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The Effect of Bureaucracy on

Corruption:

Evidence from the Regions of the

Russian Federation

Ryan Gillette

Middlebury College Department of Economics

2008

Abstract:

What is the relationship between bureaucracy and corruption? Using firm-level

data from the 2005 BEEPS survey along with information from the Russian State

Statistics department, this paper investigates the connection between bureaucracy and

bribery across 14 of the regions of Russia. Results reveal a robust inverse relationship

between size of bureaucracy and corruption. This could be due to competition between

bureaucrats, increased oversight, or better staffing.

*Special thanks to professors Pyle, Prasch, Holmes, Maluccio, Warin, and all my family and friends for their support and guidance along the way.

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Introduction:

A bureaucrat is the most despicable of men, though he is needed as vultures are needed, but one

hardly admires vultures whom bureaucrats so strangely resemble.

-Marcus Tullius Cicero

Bureaucracy and corruption have long been considered to be intrinsically

connected. Bureaucrats are stereotyped as greedy, corrupt drains on the economy,

siphoning away public funds and extracting bribes for their own personal gain. While

bureaucrats are often in a unique position to abuse their governmental powers, the

connection between bureaucracy and corruption is not as simple as it initially appears.

The presence of corruption can undermine the legitimacy of a government, and

inhibit the growth of efficient and equitable markets. If left unchecked, it can incite crime

and destabilize formal legal institutions. While virtually every country experiences some

level of corruption, it is especially prevalent in post-communist countries. As these

countries underwent transitions towards a market based economy, the resulting liberation

of prices and privatization of firms proved to be extremely detrimental to governments

and economies. Uncertainty and confusion associated with the transitions exacerbated

problems with rent seeking and clouded the chain of authority. As a result of the lack of

clear leadership, government officials were better able to take advantage of their power to

extract bribes from newly privatized firms, which needed support to adapt to the rapidly

changing free market.

This phenomenon was especially prevalent in Russia. The communist legacy

coupled with the troubles of transition resulted in serious problems with bribes and

corruption throughout the government and across the country. In 1998, the INDEM

foundation estimated that the monetary amount paid out in bribes in Russia exceeded

combined expenditures on science, education, health care, culture and art. Another study

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suggested US$500 million was paid by small businesses in corruption payments every

month towards the end of the twentieth century (Levin & Satarov, 1999). In some

instances, these bribes proved to be nothing more than a trifling hurdle, but in other cases

they became a serious impediment. For example, to enroll a child in kindergarten, parents

had to typically pay US$20 and supply the director with chocolates or flowers; to secure

a job at a public school, however, applicants often had to forego a whole year’s salary in

bribes (Leonhardt, 1998).

In addition to extensive corruption, Russia also has an expansive bureaucracy.

The country’s vast size and considerable population have historically necessitated a large

government with a dense bureaucracy that has carried over into modern times. Combined

with the high prevalence of corruption, Russia provides an opportunity to study the

connection between bureaucracy and corruption in a controlled manner. The current

Russian Federation consists of 84 regions, each with diverse resources, populations, and

regional governments. However, their differences are bounded by their shared cultural

and political history. Furthermore, the overarching communist legacy enables easier

control of political factors, as the central planners dictated development and growth of

local governments in each region.

After the fall of the Soviet Union, these regions gained unprecedented levels of

independence, enabling them to grow in unique directions. This divergent development

allows for a thorough examination of the effect of size of government on corruption.

Recent data includes detailed information about bureaucratic concentration and bribes

paid to government officials in several of the regions of Russia. While it is generally

believed that bureaucrats are associated with higher instances of bribes and corruption,

this may not be the case. In fact, there is extensive literature which suggests an opposite

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effect of bureaucracy on corruption. Through various mechanisms, such as competition,

heightened oversight, and increased efficiency of public services, it is possible that

increases in the size of bureaucracy may lead to a decrease in the frequency and size of

bribes.

The purpose of this paper is to investigate the effect of bureaucracy on corruption

by examining the relationship between the concentration of bureaucrats in each region

and the frequency and size of bribes. Using this information, it becomes apparent that

having more bureaucrats in a region is typically associated with lower levels of reported

corruption.

Literature Review:

Definition of Corruption:

An especially difficult aspect of corruption is the complexity in creating a precise

definition of the problem. One of the most widely accepted definitions is provided by

Treisman (2000), who states that corruption is defined as “the misuse of public office for

private gain.” While this definition provides a reasonable generalization of the topic, it

fails to cover many of the deeper intrinsic qualities of corruption. For example, the

definition makes no distinction between types of corruption. Prasch (2007) describes

different forms of fraud, and how “simple fraud” can occur at the employee level, but a

more damaging “control fraud” can occur at the highest levels of an organization, in

which a single corrupt executive can undermine an entire corporation.

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Governmental corruption functions in a similar manner. Corruption can occur at

different points of the government hierarchy and have varying effects. High level

corruption is corruption that occurs in the upper echelons of a government, reaching up to

those officials with the most power and influence. This is typically characterized by

activity such as state capture, in which firms exert enough influence over law makers that

laws are actually amended to give advantages to certain parties, or large government

funds are transferred away from one sector of the economy to another where it is easier to

extract bribes.

Low level corruption, on the other hand, occurs at a more local level among

inferior officers. Examples of this include bribes to hasten the procurement of certain

government permits or patents, payments to government agencies to prevent inspections

and thus avoid compliance, or kickbacks to regional decision makers to secure local

government contracts and other public services. This sort of corruption is much more

widespread, as it often goes undetected and unnoticed due to the relative insignificance of

any one instance of petty bribery. In aggregate, however, the broad reaching implications

of low level corruption make it at least as great a threat to a country as high level

corruption.1

1 There is also the issue of economic and political rent seeking. Rent seeking occurs when officials abuse their power to profit from a good, without increasing or contributing to productivity. This was especially prevalent under command economies, as officials who had access to resources could sell them on the black market for significant profits. This phenomenon continues to be a problem, even in liberal capitalist economies. Rent seeking can be considered a form of corruption, but it falls under a different category than the other two types. The primary difference is that rent seeking is not limited to government officials. Anyone with access to below-market-price goods can engage in rent seeking. It can also be done in a semi-legal fashion, as exhibited through the powerful agriculture lobbies in the United States, who transfer some of the burden of costs to the government by petitioning for subsidies. As a result, rent seeking is very hard to measure and define, and will not be a major focus of this paper.

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Effects of Corruption:

The consequences of corruption can be significant. Several studies have found a

robust relationship between perceived corruption and the level of investment. Anderson

& Gray (2006) show in a multinational study of countries across Europe and Central Asia

that as perceptions of corruption increase, investment tends to decline, as firms fear their

investments will be seized by corrupt bureaucrats. This not only discourages foreign

direct investment, but also inhibits the formation of new small businesses. Entrepreneurs

are less likely to invest capital in a new company if they fear they will be unable to pay

the necessary bribes to get the company off the ground. As a result, corruption can

function like a tax, creating a similar barrier to entry without providing the social benefit

of increased public revenue.

Other works examine how corruption encourages inefficiencies in markets.

Contrary to a model proposed by Mauro (1998), in which it is suggested that bribes might

encourage government employees to work harder, and speed the most efficient firms

through the permit queue, Leite & Weidmann (1999) find that corruption actually

decreases the efficiency of markets. The ability to extract bribes instead provides

bureaucrats with an incentive to devise a slow and inefficient system for the processing of

various documents, forcing firms to pay bribes to receive them in a timely manner.

Mauro (1998) also shows that corruption can lead to an inefficient allocation of

government funds. Resources can be siphoned away from beneficial public programs to

areas in which it is easier to disguise costs and seize funds. For example, a corrupt

politician may use money earmarked for public school textbooks to purchase a new

fighter jet or finance a large government building, since it is easier to disguise costs in

these fields. Dreher, Kotogiannis & McCorriston (2007) attempt to model this by

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measuring cement consumption across countries, proposing that more corrupt countries

will have more large scale construction projects to hide fund seizure. Results reveal a

positive relationship between cement consumption and corruption, confirming the theory

that corruption may lead to a misallocation of funds.

