Determinants of bribery for infrastructure provision in East African manufacturing firms

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Determinants of bribery forinfrastructure provision in East Africanmanufacturing firmsSheshangai Kaniki & Tendai GwatidzoPublished online: 30 Oct 2012.

To cite this article: Sheshangai Kaniki & Tendai Gwatidzo (2012) Determinants of bribery forinfrastructure provision in East African manufacturing firms, African Security Review, 21:4, 17-37, DOI:10.1080/10246029.2012.712874

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African Security Review 21.4, December 2012, 17–37

Tendai Gwatidzo is a senior lecturer at the School of Economic and Business Sciences, at the University of the Witwatersrand, Johannesburg, South Africa ([email protected])

Sheshangai Kaniki is a senior researcher at Momentum Group in South Africa ([email protected])

Determinants of bribery for infrastructure provision in East African manufacturing fi rmsSheshangai Kaniki and Tendai Gwatidzo

Existing empirical evidence suggests that corruption in infrastructure is prevalent in developing countries. Using data on manufacturing fi rms in Kenya, Uganda and Tanzania, this study investi-gates what type of fi rms are asked to pay bribes by public offi cials in order to access infrastruc-ture. We fi nd that fi rms in Tanzania and Uganda face more severe problems with infrastructure than those in Kenya. Despite facing fewer infrastructure constraints, we fi nd that Kenyan fi rms are more likely to be asked for bribes than Ugandan and Tanzanian fi rms; suggesting that pay-ing bribes could be enabling Kenyan fi rms to access limited infrastructure. We also fi nd that larger fi rms are less likely to be asked for bribes, and that an effi cient court system reduces the propensity of public offi cials to ask for bribes. In addition, fi rms located in capital cities are more likely to be asked for bribe payments. These fi ndings provide policy makers with specifi c targets to aim for in the design of policies meant to address corruption in infrastructure provision.

Keywords corruption, bribery, infrastructure, East African Community, manufacturing fi rms

Introduction

A high incidence of corruption indicates a poor institutional environment. According to Aidt,

a weak institutional framework is a necessary condition for corruption to arise and persist.1 He

argues that corruption is found where incentives in political, administrative and legal institu-

tions lead to the exploitation of discretionary power for the extraction of rents. Ndikumana

explains that eradicating corruption in sub-Saharan Africa is a diffi cult undertaking that

requires a fundamental change to the incentive structures that regulate the interactions be-

tween bureaucrats and private agents.2 This involves modifying the payoffs and sanctions that

characterise these interactions.

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18 African Security Review 21.4 Institute for Security Studies

Like many countries in sub-Saharan Africa, three countries that are part of the East African

Community (EAC), namely Kenya, Tanzania and Uganda, have a high incidence of corruption.

Out of 183 countries, Kenya was ranked 154th, Tanzania was ranked 100th and Uganda was ranked

143th on the Transparency International 2011 Corruption Perception Index (CPI).3 Although this

index can be questioned on the basis of its subjective nature, it still indicates that, relative to other

countries, corruption is a serious problem in the EAC. A 2009 study by Transparency International

on corruption in East Africa also found that the incidence of bribery was highest in Kenya (45 per

cent), followed by Uganda (35 per cent) and Tanzania (17 per cent).4 The three main power utility

companies in the EAC – TENASCO (from Tanzania), UMEME (from Uganda) and the Kenya

Power Lighting Company – were found to be some of the most corrupt institutions.5

Corruption can manifest as the diversion of public resources allocated to infrastructure

projects.6 When public resources are diverted from their intended use, it retards the quality

and quantity of infrastructure. This, argues Calderon and Serven, undermines the robust

positive relationship between infrastructure and growth in Africa.7 Given the low infrastruc-

ture endowment in Africa, it is essential that limited resources aimed at infrastructure devel-

opment are not stolen or poorly allocated. Yepes et al. investigate the infrastructure defi cit in

Africa and fi nd that Africa has the worst infrastructure endowment of any developing region.8

Within Africa they fi nd that the EAC has the lowest infrastructure endowment on a number

of measures, including access to electricity and density of fi xed line telephones.

Corruption that leads to suboptimal public investments in infrastructure is likely to result in

unmet demand among fi rms attempting to pursue productive investment opportunities. This

can lead to another form of corruption, namely payment of bribes by fi rms to public offi cials in

order to access the limited infrastructure. Paying bribes for infrastructure presents fi rms with an

additional cost to doing business. However, the large infrastructure defi cit in the EAC is likely

to be a motivator for fi rms to bribe public offi cials for infrastructure such as electricity, water

and fi xed telephone lines. Furthermore, the incentive to pay bribes is likely to be higher where

institutions such as courts are poorly equipped to discipline bribe payers and bribe takers.9

This study investigates what factors determine whether manufacturing fi rms in Kenya,

Tanzania and Uganda are asked to pay bribes for infrastructure. It deviates from previous

studies in two important ways. First, it focuses specifi cally on bribery for infrastructure.

Second, it covers three countries. Previous studies such as Fisman and Svenson, and Kimuyu

are single-country studies.10 This study also uses more recent data compared with earlier

studies, giving a more up-to-date assessment of the state of bribery in East Africa.

The rest of the article is organised as follows. The next section reviews the theoretical

and empirical literature on corruption. Within this substantial literature, an effort is made

to discuss studies that have focused specifi cally on bribery and on the role of corruption in

infrastructure. This is then followed by a section on methodology which sets out the empiri-

cal specifi cation, describes the data and interprets the results. Finally, conclusions and policy

implications are drawn in the last section.

Literature review

Factors that lead to corruption

■ Corporate governance: Wu explains that principles of good corporate governance such as re-

sponsibility, accountability and transparency can alleviate the problem of bribery by solving

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Features 19

principal-agent problems and information asymmetry.11 Additionally, good governance

reduces the incentive for public offi cials to pursue corrupt practices because it increases

the likelihood that they will be caught. Principles of good governance are more diffi cult to

implement in family-owned fi rms compared with other fi rms such as publicly held fi rms

because corrupt offi cials are less likely to be exposed when dealing with a few individuals.

Governance issues also differ between locally-owned fi rms and foreign-owned fi rms, affect-

ing the extent to which they are willing to participate in corruption activities.

■ Endogenous harassment theory: According to this theory, the predatory offi cials of utility

service providers can use observable information such as fi rm profi tability, growth and size

to differentiate among targeted fi rms based on their willingness to pay.12 In this framework

more profi table fi rms and fast-growing fi rms have a higher likelihood of participating in

bribery and of paying larger bribes.

■ Excess demand for public services: Private agents will be willing to pay bribes for public

services when their demand for these services is not being met.13 Excess demand can arise

from price ceilings or through inadequate public investment.14 As a result, employees of

public institutions such as utilities can demand bribes for connections, repairs, instal-

lations and other activities required for private agents to have access to public facilities.

Excess demand is a key feature of infrastructure in sub-Saharan Africa.

■ Institutional environment: Corruption is partly a result of the poor quality of institutions.

According to Gray and Kaufmann corruption is more likely to arise where accountability

is weak and legal institutions are poorly prepared to enforce laws pertaining to ethics in

government.15 Lederman et al. argue that political institutions are particularly important

for corruption.16 They fi nd that democracies, parliamentary systems, political stability and

freedom of the press are associated with lower levels of corruption. Another important

aspect of the institutional environment is the design of regulations as contained in the

permit and licence system; with a more complicated system presenting greater opportuni-

ties for public offi cials to abuse their positions for private gain.17

■ Market competition: In a highly competitive environment delays in obtaining public ser-

vices could lead to a loss of market share.18 An increase in market competition may cause

fi rms to bribe public offi cials as a means of avoiding red tape.

