Presentation is loading. Please wait.

Presentation is loading. Please wait.

Cassia Spohn, Ph.D. Arizona State University, Phoenix, AZ, USA Steven Belenko, Ph.D. Temple University, Philadelphia, PA, USA Byungbae Kim Arizona State.

Similar presentations


Presentation on theme: "Cassia Spohn, Ph.D. Arizona State University, Phoenix, AZ, USA Steven Belenko, Ph.D. Temple University, Philadelphia, PA, USA Byungbae Kim Arizona State."— Presentation transcript:

1 Cassia Spohn, Ph.D. Arizona State University, Phoenix, AZ, USA Steven Belenko, Ph.D. Temple University, Philadelphia, PA, USA Byungbae Kim Arizona State University, Phoenix, AZ.USA Presented at the annual meeting of the European Society of Criminology Budapest, Hungary September 2013

2  This study was funded in part by the National Science Foundation, grant # SES (C. Spohn, principal investigator) and the U.S. Department of Health and Human Services, National Institutes of Health, National Institute on Drug Abuse (NIH/NIDA), grant # U01DA (S. Belenko, principal investigator).

3  Objectives of the Study  Plea Bargaining & the U.S. Sentencing Guidelines  Methods and Data  Findings  Conclusions and Policy Implications

4  Remove the “cloak of secrecy” surrounding charging and plea bargaining decisions in the U.S. federal court system ◦ Substantial body of research on sentence outcomes in U.S. District Courts ◦ But, little research on charging and plea bargaining decisions ◦ Organizational norms vs. individual discretion

5  Do charge reductions vary across prosecutors?  Does inter-prosecutor disparity vary depending on the type of offense (drug v. non-drug offense)?  Do the factors that influence charge reductions vary across prosecutors?

6  Prosecutor offers a “deal” to the defendant to avoid trial ◦ Reduction/dismissal of charges or reduced sentence ◦ Must be approved by the judge ◦ “Trial penalty”—defendant told that more severe sentence will be recommended if case goes to trial  90% of criminal convictions in U.S. occur through plea bargain

7  Handle criminal cases based on violations of U.S. laws  78,440 criminal cases filed in 2011 ◦ Drug trafficking or similar cases are the most common, accounting for about one-third of all criminal case filings excluding immigration, regulatory, or traffic offenses ◦ 51% of Federal prison inmates are serving time for a drug offense  U.S. Attorneys’ offices in each of 94 Federal Court districts handle the prosecution and are responsible for charging and plea bargaining decisions

8  Guidelines were enacted in 1987 (Sentencing Reform Act) ◦ Designed to achieve uniformity, proportionality, and fairness in sentencing by constraining judicial discretion  Sentences based on intersection of crime severity score (1-43) and criminal history score (1-6) ◦ Example: criminal history score of 3 and crime severity score of 24 = presumptive prison sentence of 63 to 78 months  Laws enacted by U.S. Congress dictate lengthy potential prison sentences for drug trafficking cases

9  Plea bargaining poses an obstacle to achieving uniformity, proportionality and fairness in sentencing ◦ Guidelines prescribe similar sentences for similar defendants convicted of same offense ◦ But if conviction offense varies because of discretionary decisions of U.S. Attorneys then similar case types and offenses may not result in same conviction charges, creating unwarranted disparity

10  U.S. Attorneys have considerable discretion ◦ Precluded (in theory) from dismissing or reducing “readily provable charges” ◦ But, can  Recommend sentence at bottom of applicable guideline range  Stipulate to various factors that reduce either the offense seriousness score or the criminal history score  Example: stipulate to amount of drugs smaller than amount in indictment  Example: stipulate to a specific criminal history score  File a motion for a departure for substantial assistance  Determine whether statutory sentencing enhancements for use of weapon should be applied

11  All offenders convicted in three U.S. District Courts, during FYs 1998, 1999, 2000 ◦ District of Minnesota, District of Nebraska, District of Southern Iowa (N = 3139)  Unique dataset that identifies the AUSA to whom the case was assigned  Cases are nested within 102 AUSAs ◦ 48 AUSAs in Minnesota (N = 1188) ◦ 29 AUSAs in Nebraska (N = 1027) ◦ 25 AUSAs in Southern Iowa (N = 924)

12  Dependent Variable (Charge Reduction)  Whether the most serious count was dismissed or was replaced with a less serious count (1 = yes; 0 = no)  Independent Variables ◦ Offender’s race/ethnicity, gender, employment status, citizenship status and education ◦ Charge severity, criminal history score, number of counts, offense type, whether offender detained pretrial ◦ District court where case adjudicated

13  Multi-level modeling ◦ 1 st Step: Estimate an unconditional model that indicates whether there is significant between-prosecutor variation in likelihood of charge reduction ◦ 2 nd Step: Estimate a random intercept model that includes relevant control variables  Two-level logistic regression model in which cases are nested with prosecutors ◦ 3 rd Step: Partition data by offense type (drug and non- drug) and re-estimate models from step two ◦ 4 th Step: Estimate a random coefficient model to determine whether effects of the Independent variables on charge reduction vary significantly across prosecutors

14

15  Question: Does use of charge reduction vary across prosecutors (w/o controlling for offender and case characteristics)  Significant variation in use of charge reductions across prosecutors ◦ Typical prosecutor: predicted probability of charge reduction was 15.9% ◦ Predicted probability ranged from 5.1% to 40.2%  Thus, whereas some prosecutors rarely offered offenders charge reductions (in only 5% of their cases), others reduced charges in a substantial proportion of the cases they handled (40% of their cases)

16  Question: Does inter-prosecutor disparity persist once controls for offender and case characteristics are added to the model?  Significant variation across prosecutors remains ◦ Adding offender and case characteristics reduces inter- prosecutor disparity by 13.4% ◦ Predicted probability for typical prosecutor in Nebraska was 11.6% (compared to 15.9% in unconditional model)  Note: use of charge reductions more common in Minnesota than in Nebraska

17  Inter-prosecutor disparity in use of charge reductions did not vary by offense type ◦ Significant variations across prosecutors for both drug and non-drug offenses

18  Question: Do the factors that influence charge reductions vary across prosecutors?  Prosecutors do attach differential weight to several factors ◦ Ethnicity (Hispanic, but not black) ◦ Gender ◦ Education ◦ Citizenship status ◦ Criminal history score

19  Significant variation in AUSAs use of charge reduction during plea bargaining process ◦ Even after controlling for offender and case characteristics  No differences in level of inter-prosecutor disparity for drug and non-drug offenses  AUSAs’ charge reduction decisions affected differently by offender and case characteristics

20  Need to study discretionary charging decisions and plea negotiations, as these affect sentence imposed by judge ◦ Judge in District of Southern Iowa, “We haven’t eliminated discretion in sentencing—we just moved it across the parking lot to the U.S. Attorney’s Office.”  Need to study how cases are assigned to AUSAs in different offices ◦ Some AUSAs may be assigned cases with weaker evidence that are more likely to be plea bargained  Role of defense counsel and judge in plea bargaining process is unclear ◦ Qualitative research designed to identify their roles is needed ◦ Implications for Just Results and Resource/Efficiency theories  Jurisdictional differences highlight problems of aggregating data across 94 district courts ◦ These courts vary on important dimensions, including use of plea bargains


Download ppt "Cassia Spohn, Ph.D. Arizona State University, Phoenix, AZ, USA Steven Belenko, Ph.D. Temple University, Philadelphia, PA, USA Byungbae Kim Arizona State."

Similar presentations


Ads by Google