metadata: authors: 0: author: Clarke,George R.
1: author: Xu,L. Colin
docna: 0: docna: Ownership, competition, and corruption :
            bribe takers versus bribe payers
docty: Policy Research Working Paper
subtopic: Social Analysis,Quality of Life & Leisure,National Governance,Government Policies,Telecommunications Infrastructure,State Owned Enterprise Reform,Public Sector Administrative & Civil
            Service Reform,Energy Policies & Economics,Youth and Governance,Privatization,Energy Privatization,Democratic Government,Economics and Finance of Public
            Institution Development,Public Sector Administrative and Civil
            Service Reform,De Facto Governments
teratopic: Finance and Financial Sector Development
abstracts: cdata!: Over the past few years, many studies
            have looked at the macroeconomic, cultural, and
            institutional determinants of corruption. This study
            complements these cross-country studies by focusing on
            microeconomic factors that affect bribes paid in a single
            sector of the economy. Using enterprise-level data on bribes
            paid to utilities in 21 transition economies in Easter
            Europe and Central Asia, the authors look at how
            characteristics of the firms paying bribes (such as
            ownership, profitability, and size) and characteristics of
            the utilities taking bribes (such as competition and utility
            capacity) affect the equilibrium level of corruption in the
            sector. On the side of bribe payers, enterprises that are
            more profitable, enterprises that have greater overdue
            payment to utilities, and de novo private firms pay higher
            bribes. On the side of bribe takers, bribes paid to
            utilities are higher in countries with greater constraints
            on utility capacity, lower levels of competition in the
            utility sector, and where utilities are state-owned. Bribes
            in the utility sector are also correlated with many of the
            macroeconomic and political factors that previous studies
            have found to affect the overall level of corruption.
repnme: repnme: Ownership, competition, and corruption :
            bribe takers versus bribe payers
display_title: Ownership, competition, and
            corruption : bribe takers versus bribe payers
listing_relative_url: /research/2002/02/1703258/ownership-competition-corruption-bribe-takers-versus-bribe-payers
topicv3: Political Economy of Reform m1326263 836,State Owned Enterprises m1326323 721,Power and Electricity Sector m1327094 515,Privatization m1327236 1283,Telecommunications and Broadband Access
            m1327340 286
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Finance and Financial Sector Development
origu: Development Research Group (DECRG)
no_of_pages: 40
model_extractions: I POIcY RESEARCH WORKING PAPER 2783

# Abstract

> Over the past few years, many studies have looked at the macroeconomic, cultural, and institutional determinants

> of corruption. This study complements these crosscountry studies by focusing on microeconomic factors that affect bribes paid in a single sector of the economy.

> Using enterprise-level data on bribes paid to utilities in 21 transition economies in Easter Europe and Central Asia, Clarke and Xu look at how characteristics of the firms paying bribes (such as ownership, profitability, and size) and characteristics of the utilities taking bribes (such as competition and utility capacity) affect the equilibrium

> level of corruption in the sector. On the side of bribe payers, enterprises that are more profitable, enterprises that have greater overdue payment to utilities, and _de nova_ private firms pay higher bribes. On the side of bribe takers, bribes paid to utilities are higher in countries with greater constraints on utility capacity, lower levels of competition in the utility sector, and where utilities are state-owned. Bribes in the utility sector are also correlated with many of the macroeconomic and political factors that previous studies have found to affect the overall level of corruption.

This paper-a product of Regulation and Competition Policv, Development Research Group-is part of a larger effort in the group to understand the economic impact of privatization and competition in network utilities. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Paulina Sintim-Aboagye, room MC3-300, telephone 202-473-7644, fax 202-522-1155, email address **iTH[.n l i..** K .. ..rld.lbm . **rg.** Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gclarke@a worldbank.org or lxu t(a worldbank.org. February 2002. (33 pages)

> _The Policy Research Working Paper Series disseminates the findings of work in progress to encotirage the exchange of ideas about dei'elopmenrt issues. Ain obhectioe of the series is to get the findings out quiickly, even if the presentatioins are less than f(lly polished. The papers carry the names of the authors and shouild be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the auithors. They do not necessarily represent the View' of the WXIorld Bank, its Executive Directors, or the counstries they represent._

Produced by the Research Advisory Staff
# **OWNERSHIP, COMPETITION, AND CORRUPTION:**

# **BRIBE TAKERS VERSUS BRIBE PAYERS**

George R.G. Clarke and Lixin Colin Xu* Key words: Corruption, bribes, ownership, competition. **JEL codes:** Illegal behavior, K4; market structure, LI; industry studies, L9.

Some of the data used in this paper are from the World Business Environment Survey (WBES) 02000 The World Bank Group. We are grateful to Luke Haggarty and Andrew Stone for their help with the WBES data. _Contact informnation:_ L. Colin Xu, The World Bank, 1818 H Street NW, MSN MC3-300, Washington, DC 20433. Phone: (202) 473-4664. Fax: (202) 522-1155. E-mail: lxulworldbank.org.
# I. **INTRODUCTION**

Since the pioneering papers on corruption and rent seeking in the sixties and seventies (Becker, 1968; Becker and Stigler, 1974; Krueger, 1974; Leff, 1964; Rose-Ackerman, 1978), many studies have looked at the determinants and consequences of corruption.' While some authors have seen bribes either as "grease money" that lubricates the squeaky wheels of rigid bureaucracy and commerce (Leff, 1964; Huntington, 1968) or as an endogenously generated price mechanism that corrects disequilibria and restores optimal allocation in the market (Lui, 1985), most have viewed corruption less positively, suggesting that it distorts economic decisions. For example, it has been suggested that corruption might result in the misallocation of talent to occupations with large opportunities for rent seeking (Baumol, 1990; Murphy _et al.,_ 1991), might bias bureaucrats towards purchases on which it is easier to collect bribes (Shleifer and Vishny, 1993), or might affect income distribution adversely (Rose-Ackerman, 1978). Consistent with the less flattering view of corruption, recent empirical studies have found that corruption hampers growth, reduces income, and increases inequality (Mauro 1995; Myrdal 1968; Li _et al.,_ 2000; Bardhan 1997),2 while other studies have found that it reduces investment (Mauro, 1995), increases the size of the unofficial economy (Friedman _et al.,_ 2000; Murphy _et al.,_ 1993), and is associated with lower levels of human capital, urbanization, financial depth and foreign trade (Li _et al.,_ 2000).3 In addition to the literature on the effect of corruption on economic outcomes, a large supplementary literature has appeared on the determinants of corruption. These studies have found that corruption is lower in countries that are more open to foreign trade; countries with protestant traditions and that were formerly British colonies; countries with longer exposure to democracy; countries that are more democratic; countries with greater political stability and

> ISee Bardhan (1997) for an excellent review of issues. Also see Rose-Ackerman (1978).

> 2 The inequality-raising effects are not observed for high levels of corruption because the income levels are likely to be low for most people, resulting in low levels of income inequality (Li _et al.,_ 2000).

> 3Other studies of corruption include Alam (1990), Ades and Di Tella (1997), Bliss and Di Tella (1997), De Long and Shleifer (1993), Fisman (2001), Johnson _et al._ (1988), Johnson _et al._ (1997) Li (1999), and Mookherjee and Png (1995).
greater freedom of the press; and countries with parliamentary systems (see, for example, Ades and Di Tella, 1999; Treisman, 2000; Lederman _et al.,_ 2001; and Wei, 2000).4 Most of these earlier studies have used cross-country subjective indices (e.g., from the International Country Risk Guide) and have focused on how macroeconomic, cultural and institutional factors affect the overall level of corruption.

Although this paper fits squarely into the existing literature on the determinants of corruption, it complements it in several ways. First, rather than using subjective survey data on the level of corruption, this paper uses data on the actual bribes that enterprise managers report paying (as percent of revenues) - a measure that does not suffer from some of the problems associated with subjective measures.<sup>5</sup> More importantly, rather than focusing on the overall level of corruption in a country, the paper looks at firm-level data on bribes paid to a single sector of the economy - infrastructure. This allows us to focus on characteristics of the enterprises that pay and receive bribes in addition to characteristics of the institutional and macroeconomic environment. For enterprises paying bribes, we look at whether willingness-topay and ownership of the enterprise offering the bribe and the nature of the enterprises' relationship with the utility affects the equilibrium level of the bribe payment. On the other side of the equation, we look at whether the equilibrium bribe payment is affected by capacity, competition and privatization in infrastructure - factors that might affect either the internal incentives of the utility companies or their ability to demand bribes.