■ Network effects: According to Ali and Isse the domination of one ethnic group in politics

leads to differential access to power.19 They explain that in societies that are ethnically di-

verse bureaucrats give preference sequentially, beginning with their close kin followed by

their ethnic group. These network effects based on ethnicity suggest that highly fragment-

ed societies will be more corrupt than homogenous societies. In the context of countries

in sub-Saharan Africa, this implies that corruption is likely to be higher among African

people given the diversity of their ethnic groups compared with white or Asian people,

who tend to be more homogenous. Wrong argues that in Kenya the Kikuyu do well in

business because of political support from the ruling party or government, which is also

dominated by people of that tribe.20

Empirical evidence on the relationship between corruption and infrastructure

The empirical literature on corruption is growing rapidly. This is largely a result of the strong

interest amongst academics, policy makers and the international community on the economic

effects of corruption, particularly in the developing world. The surveys conducted by the

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World Bank have also supplied some fi rm-level data that can be used to understand better the

problem of corruption at the micro-level.21 In line with the objectives, literature that shows

the effect of corruption on infrastructure is reviewed.

Although the relationship between corruption and infrastructure is important, the empiri-

cal evidence is limited. Much of the empirical research on the economic effects of corrup-

tion in developed and developing countries is macro in nature and has tended to focus on

investment and economic growth.22 Corruption in these studies has been measured using

mainly country-level perception data. The studies show that corruption has a negative effect

on investment and economic growth.

Nevertheless, there is some evidence that shows that corruption affects infrastructure

provision. For example, Queiroz and Visser examine the relationship between corruption

and infrastructure provision and fi nd that countries perceived to be more corrupt have less

transport infrastructure.23 Tanzi and Davoodi, using a sample of 128 countries, fi nd that cor-

ruption is associated with poor infrastructure quality.24 This is further corroborated by Lovei

and McKechnie, whose fi ndings indicate that corruption diverts funds away from projects

that would directly benefi t the poor.25 In Estache and Kouassi, corruption is found to have

a negative impact on the operations of African water utilities.26 Estache et al. also show that

corruption leads to lower rates of access to electricity.27 Herrera and Rodriguez further show

that poor infrastructure contributes to high incidences of corruption.28

Previous studies on bribery in East Africa do not focus specifi cally on infrastructure.

Nevertheless, they still provide some important results that provide guidance to this study.

Svensson uses a sample of Ugandan manufacturing fi rms to investigate the factors deter-

mining whether fi rms pay bribes and how much they pay.29 He fi nds that exporting fi rms

and fi rms that pay higher taxes are more likely to pay bribes for customs, taxes, licences,

regulations and services. His results also show that more profi table and larger fi rms pay

bigger bribes, corroborating fi ndings by Clarke and Xu.30 Using data on Ugandan fi rms,

Fisman and Svensson fi nd that bribery and taxation are harmful to fi rm growth, with brib-

ery being more harmful than taxation.31 Supporting evidence was also found by Kimuyu

using Kenyan fi rm-level data.32 In Wu it is found that smaller fi rms, fi rms controlled by

individual owners and family, and fi rms in a highly competitive market environment are

more likely to pay bribes.33 Wu’s results also show that the legal and regulatory environ-

ment also affects the probability that fi rms pay bribes. Firms that view the court system

as honest are less likely to pay bribes, while fi rms that fi nd licensing requirements to be

problematic are more likely to pay bribes.

Herrera and Rodriguez test for the importance of fi rm characteristics in driving the pro-

pensity to pay bribes and fi nd that multinational fi rms tend to pay fewer total bribes than

local fi rms.34 Larger fi rms were also found to pay fewer bribes than smaller fi rms.

Data, hypotheses and model estimation

This section discusses the data used in the study and summarises the challenges associ-

ated with measuring corruption. It also specifi es the empirical models to be estimated

and presents the regression results. The empirical analysis will be undertaken based on

hypotheses that are guided by the theoretical and empirical literature review, as well as the

availability of data.

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Features 21

Data

The main data sources for this study are the Investment Climate Assessment (ICA) surveys

of the manufacturing sectors in the EAC. The surveys were conducted by the World Bank’s

Regional Program on Enterprise Development (RPED). A broad range of topics are covered

in the ICA surveys, including investment, export participation, infrastructure, access to credit,

the use of courts to resolve disputes and corruption. The data are cross-sectional, having been

collected between 2006 and 2007. The surveys covered 396 Kenyan, 307 Ugandan, and 273

Tanzanian fi rms. The samples were drawn from censuses conducted by National Statistical

Bureaus in each country. To ensure representation of all types of fi rms, the samples were

stratifi ed across location, industry and size. The regions covered in the survey were selected

based on the relatively high concentration of manufacturing fi rms in these areas.

The surveys captured data on infrastructure. Firms were asked how many times in a

month they had (a) power outages and (b) insuffi cient water supply, and how long these oc-

currences lasted on average. They were also asked whether they think (a) telecommunications

and (b) electricity present an obstacle to their current operations. They were given the option

of fi ve responses: no obstacle, minor obstacle, moderate obstacle, major obstacle, and very

severe obstacle.

Data on corruption in general and bribery for infrastructure in particular was also cap-

tured in the surveys. Firms were asked whether they think corruption presents an obstacle

to their current operations. As with infrastructure, they were given the option of fi ve re-

sponses ranging from no obstacle to very severe obstacle. They were also asked whether an

informal payment was requested by public offi cials to obtain (a) a mainline telephone con-

nection, (b) an electrical connection, and (c) a water connection. No data on the amount of

bribes paid are available, making it diffi cult to test theories like the effi ciency enhancement

theory directly.

The measurement of corruption is particularly susceptible to data problems. First, it is a

subjective matter in the sense that the same corrupt activity may be considered as severely

constraining by one fi rm but as inconsequential by another. Second, fi rms may be unwilling

to report whether they have engaged in corruption. These problems are partly solved by the

questionnaire: it does not ask whether fi rms engaged in corruption but whether they made

unoffi cial payments for infrastructure services, which sounds less direct. Third, the meas-

urement of corruption has depended largely on perceptions. Perceptions about corruption in

infrastructure have been found to have only limited information about actual corruption and

to be biased by the underlying beliefs of those providing the information.35

Previous studies also acknowledge some of the problems associated with collecting data on

corruption.36 They agree, however, that the data should be collected by an institution or body

in which the fi rms or the private sector in general has confi dence. The data used in this study

were also collected by bodies that are largely trusted by the fi rms. The data collection exercise

was also helped by the fact that, thanks to organisations like Transparency International, the

World Bank and other international donors, corruption has been desensitised and fi rms are

more willing to talk about it. For example, a number of anti-corruption campaigns have been

carried out across Africa emphasising the effects of corruption and encouraging economic

agents to report on and desist from corruption.

It must also be pointed out that our results must be interpreted with caution since, even

after taking great care to collect the best possible data on bribery, the survey may still have

failed to capture the whole picture on this issue. However, we do not think that exclusion of

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some of the fi rms that failed to provide information on bribery is systematic. For instance,

both small and large African-owned or non-African-owned fi rms can decide not to provide

the information.

