Discussion Boards

Prevailing Party Laws and General Election Outcomes, Darren Grant, working paper here: https://profiles.shsu.edu/dpg006/ThemThatsGot.pdf. Vernacular knowledge plays a key role in this working paper of mine that examines the effect of ballot order on vote share in nearly half a century of Wyoming elections. In previous research I had analyzed quasi-randomized ballot orders, as is standard in the literature, because they offer "clean identification." Never did I pause to think that these might have milder effects than other methods of ordering candidates--a possibility unconsidered in the literature. I had never taken in the body of existing research (largely in other fields) that describes the variety of possible framing effects that could be generated by ballot order. In that sense, I lacked the requisite vernacular knowledge. Two purposes of vernacular knowledge listed in Ch. 4 are to "indicate the relevance or irrelevance of economic and social forces" and gauging "the sensitivity of the model and its findings to the institutional social and technical environment." This article shows how vernacular knowledge can do both, making it a great complement to Article #11.

Market Design of College-Major Switches for Reducing Student-Major Mismatch, Umut Dur, Yi-Cheng Kao, and Scott Paiement, working paper presented at the 2024 ASSA meetings, right here: https://www.aeaweb.org/conference/2024/program/paper/rdZaYfd2. This paper looks at major-switching in a Taiwanese university, which is done differently than the (generally) decentralized process used in most U.S. universities. The institutional setup is more complex, which motivates the procedure these authors develop to improve the matching process and allow more students to enroll in (switch to) their most-desired major. In the later sections of the paper, 6.4 and 6.5, we learn that the university in question adopted their procedure, but it didn't work as intended! It had essentially a null effect. It's a great paper for learning about the importance of vernacular knowledge.

When Should You Adjust Standard Errors for Clustering, Alberto Abadie, Susan Athey, Guido Imbens, and Jeff Wooldridge, in the Quarterly Journal of Economics, 2023, right here: https://academic.oup.com/qje/article/138/1/1/6750017. This interesting article raises the issue of experimental content in a fresh way. Among its contributions, it distinguishes between standard errors under the assumption that you have observed all the relevant clusters in the population vs. a random sample of clusters from a much larger population. The standard clustering formula assumes the latter, but researchers are often in the former position, for example when they observe data from all states in the U.S., which is taken to be the relevant population. Using the standard formula in this situation can give highly inflated standard errors. I was always suspicious of this standard approach to clustering (though I never was forced to stake out a clear position, and never did). This paper articulates one of the reasons why.