Reuniting ‘Is’ and ‘Ought’ in Empirical Legal Scholarship

Reuniting ‘Is’ and ‘Ought’ in Empirical Legal Scholarship

Scholars engaged in empirical legal research have long struggled to balance the methodological demands of social science with the normative aspirations of legal scholarship. In recent years, empirical legal scholarship has increased dramatically in methodological sophistication, but in the process has lost some of its relevance to the normative goals that animate legal scholarship. In many empirical studies, the phenomena that are readily measured have a complex relationship with the values that are relevant to legal reform, yet empirical scholars often neglect to explain how their positive findings relate to normative claims. Although some empirical studies offer prescriptions, they often rely on normative premises that are clearly untenable or simply fail to explain how they purport to derive an ‘ought’ from an ‘is.’ Other empirical studies avoid prescription altogether, reporting results without clarifying how they are relevant to meaningful questions about law or legal institutions.

Using as examples three types of measures commonly used to evaluate judges and institutions—citation counts, reversal rates, and inter-judge disparities—this Article describes widespread flaws in efforts to connect the ‘is’ and the ‘ought’ in empirical legal scholarship. The Article argues that normative implications should not be an afterthought in empirical research, but rather should inform research design. Empirical scholars should focus on quantities that can guide policy, and not merely on phenomena that are conveniently measured. They should be explicit about how they propose to measure the goodness of outcomes, disclose what assumptions are necessary to justify their proposed metrics, and explain how these metrics relate to the observable data. When values are difficult to quantify, legal empiricists will need to develop theoretical frameworks and empirical methods that can credibly connect empirical findings to policy-relevant conclusions.

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