Chinese Trade Driving Corruption in Africa? Wilson Gu

Introduction

In order to keep up rapid economic growth and stave off domestic discontent, China has

had to look for natural resources to import and new markets to export to. Africa represents major

opportunities in both areas. Yet, in its pursuit of economic growth, China has been accused of a

singular focus on economic growth at the expense of furthering bad governance in Africa. Bad

governance comes in many shapes and sizes, not limited to corruption, human rights abuses, and

wasteful spending. This differs from many Western approaches to economic development where

the foreign country often places conditions on aid disbursement, like good governance or

democratic reforms. Leaders of authoritarian states in Africa view these conditions as a thorn in

the side, and they are gladly turning to China for economic assistance and opportunities as China

has stated a non-interventionist policy. It is not clear if these accusations are clear and if China

has a significant negative influence on African countries governance and institutions.

This paper attempts to shed some light on this topic by providing an econometric model

that will complement existing accounts and reports on Chinas impact on corruption in Africa.

The existing literature on Chinas engagement with Africa lacks quantitative evidence.

Corruption is a major obstacle to development, and as China plays a larger global role, it also has

an incentive to promote sustainable growth founded on good governance. But before action can

be taken, it must be determined if Chinas trade and aid policies are actually exacerbating the

problem of corruption in Africa.

Do firms in countries that have major economic ties with China pay more bribes? What

characteristics of firms in these countries lead to a higher probability of paying bribes? Four

Gu 2

major characteristics are examined, leading to four hypotheses. 1) For firms in African countries

that have more than 20% of their exports to or imports from China, these firms are more likely to

pay bribes than firms in other African countries. 2) For firms that both operate in a major

Chinese trade country and sectors that China is interested in, these firms are more likely to pay

bribes than all other firms. 3) For firms that have high horizontal FDI (share of output produced

by foreign firms) in sector-regions in major Chinese trade countries, these firms are more likely

to pay bribes than other firms. 4) For firms that require an import license in major Chinese trade

countries, these firms are more likely to pay bribes than other firms. In addition, this paper

draws heavily from Alaimo et al., and the control rights, bargaining power, and grease the

wheels hypotheses on determinants of bribery, all of which are tested in this paper.

Hypothesis 1 is confirmed, as firms in countries that have major trade ties with China do

pay more bribes. Hypothesis 2 is also confirmed but with the caveat that the data on sectors do

not include extractive industries which tend to facilitate corruption. Hypothesis 3 is not

confirmed, but the measurement of FDI uses microeconomic data and not macro data. It cannot

be ruled out completely based on the results of this paper. Hypothesis 4 is not confirmed. There

is also evidence that supports the control right hypothesis in Africa and a lack of evidence for the

bargaining rights hypothesis, findings that corroborate those of Alaimo et al. However, the

impact of objective courts in Africa has no significant impact on the probability of paying bribes,

a finding that differs from Alaimo et al who find that court enforcement power lead to a

decreased probability of paying bribes. Standard fixed effects controls include country, sector,

and region in addition to other controls like peacefulness and country income levels.

Literature Review

Gu 3

The seminal papers on corruption are Who must pay bribes and how much? Evidence

from a cross section of firms (Svensson 2003) and Behind the investment climate: back to

basics determinants of corruption (Alaimo et al. 2009). Both differ from the majority of

papers in that they use microeconomic data instead of perception indices. Svensson focuses on

Uganda firms and the questions who pay bribes and how much. He tests the control rights

hypothesis where firms pay bribes when faced with public officials who demand kickbacks

when providing necessary services like public infrastructure. Results show that firms do have to

pay bribes when dealing with these public officials, thereby answering the question who receives

bribes. Svensson also tests a bargaining hypothesis where firms will pay bribes only if they

are able to. He proxies the ability to pay of firms with current and expected future probability,

and he proxies the refusal power of firms with the estimated alternative return on capital. The

results show that the more a firm is able to pay, the less it pays. The results also show that the

more profitable an outside option is for the firm, the fewer bribes it will pay. These two factors

answer the question of how much do firms pay in bribes.

