Abstract
In the US, federal prosecutors are appointed by the president, confirmed by the Senate, and have significant discretion over which cases they choose to take to court. Federal prosecutors handling an overwhelming majority of corruption cases invites the possibility of political influence in the monitoring of corruption. Additionally, political disparities across states may result in differences in corrupt behavior. Using individual case level data, I examine the effect political factors have on federal corruption cases, with an emphasis on states that are an important focus in the next presidential election. I find that corruption convictions tend to be higher in politically important states. This effect seems more significant when Democratic administrations are in power. In addition, it seems that these effects are relevant only for corruption crimes labeled as “federal”.
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Notes
Definition of corruption comes from Rose-Ackerman (2004).
Political importance will be explained further in the data section of the paper.
If the results indicate that politically important states experience more corruption convictions, this is despite of the possible negative effect corruption has on growth. The debate over whether corruption is growth enhancing or inhibiting is ongoing, but recent evidence suggests that it is more likely to harm than help (Dutta and Sobel 2016).
Federal prosecutors, US District Attorney, and US0 Attorney are used interchangeably throughout the paper. These three terms are to be distinguished from Assistant US Attorneys.
The conviction rate is above 90 percent.
Official corruption, or public corruption, is defined as the “criminal prosecution of public employees for misconduct in, or misuse of, office, including attempts by private citizens to bribe or otherwise corrupt public employees” (DOJ 2014; pg. A-59).
Bologna (2016) also explores the possibility that the PIN data is influenced by congressional dominance (see, e.g., Weingast 1984; Weingast and Marshall 1988; Weingast and Moran 1983) as well, given that the PIN data comes from a report to Congress. However, little evidence of congressional dominance is found when looking at these specific measures of corruption.
This finding is intuitively different from the finding in Nyhan and Rehavi (2017). Here, I am comparing disposals in election years with disposals in non-election years, whereas Nyhan and Rehavi (2017) only explore how partisan, or non-partisan, case timing differs based on election dates. They do not explore general trends. Even if the timing of partisan cases depends on the election date, it remains unclear how the election year as a whole differs from other years in general.
It is important to note here that resources available to judicial districts are controlled for in the analysis using federal expenditure data. This point relates to the Alt and Lassen (2014) finding that resources are an important determinant of corruption convictions. The current administration can influence prosecutorial output without relying on prosecutorial employment incentives in two ways: (1) they can provide the district with more resources, and (2) they can manipulate the case queue. Controlling for resource allocation takes care of (1). However, it is still possible that differences in convictions across states come from (2).
Federal judicial salary is included as a control.
Boylan (2005) argues that due to uncertainties regarding presidential politics, prosecutors are concerned about future employment immediately after instatement. However, since within term dismissals are quite rare (Scott, 2007) we can expect the prosecutors to become most concerned with future employment when they near the end of their term.
I find that disposals tend to increase in election years for corruption cases. While the incentive structures may differ for federal prosecutors, this is possibly suggesting that federal prosecutors are reallocating resources, á la Bandyopadhyay and McCannon (2014; 2015a; 2015b), to focus on corruption cases specifically. Corruption cases are highly publicized and may perhaps be used as a signaling mechanism for future employers.
Prosecutors can choose to immediately decline a case, or consider the case further for criminal investigation, but eventually decline it before taking it to trial.
Many factors affect the timing, length, and outcome of a trial. However, it is unlikely that those who could influence the timing or outcome of the conviction, and are not a part of the prosecutorial staff or current administration, have an incentive to influence outcomes for political reasons. For example, judges and juries should have no systematic political motivation as they are almost always chosen at random. Additionally, judges serve lifelong terms, therefore it is not clear that they are sympathetic to the party in power.
Boylan (2005) acknowledges that sentence length may be a function of conviction rates and tests the robustness of his results for this fact. However, he does not test whether or not the number of convictions drives the results.
An interesting question may be whether prosecutors purposely extend or shorten cases such that more convictions occur in the election year. Again, this effect may be indistinguishable from administrative queue manipulation, but it would shed some light on whether the increase in convictions come from closing ongoing cases or if new cases are pursued. Establishing where the increase in convictions come from is beyond the scope of this paper.
Government employment and population are highly correlated (0.98). However, I do estimate results using a governmental employee scaled corruption measure as this is a common alternative to population scaled measures in the literature. The results using this alternative measure mirror the population scaled results. However, in both cases, the scaling used does not control for a variety of state-level characteristics unrelated to political influence that may cause convictions per-capita to be higher in politically important states.
Note that this variable changes only after a presidential election occurs. Since I am interested in the actions of federal prosecutors during a single presidential term I match this variable with the term years of the incumbent party. For example, the election occurring in 2000 would be matched to TRAC data from 1996 to 2000.
Young et al. (2001) actually calculate a variable that is higher for states in which the election was not close and lower for states in which the election was close. Given this, they actually divided their (not) closeness variable by the standard deviation of democrat votes.
I use an interaction term between election year and an indicator equal to one if the governor is the same party as the president, finding no differential effect. Additionally, I interact importance with election year, again finding no significant differences across states. These results are available upon request.
Additional controls described in detail below.
Glaeser and Saks (2006) actually include the percent of the population that is living in an urban area rather than population density. However, since this data is only available in Census years and the Census definition of an “urban area” changes over time, this paper is using population density in its place.
Regressions with state-fixed effects are reported for robustness; however, as the results show, Political Importance is overpowered when state-fixed effects are included. The average standard deviation of Political Importance overtime within a state is 0.026, explaining less than five percent of a one standard deviation increase in Political Importance overall.
The total number of disposals is equal to the total number of cases closed. Since most cases that are pursued end in a conviction, a disposal generally involves either a dismissal or a conviction. Thus, the percent of cases disposed of with a dismissal is generally equal to 1 - the percent of the cases disposed of with a conviction. However, some cases result in a not guilty verdict. I check the robustness of the conviction results using the percentage of cases resulting in a disposal as the dependent variable instead, finding that the signs tend to mirror the signs found using the conviction counterpart, though some statistical significance is lost. Results available upon request.
Though normally these crimes are categorized as being in the program category as “official corruption” there are some cases where they are categorized differently.
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Acknowledgements
I would like to thank the Center for Free Enterprise at West Virginia University for making this project possible through data funding. I would also like to thank the people of the Mercatus Center at George Mason University, participants in the Southern Economic Association meetings, Andrew Young, and Bryan McCannon for their helpful comments.
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Pavlik, J.B. Political importance and its relation to the federal prosecution of public corruption. Const Polit Econ 28, 346–372 (2017). https://doi.org/10.1007/s10602-017-9243-0
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DOI: https://doi.org/10.1007/s10602-017-9243-0