The empirical results are largely consistent with the conceptual framework presented in the next section of the paper. We find that the bribe payments are lower in countries where infrastructure is better developed, suggesting that excess demand is an important determinant of corruption. The extent of competition in the telecommunications sector, measured by the number of cellular operators in the country, also appears to reduce the equilibrium level of

> 4 However, some results vary between studies. For example, Lederman _et al._ (2001) find that corruption is lower in democracies, while Treisman (2000) finds no evidence of this. In addition, whereas Fisman and Gatti (2001) find that corruption is lower in countries with greater decentralization, Treisman (2000) finds corruption is higher in federal states. Finally, Knack and Azfar (2000) find that the association between corruption and trade intensity is not robust when they use measures of corruption that are available for larger samples of countries. They argue that this is because larger samples are less subject to selection bias.

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bribes. After controlling for capacity and competition, we also find that bribes are lower in countries where the utility companies have been privatized. One potential explanation for this final result is that private owners might have a greater incentive than public managers to impose stiff penalties upon employees taking bribes, reducing bribe payments.

Characteristics of the enterprise offering the bribe also affect payments. For example, enterprises that are more profitable appear to pay higher bribes - a result that is consistent with both the queuing (Lui, 1985) and the endogenous harassment (Myrdal, 1968) theories of corruption. Also consistent with the endogenous harassment theory, firms with higher overdue payments to utilities appear to pay higher bribes, perhaps because they have a weaker bargaining position vis-a-vis the employees of the utility company. The duration of the relationship between the enterprises paying and receiving the bribe and ownership also appears to matter: _de novo_ private firms are found to pay higher bribes than established firms. Finally, we find strong support for the complementarity of the overall level of corruption in a country and bribes in the utility sector.

# **II.** CONCEPTUAL FRAMEWORK

Consider a company that provides utility services (i.e., power or telecommunications services) and many firms that demand the service. If there is excess demand for the service that the utility provides - for example if there are price ceiling or if limits on public investment have historically limited system expansion - there will be rents associated with access to utility services. 6 In this situation, if employees of the utility company have some discretion over which enterprises or individuals get connected to the service or have broken down connections repaired, utility employees will be able to demand additional payments from the firms and individuals

> _5_ See Bertrand and Mullainathan (2001) for a general discussion about problems related to subjective survey data.

> 6 In practice, utility services in many developing and transition economies have been severely rationed, with long waiting lists for connections. The average waiting list over the number of main lines (or the waiting list ratio) is 0.17 for the 17 countries in our sample that have non-missing values for waiting list ratio in 1998 (authors' calculation based on the ITU data). In addition, see footnote 9.

3
demanding service or repairs in return for reducing wait periods.<sup>7</sup> Enterprises will be less willing to pay bribes when the excess demand for utility service is lower - i.e., lower excess demand will force down the shadow price of utility service and, thus, lower the enterprises' willingness to pay bribes.<sup>8</sup> Since, other things being equal (including income level), we would expect excess demand for service to be highest in those countries with the largest capacity constraints, we thus expect:<sup>9</sup> _Hypothesis 1. Bribes paid to utilities will be lower in countries where the utility's capacity is greater._ Privatization of the utility company is often associated with an increase in investment and a large expansion of capacity.'<sup>0</sup> Hypothesis 1 then suggests that utility privatization will reduce bribe payments by removing capacity restrictions. However, privatization might affect bribes even after controlling for the impact of privatization on capacity. Here the penalty function imposed by management on utility employees who take bribes will play an important role. When a company is privatized, the private owners become residual claimants on the income of the company, giving them a large incentive to reduce corruption. In contrast, under public ownership, it is often not clear who the residual claimants are and who will gain from reducing corruption (e.g., whether the funds would go the Treasury, to political leaders, or to the utility itself). Further, although profits are the property of the general public in theory, individual members of the public have little incentive to monitor the employees of the utility company. Since privatization will raise the marginal benefit of monitoring employees without affecting the marginal cost, privatization will increase the optimal amount of monitoring, and thus reduce the extent of bribes.

> 7Of course, the utility employees will have to balance the gains from the bribe with the possible loss of income due to the penalty they will face if caught.

> 8 This is similar to the point made by Ades and De Tella (1999).

> 9 Although the waiting period might be seem to be a more appropriate measure of excess demand, waiting period is often poorly measured and can be endogenous if long waits deter people from bothering to request service.

> 10 See, for example, recent studies of the effect of privatization on the telecommunications sector (Ros, 1999; Wallsten 2001; Li and Xu 2001).

4
Other aspects of public ownership might also increase corruption under public ownership. In general, principal-agent problems between owners and managers might be worse for publicly owned enterprises.'" In particular, it is often difficult to tie managers' salaries to profits under civil service pay schemes or to reward public managers with stock or stock options.'<sup>2</sup> Even if contractual arrangements linking the managers' wages to profitability are politically feasible, in the weak institutional environments found in many developing and transition economies, it would be difficult to find credible third parties that could force the government to honor it contractual obligations (Shirley and Xu, 1998). Under these circumstances, and especially if side-payments from corrupt employees are possible, managers might not be willing to exert much effort to reduce corruption. Finally, in countries where inflation or pay freezes have eroded salaries in the civil service and public utility, threats to fire corrupt employees will generally be less effective. These factors, combined with greater monitoring by private owners (relative to public owners), will mean that privatization should reduce both stealing and bribes even if privatization fails to reduce excess demand. We thus have _hypothesis 2._

# _Hypothesis 2. Bribes will be lower when the utility company is privately owned._

Competition faced by the utility company might also reduce corruption. First, increased competition might increase the total supply of infrastructure services (relative to supply under a monopoly), because monopolists take into account the effect that raising output has on prices when setting output levels. According to hypothesis 1, this quantity effect should reduce bribe payments. Moreover, when there are multiple utility service providers, utility customers can respond to demands for bribes by switching to other providers. Anticipating this, the producer might be less likely to ask for bribes or to ask for lower bribe payments (Rose-Ackerman 1978; Shleifer and Vishny 1993; Ades and Di Tella 1999). The effect of competition on bribes will depend crucially upon whether the users' threats to change utility company are credible.

> I For example, Shirley and Xu (1998) note that managers of public enterprises answer to many principals, who impose differing, and sometimes conflicting, objectives and constraints upon them.

> 12 Laffont and Tirole (1991) note that because managers of public enterprises do not own stock or stock options and are not subject to corporate takeovers that could cost them their jobs, they typically have less reason to adopt a sufficiently long-term perspective focusing on productive efficiency. Similarly, this will make it more difficult to encourage managers to reduce corruption among employees even when it affects profitability.

_5_
Because of this, in the telecommunications sector, the number of cellular operators should provide a better measure of competition that the number of fixed-line operators. Even when there are multiple fixed-line operators, local monopoly provision of services is likely - in contrast, cellular operators will often compete locally with fixed line operators. We thus expect bribes to be lower in countries with greater competition, as measured by competition from cellular operators.

_Hypothesis 3. Bribes to utilities will be lower in countries with greater competition in infrastructure._ So far we have focused on the bribe taker, the utility companies. However, characteristics of the bribe payer, the firm demanding utility service, might also affect bribe payments. The simplest theory about the determinants of bribing behavior of utility customers (firms in this paper) is the "speed money" or the efficiency theory of bribes (Barzel, 1974; Lui 1985; Leff 1964; Huntington 1968). Assuming there is no stigma associated with bribery - or at least that the stigma associated with paying bribes does not depend on enterprise profitability - firms that benefit more from utility service will generally offer larger bribe payments for reduced wait periods for connection or repairs. Consequently, we would observe allocation of the utility services according to the value that different enterprises place on utility service. Under this hypothesis, utility services will be allocated efficiently with the bribe acting as a perfect price discrimination mechanism. Although the benefit that an individual firm gains from utility services is unobservable, it is reasonable to assume that firms that are more profitable will generally benefit more the utility services. This can be justified, for example, by the plausible assumption of complementarity of managerial ability or monopoly rents with utility service. With this assumption, the "speed money" theory of bribery implies _Hypothesis 4. Enterprises that are more profitable will pay higher bribes than less profitable enterprises._ Hypothesis 4 can also be explained by the endogenous harassment theory, as suggested in Myrdal (1968) and further elaborated in Kaufmann and Wei (1999). The provider can use observable information such as industry, size, or profitability to guess each enterprise's maximum willingness-to-pay for utility service and endogenously offer incentive compatible bribe levels that 6
depend on such characteristics. In this case, the relationship between bribe payments and profitability will also be positive. Although the basic ingredient in both the 'speed money' and the endogenous harassment theories is that the bribe amount of different firms increase with the willingness-to-pay for service, the utility employees demanding the bribe need more information in the 'endogenous harassment' theory. In the "speed money" hypothesis the enterprise paying the bribe self-selects the amount of the bribe according to its cost of waiting, while the endogenous harassment version requires that the utility employees taking the bribe discriminate between enterprises and, thus, require that the utility has information on profitability and other firm characteristics that affect the enterprises' willingness-to-pay. In practice, the data used in this paper do not allow us to easily distinguish between these two theories.

