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2019 Cambridge Business & Economics Conference ISBN : 9780974211428

The Impact of Corruption on FDI:

Evidence from Africa

Neerputh D and Seetanah B

University of Mauritius

Abstract

This paper investigates the impact of corruption on foreign direct investment for a

sample of 30 countries over the period 1998 to 2016 using a Panel Vector Error

Correction Model (PVECM). The study supplement the literature as it accounts for the

possibility of dynamics and endogeneity in the corruption-FDI nexus. Moreover, the

research dwells into an analysis of possible reverse causal, indirect or mediating effect

of corruption on FDI, elements so far overlooked in the literature.Findings show that

corruption is a hurdle in attracting FDI inflows among African countries in the long-

run, although to a relatively lower extent as compared to the other factors affecting

FDI. The Impulse Response Function and Variance Decomposition analysis are also

consistent with the VECM results. Interestingly, corruption is reported to have some

indirect effects on FDI as it allow negatively influences economic growth, trade level

and private investment which in turn are seen to have an impact on FDI. Finally, our

analysis confirms the presence of a negative reverse causal effect from FDI to

corruption.

Keywords: Foreign Direct Investment (FDI), Corruption, PVAR, Africa.

November 24-25, 2019Cambridge, UK 1


1. INTRODUCTION

The African continent possesses abundant natural resources and a population of 1.6

billion in 2016 but yet struggles to attract Foreign Direct Investment. The Africa

Investment Report (2017) reveals that FDI inflows to Africa accounted for a meagre

12% of world FDI flows in 2016 and project numbers accounting for only 5% of global

projects. Corruption which is deeply rooted in Africa where governments have often

committed serious human rights violations may be “the” reason of low FDI inflows in

African countries. Widespread corruption in Africa is vastly undermining the huge

potential of African countries where resources are largely unexploited in some regions

while overexploited in others. Besides, FDI inflows in Africa is concentrated mostly in

countries possessing valuable natural resources like coal, oil and natural gas, where 91%

of the total capital invested and 78% of total projects were directed towards the top 10

destination countries for FDI in Africa in 2016 (Africa Investment Report, 2017).

Although the general perception is that corruption is harmful and is most likely to deter

FDI in a country, previous studies have shown that the impact of corruption on FDI

inflows is subject to much ambiguity which has led to the formation of two main

theories; the “grabbing hand” or “sand in the wheel of commerce” theory (see Barassi

and Zhou, 2012;Azam et al. ,2013;Yong et al., 2015) and the “helping hand” or “grease in

the wheel of commerce” theory (Henisz, 2000; Cuervo-Cazurra, 2006 and Wignall and

Roulet 2017 among others). Supporters of the “grabbing hand” theory are of the view

that corruption acts as a barrier to FDI inflows which is mostly supported in developed

countries while on the other hand, advocates of the “helping hand” theory suggest that

corruption can be helpful in attracting FDI inflows which is mostly believed to occur in

developing countries. There are also a number of studies which could not establish any

impact of corruption on FDI (Wheeler and Mod, 1992; Akcay, 2001 andBayar and

Alakbarov, 2016 among others)

Empirical evidence pertaining explicitly to corruption and FDI in Africa is relative

scarce and moreover is inconclusive. For instance while studies from Asiedu (2006),

Abotsi(2016) and more recently Epaphra and Massawe (2017) showcased that

corruption acts as a “grabbing hand” by reducing FDI, a few studies validated that

corruption was likely to increase FDI (see Quazi et al. ,2014 and Hakimi and Hamdi,

2017). On the other hand Calvo and Reinhart (1997) and Ardiyanto (2012) showed that

corruption is insignificant in affecting FDI inflows.



It is noteworthy that existing research, in addition to being inconclusive on the overall

and to have been scant for the African continent, has largely been based on static panel

data analysis, ignoring to a large extent the possibilities of dynamism and endogeneity

in the corruption-FDI nexus. Similarly the possible existence of indirect effects of

corruption on FDI has also been overlooked. The principal aim of this study thus is to

empirically assess the impact of corruption on FDI inflows in Africa. It is believed to

contribute to the literature by addressing the above potential gaps in the literature

byadopting a dynamic panel data approach, namely a Panel Vector Error Correction

Model (PVECM) based on 30 African countries spanning over the period 1998 to 2016.

Such a framework also allows the analysis of short and long run effects.Our research

also use Transparency International’s Corruption Perception Index as measure of

corruption contrary to many previous studies and it is a more realistic indicator of

perceived corruption in the public sector since it is a weighted score based on various

sources of data and will therefore give us more realistic results.

The rest of the paper is organised as follows: section 2 dwells into the theoretical

underpinnings and related empirical evidences, section 3 discusses the methodology as

well as provide and analysis of the empirical results while section 4 concludes.

2. LITERATURE REVIEW Theoretical BackgroundTheories on FDI

Over the years, various notable theories on FDI have been established among which

exists the monopolistic advantage theory, developed by Hymer (1976) who argue that

foreign firms need to possess specific advantages over local firms for FDI to occur. The

transaction cost and internalisation theory (Coase, 1937; Buckley and Casson, 1976)

posits that transaction costs arising during production and exchange are more efficiently

controlled within the organisation of a firm. However, the most famous work on FDI

remains the Eclectic Paradigm (Dunning1979, 1988, and 1993) which identifies the

three main motivating factors offoreign production as; ownership advantages, location

advantages and internalisation efficiencies. Dunning’s view is that a company can

engage in FDI only when these three conditions are concurrently satisfied. In the last

decades, the element of location advantages has gained renewed consideration as a



result of growing magnitude and geography of MNCs activities (Dunning, 1998).

Hence, the modern FDI theory is a mixture of the; ‘where’ aspect of FDI and the ‘why’1

aspect of FDI (Buckley, 1976). The ‘where’ factors of FDI include corruption which

have often received limelight lately and should not be left out amid the descriptors of

the attractiveness of a location. Besides, when internalising, MNCs are required to deal

with extra costs of doing business abroad (Hymer, 1976) and some of these costs

include dealing with the host country’s corruption (Calhoun, 2002).

Corruption and Foreign Direct Investment

The “Grease in the wheel of commerce” or “Helping hand” Theory

There exist two streams of thought for the impact of corruption on FDI; the “grease in

the wheel of commerce” or “helping hand” theory and the “sand in the wheel of

commerce” or “grabbing hand” theory. On one hand, corruption raises transaction costs

and risks thereby reducing FDI inflow and so depicted as sand. On the other hand,

corruption helps to circumvent superfluous costs of operation in rigid egalitarian

regimes thereby inducing FDI inflow and so depicted as grease (Cuervo-Cazurra, 2008).

