metadata: authors: 0: author: De Rosa,Donato
1: author: Gooroochurn, Nishaal
2: author: Gorg,Holger
count: Europe
docna: 0: docna: Corruption and productivity : firm-level
            evidence from the BEEPS survey
docty: Policy Research Working Paper
subtopic: Social Policy,Legal Products,Judicial System Reform,Common Property Resource Development,Energy Policies & Economics,International Trade and Trade Rules,Legal Reform,Legislation,Regulatory Regimes,Global Environment,Crime and Society
teratopic: Environment,Public Sector Development,Private Sector Development,Macroeconomics and Economic Growth
abstracts: cdata!: Using enterprise data for the economies
            of Central and Eastern Europe and the CIS, this study
            examines the effects of corruption on productivity.
            Corruption is narrowly defined as the occurrence of informal
            payments to government officials to ease the day-to-day
            operation of firms. The effects of this "bribe
            tax" on productivity are compared to the consequences
            of red tape, which may be understood as imposing a
            "time tax" on firms. When testing effects in the
            full sample, only the bribe tax appears to have a negative
            impact on firm-level productivity, while the effect of the
            time tax is insignificant. At the same time, unlike similar
            studies using country-level data, firm level analysis allows
            a direct test of the "efficient grease" hypothesis
            by investigating whether corruption may increase
            productivity by helping reduce the time tax on firms.
            Results provide no evidence of a trade-off between the time
            and the bribe taxes, implying that bribing does not emerge
            as a second-best option to achieve higher productivity by
            helping circumvent cumbersome bureaucratic requirements.
            When controlling for EU membership the effects of the bribe
            tax are more harmful in non-EU countries. This suggests that
            the surrounding environment influences the way in which firm
            behaviour affects firm performance. In particular, in
            countries where corruption is more prevalent and the legal
            framework is weaker, bribery is more harmful for firm-level productivity.
repnme: repnme: Corruption and productivity : firm-level
            evidence from the BEEPS survey
display_title: Corruption and productivity :
            firm-level evidence from the BEEPS survey
listing_relative_url: /research/2010/06/13681810/corruption-productivity-firm-level-evidence-beeps-survey
topicv3: Personal Property Rights m1326571 1907,Trade m1326924 698,Criminal Justice m1326993 1014,Power and Electricity Sector m1327094 515,Environment, Natural Resources and Blue
            Economy m1327391 566
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Environment,Public Sector Development,Private Sector Development,Macroeconomics and Economic Growth
no_of_pages: 48
author_keys: 0: author_key: 901956
1: author_key: 927478
model_extractions: WPS5348 Policy Research Working Paper 5348 Corruption and Productivity Firm-level Evidence from the BEEPS Survey _Donato De Rosa Nishaal Gooroochurn Holger Görg_ The World Bank Europe and Central Asia Region Private and Financial Sector Department June 2010
Policy Research Working Paper 5348

# **Abstract**

Using enterprise data for the economies of Central and Eastern Europe and the CIS, this study examines the effects of corruption on productivity. Corruption is narrowly defined as the occurrence of informal payments to government officials to ease the day-today operation of firms. The effects of this “bribe tax” on productivity are compared to the consequences of red tape, which may be understood as imposing a “time tax” on firms. When testing effects in the full sample, only the bribe tax appears to have a negative impact on firm-level productivity, while the effect of the time tax is insignificant. At the same time, unlike similar studies using country-level data, firm level analysis allows a direct test of the “efficient grease” hypothesis by investigating whether corruption may increase productivity by helping reduce the time tax on firms. Results provide no evidence of a trade-off between the time and the bribe taxes, implying that bribing does not emerge as a secondbest option to achieve higher productivity by helping circumvent cumbersome bureaucratic requirements. When controlling for EU membership the effects of the bribe tax are more harmful in non-EU countries. This suggests that the surrounding environment influences the way in which firm behaviour affects firm performance. In particular, in countries where corruption is more prevalent and the legal framework is weaker, bribery is more harmful for firm-level productivity.

This paper is a product of the Private and Financial Sector Department, Europe and Central Asia Region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dderosa@ 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
# **Corruption and Productivity Firm-level Evidence from the BEEPS Survey**

## Donato De Rosa

_The World Bank, Washington DC, USA_

## Nishaal Gooroochurn

_London Metropolitan University, UK_ Holger Görg _Kiel Institute for the World Economy and University of Kiel, Germany, and CEPR_ The authors are grateful to Irena Grosfeld, Erich Gundlach, Mariana Iootty, Jorge Pena, Ekaterina Zhuravskaya, and seminar participants at the Kiel Institute and the World Bank for the many helpful comments received.
# **1. Introduction**

One of the most obvious facts about corruption is that poor countries tend to be the most corrupt. Available data at the country level support this view. For instance, there is a 0.81 correlation between GDP per capita and Transparency International’s Corruption Perception Index, one of the most commonly used measures of corruption.

Beyond this simple observation, understanding corruption is not an easy task. For instance, it is debatable whether corruption is a cause of low incomes per capita, one of its consequences or, as it seems more likely, whether the relationship between corruption and income is an intricate one, made of a web of dynamic interactions, whereby some countries appear trapped in a condition of low incomes and high corruption.

An even more daunting task is to find a cure for corruption, since historical experience does not provide many obvious examples of countries that have been successful in eradicating it. Corruption may be endemic and linked to deep-rooted cultural or “institutional” features of a society, which are not easily overturned by specific policy measures. For example, increasing the wage of public officials may prove ineffective in the absence of credible mechanisms to sanction deviant behaviour. Furthermore, it is difficult to identify a reservation wage for public officials, beyond which their incentive to accept or demand bribes is reduced. From the point of view of researchers and policymakers, this is so because corruption is not easily measured or quantified. The first challenge, then, is to delimit the field of investigation by providing a working definition of corruption and to find adequate sources of information that allow quantifying its extent.

In common parlance and in academic research corruption can take many forms. Most often it is understood as _bribery_ , whereby an official demands informal payments to perform an official task - e.g. issuing a license - or to circumvent laws and regulations. _State capture_ may also qualify for the definition of corruption, when bureaucrats subject themselves to more or less legal forms of lobbying, involving monetary bribes or other forms of exchange of favours, to afford preferential treatment to certain private interests. _Political patronage_ , _nepotism_ and 2
_cronyism_ , whether or not they involve monetary kickbacks, may also be included in a broad definition of corruption.

For our purposes, and to demarcate the field of investigation, corruption is defined as a “bribe tax”, a certain amount of money necessary to enforce a contract between an individual and the state. In this asymmetrical relationship, the state – or its agents - define the property rights of individuals and enforce them with a monopoly on the legitimate use of force. The institutions that govern this type of “vertical” transactions between the state and its citizens are defined by Acemoglu and Johnson (2005) as property rights institutions and are distinguished from contracting institutions that regulate “horizontal” transactions among ordinary citizens. Property rights institutions are inefficient when they allow those who control the state to extract rents from producers (Acemoglu, 2006) and the extortion of bribes from firms may be viewed as a form of rent extraction perpetrated by bureaucrats (Shleifer and Vishny, 1998).

Another important task is to identify a suitable data source to quantify the extent of corruption. A first type of data is based on _expert assessments_ , such as the International Country Risk Guide. A second type takes the form of a _meta-database_ , assembling the results of a number of perceptionbased surveys. Popular indicators in this group include the Corruption Perception Index (CPI) estimated by Transparency International or the indicator for Control of Corruption included in the World Bank’s World Governance Indicators. All these assessments present a high degree of correlation, indicating that they concur in identifying levels of corruption across countries and are, therefore, virtually interchangeable for the purpose of cross-country econometric analysis. A third source of measurement of corruption is provided by _enterprise surveys_ , which have the benefit of allowing to link the occurrence and effects of corruption to a number of firm-level and country characteristics.

Our analysis intends to exploit the advantages of the latter type of data by using the information contained in the 2009 Business Environment and Enterprise Performance Survey (BEEPS) of over 11,000 firms in 28 countries of Central and Eastern Europe and Central Asia. The sample of countries is very diverse. It covers all the formerly communist countries of Europe and the Former Soviet Union, which have undergone the profound institutional transformation connected 3
with transition to a market economy. The group of formerly communist countries presents substantial variation, ranging from the low income economies of Central Asia, to high income Central European countries, which, as members of the EU, tend to have a fully developed market system.

The objective of this study is to shed light on the consequences of corruption for economic performance. Unlike similar _country level_ studies using broad definitions of corruption and institutional quality combined with aggregate measures of economic performance, investigation of the effects of bribery on _firm level_ productivity will allow to be more precise regarding the incentives of economic agents to engage in corrupt behaviour and the consequences this has for productive efficiency. Variables for firm level bribery and productivity can be obtained from the BEEPS database, which, in addition to information on the occurrence of bribing and other aspects of firm operation and performance, allows estimating a measure of total factor productivity (TFP) at the enterprise level.