Alaimo et al. takes Svenssons hypotheses and adds another one, the grease the wheels

hypothesis, which states that firms will pay bribes if the gain from paying the bribe outweighs

the costs of the bribe and the costs of getting caught. However, instead of Uganda, the authors

focus on Latin America. There are four major findings. First, regulations, proxied by the degree

of regulatory compliance, affect the probability of paying bribes, a finding that supports the

control rights hypothesis. Second, there is not enough evidence for the bargaining position

hypothesis. Third, domestic firms are more likely to pay bribes. Fourth, a more credible judicial

system is associated with lower probability of paying bribes. Both of these papers provide the

conceptual framework for identifying determinants of paying bribes in Africa. They are the

Gu 4

baseline to which additional factors will be added to in order to examine Chinas economic

actions impact on corruption and more broadly, institutions in Africa.

Numerous articles propose deal with the impact of corruption on foreign direct

investment and other actions of foreign economic actors (Habib and Zurawicki 2002; Egger and

Winner 2005; Ogunmuyiwa 2012). One paper has dealt with the other direction of causality, that

economic activity actually affects corruption. Robertson and Watson (2004) test the impact of

changing levels of foreign direct investment on levels of corruption as measured by the

Corruption Perceptions Index for all countries. Results show that the more rapid the rate of

change of FDI, the higher the level of corruption. This reveals a need for similar studies that use

microeconomic data instead of indices to measure corruption, something this paper hopes to

achieve. The paper also proposes multiple sources of change in corruption in its literature review,

such as abundance of natural resources, the level of innovation, government philosophy on trade,

and cultural tendencies. The paper itself only measures FDI and some cultural factors like

masculinity of a country. FDI is measured by using publicly available macroeconomic data.

Because Robertson and Watson present evidence that foreign actions can influence corruption, it

suggests that corruption in African countries could go through similar increases or decreases

dependent on the actions of foreign countries seeking to invest or trade.

There is much variation in the literature on whether or not Chinese economic activity has

improved institutions and decreased corruption. An International Rivers Network report (2007)

Chinas role in financing African infrastructure identifies a perceived propensity of Chinese

companies to bribe abroad, and the lack of success by the Chinese government to prevent this,

may undermine international efforts to strengthen good governance and fight corruption in

development projects. This is also reflected in the Ethics Institute of South Africas (2014)

Gu 5

Africans perceptions of Chinese businesses in Africa: a survey. For South Africa, Nigeria, and

Kenya, 61% of respondents agree with the statement Chinese companies are involved in

corruption when doing business in their country. It also finds negative perceptions on Chinese

companies environmental policies, reputation, and social interactions. The China Development

Bank has a policy of non-interference in domestic affairs when giving out loans, further

propagating the notion that China does not care about governance in African countries as long as

it obtains resources and markets. At the same time, Chinese companies have made numerous

investments in transportation and infrastructure, and it is conceivable that economic growth

could positively impact corruption (Williams 2013).

Methodology

This study uses the WBES new comprehensive dataset. Many of these countries data

comes from 2007-2009, arguably before China had established a significant foothold in the

region. Other major countries that have significant trade ties with China are missing, like Egypt

and Sudan. Therefore, this study only deals with countries in Sub-Saharan Africa and not the

entire continent.

The main variables are constructed using definitions from Alaimo et al. Variable bribe is

a dummy variable with a value of 1 if the firm paid a bribe in a variety of circumstances, from

requesting utility services to applying for an import license. Variable foreign measures whether

or not a firm has any foreign ownership, and variable export measures if a firm exports more

than 50% of its direct sales. Variable govt is used to separate state-owned firms from private

firms, because it is more common for state-owned firms to have more connections with public

officials, thereby overstating the probability of paying bribes.

Gu 6

Variables public, bank, and court are taken from Alaimo et al. as independent variables.

Public signifies whether a firm requested public utilities or not. Bank measures a firms

solvency which is used as a proxy for a firms ability to pay bribes. Court is an objective

measure of courts fairness and impartiality and is used by Alaimo et al. as a measure of

enforcement power.

