Gregor Schubert

Research (by topic)

Effects of Artificial Intelligence and Technology Shocks on Firms and Labor Markets

Generative AI and Firm Values. (Revise & Resubmit, Journal of Finance)

with Andrea Eisfeldt, Bledi Taska and Miao Ben Zhang 

SSRN |  Occupation GenAI Exposure Data  | Firm GenAI Exposure Data (coming soon) | NBER WP

Best Paper Award, TCU Finance Conference (Previous draft  was also circulated as "The Labor Impact of Generative AI on  Firm Values")

• Media coverage:  Bloomberg, How the World Works Podcast, Wall Street Journal, Financial Times, Barron's Op-Ed, 

• Selected Presentations:  Purdue Fintech Conference, TCU Finance Conference, ITAM Finance Workshop, UW Summer Finance Conference, 2024 IMF-WIFPR Conference, Labor in the Age of Generative AI Conference at Chicago Booth, Wharton AI and the Future of Work Conference, AI & Big Data in Finance Research Forum, 2024 Macro Finance Society Workshop, UCSD Rady Finance Seminar, 2024 Duke/UNC Asset Pricing Conference, 2024 Kentucky Finance Conference, Federal Reserve Board AI Talks, 2023 NBER Big Data and Securities Markets Fall Conference, Chicago Booth Empirical Finance Conference 2023, KDD Finance Day 2023

How do recent advances in Generative AI affect firm value? We construct the first measure of firms' workforce exposures to Generative AI and show that an ``Artificial-Minus-Human'' (AMH) portfolio that is long high-exposure firms and short low-exposure firms earned daily returns of 0.44% in the two weeks following the release of ChatGPT. The labor-exposure effect is more pronounced for firms with greater data assets and is distinct from the effect of firms' product exposures to Generative AI. Highly-exposed workforces can be either substituted for or complemented by Generative AI technologies.  We measure whether the exposed tasks are core or supplemental to assess relative substitutability. Examining firms' labor demand and profitability following the release of ChatGPT supports a labor-technology substitution channel for the increases in exposed firms' values that we document. 

Generative AI and Finance. The Annual Review of Financial Economics. (Forthcoming).  

Eisfeldt, Andrea and Gregor Schubert

SSRN  PDF  Twitter thread   Blog summary 

Abstract: Since ChatGPT’s release in 2022, demand for AI-related skills in finance has grown rapidly, as Generative AI drives significant technological changes in both financial research and the broader economy. We show that financial occupations are highly exposed to the productivity effects of Generative AI, review the literature on the impact of ChatGPT on firm value, and provide directions for future research investigating the impact of this major technology shock. Generative AI also holds great potential as a tool for finance researchers and practitioners: we review and describe innovations in research methods linked to improvements in AI tools, along with their applications. We offer a practical introduction to available tools and advice for researchers in academia and industry interested in using these tools.

Organizational Technology Ladders: Remote Work and Generative AI Adoption

Presentations: NBER Organizational Economics Spring 2025 Meeting, CREDA Real Estate Research Symposium 2024 

Scheduled presentations: Wharton Business and Generative AI Conference 2025 (San Francisco); USC AI and Economics Conference; UEA North America Meeting 2025 in Montreal; SF Fed Micro Macro Labor Conference 2025

SSRN PDF  

What drives inequality in technology adoption among firms? In this study, I provide novel empirical evidence linking two of the biggest technology shocks to firms in the last decade: remote work and generative AI. I develop an IV approach to identify the causal effect of remote work and apply it to detailed job posting data to estimate large positive effects of remote work on generative AI skill demand. Conversely, I provide evidence from a synthetic difference-in-differences approach that firms that were more exposed to generative AI technology reduced their demand for remote workers after ChatGPT was released. I rationalize these results using a task-based model of firm investments in new technology. Moreover, I provide evidence for the mechanism through which this “organizational technology ladder” operates: When firms go remote, they invest in technology skills, which, in turn, enable a more rapid generative AI adoption. Firms that are less able to accommodate remote work because they have lower managerial or communication capabilities, or workers with lower individual decision-making skills, are more likely to adopt generative AI after they go remote, and are more likely to reduce their remote hiring after they are exposed to generative AI tools.

