Empirical Legal Studies

Ready-To-Use Data (and Codebooks) for Law & Education Research

One thing that the U.S. Department of Education does reasonably well is gather data--and make them publicly available. And as the number and complexity of law and education issues continue to increase over time, greater access to relevant data becomes even more important. To this end, the Dept. of Education's National Center for Education Statistics ("NCES") operates a landing spot (here) for direct access to ready-to-use public use data sets and codebooks. Users can select from an array of standard formats (Stata, SPSS, SAS, R, etc.) and tailor requests.

Instrumental Variables Regression--A Helpful Intro

For a brief (5+ min) introduction to--or a very quick refresher on--the basics of instrumental variables regression, including some post-estimation commands, one could do far worse than this Stata video. The video walks viewers through the basics on how to fit an instrumental variables model for endogenous covariates (using the "ivregress" command).

Comparative Competition Law & Datasets

Consistent with JELS' impulse to recently launch a new genre of papers--"Research Notes and Datasets"--a recent contribution to this initiative, Competition Law Gone Global: Introducing the Comparative Competition Law and Enforcement Datasets, by Anu Braford (Columbia), Adam Chilton (Chicago), Christopher Megaw (WilmerHale), and Nathaniel Sokol, introduces and discusses a pair of datasets that provide future researchers with "a new foundation for empirical research on the legal regimes used to regulate markets."

"Competition law has proliferated around the world. Due to data limitations, however, there is little systematic information about the substance and enforcement of these laws. In this article, we address that problem by introducing two new datasets on competition law regimes around the world. First, we introduce the Comparative Competition Law Dataset, which codes competition laws in 131 jurisdictions between 1889 to 2010. Second, we introduce the Comparative Competition Enforcement Dataset, which provides data on competition agencies’ resources and activities in 100 jurisdictions between 1990 and 2010. These datasets offer the most comprehensive picture of competition law yet assembled and provide a new foundation for empirical research on the legal regimes used to regulate markets."

So far, so good. But the authors recently undertook yet another step that warrants attention (and praise). Specifically, Bradford and Chilton created a new website (here) that already includes an array of ready-to-use datasets (and associated codebooks) germane to comparative competition law (see, e.g., here). According to the project's website, "this project generates novel data on competition regimes across time and countries. Using these data, the project studies competition law and enforcement around the world, including exploring the diffusion of these laws and the effects they have on market outcomes."

Frankly, the construction and public distribution of helpful and important datasets are among the most the most under-appreciated of endeavors. Future scholars (and scholarship) will gain from initiatives like this one.

"p-Hacking" explained

One consequence of the on-going "replication" crisis in the social (and, increasingly, hard) sciences is a spike in the salience of the term "p-Hacking." The term "p-Hacking," as defined by Robert MacCoun (Stanford) in his recent essay, p-Hacking: A Strategic Analysis, “joins a growing taxonomy for describing the myriad ways in which ‘cold cognitive’ and ‘hot motivational’ biases can creep into the interpretation and use of research findings.” In his brief essay MacCoun dives deeply into the issue and proposes “four different models of p-hacking behavior and examining what kinds of aggregate patterns they might be expected to produce.” The abstract follows.

"The phenomenon of p-hacking occurs when researchers engage in questionable practices that enable them to report findings as being statistically significant. I offer four models of p-hacking behavior – unconditional, strategic, greedy, and restrained – and explore the implications of each model. I then discuss the implications of recent reforms (routine replication, pre-registration, and blinded data analysis) with respect to these models."