Lui (1985) supports the efficient grease hypothesis by advancing that “speed money”

allows investors to circumvent bureaucratic delays. In his model, he demonstrates that

the value of waiting costs is minimised by paying bribes to have decisions made more

quickly, hence increasing efficiency of firms and greasing the wheels of commerce. In a

similar inclination, Beck and Maher (1986) showed in their bribery model that, the most

efficient firms could afford to bid the highest bribe and win contracts in bidding

competition. Hence, by allocating projects to the most efficient firms, bribes foster

efficiency. This view was furthered by Lien (1986) who postulates that efficiency in the

awarding process is not lost since the most efficient firm is assigned the contract.

Therefore, corruption acts as a “helping hand” rather than a “grabbing hand”.

According to Boddewyn (1988) and Boddewyn and Brewer (1994), without corruption,

some benefits such as obtaining official permits and escaping cumbersome bureaucratic

procedures during firm establishment and registration could not be obtained by

investors. Such benefits may increase bureaucratic efficiency (Bardhan, 1997) and

therefore promote inward FDI. Saha (2001), Glass and Wu (2002) and Egger and

Winner (2005) are also of the view that in the presence of rigid regulations and controls,

1“where” represent the location factors while “why” denotes the internalization factors. November 24-25, 2019Cambridge, UK 4


corruption may act as a helping hand in the country. Similarly, corruption is found to

hurt FDI inflows in developed economies while it is rather beneficial in developing

countries (Ardiyanto, 2012).

The “Sand in the wheel of commerce” or “Grabbing hand” Theory

Foreign investors are often reluctant to invest in highly corrupted countries as they

perceive corruption as immoral. Many highly corrupted African countries are poor

economies, barely growing and receiving a dwarf’s share of global FDI (WIR, 2017).

“Honesty has its price, however, if it means inability to compete in some markets”

(Habib and Zurawicki, 2002). Therefore, corruption may act as a “grabbing hand” or

sand the wheels of commerce. Wei (2000) argues that corruption raises the cost of doing

business by acting like a tax on FDI. According to him, corruption is more detrimental

to firms than tax as unlike the latter, it is opaque and it is subject to weaker enforcement

agreement between the briber and the bribee (Wei, 1997). In the same vein, Shleifer and

Vishny (1993) advocate that corruption is illegal and requires secrecy which makes it

more expensive and distortionary than taxation. Moreover, corruption creates

opportunity for more bribes until the bribe payment is effected (De Soto, 1989). This, in

turn leads to firms paying more bribes and wasting more time dealing with officials

(Kaufmann and Wei, 1999). Therefore, the intended “grease effect” may not materialise

due to the greed of corrupt bureaucrats (Tanzi, 1998).

While risk reduction is a common objective when operating at international level

(Boddewyn, 1988), risks and costs of non-compliance rise when bribery becomes a

necessity for potential investors (Drabek and Payne, 2001). Therefore, investors may be

reluctant to join a corrupt country due to fear of punishment (Rose-Ackerman, 1999).

Kaufmann et al. (1999) further add that TNCs will undertake these additional risks only

if returns are sufficient. Moreover, according to Habib and Zurawicki (2002), it is less

costly for MNCs to gain a similar market value in other competitive market compared to

a corrupt country as the latter “does not provide open and equal market access to all

competitors”. Hakkala et al. (2008), similarly uphold that corruption reduces the

likelihood that MNCs will invest in a nation as bribery is costly. They also advance that

corruption contorts competition as local firms have an edge in bribing. Gani (2007) puts

forward that corruption is a deterrent of FDI and that countries with strong regulations,

effective government and political stability will induce FDI. His view is consistent with

Drabek and Payne(2001) who argue that the presence of robust legal provisions to



combat bribery and their effective enforcement will be beneficial in attracting inward

FDI flows.

Corruption leads to inefficient resource allocation geared towards areas that are more

prone to bribery (Mauro, 1998) and high start-up procedures for new firms in order to

extract more rents by corrupt officials (Djankov et al., 2002) and thus keeps potential

investors away. Theoretical underpinnings by Abotsi (2014) suggests that where

corruption is pervasive, MNEs are unable to exploit their ownership, location and

internalisation advantages. According to the author, firms’ inability of minimising

transaction costs with corruption, make African countries unattractive for FDI inflows.

Firms often seek to escape poor business environments in highly corrupted countries

towards host countries with better institutions (Di Guardo et al., 2016). Barnard (2014)

postulates that corrupt countries are unattractive to FDI because firms are less likely to

invest in technology and skills in those economies. Barassi and Zhou (2012), Sanyal and

Samanta (2008) and Al-Sadig (2009) also suggest that corruption has an adverse effect

on FDI and that it acts more like a “grabbing hand” than a “helping hand” for inward

FDI. If FDI were to occur in highly corrupt countries, it would be more likely through a

local joint venture so as to reduce transaction costs dealing with public officials

(Javorcik and Wei, 2009)

The “Third Opinion”

Previous studies show that there exist a “third opinion” on the impact of corruption on

FDI where corruption can be both a “helping hand” and a “grabbing hand” depending

on various instances. Pioneering work from Shleifer and Vishny (1993) differentiates

between corruption with theft and without theft and their contradictory effect on FDI.

They posit that corruption without theft increases costs and deters FDI since the sum

paid is the official government price and bribes. Contrariwise, corruption with theft

reduces costs as the sum amount is only the bribes which are less than the government

official price. Therefore, in the latter case, corruption in fact promotes FDI as it greases

the wheel of commerce rather than sanding them. Caetano and Caleiro (2005) unveil the

presence of two well defined clusters: one consists of highly corrupted countries, where

corruption deters FDI while the other cluster is comprised of lowly corrupted countries ,

where there is no evident link between corruption and FDI. Low levels of corruption

grease commerce while at high levels, corruption is no longer favourable (Cuervo-

Cazurra, 2008). Similarly, Barassi and Zhou (2012) advocate that corruption affects



investors’ FDI choices differently. According to the authors, though corruption inhibits

FDI, once a country has been chosen as a host country, a rise in corruption rate in that

country would not impede FDI. Hence, corruption obstructs FDI inflows for countries

having a low share of FDI distribution while countries in the top quantiles such as China

and India do not observe this negative effect despite having high corruption Index.