Career Values for Labor Markets: Evidence from  Robot Adoption  (under review)

 with Maria Petrova, Pinar Yildirim & Bledi Taska

NEW DRAFT  SSRN  Old version: NBER WP 

• Media coverage: Chicago Booth Review

• Presentations: NBER Economics of Artificial Intelligence Conference 2024 (Toronto), Digital, Data, Design institute at Harvard University

Career progression is important for people's lives and economic decisions. We develop an empirical measure of an occupation's local "career value" - the long-run value of the earnings that will result from working in that job and following the career ladder associated with it. We then document that career values have been stagnating over the 2000-2016 period, in spite of growing wages,  due to a deterioration in career mobility. We estimate the effect of robot automation on career values over the same time period and find that one additional industrial robot per 1,000 workers lowered local career values by about 1.5 percent. The reason is that robotization reduces transitions into better-paid occupations and redirects workers toward similar- or lower-paid jobs. The impact is largest in high-manufacturing areas, for mid-experience workers, and for males. Demotions from management jobs that result from robotization are more likely for less-educated workers and for women, who are more likely to respond by upskilling. Declines in career values led to a reduction in housing construction and college enrollment and an increase in Republican vote shares in 2016, which highlights how the career effects of automation shape forward-looking household decisions.

Maria Petrova, Gregor Schubert, Pinar Yildirim & Bledi Taska (2025). Robotization and the Political Response of Politicians. In A. Agrawal, J. Gans, A. Goldfarb, & C. Tucker (Eds.), The economics of artificial intelligence: Political economy. NBER Conference Volume.

  NBER Book  PDF 

Abstract: Politicians respond to increased robotization in the labor market: we find that the Republican presidential candidate in 2016 targeted campaign visits to areas with high robot exposure and that representatives in high-manufacturing areas shifted their policy positions rightward in response to automation.

The Household Benefits of Generative AI    (w. Michael Blank and Miao Ben Zhang)

(Draft coming soon)

• Scheduled Presentations: AEA Meeting 2026

Who adopts generative AI, and what do households use generative AI for in practice? This paper provides the first comprehensive evidence on households' usage of generative AI, documents inequality in household benefits and changes in household behavior after adopting generative AI tools.

Labor Mobility

Employer Concentration and Outside Options  (w. Anna Stansbury & Bledi Taska) 

Reject & Resubmit, American Economic Journal: Economic Policy

 [Occupational Transition Data] [Non-Technical Write-up] [SSRN]

• Presentations: AASLE 2019, UEA 2019

We find that increases in employer concentration causally reduce wages, using a new instrument for employer concentration based on changes in large firms' national hiring patterns. We also show that measuring employer concentration within a single local occupation excludes important parts of workers' true labor markets. Moving from the median to the 95th percentile of employer concentration as experienced by workers causally reduces wages by 10.7 log points in low-outward-mobility occupations like registered nurses or security guards, and by 3 log points in high-outward-mobility occupations like bank tellers or counter attendants. We propose a new approach for defining mobility-adjusted labor markets, measuring employer concentration on clusters of local occupations identified through asymmetric mobility patterns (using new, highly granular data on occupational mobility from 16 million resumes). Overall, we estimate that around one in six U.S. workers face wage suppression of 2% or more as a result of employer concentration.

Partisan Migration  (w. Ben Pirie & Stuart Gabriel) 

(Draft coming soon)

• Scheduled Presentations: UEA North America Meeting 2025 in Montreal

We show that partisan political identity has become an increasingly important determinant of where Americans choose to live. Using individual‑level voter‑registration records for 2012‑2022, we construct county‑to‑county migration flows by party and quantify the degree to which migration is ``politically-aligned''---moving to a county whose presidential vote share matches one’s own party. We use  this data to estimate party-specific location preferences in a spatial equilibrium model and find a sharp increase in partisan preferences: in 2020‑2022 a 1 percent rise in a destination's Democratic vote share raises Democratic in‑migration by 1.8 percent and reduces Republican in‑migration by 0.8 percent. This partisan preference by Democratic movers represents a 29% increase  relative to 2012‑2015, while Republican movers' partisanship only increased in magnitude by 6\%.  This can explain the growth in county‑level partisan segregation, as the share of counties  that were similarly attractive to both parties declined substantially during the decade.

