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on Minorities Research (Ethnic, LGBTQ+, Disabilities) |
| By: | Mallory Avery; Edwin Ip; Andreas Leibbrandt; Joseph Vecci |
| Abstract: | Recent technological advancements are reshaping pathways to employment by automating the interview process. Asynchronous interviews, in which job applicants submit answers to interview questions via an online platform without interacting with an interviewer, are replacing more traditional face-to-face job interviews. At the same time, AI algorithms are now widely used to assess these interview answers. In this paper, we use a field experiment to comprehensively study how these new technologies affect applicants and employers in the recruitment process. Over 3, 000 job applicants are randomized into asynchronous audio or video interviews, live online interviews, and a control group. Their job interviews are then assessed by both professional recruiters and a commercial AI recruitment tool used by most Fortune 100 companies. We find that asynchronous interviews cause an over 50% decrease in application continuation, including among the most qualified applicants, and that this decline is largest for women. A complementary vignette experiment provides evidence that this deterrence is driven by perceptions about the competitiveness and fairness of the recruitment process. In terms of assessments, we find that the AI evaluation tool scores women and underrepresented racial minorities higher than human evaluators, while the opposite is true for men, Whites and Asians. We track our applicants' subsequent labor market outcomes and find that the AI assessment tool predicts subsequent employment success substantially better than human recruiters, suggesting that AI captures soft skills and potential that humans overlook. In addition, we provide evidence that, unlike AI, human recruiters' assessments suffer from multiple cognitive biases. Our findings provide some of the first key evidence on how recent technological advances are transforming the hiring process. |
| Keywords: | technological change, artificial intelligence, gender, field experiment |
| JEL: | C93 J23 J71 J78 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12573 |
| By: | Diogo Baerlocher (Department of Economics, University of South Florida); Renata Caldas (Department of Economics, University of South Florida); Francisco Cavalcanti (Department of Economics, Universidade Federal de Pernambuco) |
| Abstract: | Racial identity is not solely a matter of physical appearance but is also shaped by social and historical context. Using data on over 500, 000 candidates for local office in Brazil’s 2020 elections, we study how self-reported race - specifically, identification as white - relates to phenotypic appearance, socioeconomic characteristics, and local social perceptions. We use machine learning to extract appearance-based probabilities of racial classification from candidate photographs and show that these probabilities explain a significant share of variation in self-reported race. Socioeconomic factors such as education, gender, and wealth also influence racial identification, though their effects diminish among individuals whose appearance more clearly aligns with the white category. Municipality fixed effects, which we interpret as capturing local social perception bias, vary systematically across regions and are strongly associated with historical slave population shares. We further show that areas with state-sponsored European settlements - often associated with more inclusive institutions - exhibit lower rates of white self-identification, contrasting with the positive association between slavery intensity and white identification. Our findings highlight the enduring role of social and historical forces in shaping racial classification and suggest that racial inequality cannot be fully understood without accounting for the social construction of race. |
| Keywords: | Racial Classification, Social Identity, Phenotypic Appearance, Historical Legacy |
| JEL: | J15 N36 Z13 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:usf:wpaper:2026-01 |
| By: | St'Anna, Pedro (Massachusetts Institute of Technology); Sardoschau, Sulin (Humboldt University Berlin); Schmeisser, Aiko (Columbia University) |
| Abstract: | Empirical studies of racial wage disparities typically rely on self-reported race and treat racial categories as fixed. This paper shows that racial classification in the labor market is produced by social perception, and that modeling this process is essential for measuring wage gaps. We combine two large administrative data sets to construct three racial identity measures for 330, 000 workers in Brazil (2003-2015): employer classification, self-identification, and an algorithmic skin-tone measure. Self-identified and employer-ascribed race differ in over 20 percent of cases, and employers disagree about the same worker. We estimate a "race function" describing how employers map phenotypic cues, self-identification, education, and employment histories into racial categories. Holding skin tone constant, university graduates are substantially more likely to be perceived as White. Measured wage gaps vary across racial definitions, and accounting for perception meaningfully alters disparity estimates. We show that conventional approaches overstate the role of productivity differences in explaining racial wage gaps. |
| Keywords: | Race, identity, disparity, wage gap, Brazil |
| JEL: | J15 J50 J71 Z10 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18473 |
| By: | Lin, Zhuoer; Sun, Ruochen; Ross, Joseph S.; Lau, Kien; Stumpf, Sophia; Chen, Xi |
| Abstract: | Alzheimer's disease (AD) disproportionately burdens racial and ethnic minority populations, yet the extent to which US clinical trials reflect this burden remains poorly understood. We conduct a systematic review of all 88 completed US-based Phase III AD drug trials between 1997 and 2023, using a multi-source approach that integrates the Trialtrove clinical trial database with PubMed, ClinicalTrials.gov, pharmaceutical reports, and conference abstracts. We document three main findings. First, nearly half of published trials (49.3%) reported no data on patient race or ethnicity. Among trials that did report, practices were highly inconsistent in terminology, categorization, and analytical depth. Second, White patients constituted a median of 91.3% of enrollment, while Black patients represented 4.5%-7.2%, Hispanic patients 5.2%, and Asian / Pacific Islander and Native American patients less than 1% - shares that are grossly disproportionate to AD prevalence rates, which are approximately twice as high among non- Hispanic Black older adults and 1.5 times as high among Hispanic older adults relative to non- Hispanic Whites. Third, only 3 trials (4.2%) conducted any subgroup analyses by race or ethnicity, and none reported treatment safety or efficacy stratified by demographic group. Critically, regression models find no evidence of improvement in reporting or representation from 1997 to 2023. These patterns limit the generalizability of existing AD treatment evidence and raise fundamental concerns about health equity. Our findings support strengthening mandatory reporting standards, broadening eligibility criteria, and diversifying trial site selection to ensure emerging AD treatments are evaluated equitably across the populations most affected. |
