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Article Excerpt
I. INTRODUCTION
A sizable literature has emerged recently to examine factors that impact the level of corruption across countries. For example, Ades and Di Tella (1999) found that corruption is higher in countries where domestic firms are sheltered from foreign competition. Graeff and Mehlkop (2003) documented the relationship between a country's economic freedom and its level of corruption. Brunetti and Weder (2003) found that higher freedom of the press is associated with less corruption. Van Rijckeghem and Weder (2001) showed that the higher the ratio of government wages to manufacturing wages, the lower is corruption in a country. (1)
The current research on corruption has two common characteristics. First, it exclusively relies on subjective measures of corruption. Specifically, it employs various indexes of corruption perception based on the surveys of international business people, expatriates, risk analysts, and local residents. The use of a corruption perception index is justified because the actual level of corruption in a country is difficult to observe. Certain potential measures of corruption, such as the number of prosecuted corruption-related cases in a country, may be rather noisy measures. For example, a low arrest rate for bribery may indicate a low prevalence of corruption or it may indicate widespread corruption with no prevention efforts.
Second, because corruption data are available only at the aggregate (country) level, existing research has focused on explaining the cross-country variation in corruption. Two exceptions are Swamy et al. (2001) and Svensson (2003). Swamy et al. (2001) used microdata where respondents answered questions on hypothetical situations regarding corruption. In the same paper, they analyzed the responses of 350 managers from the Republic of Georgia to a question on the frequency of an official requesting unofficial payments. Svensson (2003) analyzed the bribery behavior of 176 firms in Uganda.
In its benchmark specification, this paper analyzes information obtained from more than 55,000 individuals from 30 countries pertaining to their direct experiences with bribery. Specifically, the individuals are asked whether any government official such as a government worker, police officer, or inspector in that country has asked them or expected them to pay a bribe for his services during the previous year. Using these microdata, the paper investigates the determinants of the probability of being asked for a bribe. Following the theoretical arguments put forth by Treisman (2000), this probability is explained by a number of country characteristics. In addition, personal characteristics of the individuals are controlled for, as they are expected to impact the exposure to corruption through the mechanisms discussed in Section II below. The results show that the characteristics of an individual influence his/her propensity of exposure to bribery. For example, males and individuals with higher income and education are more likely to be asked for a bribe. Country characteristics also influence exposure to bribery. Examples are the risk of expropriation, average education, and the unemployment rate in the country.
A second contribution of the paper was to create an aggregate (country level) corruption index using information provided by more than 90,000 individuals in the data set. The index is the proportion of individuals who were asked for a bribe in the country. As such, it is an indicator of the breadth of corruption. This measure of corruption is compared with three widely used corruption perception indices published by Transparency International (TI), Business International (BI), and International Country Risk Guide (ICRG).
II. WHAT DETERMINES CORRUPTION? THEORETICAL CONSIDERATIONS
A. Macrolevel
Treisman (2000) details a number of hypotheses that link the level of corruption in the country to its legal, political, and socio-economic characteristics. Following his discussion and the literature he cites, it is postulated that at the macrolevel, the following holds:
(1) [COR.sub.j] = [f.sub.1]([C.sub.j], [Econ.sub.j]),
where the extent of corruption in country j ([COR.sub.j]) depends on cultural attributes (C), as well as the level of economic development of the country (Econ). Economic development, in contrast, is argued to be negatively impacted by the extent of corruption in the country (Mauro 1995). To incorporate this connection, consider Equation (2) where corruption is postulated to have a direct impact on economic development.
(2) [Econ.sub.j] = [f.sub.2]([COR.sub.j,] [K.sub.i], [C.sub.j], [H.sub.j]).
Acemoglu, Johnson, and Robinson (2001) demonstrate that the quality of institutions in the country, such as secure property rights, has a direct impact on development. Thus, in Equation (2), K represents the institutional characteristics of the country. H stands for standard human capital measures that impact economic development, such as the level of education in the country. Substituting Equation (2) into (1) generates the macrolevel reduced form as follows:
(3) [COR.sub.j] = [f.sub.3] ([C.sub.j], [H.sub.j], [K.sub.j]).
B. Microlevel
At the microlevel, a number of formulations can be developed to demonstrate the determinants of corruption. Examples are Kaufmann and Wei (1999), Ades and Di Tella (1999), and Van Rijckeghem and Weder (2001). Similarly, one can consider that the utility of the bribe-receiving government official depends on a composite consumption good, the number of bribes he receives, and the quality of the institutions in the country. Consumption depends on the sum of earned legal income and illegal income. In this framework, it is easy to show that an increase in the income of the potential victim would increase the propensity to ask for a bribe. Alternatively, an increase in the quality of the institutions in the country, which would increase the probability of apprehension, would in turn reduce the propensity to ask for a bribe. (2)
Within this framework I estimate:
(4) [COR.sub.ij] = f([X.sub.ij], [C.sub.j], [H.sub.j], [K.sub.j]),
where [COR.sub.ij] is the propensity of the ith individual who lives in country j to be a victim of corruption, [X.sub.ij] represents personal characteristics of the individual, and [C.sub.j], [K.sub.j], and [H.sub.j] are the characteristics of the country as described above. The theoretical and empirical research have identified viable candidates for X, C, H, and K, which are described below.
C. Individual-Specific Explanatory Variables
The propensity for being targeted for a bribe is assumed to depend on age, marital status, labor market activity, wealth, education, gender, and the location of the residence of the individual. Because the dependent variable is essentially a measure of "exposure to bribery," individuals in certain age, wealth, and labor market categories may be at a higher risk of being asked for a bribe. For example, all else the same, highly educated and high-income individuals should have higher exposure to being asked for a bribe by a government official because of their higher earning capacity and because they are likely to have more opportunities to interact with government officials. The opposite should be true for the very young and old, as well as home keepers, as they may have less contact with government officials in comparison to prime-age individuals. Males are expected to be more frequent targets of bribery for a number of reasons. First, in most countries, especially in developing countries, males are more active than females in the labor market for various reasons, and therefore, they have more exposure to government officials. Second, all else the same, males have a higher propensity to engage in criminal activity or to have tolerance for illegal activity (Mocan and Rees 2005; Swamy et al. 2001).
In larger cities, the extent of bribery may be higher because economic activity may be larger and more varied in scope, which may increase the contact with government. It can also be argued that the relationship between individuals and government officials may be less personal in larger cities in comparison to smaller ones, which may make it easier to ask for a bribe (Hunt 2004).
D. Country Characteristics
Higher quality institutions are expected to reduce the incidence of being asked for a bribe. The quality of the institutions of the country can be measured in a number of ways such as the independence of the judicial system and the protection of civil liberties. Following Acemoglu, Johnson, and Robinson (2001), I use the risk of expropriation in the country (the risk of confiscation and forced nationalization of property) as a measure of the quality of the institutions. The structure of institutions is likely to change over the course of development; that is, the protection of property rights might get stronger as the country develops economically. Acemoglu, Johnson, and Robinson (2001) control for the endogeneity of institutions by using the settler mortality rates in ex-colonies as instruments. Because most countries in our sample are not ex-colonies, in this paper, institutional quality is instrumented by geographic indicators as employed by McArthur and Sachs (2001).
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