WORKS IN PROGRESS
Putting AI on the Org Chart: Evidence on Oversight and Accountability with Megan Hsu, Julie Bedard, and Matt Kropp
Abstract: Motivated by the potential for large productivity gains from AI, firms are increasingly deploying agentic AI systems capable of independent action. Moreover, they are increasingly branding these AI agents not as tools but instead as "AI teammates'' or "AI employees''. While existing research heavily explores the effects of using AI as a standalone productivity tool, the behavioral and governance consequences of treating AI as an organizational peer remain largely unexplored. We argue that framing AI as an employee fundamentally alters oversight and workplace dynamics as long as human employees remain in the loop to review, approve, or collaborate with AI. In a survey of 1,261 managers we find that 23% of managers already work in organizations where AI agents have been formally institutionalized on organizational charts. In a randomized experiment we provide those managers with identical documents containing built in errors, where we vary whether we say the document was produced by an AI Tool, an AI Employee, or a Human Employee. In the subgroup of managers whose organizations have already "put AI on the org chart’’, categorizing identical drafts as coming from an AI employee (versus an AI tool) reduces managers’ error catching by 16%, increases requests for additional review by 44%, and shifts perceived accountability away from the manager and toward the AI system. By contrast, we find little evidence of effects of the AI employee framing among managers in organizations without such institutionalization. These findings imply that how organizations categorize AI is not neutral: when institutionally credible, treating AI as an organizational member changes oversight behavior and perceived accountability in AI-mediated work.
Generative AI and Labor Market Matching Efficiency (Previously, "More, but Worse: The Impact of AI Writing Assistance on the Supply and Quality of Job Posts") with John Horton (Under review)
Abstract: Reductions in private search costs due to advances in information technology can improve market efficiency. Although, changes in private search costs can change behaviors, making the welfare implications unclear if that behavior creates negative externalities, as was the case here. We consider the market efficiency effects of the introduction of an AI tool into a labor market. In order to lower their search costs, potential employers were randomly offered AI-written first drafts of their job post. The assistance was widely accepted and treated employers were 19% more likely to post a job; those posting spent 44% less time writing. Despite the substantial increase in job posts, there was no discernible increase in matches. The lack of match formation was mostly due to marginal jobs being posted by employers with lower intent. Up to a fifth of the missing matches were caused by direct impacts on the job posts—in the sense that they were more generic and less informative to jobseekers. This combination of increased congestion and degradation in informativeness wasted jobseeker time as jobseekers applied to jobs they otherwise would not have. Quantifying this waste, the per job post loss to jobseeker welfare is six times larger than the increase to employer welfare from time saving. These negative efficiency outcomes persists after the close to market wide adoption of the technology, showing the reductions in private search costs in this context harmed market efficiency.
Presentations: Columbia’s Management, Analytics, and Data Conference, Yale AI/ML Conference, CMU Mini-Conference on AI and Future of Work
Selected media:
Financial Times: The AI Shift: Is hiring becoming less meritocratic?
New York Times: The Long Term Employed Today: College-grads
MIT Initiative on the Digital Economy: Using GenAI to Write Job Descriptions? Put Some Effort Into It
Human Assistance Can't Beat Algorithms with Online Hiring with John Fallon and John Horton
Abstract: We study a randomized experiment in which a large online labor market assigned human assis- tants to 83,017 job posts, with 25% serving as controls. Treated employers received 13% more applications and 35% more interviews, but hired at the same rate as control employers who had access only to algorithmic tools. Treated employers also spent 10% less on hires in the first 30 days, suggesting human assistance produced worse matches. We develop a model of delegated recruiting with shared information noise to explain these findings: as algorithmic screening improves and more worker quality becomes easily observable from profiles, the scope for recruiter-added value shrinks further.
Workers Response to Price Uncompetitiveness: Evidence from a Field Experiment with Apostolos Filippas and John Horton
Abstract: If and how to regulate online marketplaces is an open question important to both platform designers and policy makers. Using a large field experiment in an online labor market, we analyze the effects of a platform minimum wage. Workers were randomly assigned individual price floors which prevented treated workers from bidding hourly rates below their floor. Workers for whom the floor was likely binding—those historically bidding below the floor—suffered a decline in job-finding probability(30%), but higher wages conditional upon being hired(9%). Treated workers made lower earnings overall, but higher earnings conditional on working at least one hour on the platform. Despite a job being “worth more” if hired, affected workers lowered their search intensity. They did not move to the “uncovered sector”—jobs with a fixed price rather than an hourly wage, nor did they direct their search to better fitting jobs. They were also more likely to exit the platform. After the conclusion of the experiment, the platform rolled out the $3 per hour minimum wage platform wide, allowing us to observe the the employment outcomes and job search behavior in equilibrium.