These studies suggest that there is a link between higher levels of corruption and

lower GDP growth. However, as Schleifer & Vishny (1993) show, once controlling for

level of investment, there is no relationship between corruption and GDP growth.

Combined with findings from Anderson & Gray (2006) which reveal no clear link

between corruption and GDP growth, these studies conclude that any effect corruption

may have on GDP growth is channeled primarily through lost investment spending.

Another study by Slinko, Yakolev & Zhuravskaya (2004) shows that while regions with

high instances of state capture exhibit lower rates of development for small businesses,

overall growth is not significantly affected, suggesting the effects of corruptive

protectionism to be more redistributive than draining. Furthermore, while it is possible

that corruption could be causing a decline in GDP growth, it is also plausible that

countries that already exhibit low GDP growth could have higher levels of corruption due

to some third factor, such as poor political or legal institutions, which influences both.

Alternatively, causation could run in both directions, with each factor being affected by

the other.

There is also literature showing that many of the detrimental effects of corruption

may be caused by perceptions of corruption rather than actual corruption experiences. As

Treisman (2006) explains, many commonly used corruption indices determine values by

polling experts and scholars instead of the actual population, resulting in discord between

real and predicted values of corruption. In a survey of countries from Burkina Faso to

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Togo, experts predicted 54% of the population would encounter corruption during the

year, while reported experiences revealed that only 13% actually experienced acts of

corruption (Razafindrakoto & Roubaud, 2005). Furthermore, Mocan (2004) shows that,

controlling for endogeneity and institutional quality, perceived corruption is determined

more by institutional quality than by actual corruption experience. This is especially

troubling, since many investors, both foreign and local, rely on corruption perception

indices to evaluate investment risks in countries. It is thus possible that many of the

apparent detrimental effects of corruption are caused by unfounded assumptions about

levels of corruption within a country.

Causes of Corruption:

The causes of actual corruption are varied. According to Ades & Di Tella (1999),

one of the most prominent sources is an abundance of natural resources. Natural

resources create significant rent seeking opportunities, attracting corruption and

inhibiting proper development of markets. This is especially problematic with high value

resources such as oil and diamonds. In some African countries, competition over valuable

gem deposits has even lead to civil war, demonstrating the incredible corruptive power of

natural resources (Lujala et al, 2005).

Other studies hypothesize that the concentration of population contributes to

corruption. Rose-Ackerman (1996) and Anderson & Gray (2006) both find that

corruption thrives in cities and other densely populated regions. Mocan (2004) suggests

this could be due to more impersonal relationships between bureaucrats and citizens in

urban areas, allowing larger abuses of their governmental powers and greater ease in

asking for bribes. It is also possible that corrupt officials are drawn to metropolitan areas

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due to larger public budgets from which they can seize funds. However, it is also possible

that regions with lower populations will experience higher corruption, due to decreased

political competition and a greater ability to retain power despite suspicions of scandal.

Thus, the quality of elections and democracy seems to play a role in corruption, as areas

with poor democratic institutions may not be able to remove corrupt officials from office.

Treisman (2000) also proposes that education plays a role in corruption. Along

with Dreher et al. (2007) and Dininio & Orttung (2005), they find that highly educated

societies typically exhibit lower levels of corruption. This could be due to the fact that

more educated regions will tend to have higher literacy rates, which yields a more literate

populace that is better informed and well read about issues such as corruption, and thus

more capable of combating it. As a result, the degree to which the press is free to report

news also has an influence on the level of corruption. Furthermore, as Alt & Lassen

(2003) show, societies with higher incomes often enjoy lower levels of corruption, so

education could be affecting corruption through the income channel as well.

Bureaucratic wage is theorized to have an effect on corruption by Di Tella &

Schargrodsky (2003). They hypothesize that by paying bureaucrats higher wages, they

will have less incentive to attempt to extract bribes due to higher opportunity costs should

they be caught. As Larina (2001) describes, in many cases it is not even possible to earn a

living wage on bureaucratic pay. As such, bribes are considered a benefit of the job to

boost income to an equilibrium level. Furthermore, when the difference between business

pay and government pay is substantial, bribe size is hypothesized to increase to balance

out the pay differential.

Perhaps the most interesting factor is the role of bureaucratic concentration on

corruption. The literature on this topic is varied and comes to diverse conclusions.

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Dininio and Orttung (2005) undertook a study similar to this one of bureaucracy in

regions of Russia, and found that an increase in the number of bureaucrats per region

resulted in an increase in perceived corruption. However, their study relies heavily on

measures of corruption provided by Transparency International and INDEM. As

discussed earlier, these indices are often poor measures of actual corruption experience,

and in some cases are not correlated at all. Thus, while they find a positive relationship

between bureaucracy and corruption, their results must be regarded with caution due to

the nature of their data.

Furthermore, Schleifer & Vishny (1993) model the market for bribes, and find

that with the presence of several corrupt officials, competition between these bureaucrats

will drive down the price of bribes. When attempting to obtain permits and licenses, firms

will seek out the bureaucrat who charges the lowest bribe. Barring any collusion among

officials, other bureaucrats will be forced to match this price, or risk losing market share

in the arena for bribes. Increasing the pool of bureaucrats could thus stimulate

competition, dampening the level of corruption in a region.

Rose-Ackerman (1996) suggests that there are two types of markets for bribes. In

some cases, the market is roughly competitive, and approximately the same bribe price is

charged to all firms. This typically occurs in the procurement of routine government

services, such as general permits and documents. In the absence of competition, however,

bureaucrats may be able to price discriminate, charging higher bribe prices to those most

wiling to pay. Those firms that stand to gain the most from the bribe will be willing to

pay the highest price. For example, a firm with a high volume of exports would be much

more willing to pay large bribes to customs officials than small firms that trade mostly

domestically.

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Brown et al. (2007) find a negative relationship between size of bureaucracy and

frequency of bribes in Russia, but proffer a different explanation. Instead of competition,

they suggest that more bureaucrats could lead to better staffed public agencies. As a

result, firms will not be forced to resort to bribery to receive government services in a

timely fashion.

Mauro (1998) and Rose-Ackerman (1996) both discuss how heightened oversight

can lead to lower levels of corruption. By increasing the chances of getting caught taking

or giving a bribe, some bureaucrats and firms will find it too risky to engage in corrupt

practices. As a result, increasing the number of supervising bureaucrats could help curtail

corruption. However, as Treisman (2000) finds, countries with more tiers of government

tend to have higher levels of perceived corruption. Instead of regulating their underlings,

these supervising bureaucrats become just another obstacle, generating even more bribes.

Waller, Verdier & Gardner (2002) reach an interesting mixed conclusion. By

examining the number of tiers of government, they find that a single bribe setting

monopolist will set a lower bribe price per investment project relative to a chain of

successive monopolists. However, the total volume of bribes obtained will be higher, due

to the increased amount of investment. Yang (2005) also finds that an increase in the

perceived probability of meeting an honest official can drive down bribe frequency.

The wide diversity of opinions regarding the role of bureaucracy in determining

corruption demonstrates the importance of the issue. Combining the findings of all the

various studies suggests that bureaucracy plays an uncertain role in determining

corruption. The presence of more bureaucrats in a region has the potential to increase the

volume of bribes due to additional bureaucratic hurdles. At the same time, however,

competition between these bureaucrats may drive down the price of any one bribe. The

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added risks of heightened oversight and greater efficiency of government agencies could

also affect the amount of corruption in an area.

There are many other potential factors that could influence corruption. Studies

have found connections between colonial history, cultural factors, legal origins, religion,

and other sociopolitical indicators to affect levels of corruption. For the scope of this

paper, however, these variables are insignificant since this study examines regions of

Russia, which share a common political and cultural background. As a result, strong

entho-cultural unity throughout Russia will effectively control for many of these aspects.

