_ where  ln _TFPi_ , _t_ is the change in the logarithm of TFP of manufacturing establishment _i_ from 2002 to 2004, estimated by the semiparametric estimation technique developed by Levinsohn and Petrin (2003);  _INFRASTRUCTURE ts_  ,1 _l_ , _c_ ,  _FINANCE ts_  ,1 _l_ , _c_ ,  _GOVERNANCE ts_  ,1 _l_ , _c_ **,**  _LABOR_ MARKETt_  _s_ ,1 _l_ , _c_ **,** and  _LABOR_ QUALITYt_  _s_ ,1 _l_ , _c_ are the changes from 2001 to 2003 in respective business environment indicators for groups of firm size _s_ , location _l_ , and country _c_ ;  ln _TFPi_ , _t_  1 is the change in the logarithm of TFP from 2001 to 2003;  _Z in_ is a vector of logarithmic changes in firm characteristics from 2002 to 2004 that include the number of 13
employees, the value of tangible fixed assets (thousands of 2001 U.S. dollars), and cost of materials (thousands of 2001 U.S. dollars); _INDUSTRYs_ is a vector of industry dummy variables defined at the 4-digit NACE level (1510 to 3663); _LOCATIONs_ is a vector of location dummy variables including a capital city dummy variable (equal to 1 if the firm is located in a capital city—that is, Belgrade, Bucharest, Kyiv, Prague, Sofia, Tallinn, Warsaw, or Zagreb—and 0 otherwise) and a large city dummy variable (equal to 1 if the firm is located in a city with a population of 250,000 or greater, and 0 otherwise); and _COUNTRY c_ is a vector of country dummy variables for Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine.

Lastly,  _COMPETITION tm_  1, _c_ is the change in the level of competition in each industry _m_ of country _c_ from 2001 to 2003 and is equal to 1 minus the change in the four-firm concentration ratio for industries defined at the 4-digit NACE level (so that positive changes indicate higher levels of competition). The four-firm concentration of an industry is equal to the market share as measured by operating revenues of the four largest firms in each 4-digit NACE level industry and is country-specific.11 Given the lower data requirements, a much larger AMADEUS sample is used to calculate the competition indicator: 69,116 firms in 1,935 4-digit NACE industries from the 2001 sample and 77,265 firms in 1,970 4-digit NACE industries from the 2003 sample. Table 2 presents descriptive statistics of the competition indicator.

The above specification of the model addresses a number of econometric concerns. Given that the objective of the paper is to capture the effect of changes in the business environment on productivity growth of the average firm, the regression analysis opts for a balanced panel design, pooling observations across 296 NACE industries at the 4-digit level in eight countries with data in the years 2001 through 2004. Second, first-differencing firm characteristics and lagging business environment indicators by one year mitigates further endogeneity between unobservable firm heterogeneity and factor input choices. Third, the inclusion of lagged changes in log TFP addresses serial correlation that is not eliminated by first differencing. Given the necessary assumption made in the TFP estimation technique of Levinsohn and Petrin (2003) of a Markov process for productivity—that is, the conditional probability distribution of future states of the productivity, given the present state and all past

> 11 Market forms are often classified by their four-firm concentration ratio.  Perfect competition is associated with a very low ratio, monopolistic competition with ratios below 0.4, oligopoly with ratios above 0.4, and monopoly with a near-1 four-firm measurement.

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states, depends only upon the present state and not on any past states—lagged productivity must be included in the regression model for consistency (Fernandes, 2007). Fourth, the inclusion of industry, sub-national location, and country fixed effects controls for time trends and unobserved sub-national location-, industry-, and country-specific characteristics that might affect the correlation between productivity growth and changes in the business environment.

Fifth, as described in the previous section, merging the AMADEUS and BEEPS manufacturing datasets on country, sub-national location, firm size, and year mitigates the endogeneity between firm productivity and business environment indicators. The econometric analysis in this paper treats BEEPS variables as exogenous determinants of firm productivity; however, firms can be proactive in reducing the constraints they face in the business environment, producing a simultaneity bias in the estimation exercise. For example, a wellmanaged firm with high productivity growth may have worked with authorities to secure a more reliable power supply or to relax hiring and firing restrictions. Statistically, a balance must be struck so that the set of variables, on which the AMADEUS and BEEPS manufacturing datasets are merged, is large enough so that resulting average values not only mitigates the endogeneity problem but also retain sufficient variation for regression analysis. To the extent that sub-sample groupings as defined are sufficiently aggregated so that individual firms are less likely to influence averages but varied enough so that heterogeneous “pockets” of business environments are reflected, using year-specific averages of BEEPS indicators taken across firms in the same country, sector, sub-national location, and size groups is a valid way to instrument out the simultaneity problem (Bastos and Nasir, 2004).

