New ESRC project: Disproportionality
CERS member Dr Jose Pina-Sánchez (School of Law, Leeds) will lead an ESRC grant as PI, exploring 'Disproportionality: Exploring the Nature of Ethnic Disparities in Sentencing through Causal Inference'.
Empirical sentencing research shows how offenders from ethnic minority groups tend to receive harsher punishments than white offenders who committed similar crimes. These disparities have been documented in great detail; corroborated across jurisdictions, offence types, and sentence outcomes. However, one key question remains: can such disparities be taken as evidence of discrimination?
Research based on real cases cannot randomise offenders by their ethnicity, hence, how do we know that those disparities are not due to unobserved relevant case characteristics? For example, we would expect to see differences in sentence severity for similar crimes committed by Black and White offenders if White offenders are shown to plead guilty more often than black offenders.
The analytical response to this problem has been to ‘control for’ any relevant case characteristics. But what if those differences are based on case characteristics such as offender dangerousness, that cannot be easily measured, nor controlled for? And, what if those case characteristics are not neutrally defined but subject to potential discriminatory practices? These are important methodological questions that remain unresolved.
Here, we suggest using the new sentencing datasets made available by Administrative Data Research UK, and some of the latest sensitivity analysis techniques developed in Epidemiology to overcome this methodological impasse.
Rather than uncritically dismissing ethnic disparities because of their inability to make perfect ‘like with like’ comparisons, we pose the following question: What should the strength of the unobserved relevant case characteristics be to explain away the ethnic disparities observed in the literature?
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