Data & Methodology:

The primary source of data for this paper is the 2005 Business Environment and

Enterprise Performance Survey (BEEPS) jointly provided by the World Bank and

European Bank for Reconstruction and Development. The survey was first carried out in

1999 in 25 different transition countries, and was repeated in 2002, 2004 and 2005. The

2005 survey was conducted through a series of face-to-face interviews with over 9000

firms in 27 different transition countries. The questionnaires were designed to illuminate

issues in transition, ranging from success of privatization to levels of competitiveness to

sources of corruption.

Firms were selected randomly, but the sample was designed to ensure certain

sampling thresholds. At least 10% of the firms were required to be “small” (2-49

employees), and 10% “large” (250+ employees). Firms larger than 10,000 employees

were removed from the sample, as were new firms that had been established after 2002.

A minimum of 10% of the firms had to have foreign ownership and another 10% state

control; additionally, 10% of firms had to be in a “small city/countryside” location

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(population under 50,000). The sectoral composition of the sample was selected to reflect

relative contribution to GDP. Of all the firms that were contacted in Russia, 31%

completed an interview, 41% refused or were unavailable, and the remaining 28% were

not eligible or unnecessary due to fulfillment of sampling quotas. Refusals were typically

attributed to lack of time, disinterest, or need for approval from those in charge. Usually,

the owner or director of the firm was interviewed. However, in some cases a manager or

financial officer was surveyed. There is no reason to believe any of these factors would

affect a firm’s answers regarding bribery and corruption.

To ensure honesty of answers, firms were assured of their anonymity throughout

the survey. Each interview was carried out privately and personally in the local language

by trained individuals, and firms were coded with a serial number so that no names were

attached to their responses. Before sensitive questions regarding bribes were asked,

interviewers were instructed to read to participants the following assurances:

We are interested in your opinions in a personal capacity, we do not imply in any way that your company makes unofficial payments/gifts, and we recognize that your company neither approves nor condones the use of unofficial payments/gifts. The responses you give will be aggregated and presented in purely statistical terms; any comments you give me cannot be attributed to either you or your company.

Sensitive questions were phrased in a manner to encourage honesty through

depersonalization; two of the critical questions to this paper are structured as such: “It is

common for firms in my line of business to have to pay some ‘additional payments/gifts’

to get things done” and “on average, what percentage of total annual sales do firms like

yours typically pay in unofficial payments/gifts to public officials?” As a result, instances

of no response are low, but it is unclear how truthfully firms chose to respond. However,

since there is little incentive to inflate the actual bribe values, any bias would probably

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downplay the true level of corruption. If these real values could be measured, they would

likely further strengthen the robustness of this study.

The most valuable component of the 2005 survey is that it contains a locational

variable for firms in Russia, allowing for distinction between different cities and regions

within the country. This makes it possible to build a database of firm-reported corruption

and bribery within different regions of Russia. The 2005 BEEPS survey covered 601

enterprises in Russia, but unfortunately the firms were only distributed among 14 of

Russia’s 84 regions. Figure 2 in Appendix 1 shows the breakdown of firms by region.

The BEEPS data provide valuable measures of corruption and bribery based on

actual experiences rather that perceptions. There are two central questions from this

survey that serve as the focal point for this paper. The first is a question regarding the

frequency of “additional payments/gifts to get things done with regard to customs, taxes,

licenses, regulations, services, etc.” This is measured on a scale of 1 (never) to 6

(always). Another question asks what percentage of annual sales is used to make

“unofficial payments/gifts to public officials.” This, combined with another question at

the end of the survey asking for an estimate of total sales makes it theoretically possible

to determine the total monetary size of bribes paid per year. Unfortunately, nearly half the

firms declined to report sales estimates, significantly affecting the usefulness of this

information in larger regressions.

There are several other important variables from the 2005 BEEPS survey that are

included in regressions. One is another question pertaining to corruption, but at a higher

level. It asks on a yes-no basis if firms attempt to affect legislation for the benefit of their

company, providing a potential proxy for state capture. Lobbying, however, would be

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included under this variable, so it is difficult to distinguish illicit imposition of power

from simple legal influence.

Another series of variables inquires about the frequency with which various

departments are bribed, again on a 1-6 scale. This allows for partial control of the “type”

of official being bribed. Since it is unreasonable to assume all bureaucrats have the same

governmental powers and hence the same ability to extract bribes, this variable allows for

a closer comparison of similar officials. The types of departments range from tax

collection to the court system to fire inspection.

The 2005 BEEPS survey also included a panel component, allowing for

comparison between the 2005 and 2002 surveys. While there were over a thousand firms

surveyed in both 2002 and 2005, only 41 of those were in Russia, preventing any sort of

useful analysis of trends. A preemptory glance at all the data from both surveys, however,

shows some interesting patterns, illustrated in Table 1 below. The frequency of bribes is

similar in both data sets, both in terms of median and mean. The percentage of sales paid

in bribes, however, is quite different. The 2002 data set has a much wider variance than

the 2005 survey, with a median of .5% and a maximum of 20% of sales. The 2005 data

set, however, has a median of only .1% and a maximum of 10%. These differences are

significant at the 1% level.

Mean Median Std. Deviation Max

2002 1.43 0.5 2.49 20

2005 1.06 0.1 1.66 10

Table 1: % of sales paid in bribes in 2002 and 2005

When extrapolating monetary size of bribes based on the firms that chose to

report annual sales data, however, we find that more money was paid out in 2005 than in

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2002. In 2002, an average of $11,728 was paid in bribes, while in 2005 over $18,000 was

paid (see Table 2 below). This shows that while larger percentages were paid in 2002,

more money was actually lost to bribes in 2005. Since the reported frequency of bribes is

essentially identical in both data sets, this means the increase is likely due to growth in

reported sales from 2002 to 2005. It also suggests that bureaucrats have some knowledge

of firm profit margins, and are able to charge higher bribe prices as companies become

more successful. This difference, however, is only significant at the 88% level, perhaps

due to the fact that nearly 40% of the firms in 2005 declined to report sales data. As a

result, the monetary size of bribes was excluded as a dependent variable in regressions

due to the low response rate and lack of significance of the overall regressions.

Mean Median Std. Deviation Max

2002 $11,728 $488 $11,728 $600,000

2005 $18,500 $500 $114,490 $1,851,800

Table 2: Monetary size of bribes paid in 2002 and 2005

The BEEPS data also contains questions pertaining to various firm characteristics,

such as size and origin. These questions are used as controls in some regressions, but it is

possible the survey does not accurately represent the different types of firms in a region.

For the regions with a high volume of survey respondents, such as Moscow, it is safe to

assume there is an accurate representation of the different sizes and types of firms within

the region. For many of the smaller regions, such as Kemerovo, it is possible that only the

largest or most prominent firms were interviewed, potentially biasing the true effects of

firm characteristics on corruption.

The Russian Federation is particularly discreet regarding its bureaucracy. As a

result, it is difficult to find constant, detailed data on the size of bureaucracy at the

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regional level. The most recent available data was published in the 2001 Statisticheskii

Builleten’ created by the Russian State Statistics Department Goskomstat. This summary

of Russian bureaucracy includes the number of bureaucrats per region, both at the federal

and regional levels. It also divides bureaucrats by branch of government, separating

executive from legislative from judicial bureaucrats.

Throughout this study, bureaucracy was measured as both the absolute number of

government employees per region and the number of bureaucrats per capita. When

running regressions, coefficients followed the same general trends regardless of the

measure, but the results were often far less statistically significant and the adjusted R-

squared values were lower when using the per capita measure of bureaucracy. As

suggested in Dininio & Orttung (2005), using the number of bureaucrats is a more

accurate indicator of the absolute size of the state than using a per capita measure. Due to

the drastically different sizes of Russian regions, both in terms of population and

territory, measuring bureaucracy on a per capita basis places too heavy an emphasis on

population as a determinant of the size of bureaucracy. More expansive regions will

require larger numbers of bureaucrats in assorted local government positions, despite the

fact that they may have drastically smaller populations. For example, Moscow has the

largest number of bureaucrats, but the smallest number of bureaucrats per capita. This

does not accurately reflect the fact that in Moscow all the bureaucrats are in a much

closer proximity to the population relative to a remote, expansive region such as

Krasnoyarsk. As a result, using a per capita measure of bureaucracy is not as useful a tool

for this study as the absolute number of bureaucrats. Nonetheless, once controlling for

the size of the region, results were similar, but less significant. Regressions using a

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measure of bureaucrats per area suffered from similar problems, and were typically

highly insignificant.