Sixth, in order to mitigate the problems of multicollinearity in the full model regression, principal component analysis (PCA) is used to reduce the dimensionality of the BEEPS data and construct indicators that summarize various dimensions of the business environment. In the BEEPS database, there are typically several variables that address a particular issue that affect the productivity and growth of firms. Several questions, for example, collect information on the quality of infrastructure, namely the number of days of power outages or surges from the public grid, the number of days of insufficient water supply, and the number of days of unavailable mainline telephone service. Inclusion of two or more highly correlated explanatory variables in a regression model generally leads to difficulties in ascertaining the effects of individual factors 15
on the dependent variable. The follow section explains the construction of the five business environment indicators that are used in the paper.

# **_3.3. Business Environment Indicators: Principal Component Analysis (PCA)_**

Synthetic indicators are constructed using PCA on the BEEPS manufacturing dataset for the following five distinct aspects of the business environment: (a) infrastructure quality, (b) financial development, (c) governance, (d) labor market flexibility, and (e) labor quality. Intuitively, the method of principal components is used to describe a set of variables with a set of variables of lower dimensionality; for this paper, the objective of PCA is to construct one series that summarizes the behavior of a group of three or more underlying BEEPS variables that describe a particular aspect of the business environment. Statistically, PCA reduces the number of variables in the analysis by specifying linear combinations (“principal components”) of the underlying BEEPS variables such that the resulting series contains most of the information, i.e. has maximum variance.<sup>12</sup> Specifically, BEEPS variables are first mapped into one of five distinct aspects of the business environment, and then the main variation commanded by each aspect is extracted through the use of their respective principal components. Before applying PCA, the underlying variables are rescaled so that higher values indicate improvements in the business environment and then standardized to having mean zero and standard deviation one in order to abstract from units of measurements.

Variable selection for PCA is guided by the preference for quantitative over qualitative indicators. First, quantitative responses link directly to objective, actionable policy actions, as opposed to firm perceptions. Second, there are numerous statistical problems associated with the use of perception-based data, such as Likert-scale survey responses. The most fundamental is whether responses along a semantic continuum can be treated as if they were interval data. Additionally, there are several potential sources of measurement error with perception-based data. For example, individual respondents may differ in their use of the Likert scale owning to

> 12 Algebraically, this method locates _n_ linear combinations of the _n_ columns of the _X'X_ matrix, all orthogonal to each other, with the following property: the first principal component _p_ l minimizes _tr_ ( _X_  _p_ 1 _a_ 1  )  ( _X_  _p_ 1 _a_ 1  ) , where _a_ 1 is the eigenvector of the _X_  _X_ matrix associated with the largest eigenvalue.  Intuitively, _p_ l summarizes the _n_ variables in _X_ by giving the best linear description of the columns of _X_ in a least squares sense.  The second principal component of _p_ 2 also describes what is not “captured” by the first component _p_ l by minimizing the sum of squared residuals after subtracting _p_ l, i.e. _p_ l minimizes _tr_ ( _X_  _p_ 1 _a_ 1   _p_ 2 _a_ 2  )  ( _X_  _p_ 1 _a_ 1   _p_ 2 _a_ 2  ) where _a_ 2 is now the eigenvector associated with the second largest eigenvalue, and so on.  See Alesina and Perotti (1996).

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his or her subjective frame of reference. An issue perceived as a major obstacle to doing business in one country may actually impose a lower cost in actuality than it does in a country where the problem is rated as merely a minor problem. For example, based on data from Enterprise Surveys in 33 African and Latin American countries that used instruments similar to those used for the BEEPS, González, López-Córdova, and Valladares (2007) show that perceptions adjust slowly to firms’ experience with corrupt officials and hence are an imperfect proxy for the true incidence of graft.

Consequently, quantitative measures of an issue in the business environment are always selected over perception-based indicators whenever available. For example, the number of power outages or surges from the public grid is used rather than the perceptions of the manager on how problematic electricity is for the operation and growth of the business. Similarly, the level of bribes paid as a percentage of total annual sales number is used rather than the perceptions of the manager on how problematic corruption is for the operation and growth of the business. However, because of the inadequate number of quantitative measures available in the areas of governance (legal system), labor market flexibility, and labor quality, one perceptionsbased question is used in the construction of these indicators in order to meet the three variables per component minimum threshold recommended for exploratory factor analysis (Thurstone, 1935; Kim and Mueller, 1978b).