Real Estate Finance and Housing Market Dynamics

When Money Moves In: The Consequences of Housing Wealth    (w. Darren Aiello & Jason Kotter)

Revise & Resubmit, Journal of Financial Economics     [SSRN]

• Overall Best Paper Prize, AREUEA National Meeting 2023

• Presentations: USC Lusk Real Estate Seminar, UEA North America Meeting 2024, Boston College Finance Seminar, Oxford Said Business School, UC Irvine

• Media Coverage:  Wall Street Journal

We construct a novel dataset tracking households across property purchases covering 25 years of moves within the U.S. We find that information frictions in residential real estate markets cause movers with larger exogenous housing wealth to overpay for their next house, relative to both time varying local prices as well as time invariant characteristics of the property itself. These housing wealth driven overpayments are associated with larger positive price impacts to the immediately surrounding neighborhood and are larger for local movers relative to non-local movers. The aggregate effect of housing wealth inflows is to increase county-level house prices.

House Price Contagion and U.S. City Migration Networks (Job Market Paper)

  SSRN Non-technical write-up    Presentation video    Download migration network data

• Media coverage: Mortgage Banker Magazine

• WFA Ph.D. Candidate Award for Outstanding Research, 2021

• Real Estate Cycles Manuscript Prize of the American Real Estate Society, 2021

• Presentations: NBER SI Real Estate 2021, WFA 2021, EUEA 2021, EALE 2021, ARES 2021, UEA 2020, UBC Urban Economics, Univ. of Toronto Urban Economics, Stanford GSB Finance Seminar, ITAM Finance Workshop

How do shocks propagate between city-level housing markets? I propose an explanation based on migration spillovers between U.S. cities. I derive an empirical measure of a city’s migration exposure to other cities, and use the network structure of migration to estimate that a 10 pp house price growth shock in a city’s network causes a 4.3 pp increase in house price growth in the focal city. I show that differences in migration networks can explain differences in local housing cycles, and show that accounting for spillover effects improves the ability to explain housing market effects in multiple applications from the finance literature, including 2002-2006 housing boom dynamics, and the effects of the Covid pandemic.

The Financial Consequences of Wanting to Own a Home  (w.  Caitlin Gorback)

 [Working Paper]  [SSRN]

• Presentations: NBER SI 2023, Real Estate,  Urban Economics Association North America Meeting 2024

We study the causal effects of homeownership affinities on tenure choice, household sensitivity to credit shocks, and retirement portfolios. Exploiting exogenous variation in affinities across U.S. immigrants’ countries of origin, we find that a 10pp higher affinity causes a 1.5pp higher homeownership rate in the U.S. Using exogenous credit-supply shocks, we show that high-affinity households are more responsive to credit availability, and less likely to default on mortgage payments. By retirement, high-affinity households realize higher homeownership, greater total wealth, and larger real estate shares in their portfolios. These effects are largely driven by appreciation.

Research in Progress

Age Structure and Housing Affordability

 (w. Erica Moszkowski, Shiyan Wei)

Presentations: Urban Economics Association North America Meeting 2024

The Geography of Entrepreneurship

(w. John Barrios, Yael Hochberg)

The Long Term Effects of Household Migration on Employment and Education (Work in Progress)

 (w. D. Aiello and J. Kotter)

Using AI and Property Listings To Understand Housing Market Transformations

 (w. D. Aiello and J. Kotter)

A Demand System Approach to Residential Housing Supply

 (w. D. Aiello, M. Kargar, and J. Kotter)