The data also include information regarding government salaries by region, which

will help control for wage differentials. Several other control variables, such as regional

population, regional domestic product, geographic size, and education are taken from

other surveys provided by Goskomstat. Presence of natural resources would be included

in regressions, but none of the 14 regions covered in the BEEPS survey exhibit

significant amounts of any valuable natural resource.

A series of political variables was taken from the Социальной Атлас Российских

Регионов (Social Atlas of the Russian Regions) provided by the independent institute

Social Politics. This group evaluated the quality of different political factors in the

regions of Russia, such as fairness of elections, freedom of press, and political

transparency. Unfortunately, their methodology was largely based on aggregating expert

opinions, calling into question the validity of their results. As such, these variables will

be used with caution, since this paper has already discussed the flaws of perception-based

measurements.

One of the biggest concerns when studying corruption is endogeneity. There are

many reasons why the concentration of bureaucrats in any region may not be exogenous

to the amount of corruption. It is possible that the presence of significant corruption

profits for government employees would draw other officials to the region, increasing the

concentration of bureaucrats. Another possibility is that the poor business environment

created by high levels of corruption could encourage firms to move to less corrupt

regions, thus decreasing tax revenues in these corrupt areas and lowering the regional

budget for employing bureaucrats.

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The nature of this study helps alleviate some of these problems. As Hill and

Gaddy (2003) discuss in their book, the central planners played a critical role in the

development of regional governments, especially in the later years of the Soviet Union.

This means that the initial distribution of bureaucrats at the time of the collapse of the

USSR was almost entirely determined by economic factors and not affected by other

elements. As a result, there has been a much shorter period of time, largely characterized

by significant changes in economic and political structure, for bureaucrats to discover

opportunities for corruption profits and seek entry into these high corruption regions. The

desire of local bureaucrats to prevent entry of other corrupt officials combined with the

short time horizon for transferring means that any movement trends are likely small and

still in progress, and thus not a significant impediment to the results of this paper.

Other questions in the 2005 BEEPS data help show that corruption does not affect

the performance of a business enough to make a firm move to a different region. When

asked if corruption was an impediment to business success, most firms responded that

corruption was no more than a “minor obstacle.” This suggests that corruption does not

drastically affect firms in a way that would encourage them to relocate. Also, a question

regarding percentage of actual sales reported for tax purposes shows only a small, weak

correlation between reported sales and corruption, demonstrating that, all else equal,

more and less corrupt regions should expect to collect comparable tax revenues. This

means the regional governments should be equally well funded and equally capable of

hiring and retaining bureaucrats.

The host of controls introduced helps eliminate many of the other pathways by

which corruption could influence the size of the bureaucracy. Controlling for regional

population is an important factor, as it directly affects both corruption and bureaucracy.

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More highly populated regions likely require more bureaucrats simply due to greater

demand for government services, while at the same time population density is shown to

be associated with higher levels of corruption. Regional domestic product is likely to

have a similar effect. For the regressions in this paper, both of these factors were

controlled for using the average of data from the years 2001 and 2005. Since the

bureaucracy data are from 2001, and the BEEPS survey is from 2005, using the mean

values of data from the two years helps avoid bias. Regressions were checked using the

individual year data to ensure accuracy of results. For other variables this is either not

possible or not relevant, so the most appropriate available data were used. Detailed

descriptions of all the variables included in this study can be found in Figure 1 of the first

appendix.2

Multicollinearity also proved to be a significant issue with many of the

regressions. Figure 3 in Appendix 1 lists correlation coefficients between several of the

independent variables. This may have limited the statistical significance of the

regressions, and inhibited the ability to accurately determine the individual sources of

variation in corruption. Removing highly correlated variables did not significantly alter

the magnitude of coefficients, but lowered R-squared values. Thus, they were all

typically included in regressions due to the extensive literature noting their importance.

2 An instrumental variable model was also run in addition to the regressions presented as a validity check. Results are robust and similar to those found in the body of the paper, suggesting endogeneity is not a significant impediment to this analysis. See Appendix 2 for more details.

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Results:

To investigate the role of bureaucracy on corruption, a series of regressions were

run using a variety of models (see Table 3 below). The primary dependent variables used

in the regressions were bribesize and bribefreq, which measure the size and frequency of

bribes. The first set of regressions examines the effects of bureaucracy on bribe

frequency. Since the indicator of bribe frequency from the BEEPS data is measured on a

scale of 1-6, binary variables representing different ranges of bribe frequency were

created, and probit models were run to examine changes in the probability of paying

bribes.3 The first regression uses the binary variable anybribe, which is equal to “1” if a

firm pays any bribe. This basic model confirms that bureaucracy is closely tied to

corruption, demonstrating that the likelihood of paying a bribe declines as the number of

bureaucrats increases. Although statistically significant, the magnitude of the drop not

large: adding an additional 1000 bureaucrats to a region only leads to a 1% drop in the

probability of paying a bribe.

Furthermore, this initial model is consistent with many of the patterns of

corruption found in other literature. It shows that the probability of paying a bribe

increases in more populated regions along with areas exhibiting a higher regional

domestic product. The coefficient on diffwage shows the wage differential between the

average employee and the average bureaucrat is associated with a lower existence of

bribes. Past theories have proposed the opposite relationship, but it is possible that

bureaucrats make up the wage gap by simply extracting larger bribes as opposed to more

bribes. The coefficients on unistudents and democracy have expected signs, but are not

3 An alternative model was run using an ordered probit model. Overall results were similar to probit analysis. This model is described in more detail in the second appendix.

22

Table 3: Probit regressions on bribe presence and frequency4

anybribe oftenbribe

I II III IV V

bureaucrats -0.00154 -0.0033 -0.0033 -0.0075

(0.0009)* (0.00098)*** (0.0009)*** (0.00268)***

burpc -0.051

(0.0280)*

popavg 0.000046 0.0001 0.00014 0.0003 -0.00006

(0.00005) (0.00006)** (0.00006)** (0.0001)*** (0.00003)**

territory -0.0000571 0.000004 0.000003 0.0001 -0.00008

(0.00006) (0.00011) (0.0001) (0.0002) (0.0001)

rdpavg 0.0004 0.0003 0.0004 -0.001 0.0005

(0.0002)** (0.00019)** (0.0001)** (0.0010) (0.0002)**

diffwage -0.00005 -0.00005 -0.00006 0.0001 -0.00005

(0.00002)* (0.000025) (0.00002)** (0.0001) (0.0000)

unistudents -0.00018 -0.00003 -0.00006 0.0008 -0.00001

(0.00017) (0.00019) (0.0001) (0.0006) (0.0002)

mediafreedom 0.0153

(0.0270)

democracy -0.0406

(0.0386)

privatized 0.1351 0.1641 0.1765 0.1691

(0.0942) (0.0965)* (0.0971)* (0.0959)

private 0.1186 0.125 0.1204 0.1174

(0.070)* (0.0723)* (0.0733) (0.0726)

foreign 0.1766 0.206 0.1937 0.2022

(0.153) (0.153) (0.1543) (0.1532)

subsidary 0.0655 0.1084 0.1008 0.1115

(0.171) (0.1744) (0.1739) (0.1725)

small -0.151 -0.143 -0.1508 -0.1437

(0.0709)** (0.0763)* (0.0767)** (0.0762)*

medium -0.0522 -0.0574 -0.0596 -0.0675

(0.0739) (0.075) (0.0762) (0.0754)

mine -0.121 -0.0608 -0.1475

(0.159) (0.1856) (0.1501)

construction 0.0970 0.0969 0.1006

(0.093) (0.0936) (0.0928)

manuf -0.030 -0.0323 -0.0239

(0.083) (0.0835) (0.0828)

transport 0.054 0.0476 0.0594 (0.114) (0.1148) (0.1150)

trade -0.0056 0.0101 -0.0036

(0.081) (0.0824) (0.0810)