Hypotheses

In line with the theoretical and empirical literature, Table 1 presents the hypotheses to be

tested in this study. Data availability is the other determining factor for the choice of hypoth-

eses to be tested.

Table 1 Hypotheses on the determinants of being asked for bribes for infrastructure

Basis for hypothesis Hypotheses

Endogenous harassmentH1: Small fi rms have a greater likelihood of being asked to pay bribes than large fi rms

Excess demandH2: Firms where infrastructure is a major obstacle have a higher propensity of being asked to pay bribes

Institutional environmentH3: Firms that perceive the court system to be corrupt are more likely to be asked to pay bribes

Network effectsH4: Firms that are owned by Africans have a higher propensity of being asked to pay bribes

Capital cities have the highest concentration of fi rms. The frequency with which public offi cials engage with fi rms will be higher in these cities

H5: Firms in capital cities have a higher propensity of being asked to pay bribes

Descriptive statistics

Table 2(a) shows the descriptive statistics for the sample countries. The data show that Kenyan

fi rms have a higher likelihood of being asked to pay bribes than either Ugandan or Tanzanian

fi rms. More Ugandan fi rms tend to face requests to pay bribes compared with Tanzanian

fi rms. This is despite the fact that Kenya has the smallest number of fi rms that stated they

faced infrastructure problems. About 33 per cent of the Kenyan fi rms stated that they faced

problems of infrastructure inadequacy, compared with Uganda’s 48 per cent and Tanzania’s

40 per cent. This is in line with fi ndings by Transparency International, which found that

bribery incidence was highest in Kenya (45 per cent), followed by Uganda (35 per cent) and

lastly Tanzania (17 per cent).37 About 32 per cent of Kenyan fi rms surveyed exported goods,

while about 12 per cent of Ugandan and 13 per cent of Tanzania fi rms exported goods. Most

of the fi rms in the sample were located in the capital cities of the respective countries. About

66 per cent of the Kenyan fi rms were located in Nairobi, while 82 per cent of the Ugandan

fi rms were located in Kampala and 69 per cent of the Tanzanian fi rms were located in Dar

es Salaam.

Table 2(b) shows that the average age of the fi rms surveyed is about 20 years in Kenya,

13 years in Uganda and 13 years in Tanzania. On average, Kenya had the biggest fi rms with

a mean size of 114 employees. Tanzanian and Ugandan fi rms had similar fi rm sizes at about

52 employees.

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Features 23

More details on the data and variables used in the study can be gleaned from the appendix.

Table A1 in the appendix provides the full list of all variables used in the study.

Table 2(a) Descriptive statistics for binary variables

Variable

Kenya Uganda Tanzania

Obs. Mean(proportion) Obs. Mean

(proportion) Obs. Mean(proportion)

BRIBE 452 0.62 307 0.48 272 0.44

LOCATION 453 0.66 307 0.82 273 0.69

OWNER 453 0.9 307 0.88 273 0.89

ETHNIC 450 0.54 307 0.88 273 0.73

EXPORT 453 0.32 307 0.12 273 0.13

SOLETRADER 453 0.21 307 0.4 273 0.32

EXPORT PROCESSING ZONE 453 0.42 307 0.33 273 0.42

COURTS 453 0.49 307 0.63 273 0.64

COMPETITION 453 0.83 307 0.93 273 0.89

FOOD 453 0.25 307 0.29 273 0.26

GARMENT 453 0.2 307 0.02 273 0.19

TEXTILE 453 0.07 307 0.13 273 0.01

MACHINE AND EQUIPMENT 453 0.2 307 0.02 273 0.01

CHEMICAL 453 0.06 307 0.03 273 0.05

WOOD 453 0.11 307 0.3 273 0.22

NONMETAL 453 0.03 307 0.04 273 0.03

Notes BRIBE is a binary variable taking a value 1 if a bribe was requested, 0 otherwise.LOCATION is a binary variable taking a value 1 if fi rm is located in capital city, 0 otherwise.OWNER is a binary variable taking a value 1 if local ownership is above 50%, 0 otherwise.ETHNIC is a binary variable taking a value 1 if the principal shareholder(s) is African, 0 otherwise.EXPORT is a binary variable taking a value 1 if fi rm is exporting goods/services, 0 otherwise.COURTS is a binary variable taking a value 1 if fi rm considers the courts to be effi cient and reliable, 0 otherwise.EPZ is a binary variable taking a value 1 if fi rm is located in an export processing zone, 0 otherwise.SOLETRADER is a binary variable taking a value 1 if the legal status of the fi rm is sole proprietorship, 0 otherwise. COMPETITION is a binary variable taking a value 1 if fi rm faces competition, 0 otherwise.

Source Survey Data

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Table 2(b) Descriptive statistics for continuous variables

Variable Obs. Mean SD Min Max

Kenya

AGE 453 20.03 15.55 0 86

SIZE 453 114.36 272.12 0 2 700

Uganda

AGE 307 12.83 10.12 1 79

SIZE 307 51.64 239.53 5 4 000

Tanzania

AGE 273 13.23 12.09 0 97

SIZE 273 51.99 108.39 5 1 118

Notes AGE is the number of years since fi rm’s inception;SIZE is the number of employees employed by the fi rm.

Source Survey Data

Table 2(c) Percentage of fi rms asked for a bribe

Description Kenya Uganda Tanzania

Percentage of fi rms located in the capital city that were asked for a bribe (Location) 47.17 41.43 40.43

Percentage of locally owned fi rms that were asked for a bribe (Owner) 44.46 42.59 43.21

Percentage of African-owned fi rms that were asked for a bribe (Ethnic) 44.75 41.26 39.50

Percentage of exporting fi rms that were asked for a bribe (Export) 47.00 36.24 51.43

Soletrader 40.42 38.71 34.44

Percentage of fi rms in the export processing zones that were asked for a bribe (EPZ)

45.71 34.95 43.36

Percentage of fi rms that fi nd the court system to be fair that were also asked for a bribe (Courts)

42.16 34.92 38.07

Percentage of fi rms that face competition that were also asked for a bribe (Competition)

43.80 41.18 46.91

Percentage of fi rms in the food sector that were asked for a bribe (Food) 13.91 35.56 44.93

Percentage of fi rms in the garment sector that were asked for a bribe (Garment) 14.13 33.33 31.37

Percentage of fi rms in the textile sector that were asked for a bribe (Textile) 8.82 0.00 0.00

Percentage of fi rms in the machine and equipment sector that were asked for a bribe (Machandequip)

22.22 40.00 50.00

Percentage of fi rms in the chemicals sector that were asked for a bribe (Chemical) 17.86 25.00 53.33

Percentage of fi rms in the wood sector that were asked for a bribe (Wood) 10.42 36.96 40.00

Percentage of fi rms in the nonmetal sector that were asked for a bribe (Nonmetal) 7.14 0.00 50.00

Source Survey Data

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Features 25

Infrastructure problems faced by fi rms in East Africa

This section looks at the extent of infrastructure inadequacy in the sample countries. This

is important as it helps us better understand the heterogeneity of fi rms when it comes to

paying bribes.