PUBLICATIONS
Generative AI and the Temporary Upskilling of Knowledge Workers with Lisa Krayer, Mohamed Abbadi, Urvi Awasthi, Ryan Kennedy, Pamela Mishkin, Daniel Sack, Francois Candelon (Previously, "Generative AI as an Exoskeleton")
(Accepted at Nature Human Behaviour)
Abstract: "Upskilling" often refers to the process by which workers acquire and expand their skills, enabling them to perform different types of work as market demands change.This paper demonstrates that while generative artificial intelligence (GenAI) can act as an “exoskeleton,” enhancing workers’ capabilities while they attempt new skills, these gains are dependent on the continued use of the technology. When the “exoskeleton” is removed, little to no knowledge is retained independently, revealing that the newfound capabilities are temporary and reliant on the external support provided by GenAI. We run a randomized controlled trial on “reskilling” with GenAI by providing Boston Consulting Group (BCG) consultants with access and training in using ChatGPT to solve technical problems. We measure their performance on real data science tasks outside their skill sets, which cannot be independently solved by ChatGPT. Treated workers score 49, 20, and 18 percentage points higher than those in the control group on the three tasks and perform close to the level of real BCG data scientists on two of the three tasks. However, treated workers are no better at answering technical questions without the use of ChatGPT post-experiment, suggesting their demonstrated newfound technical capabilities do not imply knowledge acquisition.
Job Market Paper: Algorithmic Writing Assistance on Jobseekers' Resumes Increases Hires with Zanele Munyikwa and John Horton, 2025. (Management Science)
Working paper version: NBER Working Paper No. 30886
Abstract: There is a strong association between writing quality in resumes for new labor market entrants and whether they are ultimately hired. We show this relationship is, at least partially, causal: in a field experiment in an online labor market with nearly half a million jobseekers, treated jobseekers received algorithmic writing assistance on their resumes. Treated jobseekers were hired 8% more often. Contrary to concerns that the assistance takes away a valuable signal, we find no evidence that employers were less satisfied. We present a model where better writing does not signal ability but helps employers ascertain ability, rationalizing our findings.
Yahoo News: AI can help job seekers get noticed and hired, study finds
Market Watch: Looking for a new job? Brush up your résumé with a computer algorithm. Seriously, it could pay off.
Presentations: NBER Lightning Talk, LinkedIn TechTalk, INFORMS 2022, Wharton Generative AI & Business Conference 2023, Oxford Platform Economics Seminar Series
Boundary Discontinuity Methods and Policy Spillovers with Ekaterina Jardim, Mark C. Long, Robert Plotnick, Jacob Vigdor, 2024. (Journal of Public Economics)
Working paper version: NBER Working Paper No. 30075
Abstract: The boundary discontinuity method of causal inference may yield misleading results if a policy’s impacts do not stop at the border of the implementing jurisdiction. We use geographically precise longitudinal employment data documenting worker job-to-job mobility to study policy spillovers in the context of three local minimum wage increases. Estimated spillover impacts on wages and hours are statistically significant, geographically diffuse, and sufficient to create concern regarding interpretation of results even using not-immediately-adjacent regions as controls. Spillover effects appear less concerning with smaller interventions or those or adopted in a smaller jurisdiction.
Minimum Wage Increases and Low-Wage Employment: Evidence from Seattle: with Ekaterina Jardim, Mark C. Long, Robert Plotnick, Jacob Vigdor, and Hilary Wething, 2022. (AEJ: Economic Policy)
Working paper version: NBER Working Paper No. 23532
Abstract: Seattle raised its minimum wage to as much as $11 in 2015 and as much to $13 in 2016. We use Washington State administrative data to conduct two complementary analyses of its impact. Relative to outlying regions of the state identified by the synthetic control method, aggregate employment at wages less than twice the original minimum, measured by total hours worked, declined. A portion of this reduction reflects jobs transitioning to wages above the threshold; the aggregate analysis likely overstates employment effects. Longitudinal analysis of individual Seattle workers matched to counterparts in outlying regions reveals no change in the probability of continued employment, but significant reductions in hours particularly for less-experienced workers. Job turnover declined, as did hiring of new workers into low-wage jobs. Analyses suggest aggregate employment elasticities in the range of -0.2 to -2.0, concentrated on the intensive margin in the short run and largest among inexperienced workers.
Media coverage: The Economist, FiveThirtyEight, Los Angeles Times, New York Times, New York Times (The Upshot), Seattle Times, Washington Post
Presented at: NBER Summer Institute 2017, PAA Annual Meeting 2017, APPAM Fall Conference 2016, APPAM International Conference 2016
GRAVEYARD
Business Churn, Labor Intensity, and the Minimum Wage with Ekaterina Jardim Most Recent Draft Online Appendix
Working paper version: W.E. Upjohn Institute for Employment Research Working Paper 19-298.
Abstract: We study the effects of a large increase in Seattle's minimum wage on business churn, hours, and revenue using Washington State administrative data. We find the minimum wage affected businesses both at the intensive and extensive margins. At the intensive margin, surviving businesses increased labor costs without decreasing hours and saw no reductions in revenue. At the extensive margin, businesses experienced higher rates of exit and newly opened businesses became less labor-intensive. We find the total effect of the minimum wage to low-wage employment, defined as jobs paying 130% of the minimum wage or less, came from changes to the composition of businesses.
Tax Pass-Through with Information Asymmetry in an Online Labor Market
Presented at HBS Digital Initiative Workshop