New variables in this study are as follows. Chinatrade is a dummy variable where a

country is coded as 1 if 20% of either exports or imports go to or come from China. This data is

taken from MITs Observatory of Economic Complicity. Trade data is used from two years

before the dataset of the country until the year from the country. For example, if the dataset is

Angola2010, in any year from 2008-2010 that Angola has 20% of either exports or imports with

China, Angola will be coded as a chinatrade country. This not only matches the years of trade

data with the year of the WBES data, but it also takes into account some lag effects of trading

with China on corruption. Chinasector is a dummy variable where a value of 1 means the firm

engages in one of the following sectors that China is interested in: manufacture of chemicals,

chemical products, rubber and plastic products, other non-metallic mineral products, basic metals,

and fabricated metal products excluding machinery and equipment. Chinasectorcountry is an

interaction variable that multiplies chinasector by chinatrade. Horizontal FDI is measured in the

dummy variable DVHFDI and the interaction variable ChinaFDI. In this paper, horizontal FDI

is taken from the share of output produced by foreign firm in each region-sector. Finally, ilice

and its interaction variable chinaimport show whether or not a firm requested an import license

or not by country-sector. Interaction variables chinapublic, chinabank, and chinacourt are also

included in one regression to look at the three bribery hypotheses effect in chinatrade countries.

Gu 7

Fixed effects include country, region, and sector. For all regressions, import, export,

public, bank, and court are also included. Control variables were also created to take into

account the domestic conflict in a country (peaceful), using data from Databanks International,

and variables were created to to control for income level of the country (lowmid and uppermid),

using the income group classifications by the World Bank. The data for domestic conflict is not

available for every country, so the missing countries are dropped from the regressions.

The paper will first establish a baseline to check Alaimo et al.s variables and their effect

on probability of paying a bribe. It will then examine each new independent variables effect on

bribes individually. Finally, it will build a final regression model by individually adding

interaction variables. It will also include interaction variables concerning the three bribery

hypotheses to see how they hold in countries that trade with China. Finally, due to the number of

observations dropped when controlling for conflict, this control is removed for an additional

regression model that has a larger number of observations in order to see if the relationships still

hold.

The econometric model for this paper is:

Pr(bribe)j,c,r,il,p = c + i + r + a + Xj,c,i,s,a + vj,c,i,s

where j = firm; c = country; i = industry; r = region; il = income level; and p = peacefulness.

Pr(bribe) is the probability that any firm pays bribes, and X is a set of controls aimed at testing

the main variables: chinatrade, chinasectorcountry, and ChinaFDI.

Results

Table 1. Countries in newcomprehensive.dta and % of trade with China

Country Year

% of

exports

to

China

% of

imports

from

China Country Year

% of

exports

to

China

% of

imports

from

China Country Year

% of

exports

to

China

% of

imports

from

China

DRC2010*** 2012 77.28% 16.35% Cameroon2009 2012 14.72% 11.19% Togo2009*** 2012 3.58% 26.30%

Chad2009*** 2012 60.78% 24.39% Zimbabwe2011 2012 10.42% 5.65% Senegal2007 2010 1.91% 8.23%

Angola2010*** 2012 60.78% 20.56% Rwanda2011 2012 9.23% 12.12% Ghana2007 2010 1.64% 12.84%

Centralafricanrepublic2011*** 2012 53.70% 5.05% Rwanda2006 2009 8.88% 8.40% Uganda2006 2009 1.42% 9.03%

Sierra Leone2009*** 2012 53.31% 14.09% Gabon2009 2012 8.50% 13.68% GuineaBissau2006 2009 1.41% 7.90%

Congo2009*** 2012 49.35% 13.94% Tanzania2006 2009 7.97% 10.95% Nigeria2007 2010 1.23% 14.06%

DRC2006*** 2009 42.55% 10.23% SouthAfrica2007 2010 7.57% 13.40% Cte d'Ivoire2009 2012 0.99% 8.13%

Angola2006*** 2009 37.37% 13.39% Niger2009 2012 6.93% 18.82% Kenya2007 2010 0.78% 11.73%

Mauritania2006*** 2009 34.69% 13.17% Madagascar2009 2012 6.63% 15.07% Burundi2006 2009 0.56% 7.14%

Zambia2007*** 2010 31.08% 6.03% Gambia2006*** 2009 6.50% 20.24% Mauritius2009 2012 0.41% 17.75%

BurkinaFaso2009*** 2012 29.00% 3.00% Mozambique2007 2010 5.00% 6.12% Eritrea2009 2012 0.31% 14.58%

Benin2009*** 2012 23.15% 39.67% Mali2007 2010 4.73% 10.06% Ethiopia2011 2012 0.31% 14.58%

Liberia2009*** 2012 20.09% 15.56% Malawi2009 2012 4.53% 18.38% CapeVerde2009 2012 0.00% 4.81%