Cuervo-Cazurra (2006) provides a different perspective of corruption with respect to

FDI. He argues that investors from countries with high level of corruption may be

undeterred by host-country corruption but may in fact be even attracted by it for two

main reasons. Firstly, given they come from highly corrupted countries, they probably

already know how to best deal with corruption (Ades and Di Tella, 1997) and face

lower cost in the host country. Secondly, similar institutional conditions in their home

country means that they are close to the host country in terms of psychic distance and

therefore more likely to expand there than in more distant countries (Johanson and

Wiedersheim-Paul, 1975; Johanson and Vahlne, 1977). Cuervo-Cazurra (2006)

ironically posits that strong regulatory system of host countries keep investors from

highly corrupted countries away as “these laws increase the cost of engaging in bribery

abroad”.

Empirical Review

Earlier studies on Corruption were based on its impact on economic growth and such a

relationship was first analyzed by the seminal work of Leff (1964) who found a close

linkage between corruption and economic growth. Since then, several papers have been

conducted to assess the real effects of corruption (Baylay 1966, Nye 1967, Rose-

Ackerman 1978, Heidenheimer 1970, Kaufmann, 1997). The studies have provided

inconclusive results on the impact of corruption on growth.Over the past decades, other

studies looked at corruption’s effect on private investment (Ades and Di Tella, 1994,

Mauro, 1995,Braguinsky, 1996,Swaleheen and Stansel, 2007, Houston, 2007and

Agostino,2016) and also on poverty (Gupta et al., 1998).

Empirical work on the corruption- FDI nexus, although relatively scarce, are indeed

relatively recent and the impact of corruption on FDI is subject to much ambiguity as

the findings of studies diverge significantly. The literature can be divided into 2 major

strands, with some reveal a negative impact, some affirm a positive reaction while

others fail to find any relationship among the two variables.



The first strand was based on corruption as a ‘grabbing hand hypothesis’, reporting a

negative impact of corruption of FDI was impact.Among the early studies featuresWei

(2000) who studied bilateral FDI from 12 developed countries to 45 destination

countries in 1989 and 1990 to validate a negative impact of corruption on FDI. Habib

and Zurawicki (2002) who used the OLS regression and the PROBIT model to analyse

the impact of corruption on FDI from 7 source countries to 89 host countries using three

year data, concluding in both models that corruption is a significant barrier to FDI. To

support the findings from the previous study, Zhao et al. (2003) analysed the

relationship between corruption and FDI based on a panel data of 40 countries over a

period of 7 years to reach a similar conclusions usingOLS Fixed effects model. Voyer

and Beamish (2004), investigated the link between corruption and Japanese outward

FDIto 59 countries using an OLS regression with two sets of predictors and found that

Japanese FDI is negatively correlated to the level of corruption in emerging countries.

Al-Sadig (2009) conducted an OLS cross-sectional regression using panel data from

117 host countries over the period 1984–2004 and revealed that host country corruption

is significantly negative in affecting FDI inflows where a one point rise in corruption

reduce per capita FDI inflows by approximately 11 percent. In a similar inclination,

Javorcik and Wei (2009) also showed that FDI is negatively correlated to host country

corruption when considering unique firm-level data set from 22 transition economies

using a Minimalist Model with a single-equation Probit approach and a system of

equations Double-Probit approach.Barassi and Zhou (2012) using parametric and non-

parametric methods with data from a sample of 20 OECD source countries and 52 host

countries over the period 1996 to 2003 reported that the overall result of corruption on

FDI was negative. Such results were a rejoinder to Craigwell and Wright (2011) who

studied 42 developing countries over the period 1998 to 2009. Azam et al. (2013)

subsequently employed a sample of 33 Less Developed Countries (LDCs) observed

over the period 1985-2011 to test whether corruption affects FDI or not. Using a panel

data methodology they found that the level of FDI inflows in LDCs is negatively

influenced by the level of corruption, market size and inflation rate. Similar results were

obtained by Azam and Ahmad (2013) for the case of 33 LDCs over the period 1985 to

2011. More recently, Yong et al. (2015) analysed the relationship between corruption

and FDI in 5 selected ASEAN countries2 using Fixed Effect Model, Random Effect

Model and Hausman Specification Test and found that corruption deters FDI in these

countries.2Vietnam, Thailand, Singapore, Malaysia and IndonesiaNovember 24-25, 2019Cambridge, UK 8


African evidence on the adverse effect of corruption and FDI can be traced from Asiedu

(2006) who assessed the factors influencing FDI in 22 countries in sub-Saharan Africa

over the period 1984 to 2000, Asiedu (2006). A subsequent study by Abotsi (2014),

using the dynamic panel data framework among 50 African countries for the period

1996 to 2012 confirmed that corruption negatively impacts on FDI in Africa. Likewise,

empirical results from the recent study by Epaphra and Massawe (2017) showcased that

corruption acts as a “grabbing hand” by reducing FDI. The authors work was based on

panel data from 5 East African countries over the period 1996 to 2015.

Another strand of empirical work lend support to the “helping hand” hypothesis, that is

corruption promotes FDI. Egger and Winner (2005) for the case of 73 developed and

less developed countries over the period 1995 to1999,using statistic Fixed-Effects

models and a Hausman-Taylor model, showed support to the hypothesis. This

conclusion is in line with that of a prior research conducted by Henisz (2000) who used

a two-stage probit estimation technique from U.S. firm-level data. Subsequently,

Cuervo-Cazurra (2006), in his study of bilateral FDI inflows from 183 home economies

to 106 host economies reported similar result.Hakkala, Norbaack, and Svaleryd (2008)

analysed Swedish multinational firms in manufacturing industries for 1998 and found

that corruption fostered vertical FDI while Hasan, Rahman and Iqbal (2017) reported

similar findings for China. Wignall and Roulet (2017) analysed 54 origin-destination

countries, Using OLS fixed effects and Arellano and Bond GMM estimators in a

dynamic FDI gravity model Corruption was found to act as a “helping hand” by

promoting FDI when CPI was used as a measure of corruption.

Turning now to the African continent Quazi et al.(2014) used a dataset of 53 African

countries over the period 1995-2012 to analyze the impact of corruption on FDI

inflows. They conducted the dynamic System Generalized Method of Moments

modeling framework and their empirical results indicate that corruption facilitates FDI

inflows in Africa, validating the ’helping hand” hypothesis. More recently Hakimi and

Hamdi (2017) analysed the impact of corruption on FDI and growth in the Middle East

and North African (MENA) countries over the period 1985 to 2013 and confirmed

Quazi et al.(2004) results.