4 Standard deviations presented in parentheses below. * denotes significance at 10%, ** at 5%, *** at 1%

23

(continued)

I

II

III

IV

V

real_estate 0.0094 0.0188 0.0198

(0.099) (0.1005) (0.0995)

hotel_restau 0.011 0.0093 0.0202

(0.130) (0.1316) (0.1320)

central -0.0486

(0.1441)

northwest -0.3745

(0.1379)*

siberian 0.7385

(0.0959)

southern 0.5315

(0.2511)

urals 0.6395

(0.1832)

distmosc -0.0011

(0.0009)

Pseudo R2 0.0355 0.0431 0.049 0.0545 0.0377

N 548 548 548 548 548

significant. Mediafreedom has a counterintuitive sign, but is also insignificant. Since both

mediafreedom and democracy are subjective measures, however, results must be

interpreted with caution. Due to the lack of significance and concerns about

multicollinearity, these variables were dropped in subsequent models.

Models II through V use the variable oftenbribe, which is equal to “1” if firms pay

bribes often, and “0” otherwise.5 Model II reveals a strengthened negative relationship

between bureaucrats and bribe frequency with a coefficient over double in size.

Furthermore, this regression introduces firm level characteristics to control for firm type

5 All regressions were checked using each of the dependent variable measures. Trends were the same no matter the measure, and variations in coefficients can be reliably attributed to changes in included independent variables as opposed to the specific measure of the dependent variable. Other measures are included to demonstrate consistency of results.

24

and size. Small firms appear to enjoy a hearty decline in bribe frequency relative to large

sized firms, suggesting bureaucrats prey more heavily on larger firms, which likely have

a greater ability to pay. Private firms with no state history also show a statistically

significant increase in bribe frequency relative to the omitted category, otherorigin. This

could be due to the fact that privatized firms often already have established relationships

with government employees, reducing the need for impersonal bribes. Once controlling

for sector, however, privatized firms also exhibit an increase in bribe frequency.6

Sector dummies were also introduced to examine trends in different areas of the

economy. While these dummies tended to be insignificant in regressions, the dummy for

construction firms was routinely positive. When excluding other firm level

characteristics, it even proved to be significant at the 10% level. This suggests it is easier

for officials to extract bribes from construction firms, perhaps due to the high volume of

permits and inspections required, along with large, soft budgets in which it is easy to hide

unofficial extra payments. Because of this, firms that specialize in construction tend to

pay more bribes on average than firms that work in other sectors.

Model IV introduces controls for geographic features in an attempt to capture any

differences that may arise from a region’s location within Russia. One method of

accomplishing this was to study the next level of regional organization. In 2000,

President Putin divided the country’s oblasts and regions into seven federal districts, each

with its own presidential envoy to oversee activity in the region. These envoys are

appointed by the president, and are charged with ensuring regional compliance with

federal laws and regulations. As a result, it is possible that the degree to which corruption

6 Several regressions were also run using interaction terms between bureaucracy and assorted firm characteristics including size, sector and origin; these coefficients proved to be highly insignificant.

25

is detected and punished may vary depending on the district and the envoy, allowing for

variation in the extent to which bureaucrats attempt to extract bribes. Figure 4 in

Appendix 1 shows the distribution of observations among the districts in the data set.

Regressions including these dummies revealed that on average, firms located in

the Northwest federal district are 37% less likely to pay frequent bribes relative to the

other federal districts. All the Northwest firms in this data set are situated in or around St.

Petersburg7, so it is difficult to assess whether the effect is caused by characteristics of

this particular city, or some aspect of the Federal supervision. St. Petersburg is often

considered the most “European” of Russian cities, but it is unclear if this notion

transcends into the realm of politics. Citizens appear to be more politically aware,

however, as evinced by large public unrest over a 2003 campaign for governor that was

deemed unfair and dishonest (BBC, 2003). This could perhaps translate into a higher

intolerance of fraud and corruption. It is also the birth city of Vladimir Putin, whose

diligent pursuit of law and order may resonate especially strongly in his home town.

As Francken et al (2005) show, the remoteness of a region or distance from

central authority can also affect corruption. If regions are more isolated from the central

government, it may be easier for bureaucrats to extract bribes without fear of

repercussion. To control for this, another variable was introduced measuring the linear

distance of each region’s administrative center from Russia’s capital, Moscow. This

variable, however, proved to be largely insignificant, but often negative in probit

regressions.

7 The administrative center of Leningradskaya Oblast is the city of St. Petersburg, but due to the immense size, the city itself is an independent oblast. The same is true of Moscow and Moscovskaya Oblast.

26

Model V uses bureaucrats per capita as a measure of bureaucracy to illustrate the

consistency of results. When using the variable burpc, the statistical significance of many

coefficients declines, along with pseudo R-squared values. General patterns, however,

remain similar to other regressions.

Another set of probits was run to examine the effects of bureaucracy on bribes to

specific departments. These results are presented below in Table 4. Firms were asked

how often, on a scale of 1 (never) to 6 (always) they paid bribes to individual

departments and for certain services. In this study, all of the dependent variables are

binary, taking on a value of “1” if a firm ever paid a bribe to that department (similar in

definition to anybribe). This measures the existence of bribes in different departments

and agencies.

Results reveal that the number of bureaucrats has a negative, statistically

significant effect on the probability of paying a bribe in several processes, including

obtaining permits, getting connected to basic infrastructure, dealing with the health and

fire inspectors, and resolving legal affairs in courts. While the data do not break down

bureaucrats by agency, the trends suggest that as the number of government officials in a

region increases, the presence of bribes in specific departments tends to decline. Some of

these services, such as obtaining permits and getting connected to local infrastructure, are

likely rendered by bureaucrats who serve essentially identical functions. Since it is easy

to turn to another bureaucrat to get a basic permit, it is possible competition between

officials helps reduce corruption. Similarly, in departments in which bureaucrats offer

unique services, such as the procurement of government contracts, there is no apparent

connection between bureaucracy and the presence of bribes. This could be due to the fact

that there is no substitute for a government contract. As a result, firms have much less

27

Table 4: Probit regressions on existence of bribes in different departments

Dependent Variables:

permits getconnected govcontracts healthinspector fireinspector environinspect taxcollector customsimports courts

bureaucrats -0.0083 -0.0124 -0.0183 -0.0054 -0.0109 0.0006 -0.0024 -0.0019 -0.0064

(0.0047)* (0.0028)*** (0.0490) (0.0026)** (0.0049)** (0.0053) (0.0030) (0.0080) (0.0024)***

popavg 0.0003 0.0005 0.0006 0.0001 0.0003 -0.0002 0.0001 -0.0001 0.0003

(0.0002) (0.0001)*** (0.0018) (0.0001) (0.0001)* (0.0004) (0.0001) (0.0006) (0.0001)**

territory 0.0001 0.0005 0.0007 0.0003 0.0006 -0.0002 -0.0006 -0.0001 0.0002

(0.0002) (0.0002)** (0.0207) (0.0002) (0.0002)** (0.0003) (0.0008) (0.0007) (0.0002)

rdpavg 0.0011 -0.0018 -0.0054 0.0008 0.0007 0.0017 0.0010 0.0007 -0.0002

(0.0010) (0.0009)* (0.0191) (0.0009) (0.0010) (0.0016) (0.0009) (0.0023) (0.0009)

diffwage -0.0001 0.0002 0.0007 -0.0001 -0.0001 -0.0002 -0.0001 -0.0001 0.0000

(0.0001) (0.0001)* (0.0023) (0.0001) (0.0001) (0.0002) (0.0001) (0.0002) (0.0001)