All synthetic indicators are given by the first principal component of their respective set of underlying BEEPS variables, and three separate tests confirm the decision to retain only the first principal component. The first test is the most frequently used Guttman-Kaiser criterion, which states all components with eigenvalues greater than 1 should be extracted as variables. The rationale behind this criterion is that the interpretation of proportions of variance smaller than the variance contribution of a single variable is of dubious value (Guttman, 1954; Kaiser, 1961). The second test is the Cattell’s scree test, which plots the components along the X-axis and the corresponding eigenvalues along the Y-axis and is also a widely used criterion. Cattell (1996) suggests visual inspection to identify an inflection point of the resulting curve (scree), where components to the left are retained and those to the right are dropped.<sup>13</sup>

> 13 “Scree” is the geological term referring to the debris that collects on the lower part of a rocky slope (Cattell, 1966).

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The final test is Humphrey-Ilgen parallel analysis, which is now often recommended as the best method to assess the true number of factors (Velicer, Eaton, and Fava, 2000; Lance, Butts, and Michels, 2006). Parallel analysis compares obtained eigenvalues to those one would expect to obtain from random data. To use this procedure, a matrix of random numbers representing the same number of observations and variables is factor analyzed. If the first _n_ eigenvalues given by the actual data are those which have values greater than those generated from random data, then _n_ components are retained. Graphically, eigenvalues from the actual and random data are represented on the same scree plot; the intersection of the two lines determines the number of components to be retained. All three tests determined that for each set of BEEPS variables only one component should be retained.

A detailed explanation for each of the underlying BEEPS variables used in the construction of the synthetic indicators for infrastructure quality, financial development, governance, labor market flexibility, and labor quality follows below. Given that principal components are used to summarize a group of variables that describe a particular aspect of the business environment, the resulting indices are expected to be correlated with their underlying BEEPS variables. Tables 3 through 7 show that all five indices are indeed strongly associated with their corresponding BEEPS variables. Figures 4 through 8 graphically show that the Guttman-Kaiser criterion, Cattell’s scree test, and Humphrey-Ilgen parallel analysis all confirm the retention of only the first principal component for each set of BEEPS variables.

# _Infrastructure Quality_

The _infrastructure quality indicator_ measures the quality in the provision of infrastructure services. Underlying variables are rescaled as explained below so that higher values of the indicator signify higher levels of infrastructure quality. The indicator is based on a PCA of the following three BEEPS variables:

- _Power outages_ .  The number of days over the last 12 months that each establishment experienced power outages or surges from the public grid (multiplied by -1) (Question 23).

- _Insufficient water supply_ .  The number of days over the last 12 months that each establishment experienced insufficient water supply (multiplied by -1) (Question 23).

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- _Unavailable mainline telephone service_ .  The number of days over the last 12 months that each establishment experienced unavailable mainline telephone service (multiplied by -1) (Question 23).

# _Financial Development_

The _financial development indicator_ measures the reliance of firms on various sources of finance for new fixed investments (i.e., new machinery, equipment, buildings, and land). Underlying variables are rescaled as explained below so that higher values of the indicator signify higher levels of financial development. The indicator is based on a PCA of the following three BEEPS variables:

- _Local private commercial banks_ .  The percentage of new fixed investment financed by borrowing from “local private commercial banks” (Question 45a).

- _Foreign banks._ The percentage of new fixed investment financed by borrowing from “foreign banks” (Question 45a).

- _Informal (family/friends/money lenders_ ).  The percentage of new fixed investment financed by borrowing from loans from family or friends, money lenders, or other informal sources (subtracted from 100 percent) (Question 45a).

# _Governance_

The _governance indicator_ measures the control of corruption, bureaucratic efficiency, and judicial effectiveness in resolving business disputes. Underlying variables are rescaled as explained below so that higher values of the indicator signify higher levels of good governance. The indicator is based on a PCA of the following three BEEPS variables:

- _Bribe level_ .  The estimated percentage of total annual sales firms typically pay in unofficial payments or gifts to public officials (subtracted from 100 percent) (Question 40).

- _Tax compliance_ .  The response of the firm to the question, “Recognizing the difficulties that many firms face in fully complying with taxes and regulations, what percentage of total annual sales would  you estimate the typical firm in your area of business reports for tax purposes” (Question 43a).

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- _Confidence in the legal system_ .  The response of the firm on a six-point scale (1=“strongly disagree” to 6=“strongly agree”) when asked the question, “To what degree do you agree with this statement.  ‘I am confident that the legal system will uphold my contract and property rights in business disputes.  (Question 27).