Related to the endogenous harassment theory, another factor that might affect enterprises' willingness-to-bribe is size of the enterprises' overdue payments to the utility company - something that is a significant problem in many countries in Eastern Europe and Central Asia.'3 When an enterprise had high debts to the utility company, the utility employee can more credibly threaten to cut the enterprises' utility connection - the enterprise manager would find it more difficult to complain to either the judiciary or to the employee's superiors within the utility company about being disconnected if he has overdue payments. In bilateral bargaining between the utility employee and the firm, the fallback position of the firm is worse and, hence, its bargaining power is weaker. Consequently, the utility employee will be able to extract higher bribes from firms that have overdue payments to the utility. In contrast, overdue payments to workers or suppliers should not have this effect - although the utility employee would be able to threaten to cut off service to a non-paying customer, it would not be able to do the same to a customer that pays its utility bills in a timely way but has overdue payments to suppliers or employees. Further, to the extent that overdue payments suggest that the enterprise is distressed or has cash flow problems, we might expect enterprises with other types of overdue payments to be less willing (or able) to pay cash bribes. Since several authors have argued that overdue payments to workers are a better measure of financial distress than overdue payments to suppliers, we might expect enterprises with overdue

> 13 In the World Business Environment Survey (WBES) for the transition economies, 33 percent of enterprises reported having overdue payments to utilities. For a general discussion of non-payment in the power sector in Eastern Europe and Central Asia, see World Bank (1999).

7
payments to workers to pay lower bribes than enterprises without overdue payments to workers.<sup>14</sup> We thus arrive at hypothesis 2.

_Hypothesis 5. Enterprises with higher overdue payments to the utility company will pay higher bribes than other enterprises. In contrast, enterprises with overdue payment to workers and suppliers will pay similar or lower bribes to utilities._ The relationship between the enterprise paying the bribe and the utility receiving the bribe might also influence the size of the bribe. For example, _de novo_ private enterprise might pay higher bribes than other enterprises for several reasons. First, if _de novo_ private firms were more profitable than other enterprises, we would expect them to pay higher bribes.<sup>15</sup> Second, bribe taking might be more risky for the utility employees in the early stages of the relationship. For instance, before the utility employee has developed a relationship with the bribe payer, there is higher likelihood that the enterprise paying the bribe would inform others about the corrupt deal or that the deal is part of a 'sting' operation by either law enforcement or the employee's managers. Since this increases the risk that the utility employee will be detected and punished, the employee might demand higher payments, as a form of risk premium, to let the deal proceed. As the relationship becomes consolidated with years of collusion, the bond is no longer needed and a stream of variable payment is sufficient.'<sup>6</sup> Consequently, we might expect _de novo_ private firms to pay higher bribes than other types of firms, which will have already developed relationships with employees of the utility company.

# _Hypothesis 6. De novo privatefirms pay higher bribes than establishedfirms._

> **14** Schaffer (1998) argues that since suppliers can always stop shipping to non-paying customers, inter-enterprise arrears do not necessarily signal financial distress. In contrast, given the high rates of unemployment in many transition economies - and regional economies heavily dependent on single enterprises - the same might not be true for workers.

**_15_** Megginson and Netter (2001) and Shirley and Walsh (2000) discuss reasons why privately owned enterprises might perform better than state-owned enterprises and present evidence that supports these hypotheses. Since the start of the transition over ten years ago, many studies have compared the relative performance of state- and privately owned enterprises in Eastern Europe and Central Asia. A recent meta-analysis of these studies found that privately owned enterprises appear to generally perform better than state-owned enterprises in these economies (see, Djankov and Murrell, 2000).

**16** Maybe **it** is in this sense that "It used to be said of General Noriega of Panama in his heyday that he could not be _bought,_ he could only be _rented"_ (quoted by Bardhan 1997, p. 1324).

8
In addition to characteristics of the utility taking the bribe and the enterprise paying the bribe, the enviromnent in which the enterprise and utility operate might also affect bribe payments, especially in light of the multiple-equilibria nature of corruption. The incentives<sup>of an</sup> individual to be corrupt depend on how many other people are corrupt (Andvig 1991). When the society is already corrupt, the moral costs of corruption are low, making the strategy of being corrupt a Nash equilibrium. Moreover, given limited enforcement resources, the possibility<sup>of</sup> being detected might also be lower in more corrupt societies. We thus expect bribes in the _utility sector_ to be higher in countries where _other forms of corruption_ are more common. In other words, factors that raise the general level of corruption in a country might also increase<sup>bribe</sup> taking in the utility sector even if they have little direct effect on the incentives of either<sup>the</sup> enterprise paying the bribe or the utility receiving the bnibe.

As previously discussed, there is a large literature that discusses factors that might affect the _overall_ level of corruption in a given country. First, several authors have argued that<sup>the</sup> rents might be lower in more competitive economies and, therefore, that corruption might also be lower in these countries (Rose-Ackerman 1978; Shleifer and Vishny 1993; Ades and De<sup>Tella</sup> 1999). Consistent with this, Ades and De Tella (1999) find that corruption is higher when domestic firns are sheltered from foreign competition by natural or policy induced barrier<sup>to</sup> trade.'<sup>7</sup> To control for this, our base regression includes measures of the extent of competition and the existence of rents similar to those used in previous studies - the ratio of imports to GDP (to measure competition) and the ratio of mineral, fuel and metal exports to total exports (to measure rents). Second, corruption tends to be lower in countries with political institutions that highlight political accountability and give voice to voters. For instance, past studies have found that corruption is lower in countries with longer exposure to democracy (Treisman, 2000) or in countries that are more democratic (Lederman _et al.,_ 2001). Third, corruption should be lower in countries that are growing more rapidly. For example, Baumol (1990) and Murphy _et al._ (1991) suggest that occupational choice is affected by the way in which talents are rewarded. When growth is faster, talent will tend to flow to productive sector instead of the rent-seeking sector and, therefore, we might expect corruption to be lower in countries that are growing faster.

> 17 Knack and Azfar (2000) find that this result, however, is not highly robust. See footnote 4.

9
_Hypothesis 7. Given the complementarity of corruption in the society with corruption in the utility industry, we expect utility bribes to be lower in countries with lower level of rents (as measured by a higher level of imports, and lower export shares of fuel, mineral, and metal export), in countries that are more democratic or have longer exposures to democracy; and in countries that are growing more rapidly._

# III. EMPiRICAL **IMPLEMENTATION**

## **111.1** Data

The main source of data used in this paper is the World Business Environment Survey (WBES), a cross-sectional survey of industrial and service enterprises conducted in mid-1999 by the World Bank and several other agencies.'<sup>8</sup> The main purpose of the WBES is to identify perceived constraints on enterprise performance and growth in developing and transition economies. The survey, therefore, has a large number of questions on how taxation, regulation, the performance of the financial sector, the institutional environment and corruption affect business operations. In contrast, the survey includes little information on enterprise characteristics or performance. In particular, although some information on assets, sales, broad sector of operations, ownership, employees, and enterprise growth was collected, detailed balance sheet information and profit and loss statements were not collected from participating enterprises.

Although the WBES was conducted in many countries throughout the world, and some effort was made to ensure cross-country comparability, the degree of detail varies greatly between regions. For example, although data was collected on actual sales, fixed assets, and debts in some regions, only categorical data on the same information was collected in other regions. For the purpose of this study, the most important difference between the surveys completed in various regions is that questions that allow us to calculate bribes paid to utilities

> 18 The survey of the transition economies was conducted in collaboration with the European Bank for Reconstruction and Development. Hellman _et al._ (2000) and European Bank for Reconstruction and Development (1999) provide more complete descriptions of the survey.

10
were asked only in the transition economies of Eastern Europe and Central Asia. The sample includes about 3000 enterprises from 21 transition economies.<sup>19</sup> The enterprise level data from the WBES is supplemented with data from a variety of other sources. In addition to characteristics of the enterprise paying the bribe, the analysis also includes characteristics of the utilities, the enterprises receiving the bribe payments. In the electricity sector, we focus on the distribution utilities, since these are the enterprises that will generally interface with the (mostly small) enterprises in the WBES sample. However, for the most part, we focus on the telecommunications sector because there are readily available measures of competition and privatization in the telecommunications sector. By the late 1990s, cellular services provided significant competition for fixed line services in many developing and transition economies. By 1999, most of the countries included in this analysis had significant penetration by cellular services (see Figure 1) and in some countries there were nearly as many cellular subscribers as fixed main lines. Further, since the WBES does not provide information on the enterprises' locations within the country, and because electricity distribution is often handled on a local or regional basis, it is generally easier to observe privatization in the telecommunications sector than it is in the power sector.<sup>20</sup> The information on the privatization of telecommunications operators was provided by the World Bank Telecommunications Department and information on the privatization of electricity distribution was obtained from Bacon (1999). Information on number of fixed lines come from International Telecommunications Union (2000), while the number of cellular companies operating in each country was calculated using information from EMC (2001) and _Telecoms and Wireless Reports: Eastern Europe/Commonwealth of Independent State_ by Pyramid Research.