unistudents 0.0000 0.0018 0.0048 0.0005 0.0010 0.0002 -0.0004 0.0007 0.0003

(0.0006) (0.0006)*** (0.0168) (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) (0.0006)

mine 0.2471 0.1266 0.4221 0.3704 -0.0048 0.2531 -0.0764 0.4884 0.3878

(0.1244) (0.1983) (0.2124)* (0.1618)** (0.1770) (0.1914) (0.1902) (0.1977)** (0.1873)**

construction 0.2908 0.0713 0.5542 0.1419 0.2294 0.1865 0.1423 0.0482 0.2863

(0.0664)*** (0.0901) (0.0951)*** (0.0952) (0.0677)*** (0.0959)** (0.0853) (0.0928) (0.1146)***

manuf 0.1090 -0.0277 0.4606 0.0118 0.0504 0.1032 -0.0337 0.0717 0.1860

(0.0807) (0.0785) (0.1330)*** (0.0854) (0.0789) (0.0878) (0.0862) (0.0901) (0.1101)*

transport 0.1273 -0.0262 0.1827 -0.0159 0.0606 0.0890 0.1198 0.2631 0.1526

(0.1034) (0.1069) (0.1669) (0.1169) (0.1052) (0.1227) (0.1088) (0.1423)** (0.1443)

trade 0.0966 -0.0488 0.3139 0.0435 0.0939 0.0654 0.0421 0.1648 0.2931

(0.0793) (0.0750) (0.1580)*** (0.0831) (0.0746) (0.0838) (0.0815) (0.0898)** (0.1030)***

real_estate 0.0331 -0.0376 0.1503 -0.2214 -0.0112 0.0051 0.0181 -0.0064 0.2906

(0.0983) (0.0912) (0.1483) (0.079)** (0.0971) (0.1020) (0.1003) (0.0923) (0.1247)*

hotel_restau 0.0981 0.0326 0.0800 0.2013 0.0729 -0.1030 -0.0395 -0.1181 -0.1862

(0.1211) (0.1267) (0.1791) (0.1337) (0.1164) (0.1232) (0.1334) (0.0773) (0.1070)

firm level controls yes yes yes yes yes yes yes yes yes

fed district controls yes yes yes yes yes yes yes yes yes

Pseudo R2 0.0755 0.0803 0.1722 0.0772 0.0652 0.0792 0.0488 0.122 0.0988

N 542 550 480 535 549 549 541 486 487

28

bargaining power when negotiating for bribes, as it is more difficult to turn to another

official to receive the same service.

The court system is a curious intermediary, since firms are typically stuck with

one judge. However, there are many aspects of the legal process in which bureaucrats

play a role. In the Russian court system, claimants are required to pay legal fees before

any hearings or trials. It is possible to ask for postponement of payment, but there is a

complicated procedure associated with the process. Additionally, claimants can request to

have a panel of three judges instead of just one, requiring an equally lengthy application

(Hendley, 1998). This provides judicial bureaucrats the opportunity to demand bribes to

hasten or accept these requests, causing them to be subjected to mechanisms such as

competition in the same fashion as other bureaucrats.

At the same time, however, an overburdened court system could be streamlined

by the addition of more employees. It is possible that regions with more bureaucrats have

better staffed courthouses that are more capable of efficiently handling cases. As a result,

firms do not need to make extra payments to receive a court ruling in a timely fashion,

suggesting better staffing plays an important role. Alternatively, a higher number of court

affiliated bureaucrats could simply imply a better functioning legal system, reducing the

need for bribery and increasing the consequences if caught.8

It is also possible that declines in bribe frequency are a result of better staffed

agencies. As Brown et al (2007) suggest, if a department is understaffed, it may function

inefficiently. This could cause queuing problems and create delays, encouraging firms to

pay extra to expedite the process. With a well staffed bureaucracy, agencies may perform

8 A set of regressions was run using the number of “judicial” bureaucrats in a region. The coefficient on this variable was negative, but only significant at the exclusion of the other types of bureaucrats. As a result, it is unclear if lower instances of bribery in the legal system are associated with more judicial bureaucrats, or simply more bureaucrats in general.

29

better and faster, reducing the need for these efficiency payments. Examining sector

dummies also shows which industries are most affected by which departments. As

discussed earlier, construction firms seem have the greatest likelihood of offering a bribe.

Results reveal that businesses in the construction sector are roughly 25% more likely to

pay bribes for permits, fire inspection, and court services, and over 55% percent more

likely to pay bribes to obtain government contracts relative to the omitted sector, “other

industries.” This seems logical, since these services are all essential to the construction

industry. Similarly, the mining sector exhibits significant increases in the probability of

paying a bribe to the health inspector and customs officials, two of the major departments

that mining operations interact with on a regular basis.

In addition to the differences between departments, it is possible there is a

difference between types of bureaucrats. The government of the Russian Federation is

structured similarly to the American government, separating power into three branches –

legislative, executive, and judicial. Additionally, there are three levels of governmental

hierarchy – federal, regional, and local. Thus far, the measure of bureaucracy has

included all bureaucrats in each region, but there is reason to believe bureaucrats in

certain branches and hierarchies may have differential effects on corruption. For

example, there is an agency of the Kremlin ominously entitled The Control Directorate.

Founded in 1991 as an arm of the Presidential Executive Office, its purpose is essentially

to oversee implementation and adherence to federal law and to punish violators.9As such,

the Directorate is also charged with curtailing and controlling corruption within regions.

This suggests that larger quantities of federal level executive bureaucrats may translate

into a greater presence of the Control Directorate, discouraging bribery and corruption in

9 For a more complete description, check the Kremlin website http://www.kremlin.ru/eng/articles/Control_Functions.shtml

30

the area. The data on bureaucracy breaks down bureaucrats by branch, and executive

bureaucrats by hierarchy (see Figure 5 in Appendix 1 for breakdown). Using a probit

model with oftenbribe as the dependant variable, Regression I in Table 5 below shows

that increases in the number of executive branch bureaucrats leads to a statistically

significant decline in the probability of paying frequent bribes. Regression II further

breaks down executive branch bureaucrats by hierarchy, with interesting results.

Increases in regional and local level bureaucrats leads to decreases in corruption, but for

federal bureaucrats, the coefficient is insignificant but positive.

The coefficients for regional and local bureaucrats are not surprising, as they

mirror earlier findings, but the federal coefficient is perplexing. It is possible that the

number of federal level bureaucrats has no effect on the size or effectiveness of

departments such as the control directorate, negating any causal link to corruption.

Furthermore, since federal level bureaucrats are likely involved with larger governmental

affairs, it is probable they are not in a position to extract bribes from local firms, causing

them to have no impact on bribery. This suggests that the decline in corruption associated

with higher levels of bureaucracy is not a result of better monitoring: the increased

prevalence of overseeing officials appears to be unconnected to changes in bribe

frequency. The strong coefficients for regional and local bureaucrats, however, reinforce

the mechanisms of competition and better staffing: as regions become more saturated

with low-level government employees, corruption tends to decline. Greater competition

among these officials could drive down bribe prices, or better staffing could reduce the

need for firms to pay bribes. Alternatively, since federal level bureaucrats are typically

appointed and thus easily transferable, it could be that federal level bureaucrats are sent

to regions already exhibiting higher instances of bribery in an attempt to better combat

31

corruption, leading to an endogeneity problem. However, since bureaucracy data is

already effectively lagged by four years relative to corruption data, an accurate analysis

of the effects of federal level bureaucrats would require a more in-depth study with more

recent data.

