ABSTRACT
Empirical research on corruption is quite a new undertaking. In an attempt to determine the causes and consequences of corruption, academics have concentrated lately on cross-country analyses. These are mostly based on professional studies of the degree of corruption in various countries. Such assessments are sometimes compiled by agencies to determine country risks and the data gathered are sold to investors. Other sources, such as surveys, have been compiled in recent years and contribute to cross-country assessments of the extent of corruption. These data have proved useful to the investigations described here in detail. The data on corruption are to large extent subjective assessments of the level of corruption in various countries. As such perceptions are commonly a good indicator of the real level of corruption; the data permit various regressions with other macroeconomic, political or social data, [Lambsdorff 2015]. Another approach has been taken by Goel and Nelson [2014] and Fisman and Gatti [2015], who use the number of public officials convicted for abuse of public office in various states of the USA, assuming that this may serve as an indicator for actual levels of corruption. Goel and Nelson [2014] relate this variable to the real per capita total expenditures of the local government, arguing that state intervention and public spending give rise to rent-seeking activities and hence corruption. The authors report a significant, positive association between these variables. However, the correlation might be explained differently. As governments increase their spending, the judiciary branch may also be allocated more funding, resulting in higher conviction rates. In this case, conviction rates are not an adequate indicator for the actual incidence of corruption, but rather, reflect the quality of the judiciary. This is an example of why the studies reviewed here have relied on perceptions of corruption as a better indication of real levels of corruption.