# _Labor Market Flexibility_

The _labor market flexibility indicator_ measures the efficiency of employment protection legislation and the degree to which labor markets can adapt to fluctuations and changes in the economy or the demands of production. Underlying variables are rescaled as explained below so that higher values of the indicator signify higher levels of labor market flexibility. The indicator is based on a PCA of the following three BEEPS variables:

- _Underemployment and overemployment_ .  The percentage of firms that either report underemployment because of labor restrictions regarding the hiring of workers (i.e., seeking and obtaining permission, etc.) or report overemployment because of labor restrictions regarding the firing of workers (i.e., making severance payments, etc.). Specifically, this dummy variable is equal to 1 if the optimal level of employment estimated by the firm is equal to or greater than 120 percent (underemployment) or equal to or less than 80 percent (overemployment) of their existing workforce, and is equal to 0 otherwise (subtracted from 100) (Question 73).

- _Change in the use of temporary workers_ .  The change in the number of parttime/temporary workers (as a percentage of permanent, full-time workers) over the last 36 months (Questions 66 and 67).  Atkinson (1984) and Atkinson and Meager (1986) study the labor management strategies companies use and identify four types of labor market flexibility.  One category is called “external numerical flexibility,” which refers to the adjustment of labor intake, or the number of workers from the external market.  External numerical flexibility can be achieved by employing workers on temporary or fixed-term contracts or through relaxed hiring and firing regulations, where employers can hire and fire permanent workers according to the needs of the firm.

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- _Labor regulations as a constraint_ .  The responses of firms on a four-point scale (1=“major obstacle” to 4=“no obstacle”) to the question: How problematic are “labor regulations” to the operation and growth of your business? (Question 63).

# _Labor Quality_

The _labor quality indicator_ measures the skill level and educational attainment of workers. Underlying variables are rescaled as explained below so that higher values of the indicator signify higher levels of labor quality. The indicator is based on a PCA of the following three BEEPS variables:

- _Skilled workers/Total employees_ .  The percentage of the firm’s current permanent, full-time workers that are managers, professionals, or skilled production workers (Question 68).

- _Time to fill vacancy_ .  The average number of weeks it took to fill the most recent vacancy for a manager, professional, or skilled production worker (multiplied by −1) (Question 70).

- _Labor quality as a constraint_ .  The responses of firms on a four-point scale (1=“major obstacle” to 4=“no obstacle”) to the question: How problematic are the “skills and education of available workers’” to the operation and growth of your business? (Question 63).

Table 8 presents descriptive statistics of the five synthetic indicators, constructed using PCA on the BEEPS manufacturing dataset for the following five distinct aspects of the business environment: (a) infrastructure quality, (b) financial development, (c) governance, (d) labor market flexibility, and (e) labor quality. For years 2001 and 2003, means are provided for the whole sample, by country, and also by groups defined by country, sub-national location, and firm size upon which the AMADEUS data is merged. On average, countries from 2001 to 2003 improved in the areas of infrastructure quality, governance, and labor quality, but faced worsening financial development and decreasing labor market flexibility. All countries improved infrastructure quality over this period, but results were mixed across countries in the other four areas. Labor market flexibility worsened in the largest number of countries, only improving in Poland and Romania.

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# **_3.4. The Olley and Pakes Decomposition: Relative Percentage Contribution of Changes in Business Environment Indicators to Log TFP Growth, 2001-2004_**

To complement the productivity analysis that is based on the OLS estimation of equation (4), the paper follows Escribano and Guasch (2005) and measures the partial direct effect of the change in each business environment indicator on average productivity for each country by calculating the average productivity term of the Olley and Pakes (1996) decomposition of productivity.

The Olley and Pakes decomposition of productivity has two components: average _N c c Y c c_ productivity and the efficiency or covariance term. Formally, let _TFPt_   _sit TFPit_ be the _i_  1 productivity of country _j_ at year _t_ obtained as the weighted average productivity of firm _i_ in country _c_ at year _t_ , where _N c_ is the number of firms in country _c_ . The weights<sup>_s_</sup> _itY c_ indicate the share of firm _i_ in aggregate operating revenue of country _c_ in year _t_ , and is equal to the operating revenue of firm _i_ divided by the total operating revenue of country _c_ at year _t_ : _sitY c_  _NYc_ _<u>it</u>_ . Let _Y_  _it i_  1 _s_  _itY c_  _sitY c_  _~~s~~ tY c_ and _TFP_ � _tc_  _TFPitc_  _TFPtc_ be deviations to the mean. Since _s_  _itY c_  _N_ 1 _c_ , the annual aggregate productivity of country _c_ can then be decomposed as: The first term _TFPt c_ is the average productivity of country _c_ at year _t_ and the second term _N c_  _s_<sup></sup> _itY c TFP_ � _itc_  _N c_ cov  _scY_ , _it_ , _TFPitc_  measures the allocative efficiency or covariance between the _i_  1

> share of operating revenue and productivity, cov  _scY_ , _it_ , _TFPit_<sup>_c_</sup>  , multiplied by the number of firms, _N_<sup>_c_</sup> , that operate in country _c_ .  A covariance that is negative indicates that there are allocation inefficiencies.  That is, as the share of output for less productive firms increases, the covariance becomes more negative and the productivity of country _c_ decreases.