Table 5: Probit models breaking down bureaucratic hierarchy (dependent variable oftenbribe)

I II

executive -0.00005

(0.00002)*

federal 0.00092

(0.0007)

regional -0.00054

(0.0003)*

local -0.00051

(0.0003)*

legislative -0.00010 -0.00577

(0.0006) (0.0038)

judicial 0.00009 -0.00118

(0.0003) (0.0009)

otherorgans -0.00229 0.02690

(0.0038) (0.0199)

popavg 0.00017 0.00026

(0.0002) (0.0003)

territory 0.00003 -0.00063

(0.0002) (0.0005)

rdpavg 0.00018 0.00276

(0.0004) (0.0018)

diffwage -0.00005 0.00026

(0.0001) (0.0002)

unistudents -0.00004 -0.00923

(0.0004) (0.0062)

sector controls yes yes

firm level controls yes yes

fed district controls10 no no

Pseudo R2 0.057 0.0536

N 510 510

10 Dummies for the different federal districts were omitted due to perfect co-linearity of a few of the districts with the disaggregated bureaucratic data.

32

The high levels of correlation between the independent variables complicated the

analysis of bribe size (see Table 6 below). Multicollinearity made it difficult to obtain

significant coefficients on several of the independent variables. Since

Table 6: Tobit regressions using bribesize as the dependent variable

I II III

bureaucrats -0.0104 -0.0027 -0.0120

(0.0041)** (0.0051) (0.0102)

popavg 0.0005 -0.0002 0.0000

(0.0001)*** (0.0003) (0.0005)

territory 0.0001 -0.0002 0.0007

(0.0005) (0.0005) (0.0007)

diffwage 0.0001 -0.0002

(0.00005)** (0.0003)

rdpavg 0.0030

(0.0025)

unistudents 0.0001

(0.0014)

small 0.2472

(0.3943)

medium 0.6664

(0.412)*

privatized 0.4801

(0.4775)

private 0.5119

(0.3953)

subsidary 0.8564

(0.8504)

foreign 2.5148

(0.785)***

_cons 1.1092 1.6433 1.5123

(0.415)*** (0.4629) (1.1277)

sector controls no no yes

fed district controls no no yes

Pseudo R2 0.0041 0.0075 0.021

N 556 556 556

the variable for bribe size as a percentage of sales is bounded by 0 and 100, Tobit models

were run to examine trends in corruption. Although Model I in Table 6 showed a

33

statistically significant decline in bribe size as the concentration of bureaucrats increased,

this significance vanished with the inclusion of more variables in Models II and III.

The difference between average regional per capita income and bureaucratic wage

proved to be an important factor in determining bribe size. Model II shows that as the

difference increases, the size of bribes tends to grow as well, suggesting that bureaucrats

augment their salaries to closer match this outside opportunity. However, when

controlling for regional domestic product, even this significance vanishes. This could be

due to the fact that bureaucratic wage, per capita income, and regional domestic product

are all very highly correlated, with correlation coefficients upwards of .98. This means it

is possible the increase in bribes could be attributed to the size of the regional economy

and greater potential for extortion, reducing the relevance of the wage differential.

Model III introduces firm, sector, and regional level controls. Coefficients on the

individual sectors and federal districts were highly insignificant and hence omitted from

tables. Firm level characteristics reveal medium sized firms pay larger bribes as a

percentage of sales relative to large firms, with statistical significance just shy of 10%.

Furthermore, firms owned by foreign partners suffer a huge penalty in bribe size, paying

over 2.5% more of annual sales in bribes than other types of firms. This could be because

foreign firms do not have the same connections as local firms, and bureaucrats may

choose to exploit the ignorance of foreign owners about local customs and traditions.11

11 In addition to investigating low level corruption, several regressions were run to examine the

effects of bureaucracy on high level corruption. Results proved to be largely inconclusive due to several factors. There were only two variables pertaining to high level corruption in the data set, asking whether firms had attempted to influence national or regional law. Less than 10% firms interviewed responded yes, with no indication of frequency of success. Regressions on these variables yielded no significant coefficients, and the overall regressions were often insignificant themselves. Only the dummy for small firms exhibited a significant, negative coefficient – a rather obvious result, as high level corruption is much more expensive and outside the scope of a small firm’s budget. Furthermore, it is unclear what, if any, effect size of bureaucracy should have on high level corruption. If anything, lower levels of bureaucracy should encourage a greater frequency of high level corruption, as each bureaucrat would have more power and potential influence over regional law, making high level corruption easier and perhaps cheaper. Still,

34

Conclusion:

Contrary to common assumptions about bureaucracy, this paper finds that larger

bureaucracies tend to be associated with less corruption than smaller bureaucracies. This

may be because in areas with a larger pool of bureaucrats to choose from, it is easier to

find bureaucrats who will perform their duties without demanding additional payments.

Furthermore, bribes could be a choice by firms due to inefficient, understaffed

bureaucracies. As a result, regions with more government officials may be able to

provide public services more effectively, reducing the need for bribes to hasten the

process. Improved oversight may play a role as well, but this paper found no apparent

connection between increases in supervising officials and lower levels of corruption.

Since Russia is considering halving the number of regions and significantly

trimming the bureaucracy, it is important that the implications of such an action be

considered. Reducing the size of bureaucracy has the potential to increase levels of

corruption, perhaps due to easier collusion among bureaucrats, fewer alternatives for

services, or reduced efficiency of government agencies. Thus, while bureaucrats have

long been chastised for their corrupt practices, an ironic solution to the problem may lie

in simply increasing the quantity of these loathed officials.

the connection is tenuous at best, and would warrant a more in-depth study than is possible with the available data.

35

Appendix 1: Variables and Statistics

Figure 1: Descriptions of variables

Variable Name Description Mean

(Std Dev)

bribesize size of total bribes as % of annual sales 1.06

(1.66)

bribefreq frequency of bribes on a scale of 1 (never) to 3.18

6 (always) (1.62)

anybribe a binary variable, equal to 1 if a firm ever pays .790

a bribe (bribefreq>1) (.407)

oftenbribe a binary variable, equal to 1 if a firm often pays bribes .392

(bribefreq>3) (.488)

bureaucrats hundreds of bureaucrats in a region 298.3

(121.7)

burpc number of bureaucrats per captia in a region 6.56

(1.57)

territory size of a region in thousands of sqaure km 90.2

(252.4

pop2005 regional size of population in 2005, measured 5184

in thousands of inhabitants (3263)

pop2001 regional size of population in 2001, measured 4754

in thousands of inhabitants (2461)

popavg average regional population from 2001 to 2005 4969

measured in thousands (2859)

rdp2005 regional domestic product in 2005, measured 1292612

in millions of rubles (1621599)

rdp2001 regional domestic product in 2001, measured 461522

in millions of rubles (543729)

rdpavg average regional domestic product from 877.1

2001 - 2005, billions of rubles (1082)

36

pcmincome per capita monthly income in rubles, 2001 9899

(7070)

msalary average monthly bureaucratic salary in 2001. 7389

Across Russia, all bureaucrats receive a base (366)

salary of 4050 rubles/month, but receive

"bonuses" that vary by region. This variable measures base salary plus any

bonuses.

diffwage difference between monthly per capita income 2509

and monthly bureaucratic salary in rubles, 2001 (6904)

unistudents number of university students per 10,000 people 512.5

(210.4)

mediafreedom freedom of media on a scale of 1 (poor) to 3.74

5 (excellent) (1.01)

numemployees the number of employees in the firm in 2005 154.4

( 589.8)

numemp2 number of employees in 2005 squared 371209

(4341767)

distmosc linear distance (in miles) of administrative 465.3

center of region to Moscow (503.6)

extaudit dummy equal to 1 if firm uses an external certified .416

auditor to check books

small dummies for size of company based on number .66

medium of employees. 2-49 is small, 50-249 medium, 250 .22

large and over is large. .11

privatized dummies representing origin of company. .11

private Privatized companies are former state owned .73

subsidary enterprises. Private companies have no state .01

foreign history. Subsidaries are offshoots of former state .02

otherorigin owned enterprises. Foreign firms are joint .10

venture private firms with a foreign partner

37

getconnected a series of variables indicating the frequency with which firms pay bribes for

permits various services/departments, ranging

govcontracts from getting connected to basic

healthinspector infrastructure to avoiding inspections to paying

fireinspector off courts. Measured as binary variables, equal

taxcollector to 1 if a firm has ever paid a bribe for this

customsimports service

courts

mine dummies representing self-identified

construction Sector firm works in. Ommited sector is

manuf "other"

transport

trade

real_estate

hotel_restaurant

central dummies representing the administrative

northwest district in which each firm resides.