The need to recur to bribery is often linked to the power of government officials to impose and enforce regulatory requirements on individuals and firms and to exact bribes in the process (see, for example, Djankov et al. 2002). In order to account for this possibility, it is necessary to identify some measure of the power that officials have over firms as enforcers of regulatory requirements. The BEEPS survey offers such a measure at the firm level. It refers to the time that enterprise managers are required to spend complying with government regulations, amounting to a _time tax_ imposed on firms. This may be interpreted as an opportunity cost borne by firms, which, in isolation or in combination with the bribe tax, potentially constitutes a drag on enterprise performance. The availability of a firm level measure for the time spent dealing with bureaucracy offers the opportunity to perform a _direct_ test of the so-called “efficient grease” hypothesis, which is explicitly defined in the literature in terms of bribery helping reduce the _time_ required for some interaction between an economic agent and the state, as in Lui’s (1985) queuing model.<sup>1</sup>

> 1 Use of the time tax as a specific outcome of the institutional inefficiencies linked with corruption distinguishes our paper from other studies, such as Méon and Sekkat (2005) and Méon and Weill (2008). These authors, for lack of more precise country level data explicitly reflecting the time involved in interactions with the state, use generic country level proxies of “governance” to test whether corruption does indeed act as efficient grease.

4
The effects of corruption on productivity are modelled based on a firm level production function.<sup>2</sup> This paper offers a number of novelties with the respect to the existing literature on the consequences of corruption. First, the time spent dealing with bureaucracy, which is intimately linked to bribe payments, is directly examined. Second, the analysis makes use of cross-country firm level data in order to investigate the effects of country-level characteristics in mitigating the negative productivity impact of bribe payments. Third, we deal with the potential endogeneity of corruption at the firm level using an instrumental variables approach. To do so, we make use of the rich firm level data we have available in the BEEPS data to come up with reasonable instruments.

Results of econometric analysis highlight some differences between the effects of corruption _per se_ and those of the time tax. Across the entire sample, whereas the time devoted to complying with government regulations has no significant effect on firm level productivity, corruption has a statistically significant negative effect in instrumental variable specifications. Additionally, regression results show no evidence in favour of the “efficient grease” hypothesis, whereby bribing would be a second best option to achieve higher productivity levels by helping firms circumvent burdensome regulatory requirements. Namely, when bribing is made conditional on the time spent dealing with government regulations, the interaction term has no significant effect on productivity, implying that no trade-off emerges between the time and the bribe tax.

When controlling for the diversity of the sample distinguishing between recent EU members from Central and Eastern Europe and non-EU countries, the negative impact of the bribe tax on productivity is higher in non-EU countries. Observing that levels of institutional quality are generally higher in EU countries, this may suggest that the effects of corrupt behaviour on firm performance vary depending on broader country characteristics.

In order to test this environmental effect, bribery experienced by individual firms is made conditional on broader country characteristics that may play a role in influencing individual

> 2 Fisman and Svensson (2007) look at the relationship between corruption and firm growth using firm level data for Uganda.  Our paper is different in a number of aspects.  First, we use firm level data for a number of countries in Central and Eastern Europe and Central Asia, which allows us to explore cross country heterogeneity. This is important, as we show, since we find strong differences in the relationship between corruption and productivity in different countries.  Second, we look at productivity, not growth of sales as in Fisman and Svensson (2007).  .

5
choices to engage in corrupt behaviour. The first measure used is the Transparency International Corruption Perception Index (CPI), which provides an independent measurement of perceived corruption in 180 countries, based on 13 different expert and business surveys. Inclusion of country-wide corruption may be interpreted to reflect the extent to which peer effects may be conducive to corrupt behaviour, as in theoretical models that explain the persistence of corruption with social effects, such as Andvig and Moene (1990) and Tirole (1996). The second country-wide measure, used to probe the robustness of the environmental effect of corruption, is the World Economic Forum (WEF) index of the effectiveness of the legal framework in resolving disputes, intended to capture the possibility that a higher likelihood of sanctioning by the legal system may act as a deterrent.<sup>3</sup> Regression analysis shows that firms that do _not_ pay bribes in environments with a high prevalence of corruption and inefficient legal frameworks experience higher productivity. Furthermore, when overall levels of corruption exceed a certain threshold, the total effect of corruption on productivity - i.e. the combination of individual and country effects - is increasingly negative. This indicates that, whereas environmental circumstances are beyond the choice set of individual firms, managers still have some degree of autonomy in deciding whether to recur to bribery or not and this affects firm level productivity.

The remainder of the study is structured as follows. The following section provides an overview of the relevant literature on the possible causes of corruption and on its effects on economic performance. Next is a description of the BEEPS 2009 data, as well as an exposition of the econometric methodology. The fourth section demonstrates the effects of corruption on productivity, both unconditional and conditional on the time tax experienced by individual firms and on country characteristics in terms of prevalence of corruption and efficiency of the legal framework. The final section concludes.

> 3 North (1990) distinguishes between “informal norms,” “formal rules” and “enforcement” as pillars of the institutional framework of an economy. For our purposes, the CPI might also be viewed as a proxy of informal norms of behaviour dictated by social networks, while the WEF index of the efficiency of the legal framework might be interpreted as a proxy for the quality of formal rules or their enforcement.

6
# **2. Causes and Consequences of Corruption**

The occurrence of corruption can be directly linked to the quality of the overall institutional environment, which, in turn, is seen by several authors as the fundamental ingredient of economic development. For example, Acemoglu, Johnson and Robinson (2001) focus on the persistence of inherited institutions, by maintaining that the disease environment determined different settlement patterns of European colonists, which, in turn, shaped subsequent institutions. Notably, where Europeans settled in large numbers they established solid “property rights” - as opposed to “extractive” - institutions aimed at benefiting residents of the colony, resulting in higher institutional quality and lower incidence of corruption today. In a similar spirit, other theories stress legal origin (La Porta, Lopez-de-Silanes, Shleifer and Vishny, 1998 and 1999) as the source of institutional inefficiencies. According to this view, corruption is more likely to be observed in countries based on civil law systems, due to their greater tendency to regulate economic activity, which provides more frequent opportunities for corrupt behaviour.

Given the strong empirical association between various measures of institutional quality, including corruption, and incomes per capita, it is plausible to assume that institutions in general - and the extent of corruption in particular - develop in response to a country’s _income level_ and to the differential needs associated with various stages of development (Lipset, 1960). In this spirit, better institutional outcomes would emerge when, in response to economic development, the benefits of internalizing higher income opportunities - for instance by keeping corruption under control - exceed the transaction costs of doing so (Demsetz, 1967).

Institutional outcomes, including corruption, and levels of income per capita may crucially depend on the accumulated stock of _human capital_ (Glaeser, La Porta, Lopez-de-Silanes and Shleifer, 2004). The central role of human capital becomes evident when considering that formal institutions - e.g. courts - require a high level of competence to effectively perform their function. Furthermore, together with a free press (Besley and Burgess, 2001; Brunetti and Weder, 2001), widespread literacy is a precondition for the population to be able to scrutinize government activity and prevent abuses.

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Another possibility is that _economic growth_ itself, rather than income _levels_ , could play a role in determining the occurrence of corruption. For instance, a growing economy would have more resources available to keep corruption under control, thus generating better institutional outcomes and reducing observed levels corruption (Paldam, 2002). At the same time, economic growth can reduce corruption because corrupt elites have an interest in collecting bribes from a growing pie. This implies that, at least in the short term, they have to ensure that institutions are sufficiently immune to corruption to allow incomes to increase (Aidt and Dutta, 2008).

Policies aimed at increasing _competition_ in product markets may be instrumental in reducing corruption, since competitive pressures leading to a reduction in mark-ups and profits of firms may limit the resources available to pay bribes. In support of this view, Ades and di Tella (1999) find that corruption levels are higher in countries where domestic firms are sheltered from foreign competition by the existence of barriers to trade, while economies dominated by a small number of firms, or with ineffective antitrust regulations, experience higher degrees of corruption.

More generally, a _regulatory environment_ that stifles market entry and competition is likely to increase opportunities for corruption. More stringent regulatory requirements pander to the discretionary power of regulators and enforcers to collect bribes from producers, thus increasing the prevalence of corruption (Djankov, La Porta, Lopez de-Silanes and Shleifer, 2002). Such a view is in line with public choice theories, whereby regulation is pursued for the benefit of politicians and bureaucrats to create rents and extract them through political patronage or bribery (Shleifer and Vishny 1998). Rent extraction on the part of bureaucrats and politicians is inefficient because regulators are disorganized and their actions discretionary. As a consequence, more restrictive regulation may result in a “time tax” on entrepreneurs, which diverts entrepreneurial time and talent away from productive activities, with negative consequences for economic performance.

A separate strand of the literature highlights the importance of _social (or peer) effects_ in determining individual incentives for corruption and its persistence. Andvig and Moene (1990) model a situation where the individual propensity for corruption increases when corruption is 8
more pervasive, since, in this situation, it is easier to find corrupt officials and escape punishment. Tirole (1996) provides a rationale for the persistence of corruption. In his model members of a group with a bad reputation (i.e. corrupt individuals) have less of an incentive to behave honestly since individual behaviour is imperfectly observed and individual reputation partly depends on group reputation.