There are a number of infrastructural problems faced by fi rms in Kenya, Tanzania and

Uganda. Based on the data, we specifi cally focus on electricity and water provision. The

survey asked a number of questions around infrastructure (see section on data and survey

description).38 About 88,77 per cent of the fi rms in Kenya stated that they experienced power

outages. In Tanzania and Uganda about 79,12 per cent and 94,14 per cent, respectively, stated

that they experienced power outages. It is thus not surprising that a signifi cant number of

fi rms in the sample countries have their own generators. About 55 per cent of the fi rms

in Kenya owned a generator while about 49 per cent and 27 per cent owned a generator in

Tanzania and Uganda, respectively.39

The average number of power outages per month is 6,90 (Kenya), 12 (Tanzania) and 11

(Uganda). The average duration of each outage is 4,45 (Kenya), 7,88 (Tanzania) and 10,01

hours in Uganda. This implies that an average Kenyan fi rm goes without electricity for about

31 hours a month (about 1,3 days), compared with 95 hours in Tanzania (about 4 days) and

111 hours in Uganda (about 4,6 days; see Figure 1 and Table 3).

About 46 per cent of the fi rms in Kenya stated that they experienced incidents of water in-

suffi ciency. The fi gures are 27 per cent and 9 per cent in Tanzania and Uganda, respectively.

In Kenya the average number of such incidents in a month is 6,5, with each lasting for about

14,6 hours. This implies that an average Kenyan fi rm can go without suffi cient water for pro-

duction for about 95 hours a month (or about 4 days). In Tanzania the average number of such

incidents is 12,44, with each lasting for about 12,75 hours. Thus an average Tanzanian fi rm is

likely to go for 158,61 hours (or about 7 days) with insuffi cient water in any given month. The

average number of water insuffi ciency incidents is 3,92 in Uganda, with each lasting 10,66

Source Survey Data

Figure 1 Average duration of power and water interruptions (in hours) per month in Kenya, Tanzania and Uganda

Hou

rs p

er m

onth

180

140

160

120

80

100

60

20

40

0Kenya Tanzania Uganda

Average duration of power outages per month Average duration of water outages per month

Kenya Tanzania Uganda

31

95

111

95

158

42

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hours. This implies that, on a monthly basis, an average Ugandan fi rm can go for about 42

hours (or about 2 days) without suffi cient water (see Figure 1 and Table 3).

Figure 2 shows the indirect cost of power outages as a percentage of sales. Even though fi rms

in Uganda face the highest duration of power outages, most of the fi rms in that country do not

own private generators (only 27 per cent of the Ugandan fi rms owned generators, compared

with 55 per cent in Kenya and 49 per cent in Tanzania). It is thus not surprising that the cost of

power outages is highest in Uganda (10,23 per cent of sales). The respective costs for Kenya and

Tanzania are 6,35 per cent and 9,62 per cent of sales. According to the World Bank, the aver-

age cost due to power outages among sub-Saharan countries surveyed in the RPED surveys is

about 5,8 per cent and 4,9 per cent when other nonregional countries are considered.40

Firms were also asked to identify the key constraints to their operations. Among the con-

straints identifi ed, some related to infrastructure and corruption. Fifty-one per cent of the

fi rms in Kenya stated that electricity was a severe or major obstacle, compared with about 91

per cent in both Tanzania and Uganda. Corruption was also found to be a major or severe

Table 3 Water and power outages data for Kenya, Tanzania and Uganda

CountryNo. of power outages per

month

Ave. duration

per outage (hours)

Ave. duration of power outages month (hours)

No. of water insuffi ciency incidents per

month

Ave. duration

per incident (hours)

Ave. duration of water

insuffi ciency per month

(hours)

Kenya 6.90 4.45 30.71 6.50 14.60 94.90

Tanzania 12.00 7.88 94.56 12.44 12.75 158.61

Uganda 11.00 10.07 110.77 3.92 10.66 41.79

Source Survey Data

Figure 2 Indirect cost of power outages: value lost due to power outages (% sales)

Sale

s (%

)

12

Kenya

Countries

10

8

6

4

2

0Tanzania Uganda

6.35

9.6210.23

Source Survey Data

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Features 27

problem by 45 per cent of the fi rms in Kenya, 23 per cent of the fi rms in Tanzania and 21 per

cent of the fi rms in Uganda (see Table 4).

Empirical models

We test the study hypotheses by estimating probit models. An important limitation of probit

estimation by maximum likelihood, argues Greene, is that it requires a complete specifi cation

of the distribution of the observed random variable.41 In the event that the correct distribution

differs from what is assumed in the study, the likelihood function will be mis-specifi ed, and

the estimator will be misleading. With cross-sectional data, problems such as heteroscedastic-

ity can arise. To account for this the results are based on an estimator due to Huber and

White,42 which is robust to several forms of misspecifi cation error.43

Model

Firms were asked whether an informal payment was requested by public offi cials to obtain

(a) a mainline telephone connection, (b) an electrical connection, and (c) a water connection.

The dependent variable is whether or not fi rms received requests for unoffi cial payments for

any of these infrastructure services. It is a binary variable that equals 1 if fi rms were asked to

make an unoffi cial payment and equals 0 otherwise.44 Firms were not asked for the amount of

the bribe paid. A fi rm’s likelihood of facing a bribe request is estimated using a probit model.

The model is estimated by regressing the probability of being asked for a bribe as a function

of fi rm characteristics. That is,

Bribe propensity = F(age, size, ownership, ethnicity, export, courts, competition, location, sector dummies)

Probit coeffi cients cannot be interpreted in the same way as coeffi cients in standard linear regres-

sion models. They do not equal the marginal impact of the explanatory variables. To gain further

insight into the models, the marginal probability elasticity technique will be used. This gives the

marginal impact on the explanatory variable of a unit change in one variable while holding the

others constant at some value. In the case of discrete variables, marginal effects calculated as the

fi nite changes in these variables as their values change from 0 to 1 will be obtained.

Data analysis and empirical results

The main question we seek to address here is whether fi rms were asked to pay bribes given

that they have requested access to infrastructure.45 To address this question, we regressed

Table 4 Firms reporting major or very severe constraints (%)

Obstacle or constraint Kenya Tanzania Uganda

Telecommunications 22.74 5.13 8.47

Electricity 51.43 91.58 90.55

Transportation 51.88 17.95 30.62

Corruption 45.47 23.08 21.17

Source Survey Data

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our measure of the propensity to be asked for bribes on fi rm characteristics. Firms that do

not request infrastructure connections do not have to pay bribes for infrastructure provi-

sion. Excluding such fi rms can result in sample selection bias. To correct for the problem

of self-selection we used the Heckman procedure. Since both dependent variables in the

selection equation and the equation of interest are dummy variables we used STATA 10’s

Table 5 Bribery results

Explanatory variables Kenya Uganda Tanzania

CONSTANT −13.56*** (−10.26) 2.319*** (3.10) 1.897*** (4.660)

LOCATION 2.09*** (3.61) −0.933** (−2.59) −0.021 (−0.07)

OWNER 11.452*** (7.89) 0.280 (1.02) −0.535* (−1.67)

ETHNIC 0.126 (0.33) −0.231 (−0.78) 0.242 (0.79)

AGE −0.412* (−1.67) −0.154 (−1.53) 0.035*** (2.61)

SIZE 0.278** (2.11) −0.001 (−0.63) −0.004*** (−3.34)

EXPORT −0.806* (−1.72) 0.580 (1.41) 0.882* (1.80)

EPZ 0.133 (0.39) 0.064 (0.29) −0.081 (−0.30)

SOLETRADER 0.321 (0.55) 0.039 (0.16) −0.116 (−0.44)