> 19 The countries in the sample for transition economies are: Albania, Armenia, Azerbaijan, Belarus, Bulgaria, Croatia, the Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, the Kyrgyz Republic, Lithuania, Moldova, Poland, Romania, Russia, Slovenia, the Slovak Republic, Ukraine, and Uzbekistan.

> 20 Bacon (1999) provides information on whether any privatization of electricity distribution had occurred by 1999, but did not provide information on the extent of privatization.
<!-- Start of picture text --> 100%<br>80% -_<br>60%- 1<br>40% -7<br>2%<br>0%.- 111111<br>Cu m C .) 0 u Cu to to Cu Cu Cu C C £ C u u C<br>C ~~ ~~ ~ *E Cu E .E. ~~ j ~ ° .~ E 2<br>C S c 0 E X<br>On N<br><!-- End of picture text --> Figure 1: Cellular subscribers per main fixed line. Data Source: Intemational Telecommunications Union (2001).

The macroeconomic and political data used to control for factors that might affect the overall level of corruption in the countries in the sample are taken from a variety of sources including World Bank (2001), Beck _et al._ (2001), and Freedom House (2000). Table 1 and Table 2 provide sources, brief descriptions and summary statistics for the main variables used in this analysis.

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# **111.2 Empirical Specification**

<!-- Start of picture text --> 100%<br>75 _ _-* *E<br>5*% * * * *utilities<br>50%__<br>25%<br>* flflflfl<br>0% X_<br>n a0X 0ss<br>0°C a) O D ° D<br>z -1 -j -i -. -.<br><!-- End of picture text --> Figure 2: Upper Bounds of Bribe Payments to Utilities (as % of Revenues) in the Sample Countries.

Note: The categories are cumulative. Because each enterprise has unique upper and lower bounds, enterprises are classified by upper bounds. For example, an enterprise that reported paying between 0.1% of revenues and 1.9% of revenues in bribes to utilities would be counted only in the 'less than 2.0%' and 'less than 5.0%' percent categories (even if its actual payments - which are not observed - were only 0.2% of revenues).

The dependent variable used in this study is the percent of revenues paid by the enterprise to electricity and telecommunications in the form of bribes.<sup>2</sup> 1 This variable is calculated using answers from two questions in the WBES - the percent of revenues paid per annum in 'unofficial' payments to public officials (including to the employees of electricity and telecommunications companies) and the share of those unofficial payments that were spent 'to get connected to and maintain public services (electricity and telephone)'. The enterprise manager's response to the first question about total unofficial payments was categorical (i.e., 0% of revenues; less than 1%; between 1 and 1.99%; between 2 and 9.99%; between 10 and 12%; between 13 and 25%; and over 25%), while the manager's response to the second question was any number between 0 and 100% (of total 'unofficial payments'). From this information, it is possible to calculate a range for the percent of revenues that each enterprise reported paying to electricity and telecommunications utilities.<sup>22</sup> About 75 percent of enterprises 21 The question refers specifically to power and telecommunications, but does not separate between the two. To encourage honest responses to questions about bribery, and to allow enterprise managers to avoid implicating themselves when answering questions about frequency and level of bribe payments, the WBES asked about bribes paid by 'firms like yours' rather than about the manager's own firm. In the empirical analysis, we assume that the manager was answering the question for a firm similar to the manager's own enterprise in terms of the independent variables.

22 That is, the share to the utilities times the percentage of revenue as unofficial payment. Because the second response could take any value between 0 and 100%, the ranges are distinct for each enterprise.

13
reported paying no bribes to public utilities (see Figure 2), while about 97.5 percent of enterprises reported an upper bound for bribes to utilities of less than 5.0 percent of revenues.<sup>23</sup> It is assumed that the percent of revenues paid as bribes to telecommunications and electricity utilities by enterprise i in countryj _(Bij)_ is a function of enterprise characteristics (x,j), characteristics of the utilities (uj), country-level characteristics (zj) and an unobserved disturbance term (-j,).

As discussed above, lower and upper bounds for bribes as a percent of revenues, _b,, bH_ can be calculated for each enterprise. The contribution to the likelihood function for each enterprise is, therefore, Pr(b, _< Bij <b,hH)24_ Assuming that the disturbance term is normally distributed, the log-likelihood function, which can be maximized using standard maximum likelihood estimation, is: where 4) is the standard normal distribution.

> 23 Note that although we know that an enterprise that reports an upRer bound of less that _5_ percent definitely paid less than five percent of revenues as bribes (ignoring reporting errors), it does not follow that those enterprises that reported an upper bound greater than _5_ percent of revenues necessarily paid over _5_ percent of revenues in bribes to utilities. For example, an enterprise that paid 2 percent of revenues in bribes could report lower and upper bounds of 1.2 and 6 percent (i.e., the actual level of bribes is between the two bounds). Only 0.3 percent of enterprises reported lower bounds greater than 5.0 percent of revenues (i.e., only 0.3 percent of enterprises reported ranges that were entirely above 5.0 percent of revenues) and no enterprises reported a lower bound greater than 8.0 percent of revenues.

> 24 The estimation takes truncation below, at 0% of revenues, into account (i.e., negative bribes are not observed).

14
The measure of corruption used in this study has some advantages over the<sup>subjective</sup> indices used in previous studies of the determinants of corruption.<sup>25</sup> One problem with subjective indices is the question of what benchmarks respondents use for rating the<sup>extentof</sup> corruption. For example, some respondents might compare corruption in a country to corruption under a previous regime, others might compare it with neighboring countries, while others might even compare it with their own personal ideals. If different respondents use different benchmarks, subjective indices might suffer from large noise-to-signal ratios.<sup>26</sup> Moreover, there might be systematic errors due to cognitive problems, social desirability of answers,<sup>non-</sup> attitudes, wrong attitudes, and soft attitudes (Bertrand and Mullainathan, 2001; Tanur,<sup>1992;</sup> Sudman _et al.,_ 1996). If these systematic errors are correlated with enterprise (or country<sup>level)</sup> characteristics, and it is difficult to obtain instruments that are correlated with the explanatory variables but not the systematic errors, results using the indices as dependent variables<sup>will be</sup> biased. Consequently, some authors have suggested that although subjective indices might<sup>be</sup> useful as explanatory variables (although they will still suffer from attenuation bias and when correlated with other explanatory variables, inconsistency), they are less likely to be effective<sup>as</sup> dependent variables (Bertand and Mullainathan 2001).27

# **111.3 Econometric Results**

**_Enterprise Ownership for Enterprise Paying Bribe._** The base regression (see Table 3) includes several dummy variables to control for the ownership of the enterprise paying the bribe.

25 For instance, a commonly used index, the Business International rating, is based on the assessment of "the degree to which business transactions involve corruption or questionable payments" on a scale from 0 to 10. The remarks of Glaeser _et al._ (2000) about another perception index can be applied here: "While these survey questions<sup>are</sup> interesting, they are also vague, abstract, and hard to interpret." See Treisman (2000) for a comprehensive discussion of the existing cross-national corruption indices.

26 Some studies have found evidence consistent with this. For example, the account of the land consolidation program in villages in U.P. in Northern India described by Oldenberg (1987) suggests that, there may often be discrepancies between personal assessment about corruption frequency and its actual incidence (Bardhan<sup>1997).</sup> Measurement error might be especially problematic in studies that include fixed country effects (see Bertand and Mullainathan, 2001).