Empirically, there are a number of studies which are neither fully in support of the

“grabbing hand” hypothesis nor the “helping hand” hypothesis but they are rather of the

view that the impact of corruption on FDI is not significant or varies depending on



various instances. Past studies by Wheeler and Mody (1992)panel data of 42 countries,

Hines (1995) panel sets of 35 countries, Akcay (2001) 52 developing countries and

Caetano and Caleiro (2005) for a sample of 97 countries over the period 2001 to 2003,

all failed to identify a significant relationship between corruption and inward FDI.. To

explore the relationship between corruption and foreign direct investment, Bayar and

Alakbarov (2016) conducted a study for 23 emerging market economies during the

period 2002-2014 andtheir results showed that control of corruption and rule of law had

no statistically significant impact on attraction of foreign direct investments in overall

panel.Empirical evidence pertaining explicitly to corruption and FDI in Africa is scare.

A study contributing to the empirical review of the link between corruption and FDI

among 15 African countries over the period 1996 to 2010 has been carried out by

Ardiyanto (2012). The author used dynamic panel data framework and results from his

study showed that corruption is insignificant in affecting FDI inflows in Africa in all the

5 models. According to the author, corruption is widespread in Africa and therefore,

foreign investors take it as a given.

A summary of the empirical literature reveals that existing research, in addition to being

inconclusive on the overall and to have been scant for the African continent, has largely

been based on static panel data analysis, ignoring to a large extent the possibilities of

dynamism and endogeneity in the corruption-FDI nexus. Similarly the possible

existence of indirect effects of corruption on FDI has also been overlooked. Thus this

research attempts to fill in the address the above gaps and supplement the literature.

3. CORRUPTION AND FDI IN AFRICA: AN OVERVIEW

Africa has historically struggled in attracting FDI but the story seems to start to reverse

at a time of global flux where investors are beginning to realise that the so-called “safe

havens” are not the only “safe bets” with the rise in risk in Europe and the US. From

2015 to 2016, the number of FDI projects fell by 16% to 602 but Africa’s share of

global FDI rose from 8% to 12%, representing a staggering rise in capital investment by

40% to $92.3bn (Africa Investment Report, 2017).Doubtless that investors perceive a

golden opportunity to “house and serve Africa’s rapidly growing and fast urbanising

populations” as stated by Kiasa (2017).



Investors in Africa are gradually looking beyond traditional energy and extractives as

construction becomes the top business activity in 2016, accumulating 40% of inward

FDI. Moreover, there has also been a shift in the top sector by capital investment from

coal, oil and natural gas which accounted for 24% ($15.7bn) of FDI in 2015 to real

estate which boasts 40% ($36.5bn) of FDI in 2016. Most importantly, Africa has now

the partner with the required resources and motivation to propel the continent to another

level as China becomes the most prolific investor by capital expenditure for the first

time in 2016, surpassing the US. Egypt remains by far the top recipient of FDI by

capital investment while South Africa leads in the number of FDI project received in

2016 as illustrated in the following tables:

Table 1: FDI by capital investment in 2016 Table 2: FDI by project numbers in 2016

The graph below illustrates how FDI inflows evolved over the period 1998 to 2016 for

the 30 selected African countries in our sample. There is a rising trend in net FDI

inflows from our start period, however 2008 was marked by the global financial crisis

which shrunk FDI inflows but the latter has picked up again in later years.



19982000

20022004

20062008

20102012

20142016

0.00

20.00

40.00

60.00

80.00

100.00

Net FDI inflows in Africa

Net

FD

I inf

low

s ($

Bn)

Figure 1: Net FDI Inflows in Africa

Overview of Corruption in Africa

The global corruption rate is alarmingly high. Transparency International reveals that

69% of countries in the world score a Corruption Perception Index (CPI)3 of less than

50 out of 100. The SSA countries are the most corrupted one in the world with an

average score of 31 in 2016. 2016 was marked by the resignation of several African

leaders4 who were accused of financial crimes. Despite of suspected wrongdoings, the

head of several countries like Uganda, Equatorial Guinea, Gabon, Congo, and

Cameroon continue to remain in power.

The figure below demonstrates that African countries have always been more corrupted

than the world on average. The trend also shows that corruption has increased over time.

19981999

20002001

20022003

20042005

20062007

20082009

20102011

20122013

20142015

20160

102030405060

Corruption in Africa in comparison to the World

Global Average African Average

3 CPI score of 100 being least corrupt and 0 being most corrupt.4Gambia’s Yahya Jammeh, Angola’s Jose Eduardo dos Santos, Zimbabwe’s Robert Mugabe, and Jacob Zuma of South Africa.November 24-25, 2019Cambridge, UK 12


Figure 2: Corruption in Africa with World comparison

Most African governments are failing their fights against corruption. Transparency

International estimates that approximately 75 million people paid bribes in 2015 to

avoid judicial punishment. Moreover, to have access to basic services that they are

badly in need of, many are forced to pay bribes. Though the average CPI score in Africa

is 32 which is worrying, corruption does not appear to be a major concern in countries

like Botswana, Cape Verde, Rwanda, Namibia and Mauritius who scored over 50 in

2016.

Table 3: CPI and FDI in 9 most corrupted Table 4: CPI and FDI in 9 least corrupted

African countries in 2016 African countries in 2016

Top 9 Most Corrupted African

Countries in 2016

Country Net FDI

Inflows

($ Bn)

CPI

score

Sudan 1.06 14

Angola 4.10 18

Burundi 0.00 20

Chad 0.56 20

DRC 1.20 21

Zimbabwe 0.34 22

Uganda 0.52 25

Cameroon 0.13 26

Kenya 0.39 26

Total 8.32

The tables above illustrate a comparison between the top 9 most corrupted and least

corrupted African countries from our selected sample.


Top 9 Least corrupted African

countries in 2016

Country Net FDI

Inflows

($ Bn)

CPI

score

Botswana 0.01 60

Mauritius 0.35 54

Rwanda 0.25 54

Namibia 0.30 52

Senegal 0.39 45

South Africa 2.25 45

Ghana 3.49 43

Burkina Faso 0.31 42

Tunisia 0.70 41

Total 8.05


3. METHODOLOGY AND ANALYSIS

METHODOLOGY

Historically, corruption has been a major concern for many investors and its impact is

not limited to solely the growth of an economy but can also extend to its FDI inflows, as

documented by many previous researchers. Inspired by previous diligent studies carried

by Habib and Zurawicki (2002), Cuervo-Cazurra (2006), Barassi and Zhou (2012) and

Ardiyanto (2012), we conducted a research which seeks to evaluate the impact of

corruption on inward FDI in a selected sample of African countries. This study will also

allow us to have a glimpse of some other factors that influence FDI in African countries.