COURTS −0.874** (−2.52) −0.078 (−0.35) −0.968*** (−10.42)

COMPETITION 0.144 (0.19) 0.172 (0.36) −0.180 (−0.74)

FOOD −0.135 (−0.25) −0.351 (−1.24) 0.404 (1.30)

GARMENT 0.930** (2.22) −0.658 (−1.03) −0.159 (−0.66)

TEXTILE 6.132*** (3.10)

CHEMICAL 5.747*** (6.54) −1.008* (−1.77) 0.702** (1.73)

WOOD −0.324 (−0.37) −0.312 (−1.03) 0.171 (0.59)

NONMETAL 0.449 (0.69)

Observations 392 295 270

Censored 274 224 204

Uncensored 118 71 66

Wald chi2 (13) 21.89** 5963*** 42761.76***

Log pseudo-likelihood −248.08 −190.92 −170.15

t−statistics are in parenthesis and *, ** and *** show levels of signifi cance at 10%, 5% and 1%, respectively. The dependent variable is BRIBE which is a binary variable taking a value 1 if a bribe was requested, 0 otherwise. LOCATION is a binary variable taking a value 1 if fi rms is located in capital city, 0 otherwise. OWNER is binary variable taking a value 1 if local ownership is above 50%, 0 otherwise. ETHNIC is a binary variable taking a value 1 if the principal shareholder(s) is African, 0 otherwise. AGE is the number of years since fi rm’s inception. SIZE is the number of employees employed by the fi rm. EXPORT is a binary variable taking a value 1 if fi rm is exporting goods/services, 0 otherwise. COURTS is a binary variable taking a value 1 if fi rm considers the courts to be effi cient and reliable, 0 otherwise. EPZ is a binary variable taking a value 1 if fi rm is located in an export processing zone, 0 otherwise. SOLETRADER is a binary variable taking a value 1 if the legal status of the fi rm is sole proprietorship, 0 otherwise. COMPETITION is a binary variable taking a value 1 if fi rm faces competition, 0 otherwise.

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Features 29

heckprob command to run the regression. To ensure model identifi cation we also include some

variables that affect the decision to request for connection but need not affect the likelihood

of receiving bribe requests. We chose variables that capture the use of such technology as

internet and fi xed telephones. Firms that use such technology to communicate with clients

and suppliers are more likely to request connection than those that do not.46 The results are as

shown in Table 5. Table 6 shows the corresponding marginal effects.

The main variables that are important when it comes to whether public offi cials request

bribes are location, ethnicity, ownership, exports, courts, age and fi rm size. We fi nd mixed

results when it comes to the location variable: the parameter is positive for the case of Kenya

but negative for the case of Uganda. In the case of Kenya the results indicate that fi rms located

in the capital city are more likely to be asked for bribes compared with those that are located

in other cities or regions of the country. More specifi cally, the probability of fi rms located in

the capital city being requested to pay bribes, compared with those located elsewhere, is higher

by 0,69 (see Table 6, which shows the marginal effects). It is likely that fi rms located in the

capital city tend to interact more often with service providers; thus creating a fertile ground for

bribe solicitation and possibly bribe payments. This positive relationship is signifi cant at 1 per

cent. One possible reason why the location parameter is negative and signifi cant in Uganda is

that the infrastructure to deal with corruption may be more effi cient in the capital city than in

other cities. This may drive corruption out of the capital city of Kampala. The marginal effects

table shows that the probability of being asked to pay bribes by fi rms located in the capital city,

Table 6 Marginal effects

Explanatory variables Kenya Uganda Tanzania

LOCATION 0.691 −0.143 −0.004

OWNER 0.919 0.060 −0.077

ETHNIC 0.049 −0.045 0.048

AGE −0.162 −0.033 0.006

SIZE 0.109 −0.002 −0.001

EXPORT −0.313 0.097 0.109

EPZ 0.052 0.014 −0.015

SOLETRADER 0.123 0.008 −0.022

COURTS 0.332 −0.017 −0.154

COMPETITION 0.144 0.040 −0.031

FOOD −0.053 −0.082 0.067

GARMENT 0.327 −0.190 −0.032

TEXTILE 0.612

MACHINEANDEQUIP 0.707

CHEMICAL 0.584 0.320 0.087

WOOD −0.219 −0.071 0.030

NONMETAL 0.166

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compared with those located elsewhere, is lower by 0,143. It must be noted that the variable of

location may fail to capture the interaction that may occur if most of the requests for infrastruc-

ture provision were made at regional offi ces, outside the capital city. It is possible, however, that

fi rms that fail to get timely access to infrastructure, as is expected in developing countries, can

still approach the headquarters, which are more likely to be located in the capital city.

It is not easy to state whether fi rms owned by locals are more or less likely to be asked

to pay bribes compared with foreign-owned fi rms (multinationals, for example). First, most

multinational fi rms are larger than local fi rms and are thus more visible and have reputational

capital to protect. They also have internal codes of conduct and comprehensive internal pro-

cedures that they can use to minimise corruption. But multinational fi rms are cash-rich and

have resources to pay bribes; they thus have higher willingness to pay.47 They may also be

naive when it comes to local customs and suffer from ‘a liability of foreignness’. It is thus pos-

sible that foreign fi rms may actually pay more bribes than local ones. It is therefore diffi cult

to state categorically what the expected relationship between ownership and bribe payments

would be. We fi nd mixed results on this variable: the OWNER coeffi cient is positive for

Kenyan and Ugandan fi rms but negative for Tanzanian fi rms. The coeffi cients are signifi cant

in the cases of Kenya and Tanzania.

We hypothesised that ethnicity has a potential role in an African setting. Firms that have

an African as a major shareholder are more likely to be asked to pay bribes than those owned

by non-Africans. This is in line with hypothesis 4, which states that Africans tend to know

how to approach other Africans on how to pay bribes. This is important since bribery is il-

legal, so the bribe takers are doing this at the risk of being caught. This implies that bribery

is more likely to occur among people speaking the same language or from the same tribe, for

example. However, we do not fi nd any evidence to support this from the data; the coeffi cient

of the ethnicity variable is insignifi cant across all the countries. This suggests that ethnicity is

not important when it comes to being asked for bribes and possibly payment of bribes.

We fi nd mixed results on the impact of age. Older Kenyan fi rms are less likely to be asked

to pay bribes; older Tanzanian fi rms are more likely to be asked for bribes; and for Ugandan

fi rms age does not seem to matter. An increase in age by 1 year reduces the probability of be-

ing asked to pay bribes by 16,2 per cent in Kenya and increases it by 0,6 per cent in Tanzania

(see the marginal effects in Table 6). The age coeffi cient is signifi cant in Kenya (at 10 per cent

level) and Tanzania (at 1 per cent level).