# **_2.1 Corruption as “Efficient Grease”_**

Corruption is sometimes seen a second-best option when it helps reduce the time involved in dealing with burdensome regulatory requirements. According to the proponents of this “efficient grease” hypothesis this would happen because, in spite of the transaction costs it entails, bribery would lead to lower effective red tape for the firm. A theoretical framework for this efficiency enhancing role of corruption is provided by Lui’s (1985) queuing model, where the size of bribes by different economic agents reflects their different opportunity cost, with more efficient agents more able or willing to buy lower effective red tape, reflected in a lower “time tax”. As a consequence, a license or contract awarded on the basis of bribe size could achieve Paretooptimal allocation. Kaufmann and Wei (1999) identify a major shortcoming in Lui’s (1985) assumptions, namely that the regulatory burden is treated as exogenous, independent of the incentives for officials to take bribes. This may not be the case since the incentives of bureaucrats can be modified by specific policy measures. Ultimately, because of this assumption, Lui’s theory is partial equilibrium in nature, and may not hold in a general equilibrium.

More generally, Bardhan (1997) argues that red tape and corruption are not exogenous, as they are caused - or at least preserved or aggravated - by those who benefit from an overregulated and corrupt system. Hence, as argued by Aidt and Dutta (2008), even if corruption helps overcome cumbersome regulation in the short term, it creates incentives to create more such regulation in the long term. Empirical evidence, especially at the micro level, is generally not supportive of the efficient grease hypothesis<sup>4</sup> , with corruption found to increase the time spent by managers

> 4 One of few exceptions is Egger and Winner (2005) who, based on country-level evidence, argue that corruption can help overcome regulatory obstacles and stimulate FDI,

9
dealing with red tape (Kaufmann and Wei, 1999) and to hamper firm growth (Fisman and Svensson, 2007).<sup>5</sup>

# **_2.2 Corruption and Economic Performance_**

If corruption was a means to “greasing the wheels of commerce” it could possibly have positive effects on economic performance by reducing transaction costs in the vertical transactions between the state and its citizens. However, the theoretical and empirical evidence in favour of the opposite argument appears more convincing, highlighting the negative consequences of corruption for resource allocation, entrepreneurship, investment and innovation.<sup>6</sup> The main argument is that the prevalence of corruption may distort _resource allocation_ by increasing the returns to rent-seeking compared to those of productive activities (Baumol, 1990). An extremely corrupt environment may induce individuals to minimize interaction with the state by expanding more slowly, operating in the informal sector or even forgoing _entrepreneurial activity_ altogether. Corroborating this point, Djankov et al. (2002) find that entry of new firms is more difficult in the presence of greater corruption and larger unofficial economies.

Corruption also affects the allocation of entrepreneurial _talent_ , when, in highly corrupt environments, entrepreneurs may devote greater efforts to obtaining valuable licenses and preferential market access than to improving productivity (Murphy, Shleifer and Vishny, 1991). When entrepreneurial talent is directed towards productive activity, the rate of innovation and investment is likely to increase with positive consequences for productivity and income growth. In contrast, when talent is directed towards rent extraction, returns to talent are maximized by appropriating wealth rather than wealth creation (Murphy, Shleifer and Vishny, 1991, 1993; Acemoglu and Verdier 1998).

> 5 Hellman, Jones, and Kaufmann (2000) also find a negative correlation between bribe payments and sales, although these authors do not instrument for bribe payments.

> 6 For an overview of the consequences of corruption, see Lambsdorff (2003), Svensonn (2005), Krueger (1974), Rose-Ackerman (1975 and 1999) and Shleifer and Vishny (1993 and 1998).

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The sources of productivity enhancements, _technological progress_ and _investment_ , may be directly affected in corrupt environments. For instance, entrepreneurs may have incentives to adopt inefficient “fly-by-night” technologies of production with an inefficiently high degree of reversibility, which allows them to react more flexibly to future demands from corrupt officials—and more credibly threaten to shut down operations (Svensson, 2003). Additionally, vested interests may directly oppose the adoption of new technologies, which would threaten their position of influence by rendering obsolete the older technological vintages they control (Krusell and Rios-Rull, 1996). Finally, corruption may erect de facto entry barriers into otherwise competitive markets with discouraging effects for investment decisions, in a mechanism similar to the one proposed by Alesina et al. (2005). Alternatively, the monetary cost involved in the payment of bribes may simply limit the amount of resources available to expand productive capacity via investment.

In addition to distortionary allocation effects, the discretionary power of state officials will increase the _risk of expropriation_ thus reducing the appropriability of returns to investment and innovation (Demsetz, 1967 and Alchian and Demsetz, 1973). This will further diminish rewards for entrepreneurial behaviour, while propping up inefficient firms engaged in corrupt practices. In this spirit, Johnson, McMillan, and Woodruff (2002), using firm-level data from former communist countries in Europe, find a negative effect of paying bribes on investment and interpret this finding as the effect of insecure property rights.

Whereas corruption can impact economic performance through all these channels, its adverse effects may be non-linear and depend on the overall level of institutional quality (or governance) in the country. Two studies – both based on country level data - find such non-linearities in the relationship between corruption and growth, namely a _more negative effect when institutional quality is poor_ . Méon and Sekkat (2005), based on sample of 71 countries between 1970 and 1998 and using various proxies for both corruption and governance,<sup>7</sup> find that corruption is most harmful to growth where governance is weak. Méndez and Sepúlveda (2006) examine country-

> 7 The authors use the Transparency International CPI, as well as a number of indicators from the World Bank Governance Indicators, as proxies for corruption and governance.

11
level evidence by using different proxies for corruption<sup>8</sup> , as well as the Freedom House index of political freedom as a proxy for overall institutional quality. They find that the relationship between corruption and growth is non-monotonic with corruption having negative effects only at high levels of incidence.

# **3 Data and Methodology**

To assess the effects of corruption on firm performance this paper uses the 2009 EBRD/World Bank Business Environment and Enterprise Performance Survey (BEEPS) of over 11,000 firms in 28 transition and developed countries.<sup>9</sup> The BEEPS was specifically conceived to assess the extent to which government policies and practices facilitate or impede business activity. It therefore provides a vast array of information on the behaviour and performance of firms, which allows to explicitly model the possible influence of various firm characteristics on the occurrence and impact of corruption at the firm level.

Table 1 lists the countries included in the sample. It shows that there is substantial variation in terms of income group (based on the World Bank classification for 2008)<sup>10</sup> and EU membership. Such high dispersion in income per capita provides a particularly rich sample, that allows controlling for specific country characteristics linked to the level of development and, in particular, to the quality of the institutional environment. The business environment is examined by asking firms to assess how various factors affect business operations, including infrastructure, financial services, government regulation, tax administration, judiciary functions. Corruption is also examined, allowing us to model its occurrence and impact on the operation and performance

> 8 The authors use the International Country Risk Guide (ICRG), the IMD index of corruption is published by the Institute for Management Development (IMD) and the corruption perceptions index (CPI) compiled by Transparency International.

> 9 Previous rounds of the BEEPS surveys were carried out in 1999, 2002 and 2005.  Unfortunately, given the changes in survey design, a meaningful link between the 2009 and earlier versions is not feasible.  Also, the survey nature of the data leads to the loss of many observations in multivariate regressions, owing to non-response rates. 10 Economies are divided according to 2008 GNI per capita. The groups are: low income, $975 or less; lower middle income, $976 - $3,855; upper middle income, $3,856 - $11,905; and high income, $11,906 or more.  See http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20420458~menuPK:641331 56~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html (accessed 15 October 2009).

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of firms. A list of variables used and their description is given in Table A.1, and their descriptive statistics are in given in Table A.2 in the appendix.

**Table 1 List of countries in the sample**

|**Economy**|**Incomegroup**|**EU member**|
|---|---|---|
|Croatia|High income||
|Estonia|High income|<br>|
|Slovenia|High income|<br>|
|Czech Republic|High income||
|Hungary|High income|<br>|
|Slovak Republic|High income||
|Belarus|Upper middle income||
|Bosnia and Herzegovina|Upper middle income||
|Bulgaria|Upper middle income||
|Kazakhstan|Upper middle income||
|Latvia|Upper middle income|<br>|
|Lithuania|Upper middle income||
|Macedonia, FYR|Upper middle income||
|Montenegro|Upper middle income||
|Poland|Upper middle income||
|Romania|Upper middle income||
|Russian Federation|Upper middle income||
|Serbia|Upper middle income||
|Turkey|Upper middle income||
|Albania|Lower middle income||
|Armenia|Lower middle income||
|Azerbaijan|Lower middle income||
|Georgia|Lower middle income||
|Moldova|Lower middle income||
|Ukraine|Lower middle income||
|Kyrgyz Republic|Low income||
|Tajikistan|Low income||
|Uzbekistan|Low income||

_Source: World Bank._ A typical concern when using survey data is that of individual perception bias (Kaufman and Wei, 1999). Some firms may, for instance, consistently provide positive or negative answers depending on their overall perception of the business climate. In principle, assuming that the bias is uncorrelated across groups of respondents, individual perception bias contributes only to the standard error of estimates obtained from the survey responses. In cross-country surveys, such as the BEEPS, the group within which the bias is likely to be correlated is the particular country in which respondents operate. Perception bias at the country level could originate from different cultural norms and degrees of political freedom across countries, which may influence the choice 13
of specific ratings and the willingness of business people to criticise state institutions. Fries et al. (2003) check for such perception bias in the BEEPS 2002 by statistically comparing measures obtained from the aggregation of survey responses to related objective measures and find no significant perception biases across the countries in the sample. Since the BEEPS 2009 follows a similar methodology, we may be reasonably confident that perception bias will not affect the results of the analysis. However, as a further control, the analysis that follows will make use of sector and country level fixed effects.

