Calvin J. Wright

This paper provides causal evidence on how editorial ideology influences the production and direction of scholarly discourse. Using machine learning applied to 60,000 economics and finance articles published between 1957 and 2013, we construct novel measures of political ideology for both papers and editorial boards. We find that conservative and liberal editorial boards tend to publish papers that align with their ideological orientation, with associations large enough to suggest systematic editorial gatekeeping. However, these relationships disappear when we compare papers published within the same journal across different editorial regimes. This null result challenges accounts of active editorial filtering and instead points to powerful sorting mechanisms in academic markets. Different journals appear to serve ideologically distinct scholarly communities, with authors strategically targeting outlets that match their ideological approach. At the same time, extreme editorial transitions increase the probability of publishing ideologically extreme papers by 4.5 percentage points, or 43%, suggesting that editors can influence the boundaries of acceptable discourse. In addition, papers that are ideologically distant from their handling editors receive significantly fewer citations. Together, these findings suggest that ideological patterns in academic publishing arise primarily through institutional sorting rather than routine editorial gatekeeping, with important implications for understanding how scientific communities organize knowledge.

Whether judges’ politics shape firm legal outcomes matters for litigation risk, but ideology is hard to separate from the cases judges hear. I assemble judicial opinions from 22 federal district courts and use the random assignment of cases to judges within court and year to measure each judge’s tendency to rule against firms. Judges appointed by Democratic presidents rule against firms more often than Republican appointees drawing from the same pool of cases, a gap of about a third of a standard deviation in judge tendency, and it widens with the political controversy of the judge’s confirmation. Firms drawing a judge one standard deviation more firm-adverse face settlements roughly 40 percent higher and penalties roughly 20 percent higher. Against a machine learning benchmark trained on case facts, these judges impose penalties above merits-based predictions, so the gap reflects the judge rather than the docket. Judges’ personal financial circumstances do not predict their rulings, which rules out pecuniary self-interest and points to ideology itself.

This paper investigates the market efficiency of the Jamaica Stock Exchange (JSE). Together, weak and semi-strong form market efficiency claim that historical and newly released public information do not predict future stock price movement. We test both forms of market efficiency by analyzing stock price behavior during times of abnormal trading volume and around the release dates of earnings information. Abnormal trading volume may be driven by liquidity demand or reflect new or private information flow to the market. Using data from the Jamaica Stock Exchange over the period 2000 to 2021, we find price dynamics consistent with price pressure as firms experience negative abnormal returns on the day of abnormal trading activity but offsetting positive abnormal stock returns on the following day. Further findings show post earnings announcement drift on the JSE. Taken as a whole, the evidence suggests violations of market efficiency and has implications for capital allocation in this emerging market.