Size does matter when it comes to being asked to pay bribes. The results show an inverse

relationship between the likelihood of being asked to pay bribes and size in the case of Ugandan

and Tanzanian fi rms. The coeffi cients are however signifi cant in the case of Tanzanian fi rms

(at 1 per cent level). For Tanzanian fi rms, a unit increase in size reduces the probability of being

asked for bribes by 0,1 per cent. The results for Tanzanian fi rms corroborate fi ndings by Clarke

and Xu as well as those by Herrera and Rodriguez, suggesting that smaller fi rms were being

targeted for bribes. There are several reasons why smaller fi rms are more likely to be targeted

for bribes and why they are more likely to pay bribes. First, small fi rms are more likely to be

easily targeted by predatory offi cials seeking bribes. According to Wu, the small fi rms also lack

power to resist such offi cials’ demands.48 Second, argues Wu, smaller fi rms, because they are less

visible and do not ordinarily attract much attention from law enforcers, can easily participate

in bribery. Third, small fi rms, unlike larger fi rms that have comprehensive systems to control

fraud, are less likely to have such robust internal protocol.49 On the other hand, larger fi rms are

more likely to be infl uential and individually important in the economy so that they may actu-

ally be less vulnerable to the demands of predatory offi cials soliciting for bribes.50 According to

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Features 31

Clarke and Xu, the predatory offi cials use information on these fi rm characteristics to propose

incentive-compatible bribe levels to the targeted fi rms.51 We fi nd a positive relationship, how-

ever, between size and the probability of being asked to pay bribes in Kenya. The size coeffi cient

is positive and signifi cant at the 5 per cent level, indicating that bigger Kenyan fi rms are more

likely to be approached for bribes compared with smaller ones.

We also investigated whether improving the court system by making them fair and

impartial increases the likelihood that public offi cials will request bribes. Across all the

sample countries, improved and fair court systems tend to reduce the propensity to ask

fi rms for bribes; the coeffi cients of COURTS (a variable that takes a value 1 if the courts

Table 7 Pooled regression bribery payment

Explanatory variables 1 2 3

CONSTANT 0.929** (2.56) −1.849*** (−2.70) −2.221*** (−4.22)

TANZANIA‡ −0.492* (−1.85) −0.571*** (−2.77) −0.582*** (−2.74)

UGANDA −1.121*** (−3.51) −1.337*** (−4.83) −1.1952*** (−3.59)

LOCATION 0.787*** (3.49) 0.741*** (3.87)

OWNER 0.216 (0.71) 0.298 (0.96)

ETHNIC 0.085 (0.42) 0.151 (0.81)

AGE 0.005 (0.78) 0.003 (0.44)

SIZE −0.004 (−0.99) −0.0002 (0.44)

EPZ 0.138 (0.80) 0.32 (0.84)

SOLETRADER −0.041 −0.21 −0.068 (−0.39)

COURTS 0.159 (0.89) 0.187 (1.19)

COMPETITION 0.618 (1.65) 0.470 (1.36)

GARMENT 0.316 (1.27)

TEXTILE 6.572*** (10.88)

MACHINEANDEQUIP 5.356*** (4.82)

CHEMICAL 0.547* (1.66)

WOOD 0.249 (1.11)

NONMETAL −0.090 (−0.24)

Observations 962 962 962

Censored 697 697 697

Uncensored 265 265 265

Wald chi2 (13) 13.56*** 49.48*** 361.63***

Log pseudo-likelihood −699.82 −692.13 −680.30

t-statistics are in parenthesis and *, ** and *** show levels of signifi cance at 10%, 5% and 1%, respectively. The dependent variable is BRIBE, which is a binary variable taking a value 1 if a bribe was requested, 0 otherwise. ‡ Kenya is the reference country

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are considered fair and impartial, but 0 if they are not) are all negative and signifi cant in the

case of Kenya and Uganda.52 The results, which confi rm our hypothesis that an improve-

ment in African court systems reduces the incidence of bribery, also corroborate fi ndings

by Wu.53 For all fi rms across the sample countries, we did not fi nd any evidence on the im-

portance of export-processing zones, competition and type of business ownership (whether

sole trader or not).

Table 7 shows the regression results for the pooled data. Column 1 shows the regression of

the likelihood to receive bribe requests on country dummies. The aim here is to use this as a

baseline equation. Can we, for example, tell a fi rm’s propensity to be asked for bribe payments

just by looking at its nationality? Column 2 shows the results of the regression of the depend-

ent variable on fi rm characteristics, excluding sectoral dummies. Column 3 shows the results

when all variables are included. The pooled results in column 1 show that compared with

Kenyan fi rms, Tanzanian and Ugandan fi rms are less likely to be requested to pay bribes.

More specifi cally, the probability that an average Kenyan fi rm will be asked to pay bribes

exceeds that of Tanzanian fi rms by 0,18 (see results in Table 7 and the marginal effects in

Table 8). The likelihood that Ugandan fi rms are requested to pay bribes is smaller than that

of Kenyan fi rms by 0,42. This is in line with the Transparency International survey, which

shows that corruption is higher in Kenya than in Tanzania and Uganda.54 The coeffi cients

on the country dummies are negative and signifi cant for both Tanzania and Uganda. Such

Table 8 Marginal effects (pooled regression)

Explanatory variables 1 2 3

TANZANIA −0.184 0.145 −0.158

UGANDA −0.415 −0.301 −0.297

LOCATION 0.190 0.195

OWNER 0.061 0.091

ETHNIC 0.024 0.045

AGE 0.001 0.001

SIZE −0.0001 0.0005

EPZ 0.040 0.040

SOLETRADER −0.011 −0.020

COURTS 0.045 0.056

COMPETITION 0.135 0.119

GARMENT 0.103

TEXTILE 0.838

MACHINEANDEQUIP 0.800

CHEMICAL 0.191

WOOD 0.080

NONMETAL −0.026

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Features 33

a relationship is stronger with the inclusion of other variables as shown in columns 2 and 3.

For example, in the case of Tanzania the country dummy was, as shown in column 1, nega-

tive and signifi cant at the 10 per cent level; after controlling for sectoral dummies and fi rm

characteristics the country dummy coeffi cient is now signifi cant at the 1 per cent level (as

shown in column 3).

The location of the fi rm tends to affect the propensity to be asked for bribes. Columns 2

and 3 show that being located in the capital city tends to increase the probability that fi rms

are asked to pay bribes. This may be due to the increased interaction between government

offi cials and fi rm representatives. Table 8 shows the marginal effects that correspond to the

pooled regression results.

Conclusion and policy implications

This paper investigated the type of fi rms that are requested to pay bribes in order to access

infrastructure. The paper started by accessing the problems of infrastructure that fi rms in

East Africa face. Firms that face severe problems when it comes to infrastructure can pay

bribes to jump the queue and quickly get access.55 The two main questions we tried to answer

are: Which fi rms are facing severe infrastructure problems? Are such fi rms being asked to pay

bribes to access infrastructure? Comparing the three sample countries, we found that Uganda

is the country where the problems of infrastructure are most severe, followed by Tanzania

and then Kenya. About 48 per cent of the fi rms in Uganda were considered to be facing severe

infrastructural problems, Tanzania had 40 per cent of the fi rms and Kenya had 33 per cent of

the fi rms.

The results indicate that Kenyan fi rms are more likely to be asked to pay bribes than

fi rms in Uganda and Tanzania. This is despite the fact that Kenyan fi rms do not have severe

infrastructure problems as compared with Tanzanian and Ugandan fi rms. The descriptive

statistics showed corruption to be more rampant in Kenya than the other sample countries.

One way of interpreting the less severe infrastructure constraints in Kenya is that the greater

incidence of corruption ensures better access to infrastructure. Corruption is playing a greater

part in raising the cost of doing business in Kenya, but at the same time allowing Kenyan

fi rms to have better access to infrastructure than their Tanzanian and Ugandan counterparts.

This supports the enhancement of effi ciency theory.