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For the calculation of the relative percentage contribution of changes in business environment indicators to log TFP growth over the period 2001 to 2004, the Olley and Pakes decomposition of productivity is also similarly computed for aggregate productivity in logs. Let _N c c_ ln _Y c c_ ln _TFPt_   _sit_ ln _TFPit_ be the log productivity of country _j_ at year _t_ obtained as the weighted _i_  1 average log productivity of firm _i_ in country _c_ at year _t_ . The weights<sup>_s_</sup> _it_ ln _Y c_ indicate the share of firm _i_ in aggregate log operating revenue of country _c_ in year _t_ , and is equal to the log operating revenue of firm _i_ divided by the total log operating revenue of country _c_ at year _t_ : _N c sit_ ln _Y c_  _N_ ln _c_ ln _YitY_ . Let ln _TFPtc_  _N_ 1 _c_  _i_  1 ln _TFPitc_ be the average log productivity of the firms in  _it i_  1 country _c_ at year _t_ . Let _s_  _it_ ln _Y c_  _sit_ ln _Y c_  _~~s~~ t_ ln _Y c_ and ln � _TFPtc_  ln _TFPitc_  ln _TFPtc_ be deviations to the  ln _Y c_ 1 mean. Since _sit_  _N c_ , the annual aggregate log productivity of country _c_ can then be decomposed as: The first term ln _TFPt c_ is the average log productivity of country _c_ at year _t_ and the second term _N c_  _s_<sup></sup> _it_ ln _Y c_ ln � _TFPitc_  _N c_ cov  _sc_ ln, _itY_ ,ln _TFPitc_  measures the allocative efficiency or covariance _i_  1 between the share of log operating revenue and log productivity.

Equation (5) estimated by OLS with a constant term implies that the mean of the residuals is zero, and therefore, the estimation results of equation (5) can be evaluated at their sample mean values without including an error term (Escribano and Guasch, 2005). The corresponding expression for the first term of Olley and Pakes decomposition in changes then becomes: 23
where the variables with bars on top indicate the country averages of each covariate. Following Escribano and Guasch (2005), the relative contribution of each business indicator is derived by dividing the change in each business environment indicators by the dependent variable _c_  ln _TFPt_<sup>and multiplying by 100:</sup>

|, ,<br>1<br>1<br>1<br>3<br>1<br>2<br>ˆ<br>ˆ<br>100<br>100<br>100<br>100<br>_s l c_<br>_c_<br>_c_<br>_t_<br>_t_<br>_t_<br>_c_<br>_c_<br>_c_<br>_GOVERNANCE_<br>_INFRASTRUCTURE_<br>_FINANCE_<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>|
|---|
|ln<br>ln<br>ln<br><br><br><br>_t_<br>_t_<br>_t_<br>_TFP_<br>_TFP_<br>_TFP_<br><br><br>|
|, ,<br>, ,<br>4<br>5<br>1<br>1<br>ˆ<br>ˆ<br>_<br>_<br>100<br>100<br>ln<br>ln<br>_s l c_<br>_s l c_<br>_t_<br>_t_<br>_c_<br>_c_<br>_t_<br>_t_<br>_LABOR_<br>_MARKET_<br>_LABOR_<br>_QUALITY_<br>_TFP_<br>_TFP_<br><br><br><br><br><br><br><br><br><br><br>|
|,<br>1<br>6<sup>ˆ</sup><br>100<br>ln<br>_m c_<br>_t_<br>_c_<br>_t_<br>_COMPETITION_<br>_TFP_<br><br><br><br><br><br><br>(8)|

Equation (8) represents the sum of the percentage productivity gains and losses from the change in each business environment indicators relative to the average log TFP growth of country _c_ over the period 2002 to 2004. In this way, the relative impact of the average change in each business environment indicator over the period 2001 to 2003 on average log TFP growth over the period 2002 to 2004 can be estimated.