Model Specification

Motivated by the above mentioned studies and availability of data, we adopted a classic

function that establish the relationship of corruption and other macroeconomic variables

like GDP, Education, Investment, Trade Openness and Corporate Tax. The function is

thus illustrated below:

Sample Selection

Our full sample consists of an unbalanced panel data from 30 selected African countries

from 1998 to 2016 where each country has at least a 10 year observation of the

variables. Outliers and non-availability of data for some African countries influenced



our sample selection. We deemed appropriate to start our observation as from 1998

since our main determinant, the Corruption Perception Index (CPI) was first issued in

1995 but had only a small number of African countries at that time. By 1998, most of

the major African countries were computed a CPI index and hence we believe that the

time frame 1998 – 2016 will allow us to better analyse the impact of corruption on FDI

in Africa.

Selected African countries

Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Chad, Ivory

Coast, Democratic Republic of Congo, Egypt, Ethiopia, Gabon, Ghana, Kenya,

Madagascar, Malawi, Mauritius, Morocco, Mozambique, Namibia, Nigeria, Rwanda,

Senegal, South Africa, Sudan, Tanzania, Tunisia, Uganda, Zimbabwe

Table 4.1: List of selected African Countries

Definition and Measurement of Variables

Dependent variable – Foreign Direct Investment (FDI)

The indicator to quantify the FDI being used here is net FDI inflows as a percentage of

GDP as measured by the World Bank. Our choice for this proxy stems mostly to

facilitate the interpretation of our regression results. Moreover, previous researches by

Wignall and Roulet (2017), Habib and Zurawicki (2002), and Hakkala et al. (2008)

showed that corruption has a significant relationship with FDI which is considered to be

a growth stimulus. FDI are net inflows (new investment less disinvestment) of

investment to gain ownership of permanent management interest of at least 10% of

voting stock in a foreign company. Here, it relates to the amount of reinvestment

earnings, equity capital, short-term and long-term capital inflows in a country’s BOP

divided by the country’s GDP (World Bank, 2016).

Independent variables

Corruption Perception Index (CPI)

The CPI is an aggregate indicator of corruption in the public sector computed by

Transparency International since 1995. The index is calculated using several sources of

information about corruption (Table 5) by assigning a weight to each data source. A

country’s CPI is calculated only if there is at least three different data sources. The CPI

ranges from 0 to 100, where 100 is the highest score implying the lowest level of

corruption and 0 being the lowest score, implying the highest level of corruption. The November 24-25, 2019Cambridge, UK 15


CPI is relatively reliable5 and has been used as a measure of corruption in several

pioneering works by Wei (2000), Habib and Zurawicki (2002), Zhao et al. (2003),

Voyer and Beamish (2004), Egger and Winner (2005) and Barassi and Zhou (2012).

Table 5: Data sources of the Transparency International’s CPI

Data Sources used to construct CPI 2016

1. African Development Bank Governance Ratings 2015

2. Bertelsmann Foundation Sustainable Governance Indicators 2016

3. Bertelsmann Foundation Transformation Index 2016

4. Economist Intelligence Unit Country Risk Ratings 2016

5. Freedom House Nations in Transit 2016

6. Global Insight Country Risk Ratings 2015

7. IMD World Competitiveness Yearbook 2016

8. Political and Economic Risk Consultancy Asian Intelligence 2016

9. Political Risk Services International Country Risk Guide 2016

10. World Bank - Country Policy and Institutional Assessment 2015

11. World Economic Forum Executive Opinion Survey (EOS) 2016

12. World Justice Project Rule of Law Index 2016

13. Varieties of Democracy (VDEM) Project 2016

Gross Domestic Product (GDP)

The annual GDP growth rate based on constant local currency as measured by the

World Bank has been used as indicator of economic growth for each country. GDP

refers to the market value of all goods and services produced in a country in a year, all

double counting being avoided. Several previous studies (Lee, 2009; Agrawal and

Khan, 2011; Maheswari, 2015) have pointed out that GDP is a driving force in

attracting FDI inflows in a country. Hence, we have not left GDP out amid the

descriptors of FDI.

Education

Also implemented by Al Sadig (2009), education believed to assist in attracting inward

FDI in a country. Education is found to upgrade the skills and productivity of labour

5 According to Wei (2000), there is a high correlation coefficient (0.89) between CPI and the Business International (BI) index, used by Mauro (1995) and thus, these two indices can easily be extended to each other.November 24-25, 2019Cambridge, UK 16


(Bodman and Le, 2013; Mankiw et al., 1992) and hence investors are more willing to

invest in a country as education rises. The mean years of schooling, computed by the

Human Development Report, has been used as a measure of education and is in line

with the studies of Al Sadig (2009) and Gordinez and Liu (2014).

Investment

Investment is an important element which foreign investors consider before investing in

a country. We used the gross capital formation as a percentage of GDP as measured by

the World Bank in our function. It relates to expenditures on additions to the fixed

assets of the country and adding any net changes in the inventory level. Barassi and

Zhou (2012) and Ardiyanto (2012) also included investment in their function to

determine the factors influencing FDI.

Trade Openness

Trade openness is generally a measure of the extent to which a country is open to the

rest of the world. It is measured using trade as a percentage of GDP which is calculated

by dividing the total flows of exports and imports over GDP. Kandiero and Chitiga

(2006) and Wahid et al. (2009) are among the researchers who posit that trade openness

is an important factor that trigger FDI inflows among African countries.

Corporate Tax

In line with several previous studies (Hakkala et al., 2008; Javorcik and Wei, 2009;

Wignall and Roulet, 2017), tax has been included in our model among the descriptors of

FDI. The total tax rate as measured by the World Bank is the amount of taxes and

mandatory contributions which a business has to pay after allowing for deductions and

exemptions expressed as a percentage of commercial profits.