27 The most comprehensive study of the cross-national determinants of corruption is Treisman (2000), who is keenly aware of the limitation of such subjective measures. He offers three justifications for the use of these indices: (1) the Transparency International Ratings are highly correlated among themselves, (2) they are also<sup>highlycorrelated</sup> among themselves across years, and (3) in a footnote, "a third reason, of course, is that there _are_ no objective<sup>data</sup> on the extent of corruption." 15
When margin (the measure of enterprise profitability) is included in the base regression, most of the coefficients on the dummy variables indicating ownership are statistically insignificant. This suggests that enterprises owned by foreigners, 'insiders' (i.e., managers and workers) and privatized enterprises owned by outsiders pay similar levels of bribes to state-owned enterprises (the default category). In contrast, the coefficient on the dummy variables indicating that the enterprise is a _de novo_ enterprise (i.e., newly established private enterprises) is statistically significant and positive, suggesting that _de novo_ enterprises generally pay higher bribes than privatized or state-owned enterprises.28 The effect appears to be large in quantitative terms - _de novo_ private enterprises reported paying between about 0.5 and 0.9 percent more of revenues in bribes than similar state-owned enterprises in the different model specifications.<sup>2 9</sup> As discussed previously, there are several plausible reasons why _de novo_ enterprises might pay higher bribes than other enterprises. First, many studies have found that private enterprises in general and _de novo_ enterprises in particular perform better than state-owned enterprises in the transition economies (see Djankov and Murrell, 2000). As noted previously, both the 'speed money' and 'endogenous harassment' hypotheses suggest that more profitable enterprises should pay higher bribes than less profitable enterprises. Consequently, to the extent that the measure of profitability (i.e., 'margin') does not fully account for perfonnance differences between state and privately owned enterprises, the ownership dummies might be partially proxying for performance differences.<sup>3 0</sup> Consistent with the idea that performance differences between private and state-owned enterprises might partially explain the result, the coefficients on the dummy variables indicating _de novo_ private enterprises and privatized enterprises increase in magnitude and the coefficient on privatized enterprises becomes statistically significant when margin is excluded (see column 4 of Table 3). Second, as noted

> 28 This pattern is consistent with the pattern observed for total bribes (i.e., bribes to all government officials not just utilities) before controlling for other factors that might affect corruption (see, European Bank for Reconstruction and Development, 1999, p 125-26).

> 29 Note that the actual average reduction would be smaller than this since most enterprises did not report paying bribes and, therefore, bribes could not be reduced for these enterprises.

> 30 Although foreign-owned enterprises also appear to consistently out-perform state-owned enterprises in the transition economies (see Djankov and Murrell, 2000), the small number of these enterprises in the sample (see Table 2) might make it difficult for the coefficient on this dummy variable to achieve statistical significance.

16
earlier, if managers of _de novo_ enterprises have less well developed relationships with either the utility employees demanding bribes, utility employees might demand higher bribes<sup>to</sup> compensate them for the additional risk of taking bribes for performing favors for the entrepreneur (for example, for 'misreading' meters). Finally, managers of _de novo_ enterprises might, in general, have less political influence with government officials (e.g., judges or police officials) meaning that they are less likely to be able to appeal to them when threatened by utility employees (e.g., to avoid 'sudden' breakdowns in service).

_Enterprise Profitability._ Enterprises that were more profitable reported paying higher bribes than less profitable enterprises. This is consistent with either the endogenous harassment or 'speed money' theory of corruption (see Hypothesis 4). A one-standard-deviation increase in margin raises reported bribes to utilities by about 0.2 percent of revenues.

_Overdue Payments to Utilities._ Since the coefficient on the index variable indicating overdue payments to utilities is negative and statistically significant (and higher values of<sup>the</sup> index mean lower overdue payments), this implies that enterprises with overdue payments to utilities generally reported paying higher bribes to utilities than enterprises without overdue payments (see Table 3). In contrast, the coefficients on overdue payments to suppliers and workers are either statistically insignificant or have a positive coefficient. The (sometimes statistically significant) positive coefficient on overdue payments to workers might be consistent with the previous results regarding profitability - if enterprises with overdue payments are more financially troubled than other enterprises, they might on average have lower ability/willingness to pay bribes. Although this will also be true for enterprises with overdue payments to utilities, other factors work in the opposite direction. In particular, enterprises with overdue payments might be willing to pay bribes (of less than the amount of the overdue payment) to avoid being disconnected from utility service. The coefficient on overdue payments to utilities suggests that enterprises with substantial (manageable/modest) overdue payments reported paying about 0.15 percent more of revenues to utilities in bribes than enterprises with manageable (modest/no) 17
overdue payments.<sup>3</sup> 1 These findings are consistent with hypothesis 5 that bribe extraction is higher when the firm is vulnerable to threats by the utility employees.

**_Enterprise_** _Size._ European Bank for Reconstruction and Development (1999, p. 125-126) finds small enterprises in Eastern Europe and Central Asia generally paid higher total bribes (i.e., to all sources not just than large enterprises. Consistent with this, and even after controlling for other factors that might affect corruption, we find that small enterprises generally pay higher bribes than large enterprises (see Figure 3). Enterprises with fewer than 50 enterprises paid about 1.5 percentage points as bribes to utilities po of revenues than enterprises with more than 500 I employees, while enterprises with

<!-- Start of picture text --> 2.0-<br>1.5 -7 |that<br>1.0 --<br>0.5 | | | _utilities)<br>0.0 . . . . _<br>Less than Between Between Between Between<br>9 10 and 49 50 and 99 100 and 200 and<br>199 499<br><!-- End of picture text -->

Figure 3: Coefficients on enterprises size dumrnies in regressions on bribes paid to utilities as percent of revenues (relative to enterprises with over 500 employees).

Data Source: The World Business Environment Survey (WBES) 02000 The World Bank Group.

Note: Graph shows coefficients on size dummies from regression shown in column I of Table I. Omitted category is enterprises with more than 500 employees. Consequently, coefficients can be interpreted as the additional percent of revenues paid by enterprises of that size over a similar enterprise with more than 500 employees, between 50 and 500 employees paid about 1.0 percent of revenues more in bribes (see Figure 3). The differences between the amounts paid by enterprises of different sizes are statistically significant.<sup>32</sup>

**_Ownership_** _of_ **_Utilities._** Consistent with hypothesis 2, enterprises paid lower bribes to utilities in countries where the fixed line telecommunications and electricity distribution were privately owned (see Table 3). In the regression that excludes the measure of enterprise profitability (i.e., margin), the coefficients on both dummy variables are statistically significant.

> 31The index variable is coded as I for substantial, 2 for manageable, 3 for modest and 4 for none.

> 32 The null hypothesis that enterprises of all sizes pay similar share of revenues in bribes can be rejected at less than a 1 percent significance level (X2 (5) = 30.37, Prob.> X<sup>2</sup> is 0.00).

18
When margin is included, sample size is dramatically reduced and the coefficient on the dummy variable indicating that electricity distribution has been privatized become statistically insignificant at conventional significance levels. Although this might suggest that electricity privatization is less important than telecommunications privatization in this respect, it is important to interpret the result carefully. As noted previously, the local nature of electricity distribution might mean that electricity privatization is measured poorly. Excluding the dummy variable indicating privatization of electricity distribution does not affect any of the other results (see column 2 of Table 3).

To the extent that the variables proxying for the effect of competition and capacity constraints on corruption control for these factors, the variables indicating utility ownership should proxy for the direct effect of ownership. By creating clear residual claimants for the utility's profits, private ownership might increase pressure on enterprise managers to reduce corruption. The point estimates of the coefficient on the dummy variable indicating that the fixed line telecommunications operator is privately owned suggests that utility privatization has a large impact on corruption in the sector, with privatization reducing the percent of enterprise revenues paid in bribes by about 0.6 percentage points.

_Capacity and Competition._ Consistent with hypothesis 1, enterprises in countries with better-developed telecommunications systems appear to pay lower bribes than enterprises in countries with less developed systems. The coefficient on fixed lines per capita is negative and statistically significant throughout most of the analysis. Since the analysis includes several macroeconomic controls, including per capita income, one plausible explanation for this result is that excess demand for the utility's services is lower in countries with better-developed systems. Increasing the number of fixed lines by about one standard deviation for the transition economies in the samples (see Table 1) would decrease the share of revenues paid as bribes to utilities by about 0.4 percentage points.<sup>33</sup> Consistent with hypothesis 3, competition also appears to lower bribes paid to utilities - the coefficient on the variable indicating the number of cellular companies is statistically

> 33 This was close to the difference between Ukraine (19.1) and Slovakia (28.6) in 1998.

19
significant and negative. The point estimate of the parameter suggests that increasing the number of cellular companies by one reduces the share of revenues paid as bribes to utilities by about 0.2 percentage points.

**Macroeconomic and Political Controls.** Since the overall level of corruption in a given country might affect the level of bribes paid to utilities, the analysis also includes some macroeconomic and political variables that might affect corruption in other areas. To avoid problems associated with reverse causation, the macroeconomic and political controls are lagged at least one year.<sup>34</sup> Given the relatively modest number of countries in this analysis, it is possible to include only a small number of the many variables suggested in the literature in the base regression. However, many additional macroeconomic and political variables - most of which have statistically insignificant effects on bribes paid to utilities in this sample - are included in the sensitivity analysis (see Table 4 and Table 5).