# **4. Results**

## **_4.1. OLS Regression Estimation_**

As presented in Table 9, the results obtained from the estimation of equation (4) by OLS with robust standard errors (White correction for heteroskedasticity) show a positive and statistically significant impact of improvements in each of the six aspects of the business environment on firm TFP over the period 2001 to 2004. Entering changes in the PCA indicators into the model one by one, the effects are statistically significant at the 1 percent level for infrastructure quality (column 1), financial development (column 2), governance (column 3), labor market flexibility (column 4), and labor quality (column 5), and at the 5 percent level for competition (column 6). Entering changes in all six aspects of the business environment into the model jointly (column 7), the effects remain strong. Changes in all BEEPS-based indicators are again statistically significant at the 1 percent level, with changes in the competition indicator significant at the 5 percent level.

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From the point estimates of the full regression model presented in column 7 of Table 9, and given the joint significance of the coefficients on the changes in all six business environment indicators, the following causal relationships can be inferred<sup>14</sup> :

- A one standard deviation increase in the _infrastructure_ indicator over the period 2001 to 2004 (1.532) raises TFP of the average firm by 9.8 percent.

- A one standard deviation increase in the _financial development_ indicator over period 2001 to 2004 (1.177) raises TFP of the average firm by 7.8 percent.

- A one standard deviation increase in the _governance_ indicator over period 2001 to 2004 (1.392) raises TFP of the average firm by 3.2 percent.

- A one standard deviation increase in the _labor market flexibility_ indicator over period 2001 to 2004 (1.198) raises TFP of the average firm by 3.4 percent.

- A one standard deviation increase in the _labor quality_ indicator over period 2001 to 2004 (1.175) raises TFP of the average firm by 5.8 percent.

- A one standard deviation increase in the _competition_ indicator over period 2001 to 2004 (0.234) raises TFP of the average firm by 3 percent.

The results of the regression analysis confirm that firm-level productivity growth is directly linked to each of these factors in the business environment and strongly support the presence of large TFP gains from successful efforts to improve the business environment. On the whole, while evidence shows that each of the six dimensions of the business environment is important and significant, one caveat is that the results do not provide clear implications for reform priorities in specific countries.

# **_4.2. Olley and Pakes Decomposition_**

Figure 9 presents the results of the Olley and Pakes decomposition in levels by country for 2001, 2002, 2003, and 2004. There are no significant differences across years. Poland has the largest aggregate productivity followed by Serbia and the Czech Republic.<sup>15</sup> The efficiency terms are likewise high for these three countries, whereas their role in the other five countries is

> 14 With the dependent variable in logarithmic form, the exact percentage change in the predicted TFP associated ˆ with a change in the regressor is calculated as [exp(  _i_ <sup>_x_</sup> _i_ )  ]1  100 where<sup>ˆ</sup> _i_ is the estimated coefficient. 15 Again, given the limited data sources available for Serbia, firms that have the prerequisite data on production function variables in all four years of the panel exhibit very high log TFP levels, resulting in a distribution much higher than those of the other countries at similar levels of economic development.

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marginal. Nonetheless, the efficiency term is positive in all countries, indicating no allocative inefficiencies in any of the eight countries over the period 2001 to 2004.

Figure 10 graphically presents the relative percentage contribution of changes in each business environment indicator to log TFP growth over the period 2001 to 2004 calculated according to equation (8) by country. That is, each bar in Figure 10 shows the relative weight of the average change in each business environment indicator with respect to the total impact of the changes in all six business environment indicators for the respective country sample. Because all coefficient estimates from the OLS regression of equation (4) are positive (see column 7 of Table 9), a positive (negative) relative percentage indicates an improvement (worsening), on average, in the respective business environment indicator. For example, infrastructure quality improved, on average, in all countries over the period 2001 to 2003, while labor quality, on average, increased in the Czech Republic, Estonia, Poland, Romania, and Ukraine, but decreased in Bulgaria, Croatia, and Serbia.

For the sample as a whole (first column in Figure 10), all aspects of the business environment, on average, improve over the period 2001 to 2003. Improvements in the infrastructure quality and governance indicators have relative contributions of 27.8 and 22.7 percent. Changes in the labor quality (7.5 percent), financial development (5.6 percent), labor market flexibility (2.6 percent), and competition (2.2 percent) indicators account for the remaining positive business environment impacts on log TFP growth. These results from the Olley Pakes decomposition of log TFP growth by country are consistent with the OLS regression results for the full sample presented in Table 9.

In Bulgaria, only two aspects of the business environment improve over the period 2001 to 2003. Relative to the total change in all six business environment indicators, increases in the infrastructure quality and financial development indicators contribute 14.7 and 6.0 percent, respectively, to log TFP growth over the period 2002 to 2004. Conversely, negative changes in the governance, labor market flexibility, labor quality, and competition indicators dominate the positive contributions of increases in infrastructure quality and financial development. A worsening in the competition indicator accounts for a third (33.3 percent) of the total impact of business environment changes on log TFP growth over the period. Negative changes in labor quality (−24.8 percent), labor market flexibility (−14.7 percent), and governance (−6.5 percent) account for the remaining impacts on log TFP growth.