Summary of Data Measurement and Sources

Table 6: Summary of Data Measurement and Sources

Dependent Variable Proxies SourcesFDI Net FDI inflows (% of GDP) World Bank

Development Report (WDI)

Independent Variables Proxies SourcesNovember 24-25, 2019Cambridge, UK 17


CPI Corruption Perception Index (Score) Transparency International

GDP Annual GDP growth rate (%) WDIEDU Mean years of schooling (Years) Human

Development Report

IVT Gross Capital Formation (% of GDP) WDIOPEN Trade (% of GDP) WDITAX Total Tax Rate (% of Commercial

Profits)WDI

Econometric Methodology

The conceptual model as per equation can be rewritten econometrically as

Where i and t represent countries and time periods where i=1,2,3…..30 and t=1998,

1999, 2000….2016. β1-β6 are parameters for the exogenous variables while β0is the

constant term. ɛit is the random disturbance term, and αi is unobservable individual-

specific effect that is time invariant.FDIit represents net FDI inflows at time t, CPIit

refers to Corruption Perception Index at time t, GDPit is annual GDP growth rate at time

t, EDUit is the education level at time t, IVTit is the level investment at time t, OPENit is

trade openness at time t and TAXit is the level of corporate tax at time t.

We apply natural logarithm on the equation in order to reduce the cases of non-linear

relationships between the dependent and independent variables. It also increases the

robustness of the model and enables the preservation of the original features of the

model. More importantly, a double log specification will ease the interpretation of the

estimated coefficient.

The Panel Vector Autoregressive framework (PVAR)

Given the presence of dynamism in FDI modelling and the possibility of endogenous

relationship among the variables, elements which have been largely ignored in the

literature, we accordingly employ a Panel Vector Autoregressive model (P-VAR)6 to

6 A Vector Autoregressive framework on panel data (the Panel Vector Autoregressive framework (PVAR)) and this takes into account the complex links between the various growth determinants and November 24-25, 2019Cambridge, UK 18


evaluate the impact of corruption on FDI.Indeed, FDI is a dynamic phenomenon and

should be modelled as such, explain a bit (see Khadaroo and Seetanah, 2009 and

Asiedu, 2012). Moreover it is believed that FDI may possibly also have anegative effect

on and reduce corruption since government are more alert and are more likely to make

enforcement in the presence of increased foreign investors and multinationals(Hakimi

and Hamdi,2017).The latters are better able to accentuate pressure on government and

authorities with respect to corruption and also can threaten to alert international

community on corruption practice.Thus the possibility of a reverse causation is present.

Our framework additional allows us to dwell into deeper analysis with respect to

indirect effects of corruption on FDI, for instance via economic growth or domestic

private investment as well

Unit Root andPanel Co-integration testing

It is important to analyse the relationship between the variables and ensure that our

panel data is free any spurious correlation.The Levin, Lin and Chu test, Im, Pesaran and

Shin test and Augmented-Dickey Fuller test have been employed to assess the

stationarity of the variables. The results of the three tests reject the null hypothesis at

5% significance level at first difference, confirming all the variables are stationary and

integrated of order 1, that is I(1). We thus proceed with the panel cointegration.

Our results from the Panel and Group PP-Statistic as well as the Panel and Group ADF-

Statistics tests confirm that co-integration occurs among our variables as the null

hypothesis is rejected at 5% level of significance. The Kao (1999) residual based panel

co-integration approach also validate the existence of cointegration, thus a long run

relationship. Conclusively, with the existence of co-integration, we are allowed to

proceed with the equation estimation under the Vector Error Correction Model (VECM)

to also analyse short term relationships.

Analysis of Test ResultsThe long run coefficients estimated from the PVAR are summarised and discussed in

what follows.

Table 7: Estimation of long-run results of the PVAR model

Variables FDI Corruption GDP Education Investment Openness Tax

output level, with particular emphasis on higher education. Panel data VAR thus combines the traditional VAR approach in a time series, which treats all the variables in the system as endogenous (both in a dynamic and in a static sense), and interdependent while also allow for unobserved country specific heterogeneity.November 24-25, 2019Cambridge, UK 19


Equation Equation Equation Equation Equation Equation Equation

FDI 1.000000 0.2343** 0.1454** 0.134** 0.323** 0.154** 0.064

CPI 0.1954** 1.0000 0.234** 0.113 0.245** 0.144** 0.112

GDP 1.0683*** 0.854*** 1.000 0454*** 0.654*** 0.354** 0.164

EDU 0.2734*** 1.254*** 0.4434*** 1.000 0.235** 0.134 0.134

IVT 0.3751** 0.0645 0.535*** 0.153* 1.000 -0.153 0.064

OPEN 0.5123*** 0.174 0.345*** 0.075** 0.344** 1.000 0.001

TAX -0.0354 -0.2954 -0.134 0.045 -0.23** -0.145 0.0455

Source: Author’s computations*denotes significance at 10%; ** at 5% and *** at 1%

Focussing on our main variable in this study, it is observed that corruption has a

negative and significant effect on African FDI in the long run, that is as CPI increases

or when corruption falls (since 0 being maximum corruption and 100 least), FDI

increases. In a 1% increase in corruption is reported to lead to a0.2% decrease in FDI.

Our main findings validates the “Grabbing hand” Theory and may suggest that foreign

investors are often reluctant to invest in highly corrupted countries as they perceive

corruption as immoral or because corruption raises the cost of doing business by acting

like a tax on FDI (Wei, 2000). Moreover as (Drabek and Payne, 2001) posits, risks and

costs of non-compliance rise when bribery becomes a necessity for potential investors.

Therefore, investors may be reluctant to join a corrupt country due to fear of

punishment (Rose-Ackerman, 1999). Hakkala et al. (2008), similarly uphold that

corruption reduces the likelihood that MNCs will invest in a nation as bribery is costly.

They also advance that corruption contorts competition as local firms have an edge in

bribing. Theoretical underpinnings by Abotsi (2014) suggests that where corruption is

pervasive, MNEs are unable to exploit their ownership, location and internalisation

advantages. According to the author, firms’ inability of minimising transaction costs

with corruption, make African countries unattractive for FDI inflows.

Our findings are also in line7 with those of Asiedu (2006) who 22 countries in sub-

Saharan Africa, Abotsi (2014) for the case of 50 African countries and from more recent

study by Epaphra and Massawe (2017) for the case of a sample of 5 East African

countries. However, other authors could not establish such link in the African context

for instance, Ardiyanto (2012) who reported insignificant link for the case of 15 African

countries , Quazi et al. (2014)reported positive and significant effect for the case of 53

7Wei (2000), Habib and Zurawicki (2002), Zhao et al. (2003),Voyer and Beamish (2004), Javorcik and Wei (2009), Craigwell and Wright (2011) and Barassi and Zhou (2012) among others, also showed that corruption deters FDI in these countries.November 24-25, 2019Cambridge, UK 20


African countries and lately. It is noteworthy that existing works, including those on

Africa, were based mainly on static panel data approaches and largely ignore elements

of dynamism and more importantly endogeneity and causal effect.