The coefficients on the macroeconomic and political control variables included in the base regression are generally statistically significant with signs consistent with theory and previous analyses. Corruption appears lower in countries with higher levels of democracy; that are more open to imports; where exports of natural resources are less important and where growth is faster. After controlling for these variables, the coefficient on per capita GDP is not statistically significant at conventional levels.

The main results in this study are also robust to several different assumptions regarding the control variables. One concern is that some of the control variables might be endogenous. In particular, Ades and Di Tella (1999, p. 988) suggest that if bureaucrats determine market structure, the level of corruption in any given country might affect the share of imports in GDP. Similarly, others have suggested that corruption might also affect growth (see, for example, Mauro, 1995). In practice, however, the main results from this study are robust to the exclusion

> **34** Macroeconomic data are for 1998. Data from Beck _et al._ (2001) are for 1997, which was the most recent year available at the time of writing. The regulation and corporate tax indices used in the sensitivity analysis are also for 1997, since data for 1998 were also not available.

20
of either of these variables.<sup>35</sup> Finally, although most of the control variables are available for the all the countries in the sample, exports of minerals, metals and fuel was not available for either Ukraine or Uzbekistan. However, the main results are robust to replacing this variable with a dummy variable indicating that the country is an oil exporter (see column _5_ of Table 3), which is available for the entire sample. Since mineral, fuel and metals made up a considerably greater share of exports for oil exporters than for non-exporters, this dummy variable seems to be a reasonable measure of the importance of natural resources in a country's exports.<sup>36</sup> Consistent with the previous results, the coefficient on the dummy is positive and statistically significant, providing more evidence that corruption is higher in countries where economic rents are more important.

**Sensitivity Analysis.** Over the past decade, many studies have looked at country level variables that might affect the overall level of corruption. Although most of these determinants would not be expected to affect corruption in the utility sector _per se,_ to the extent that they affect the overall level of the corruption in the country, they might have an indirect effect on corruption in the utility sector. Given the relatively modest number of countries in the sample (see footnote 19) and the large literature on the potential determinants of corruption, it would be impossible to simultaneously include all possible regressors in a single regression, especially since many are unavailable for some countries in the sample. However, to check robustness, we add many variables suggested in the literature - including regional dummies, measures of regulation and taxation, alternative measures of the political and institutional environment, factors that might affect natural openness, and measures of government spending and decentralization - to the base regression (i.e., column 2 of Table 3).37 In addition, we also include a subjective measure of corruption in the regression as an additional robustness check.

> 35 The coefficients on the main variables of interest (i.e., the variables listed in Table 4) remain similar in terms of size and statistical significance when either variable is excluded.

> 36 For the countries in this sample 63 percent of exports were mineral, fuel or metals for oil exporters in 1998 compared to 10 percent for non-exporters (authors' calculations). The difference in means is statistically significant at higher than a I percent level.

21
Although this variable is potentially endogenous (i.e., corruption in the utility sector might be reflected in the measure of corruption), it does not appear to have a significant impact on the main results.

For the most part, the coefficients on the additional variables are statistically insignificant and they have little impact on the main results (see Table 4 and Table 5). The coefficients on margin, the _de novo_ dummy, and the number of cellular companies remain significant at a five percent level or lower, while the coefficients on the dummy indicating privatization of the fixed line telecommunications operator and the index of overdue payments remain significant at a 10 percent level. The coefficient on the number of fixed lines remains negative in all regressions but becomes statistically insignificant at conventional levels in one of the thirteen additional regressions.

Although the small sample of countries and the measure of bribes to a single sector (i.e., utilities) mean that this sample is not necessarily well suited to looking at the impact of macroeconomic or political factors on overall corruption, the two statistically significant results are of interest. First, in contrast to the results in Lederman _et al._ (2001), we do not find any evidence that corruption is lower in countries with parliamentary systems. Second, it appears that corruption is generally lower in countries that have privatized more (i.e., with higher scores on the EBRD's index of privatization). Although this might seem inconsistent with previous results that suggest that privatized enterprises pay similar levels of bribes to state-owned enterprises (i.e., the coefficient on the dummy variables indicating privatized enterprises was statistically insignificant once the measure of profitability is included in the analysis - see Table 3), this is not necessarily the case. Although privatization might not affect the bribes paid by the privatized enterprise itself, if privatization increases competition for all enterprises in the economy (i.e., including enterprises that remain state-owned), it might be correlated with lower levels of bribes for all enterprises facing this increased competition. That is, by increasing competition and decreasing rents for all enterprises throughout the economy, privatization might

> 37 Some variables suggested in the literature are omitted from the sensitivity analysis because there is insufficient variation (e.g., none of the countries in the sample were former British colonies), while others are omitted because there is insufficient data available for the countries in the sample (e.g., measures of ethno-linguistic

22
lower bribes for both privatized and state-owned enterprises. This interpretation is consistent with results from previous studies (see, e.g., Ades and Di Tella, 2000) and the results in this paper that suggest that openness to imports, which might also increase competition, is correlated with lower levels of corruption.

# **IV. CONCLUSION**

Rather than discussing the political, macroeconomic and cultural factors that affect the overall level of corruption (see, e.g., Ades and Di Tella, 1999; Treisman, 2000; Lederman _et al.,_ 2001), this paper primarily focuses on how characteristics of firms paying and receiving bribes affect the equilibrium level of bribes in the utility sector. Our conceptual framework suggests that characteristics of bribe takers (i.e., the rents available for extraction in the utility sector, the extent of competition in the sector and the penalty functions faced by utility employees) and bribe payers (i.e., the firm's willingness to pay bribes, the leverage that bribe takers have over the bribe payers, and the length of the payers relationship with the takers) should both be important. Further, the multiple-equlibria nature of corruption means that bribe payments in the utility sector should be higher in countries where the overall level of corruption is higher.

The empirical evidence from a survey of around 2000 enterprises in 21 countries in Eastern Europe and Central Asia is remarkably consistent with the conceptual framework. We find strong evidence that bribes paid to utilities are lower in countries with greater capacity and competition in the utility sector and where the utility has been privatized. On the side of bribe payers, enterprises that are more profitable, enterprises that have greater overdue payments to utilities and _de novo_ private firms appear to pay higher bribes. Macroeconomic and political factors that contribute to higher corruption at the national level also appear to increase bribes in the utility sector.

The results from this study suggest that countries can reduce corruption in the utility sector through utility privatization and increased competition in the utility sector. In addition to reducing corruption by easing capacity constraints, privatization might improve internal incentives to reduce corruption while competition might reduces the utilities' ability to demand fractionalization).

23
bribes from enterprises using their services. Steps to reduce corruption in the utility sector might also have beneficial side effects on the overall level of corruption, due to the multiple equilibria nature of corruption.

It is less clear how to interpret the finding that more profitable firms pay higher bribes. If the "speed money" theory is correct (i.e., bribes are acting as price mechanism), then a modest level of corruption might actually improve efficiency (over no corruption) so long as capacity constraints remain in the utility sector. This is because under this hypothesis bribes ensure that utility connections are allocated to the enterprises that value them most highly, resulting in an efficient allocation of resources. However, if the "endogenous harassment" theory is correct, rent seeking and stealing mutes the firms' incentives to take costly actions to improve efficiency and increases incentives to hide profits. Consequently, corruption will have an adverse impact on efficiency. While our data do not allow us to distinguish between these two scenarios, future empirical work to do so is clearly desirable.