# **_3.1 The Bribe Tax and Productivity_**

The aim of this study is to evaluate the extent to which institutional inefficiencies experienced by firms –namely corrupt practices - may be a drag on their productivity. At the micro level, there are a number of reasons for expecting negative consequences of corruption for productivity. Firstly, as discussed in the previous section, corruption distorts the allocation of scarce resources away from the most productive use. This, all other things equal, should have a negative effect on productivity. Secondly, corruption may decrease firms incentives - or increase costs - of expanding productive capacity or investment (as in Alesina et al., 2005), which, again, would have a negative impact on productivity. On the other hand, as foreseen in the “efficient grease” hypothesis previously discussed, corruption may help a firm to cut through red tape and hence increase productivity. At the same time, both the occurrence and the effects of corrupt behaviour for individual firms may be linked to the quality of the institutional environment in the country. In this sense, a crucial role may be played by the degree to which corruption is a widely recurrent and accepted phenomenon, as well as by the ability of legal structures, such as courts or administrative recourse mechanisms within the public administration, to enforce contracts between individuals and the state and sanction deviant practices.

In order to capture the complexity of the phenomenon of corruption and its potentially varied effects on the performance of individual firms, the empirical methodology will proceed in three steps. First, is an analysis of the effects of the bribe tax and of the time tax on individual firms, controlling for firm, sector and country characteristics that may influence both phenomena.

14
Second, we proceed with an explicit test of the “efficient grease” hypothesis. Possible trade-offs between time consuming compliance with government regulation and the payment of bribes are modelled by including an interaction term between the time and the bribe tax and observing its effects on firm level productivity. Finally, the effect of individual corrupt conduct on firm level productivity is made conditional on the level of institutional quality in the country. That is, in addition to country fixed effects, the econometric specification includes an interaction term between the firm level bribe tax and independent assessments of the prevalence of corruption or the quality of the legal framework in the country.

We model the effect of corruption on TFP using an augmented production function, including, in addition to factor inputs, the set of firm, industry and country characteristics that are assumed to have an effect on output. Hence, we include corruption explicitly in the determination of output, as in (1): where _yijc_ is log output by firm _i_ in industry _j_ and country _c_ and _K_<sup>_land_</sup> _, K_<sup>_equipment_</sup> _, L and M_ are log of land, machinery, employment and materials, respectively.<sup>11</sup> The main variable of interest is _corruptionijc_ , which is the measure of corruption at the firm level. It is defined as a “bribe tax”, in the form of a dummy equal to one if a firm replies “frequently”, “usually” or “always” to the question “is it common to have to pay some irregular additional payment or gifts to get things done with regard to customs, taxes, licenses, regulations, services, etc.” The same specification can be used to test the direct effects of the “time tax”, defined as the percentage of senior management time devoted to dealing with bureaucratic requirements, by including it in the model as a substitute for the “bribe tax”. Consideration of both variables allows verifying the extent to which the time and the bribe tax are different phenomena, with

> 11 Estimation of TFP at the firm level is, of course, a task fraught with methodological difficulties. Prime among these is the simultaneity problem in the estimation of factor inputs, which are likely to be endogenously determined with output (see, for example, Levinsohn and Petrin, 2003). However, due to the cross section nature of our data we are not able to implement an approach a la Levinsohn and Petrin. This should be borne in mind in the interpretation of our empirical results.

15
different implications for firm productivity. It should be noted that the overall effect of bribes on productivity might be underestimated due to selection bias, as firms that had to pay the largest bribes may have been driven out of business altogether and, therefore, they are not in the dataset.

**Xijc** is a vector of control variables that serve to detect observable aspects of firm heterogeneity in our data to allow identification of the effect of the bribe tax on productivity. It consists of _sizeM, sizeL, age, exporter, innovator_ and _foreign-owned_ . _SizeM_ and _sizeL_ are dummy variables representing medium and large firms, respectively.  Larger firms are expected to have higher productivity due to the effects of scale economies. The variable _age_ represents the age of firms in 2008, capturing effects such as the vintage of the firm or learning by doing externalities on TFP.

_Innovator, exporter,_ and _foreign-owned_ are all dummy variables indicating whether the firm is engaged in innovation (in terms of having positive R&D expenditure), involved in exports and owned by foreign investors. These variables are expected to have a positive effect on productivity. In particular, innovation and R&D expenditures tend to positively affect firm productivity since they lead to the development of more efficient production technologies or to the more effective adoption of technologies developed outside the firm (Aw, Roberts and Xu, 2008).<sup>12</sup> At the same time, exporting activity has been found in several empirical studies to be positively associated with firm-level productivity.<sup>13</sup> FDI, on its part, is associated with various measures of firm performance, including investment, innovation and productivity, since foreign owners can be expected to transfer technology and know-how to domestic affiliates (see, for example, Girma and Görg, 2007).<sup>14</sup> In order to account for the possibility that increased _competition_ may act as a form of control on corruption, while, at the same time, affecting firm level productivity, **Xijc** also includes a variable for the perceived intensity of competition. The variable is defined “How much of an obstacle are competitors to your operations?”. Specifically, firms are asked to rank whether competition is an

> 12 Klette and Kortum (2004) provide a rationale for the effects of firm-level innovation on aggregate technological change and growth.

> 13 Wagner (2007) offers an overview of the vast empirical evidence on the strong association between exporting and productivity.

> 14 Hoekman and Smarzynska Javorcik (2006) present a number of instances of the interaction between innovation, trade and FDI. In particular, they show that the innovation activity associated with the technology transfer occurring with FDI and trade results in sizeable productivity gains at the firm level.

16
obstacle on a scale from 0 (no obstacle) to 4 (very severe obstacle). We define our variable as the difference between the individual firm’s response and the country average. As mentioned earlier, the rationale for including the competition variable is that, as firms’ profits are driven down by competitive pressure, there are no excess profits from which to pay bribes (Ades and Di Tella, 1999).

**Xijc** also includes two measures of the firm’s perception of the quality of the institutional environment.  The first is a dummy variable equal to one if the firm responds that the _quality of courts_ is a major or very severe obstacle to operating a business.  The second is a dummy variable that is similarly defined if a firm sees _political instability_ as a severe problem.  Including these two measures allows us to capture some aspects of institutional quality that may be correlated with corruption and, if not controlled for, may therefore bias our results.

Finally, _dj_ and _dc_ include a full set of industry and country dummies, respectively, and _uijc_ is the idiosyncratic error term, which allows for clustering at the country-industry level.

# **_3.2 Efficient grease: Trade-offs between the Bribe Tax and the Time Tax_**

The model in equation (1) can be expanded to verify the extent to which bribes may be a second best outcome in a context where inefficient bureaucracy leads to a time tax for producers. In other words, when regulation is overly restrictive, corruption may aide entrepreneurs in their interaction with the state, thus leading to a beneficial impact on productivity. A direct way to test this hypothesis would be to include the bribe tax and the time tax jointly in the empirical specification, together with their interaction. The latter would test the extent to which the effect of bribes on productivity is conditional on time consuming dealings with bureaucracy; in other words, it would allow a direct test of the efficient grease hypothesis, as in equation (2): 17
The net effect of _corruption_ (i.e., the bribe tax) on TFP is then given as In equation (3) a significant coefficient for _β3_ will indicate that the effect of corruption on productivity depends on the degree to which the firm is engaged in time consuming relations with the state. In particular, a positive coefficient for _β3_ would indicate that a high _time tax_ is accompanied with less negative - or even positive - effects of _corruption_ on productivity, thus providing evidence in favour of “efficient grease”, with corruption helping to mitigate the effects of burdensome regulation. The same result could also be consistent with a setting where briberevenue maximizing bureaucrats may use red tape (the time tax) as a screening device to give production licenses to high-productivity firms (Banerjee, 1997). In such a model, every firm pays the same amount of bribes while high productivity firms spend more time with bureaucrats.

