Empirical Legal Studies

"Younger" Federal Judges & Executive Power

While modeling judicial decisions--especially for federal judges--is not a terribly new endeavor, a recent paper in the Journal of Law and Economics (2020), Younger Federal District Court Judges Favor Presidential Power, caught my eye. In it, the authors, Tom Campbell (Chapman) and Nathaniel Wilcox (Chapman--economics), assess how federal judges' votes on challenges to assertions of executive power evolve over a judge's life cycle. And they do so with an eye toward federal district court judges' potential appetite for judicial "promotion."

Drawing on district court opinions spanning from 1960 to 2015, what the authors find is that “The probability that district judges favor the executive (statistically) significantly increases with the hazard for elevation (and so declines with age) for all specifications with that regressor. Moreover, the estimated average marginal effects show that the estimated size of the effect is highly robust to the particular specification: in about one of every five cases, 50-year-old judges appear to rule differently than 65-year-old judges.” The paper's abstract follows.

"From 1960 to 2015, opinions of US federal district court judges (trial judges) in cases involving challenges to executive branch authority show that these judges favor executive authority less as they age. We suggest that district judges know that elevation to the federal circuit court of appeals becomes increasingly improbable, and hence have less reason to cooperate with the executive, with advancing age. Political variables, seniority of judges, and other variables introduced as extra regressors do not reverse this main result, nor does it appear to be the product of cohort effects or selection off the district court. When there are contemporaneous vacancies on their circuit courts, district judges in the 11 state circuits (but not the District of Columbia Circuit) are also more likely to favor the executive."

"Quick-and-Dirty" Codebooks

While it's difficult to over-state the importance--indeed, necessity--of an accurate, readable codebook, what is also true is that they can be a "pain" to generate (especially if one waits until the very end of a project). Stata can generate a "bare-bones," though perfectly serviceable, codebook with relatively little effort. A helpful "how-to" is included in this discussion (here).

Impacts on Firms From Joining the S&P 500 Index

For those whose financial portfolios include investments in low-cost index funds, most assume that companies benefit from inclusion in the leading indexes, such as the S&P 500. As a recent paper, Does Joining the S&P 500 Index Hurt Firms?, by Benjamin Bennett (Tulane) et al., notes, being added to the index is like "joining a prestigious club. A firm gains prestige by joining the club, but at the cost of becoming compared to other firms in the club."

To better assess the costs and benefits, the paper executes "difference-in-differences (DiD) analyses based on S&P 500 index addition. Specifically, we first identify all S&P 500 additions in our sample period. Added firms are our treated firms. For each treated firm, we search for a control firm that is never a member of the S&P 500 index in our sample period and use a propensity-score match on total assets, Tobin’s q, stock return, and 2-digit SIC industry (exact match) in the year before a treated firm is added to the index, so that the matching process is not affected by index additions." The main findings are summarized in the paper's abstract.

“We investigate the impact on firms of joining the S&P 500 index from 1997 to 2017. We find that the positive announcement effect on the stock price of index inclusion has disappeared and the long-run impact of index inclusion has become negative. Inclusion worsens stock price informativeness and some aspects of governance. Compensation, investment, and financial policies change with index inclusion. For instance, payout policies of firms joining the index become more similar to the policies of their index peers. ROA falls following inclusion. There is no evidence of an impact of inclusion on competition.”

Testing a "Ratio Problem" in Finance Scholarship

On the heels of a recent post (here) describing a Stata regression command tailored to the unique needs of a dependent variable that takes the form of a ratio/percentage/fraction, comes a recent paper that explores potential problems with finance papers that model ratios. Potentially compounding the problem is that ratios as dependents variables are certainly not uncommon in the finance literature (see, e.g., papers featuring Tobin's Q). In The Ratio Problem, Robert Bartlett (Berkeley) and Frank Partnoy (Berkeley) frame the ratio "problem" in terms of two challenges–omitted variable and measurement error bias–"that arise anytime a researcher uses linear regression to estimate a production function that has a ratio as an output." As the paper notes, "In theory, the [ratio] denominator is not necessarily problematic; in practice, however, there are statistical concerns whenever the output of a production function is a ratio." The paper's abstract follows.

“We use the term ‘ratio problem’ to describe the omitted variable and measurement error bias that can arise when a ratio is the dependent variable in an economic model. First, we show how bias can arise from the omission of two classes of variables based on a ratio’s denominator. As an example, we demonstrate that the widely-cited ‘inverse U’ relationship between managerial ownership and Tobin’s Q is reversed when these variables are included. Second, we show how measurement error in the ratio’s denominator can produce bias. We provide empirical tests and solutions, and urge caution about ratios as dependent variables.”