Mali2010 2012 16.51% 10.75%

Guinea2006 2009 0.00% 15.02%

*** denotes inclusion in variable chinatrade as a country that has prominent trade ties with China

Gu 9

Table 2. African countries and probability of paying bribes

Country Probability of

paying a bribe Country

Probability of

paying a bribe Country

Probability of

paying a bribe

Guinea2006 0.94 DRC2013 0.43 Togo2009*** 0.21

DRC2006*** 0.94 Centralafricanrepublic

2011*** 0.43 Uganda2013 0.20

Mauritania2006*** 0.85 Chad2009*** 0.43 Gabon2009 0.20

Kenya2007 0.77 Angola2010*** 0.43 Madagascar2009 0.19

Liberia2009*** 0.69 Benin2009*** 0.39 Zimbabwe2011 0.18

Nigeria2007 0.65 Senegal2007 0.35 Malawi2009 0.17

GuineaBissau2006 0.62 Kenya2013 0.33 SouthAfrica2007 0.17

Burundi2006 0.60 Cte d'Ivoire200 0.32 Zambia2013*** 0.13

DRC2010*** 0.57 Niger2009 0.31 Namibia2006 0.13

Tanzania2006 0.57 Congo2009*** 0.30 Tanzania2013 0.12

Gambia2006*** 0.56 Botswana2006 0.25 BurkinaFaso2009*** 0.11

Uganda2006 0.51 Sierra Leone2009*** 0.25 Botswana2010 0.10

Angola2006*** 0.49 Mozambique2007 0.24 Ethiopia2011 0.08

Cameroon2009 0.48 Rwanda2006 0.24 Rwanda2011 0.07

Mali2007 0.48 Zambia2007 0.24 CapeVerde2009 0.05

Ghana2007 0.44 Lesotho2009 0.23 Mauritius2009 0.05

Swaziland2006 0.44 Mali2010 0.22 Eritrea2009 0.00

Average 0.35

*** denotes inclusion in variable chinatrade as a country that has prominent trade ties with China

Table 1 shows a list of countries and their percentages of exports to or imports from

China. Highlighted countries receive a value of 1 for the dummy variable chinatrade. This

variable does have some imperfections in that the trade data is only taken from one year and not

an average of several years. Taking averages would give a better overall picture of whether or

not a country considers China a major trade partner, because it would reduce the effect of large

swings of trades or imports between different years. 40% of imports could be from China one

year and decrease to 5% the next, an inaccurate measure of a countrys trade history. Time

concerns prevented the use of this more accurate method.

Table 2 shows a list of the African countries in the WBES surveys and the probability of

paying a bribe by using the dummy variable bribe as defined by Alaimo et al. After a quick

comparison with Table 1, its clear that numerous countries that have a high probability of

paying bribes also have significant trade ties with China. However, several countries that trade

with China have low probabilities of paying bribes, like Sierra Leone, Togo, Zambia, and

Burkina Faso. Due to the inclusion of a control of peaceful, numerous countries were dropped in

the majority of regressions. The remaining countries have stars.

In Table 3, Dprobit regressions first replicate the results of Alaimo et al. Baseline

coefficients for public, bank, and court have the same direction as the coefficients from Alaimo

et al., and they have significance at the 1% level, too. This confirms the results of Alaimo et al.s

paper, which finds support for the control rights hypothesis and grease the wheels hypothesis but

no evidence for the bargaining hypothesis.

The coefficient for variable chinatrade is positive and significant whenever regressed.

The magnitude of this coefficient is also extremely high. Whether or not a country trades with

China is an important determinant of if firms in that country pay bribes or not. However, that

Table 3. Baseline Regression Results

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES bribe bribe bribe bribe bribe bribe bribe bribe

chinatrade

0.409***

0.393*** 0.417***

0.418*** 0.405***

(0.140)

(0.142) (0.139)

(0.142) (0.141)

chinasectorcountry

0.156** 0.137**

(0.064) (0.067)

chinasector

0.039

(0.038)

ChinaFDI

-0.031

(0.040)

DVHFDI

0.021

(0.020)

chinaimport

-0.039 -0.003

(0.034) (0.040)

isector

-0.038*

(0.020)

chinaexport

0.143

(0.116)

public 0.157*** 0.157*** 0.158*** 0.157*** 0.157*** 0.158*** 0.159*** 0.157***

(0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)

bank -0.067*** -0.067*** -0.066*** -0.067*** -0.067*** -0.067*** -0.067*** -0.067***