# **_3.3 Institutional Quality: Interaction between Firm-level and Country-level Effects_**

As a further step in our analysis, in order to check whether the effect of corruption on firm level productivity differs depending on country characteristics, we extend equation (1) and interact _corruption_ with country level measures of institutional quality that may be hypothesized to be relevant in determining individual incentives for corrupt behaviour. For this we use two alternative indicators. First is the _Transparency International Corruption Perception Index (CPI)_ for the year 2008, which allows investigating whether the prevalence of corruption at the country level has implications for firm behaviour and, consequently, productivity performance. The CPI captures the perceived levels of public-sector corruption in a given country and is a composite index, drawing on different expert and business surveys. It may be interpreted to reflect the possibility of social effects as described earlier, whereby in a more corrupt environment individual entrepreneurs would have stronger incentives to behave corruptly. The CPI ranges from zero (highly corrupt) to ten (highly clean). It varies across countries and is fixed across sectors for a given country.

18
As can be seen in Table 2, the average levels of productivity, bribe tax and CPI are different between low CPI and high CPI countries<sup>15</sup> and between EU and non-EU countries. These variations could imply that the impact of bribe tax on productivity could be different for high and low CPI countries as suggested by equation (5), as well as for EU and non-EU countries. No major differences can be depicted in the level of time tax, however.<sup>16</sup>

**Table 2 Summary statistics by group of countries**

||**Tot**|**al**|**Non**|**EU**|**E**|**U**|**_CPI_**|**_CPI_**<br>|**_CPI_**|**_CPI_**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|
|**TFP**|0.148|0.916|0.150|0.941|0.144|0.874|0.239|0.983|0.090|0.867|
|**Bribe Tax**|0.099|0.298|0.139|0.346|0.030|0.171|0.195|0.396|0.030|0.169|
|**Time Tax**|14.528|18.223|14.313|19.819|14.884|15.220|14.669|21.172|14.430|15.851|
|**CPI**|3.393|1.150|2.863|0.904|4.288|0.949|2.287|0.362|4.191|0.814|

_Note: TFP is calculated as a residual from a simple production function, see appendix_ As a robustness check, we also use an alternative measure of institutional quality, namely the World Economic Forum’s index of the _effectiveness of the legal framework_ is solving legal disputes, which can be interpreted as a proxy for the ability of formal institutions to enforce contracts and prevent or sanction the occurrence of corrupt practices. This is also a country level index for the year 2008, where increases in the index imply better legal quality.

Including either of the indices in the model gives the following equation (4) as

> 15 Low and high CPI countries are defined as countries with CPI below and above the mean level, respectively. 16 The use of perceived corruption (the CPI index by Transparency International) may be problematic if there are discrepancies between perceived and actual corruption. Olken (2009) shows that this may be the case by examining a specific case of a road building project in rural Indonesia.  He compares corruption perceived by villagers with a more objective measure of corruption based on missing expenditure.  While a difference between actual and perceived corruption may potentially be a problem, one should keep in mind that Transparency International reports broad country level indices which are based on expert and business surveys.

19
_+ γXijc + dj + dc + εijc_ (4) In equation (4) a significant coefficient for _λ_ will indicate that the effect of corruption on productivity depends on the country’s level of institutional quality, as represented by the diffusion of corruption and the efficiency of the legal system. In particular, a positive (negative) coefficient of _λ_ will indicate that high _institutional quality_ will lessen (strengthen) the negative effect of _corruption at the firm level_ on productivity.

# **4 Econometric Analysis**

We now turn to the results of econometric analysis in the three stages outlined above. Namely, we examine the effects of corruption on productivity; of the interaction between corruption and the time tax; and of the relevance of country characteristics for firm level outcomes.

As discussed, the determinants of productivity are estimated using a one-step augmented production function. In order to address the potential endogeneity of firm level bribe tax and time tax, equation (1) is estimated with an instrumental variable (IV) approach. Implementation of the IV method requires the utilization of adequate instruments that must fulfil two conditions, namely being correlated with the endogenous variable and being uncorrelated with the error term in equation (1).

While it is difficult to find fully excludable instruments, the BEEPS data set offers a number of potential candidates. For instance, firms are asked whether or not the owner is female. There is evidence in the literature that women are more risk averse than men (Barsky et al., 1998). If that is the case, then they may also be less likely to engage in corrupt behaviour, which can be seen as a risky activity. While risk aversion may also play a role for the productivity of females, it is not a priori obvious that gender should have a direct influence on productivity that is not related to the indirect channel of risk aversion. Also, the data relate to the owner of the firm, not the manager. While the owner may have more influence on fundamental decision such as whether or not a firm should engage in illegal activities such as corruption, the owner may matter less for 20
operational decisions that affect productivity. Hence, we would argue that this may be a relevant and valid instrument for corruption.

An alternative instrumental variables candidate is a firm’s reply to a question as to whether they submitted an application for an electricity connection over the last two years. This allows us to generate a dummy variable equal to 1 if the firm did not submit an application and 0 if it did. This variable is arguably likely to be correlated with corruption, since it would imply interaction with public officials who have to grant the firm its right to be connected with the electricity grid. This would, hence, be a good opportunity for corrupt officials to demand a payment, either in cash or in terms of time. On the other hand, a dummy whether or not a firm submitted an application for electricity is unlikely to be correlated with productivity. Since one may assume that a firm needs some access to electricity to start operating, it appears reasonable that the application that is mentioned in the survey must relate to an additional or new connection. There is therefore no a priori reason why, conditional on the covariates in equation (1), there should be a correlation between the incidence of the application and TFP. It is also important to point out that this variable relates to the incidence of the application, not the actual connection to electricity. While this may of course lead to a new connection in the future, which may then possibly (but not necessarily) lead to an increase in productivity, this is unlikely to be the case in the current period.

Furthermore, we employ two additional instruments. These are the country-industry averages of _bribe tax_ and _time tax_ . These instruments are also employed by Fisman and Svensson (2007). Firms’ experiences and perceptions of corrupt practices or of the burden in terms of time associated with red tape are likely to be influenced by the experiences of other competitors in the same industry. Hence, we would expect our additional instruments to be correlated with the firm-level bribe tax and time tax variables. The necessary assumption for the validity of the instruments is that there is no direct effect of the sectoral average on a firm’s level of productivity conditional on the included covariates. This would not be the case if there were processes at the industry level that affect firm level productivity and bribes. An example may be governments favouring sectors that are particularly productive (or unproductive) in their attitude towards corruption. As in Fisman and Svensson (2007), we are not aware of any systematic 21
evidence to support this claim. Hence, we are cautiously confident that our instrument does not just pick up any unobserved industry effects that are correlated with firm level productivity.

We based our selection of instruments on initial tests for the validity and relevance of the instruments using standard tests. These tests indicate that the dummy whether the owner is female and the industry average are valid instruments in all cases. The dummy capturing whether a firm applied for an electricity connection is only valid and relevant in the case of the bribe tax, hence, we only use it when looking at the effects of bribes on productivity.

The results reported in Table 3 are, hence, based on using a female dummy, electrical connection application dummy, and industry level bribes, as instruments for bribe tax. For time tax, we do not use the electrical connection dummy, only the female dummy and industry level time tax as instruments. We report tests for the relevance of the instruments in these specifications, using a joint F test to verify whether the instrument candidates are correlated with the endogenous variable (e.g., Staiger and Stock, 1997) in Table 3. The F-statistics are higher than 20 in both cases confirming that the instruments are jointly highly correlated with the respective firm level corruption variable. Furthermore, we provide a Hansen-Sargan J test of overidentification restrictions to check that the IV candidates are uncorrelated with the error term in equation (1). The p-values of the Hansen-Sargan test confirm the validity of the chosen IV, as we cannot reject the null of instrument validity. We also present the full first-stage results of the IV model in the appendix (Table A4).

The second stage results of the effects of time and bribe taxes on productivity according to the baseline estimation of equation (1) are presented in Table 3. Columns (1) and (2) present the results using an OLS estimator, while columns (3) and (4) show IV estimates.

