AI & Economics Lab

Online creators have become a central source of political information for young adults in recent years, and over the same period, young men have shifted toward the political right relative to young women. We examine whether these two major developments are connected by focusing on “The Joe Rogan Experience”, the leading long-form podcast, which has a disproportionately male audience. Our identification leverages the timing and location of Ultimate Fighting Championship (UFC) events, for which Rogan has long served as the lead commentator. In a difference-in-differences design, we find that local UFC events trigger a significant increase in Google searches and in time spent consuming Joe Rogan episodes among young men. Applying text analysis to podcast transcripts, we document a pronounced shift in Rogan’s political rhetoric: until 2020, Rogan frequently expressed support for Sanders with left-populist rhetoric; starting in 2021, his commentary became supportive of Trump with a right-populist slant. Using administrative voter files, we show that exposure to UFC events until 2020 increased Democratic voting in primaries with Sanders, while after that, exposure to UFC events increased Republican voting in primaries with Trump with all effects concentrated among young men.

Recent work has used LLM agents to reproduce empirical social science results with access to both the data and code. We broaden this scope by asking: Can they reproduce results given only a paper’s methods description and original data? We develop an agentic reproduction system that extracts structured methods descriptions from papers, runs reimplementations under strict information isolation—agents never see the original code, results, or paper—and enables deterministic, cell-level comparison of reproduced outputs to the original results. An error attribution step traces discrepancies through the system chain to identify root causes. Evaluating four agent scaffolds and four LLMs on 48 papers with human-verified reproducibility, we find that agents can largely recover published results, but performance varies substantially between models, scaffolds, and papers. Root cause analysis reveals that failures stem both from agent errors and from underspecification in the papers themselves.

Collective bargaining agreements (CBAs) specify the contractual rights of unionized workers, but their full legal content has not yet been analyzed by economists. This paper develops novel natural language methods to analyze the empirical determinants and economic value of these rights using a new collection of 30,000 CBAs from Canada in the period 1986-2015. We parse legally binding rights (e.g., “workers shall receive…”) and obligations (e.g., “the employer shall provide…”) from contract text, and validate our measures through evaluation of clause pairs and comparison to firm surveys on HR practices. Using time-varying province-level variation in labor income tax rates, we find that higher taxes increase the share of worker-rights clauses while reducing pre-tax wages in unionized firms, consistent with a substitution effect away from taxed wages toward untaxed rights. Further, an exogenous increase in the value of outside options (from a leave-one-out instrument for labor demand) increases the share of worker rights clauses in CBAs. Combining the regression estimates, we infer that a one-standard-deviation increase in worker rights is valued at about 5.7% of wages.

Interviews allow employers to learn about workers, but do they also enable workers to learn about firms? Studying 500,000 interview reports from Glassdoor, we find candidates for high-paying jobs are more likely to reject a job offer if they believe the interview was easy. Easy interviews appear to convey poor “fit” as those who accept offers after easy interviews are two-fifths of a standard deviation less satisfied with their jobs and 10 percent less likely to remain with their employer for at least one year. Analysis of interview narratives using large language models reveals difficult interviews signal colleague ability whereas easy interviews convey a nonselective process. In a small-scale randomized field experiment, an exogenous increase in difficulty elevated perceived difficulty and boosted applicant engagement with the vacancy. Interviews offer workers a preview of match quality, highlighting a channel through which labor markets may become less efficient if firms automate hiring with AI.

We study the effect of televised broadcasts of floor debates on the rhetoric and behavior of U.S. Congress Members. First, we show in a differences-in-differences analysis that the introduction of C-SPAN broadcasts in 1979 increased the use of emotional appeals in the House relative to the Senate, where televised floor debates were not introduced until later. Second, we use exogenous variation in C-SPAN channel positioning as an instrument for C-SPAN viewership by Congressional district and show that House Members from districts with exogenously higher C-SPAN viewership are more emotive in floor debates. Looking to electoral pressures as a mechanism, we find the emotionality effect of C-SPAN is strongest in competitive districts. C-SPAN exposure increases the vote share for incumbent Congress Members and citizens’ approval of their job in Congress, and more so among Members who speak more emotionally. Contra accountability models of transparency, C-SPAN has no effect on measures of legislative effort on behalf of constituents, and if anything it reduces a politician’s constituency orientation. We find that local news coverage — that is, mediated rather than direct transparency — has the opposite effect of C-SPAN, increasing legislative effort but with no effect on emotional rhetoric. These results highlight the importance of audience and mediation in the political impacts of higher transparency.