It should be highlighted that the impact of corruption on FDI is relative low as

compared to the other ingredients of FDI, confirming thatinvestors seem to be attracted

by other factors other than corruption in Africa (Ardiyanto, 2012) also posited that

corruption is so pervasive in Africa, that investors take it as a given and this may

explain the relatively low impact. Moreover, the author added that FDI continue to flow

to these economies as their natural resources are “simply too tempting to resist”, where

most of the foreign investments has been for oil-related and large mining projects.

Calvo and Reinhart (1997) also reported that that world commodity prices is the only

external element that systematically influence FDI inflows to Africa. The possible trade-

off between the “grease effect” of corruption and the “sand effect” also renders the

overall effect of corruption on FDI relatively small. Countries like Angola, Morocco,

Tunisia, Chad, Algeria and Egypt among others seem to have a positive relationship

between their level of corruption and FDI inflows using preliminary country level

regression analysis while countries like Nigeria8, Uganda, Ghana, Kenya, Mauritius, and

Mozambique among others seem to have an inverse relationship between their

corruption level and FDI inflows. In fact,Asediu (2008)support the “grabbing hand”

hypothesis of corruption in Nigeria and Uganda respectively where they found that

corruption acts like “sand” in the wheel of commerce. Therefore, the “grease” effect of

corruption seems to offset the “sand” effect of the latter, thereby leading to an overall

insignificant effect of corruption on FDI among African countries.

As far as the other explanatory variables are concerned, GDP has the strongest

coefficient with a positive and significant relationship with FDI in the long run. A 1%

increase in GDP is likely to increase FDI of African countries by about 1.1% in the long

run. Hence, as an economy progresses, it is expected to attract more FDI inflows. Such

results are consistent with previous studies (see Balasubramanyan et al., 1996; De

Mello, 1999; Yussof and Ismail,2003, Farkas, 2012, Maheswari, 2015 among others).

The positive and significant coefficient of education (0.27) implies that a 1% increase in

the mean years of schooling among African countries will increase FDI inflows by

about 0.27% in the long run. Indeed, education upgrades the skills and productivity of

8For instance, Nigeria’s CPI index rose by about 38% from 1998 to 2016 and its FDI inflows also rose by about 4 times since then, implying there is an inverse relationship between corruption and FDI (validated by study from Fisman and Svensso, 2007)November 24-25, 2019Cambridge, UK 21


labour (Bodman and Le, 2013; Mankiw et al., 1992) thereby leading to increased

efficiency of workers (Hall and Jones, 1999). Moreover, a more educated workforce

allow a nation to better embrace latest equipment and technologies and the absorption

capacity of countries increases as the level of education in a country rises (Benhabib

and Spiegel, 1994; Bodman and Le, 2013, Seetanah and Neeliah, 2016), thereby

increasing the scope of business activities by MNEs in the host countries. Trade

openness is also seen to be positively related with African FDI in the long run and such

resultsvalidates the findings of Kandiero and Chitiga (2006) and Wahid et al.

(2009)who also observed that trade openness is one of the most important factor that

triggers FDI inflows among African countries. Investment is reported to have a

positivelyinfluence FDI for our sample of countries and this tallies with the findingsof

Barassi and Zhou (2012) andKhadaroo and Seetanah (2009). As such, our findings

demonstrate that tax is insignificant in explaining FDI inflows in Africa, suggesting that

foreign investors are unlikely to be affected by prevailing tax rates in African countries

when considering to invest in those nations. Studies from Hakkala et al. (2008), Barassi

and Zhou (2012) and more recently from Wignall and Roulet (2017) affirmed that tax

was insignificant in explaining FDI inflows.

Reverse and indirect effects

Our PVAR framework also allows us analyse issues related to reverse causality and

from column 3 above, it is observe that an increase in FDI decrease corruption (positive

effect on CPI) for the sample of countries under study. This is quite interesting and

indicates that as FDI reaches a country, this triggers probably combating of corruption

or that foreign investors are less prone to engage into corruption.. From column 3, the

other determinants of corruption appear to be the development level of the country as

well as education (with a relatively higher influence).

As such, further analysis shows the existence of positive effects of corruption reduction

on economic growth (refer to column 4), domestic private investment (column 6) and

trade level (as measured by openness, refer to column 7). Since these factors in turn

affects FDI (refer to column 2), one can conclude that corruption has indirect effect or

mediating effect as well though the above mentioned variables.

Discussion of the short-run results



The table below presents the short run coefficients from the error correction mode

(ECM). Our main focus lies in the second column, (FDI equation) with FDI as the

dependent variable.

Error Correction D(LNFDI) D(LNCPI) D(LNGDP) D(LNEDU) D(LNIVT) D(LNOPEN) D(LNTAX)

D(LNFDI(-1))-0.40543 0.00185 -0.27676 -0.00597 0.00637 0.03298 -0.00800

[-7.7075]*** [ 0.1112] [-2.3231]*** [-1.5295]* [ 0.2392] [ 1.5402]* [-0.3259]

D(LNFDI(-2))-0.14854 0.01701 -0.12765 0.00472 0.05259 0.03458 0.02058

[-2.7382]*** [ 0.9931] [-1.0390] [ 1.1732] [ 1.9137]** [ 1.5656]* [ 0.8126]

D(LNCPI(-1))0.04631 -0.10921 0.23816 0.01716 0.14519 0.09377 0.01388[ 0.2796] [-2.0883]** [ 0.6348] [ 1.3969]* [ 1.7301]** [ 1.3904]* [ 0.1794]

D(LNCPI(-2))-0.01073 -0.01954 0.28861 -0.01112 -0.11103 -0.13991 0.10208[-0.0704] [-0.4061] [ 0.8364] [-0.9842] [-1.4386]* [-2.2557]** [ 1.4350]*

D(LNGDP(-1))-0.02084 -0.00258 -0.22873 -0.00072 0.00666 0.02799 0.00083[-0.7610] [-0.2988] [-3.6872]*** [-0.3562] [ 0.4798] [ 2.5107]*** [ 0.0650]

D(LNGDP(-2))-0.04483 0.01561 -0.10755 -0.00099 0.01663 0.00652 0.00118

[-2.2249]** [ 2.4545]*** [-2.3568]*** [-0.6596] [ 1.6295]* [ 0.7943] [ 0.1258]

D(LNEDU(-1))0.09036 0.21674 -0.78739 0.20824 0.27719 0.32376 -0.06417[ 0.1245] [ 0.9457] [-0.4789] [ 3.8686]*** [ 0.7538] [ 1.0955] [-0.1893]