24
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> TABLE 1: SUMMARY STATISTICS AND SOURCES FOR MACROECONOMIC AND POLITICAL

VARIABLES.

|_Variable_|_Description_|_Source_|_Mean_|**_Std._**_Dev._|
|---|---|---|---|---|
|NumberofCellularCompanies|Number|Authors'Calculations<br>(seetext).|2.815|1.543|
|ElectricityDistribution Privatized|Dummy|Bacon(1999)|0.424|0.494|
|CountryhasParliamentarySystem|Dummy|Beck_etal._(2001)|0.246|0.431|
|YearsofDemocracy<br>Years of Democracy|Yearsthatcountryhasscoredsixorhigheron<br>legislativeandexecutiveindexofelectoral<br>~~~competitiveness(IEC)sincestartoftransition.<br>Seebelow.|Beck_etal._(2001)<br>Bc<br>ta.(01|3.779<br>.7|2.751<br>_.5_|
||Index(0-7). Avg.scoreonlegislativeand||||
|DemocracyIndex|executive indexofelectoralcompetitiveness<br>(fIEC).Highnumbersmean greaterlevel of<br>democracy|Beck_etal._(2001)|6.760|0.539|
|EBRDIndexfor large-scale<br>privatization|Index(1-4)--Higherscoresmeangreater<br>extentofprivatization|EBRD(1999)|3.041|0.676|
|PoliticalRights|Index (1-7)--Higherscoresmean greater<br>democracy|Freedom House (2000)|3.184|1.804|
|Decentralization|LocalandStateSpendingasshareoftotal<br>govemment spending|Intemational Monetary<br>Fund(2001)|0.326|_0.115_|
|RegulationIndex|Index_(1-5)._Higherscoresmeanregulationis<br>morerestrictive|HeritageFoundation<br>(1997)|3.379|0.780|
|CorporateTaxIndex|Index(1-5).Higherscoresmean corporate tax<br>ratesarehigher|HeritageFoundation<br>(1997)|3.079|0.651|
|Corruption(Overall)|Index(1-6).Intemational Country RiskGuide<br>IndexofCorruption.Higherscoresmeanless<br>corruption.|PRSGroup(1999)|3.200|1.097|
|NumberofFixedLines|Per100inhabitants|Intemational<br>Telecommunications<br>Union(2000)|20.987|9.930|
|Fuel,MineralandMetalExports|As%ofGDP|WorldBank(2001)|24.075|22.494|
|Population|Natural Log|WorldBank(2001)|16.414|1.384|
|Inflation|Averagebetween1996and1999|World Bank(2001)|36.939|67.545|
|Openness|Imports aspercentofGDP|World Bank(2001)|46.675|17.913|
|GovernmentExpenditures|As%ofGDP|WorldBank(2001)|16.900|5.115|
|GDPGrowth.|Averagebetween1996and1999|WorldBank(2001)|1.978|4.244|
|PerCapita GDP|000sofUS$|WorldBank(2001)|5.906|3.175|
|FixedLineTelecommunications<br>Operator Privatized|Dummy|WorldBank<br>Telecommunications<br>Department|0.502|0.500|
|CountryisLandlocked|Dummy<br>__**__**_**_**_ ___ ___**___** **__**_ ___ ___ ___<br>_<br>_<br>____<br>Growth|WorldBankGlobal<br>DevelopmentNetwork<br>Database|0.388|0.487|

Note: Data is for 1998, except where noted and for data from Beck _et al_ (2001) and Heritage Foundation (1997), which are from 1997 since data for 1998 were not available.

29
Table 2: Means, Variances and Descriptions of Enterprise Level Variables.

|_Variable_|**_Description_**|_Mean_|**_Ski._**_Dev._|
|---|---|---|---|
|ExtentofOverduePaymentsto Workers|Index (1-4).Higher values mean less overdue<br>payments.|3.429|0.941|
|ExtentofOverduePaymentstoUtilities|Index(1-4).Higher valuesmeanlessoverdue<br>payments.|3.429|0.928|
|ExtentofOverduePaymentstoSuppliers|Index(1-4).Highervalues meanlessoverdue<br>payments.|3.132|1.056|
|Margin|OUnitSalesPricelessOperating Costs(aspercentof<br>operating costs)|16.508|15.643|
|_Ownership(omittedCategoryisstate-owned)_||||
|LargestShareholder- OtherPrivate - Privatized|Dummy.OtherPrivateimpliesthatitisnotownedby<br>frignenterprises or individualsorbyinsiders|0.183|0.387|
|LargestShareholder- Insiders|Dummy.Insidersareworkersandmanagers|0.153|0.360|
|LargestShareholder- Foreign|Dummy.|0.034|0.182|
|LargestShareholder-Other Private- DeNovo|Dummy.Other Privateimpliesthatitisnotownedby<br>foreignenterprises or individualsorbyinsiders|0.488|0.500|
|_Size(omittedcategory isover500employees)_||||
|Fewer than9employees|Dummy|0.265|0.441|
|Between10and49employees|Dummy|0.200|0.400|
|Between50and99employees|Dummy|0.160|0.367|
|Between100and199employees|Dummy|0.137|0.344|
|Between200and499employees|Dummy|0.154|0.361|
|_Ownership (omittedcategoryis'otherservices')_||||
|Sector--Transportation|Dummy|0.061|0.240|
|Sector -Wholesaleand Retail Trade|Dummy|0.269|0.443|
|Sector--Manufacturing|Dummy|0.297|0.457|
|Sector--MiningandConstruction|Dummy|0.099|0.298|
|Sector--Farming,fishing,forestry|Dummy|0.135|0.342|
|_egion_||||
|Region--South EastEurope|Dummy|0.164|0.371|
|Region--CormmonwealthofIndependentStates|Dummy|0.535|0.499|
|Region--CentralEuropeand theBaltic States|**Dummy**|0.301|0.459|
|DataSource:TheWorld BusinessEnvironmentSurvey (WBES)02|000TheWorldBankGroup|||

30
Table 3: Impact of enterprise and country characteristics on bribes to utilities.

|Estimation**Method**||**IntervalR**|**egression**||
|---|---|---|---|---|
|**DependentVariable**|**Percent **|**ofRevenuesPa**|**idtoUtilitiesi **|**nBribers**|
||**Ba**|**Base**<br>**se**|**~~Margin**<br>**Omitted**|**OilExporting**<br>**Dummy**|
|**NumberofObservations**|1822|1822|2327|2200|
|**Sector Dummies**|Yes|Yes|Yes|Yes|
|**SizeofEnterpriseDummies**|Yes|Yes|Yes|Yes|
|_UtilityPrivatization_|||||
|<br>FixedLine Telecom OperatorPrivatized|-0.6610***|-0.6698***|-0.5741***|-0.7136***|
|(Dummy)|(-4.00)|(-4.07)|(-3.81)|(-4.87)|
|Electricity Distribution Privatized|-0.2947||-0.5609***|-0.1222|
|(Dummy)|(-1.49)||(-3.15)|(-0.93)|
|_CapacityandCompetition_|||||
|<br>FixedLines<br>|-0.0424***<br>|-0.0364***<br>|-0.0531***<br>|-0.0348***<br>|
|(Per1000people)|(-3.60)|(-3.30)|(-4.85)|(-3.20)|
|Cellular Companies|-0.2182***|-0.2278***|-0.1769***|-0.0970**|
|(Number)|(-3.16)|(-3.32)|(-2.76)|(-2.09)|
|_nterprise Profitability_|||||
|<br>Margin|0.0127***|0.0126***||0.0113***|
|<br>(UnitSalesPricelessOperatingCostsas%ofoperatingcosts)|(3.73)|(3.70)||(3.81)|
|_EnterpriseOwnership_|||||
|LargestShareholder- OtherPrivate- _DeNovo_|0.6768***|0.6529***|0.7915***|0.4942**|
|(Dummy)|(2.92)|(2.84)|(3.72)|(2.59)|
|LargestShareholder-OtherPrivate- Privatized|0.3139|0.2882|0.4236**|0.2573|
|(Dummy)|(1.38)|(1.28)|(2.03)|(1.35)|
|LargestShareholder- Foreign|0.2370|0.2519|0.3818|0.0131|
|(Dummy)|(0.62)|(0.66)|(1.11)|(0.04)|
|Largest Shareholder- Insiders (ManagersorEmployees)<br>|0.3444<br>|0.3249<br>|0.3632<br>|0.1312<br>|
|(Dumnmy)|(1.42)|(1.35)|(1.61)|(0.66)|
|<br>_OverduePaymentstoUtilitiesandOthers_|||||
|<br>OverduePaymentstoUtilities|-0.1491|-0.1641******|-0.1345*|-0.1317*|
|(Index- highervaluesmeanlessoverduepayments)|(-1.87)|(-2.08)|(-1.85)|(-1.96)|
|Overdue Payments toWorkers|0.1092|0.1214|0.0535|0.1237*|
|(Index- highervaluesmeanlessoverduepayments)|(1.32)|(1.48)|(0.71)|(1.81)|
|Overdue PaymentstoSuppliers|-0.0556|-0.0614|-0.0526|-0.0492|
|(Index- highervaluesmeanlessoverduepayments)|(-0.83)|(-0.92)|(-0.85)|(-0.85)|
|_CountryControls_|||||
|<br>Per CapitaGDPin1998|0.0067|-0.0086|0.0366|-0.0330|
|(000sofUSdollars)<br>|(0.18)<br>|(-0.24)<br>|(1.06)<br>|(-0.89)<br>|
|Political Rights|-0.2873***|-0.2767***|-0.2307***|-0.2010***|
|<br>(Index- Highervaluesmeangreaterdemocracy)|(-4.25)|(-4.14)|(-3.84)|(-3.44)|
|GDPgrowth<br>|-0.0584***<br>|-0.0599***<br>|-0.0690***<br>|-0.0615***<br>|
|(Average between1996and1998)|(-3.47)|(-3.58)|(-4.49)|(-4.00)|
|<br>Openness|-0.0157**|-0.0106**|-0.0185***|-0.0099**|
|(Importsas shareofGDP)|(-2.58)|(-2.14)|(-3.23)|(-2.27)|
|<br>Fuel,MineralandMetalExports|<br>0.0186***|<br>0.0181***|<br>0.0138***||
|(Assharefttlt)|(381)|372|311||
|ooaexpors|.|(.)|(.)||
|OilExporter||||0.5848***|
|<br>(Dummy)||||(2.76)|
|<br>**Log-Likelihood**|-1736.8|-1737.9|-2084.2|-2261.6|
|<br>DataSource:The WorldBusinessEnvironmentSurvey (WBES)0)|2000TheWorld Ba|nkGroup.|||
|T-statisticsare inarentheses<br>*Sinificantat10%level|**Sinificantat5|level<br>*******|inificantat10|rcentlevel|
|p.<br> g .<br>aRegressionsinclude6dummiesforenterprisesizebasedupon|g <br>employment.<br>The|.<br> <br>categoriesare:ent|g p<br>erprises withfewe|.<br>rthan10(full-time)|
|<br>employees;between10and49 employees;between50and99 emp<br>over500 emloees<br> Reressionsinclude seendummies base|<br> <br>loyees;between10<br>duonsectorofo|<br>0and199employee<br>erations Th t|<br>s;between 200an<br>i f|d499employeesand<br>tiilt|
|py.<br>bgv <br>otherindustry; retailandwholesale trade;transportation; otherservi|p <br>ces;andother.|p. e ca|goresare:manu|curng;agrcuure;|

