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Transcript
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

This study was funded in part by the National Science Foundation,
grant # SES-0136236 (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 #
U01DA025284 (S. Belenko, principal investigator).
Objectives
Plea
of the Study
Bargaining & the U.S. Sentencing Guidelines
Methods
and Data
Findings
Conclusions
and Policy Implications

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



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?

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


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

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

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

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

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)

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

Multi-level modeling
◦ 1st Step: Estimate an unconditional model that indicates
whether there is significant between-prosecutor variation
in likelihood of charge reduction
◦ 2nd Step: Estimate a random intercept model that
includes relevant control variables
 Two-level logistic regression model in which cases are
nested with prosecutors
◦ 3rd Step: Partition data by offense type (drug and nondrug) and re-estimate models from step two
◦ 4th Step: Estimate a random coefficient model to
determine whether effects of the Independent variables
on charge reduction vary significantly across prosecutors
Dependent Variable: Charge Reduction
Independent Variables
Race/Ethnicity
White
Black
Hispanic
Other
Offender is female
Offender’s age
Offender has high school degree
Offender is a non-citizen
Offender’s criminal history score (scale that ranges from 1 to 6)
Offender detained pretrial
Charge severity (scale that ranges from 1 to 11)
Number of counts
Offense type
Violent
Drug
Property
Fraud
Immigration
Other
Mean
.16
SD
.37
.47
.23
.26
.04
.16
33.4
.41
.21
2.5
.56
6.9
2.9
.68
.42
.44
.20
.37
10.2
.49
.41
1.7
.50
1.9
4.5
.08
.60
.09
.09
.06
.09
.28
.49
.28
.28
.23
.29


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)


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 interprosecutor 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

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


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

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

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