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1. Contract Employment: Measurement and Implications for Employer–Employee Relationships

   https://doi.org/10.1177/00197939221132530  

   Osterman, Paul (January 2022, ILR Review)

   This article utilizes a new nationally representative survey, executed in January 2020, that measures non-standard work. The author estimates the incidence of contract company employment and freelancing and describes who goes into non-standard employment. He then studies earnings and access to employer-provided training among contract company employees—the largest and most mis-measured group of non-standard workers. Training is important because it affects wage growth and career trajectories and also gives insight into the evolving character of employment relationships. Findings indicate that contract company employees face an earnings penalty but that considerable heterogeneity occurs within this category. The analysis of multiple forms of formal training finds that contract company employees receive less training than do standard employees even after multiple controls. Informal training is more textured due to the nature of social interactions inherent in its availability. Throughout the analysis, racial and ethnic disparities are apparent. 

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2. Long-term Effects of STEM Enrichment Programs on Wage Differentials Among Under-Represented Minority Students

   https://doi.org/10.18060/26063  

   Ghazzawi, Dina; Pattison, Donna; Horn, Catherine (February 2023, Metropolitan Universities)

   Holton, Valerie (Ed.)

   This study focuses on the increasing disparities in STEM education achievement and long-term wage earnings of under-represented minority groups. As part of national efforts to improve the diversity of the STEM workforce, this study uses longitudinal data from the University of Houston’s Education Research Center (UH-ERC) to examine the effect of participation in a STEM focused intervention program (Houston-Louis Stokes Alliance for Minority Participation) on wage earnings across students from traditionally under-represented groups. Data analysis consisted of propensity score matching analysis, followed by an ordinal logistic regression model to measure program participation effects on wage earnings. Findings indicate a significant negative association between participation in STEM intervention program and long-term wage earnings. Results highlight the role of structural racism and human capital on perpetuating achievement and wage gaps across race and socio-economic status. Recommendations focus on career-preparedness as a tool to increase the human capital of under-represented groups, and institutional shifts in policy and program components that strive to reduce the impact of structural racism on this subpopulation of students. 

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3. AI exposure predicts unemployment risk: A new approach to technology-driven job loss

   https://doi.org/10.1093/pnasnexus/pgaf107  

   Frank, Morgan_R; Ahn, Yong-Yeol; Moro, Esteban; Kimbrough, ed., Erik (April 2025, PNAS Nexus)

   Abstract Is AI disrupting jobs and creating unemployment? This question has stirred public concern for job stability and motivated studies assessing occupations’ automation risk. These studies used readily available employment and wage statistics to quantify occupational changes for employed workers. However, they did not directly examine unemployment dynamics primarily due to the lack of data across occupations, geography, and time. Here, we overcome this barrier using monthly occupation-level unemployment data from each US state’s unemployment insurance office from 2010 to 2020 to assess AI exposure models, job separations, and unemployment through a new measure called unemployment risk. We demonstrate that standard employment statistics are inadequate proxies for occupations’ unemployment risk and find that individual AI exposure models are poor predictors of occupations’ unemployment risk states’ total unemployment rates, and states’ total job separation rates. However, an ensemble approach exhibits substantial predictive power, accounting for an additional 18% of variation in unemployment risk across occupations, states, and time compared to a baseline model that controls for education, occupations’ skills, seasonality, and regional effects. These results suggest that competing models may capture different aspects of AI exposure and that automation shapes US unemployment. Our results demonstrate the power of occupation-specific job disruption data and that efforts using only one AI exposure score will misrepresent AI’s impact on the future of work. 

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4. Who Profits from Patents? Rent-Sharing at Innovative Firms*

   https://doi.org/10.1093/qje/qjz011  

   Kline, Patrick; Petkova, Neviana; Williams, Heidi; Zidar, Owen (March 2019, The Quarterly Journal of Economics)

   Abstract This article analyzes how patent-induced shocks to labor productivity propagate into worker compensation using a new linkage of U.S. patent applications to U.S. business and worker tax records. We infer the causal effects of patent allowances by comparing firms whose patent applications were initially allowed to those whose patent applications were initially rejected. To identify patents that are ex ante valuable, we extrapolate the excess stock return estimates of Kogan et al. (2017) to the full set of accepted and rejected patent applications based on predetermined firm and patent application characteristics. An initial allowance of an ex ante valuable patent generates substantial increases in firm productivity and worker compensation. By contrast, initial allowances of lower ex ante value patents yield no detectable effects on firm outcomes. Patent allowances lead firms to increase employment, but entry wages and workforce composition are insensitive to patent decisions. On average, workers capture roughly 30 cents of every dollar of patent-induced surplus in higher earnings. This share is roughly twice as high among workers present since the year of application. These earnings effects are concentrated among men and workers in the top half of the earnings distribution and are paired with corresponding improvements in worker retention among these groups. We interpret these earnings responses as reflecting the capture of economic rents by senior workers, who are most costly for innovative firms to replace. 

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5. The long-run impacts of banning affirmative action in US higher education

   https://doi.org/10.1093/oxrep/grae034  

   Antman, Francisca M; Duncan, Brian; Lovenheim, Michael (November 2024, Oxford Review of Economic Policy)

   Abstract This paper estimates the long-run impacts of banning affirmative action on men and women from under-represented minority (URM) racial and ethnic groups in the United States. Using data from the US Census and American Community Survey, we use a difference-in-differences framework to compare the college degree completion, graduate degree completion, earnings, and employment of URM individuals to non-URM individuals before and after affirmative action bans went into effect across several US states. We also employ event study analyses and alternative estimators to confirm the validity of our approach and discuss the generalizability of the findings. Results suggest that banning affirmative action results in a decline in URM women’s college degree completion, earnings, and employment relative to non-Hispanic White women, driven largely by impacts on Hispanic women. Thus, affirmative action bans resulted in an increase in racial/ethnic disparities in both college degree completion and earnings among women. Effects on URM men are more ambiguous and indicate significant heterogeneity across states, with some estimates pointing to a possible positive impact on labour market outcomes of Black men. These results suggest that the relative magnitude of college quality versus mismatch effects vary for URM men and women and highlight the importance of disaggregating results by gender, race, and ethnicity. We conclude by discussing how our results compare with others in the literature and directions for future research. 

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