Government funding incentives and felony charge rates

ABSTRACT

Using data from the State of Wisconsin’s Consolidated Court Automation Programs (CCAP), we find evidence that Wisconsin’s District Attorneys decisions on how to file criminal complaints (charges) changed meaningfully in 2001 and 2009. These changes align with changes in the State’s funding process for District Attorneys (DAs). Specifically, after the introduction and alterations of a formula-based funding process, which is determined by charges filed, we find an increase in the number of felony charges. Further, we only observe this increase where prosecutorial discretion exists. Finally, relative to misdemeanors, we find a discontinuous increase in charges that is consistent with the formula. In summary, we find evidence consistent with a relationship between DA funding policies and DA behavior.

KEYWORDS:

• Government funding formula
• prosecutorial discretion

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the author(s).

Notes

1. There are 71 DA offices in total covering 72 counties. Shawano and Menominee counties are combined.

2. The State Prosecutors Office has provided the legislature with annual reports on DA office workloads based on the caseload formula with minor adjustments as noted in Legislative Fiscal Bureau (2009).

3. Beyond discretionary and violent felonies, one might want to consider types of misdemeanors and felonies. The issue with this approach is the data is not available in CCAP. We do observe the type of felony or misdemeanor, but the data is only what was originally charged and the defendant was not found guilty or the final charge the defendant was found guilty of. So, the type of felony/misdemeanor (Type A, B, etc) is not used in our analysis.

4. The correlation between county-year arrests and county-year felony charges is 0.795, so this measure capture a lot of thevariation in felony charges, hence is a very strong control variable.

5. We conduct a ”leave-out” analysis analogous to the estimation presented in Column 1 of Table 3 where we systematically drop each district in the state one at a time in order to investigate if estimates are potentially driven by a single district. Results of this robustness exercise show that the two treatment variables (e.g. Treatment2001 and Treatment2009) are qualitatively and largely quantitatively unchanged across all 72 regressions. In all cases statistical significance is unchanged, while point estimates for Treatment2001 range from 12.36–31.70 and from 43.74–46.44 for Treatment2009.

Additional information

Notes on contributors

Bryan Engelhardt

Dr. Chad Cotti is the Oshkosh Corporation Endowed Professor and John McNaughton Rosebush Professor at the University of Wisconsin-Oshkosh, as well as a Research Affiliate of the Center for Demography of Health and Aging at the University of Wisconsin-Madison. Dr. Cotti’s research interests lie broadly within health economics and public policy, where he has studied a wide range of topics, including tobacco control policies, drunk driving, food stamp distribution, and areas of criminal justice policy. He holds a B.S. in Quantitative Economics from the University of Wisconsin Oshkosh, an M.P.A. from the Robert M. La Follette School of Public Policy at the University of Wisconsin-Madison, and a Ph.D. in Economics from the University of Wisconsin-Milwaukee.

Bryan Engelhardt is an Associate Professor of Economics at the University of Wisconsin - Oshkosh. He earned his Ph.D. in Economics at the University of Iowa where he began his studies on the impacts of unemployment on crime. Dr. Engelhardt previously worked at the Federal Reserve Bank of Cleveland and the College of the Holy Cross.

Matt Richie is an assistant professor of criminal justice at the University of Wisconsin – Oshkosh. His research focuses on jail recidivism and operations as well as pre-trial/post-conviction treatment diversion programming.