Do Judges Vary in Their Treatment of Race?

1. Do Judges Vary in Their Treatment of Race? David Abrams (U of Chicago) Marianne Bertrand (U of Chicago) Sendhil Mullainathan (Harvard) June 5, 2007

2. Research Questions • Does the legal system discriminate? • Are African-Americans more likely to be incarcerated? • Do they receive longer sentences?

3. Standard Approach sentenceijt = α + βraceijt + Xijt + εijt jailijt = α + βraceijt + Xijt + εijt • Problem: Race is not randomly assigned, so betas may be biased due to unobservables!

4. Our Approach Use random assignment of cases to judges to answer a related question: Do judges vary in their treatment of race? sentenceijt = α + βraceijt + Xijt + ΣδjDj + ΣγjDj*raceijt + mot + εijt Test for equality of the γj

5. Why is this interesting? • Large variance of sentencing disparities may have negative implications for perceptions of fairness of judicial system • Could also help explain different findings in different studies • Legally important • Would such variation violate constitutional rights? No State shall…deny to any person within its jurisdiction the equal protection of the laws. (14th Amendment)

6. Objectives • Test that cases are randomly assigned to judges • Establish counterfactual where judges don’t vary in treatment of race • For Both: Use Monte Carlo Simulation • Allows for small cell sizes • Allows for skewed Bernoulli variables

7. Monte Carlo Simulation • Use for both test of random assignment and heterogeneity in racial gap • Create cells at the month level • Simulate each observation 500 times, draw simulated data from same cell, with replacement. • For inter-judge heterogeneity in racial gap in sentencing: • Create cells at the month-race level

8. Data Description-Chicago Data • Circuit Court of Cook County • Largest unified court system in the country • Main Chicago location handles 85% of cases • Assignment procedure: • Daily assignment of cases uses random number generator • Exceptions include drugs, murder, some sex crimes • Suburban court locations perform their own random assignment • Data includes all felony cases from 1985-2004 • Over 500,000 cases • Includes charge(s), judge(s), defendant characteristics, plea, disposition, sentence • We use small subset of the data

9. Case Characteristics

10. Case types and outcomes

11. Bootstrapping (I) Testing for Random Assignment

12. 25% 75% 25% 75%

15. Random Assignment Checks • Race • Gender • Age • Total Number of Charges • Charge Type • Also use 10%-90% and 5%-95% ranges

16. Bootstrapping (II)Testing for Racial gap Heterogeneity

20. Numerical Implications • How much does the sentencing gap between black and white defendants vary across judges?

21. Robustness Check • Perhaps race is a proxy for other characteristics, (such as charge) that receive heterogeneous treatment by judges. • Address this concern by looking at subsets of the data • Drug crimes • EFT • Violent • Other

22. Interpretations Evidence of “too much heterogeneity” in Chicago incarceration data need not imply discrimination against Blacks. • Suppose the “appropriate” gap is 25% • Variation in judges between 15 and 25% would be a sign of reverse discrimination • Possible approaches to deal with this issue in the future: • Recidivism?

23. Focus on Restricted Sample • Eliminate “drug” judges • 8 judges receive only drug cases • Overflow drug cases are randomized among remaining judges • Keep only central location • Can expand to other locations, but central location accounts for 85% of cases • Restrict to initial appearance of defendant • Subsequent appearances often assigned to the same judge • Restrict to judges known to have been regular judges under current judicial administration (through consultation with presiding judge’s office)