Empirical Legal Studies

"Hot Hand" Fallacy With A (Potential) Monte Carlo Twist

Today's WSJ contributes reports on a recent paper (here) that seeks to contribute to a long-standing debate surrounding the proverbial "hot hand" fallacy. In their paper, Surprised by the Gambler's and Hot Hand Fallacies? A Truth in the Law of Small Numbers, Joshua Miller (Bocconi Univ.) and Adam Sanjurjo (Univ. de Alicante) suggest that a form of selection bias contributes to an underestimation of the true probability of an outcome when conditioned on a prior (and similar) outcome. Not sure if the paper works, but an intriguing potential wrinkle nonetheless.

UPDATE: More discussion (here).

Data on Equal Educational Opportunity

While reasonable minds can (and do) differ on how best to legally operationalize the equal educational opportunity doctrine, if the doctrine is to stand for anything meaningful it has to at least mean that a child's educational "destiny" should be independent of the household income that a child happens to have been born into. Data, however, make painfully clear that on balance we fall short--far, far short--of this aspiration. Helping make these data "come alive" is an interactive graphic (in the New York Times) that allows users to "guess" at the relation between a child's chance at going to college and parental income levels. (After a user draws his/her own regression line onto the graph the user can then compare it with the actual regression line.) While depressing from a policy perspective it's a clever interactive graphic.

Empirical Legal History: The Influence of Race, Income, and Geography on State Tort Doctrine

In a provocative, data-driven, legal history paper, Keeping Cases from Black Juries: An Empirical Analysis of How Race, Income Inequality, and Regional History Affect Tort Law, Donald Gifford (Maryland) and Brian Jones (Villanova--Sociology) set out to assess empirically the degree to which state tort doctrine varies when it comes to enabling judges to dismiss cases before they reach the jury. The authors then seek to match this variation with the influence of race and a state's slave-holding experience.

The paper's findings suggest that "some appellate courts, particularly those in the South, afraid that juries with substantial African-American representation would redistribute wealth or retaliate for grievances, struck preemptively to prevent cases from reaching them. Surprisingly, we do not find a consistent association between a state’s JADI and either income inequality or its political leanings. In other words, race and region, rather than economic class or politics, explain the failure to embrace pro-plaintiff changes that occurred elsewhere."

Thoughts On Data Sharing, Journals, Etc.

Norms concerning data sharing appear to be in some flux and variation across disciplines (and journals) is on the rise. Some interesting thoughts on why more scholars do not share their data to facilitate replication (here). A strong form of the argument favoring data sharing--that journals should make data (and/or code) sharing a condition of peer-review--is found here.

Database on Ideology, Money in Politics, and Elections (DIME)

While simply massive and (potentially) a bit unwieldy, the Database on Ideology, Money in Politics, and Elections (DIME) is, quite literally, unparalleled in terms of scope. Indeed, the database contains over 100 million political contributions made by individuals and organizations to local, state, and federal elections spanning a period from 1979 to 2012.

DIME "is intended as a general resource for the study of campaign finance and ideology in American politics. The database was developed as part of the project on Ideology in the Political Marketplace, which is an on-going effort to conduct a comprehensive ideological mapping of political elites, interest groups, and donors using the common-space CFscore scaling methodology (Bonica 2013). Constructing the database required a large-scale effort to compile, clean, and process data on contribution records, candidate characteristics, and election outcomes from various sources. The resulting database contains over 100 million political contributions made by individuals and organizations to local, state, and federal elections spanning a period from 1979 to 2012. A corresponding database of candidates and committees provides additional information on state and federal elections."

Motivations for the database's creation include: "to make data on campaign finance and elections (1) more centralized and accessible, (2) easier to work with, and (3) more versatile in terms of the types of questions that can be addressed." Housed at Stanford University, the database website requires users wishing to access the data to establish an account (free of charge) and includes direct access to a codebook.

Modeling When Lawmakers Legislate

In Quantifying Legislative Uncertainty: A Case Study in Tax Policy, Jason Oh (UCLA) and Christopher Tausanovitch (UCLA--Poli Sci) set out to articulate (and test) an empirical model designed to quantify when a legislative body will (or will not) enact law. The paper nests this difficult conceptual task in the federal income tax context.  More specifically, the paper presents a "model of legislator preferences on tax rates and show that the political process can be well understood in terms of the preferences of key legislators. We use our statistical model to quantify the uncertainty of tax rates and forecast the direction of likely rate changes in the future."

The paper--and argument--stimulate an array of implications and take-aways. "First, quantifying legislative uncertainty offers insight into the behavioral effects of the law. How people respond to the law depends on their perception of the law’s future trajectory. Second, our analysis allows us to explore the stability of major legislative reform. Our methodology allows us to demonstrate that reforms are sometimes predictably unstable. Such reforms can have the perverse result of increasing future legislative uncertainty."

Cornell's 2015 Junior Empirical Legal Scholars Workshop

Cornell Law School will host its Junior Empirical Legal Scholars Workshop next weekend in Ithaca, NY. Similar to past workshops, "Junior" is defined to include pre-tenure (or pre-tenure-track) individuals who, through their early empirical scholarship, have demonstrated considerable scholarly promise. At the Workshop, each junior presenter is matched with a field-appropriate "senior" empirical legal scholar discussant. The 2015 Junior scholars reflect a diverse range of academic disciplines and include: Maya Sen (Harvard), Sarath Sanga (UC Berkeley), William Hubbard (Chicago), Jed Stiglitz (Cornell), Jacob Goldin (Princeton), and Avani Sood (UC Berkeley).