(0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014)

court -0.107*** -0.107*** -0.107*** -0.107*** -0.107*** -0.107*** -0.107*** -0.107***

(0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)

medium -0.052*** -0.052*** -0.052*** -0.052*** -0.052*** -0.052*** -0.050*** -0.053***

(0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019)

large -0.113*** -0.113*** -0.112*** -0.113*** -0.114*** -0.113*** -0.111*** -0.111***

(0.025) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025)

foreign -0.038** -0.038** -0.037** -0.037** -0.039** -0.037** -0.038** -0.038**

(0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)

export 0.013 0.013 0.015 0.014 0.011 0.015 0.018 -0.009

(0.045) (0.045) (0.045) (0.045) (0.045) (0.045) (0.045) (0.049)

import 0.008 0.008 0.006 0.006 0.008 0.007 0.006 0.008

(0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016)

Gu 12

lowmid -0.042 -0.149 -0.042 -0.158 -0.147 -0.397*** -0.137 -0.147

(0.156) (0.130) (0.156) (0.127) (0.130) (0.053) (0.132) (0.130)

uppermid 0.517*** 0.414** 0.517*** 0.406* 0.175 -0.023 0.395* 0.418**

(0.165) (0.211) (0.165) (0.211) (0.214) (0.200) (0.215) (0.209)

peaceful 0.021 0.143 0.020 0.152 0.141 -0.261* 0.149 0.139

(0.233) (0.162) (0.233) (0.161) (0.162) (0.142) (0.162) (0.162)

Observations 8,749 8,749 8,749 8,749 8,749 8,748 8,748 8,749

Robust standard errors in parentheses

*** p

conclusion alone is not enough to draw any useful insight, so other variables must be included.

Chinasectorcountry is significant and increases the probability of paying bribes. As a reminder,

this variable interacts chinatrade with chinasector sectors that China is active in (manufacture

of chemicals, chemical products, rubber and plastic products, other non-metallic mineral

products, basic metals, and fabricated metal products excluding machinery and equipment). The

direction of causality is unclear here. It is unclear whether bribes in these sectors were already

paid before China began trading heavily with these countries or only increased (or decreased

from an even higher level) after China started to engage in trade. It is even possible that China is

especially attracted to sectors that have high instances of paying bribes. However, the finding

that firms in these sectors in chinatrade countries pay more bribes holds important policy

implications, because China has leverage in these countries which it could use to implement

good governance. This is discussed in the policy recommendations section.

All other potential variables FDI, import, and export - that could impact bribery are not

significant. Even though chinatrade is related to percentage of exports and imports with China,

the variables chinaimport and chinaexport themselves have no effect on the probability of paying

bribes. This gives added support to the importance of the sector that firms operate in. Its not

whether or not a firm exports or imports, but rather what that firm exports or imports. FDI is

also not important, but this could be due to a measurement issue. This paper measures FDI using

horizontal FDI as a summation of share of output produced by foreign firms. A better variable of

Chinese influx of capital and investments could be ODA figures and private voluntary assistance

data.

Finally, the sector variable has faults, because it does not measure extractive industries

which are linked with higher corruption. Natural resources are often owned by the government,

Gu 14

and with weak institutions, these rights can easily be sold off for private gain. From a political

perspective, natural resources also take away from the need of governments to tax its citizens.

Without taxation, government officials can act with impunity with no accountability to anybody,

because government revenue comes from resource rents. Therefore, by not including extractive

industries like petroleum, the chinasectorcountry variable underestimates the magnitude of

sectors effect on bribe probability.

Table 4 shows the results of the full regression model. When adding variables

individually to the model in columns 1-5, chinatrade maintains a high, positive coefficient and

significance at the 1% level. The same goes for chinasectorcountry which is significant at the 5%

level. In column 5, the variables chinapublic, chinabank, and chinacourt are interactions of

chinatrade and public, bank, and court to examine the effects of the three hypotheses on bribery

in African countries that trade with China. The control rights hypothesis holds but with less

robust results, and the absence of evidence for the bargaining rights hypothesis continues with a

negative coefficient on chinabank. However, chinacourt loses significance. It is expected that

with more court enforcement, the probability of bribes should decrease as shown by the negative

coefficients across the board for variable court. This does not hold in chinatrade countries,

suggesting that courts do not matter in the probability of paying bribes in those countries. This

could be due to the court systems in those countries not having significant dealings with sectors

that China is interested in, by design or accident, as its already shown that the sector a firm is in

affects the probability of paying a bribe.