22
**Table 3 Productivity regression results: baseline specifications**

|Dependent Variable: Log Output<br>_O_<br>(1)|_LS_<br>(2)|_I_<br>(3)|_V_<br>(4)|
|---|---|---|---|
|Bribe Tax<br>-0.031||-0.608||
|[0.069]||[0.252]**||
|Time Tax|0.001||-0.009|
||[0.002]||[0.008]|
|L<br>0.319|0.311|0.320|0.309|
|[0.045]***|[0.046]***|[0.048]***|[0.048]***|
|M<br>0.420|0.418|0.423|0.425|
|[0.030]***|[0.031]***|[0.031]***|[0.031]***|
|K<sup>Equipment</sup><br>0.070|0.074|0.072|0.075|
|[0.017]***|[0.017]***|[0.017]***|[0.016]***|
|K<sup>Land</sup><br>0.003|0.011|-0.003|0.004|
|[0.013]|[0.013]|[0.013]|[0.013]|
|Exporter<br>0.219|0.206|0.233|0.209|
|[0.050]***|[0.054]***|[0.052]***|[0.052]***|
|Age<br>-0.000|-0.000|-0.000|-0.000|
|[0.001]|[0.001]|[0.001]|[0.001]|
|SizeM<br>-0.440|-0.454|-0.406|-0.458|
|[0.104]***|[0.109]***|[0.113]***|[0.113]***|
|<br>SizeL<br>-0.254|<br>-0.257|<br>-0.237|<br>-0.242|
|[0.060]***|[0.064]***|[0.066]***|[0.066]***|
|Foreign Owned<br>0.175|0.183|0.205|0.169|
|[0.067]***|[0.073]**|[0.073]***|[0.072]**|
|Innovator<br>0.068|0.072|0.066|0.048|
|[0.054]|[0.056]|[0.054]|[0.055]|
|Competition<br>-0.022|-0.016|-0.009|-0.011|
|[0.015]|[0.015]|[0.016]|[0.017]|
|Courts<br>-0.039|-0.065|0.048|-0.046|
|[0.052]|[0.055]|[0.067]|[0.056]|
|Political Stability<br>0.056|0.037|0.071|0.024|
|[0.047]|[0.048]|[0.048]|[0.047]|
|Constant<br>YES|YES|YES|YES|
|Country Dummies<br>YES|YES|YES|YES|
|Sector Dummies<br>YES|YES|YES|YES|
|R squared<br>0.86|0.87|0.79|0.80|
|Wu-Hausman (p-value)||0.03|0.17|
|<br>Hansen J (p-value)||0.66|0.45|
|F-Stat||43.11|27.24|
|Observations<br>1666|1519|1629|1490|
|_Note_<br>_: Standard errors clustered by country-industry in brac_<br>_at 5%; *** significant at 1%. Instruments: bribe tax: countr_|_kets. * signific_<br>_y-industry bri_|_ant at 10%; *_<br>_be tax, dumm_|_* significant_<br>_y for female_|
|_owner, dummy for electricity connection application. Time_<br>_forfemale owner. * significant at 10%; ** significant at 5%_|_tax: country-i_<br>_; *** significa_|_ndustry time t_<br>_nt at 1%_|_ax, dummy_|

Note firstly that the coefficient on the production factors capital, labour, land and materials are all positive as expected. Furthermore, exporters and foreign-owned firms are more productive, _ceteris paribus_ , as expected. Strikingly, larger firms tend to be less productive, perhaps a sign of 23
incomplete restructuring that prevents firms from exploiting the benefits of scale economies. Whereas innovation would be expected to be associated with higher productivity, the innovation dummy appears as insignificant in all specifications. This may indicate that the innovation activities carried our within firms may be insufficient to have an impact on productivity. This result could indicate a prevalence of defensive as opposed to strategic restructuring by the firms in the sample, where the former is related to short-term cost-cutting measures, while the latter is focused on increasing the long-term efficiency and viability of the firm, by investing in labour training, fixed assets and other innovation related activities such as R&D (Grosfeld and Roland, 1997; Aghion et al., 1997; Frydman et al., 1999). The other controls are statistically insignificant.

Examination of the OLS results in columns 1 and 2 shows that the coefficients on both time and bribe tax are statistically insignificant. It is, however, unlikely that the corruption variables are exogenous in this productivity estimation. For example, highly productive firms may have a better ability to engage in bribing or may be preferred targets of bureaucrats aiming at exacting bribes. This would introduce reverse causality in the equation or, more formally, a correlation between the right-hand-side variable and the error term. Another potential source of endogeneity is the impact of unobserved institutional characteristics at the firm level. We argue that our measures of perception of the quality of courts and political instability go some way to address these concerns.

The Wu-Hausman test is performed to check whether bribe is endogenous and the results are given at the bottom of Table 3. The significant p-value rejects the null hypothesis of exogeneity of bribe tax. This is not the case for time tax, however, where we cannot reject exogeneity. However, in both cases we implement an instrumental variables (IV) technique to estimate equation (1) to check the implications this has for the coefficient on corruption.

The _bribe tax_ has a negative and significant effect on productivity when adjusting for potential endogeneity bias (Table 3, column 4). The negative and significant coefficient of bribe tax indicates that firms that pay bribes to officials experience lower productivity than other firms. The size of the coefficient suggests that a firm that pays bribes is on average around 45 percent 24
less productive than a non-corrupt firm.<sup>17</sup> The table also shows that, none of the controls for institutional quality are significantly correlated with firm-level productivity. We also still fail to find a statistically significant impact of _time tax_ on firm level productivity (Column 3).

# **_4.1 Trade-offs between the Bribe Tax and the Time Tax_**

The preceding analysis has shown that corruption proper and inefficient bureaucracy have differentiated effects on firm level productivity in our sample. Namely, while the payment of bribes is negatively associated with the productivity of the bribing firm, time spent dealing with bureaucratic requirements _per se_ appears to be irrelevant. However, it has been argued that the occurrence of corruption may not be independent of the length of bureaucratic processes. These may, in fact, be deliberately established by state officials with the intent of exacting bribes. In this context, the payment of bribes might help “grease the wheels of commerce” by speeding up bureaucratic requirements, as captured by the time tax, and lead to a second best outcome for the bribing firm. The challenge is, therefore, to examine whether the (negative) effect of bribes on productivity is somehow dependent on the time that firms have to spend dealing with red tape.

In particular, we could find evidence of “efficient grease” if the time tax mitigated the negative impact of bribes. This could be the case if bribe payments were used to buy lower effective red tape thus reducing the inefficiencies associated with the time firms have to spend with bureaucrats. On the contrary, if bureaucrats intentionally targeted more productive firms, as in Banerjee (1997), the time tax could exacerbate the negative effect of bribe payments on productivity.

> 17 Calculated as exp(-0.608)-1

25
# **Table 4 Trade-off between the** **<u>bribe tax and the time tax</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dependent Variable: Log Output|OLS|IV|OLS|IV|
|Bribe tax|0.019|-0.509|0.021|-1.151|
||[0.075]|[0.261]*|[0.099]|[0.859]|
|Time tax|0.001|-0.008|0.001|-0.008|
||[0.002]|[0.008]|[0.002]|[0.008]|
|**Time tax * Bribe tax**|||**-0.000**<br>|**0.050**<br>|
||||**[0.004]**|**[0.062]**|
|L|0.311|0.314|0.311|0.306|
||[0.046]***|[0.050]***|[0.046]***|[0.049]***|
|M|0.418|0.422|0.418|0.421|
||[0.031]***|[0.032]***|[0.031]***|[0.032]***|
|K<sup>Equipment</sup>|0.074|0.076|0.074|0.084|
||[0.017]***|[0.017]***|[0.017]***|[0.021]***|
|K<sup>Land</sup>|0.011|0.002|0.011|0.003|
||[0.013]|[0.014]|[0.013]|[0.015]|
|Exporter|0.206|0.226|0.206|0.237|
||[0.054]***|[0.055]***|[0.053]***|[0.062]***|
|Age|-0.000|-0.000|-0.000|-0.000|
||[0.001]|[0.001]|[0.001]|[0.001]|
|SizeM|-0.455|-0.432|-0.455|-0.440|
||[0.109]***|[0.121]***|[0.109]***|[0.123]***|
|SizeL|-0.257|-0.235|-0.257|-0.230|
||[0.064]***|[0.071]***|[0.065]***|[0.076]***|
|Foreign Owned|0.181|0.203|0.181|0.166|
||[0.073]**|[0.078]***|[0.073]**|[0.093]*|
|Innovator|0.072|0.053|0.072|0.056|
||[0.056]|[0.055]|[0.056]|[0.058]|
|Competition|-0.016|0.000|-0.016|-0.004|
||[0.015]|[0.018]|[0.015]|[0.018]|
|Courts|-0.067|0.026|-0.067|-0.001|
||[0.055]|[0.073]|[0.055]|[0.087]|
|Political Stability|0.036|0.047|0.037|0.021|
||[0.049]|[0.049]|[0.049]|[0.059]|
|Constant|YES|YES|YES|YES|
|Country Dummies|YES|YES|YES|YES|
|Sector Dummies|YES|YES|YES|YES|
|R squared|0.87|0.80|0.87|0.78|
|Wu-Hausman [p-value]||0.06||0.10|
|Hansen J [p-value]||0.78||0.90|
|F-Stat [time tax]||11.14||11.14|
|F-Stat [bribe tax]||29.25||29.25|
|F-Stat [time tax * bribe tax]||||6.83|
|Observations<br>_Note_<br>_: Standard errors clustered by c_<br>_5%; *** significant at 1%._|1519<br>_ountry-industry in_|1490<br>_brackets. * signif_|1519<br>_icant at 10%; **_|1490<br>_significant at_|

_<u>Note: Instruments used in column (2) are the same as in Table 4. In column (4), we include the interaction</u> of industry level bribe tax and industry level time tax as additional instrument in order to be able to test for overidentification restrictions using the Hansen J test._ 26
A direct way to investigate the trade-offs between bribes and red tape and to test whether such trade-offs are of the “efficient grease” type, is to include the bribe tax and the time tax jointly in the empirical specification, together with their interaction. Examination of the sign, significance and magnitude of the coefficient on the interaction term would allow drawing conclusions on the nature of the relationships between bribe payments and red tape in our sample and to verify the extent to which the effect of corruption on productivity is conditional on time consuming dealings with bureaucracy.

