RESEARCH RESOURCES
Data
Publicly available datasets developed for or released alongside selected research papers. Each resource includes a short description and a suggested paper citation.
01 / AI & LABOR
Occupation & Firm Generative AI Exposure
Measures of exposure to generative AI at the occupation and firm levels, distinguishing core from supplemental tasks. The associated repository also provides firm data-asset measures. Occupation scores use SOC codes; firm scores use GVKEY identifiers.
Occupation-level exposure
Overall, core-task, and supplemental-task AI exposure measures.
Download CSV ↗Firm-level exposure
Firm measures based on the occupational composition of the workforce.
Download CSV ↗Suggested paper citation
Eisfeldt, Andrea L., Gregor Schubert, Miao Ben Zhang, and Bledi Taska (2026). “Generative AI and Firm Values.” Journal of Finance. doi:10.1111/jofi.70089.
03 / REGULATION & INVESTMENT
State Section 179 Limits
State-level Section 179 expensing-limit data associated with research on investment tax incentives, firms’ equipment investment, and changes in demand for routine-task versus skilled workers.
Suggested paper citation
Tuzel, Selale, and Miao Ben Zhang (2021). “Economic Stimulus at the Expense of Routine-Task Jobs.” Journal of Finance 76(6), 3347–3399.
04 / REGIONAL ASSET PRICING
Local Beta by Metropolitan Area, 1986–2016
Measures of local economic cyclicality (“local beta”) for U.S. metropolitan statistical areas. Used to study how the industrial composition of local economies affects wages, real-estate prices, firm risk, and stock returns.
Suggested paper citation
Tuzel, Selale, and Miao Ben Zhang (2017). “Local Risk, Local Factors, and Asset Prices.” Journal of Finance 72(1), 325–370.
For additional papers and supporting materials, see the Research page. Individual datasets may have their own usage conditions; please review the associated repository or download page.