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In Croatia, several aspects of the business environment improve over the period 2001 to 2003 and have large relative contributions, while indicators with negative changes have relatively little impact, in sharp contrast to Bulgaria. Improvements in the infrastructure quality and governance indicators have relative contributions of 35.8 and 27.9 percent. Changes in the financial development (16.2 percent) and labor market flexibility (7.5 percent) indicators account for the remaining positive business environment impacts on log TFP growth. A worsening in labor quality (−11.7 percent) and competition (−0.9 percent) have limited negative impact on log TFP growth relative to the positive changes in other aspects of the business environment.

In the Czech Republic, several aspects of the business environment also improve over the period 2001 to 2003, but have more moderate relative contributions, in comparison to Croatia, while indicators with negative changes have larger relative impacts on log TFP growth. Improvements in the infrastructure quality and financial development indicators have relative contributions of 25.1 and 16.5 percent. Changes in the competition (13.3 percent) and labor market quality (10 percent) indicators account for the remaining positive business environment impacts on log TFP growth. Worsening labor market flexibility (−22.9 percent) and governance (−12.2 percent) over the period have significant negative impacts on log TFP growth relative to the positive changes in other aspects of the business environment.

In Estonia, the positive impacts in several aspects of the business environment are also somewhat diminished by the large negative relative contribution of worsening labor market flexibility, similar to the Czech Republic. Improvements in the labor quality and infrastructure quality indicators have relative contributions of 29.5 and 26.7 percent. Changes in the financial development (13.8 percent) and competition (1.4 percent) indicators account for the remaining positive business environment impacts on log TFP growth. A worsening in labor market flexibility has a significant relative contribution of −27.3 percent on log TFP growth relative to the positive changes in other aspects of the business environment. The relative contribution of the change in the governance indicator is −1.3 percent.

In Poland, the positive impacts in several aspects of the business environment are diminished by the large negative relative contribution of worsening financial development. Improvements in the labor market flexibility and labor quality indicators have relative contributions of 34.3 and 14.3 percent. Changes in the infrastructure quality (8.3 percent) and competition (0.5 percent) indicators account for the remaining positive business environment 27
impacts on log TFP growth. A worsening in financial development had a significant contribution of −38.6 percent on log TFP growth relative to the positive changes in other aspects of the business environment. The relative contribution of the change in the governance indicator is −4 percent.

In Romania, all aspects of the business environment improve over the period 2001 to 2003. The relative contribution of improvements in the governance indicator lead the way with 42 percent of the total positive impact on log TFP growth over the period 2002 to 2004. Labor quality (17.7 percent), infrastructure quality (13 percent), and financial development (12.8 percent) have double digit relative contributions. Changes in the labor market flexibility (9.4 percent) and competition (5.1 percent) indicators account for the remaining positive business environment impacts on log TFP growth.

In Serbia, several aspects of the business environment improve over the period 2001 to 2003 and have large relative contributions, while indicators with negative changes have relatively little impact, similar to Croatia. Improvements in the infrastructure quality and governance indicators have relative contributions of 52.5 and 20.2 percent. Changes in the competition (2.5 percent) and labor market flexibility (1.4 percent) indicators account for the remaining positive business environment impacts on log TFP growth. A worsening in financial development (−17.4 percent) and labor quality (−6 percent) have limited negative impact on log TFP growth relative to the positive changes in other aspects of the business environment.

In Ukraine, improvements in infrastructure dominate the relative contributions of the changes in all other aspects of the business environment. The increase in the infrastructure quality indicator has a relative contribution 85.4 percent. Changes in the financial development (1.1 percent), labor quality (1.1 percent), and labor market flexibility (0.6 percent) indicators account for the remaining positive business environment impacts on log TFP growth. A worsening in competition (−9.4 percent) and governance (−2.4 percent) have limited negative impact on log TFP growth relative to the positive change in infrastructure quality.

Figure 11 graphically presents the relative percentage contribution of changes in each business environment indicator to log TFP growth by firm size. That is, each bar in Figure 11 shows the relative weight of the average change in each business environment indicator with respect to the total impact of the changes in all six business environment indicators for the respective samples of small-sized firms (less that 50 employees) and large-sized firms (50 or 28
more employees). Small-size firms in the sample experienced on average an increase in all the business environment indicators over this period, with improvements in governance (40.1 percent) and infrastructure quality (30.3 percent) accounting for the largest relative contributions to log TFP growth. Conversely, large-sized firms experienced a worsening in labor market flexibility that accounted for a −9.3 percent relative contribution. For large-sized firms, the largest relative contributions to log TFP growth are attributed to increases in the indicators for infrastructure quality (39.3 percent), labor quality (25.4 percent), and financial development (15.7 percent).