The results also indicate that fi rms that are located in the capital city, and thus close to the

bribe-takers, are more likely to be asked to pay bribes. This is probably because government

offi cials tend to interact more often with the offi cials of fi rms located closer to the capital city

than those in other areas; creating an environment conducive for corruption. We found that

ownership does not seem to affect the propensity to be asked for bribes. Whether a fi rm is

domestically owned or a multinational does not seem to matter when it comes to being asked

for bribes. Mixed results were obtained on the impact of age. Older fi rms in Tanzania are

more likely to be asked for bribes than younger ones. This may be explained by the fact that

older fi rms are better known by public offi cials than younger fi rms. However, in Kenya it is

younger fi rms that have a higher propensity to be asked to pay bribes.

We also fi nd mixed results when it comes to the relationship between being asked to pay

bribes and fi rm size. In the case of Kenyan fi rms, we fi nd a positive relationship between size

and the probability of being asked to pay bribes. Larger fi rms in Kenya are more likely to

have resources to pay bribe-takers. By contrast, Tanzanian fi rms have a negative relationship

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between fi rm size and the probability of being requested to pay bribes. This supports the

endogenous harassment theory whereby bribe-takers view smaller fi rms as easier targets.

The fact that improved and effi cient court systems were found to reduce the likelihood of

public offi cials requesting bribes in East Africa suggests that there are economic benefi ts to im-

proving these institutions. An effi cient court system together with a professional police force,

by increasing the probability of catching the perpetrators of corruption, increases the expected

costs of participating in corrupt activities thus reducing the net payoffs of such activities.

It is important for the governments in the EAC to ensure that as the region becomes more

economically integrated there is no importation of corruption from one country where it is

rife to another where it is low. More important for Kenya, where corruption is highest, is

the fact that, if corruption is considered to be more harmful than taxes, as found by Fisman

and Svensson, it may be possible that fi rms may relocate to another country.56 For example,

multinational fi rms that would normally locate in Kenya may strategically locate in Tanzania,

produce goods and then export them to Kenya.

Corruption is a multifaceted problem requiring a multi-pronged approach to reduce it.

African governments – especially those in the sample countries – must therefore introduce

effective anti-corruption systems that punish corruption perpetrators while rewarding

whistleblowers. This is necessary if corruption is not to morph into an endemic problem.

The infrastructure defi cit in most African countries must also be reduced not only to lessen

corruption but also to spur economic growth. Findings by Clarke and Xu indicate that more

bribes are paid in countries with lower levels of competition in the utilities sector and where

utilities are state-owned. The defi cit and the concomitant corruption can thus be reduced by

introducing competition in the utilities sector as well as facilitating private–public partner-

ships in the provision of infrastructure. This can be done while still protecting society’s most

vulnerable, who may lose out from the profi t-motivated private entrants.

One limitation of the current study is that it is based on cross-sectional data and thus

fails to capture the dynamic relationship between the parties involved in bribery (the bribe-

taker and bribe-giver). Future research needs to address such data defi cits and better explore

such a relationship, with a view to breaking the umbilical cord that binds them in this odious

relationship. Also, to understand better the magnitude of the problem, future research should

focus on the amounts of bribes changing hands between bribe-takers and bribe-givers. The

study, because it uses cross-sectional data, fails to capture the dynamic interaction between

parties involved in bribery. For example, bribery that occurs after the provision of infrastruc-

ture is not captured. Hence, future research examining such dynamic interactions should be

conducted. This can be done by conducting more specifi c surveys. Panel data that use the

same World Bank survey instrument as used in this study can also facilitate a better under-

standing of this problem of corruption.

Finally, there are several additional areas of study that can be explored. For example, how

institutionalised are bribery practices in the EAC? Is corruption by bribe-takers competitive

or institutionalised? The extant literature argues that competitive corruption, unlike mo-

nopolised corruption in which the bribe-taker also cares of the goose that lays the eggs, tends

to be destructive so as to drive the fi rms underground or force them out of business. Also,

the prevalence of corruption in state-owned utilities inevitably reignites the debate around

privatisation. Can privatisation and the introduction of several fi rms offering infrastructural

services like water, electricity and telecommunications help reduce corruption? An increase

in the number of fi rms in the sector implies that consumers can rationally move away from

those fi rms demanding bribes for a service.

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Features 35

Acknowledgements

Comments made during the African Economic Research Consortium (AERC) conferences

are acknowledged. We also thank the AERC for funding the research project.

Appendix

Table A1 Data descriptions

Variable Description

BRIBE Binary variable taking a value 1 if a bribe was requested, 0 otherwise

OWNER Binary variable taking a value 1 if local ownership is above 50%, 0 otherwise

ETHNIC Binary variable taking a value 1 if the principal shareholder(s) is African, 0 otherwise

AGE Number of years since fi rm inception

SIZE Number of employees employed by the fi rm

EXPORT Binary variable taking a value 1 if fi rm is exporting goods/services, 0 otherwise

TANZANIA Country dummy

UGANDA Country dummy

COURTS Binary variable taking a value 1 if the courts are considered fair, impartial and uncorrupted, 0 otherwise

EPZ Binary variable taking a value 1 if fi rm is located in an export processing zone (EPZ), 0 otherwise

LOCATION Binary variable taking a value 1 if fi rm is located in capital city, 0 otherwise

SOLETRADER Binary variable taking a value 1 if the legal status of the fi rm is sole proprietorship, 0 otherwise

COMPETITION Binary variable taking a value 1 if fi rm faces competition, 0 otherwise

FOOD Industry dummy taking a value 1 if fi rm is in the food sector, 0 otherwise

GARMENT Industry dummy taking a value 1 if fi rm is in the garment sector, 0 otherwise

MACHANDEQUIP Industry dummy taking a value 1 if fi rm is in the machinery and equipment sector, 0 otherwise

TEXTILE Industry dummy taking a value 1 if fi rm is in the textile sector, 0 otherwise

CHEMICAL Industry dummy taking a value 1 if fi rm is in the chemical sector, 0 otherwise

WOOD Industry dummy taking a value 1 if fi rm is in the wood sector, 0 otherwise

METALLIC Industry dummy taking a value 1 if fi rm is in the metallic sector, 0 otherwise

NONMETAL Industry dummy taking a value 1 if fi rm is in the nonmetallic sector, 0 otherwise

Bribe propensity = F(age, size, ownership, ethnicity, export, courts, competition, location, sector dummies)

Notes

1 T S Aidt, Economic analysis of corruption: a survey, Economic Journal, 113(491) (2003), 632–652.

2 L Ndikumana, Corruption and pro-poor growth outcomes: evidence and lessons from African countries, Research

Paper, University of Massachusetts, 2006.

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3 Transparency International, Corruption Perception Index, 2011, http://cpi.transparency.org/cpi2011/results/ (accessed 22

June 2012).

4 The fi gures were calculated as the proportion of those who interacted with a government organisation and a bribe was

expected of them divided by the total number that interacted with government organisation.

5 Transparency International, The East African bribery index, Kenya: Transparency International Kenya, 2009.

6 See, for example: P Bardhan, Corruption and development: a review of issues, Journal of Economic Literature 35 (1997),

1320–1346; V Tanzi and H Davoodi, Corruption, public investment and growth (Working Paper No. 97/139), International

Monetary Fund, Washington DC, 1997; B A Olken, Corruption perceptions vs corruption reality, Journal of Public

Economics 93(7) (2009), 950–964.

7 C Calderon and L Serven, Infrastructure and rconomic development in sub-Saharan Africa (Policy Research Working Paper No.

