What Determines Corruption? International Evidence from Microdata

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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. …