Due to concerns over dropped countries from including a peaceful control, column 6

shows the results without this control. The number of observations rises from 8,748 to 20,133.

Here, chinatrade maintains significance, but all other variables lose significance. Firms in

Table 4. Full Regression Model

(1) (2) (3) (4) (5) (6)

VARIABLES bribe bribe bribe bribe bribe bribe

chinatrade 0.409*** 0.392*** 0.394*** 0.417*** 0.420*** 0.418**

(0.140) (0.142) (0.142) (0.141) (0.143) (0.182)

chinasectorcountry

0.156** 0.156** 0.153** 0.149** 0.035

(0.064) (0.064) (0.064) (0.064) (0.036)

ChinaFDI

-0.010 -0.011 -0.014 0.016

(0.035) (0.035) (0.036) (0.023)

chinaimport

-0.032 -0.039 -0.007

(0.034) (0.034) (0.022)

chinaexport

0.175 0.165 0.003

(0.116) (0.116) (0.075)

chinapublic

0.064* 0.030

(0.033) (0.022)

chinabank

-0.069** -0.034

(0.031) (0.021)

chinacourt

0.030 0.008

(0.038) (0.022)

public 0.157*** 0.158*** 0.158*** 0.157*** 0.145*** 0.151***

(0.013) (0.013) (0.013) (0.013) (0.014) (0.009)

bank -0.067*** -0.066*** -0.066*** -0.067*** -0.050*** -0.051***

(0.014) (0.014) (0.014) (0.014) (0.016) (0.010)

court -0.107*** -0.107*** -0.107*** -0.107*** -0.111*** -0.098***

(0.013) (0.013) (0.013) (0.013) (0.013) (0.009)

medium -0.052*** -0.052*** -0.052*** -0.052*** -0.052*** -0.044***

(0.019) (0.019) (0.019) (0.019) (0.019) (0.012)

large -0.113*** -0.112*** -0.112*** -0.111*** -0.110*** -0.086***

(0.025) (0.025) (0.025) (0.025) (0.025) (0.017)

foreign -0.038** -0.037** -0.037** -0.037** -0.038** -0.027**

(0.018) (0.018) (0.018) (0.018) (0.018) (0.011)

export 0.013 0.015 0.015 -0.009 -0.007 -0.012

(0.045) (0.045) (0.045) (0.049) (0.049) (0.029)

import 0.008 0.006 0.006 0.006 0.006 0.023**

(0.016) (0.016) (0.016) (0.016) (0.016) (0.011)

Gu 16

lowmid -0.149 -0.157 -0.156 -0.149 -0.174 -0.549***

(0.130) (0.127) (0.127) (0.129) (0.123) (0.037)

uppermid 0.414** 0.405* 0.406* 0.417** 0.380* -0.239*

(0.211) (0.211) (0.211) (0.209) (0.215) (0.123)

peaceful 0.143 0.152 0.149 0.141 0.173

(0.162) (0.161) (0.161) (0.161) (0.162)

Observations 8,749 8,749 8,749 8,748 8,748 20,133

Robust standard errors in parentheses

*** p

countries that trade with China as a major partner still face a .418 higher chance of paying bribes.

That is still very high. Because chinasectorcountry lost significance after dropping the peaceful

control, the presence of peace or conflict in a country is shown to have an important relationship

with the importance of firm sector as a determinant of bribes. That specific relationship, whether

direct or indirect, is not clear from this paper.

Policy Recommendations

To the international community interested in African development, convince China to

promote good governance. The data presented in this paper shows that firms in countries that

trade heavily with China and firms that operate in sectors that China is active in face a higher

probability of paying bribes. While causality and its direction, if causality exists at all, are still

not proven, the existence of a correlation is important enough to at least move forward and

emphasize the potential impact China could have on corruption. As more econometric data

comes out, the international community should use this evidence to encourage China to

strengthen accountability and transparency reforms in African countries. This can occur through

bilateral channels or multilateral economic forums, like the U.S.-Africa Business Forum, Forum

on China-Africa Cooperation, or African Economic Conference. China has the ability to do so,

given its numerous foreign aid packages to Africa, its heavy trade relationships, and investments

in infrastructure in Africa. The question is if China wants to change its policies. And it should,

because reduction in corruption spurs innovation and competition, all of which will lead to long-

term, sustainable growth that will benefit both African and Chinese firms. Stronger, more stable

Gu 18

African economies are in Chinas interests, as it will continue to pursue more natural resources

and more markets for decades to come.

