Abstract
Over the last several decades, federal courts have devoted considerable effort towards improving fairness in sentencing. Despite these efforts, research has consistently shown that racial/ethnic minority defendants receive harsher sentences than similarly situated White defendants. While a large body of research has detected these racial/ethnic disparities, relatively few studies have examined how they have changed over time in light of the different legal, societal, and priority changes in federal criminal courts (and the United States more broadly). Using 22 years of federal sentencing data, the current study assesses trends in Black-White and Hispanic-White sentencing disparities (net of factors relevant to sentencing). Results suggest that trends in racial/ethnic sentencing disparities differ by the dependent variable examined. At incarceration, Black and Hispanic disadvantages have been largely time stable. However, racial/ethnic effects on sentence length have changed over time, diminishing in early years before re-aggravating in later years. Findings suggest that the movement towards racial/ethnic equity in sentencing has been slower than many might hope, with setbacks along the way. The re-emergence of racial/ethnic disadvantages indicates the need for a renewed focus towards reducing racial/ethnic disparities and creating greater egalitarianism in sentencing.
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Notes
This is referred to as the pre-PROTECT Act in their study. For parsimony with our point, however, we refer to it as the “pre-pre-Booker era.” The USSC’s 2017 report also includes this “pre-pre-Booker” era. However, similar to the discussion in the text, Ulmer et al. (2011) find relative stability while the 2017 USSC report finds aggravation.
Immigration defendants are systemically different from the average federal defendant (more likely to be Hispanic, possess systemically lower levels of education, and close to 90% non-citizen; see Hartley & Tillyer, 2012).
We tried to use multiple imputation for the full, about 1 million observation, dataset but it wouldn’t converge. Thus, as a robustness check we used multiple imputation for several year-specific models. The results were identical.
Data on district-level predictors were not available each year. When an upper and lower bound existed we filled in missing years using linear interpolation. Alternatively, when an upper and lower bound did not exist, we utilized 5-year rates of change following the last available estimate. Sensitivity tests were run and the decision to interpolate/extrapolate missing district-level data had no discernable impact on racial/ethnic effects or trends.
We recognize there is some debate surrounding whether presumptive sentence is endogenous to the final sentencing decision (see Starr & Rehavi, 2013). To be conservative (see Light, 2022), we use presumptive sentence in our estimates of racial/ethnic effects (for similar practices see Kim et al., 2019; Ulmer & Parker, 2020). Models using additive controls for offense severity and criminal history were ran and they provided larger in magnitude, but similar in trends, racial/ethnic effects – like we would expect.
Some studies include judge-initiated (upward and downward) departures as predictors (Ulmer et al., 2011; USSC, 2012). However, this approach has been critiqued because judge-initiated departures occur simultaneous to the final sentencing decision (see Starr & Rehavi, 2013). Our main models include a control for government-sponsored departure but not judge initiated departures (see Fischman & Schanzenbach, 2012; Ulmer & Parker, 2020). Models including the full set of departure controls were estimated, and the racial/ethnic trends were identical. We also ran supplemental models with mandatory minimum eligibility as a control variable, and the results were substantively identical to those presented here.
Due to the scope of the analysis, we omit a lengthy discussion of descriptive statistics. However, a table of descriptive statistics is available for reference in Appendix A.
The utility of multi-level models has been well established in past federal sentencing research (Johnson et al., 2008; Kim et al., 2019; Light, 2014; Ulmer et al., 2011). In the context of this study, the multi-level models adjust the degrees of freedom at higher levels of analysis to the appropriate sample size and account for the dependency of cases within districts (Byrk & Raudenbush, 1992).
Unconditional models show significant variance in both incarceration and sentence length at the case- and district-level. In the sentence length models (where it is meaningful to estimate interclass correlations), 95% of variance lies at the case-level and 5% of variance lies at the district-level.
We do not include the Heckman (1976) correction term in our sentence length models due to substantive collinearity between the inverse mills ratio and both guideline minimum and presentence detention (for similar concerns surrounding the Heckman correction see Bushway et al., 2007). Supplemental models with the Heckman correction term were estimated and the results were substantively similar to those presented.
District-level controls in these models represent the average over the study period.
These raw sentence length trends partitioned by defendant race/ethnicity and drug cases are available upon request.
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Funding for this research was received from the National Institute of Justice [2020-R2-CX-0016].
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Holmes, B., Feldmeyer, B. The Only Thing Constant is Change: Temporal Analyses of Racial/Ethnic Sentencing Disparities. Am J Crim Just 48, 1080–1104 (2023). https://doi.org/10.1007/s12103-023-09725-9
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DOI: https://doi.org/10.1007/s12103-023-09725-9