31
Table 4: Coefficients on Main Variables when additional independent variables are included in regression (see Table 3, Column 2 for base regression)..

||DEPENDEN|TVARIABLE|ISPERCENT|OFREVEN|UESPAIDTO|UTILITIES|INBRIBES|
|---|---|---|---|---|---|---|---|
|**AdditionalIndependentVariable**|**None**<br>____|**RegionDummiee**<br>_________<br>~<br>~~~|**RegulationIndex**<br>~~~~~-<br>|**CorporateTax**<br>**RateIndex**<br>|**DemocracyIndex**|**YearsSince**<br>**Democracy**<br>|**Parliamentary**<br>**Dummy**<br>|
|**NumberofObservations**|1822|1822|1636|1636|1822|1822|1822|
|_CoefficientonAdditional IndependentVariable_|<br>**-**|**-**|_-0.0063_<br>|_0.0302_<br>|**_0.1610_**<br>|_0.0061_<br>|_O.5008**_<br>|
|||_-_ _|_(-0.05)_|**_(0.28)_**|_(0.94)_|_(0.16)_|_2.07_|
|_CoefficientsonothervariablesofInterest_||||||||
|FixedLineTelecomOperatorPrivatized|-0.6698***|-0.5192***|-0.6262***|-0.6233***|-0.6217***|-0.6605***|-0.5534***|
|(Dummy)|(4.07)<br>|(-2.72)<br>|(-3.57)|(-3.56)<br>|(-3.62)<br>|(-3.78)|(-3.49)|
|FixedLines|-0.0364***|-0.0488***|-0.0426**|-0.0426***|-0.0374***|0.0366***|-0.0148|
|(Per1000people)|(-3.30)|(-3.95)|(-3.33)|(-3.76)|(-3.38)|(-3.30)|(-0.98)|
|CellularCompanies|-0.2278***|-0.1922**|-0.1886**|-0.1907**|-0.2657***|-0.2272***|-0.1746**|
|(Number)|(-3.32)|(-2.58)|(-2.19)|(-2.21)|(-3.33)|(-3.30)|(-2.39)|
|Margin|0.0126**$|0.0125***|0.0113***|0.0113***|0.0125***|0.0126***|0.0128***|
|(SalesPricelessOperatingCostsas%ofoperatingcosts)|(3.70)|(3.68)|(3.17)|(3.18)|(3.66)|(3.70)|(3.75)|
|LargestShareholder- OtherPrivate--De Novo|0.6529***|0.6739***|0.5405**|0.5428**|0.6480***|0.6524***|0.6644***|
|(Dummy)|(2.84)|(2.91)|(2.21)|(2.23)|(2.81)|(2.83)|(2.88)|
|Overdue PaymentstoUtilities|-0.1641**|-0.1420*|-0.1696**|-0.1696**|-0.1555*|-0.1639**|-0.1565**|
|(Index- highervaluesmean lessoverdue payments)<br>|(-2.08)<br>|(-1.78)<br>|(-2.03)|(-2.03)|(-1.96)|(-2.08)|(-1.98)|
|DataSource:TheWorldBusinessEnviromnentSurvey(WBE|S)02000TheWo|rldBankGroup.||||||

T-statistics are in parentheses. * Significant at 10% level. ** Significant at 5% level. Significant at 10 percent level. Note: Regressions include all variables included in Column 2 of Table 3 including sector and size dummies. ' Regional dummies are dummnies for Central Europe, Southeastem Europe and the Commonwealth of Independent States.

32
Table 5: Coefficients on Main Variables when additional independent variables are included in regression (see Table 3, Column 2 for base regression).

|-<br> <br> <br>|DEPENDENT|VARIABL<br>|EISBRIBES<br>|PAIDTOUTI<br>|LITIESASP|ERCENTOF|REVENUE|
|---|---|---|---|---|---|---|---|
|Additioal<br>Indeendent<br>**ariablePrivatization**<br>AdditionalIndependentVariable|**Index**|**Landlocked**<br>**Dummy**|**Population**<br>**(naturalLog)**|AeaeIftin<br>**AverageInflation**|**Government**<br>**Expenditures**|beetaiain<br>**Decentralization**|**Overall**<br>**Corruption**|
|Number ofObservations|1822|1822|1822|1822|1822|1655|1737|
|_CoefficientonAdditional IndependentVariable_|<br>_0.4436***_<br>_(-3.29)_|_-0.2051_<br>_(-0.98)_|_0.0694_<br>_(0.58)_|_-0.0014_<br>_(-1.25)_|_-0.0037_<br>_(-0.18)_|_0.4047_<br>_(0.37)_|_-0.0556_<br>_(-0.66)_|
|_CoefficientsonothervariablesofInterest_||||||||
|FixedLineTelecom OperatorPrivatized|-0.4418*"|-0.5977***|-0.6948***|0.7413"'*|-0.6794***|-0.4498*|-0.6799"'*|
|(Dummy)|(-2.47)|(-3.34)|(-4.06)|(-4.24)|(-3.92)|(-1.71)|(-4.16)|
|FixedLines|-0.0312***|-0.0389***|-0.0358**|-0.0244*|-0.0361**"|-0.0301**|-0.0427"**|
|(Per1000people)|(-2.78)|(-3.43)|(-3.23)|(-169)|(-3.23)|(-2.02)|(-3.81)|
|CellularCompanies|-0.1617**|-0.2546***|-0.2744***|-0.2712"**|-0.2192"'*|-0.2006"*|-0.1533"*|
|(Number)|(-2.27)|(-3.44)|(-2.59)|(-3.50)|(-2.61)|(-2.39)|(-1.98)|
|Margin|0.0127**"|0.0125**"|0.0126"**|0.0123***|0.0126"**|0.0129"**|0.0119***|
|(Sales PricelessOperating Costs as%ofoperatingcosts)|(3.73)|(3.68)|(3.70)|(3.61)|(3.71)|(3.45)|(3.47)|
|Largest Shareholder- OtherPrivate-DeNovo|0.6495**"|0.6465***|0.6538***|0.6546"*'|0.6520**"|0.8611***|0.6103"*'|
|(Dummy)|(2.81)|(2.81)|(2.84)|(2.85)|(2.83)|(3.17)|(2.63)|
|Overdue PaymentstoUtilities|-0.1594**|-0.1601"|-0.1666**|-0.1652**|-0.1642**|-0.1669'|-0.1747*"|
|(Index- higher values meanlessoverduepayments)|(-2.02)|(-2.03)|(-2.11)|(-2.10)|(-2.09)|(-1.91)|(-2.17)|
|DataSource:TheWorldBusinessEnvironmnentSurvey(WBE<br>T-statisticsare inparentheses.<br>*Significantat10%level.|S)02000TheWorl<br>"*Significanta|dBankGroup.<br>t5%level.|"'Significant at1|0percentlevel.||||

> Note: Regressions include all variables included in Column 2 of Table 3 including sector and size dummies.

33
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