Papers written by Author(s): 'Matthew Dey'
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Working PaperAI Exposure and Adoption Among U.S. Firms
September 2026
Working Paper Number:
CES-26-61
Measures of exposure to artificial intelligence (AI) are widely used to study where AI is likely to affect firm and worker outcomes, yet limited evidence exists on how closely exposure relates to realized AI adoption at the firm level. We examine this relationship by linking 14 firm-level exposure measures, constructed from occupational exposure estimates in the literature and occupational employment shares from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program, to direct measures of firm AI adoption from the Census Bureau's Business Trends and Outlook Survey. Exposure is positively and statistically significantly associated with adoption, but explains only a modest share of its variation. The strength of the relationship varies considerably across standardized exposure measures: a one-standard-deviation increase in firm-level exposure is associated with a 4'11 percentage point higher firm adoption probability, falling to 1'8 percentage points after controlling for year and sub-sector fixed effects. The exposure'adoption relationship is also heterogeneous across firm-size classes and sectors. Exposure is thus an informative but incomplete signal of adoption. More broadly, the results have implications for the interpretation of exposure-based measures in studies of the economic effects of AI.View Full Paper PDF
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Working PaperJob Tasks, Worker Skills, and Productivity
September 2025
Working Paper Number:
CES-25-63
We present new empirical evidence suggesting that we can better understand productivity dispersion across businesses by accounting for differences in how tasks, skills, and occupations are organized. This aligns with growing attention to the task content of production. We link establishment-level data from the Bureau of Labor Statistics Occupational Employment and Wage Statistics survey with productivity data from the Census Bureau's manufacturing surveys. Our analysis reveals strong relationships between establishment productivity and task, skill, and occupation inputs. These relationships are highly nonlinear and vary by industry. When we account for these patterns, we can explain a substantial share of productivity dispersion across establishments.View Full Paper PDF
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Working PaperOpening the Black Box: Task and Skill Mix and Productivity Dispersion
September 2022
Working Paper Number:
CES-22-44
An important gap in most empirical studies of establishment-level productivity is the limited information about workers' characteristics and their tasks. Skill-adjusted labor input measures have been shown to be important for aggregate productivity measurement. Moreover, the theoretical literature on differences in production technologies across businesses increasingly emphasizes the task content of production. Our ultimate objective is to open this black box of tasks and skills at the establishment-level by combining establishment-level data on occupations from the Bureau of Labor Statistics (BLS) with a restricted-access establishment-level productivity dataset created by the BLS-Census Bureau Collaborative Micro-productivity Project. We take a first step toward this objective by exploring the conceptual, specification, and measurement issues to be confronted. We provide suggestive empirical analysis of the relationship between within-industry dispersion in productivity and tasks and skills. We find that within-industry productivity dispersion is strongly positively related to within-industry task/skill dispersion.View Full Paper PDF
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