siberian Districts are the next level of organization above

southern oblast.

volga

urals

38

Figure 2: Firm breakdown by region

Oblast # Firms

Kemerovo 7

Krasnoyarsk 7

Kursk 25

Leningradskaya o. 32

Moscow12 157

Moscovskaya o. 8

Nizhegorod 58

Novosibirsk 42

Rostov 61

Samara 45

St. Petersburg 52

Sverdlovsk 50

Udmurtia 10

Voronezh 47

Total 601

Figure 3: Correlation coefficients between several highly correlated variables

bureaucrats burpc popavg rdpavg diffwage unistudents

bureaucrats 1.000

burpc -0.702 1.000

popavg 0.971 -0.816 1.000

rdpavg 0.899 -0.776 0.959 1.000

diffwage 0.858 -0.788 0.932 0.989 1.000

unistudents 0.785 -0.717 0.842 0.848 0.829 1.000

12 Since so many of the observations are concentrated in Moscow, regressions were double-checked removing Moscow firms. Results were typically unaffected by the exclusion of Moscow observations.

39

Figure 4: Distribution of observations by federal district

Federal District Regions

# Observations

Central Kursk 237

Moscow

Moscovskaya

Voronezh

Northwest Leningradskaya 84

St. Petersburg

Siberian Kemerovo 56

Krasnoyarsk

Novosibirsk

Southern Rostov 61

Urals Sverdlovsk 50

Volga Nizhegorod 113

Udmurtia

Samara

Figure 5: Percent of Executive branch bureaucrats in each tier of government by region

Oblast Federal Regional Local

Kemerovo 36.7 10.3 53.0

Krasnoyarsk 38.2 10.0 51.8

Kursk 31.9 32.9 35.2

Leningradskaya 32.6 4.7 62.7

Moscow 38.7 50.2 11.1

Moscovskaya 33.9 4.4 61.7

Nizhegorod 38.2 6.0 55.8

Novosibirsk 45.0 13.2 41.8

Rostov 39.9 4.9 55.2

Samara 37.2 9.5 53.3

St. Petersburg 50.9 42.5 6.6

Sverdlovsk 38.6 21.8 39.6

Udmurtia 39.8 12.6 47.6

Voronezh 38.2 10.9 50.9

40

Appendix 2: Alternative Models

Ordered Probit:

In addition to the probit models run on bribe frequency, an ordered probit model

was estimated as well. The ordered probit allows for changes in the effects of

independent variables depending on the level of the dependent variable. In the context of

this paper, it means bureaucracy may play a different role when moving from “never

paying bribes” to “seldom paying bribes”, versus from “usually paying bribes” to

“always paying bribes”. Furthermore, it allows for differences in categorical sizes that

may be lost through a binary probit model. The results of the regression, presented below

in Table 7, represent the overall net effect of each independent variable on bribe

frequency. Bureaucracy continues to have a statistically significant negative impact on

the frequency of bribes, matching results from the probit models. Table 8 shows the

marginal effects of bureaucracy at each level of bribe frequency. The positive, significant

coefficients on bureaucracy at low levels of bribe frequency show that higher numbers of

bureaucrats increases the probability of paying no or very few bribes. The negative

coefficients at high levels of bribe frequency show that increases in the size of

bureaucracy drive down the probability of paying frequent bribes. This demonstrates that

the findings of this paper hold under the ordered probit, as increases in bureaucracy lead

to a higher probability of paying low or no bribes.

41

Table 7 (below): Ordered probit model, using bribefreq as dependent variable

Table 8 (below): Marginal effects of bureaucracy at each level of

bribe frequency

bureaucrats -0.0128

(0.0054)**

popavg 0.0007

(0.0002)***

territory -0.0001 (0.0003)

rdpavg -0.0017

(0.0019)

diffwage 0.0002

(0.0002)

unistudents 0.0009

(0.0013)

privatized 0.3325

(0.1957)*

private 0.3233

(0.1603)**

foreign 0.4752

(0.3227)

subsidary 0.0668

(0.3530)

small -0.0903

(0.1601)

medium 0.2125

(0.1657)

mine -0.0432

(0.3884)

construction 0.1687 (0.1910)

manuf -0.2930

(0.1769)*

transport -0.0669

(0.2388)

trade -0.1134

(0.1716)

real_estate -0.1028

(0.2104)

hotel_restaur -0.0935

(0.2767)

central -0.9268 (1.3611)

northwest -1.8573

(1.7397)

siberian 0.6265

(1.2148)

southern -0.2926

(0.5063)

volga -1.0391

(1.2495)

distmosc -0.0011 (0.0018)

Pseudo R2 0.0233 N 548

Level of bribe Marginal effect

frequency of bureaucray

1 (never) 0.0035 (.0015)**

2 (seldom) 0.0011

(.0005)**

3 (sometimes) .0001

(.0001)

4 (frequently) -.0008

(.0003)**

5 (usually) -.0019 (.0008)**

6 (always) -.0021

(.0009)**

42

Instrumental Variable Probit analysis:

Endogeneity is always a concern when studying corruption and bureaucracy. As

this paper discussed earlier, there are several ways by which the level of bureaucracy

could be affected by the amount of corruption. In some cases, the higher potential for

corruption in a region could attract bureaucrats seeking to profit from bribes. At the same

time, corruption that is too high could force firms out of the region, reducing regional tax

levels and budgets for employing bureaucrats. Since it is unclear which, if either, of these

effects is at work, it is difficult to ascertain if endogeneity is really a problem, as it is

even possible the two effects cancel each other out.

Nevertheless, to confirm results, an instrumental variable probit analysis was run

as well. The available data included information on the number of federal employees by

region back to 1995. In 2001, the number of federal employees proved to be an effective

predictor of the overall size of bureaucracy, exhibiting a correlation coefficient of .97.

The number of federal employees in 1995 also effectively predicts size of bureaucracy in

2001, with a slightly lower correlation coefficient of .90. This suggests that the sizes of

regional bureaucracies change at similar rates, and the ratio of federal employees to the

overall pool of bureaucrats remains relatively constant. At the same time, it is difficult to

explain how the number of federal level bureaucrats in 1995 would affect the amount of

corruption in 2001, suggesting that the number of federal bureaucrats in 1995 could serve

as an effective instrument.

Results of the regression in Table 9 below show that even after instrumenting for

potential endogeneity, bureaucracy has a negative effect on bribe frequency. These

results hold using both the maximum likelihood estimator and Newey’s two-step

43

estimator. Instrumental variable analysis on bribe size reveals no significant coefficients,

but this was often the result even without instrumentation.

Table 9: Instrumental variable probit model using oftenbribe as dependent variable, instrumenting

for bureaucracy using federal employees from 1995

(continued)

bureaucrats -0.0191

(0.0069)***

popavg 0.0009

(0.0003)***

territory 0.0004

(0.0005)

rdpavg -0.0024

(0.0025)

diffwage 0.0003

(0.0003)

unistudents 0.0020

(0.0017)

privatized 0.4489

(0.2468)*

private 0.3236

(0.2040)

foreign 0.4896

(0.3948)

subsidary 0.2493

(0.4387)

small -0.3905

(0.199)**

medium -0.1585

(0.2059)

mine -0.1929

(0.5168)

construction 0.2482

(0.2375)

manuf -0.0843

(0.2215)

transport 0.1235

(0.2925)

trade 0.0257

(0.2150)

real_estate 0.0497

(0.2600)

hotel_restaur 0.0227

(0.3423)

central -2.1335

(1.7591)

northwest -3.2111

(2.2498)

siberian 1.6525

(1.5527)

southern -0.6027

(0.6529)

volga -2.0247

(1.6149)

distmosc -0.0026

(0.0023)

constant 4.3187

(3.3242)

N 548

44

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