Figure 12 graphically presents the relative percentage contribution of changes in each business environment indicator to log TFP growth by NACE industries defined at the 2-digit level. That is, each bar in Figure 12 shows the relative weight of the average change in each business environment indicator with respect to the total impact of the changes in all six business environment indicators for the respective sample of firms operating in the indicated industry. Improvements in infrastructure and governance account for the two largest relative contributions, to log TFP growth in all industries (41.6 and 29.9 percent on average, respectively), with the exception of garments (NACE 18) and leather (NACE 19). While the increase in the governance indicator has the largest relative contribution for firms in both the garments (26.6 percent) and leather (23.5 percent) industries, improvements in labor quality (23.4 and 15.7 percent, respectively, for garments and leather) and financial development (19.8 and 16.1 percent) have higher relative contributions than that in infrastructure quality (18.3 and 14.5 percent) for garment- and leather-producing firms. Improvement in labor quality also contributes significantly to productivity growth in many of the industries (11.1 percent, on average) and is ranked third in terms of relative contribution in food processing (NACE 15), textiles (NACE 17), other non-metallic products (NACE 26), basic metals (NACE 27), motor vehicles (NACE 34), and other transport equipment (NACE 35), and furniture (NACE 36). Increases in competition relative to other changes in the business environment contributes at least 5 percent to productivity growth in textiles (NACE 17), leather (NACE 19), chemicals (NACE 24), rubber and plastic (NACE 25), other non-metallic products (NACE 26), basic metals (NACE 27), machinery and equipment (NACE 29), electric machinery (NACE 31), radio, television, and communication equipment (NACE 32), medical, precision, and optical instruments (NACE 33), other transport equipment (NACE 35), and furniture (NACE 36).

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Worsening of certain aspects of the business environment over the period 2001 to 2004 for firms in leather (NACE 19), paper (NACE 21), office, accounting, and computing machinery (NACE 30), radio, television, and communication equipment (NACE 32), and, motor vehicles (NACE 34) led to significant negative impacts on productivity growth. Decreases in the indicator for labor quality negatively impacted log TFP growth in office, accounting, and computing machinery (−13.8 percent), radio, television, and communication equipment (−7.3 percent), and paper (−2.0 percent). Decreases in the indicator for labor market flexibility negatively impacted log TFP growth in office, accounting, and computing machinery (−7.1 percent), motor vehicles (−4.0 percent), and radio, television, and communication equipment (−3.5 percent). Decreases in the indicator for competition negatively impacted log TFP growth in leather (−6.1 percent), radio, television, and communication equipment (−5.8 percent), and paper (−3.1 percent). Lastly, the decrease in the indicator for financial development negatively impacted log TFP growth in motor vehicles (−8.3 percent).

# **5. Conclusions**

This paper provides new evidence on the impact that changes in the business environment have on firm productivity, and contributes to the literature in two important respects. First, a unique dataset is constructed by merging information from two large databases in order to address shortcomings of earlier studies, namely reverse causation and unreliable TFP estimates. Second, in order to mitigate the problems of multicollinearity in the full model regression, a new set of robust indicators is constructed using principal component analysis on quantitative variables from the BEEPS manufacturing dataset to summarize the following five distinct aspects of the business environment: (a) infrastructure quality, (b) financial development, (c) governance, (d) labor market flexibility, and (e) labor quality. Regression analysis is based on production function estimates that correct the bias arising from the simultaneity between inputs and productivity. Furthermore, the paper exploits cross-cell (defined by country, subnational location, and firm size) variation in the changes of the business environment indicators across years to determine their effect on firm-level productivity, thereby circumventing the shortfalls of previous studies that focus only on a single year of business environment variables.

Results indicate that successful efforts to improve the business environment has a strong positive impact on firm productivity, even after controlling for unobserved firm, industry, sub30
national location, and country heterogeneity. Evidence from the BEEPS-AMADEUS dataset confirms that a good business environment encourages firms to operate efficiently and promote productivity growth by lowering risks, costs, and barriers to entry. In a global economy where technology diffuses rapidly, the persistence of productivity differences across countries may be largely explained by differences in the business environment in which firms operate. These microeconomic foundations of economic development—infrastructure quality, financial development, governance, labor market flexibility, labor quality, and competition—are critically linked to the success and growth of firms.

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