Data on Shareholder Oppression

Shareholder oppression laws set out to protect minority shareholders in closely held corporations. As the scope and contours of these protections are notoriously opaque, however, related litigation is typically time-consuming, fact-sensitive, and protracted.

In a recent paper, An Empirical Analysis of Shareholder Oppression Law, the authors, Benjamin Means (S. Carolina) et al., set out to bring data to the shareholder oppression doctrine. To this end, the paper levers data from 850 shareholder oppression opinions decided nationwide from January 2010 through July 2021.

Data on Asset Forfeitures

Government extractions of assets through forfeiture remain an increasingly contested topic. Asset forfeitures are often significant for people entangled with the criminal justice system. Helping complicate motivations for forfeitures include the financial implications for federal, state, and local governments that generate revenue from forfeitures.

While governmental forfeiture continues to receive scholarly attention, much of the attention lacks data. However, a recent paper, Asset Forfeiture and Inequality, by Stephanie Holmes Didwania (Northwestern), explores forfeitures with the benefit of Department of Justice data on roughly 1.2 million federal asset forfeitures between 1998 and 2019 which are matched to county-level population data over that period. The paper's main take-away involves the claim that governments use asset forfeiture in ways that exacerbate inequality. An excerpted abstract follows.

More on the RE v. FE Specification Debate

In the xtivreg world, for example, while Sargan-Hansen testing helps assess instrumental variable efficacy, researchers also typically confront the question about whether a FE or RE specification is appropriate. Such a question was posed recently on the StataList (here) and an initial response echoes prior suggestions that focus on the Mundlak approach. Mundlak advances an alternative approach to the Hausman test insofar as, unlike the latter, the Mundlak approach may be used when the errors are heteroskedastic or have intragroup correlation. (An earlier discussion goes into greater detail.)