I received the above in the mail today -- looks like a terrific set of essays on a wide range of topics using an empirical approach. Anyone have more in-depth reaction to essays they've read? I look forward to taking a closer look.
While I take no side (or sides) in the proverbial "Stata v. SPSS v. R" wars (I use all three), I want to note Stata's recent venture into the Blog universe. Though it's far too early to see if the Stata blog will succeed, it's an interesting development nonetheless. A recent post provides a helpful step-by-step guide to one of the more common (yet error-prone) data tasks: merging data sets. The explanation is helpful and includes all too common pitfalls.
For those still looking for something worthwhile to do this summer I recommend the following (click on links for detailed information):
Workshops on Research Design for Causal Inference
Northwestern University will be hosting two workshops this summer on Research Design for Causal Inference, sponsored by Northwestern University, University of Southern California, the Society for Empirical Legal Studies, and The Searle Center on Law, Regulation, and Economic Growth at Northwestern Law.
Increased attention to judicial decisionmaking has generated increased attention to related research design questions. A recent paper by Jon Nash (Emory) and Rafael Pardo (U. Wash.), Does Ideology Matter in Bankruptcy? Voting Behavior on the Courts of Appeals, attends to both by studying circuit judges' treatment of a discrete bankruptcy issue. The paper finds that non-ideological factors informed judges' decisions. An excepted abstract follows.
"This Article empirically examines the question of whether courts of appeals judges cast ideological votes in the context of bankruptcy. The empirical study is unique insofar as it is the first to specifically examine the voting behavior of circuit court judges in bankruptcy cases. More importantly, it focuses on a particular type of dispute that arises in bankruptcy - debt-dischargeability determinations. The study implements this focused approach in order to reduce heterogeneity in result. We find, contrary to our hypotheses, that circuit court judges do not engage in ideological voting in bankruptcy cases. We do find, however, non-ideological factors - including the race of the judge and the disposition of the case by lower courts - that substantially influence the voting pattern of the judges in our study."
Although, as Orin Kerr (GWU) notes, probable cause is one of the fundamental concepts of Fourth Amendment law, the Supreme Court has resisted quantification. Insofar as this blog tilts favorably toward all things quantitative, as an initial proposition such resistance strikes us as less than optimal. Of course, not all initial propositions withstand careful analysis. Indeed, Kerr's paper, Why Courts Should Not Quantify Probable Cause, benefits from an on-the-ground and granulated understanding of probable cause and articulates a persuasive (if counter-intutitive) non-empirical position. The abstract follows.
"This essay argues that courts should not quantify probable cause because quantification would produce less accurate probable cause determinations. The core problem is that information critical to probable cause is often left out of affidavits in support of warrants: Although affidavits say what techniques police tried that added to cause, they generally leave out both what the police tried that did not add to cause and what techniques the police never tried. Determining probable cause accurately often requires this information, however. By leaving probable cause unquantified, current law enables judges to use their intuition and situation-sense to recognize when missing information is likely important to assessing probable cause. Quantification would lead to less accurate probable cause determinations by disabling those intuitions, creating the false impression that the information provided in the affidavit is the only relevant information. Cognitive biases such as the representativeness heuristic and anchoring effects would allow the government to create the false impression that a low-probability event was actually a high-probability event. To ensure accurate probable cause determinations, then, probable cause should remain unquantified. The result is counter-intuitive but true: Knowing less about probable cause improves how the standard is applied."
Readers who share my admitted soft-spot for natural experiments might be interested in Francisco Perez-Arce's (RAND) recent paper, Is a Dream Deferred a Dream Denied? College Enrollment and Labor Market Search. The paper exploits a college's random assignment of admitted students into two groups, one of which must defer college entry by one year. The educational and policy consequences of the assignment are considered. From the abstract:
"A public college in Mexico City randomly assigns applicants into a group that can immediately enroll and a group that can only do so after one year. The author shows that the standard model of educational decisions predicts no (or minimal) effect of deferral on educational attainment. He surveyed the applicants to this college for the 2007/2008 academic year. Using data from that survey, he finds that, one and a half years after the first group enrolled, individuals in that group were 19 percentage points more likely to be enrolled than those that had to wait. This implies that offering more slots in a public college increases educational attainment. He finds that one additional slot increases the attainment of at least 0.3 individuals of the applicant pool and that offering them to individuals of poorer backgrounds has an even larger effect. To account for these results, he extends the standard model by placing the education decision in a model of labor market search. This suggests the importance of variability in opportunity costs for explaining who enrolls in college at any given moment. He derives testable implications of the model and show that they are verified empirically. He estimates the parameters of the model and show that the model can explain the observed patterns under reasonable assumptions. He also discusses alternative explanations of the impact of deferral and show they are inconsistent with observed patterns. The conclusion is twofold. First, public supply of college slots can impact the attainment of the target population. Second, within-individual variation in opportunity costs is an important element in determining educational attainment. This latter point can have implications for how systems of higher education systems should be designed."