4712), Washington, DC: World Bank, 2008.

8 T Yepes, J R Pierce and V Foster, Making sense of Africa’s infrastructure endowment: a benchmarking approach (Policy Research

Working Paper No. 4912), The World Bank, Washington DC, 2009.

9 A M Herrera and P Rodriguez, Bribery and the nature of corruption (Working Paper), Department of Economics, Michigan

State University, 2003.

10 Jakob Svensson and Raymond J Fisman, Are corruption and taxation really harmful to growth? Firm-level evidence, November

2000 (Policy Research Working Paper No. 2485), World Bank, Washington DC. Peter Kimuyu, Corruption, fi rm

growth and export propensity in Kenya, International Journal of Social Economics, 34(3) (2007), 197–217.

11 X Wu, Determinants of bribery in Asian fi rms: evidence from the World Business Environment Survey, Journal of

Business Ethics, 87(1) (2009), 75–88.

12 See G Myrdal, Asian drama: an inquiry into the poverty of nations, New York: Pantheon Books, 1968; D Kaufmann and S

J Wei, Does ‘grease money’ speed up the wheels of commerce? (Policy Research Working Paper No. 2254), Washington, DC:

World Bank, 1999.

13 Kaufmann and Wei, Does ‘grease money’ speed up the wheels of commerce?

14 It is also possible that public institution employees can deliberately undersupply some services or create bottlenecks in

the system. For example, in the 1980s in Indonesia’s Jakarta there were 1200 taps serving water to 2.5 million people

(Charles Kenny, Infrastructure governance and corruption: where next?, 1 August 2007 (World Bank Policy Research Working

Paper No. 4331), The World Bank, Washington DC). According to Kenny, it is possible that such a small number of taps

was aimed at increasing rents for corrupt utility staff.

15 C W Gray and D Kaufman, Corruption and development, Finance and Development (1998), 7–10.

16 D Lederman, N V Loayza and R R Soares, Accountability and corruption: political institutions matter, Economics and

Politics 17(1) (2005), 1–35.

17 Bardhan, Corruption and development.

18 Wu, Determinants of bribery in Asian fi rms.

19 A M Ali and H S Isse, Determinants of economic corruption: a cross-country comparison, Cato Journal 22 (2003), 449–466.

20 M Wrong, It’s our turn to eat: the story of a Kenyan whistleblower, London: Fourth Estate, 2009.

21 The campaign against corruption has also gained momentum. For example, in 1993 Transparency International was

formed. This was followed by an anti-corruption convention by the OECD in 1997 (The OECD Convention on

Combating Bribery of Foreign Offi cials in International Business Transactions in 1997). In 2003, the United Nations

came up with the United Nations Convention Against Corruption.

22 See, for example: Paolo Mauro, Corruption and growth, Quarterly Journal of Economics, 110(3) (1995), 681–712; Philip

Keefer and Stephen Knack, Does social capital have an economic payoff? A cross-country investigation, Quarterly Journal

of Economics, 112(4) (1997), 207–227; Aymo Brunnetti, Gregory Kisunko and Beatrice Weder, Credibility of rules and

economic growth: evidence from a worldwide survey of the private sector (Policy Research Working Paper Series 1760), World

Bank; Kwabena Gyimah-Brempong, Corruption, economic growth and income inequality in Africa, Economics of

Governance, 3(3) (2002): 183–209.

23 C Queiroz and A Visser, Corruption, transport infrastructure stock and economic development (Research Paper), World Bank,

Washington DC, 2001.

24 Tanzi and Davoodi, Corruption, public investment and growth.

25 L Lovei and A McKechnie, The costs of corruption for the poor: the energy sector (Note No. 207, Public Policy for the Private

Sector), World Bank, Washington DC, 2000.

26 A Estache and E Kouassi, Sector organization, governance, and the ineffi ciency of African water utilities (Policy Research Working

Paper Series 2890), World Bank, Washington DC, 2002.

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Features 37

27 A Estache, A Goicoechea and L Trujillo, Utilities reforms and corruption in developing countries, Utilities Policy 17(2)

(2009), 191–202.

28 Herrera and Rodriguez, Bribery and the nature of corruption.

29 J Svensson, Who must pay bribes and how much? Evidence from a cross-section of fi rms, Quarterly Journal of Economics

118(1) (2003), 207–230.

30 G R G Clarke and L C Xu, Privatization, competition, and corruption: how characteristics of bribe takers and payers

affect bribes to utilities, Journal of Public Economics 88(9–10) (2004), 2067–2097.

31 R Fisman and J Svensson, Are corruption and taxation really harmful to growth? Firm level evidence, Journal of

Development Economics 83(1) (2007), 63–75.

32 Kimuyu, Corruption, fi rm growth and export propensity in Kenya.



35 Olken, Corruption perceptions vs corruption reality.

36 See, for example, Kimuyu, Corruption, fi rm growth and export propensity in Kenya; Fisman and Svensson, Are

corruption and taxation really harmful to growth?

37 Transparency International, The East African bribery index.

38 The questions asked included whether the fi rms experienced power outages in the previous year, how many times this

occurred and how long each occurrence lasted, as well as the annual losses due to the power outage (as a percentage

of sales). When it comes to water supply, the questions asked include: Did the establishment experience incidents of

insuffi cient water supply for production? How many times in a typical month did the fi rm experience these incidents of

water insuffi ciency? How long did each occurrence last?

39 For policy-making purposes the presence of a private generator or private borehole should be considered to be a sign of

infrastructural constraints.

40 World Bank, An assessment of the investment climate in Kenya, Washington, DC: World Bank, 2009.

41 W H Greene, Econometric analysis, 5th ed, Upper Saddle, NJ, Prentice Hall, 2003.

42 H White, A heteroscedasticity – consistent covariance matrix estimator and a direct test for heteroscedasticity,

Econometrica 48(4) (1980), 817–838.

43 P Huber, The behaviour of maximum likelihood estimates under nonstandard conditions, in Lucien M. Le Cam and

Jerzy Neyman, Proceedings of the Fifth Berkeley Symposium in Mathematical Statistics Vol. 1, Berkeley, CA: University of

California Press, 1967, 221-233.

44 The dummy variable takes a value 1 if a fi rm was asked for an informal payment to get connected to at least one of the

following services: mainline telephone connection, electrical connection and water connection. It takes a value 0 if no

request for a payment was made.

45 First, fi rms were asked whether they had requested for water, electricity or telephone connection. Second, they were

asked whether a bribe was requested.

46 We also ran a regression of the propensity to pay bribes on the variables that capture internet and website usage and

found them not to affect signifi cantly the decision to pay bribes.

47 See, for example, Herrera and Rodriguez, Bribery and the nature of corruption, and Clarke and Xu, Ownership, competition,

and corruption.


49 For more details, see: Herrera and Rodriguez, Bribery and the nature of corruption; Wu, Determinants of bribery in Asian

fi rms.


51 Ibid.

52 It must be noted that the COURTS variable may endogenous, so the results should be interpreted with caution.


54 Clarke and Xu, Privatization, competition, and corruption: how characteristics of bribe takers and payers affect bribes to

utilities.

55 Lui, An equilibrium queuing model of bribery, Journal of Political Economy 93(4) (1985), 760–781.

56 Fisman and Svensson, Are corruption and taxation really harmful to growth?

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Determinants of bribery for infrastructure provision in East African manufacturing firms

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