Table 4 shows that the interaction of time tax and bribe tax is insignificant, failing to provide evidence of a link between inefficient bureaucracy, corruption and productivity. The time tax remains statistically insignificant, whereas the effect of the bribe tax for productivity remains negative but becomes statistically insignificant when including the interaction term. This suggests that the specification with the interaction term does not fit the data well. The coefficient for factor inputs and other control variables remain largely unaltered compared to Table 3.

# **_4.2 Does the Institutional Environment Matter?_**

An interesting question that can be answered with our data is whether there are any systematic variations in the effects of corruption on productivity across groups of countries. More specifically, we investigate whether there are any differences across countries that entered the EU recently and those that are not members, as well as among countries with various levels of institutional quality that may have an effect on individual incentives for corruption.

27
**Table 5 Productivity regression results: EU vs. Non-EU countries**

|Dependent Variable: Log Output|_O_<br>(1)|_LS_<br>(2)|_I_<br>(3)|_V_<br>(4)|
|---|---|---|---|---|
|Bribe tax|-0.055||-0.710||
||[0.072]||[0.283]**||
|Bribe tax * EU|0.227||0.868||
||[0.258]||[0.342]**||
|Time tax||0.001||-0.014|
|||[0.002]||[0.012]|
|Time tax * EU||-0.001||0.014|
|||[0.003]||[0.012]|
|EU Country|-0.334<br>|-0.317|-0.484|-0.717|
||[0.250]|[0.274]|[0.184]***|[0.279]**|
|L|0.321|0.311|0.327|0.310|
||[0.045]***|[0.046]***|[0.049]***|[0.048]***|
|M|0.419|0.418|0.421|0.428|
||[0.030]***|[0.031]***|[0.031]***|[0.032]***|
|K<sup>Equipment</sup>|0.071|0.074|0.077|0.075|
||[0.017]***|[0.017]***|[0.017]***|[0.016]***|
|K<sup>Land</sup>|0.002|0.011|-0.008|0.003|
||[0.013]|[0.013]|[0.013]|[0.013]|
|Exporter|0.218|0.207|0.229|0.206|
||[0.051]***|[0.054]***|[0.052]***|[0.051]***|
|Age|-0.000|-0.000|-0.000|-0.000|
||[0.001]|[0.001]|[0.001]|[0.001]|
|SizeM|-0.435|-0.454|-0.386|-0.453|
||[0.104]***|[0.109]***|[0.115]***|[0.112]***|
|SizeL|-0.251|-0.257|-0.223|-0.250|
||[0.061]***|[0.065]***|[0.067]***|[0.068]***|
|Foreign Owned|0.175|0.183|0.206|0.164|
||[0.067]***|[0.073]**|[0.074]***|[0.072]**|
|Innovator|0.068|0.072|0.066|0.047|
||[0.054]|[0.056]|[0.053]|[0.056]|
|Competition|-0.022|-0.016|-0.008|-0.013|
||[0.015]|[0.015]|[0.016]|[0.016]|
|Courts|-0.041|-0.065|0.043|-0.047|
||[0.052]|[0.055]|[0.066]|[0.058]|
|Political Stability|0.056|0.037|0.074|0.027|
||[0.047]|[0.048]|[0.048]|[0.048]|
|Constant|YES|YES|YES|YES|
|Country Dummies|YES|YES|YES|YES|
|Sector Dummies|YES|YES|YES|YES|
|R squared|0.86|0.87|0.80|0.80|
|<br>Wu-Hausman [p-value]|||0.03|0.17|
|Hansen J [p-value]|||0.62|0.43|
|F-Stat|||32.02|14.22|
|Observations<br>_Note_<br>_: Standard errors clustered by countr_<br>_*** significant at 1%. Instruments: XYZ; c_|1666<br>_y-industry in brack_<br>_ountry-industry ti_|1519<br>_ets. * signific_<br>_me tax; country-in_|1629<br>_ant at 10%; ** si_<br>_dustry bribe tax._|1490<br>_gnificant at 5%;_<br>|
As a first step, we interact the bribe and the time tax with EU membership (Table 5). First, we observe that the sign and significance of controls remains unchanged with respect to Table 4. Second, it emerges that the time tax remains insignificant, also when interacted with the EU dummy, whereas the bribe tax remains significant with a negative sign in isolation, while displaying a large, positive and significant effect in the interaction (columns 3 and 4). This indicates that bribe payments do not have a negative impact on productivity in EU countries – the effect may even be positive.

The finding that the bribe tax is less harmful in EU countries may reflect the generally higher institutional quality in countries with higher incomes per capita.<sup>18</sup> The negative effect of the bribe tax on productivity in non-EU countries may be linked to generally poorer institutional quality and to the fact that, in high corruption environments, bribing could be regarded as the norm for most interactions with the State. Bribery will hence constitute a drag on productivity, without enabling firms to reap an efficiency advantage over competitors, as it is very likely that other firms are also paying bribes.

In order to examine the possibility of country-specific effects a step further, we explicitly consider the potential influence of broader institutional characteristics of the country. For this purpose, two variables, obtained from sources other than the BEEPS, are used. The first is the Corruption Perception Index computed by Transparency International. The second is a measure of the quality of the legal framework taken from the Global Competitiveness Report. As shown in Table A3 in the Appendix, mirroring the large differences in income per capita, these two variables also present substantial variation across the countries in the sample. We, therefore, posit that the effect of bribe and time tax on productivity may depend on the overall prevalence of corruption in the country - in the spirit of theories highlighting the role of social effects on individual behaviour – and on the effectiveness of the legal framework in preventing and sanctioning corrupt behaviour. This hypothesis is tested by estimating model (4) and the results are given in Tables 6 and 7.

> 18 One may also try to detect a specific effect of EU accession, implying that the requirements of EU accession may induce countries to improve the process of formulation and enforcement of laws and regulations, thus reducing the occurrence of bribes as experienced by firms.  However, such a conclusion would require a more detailed analysis, which takes account of the time series dimension to model pre- and post-accession environments and controls for observed and unobserved country characteristics.

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**Table** **<u>6 Productivity regression results: Country-level corruption</u>**

|Dependent Variable: Log Output|_O_<br>(1)|_LS_<br>(2)|<br>(3)|_IV_<br>(4)|
|---|---|---|---|---|
|Bribe tax|0.149||-4.105||
||[0.199]||[2.364]*||
|Bribe tax * CPI|-0.069||1.372||
||[0.067]||[0.823]*||
|Time tax||0.001||0.005|
|||[0.005]||[0.064]|
|Time tax * CPI||0.000||-0.001|
|||[0.001]||[0.017]|
|cpi_2008|-0.120|-0.105|-0.745|-0.338|
||[0.061]*|[0.062]*|[0.377]**|[0.318]|
|L|0.318|0.311|0.342|0.307|
||[0.045]***|[0.046]***|[0.067]***|[0.047]***|
|M|0.420|0.418|0.436|0.422|
||[0.030]***|[0.031]***|[0.038]***|[0.033]***|
|K<sup>Equipment</sup>|0.069|0.074|0.098|0.074|
||[0.017]***|[0.017]***|[0.028]***|[0.017]***|
|K<sup>Land</sup>|0.004|0.011|-0.033|0.009|
||[0.013]|[0.013]|[0.026]|[0.015]|
|Exporter|0.218|0.206|0.287|0.205|
||[0.050]***|[0.053]***|[0.084]***|[0.057]***|
|Age|-0.000|-0.000|-0.000|-0.001|
||[0.001]|[0.001]|[0.001]|[0.001]|
|SizeM|-0.445|-0.454|-0.230|-0.449|
||[0.104]***|[0.109]***|[0.167]|[0.110]***|
|SizeL|-0.258|-0.257|-0.127|-0.245|
||[0.060]***|[0.065]***|[0.105]|[0.065]***|
|Foreign Owned|0.176|0.183|0.275|0.171|
||[0.067]***|[0.073]**|[0.091]***|[0.077]**|
|Innovator|0.070|0.072|0.040|0.060|
||[0.054]|[0.056]|[0.059]|[0.060]|
|Competition|-0.022|-0.016|-0.019|-0.019|
||[0.015]|[0.015]|[0.022]|[0.018]|
|Courts|-0.040|-0.065|0.098|-0.055|
||[0.052]|[0.055]|[0.100]|[0.057]|
|Political Stability|0.057|0.037|0.063|0.024|
||[0.047]|[0.049]|[0.057]|[0.047]|
|Constant|YES|YES|YES|YES|
|Country Dummies|YES|YES|YES|YES|
|Sector Dummies|YES|YES|YES|YES|
|R squared|0.86|0.87|0.74|0.81|
|<br>Wu-Hausman [p-value]|||0.07|0.97|
|Hansen J [p-value]|||0.67|0.94|
|F-Stat|||2.04|3.45|
|Observations<br>_Note_<br>_: Standard errors clustered by count_<br>_5%; *** significant at 1%. Instruments:_|1666<br>_ry-industry in bra_<br>_XYZ; country-indu_|1519<br>_ckets. * signif_<br>_stry time tax; cou_|1252<br>_icant at 10%; **_<br>_ntry-industry bri_|1490<br>_significant at_<br>_be tax._|
**Table 7 Productivity regression results: Quality of the legal framework**