For Chinese companies that trade with Africa, encourage participation of African and

Chinese firms in international openness and transparency initiatives like the Extractive

Industries Transparency Initiative (EITI). Data from this paper show that sectors China trades

with exhibit higher probabilities of paying bribes. These sectors are largely extractive industries

and secondary businesses connected to extractive industries. EITI is an international norm that

governments, firms, and civil society actors agree to. This norm requires firms in extractive

industries to report taxes and other payments with the aim of improving transparency in order to

gain accountability. Many countries in Africa have already agreed to EITI principles, even

major Chinese trading partners like Nigeria, Chad, DRC, and Zambia. Chinese companies are in

an especially good position to improve conditions in current African EITI countries and to bring

more African economies to agree to these principles, due to their existing significant ties with

African firms and government officials. This influence could be used to push for governance

reforms that address corruption. But just like how the decision to change foreign aid policy

ultimately lies with the Chinese government, it is up to the Chinese firms to encourage EITI

participation.

Summary

This paper presents evidence that Chinese trade is connected with probabilities of paying

bribes in Africa. It also shows that the main driver of bribery in these countries is the sector that

a firm operates in. Therefore, three possible conclusions arise. Corruption in these sectors could

Gu 19

have already been high due to the nature of extractive industries and already weak institutions,

and these weaknesses in governance persisted after China arrived on the scene. Or, after Chinese

firms began trading in these sectors, corruption increased due to China turning a blind eye

towards any rent-seeking as long as China got its resources. The third possibility is that bribery

is currently high in countries that trade with China, but it was even higher before China came.

Without time-series data, it is difficult to prove any one of these at the correct explanation. Yet,

given the existing literature, the first and second conclusions are most likely. These explanations

drive the policy recommendations to bring international attention to this issue in order to

convince China to use its influence to build good governance that prevents and punishes

corruption, and to encourage Chinese companies and African companies to adopt international

transparency and accountability measures like the EITI.

Further areas of research could first examine official development assistance and private

voluntary assistance from China. This and better measures of private sector FDI should provide

a more comprehensive picture of the economic relationship between China and Africa, because it

studies a dimension in addition to just trade. AidData has a dataset that uses crowdsourced

reports to put estimates on Chinese investments and in Africa. As mentioned earlier, time-series

or panel data could also prove helpful in establishing causality between bribery and Chinese

economic involvement. Evidence showing Chinese actions and policies leading to higher rates

of bribery provides much stronger support for anticorruption reforms. Whatever evidence does

emerge, China can potentially play an important role in helping Africa develop with solid

institutions that promote long-term, sustainable growth that can benefit all parties.

References

Alaimo, V. et al. 2009b. Behind the Investment Climate: Back to BasicsDeterminants of Corruption. In

Fajnzylber, P. et al. (Editors). Does the Investment Climate Matter? Microeconomic Foundations

of Growth in Latin America. Washington, DC: The World Band and Palgrave Macmillan: 139-

178 (Chapter 4).

Bosshard, P. 2007. Chinas role in financing African infrastructure. International Rivers Network.

Egger, P. and Winner, H. 2005. Evidence on corruption as an incentive for foreign direct investment.

European Journal of Political Economy. 21: 932-952.

Habib, M. and Zurawicki L. 2002. Corruption and foreign direct investment. Journal of International

Business Studies. 33: 291-307.

Ogunmuyiwa, M.S. 2012. An econometric analysis of the impact of corruption on FDI in Nigeria.

Journal of African Macroeconomic Review. 2: 148-159.

Robertson, C.J. and Watson, M. 2004. Corruption and change: the impact of foreign direct investment.

Strategic Management Journal. 25: 385-396.

Rossouw, G. et al. 2014. Africans perceptions of Chinese business in Africa: a survey. Ethics Institute of

South Africa.

Svensson, J. 2003. Who Must Pay Bribes and How Much? Evidence from a Cross Section of Firms. The

Quarterly Journal of Economics. 118(1): 207-30.

Wlliams, J. 2013. Rethinking Chinas exploitation of Africa. China Hands.