Corruption Convictions Index

Valid and reliable direct measurements of corruption itself are impossible. Indirect measures, however, can yield useful comparisons, indicating—even if imprecisely—major contrasts and trends. The most commonly index measuring corruption in American states uses data from the Justice Department’s “Report to Congress on the Activities and Operations of the Public Integrity Section.” These data cover a broad range of crimes from election fraud to wire fraud. In response to Watergate and growing concerns about corruption, a Public Integrity Section (PIN) was established in the Justice Department in 1976 to prosecute corrupt officials. PIN reports the total convictions for crimes related to corruption annually. The data are available starting from 1976. Most studies using these data deflate the number of convictions with the population in each state to construct a corruption index which we refer to as the Corruption Convictions Index (CCI). The most recent report with data is from 2015.

CCI, however, suffers from several significant problems.:

• The data cover public corruption convictions in federal courts only; thus, cases tried by state and local prosecutors are excluded.
• Federal prosecutors have considerable discretion over how much effort to put into investigating public corruption. Hence, the number of convictions depends not only on the level of corruption but also on levels of prosecutorial effort (Boylan and Long, 2003).
• As Rasmusen et al. (2009) argue, prosecutors choose which cases to prosecute so as to maximize their conviction rates and visibility. They are more likely to prosecute high profile cases (Gordon 2009).
• The number of federal convictions is related to prosecutorial resources in a state. Alt and Lassen (2012), for example, find that greater prosecutorial resources result in more convictions using data on corruption convictions in U.S. states over 25 years.
• There may well be partisan bias in the prosecution of public officials by federal prosecutors, i.e., the U.S. attorneys.  They are appointed by the President with the advice and support of home-state partisans (Alt and Lassen 2012). Anecdotal as well as empirical evidence supports the partisan-bias hypothesis: the unprecedented midterm dismissal of seven U.S. attorneys in 2007, for example, led to congressional investigations. Some were allegedly dismissed either because they did not pursue corruption investigations against prominent Democrats with sufficient vigor or because they did pursue prominent Republicans (Gordon 2009). Using data from public corruption prosecutions, Gordon (2009) finds evidence of partisan bias both under Bush and Clinton Justice Departments.
• While data are reported year by year, there is an unknown, and most likely variable, time lag between crimes and convictions.
• CCI covers only those officials who are caught and, of course, convicted. The data give little to no indication as to the seriousness or consequences of a case (an attribute that itself could be judged through the lenses of political culture), the effects of plea bargains and grants of immunity, the relationship between the charges on which a conviction was obtained and those resulting in acquittal for the same defendants, or between charges that could have been lodged but were not, for reasons of prosecutorial judgment.

Using CCI to measure corruption raises other problems too. Over the three decades between 1981 and 2010, both North and South Dakota appear to be two of the most corrupt states in America. This is quite surprising since the Dakotas were among the leading states in the anti-corruption movement that started in the late 19th century and continued through the 1930s (Uslaner, 2008). According to Uslaner (2008), prairie states such as the Dakotas, Minnesota, and Wisconsin are historically the least corrupt states. Figures below show the levels of CCI in the Dakotas relative to the states perceived by many as corrupt such as Illinois and New Jersey. CCI in South and North Dakota is consistently higher than it is in Illinois and New Jersey, respectively.