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on Demographic Economics |
| By: | Cory McCartan; Robin Fisher; Jacob Goldin; Daniel E. Ho; Kosuke Imai |
| Abstract: | The estimation of racial disparities in various fields is often hampered by the lack of individual-level racial information. In many cases, the law prohibits the collection of such information to prevent direct racial discrimination. As a result, analysts have frequently adopted Bayesian Improved Surname Geocoding (BISG) and its variants, which combine individual names and addresses with Census data to predict race. Unfortunately, the residuals of BISG are often correlated with the outcomes of interest, generally attenuating estimates of racial disparities. To correct this bias, we propose an alternative identification strategy under the assumption that surname is conditionally independent of the outcome given (unobserved) race, residence location, and other observed characteristics. We introduce a new class of models, Bayesian Instrumental Regression for Disparity Estimation (BIRDiE), that take BISG probabilities as inputs and produce racial disparity estimates by using surnames as an instrumental variable for race. Our estimation method is scalable, making it possible to analyze large-scale administrative data. We also show how to address potential violations of the key identification assumptions. A validation study based on the North Carolina voter file shows that BISG+BIRDiE reduces error by up to 84% when estimating racial differences in party registration. Finally, we apply the proposed methodology to estimate racial differences in who benefits from the home mortgage interest deduction using individual-level tax data from the U.S. Internal Revenue Service. Open-source software is available which implements the proposed methodology. |
| JEL: | C10 H22 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32373&r=dem |
| By: | Jennifer Hunt; Carolyn Moehling |
| Abstract: | We create a dataset of 14, 000 hand-coded help–wanted advertisements placed by employment agencies in three U.S. newspapers in 1950 and 1960, a time when help–wanted advertisements were divided into male and female sections, and collect information on agency ownership. We find that female-owned agencies specialized in vacancies for women, thereby expanding the access of female jobseekers to agency services, including for positions in majority-male occupations. Female-owned agencies advertised more skilled occupations to women than did male-owned agencies, leading to a 5.5% higher wage for women. On the other hand, female-owned agencies had a greater propensity to match male jobseekers to clerical jobs, contributing to 21% lower male wages than for male-owned agencies. The results are consistent with female proprietors having had a comparative advantage in female jobseekers and clerical occupations or with client firms having trusted female proprietors only with vacancies for women and homogeneous, lower-skill occupations. However, in choosing to establish an agency and to specialize in female jobseekers, female proprietors may have sought to mitigate employer discrimination against female jobseekers; their higher propensity to advertise majority-male occupations among professional, technical and managerial advertisements for women may also reflect discrimination mitigation. |
| JEL: | J16 J63 J71 N32 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32383&r=dem |
| By: | Ingar Haaland (NHH Norwegian School of Economics); Christopher Roth (University of Cologne); Stefanie Stantcheva (Harvard University); Johannes Wohlfart (Department of Economics, University of Copenhagen) |
| Abstract: | We survey the recent literature in economics measuring what is on top of people’s minds using open-ended questions. We first provide an overview of studies in political economy, macroeconomics, finance, labor economics, and behavioral economics that have employed such measurement. We next describe different ways of measuring the considerations that are on top of people’s minds. We also provide an overview of methods to annotate and analyze such data. Next, we discuss different types of applications, including the measurement of motives, mental models, narratives, attention, information transmission, and recall. Our review highlights the potential of using open-ended questions to gain a deeper understanding of mechanisms underlying observed choices and expectations. |
| Keywords: | Thoughts, Open-ended Questions, Text Data, Methodology, Surveys, Qualitative Research. |
| JEL: | C90 D83 D91 |
| Date: | 2024–05–14 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kucebi:2410&r=dem |