D(LNEDU(-2))0.19146 -0.24662 4.54735 0.05056 -0.15133 0.04299 -0.31239[ 0.2702] [-1.1025] [ 2.8337]*** [ 0.9623] [-0.4216] [ 0.1490] [-0.9443]

D(LNIVT(-1))0.10527 0.01299 0.37623 0.02268 -0.02942 0.03007 -0.11033[ 0.9154] [ 0.3580] [ 1.4445]* [ 2.6601]*** [-0.5049] [ 0.6423] [-2.0549]**

D(LNIVT(-2))-0.14210 -0.02669 -0.14761 0.01568 -0.17339 -0.03196 0.01571[-1.2538] [-0.7458] [-0.5751] [ 1.8659]** [-3.0199]*** [-0.6926] [ 0.2969]

D(LNOPEN(-1))0.23044 0.01546 0.17170 0.00866 0.00476 -0.06528 0.07375

[ 1.7364]** [ 0.3690] [ 0.5712] [ 0.8798] [ 0.0708] [-1.2082] [ 1.1902]

D(LNOPEN(-2))0.05637 -0.02369 -0.03921 -0.00022 -0.01911 -0.14043 -0.07183[ 0.4350] [-0.5791] [-0.1336] [-0.0228] [-0.2910] [-2.6615]*** [-1.1871]

D(LNTAX(-1))0.05104 0.01174 -0.10315 0.00229 -0.02400 0.01761 0.13278[ 0.4359] [ 0.3176] [-0.3890] [ 0.2645] [-0.4046] [ 0.3695] [ 2.4289]***

D(LNTAX(-2))-0.01319 -0.00285 0.05897 -0.00279 0.08736 0.02631 -0.03005[-0.1144] [-0.0783] [ 0.2258] [-0.3263] [ 1.4955]* [ 0.5605] [-0.5581]

CointEq1 -0.314 -0.314 -0.314 -0.314 -0.314 -0.314 -0.314[-2.4744]*** [-2.4744]*** [-2.4744]*** [-2.4744]*** [-2.4744]*** [-2.4744]*** [-2.4744]***

Table 8: Estimation results for error correction model9

9The level of significance is denoted with * for 10% level of significance, ** 5% level of significance and ***1% level of significance.November 24-25, 2019Cambridge, UK 23


Results under both a lag length of one and two, suggest that corruption does not affect

FDI in the short run and is therefore consistent with results in the long run. This result is

supported by the findings of Hakimi and Hamdi (2017) who also failed to identify a

short run relationship between corruption and FDI. Moreover, under a lag length of one,

it is observed that only trade openness significantly contributes to FDI inflows. It can be

held that a 1% increase in trade openness will lead to a 0.23% increase in FDI after one

year. However, it must be noted that GDP, education, investment and tax are all

insignificant in attracting FDI in the short run. The building of education and

investment are essentially long term phenomenon which is why they have insignificant

positive but insignificant effect on FDI inflows in the short run. Interestingly, the

coefficient of the error correction term (CointEq1) is -0.33, suggesting that when FDI

inflows is in disequilibrium, it adjusts by approximately 33% within the next period.

Consequently any fluctuations in the independent variables take about 3 years to have

full effect on FDI. Hence, in the event of any shock to the emission equation, the speed

of adjustment is very slow. Besides, looking at the results of CPI equation (column 3),

with CPI as the dependent variable allows us to conclude that there is no reverse

causality between corruption and FDI in the short run under both a lag length of one and

two. Impulse response and variance decomposition analysis validated our previous

findings to a large extent.

4. CONCLUSION

This paper investigates the impact of corruption on foreign direct investment for a

sample of 30 countries over the period 1998 to 2016 using a Panel Vector Error

Correction Model (PVECM). The study supplement the literature as it accounts for the

possibility of dynamics and endogeneity in the corruption-FDI nexus. Moreover, the

research dwells into an analysis of possible reverse causal, indirect or mediating effect

of corruption on FDI, elements so far overlooked in the literature. Findings show that

corruption is a hurdle in attracting FDI inflows among African countries in the long-run,

although to a relatively lower extent as compared to the other factors affecting FDI. The

analysis confirms the presence of a negative reverse causal effect from FDI to

corruption. Interestingly, corruption is reported to have some indirect effects on FDI as

it allow negatively influences economic growth, trade level and private investment

which in turn are seen to have an impact on FDI. Finally, the Impulse Response

Function and Variance Decomposition analysis were also consistent with the VECM



results. Results from our analysis also identified development level and education as the

main explanatory factors for reducing corruption in the long run.

The implications of this study are clear. African governments, policy makers,

international agencies and businesses should care about the high corruption level as it is

harmful to the continent at large, especially with respect FDI. They need to apprehend

and admit that corruption is an important issue that deters foreign direct investment and

threats the economic development and growth. Hence, the African governments, with

the help of international organisations and companies should take a collective stand to

combat corruption in order to create the enabling environment that will promote Africa

to its full potential, inducing higher FDI inflows in the long run.

While it is challenging to have specific guidance national measures to combat

corruption, African countries could adopt already established anti-corruption policies

provided by the World Bank, the United Nation’s Convention against Corruption and

Organisation for Economic Cooperation and Development’s Convention on Combating

Bribery of Foreign Public Officials in International Business Transactions. These

measures have as purpose to promote prevention, law enforcement, and criminalisation

of corruption. They also provide technical assistance and the mechanisms for the

implementation of anti-corruption actions.

Moreover, the highly corrupted countries need to undergo civil service reforms and

establish independent anti-corruption agency possessing strong internal affairs

department which must be politically neutral where professional civil service should be

free from conflict of interests with its duties (Adamolekun, 1993). The independent

agency should be given power to investigate corruption cases independently of police

forces, prosecute cases of corruption to Court independently of attorneys general.

African countries should make a reassessment of their existing laws pertaining to

corruption and “those retained should be made more transparent and as non-

discretionary as possible” (Tanzi, 1998). The standard operating procedures of agencies

like customs houses, tax offices and police force which are inclined to corrupt activities

should be periodically evaluated to reduce opportunities for corruption.

On the other hand, foreign investors must show intolerance towards corruption for their

personal long term benefit. Many countries provide for regulations regarding corrupt

activities whereby upon presentation of persuasive proof, such contracts or license



wrongfully obtained through corruption can be made null and void (Malta Conference,

1994). The process can be facilitated through information exchange within the business

community. Finally, the governments should also increase the public awareness of the

multiple drawbacks of corruption. In this case, media will have a key role to diffuse the

necessary information.

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