|Dependent Variable: Log Output|_O_<br>(1)|_LS_<br>(2)|_I_<br>(3)|_V_<br>(4)|
|---|---|---|---|---|
|Bribe tax|-0.432||-19.284||
||[0.432]||[10.369]*||
|Bribe tax * LF|0.126||6.435||
||[0.148]||[3.566]*||
|Time tax||-0.001||-0.087|
|||[0.008]||[0.190]|
|Time tax * LF||0.001||0.030|
|||[0.003]||[0.064]|
|Legal framework|0.346|0.346|-1.517|-0.836|
||[0.105]***|[0.117]***|[1.894]|[1.185]|
|L|0.300|0.289|0.320|0.269|
||[0.049]***|[0.050]***|[0.059]***|[0.061]***|
|M|0.423|0.421|0.401|0.450|
||[0.033]***|[0.035]***|[0.044]***|[0.040]***|
|K<sup>Equipment</sup>|0.069|0.075|0.089|0.078|
||[0.017]***|[0.018]***|[0.027]***|[0.024]***|
|K<sup>Land</sup>|0.002|0.010|-0.002|0.004|
||[0.014]|[0.015]|[0.017]|[0.017]|
|Exporter|0.230|0.215|0.135|0.223|
||[0.053]***|[0.057]***|[0.086]|[0.075]***|
|Age|-0.000|-0.000|0.001|-0.001|
||[0.001]|[0.001]|[0.002]|[0.001]|
|SizeM|-0.487|-0.506|-0.397|-0.425|
||[0.106]***|[0.110]***|[0.146]***|[0.159]***|
|SizeL|-0.270|-0.276|-0.240|-0.224|
||[0.061]***|[0.065]***|[0.098]**|[0.093]**|
|Foreign Owned|0.209|0.212|0.373|0.327|
||[0.071]***|[0.078]***|[0.137]***|[0.087]***|
|Innovator|0.069|0.073|0.080|0.082|
||[0.055]|[0.058]|[0.072]|[0.075]|
|Competition|-0.024|-0.020|-0.001|-0.033|
||[0.015]|[0.015]|[0.026]|[0.019]*|
|Courts|-0.018|-0.047|0.045|-0.001|
||[0.050]|[0.052]|[0.083]|[0.066]|
|Political Stability|0.063|0.038|0.097|0.032|
||[0.049]|[0.051]|[0.069]|[0.062]|
|Constant|YES|YES|YES|YES|
|Country Dummies|YES|YES|YES|YES|
|Sector Dummies|YES|YES|YES|YES|
|R squared|0.87|0.87|0.59|0.81|
|<br>Wu-Hausman [p-value]|||0.00|0.63|
|Hansen J [p-value]|||0.91|0.79|
|F-Stat|||2.51|2.75|
|Observations<br>_Note_<br>_: Standard errors clustered by countr_<br>_*** significant at 1%. Instruments: XYZ; c_|1504<br>_y-industry in brack_<br>_ountry-industry ti_|1363<br>_ets. * signific_<br>_me tax; country-in_|1470<br>_ant at 10%; ** si_<br>_dustry bribe tax._|981<br>_gnificant at 5%;_<br>|

31
Overall, our results indicate that the relationship between corruption and economic performance is conditional on the overall level of institutional quality. In particular, the coefficients on _time tax_ and its interaction are statistically insignificant, the coefficient of _bribe tax_ is still negative and significant in all specifications, whereas the interactive term, _bribe×institution_ , is positive and significant for both the CPI and the quality of the legal framework.

In order to illustrate the role of country features, and specifically the level of corruption in the country as represented by the CPI, we can use the estimated coefficients of column 3 in Table 6 to calculate the total effect of bribe tax on productivity as Equation (6) demonstrates that in highly corrupt environments – i.e. for lower values of the CPI - bribes have higher negative impact on productivity. At the same time, as the value of the CPI increases (less corrupt environments), the total effect of bribe on productivity becomes less negative and, beyond a certain threshold, could even be positive. This could be because, in an environment that is generally free of corruption, paying a bribe might result in a competitive advantage, perhaps reflected in a marginal gain in firm level productivity. On the other hand, in a highly corrupt environment, social effects of the type modelled by Andvig and Moene (1990) or Tirole (1996) may induce most market players to pay a bribe. Hence there would be no competitive edge or gain in productivity to be obtained by paying a bribe. Quite the opposite, paying more bribes allocates resources away from their most productive use, reducing productivity of the firm. Hence productivity gains are more likely to incur to the firms that do _not_ bear the cost of bribes.

From these results we can calculate that the cut-off point at which the sign of the total effect changes is 2.99.<sup>19</sup> Table 8 shows the countries in the sample for which the total effect of corruption on productivity is positive and negative. Interestingly, based on the CPI, the total

> 19 Here we are of course assuming that the relationship is linear and there is only one cut-off point.

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effect of corruption is negative in all former Soviet republics, with the exception of the Baltics and Georgia.

**Table 8 Effects of corruption and legal quality on productivity**

|**Negative**<br>**(CPI < 2.99)**|**Positive**<br>**(CPI > 2.99)**|**Negative**<br>**(legal quality < 2.99)**|**Positive**<br>**(legal quality > 2.99)**|
|---|---|---|---|
|**Armenia**<br>Azerbaijan<br>Belarus<br>Kazakhstan<br>**Kyrgyz**|**Albania**<br>Bosnia<br>Bulgaria<br>Croatia<br>**Czech Republic**|**Armenia**<br>Bosnia<br>Bulgaria<br>Croatia<br>**Kyrgyz**|**Albania**<br>Azerbaijan<br>Belarus<br>**Czech Republic**<br>**Estonia**|
|Moldova|**Estonia**|Poland|**FYROM**|
|**Russia**<br>Tajikistan<br>**Ukraine**<br>Uzbekistan|**FYROM**<br>**Georgia**<br>**Hungary**<br>**Latvia**<br>**Lithuania**<br>**Montenegro**<br>Poland<br>Romania<br>Serbia|Romania<br>**Russia**<br>Serbia<br>**Ukraine**|**Georgia**<br>**Hungary**<br>Kazakhstan<br>**Latvia**<br>**Lithuania**<br>Moldova<br>**Montenegro**<br>**Slovakia**<br>**Slovenia**|
||**Slovakia**||Tajikistan|
||**Slovenia**||Uzbekistan|

We can make similar calculations for the alternative measure of institutional quality, the efficiency of the legal framework in sanctioning corrupt behaviour in column (3) of Table 7, where the total effect of bribes is -19.284 + 6.435* _legal_quality_ . While the overlap of countries with negative and positive effects of firm level bribes is not complete, there are a number of countries that are in the same categories when using the two alternative indices. These are marked in bold in the table.

# **5 Concluding Remarks**

Corruption is often identified as one of the primary causes for the underdevelopment of many economies. Nevertheless, some see corruption as a necessary evil that, by “greasing the wheels of commerce”, mitigates the negative effects of inefficient bureaucracy, which may be seen as imposing a “time tax” on individuals and firms. At the same time, both the incentives for - and the impact of - corruption may be different across countries, depending on the nature of the 33
surrounding environment, namely on the diffusion of corruption and on the ability of the legal system to sanction corrupt behaviour.

Based on these premises, this study investigates the effect of corruption - interpreted as a “bribe tax” - on firm-level productivity across a diverse sample of countries in Central and Eastern Europe and the Former Soviet Union. The findings of econometric analysis corroborate the hypothesis that corruption has, on balance, negative consequences for enterprise performance. However, the relationship between corruption and economic performance presents some nuances.

First, a comparison of the effects of the bribe tax and the time tax indicates that only bribery negatively affects firm productivity, while lengthy bureaucratic requirements _per se_ have no significant consequences.

Second, an explicit test of the hypothesis that bribes help to mitigate the negative effects of time consuming dealings with bureaucracy does not find confirmation in our data. Contrary to previous studies addressing the same question at the country level, our conclusion is based on a precise definition of the institutional inefficiencies that corruption is supposed to “grease” – namely lengthy bureaucratic requirements – rather than generic measures of “governance”.

Third, broader environmental circumstances turn out to play a significant role in determining the impact of firm level corruption on productivity. In fact, results indicate that the effects on firm productivity are different in EU and non-EU countries, with the bribe tax appearing more harmful in non-EU countries. Further consideration of country-wide measures leads to the conclusion that, in highly corrupt environments and where the legal framework is weaker, firms that do _not_ pay bribes are more productive. Furthermore, as the level of institutional quality decreases, the total effect of corruption is increasingly negative. This suggests that, whereas environmental circumstances are beyond the choice set of individual firms, managers retain some degree of autonomy in deciding whether to recur to bribery or not and this affects enterprise performance.

34
A possible policy implication of these findings is that narrow measures to reduce the incentives for corruption, such as targeted wage increases for public officials, are likely to be ineffective if not embedded in a comprehensive strategy for institutional reform.

35