China and India in Africa: Implications of New Private Sector Actors on Bribe Paying Incidence

By Sankalp Gowda
The Developing Economist
2015, Vol. 2 No. 1 | pg. 2/3 |

V. Conceptual Framework, Data Description, and Empirical Specification

As discussed in the above studies (see Svensson, 2003 and Chen, et al., 2011), the factors that affect bribe payout by firms can be expressed as a function of several different factors:

where Brij is the amount of bribes paid out by firm i in country j, X is a vector of country level attributes representing culture, legal systems, and institutional capacity; c is a vector of firm level characteristics representing Control Rights; b is a vector of firm level characteristics representing Bargaining Power; g is a vector of firm level characteristics representing Grease the Wheels; and z is a vector of other firm characteristics (unrelated to the three hypotheses) that might also lead to bribe paying. The first set of vectors is macro-level while the second set is focused at the firm level.

For the sake of this analysis, I will set aside the level of bribe payouts and instead look at bribe paying incidence whether firms report having paid any bribes to a public official as the dependent variable. This can be expressed as:

where BD is a dummy variable equal to one if the firm reports paying a bribe, and zero otherwise. The dependent variable comes from several questions in the Enterprise Surveys which ask the respondent whether a "gift or informal payment" was expected or requested with regard to customs, taxes, licenses, regulation, public services, etc.

In addition to the dependent dummy variable, the other firm level variables also come from the Enterprise Surveys. The World Bank Enterprise Surveys provide a cross-sectional survey of industrial and service enterprises, with the data used in this analysis focusing on the Africa region between the years of 2006 and 2014. Data collection efforts were led by theWorld Bank, which has been administering business environment surveys since the mid 1990s. The surveys focus on the manufacturing and services sectors and 100% state owned enterprises are not allowed to participate. Important for the purposes of this paper, the surveys also do not include data from firms operating in extractive industries like oil or minerals. The surveys are administered through face-to-face interviews with business owners and top managers (World Bank, 2014).

The firm level vectors use variables that I created from responses to the Enterprise Surveys. The Control Rights vector is represented by the Government Help dummy variable, which is equal to one if a firm requested any public services in the past two years. According to the theory above, requesting government help is expected to have a positive relationship with bribe paying. The Bargaining Rights vector is represented by two dummy variables: Access to Credit and Credit Constrained. Access to Credit is used to gauge a firm's solvency, and is equal to one when firms have access to a line of credit or overdraft facility. Credit Constrained is used to gauge how difficult it would be for a firm to pick up and move to a less corrupt market, and is equal to one when firms have a) applied for a loan and been rejected, or b) not applied for a loan for reasons other than "does not need a loan." Both of these firm traits are expected to have a positive relationship with bribe paying. Grease the Wheels is measured through two dummy variables: Trust in Courts and Competition. Trust in Courts measures firms' belief in the effectiveness of government regulation and bureaucracy, and is equal to one when respondents said they believed the judicial system worked fairly and impartially. Competition measures the business environment in which firms are operating, and is equal to one if firms reported reducing prices due to competition against another firm. Trust in Courts is expected to have a negative relationship with bribe paying and Competition is expected to have a positive relationship as firms make decisions to gain an advantage over their competitors. I also created a Foreign dummy variable which equals one if the firm has any foreign ownership. This last variable will provide some insight to the impact of foreign firms on corruption in Africa but data limitations prevent us from separating Indian and Chinese firms from the rest.

Other firm level variables include Registered (=1 if the firm was officialy registered when it began operations), Government Owned (=1 if any government ownership), Medium (=1 if the firm has 20-99 employees), Large (=1 if the firm has greater than 100 employees), Young (=1 if the firm has operated for less than 20 years), Old (=1 if the firm has operated for more than 50 years), Sales (the log of last year's sales), and Trade (=1 if the firm imported or exported any goods).

Chen, et al. (2008) includes several macro-level variables, but I decided to focus on the two in particular that I felt were of the most importance to this paper. The first is a British Legal Origin dummy variable, which I adapted from a list of countries with British legal origins found in Klerman, et al (2012). For the African continent, this includes Ghana, Tanzania, Malawi, Uganda, Gambia, Zambia, Nigeria, Kenya, Mauritius, Lesotho, South Africa, and Zimbabwe. The second is an IndoChina dummy variable, which I set equal to 1 for African countries that have developed strong investment and trade relationships with India and China (Broadman, 2008; DahmanSa˜adi, 2013; Leung and Zhou, 2014; Nayyar and Aggarwal, 2014, 2). Countries coded for the IndoChina dummy are South Africa, Nigeria, Zambia, Algeria, Sudan, DRC, Ethiopia, Mauritius, Tanzania, Madagascar, Guinea, Kenya, Mozambique, Senegal, and Uganda. If Indian and Chinese firms have in fact exported their bribery-heavy management practices to these countries, this variable should be positively related to corruption. To further disaggregate this potential result, I also created an interaction term called IndoChina x Foreign to see the effect of being a foreign firm operating in a country with a strong IndoChina presence. If the claims of Indian and Chinese corruption are to be believed, this term should bear a strong positive relationship to bribe paying.

Within this particular mix of variables, there is the potential that some micro and macro variables could fall under each of the vectors on the right-hand side of equations (1) and (2). To ensure a proper model and avoid incorrect inferences due to multicollinearity, I constructed a correlation matrix (See Appendix Table 1) for all independent variables in the dataset. I then dropped variables with particularly high correlations (>.50) that could create multicollinearity issues. For example, I did not include IndoChina and British Legal Origin in the same specification, although it would have been interesting to see the effect of controlling for legal origin on the IndoChina coefficient.

After taking these results into account, I developed the following basic economic specification that I adapted for four different models: