Smarting at Work
Genius on Demand: The Value of Transformative Artificial Intelligence
Ajay Agrawal, Joshua Gans & Avi Goldfarb
NBER Working Paper, October 2025
Abstract:
This paper examines how the emergence of transformative AI systems providing "genius on demand" would affect knowledge worker allocation and labour market outcomes. We develop a simple model distinguishing between routine knowledge workers, who can only apply existing knowledge with some uncertainty, and genius workers, who create new knowledge at a cost increasing with distance from a known point. When genius capacity is scarce, we find it should be allocated primarily to questions at domain boundaries rather than at midpoints between known answers. The introduction of AI geniuses fundamentally transforms this allocation. In the short run, human geniuses specialise in questions that are furthest from existing knowledge, where their comparative advantage over AI is greatest. In the long run, routine workers may be completely displaced if AI efficiency approaches human genius efficiency.
The Impact of EITC on Education, Labour Market Trajectories, and Inequalities
Julien Albertini, Arthur Poirier & Anthony Terriau
Review of Economic Studies, forthcoming
Abstract:
As a complement to the federal earned income tax credit (EITC), some states offer their own EITC, typically calculated as a percentage of the federal EITC. In this paper, we analyse the effect of state EITC on education using policy discontinuities at US state borders. Our estimates reveal that an increase in the state EITC leads to a statistically significant increase in the high school dropout rate. We then use a life-cycle matching model with directed search and endogenous educational choices, search intensities, hirings, hours worked, and separations to investigate the effects of EITC on the labour market in the long run and along the transitional dynamics. We show that a tax credit targeted at low-wage (and low-skilled) workers reduces the relative return to schooling, thereby generating a powerful disincentive to pursue long-term studies. In the long run, this results in an increase in the proportion of low-skilled workers in the economy, which may have important implications for employment, productivity, and income inequality. Finally, we use the model to determine the optimal design of the EITC.
Downward Wage Rigidity and Corporate Investment
DuckKi Cho
Journal of Law and Economics, August 2025, Pages 671-710
Abstract:
Firms reduce investment when facing downward wage rigidity, the inability or unwillingness to adjust wages downward. To document this behavior, I exploit staggered state-level changes in minimum wage laws as an exogenous variation in downward wage rigidity. Following a 1-standard-deviation increase in the minimum wage, firms reduce their investment rate (the ratio of capital expenditure to capital stock) by 3.08 percentage points. The negative impact is more acute for firms with a higher fraction of minimum wage workers, stronger employment protections, or higher labor intensity. The investment reductions cannot be explained by labor adjustment under capital-labor complementarities. Rather, I identify the aggravation of debt overhang and increased operating leverage crowding out debt financing as two mechanisms by which downward wage rigidity impedes investment. The findings highlight the unintended consequences of minimum wage policies on corporate investment.
What Makes Scheduling "Responsible"? Evidence from 280 Million Shifts Across 20 Retailers
Borja Apaolaza, Santiago Gallino & Caleb Kwon
University of Pennsylvania Working Paper, August 2025
Abstract:
We use administrative data covering 280 million shifts, 1.3 million employees, and 17,456 stores across 20 major U.S. retail chains. We construct over 100 schedule characteristics based on prior literature and apply LASSO variable selection to identify which metrics predict turnover. We estimate models separately across companies, U.S. states, zip-code socioeconomic groups, and employee subgroups (e.g., part-time, low-tenure, female). This analysis reveals that the set of predictive scheduling variables is highly context-dependent. For example, while advance notice is a significant predictor of retention at some firms, it has no predictive power at others. Quantifying this variation, we find over 80% of selected predictors are present in fewer than half of the models. Ultimately, no single scheduling characteristic consistently predicts turnover across all analyzed contexts.
The Impact of Unemployment Benefit Extensions on Employment: The 2014 Employment Miracle?
Marcus Hagedorn, Iourii Manovskii & Kurt Mitman
American Economic Journal: Macroeconomics, October 2025, Pages 168-203
Abstract:
We measure the aggregate effect of unemployment benefit duration on employment and the labor force. We exploit the variation induced by Congress' failure in December 2013 to reauthorize the unprecedented benefit extensions introduced during the Great Recession. Federal benefit extensions that ranged from 0 to 47 weeks across US states were abruptly cut to zero. In sharp contrast to their typical dynamics, labor force and employment growth accelerated sharply in states with larger cuts in benefit duration. These findings are consistent with the equilibrium search framework that assigns an important role to endogenous job creation.
The Siren of the Labor Movement: Spillover Effects from Starbucks Organizing
Zachary Schaller, Sal McCollum & Prasiddha Shakya
Colorado State University Working Paper, September 2025
Abstract:
Since the first Starbucks store unionized in Buffalo in 2021, over 700 locations have filed for elections, and broader organizing has surged. We examine whether this campaign sparked wider labor activism. Using an event study, we find that counties with a Starbucks election saw nearly five additional non-Starbucks elections on average -- accounting for nearly 20% of the post-2021 surge. However, a staggered difference-in-differences design reveals no wage gains for restaurant workers, suggesting that union momentum has not yet shifted bargaining power. Employers may be waiting on the outcome of first contract negotiations before adjusting pay structures.
Labor Supply and Entertainment Innovations: Evidence from the US TV Rollout
George Fenton & Felix Koenig
American Economic Journal: Applied Economics, October 2025, Pages 1-28
Abstract:
We study the impact of entertainment technology on labor supply. Using Social Security work histories and a natural experiment arising from the regulated US rollout of television, we estimate that a station launch reduced the probability of working by around 0.3 percentage points, driven mainly by an increase in older-age-group retirement rates. The results support the hypothesis that television's rise contributed to the midcentury transition of retirement from a necessity to "golden years" of enjoyment. Our findings indicate that entertainment innovations have a less pronounced effect on overall labor supply trends than model calibrations in the previous literature suggest.
Inferring Fine-grained Migration Patterns across the United States
Gabriel Agostini et al.
NBER Working Paper, September 2025
Abstract:
Fine-grained migration data illuminate important demographic, environmental, and health phenomena. However, migration datasets within the United States remain lacking: publicly available Census data are neither spatially nor temporally granular, and proprietary data have higher resolution but demographic and other biases. To address these limitations, we develop a scalable iterative-proportional-fitting based method that reconciles high-resolution but biased proprietary data with low-resolution but more reliable Census data. We apply this method to produce MIGRATE, a dataset of annual migration matrices from 2010-2019 that captures flows between 47.4 billion pairs of Census Block Groups -- about four thousand times more granular than publicly available data. These estimates are highly correlated with external ground-truth datasets, and improve accuracy and reduce bias relative to raw proprietary data. We use MIGRATE to analyze both national and local migration patterns. Nationally, we document temporal and demographic variation in homophily, upward mobility, and moving distance: for example, we find that people are increasingly likely to move to top-income-quartile CBGs and identify racial disparities in upward mobility. We also show that MIGRATE can illuminate important local migration patterns, including out-migration in response to California wildfires, that are invisible in coarser previous datasets. We publicly release MIGRATE to provide a resource for migration research in the social, environmental, and health sciences.
From the Ground Up: Labor Demand and Intergenerational Mobility in the US
Brianna Felegi & Paul Shaloka
Virginia Tech Working Paper, July 2025
Abstract:
We provide evidence on the sensitivity of intergenerational mobility measurements described in Chetty et al. (2014) to local labor market shocks. We show that the fracking boom and Chinese import competition led to substantial changes in upward social mobility, and that between 10-15 percent of the spatial variation in absolute upward mobility can be explained by these two geographically concentrated shocks alone. Our results demonstrate that these mobility measures are (1) sensitive to shocks that occur between measurement of child and parental income and that (2) industry-specific shocks can explain similar amounts of the variation in mobility as social factors.