| Keywords: | Alzheimer's disease, dementia, clinical trials, racial and ethnic disparities, health equity, underrepresentation, diversity in clinical research |
| JEL: | I14 I18 J15 J14 L65 I11 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1728 |
| By: | Kadivar, Mohammad Ali; Khani, Saber |
| Abstract: | Ethnic diversity has long shaped Iran’s political landscape, yet systematic evidence on how material resources and risks are distributed across ethnolinguistic populations remains limited. To account for this variation, we present an uneven incorporation framework, which holds that ethnic groups are integrated into state institutions and resource distribution systems at different rates and across different domains, producing structured but multidimensional inequality rather than uniform minority deprivation. We then test this framework using the first district-level multivariate analysis of socioeconomic inequality across Iran’s major language groups, linking fine-grained data on ethnolinguistic composition to indicators of education, income, social protection, health infrastructure, economic activity, and environmental exposure — moving beyond province-level and identity-centered accounts. The findings show a structured but multidimensional hierarchy rather than uniform minority deprivation. Persian- and Caspian-speaking districts are consistently advantaged across literacy, income, and welfare. Baluchi districts face layered disadvantage, with low education, high poverty, and weak welfare integration, while Kurdish regions show similar but less severe patterns. Lori and Turkic-speaking areas occupy intermediate positions, with stronger incorporation into higher education, health, and welfare systems. Turkmen districts display mixed outcomes, combining gains in literacy and health with persistent poverty. Arab-majority districts show higher industrial activity alongside greater exposure to pollution, without consistent socioeconomic advantage. Overall, ethnic inequality in Iran is multidimensional and sector-specific, shaped by uneven institutional incorporation and spatially distributed economic and environmental burdens. |
| Date: | 2026–03–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:a2vyb_v1 |
| By: | Stephanie Coffey; Joshua S. Goodman; Amy Ellen Schwartz; Leanna Stiefel; Marcus A. Winters; Yunee H. Yoon |
| Abstract: | Special education serves more than one in seven U.S. students yet its causal impact remains understudied. Using longitudinal data from Massachusetts, Indiana, and Connecticut, we estimate the effect of individualized supports with an event-study design that tracks achievement around initial classification. Students' scores decline prior to placement and rise sharply afterward, yielding a consistent V-shaped pattern. Within three years, achievement is 0.2–0.4σ higher than counterfactual trends imply. Gains are similar across disability categories and subgroups, are not driven by testing accommodations, and remain under conservative assumptions. Individualized supports substantially increase learning productivity. |
| Keywords: | special education, disability, achievement, pre-trends |
| JEL: | I21 I28 H52 J24 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12587 |
| By: | Gilly, Travis (Real Safety AI Foundation) |
| Abstract: | Reality testing is a core cognitive-behavioral intervention for persecutory delusions: the clinician helps the patient examine evidence for the belief that they are being monitored, the patient discovers the belief is unfounded, and cognitive reframing occurs. This paper argues that mass surveillance infrastructure deployed by U.S. government agencies and their private contractors has eliminated the foundational precondition for this intervention by making persecutory beliefs factually accurate for the general population, creating a specific and documentable disparate impact on Americans with paranoia-spectrum conditions. We identify three distinct mechanisms of harm: reality testing destruction, in which the therapeutic intervention fails because the environment has changed to confirm the delusion rather than because the patient resists treatment; device abandonment and social isolation, in which affected individuals make the rational decision to discard communication devices upon learning that government geofencing captures every phone in their neighborhood, severing contact with support systems and crisis services; and algorithmic pattern-matching discrimination, in which surveillance systems designed to flag anomalous movement patterns systematically misidentify the behavioral symptoms of paranoia-spectrum conditions as suspicious activity, subjecting affected individuals to additional scrutiny that compounds the original harm. We document that this intersection has been entirely overlooked in both the surveillance policy literature and the clinical psychiatric literature, including a 119-page European Parliament analysis on AI surveillance and human rights that does not contain the word "disability." We frame these harms under ADA Title II and Section 504, identify a novel legal problem we term the self-sealing paradox (in which surveillance retroactively eliminates the diagnostic category it damages), and propose clinical, legal, and policy responses. |
| Date: | 2026–03–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:cfqwz_v1 |
| By: | Daas, Yousuf; Dalmon, Danilo Leite |
| Abstract: | South Africa entered the COVID-19 pandemic with one of the world's most unequal labor markets, where racial stratification shaped not only employment access but the distribution of wages within employment. This paper estimates the differential effect of the post-2020 period on monthly wages using seven waves of the Labor Market Dynamics in South Africa (LMDSA) dataset spanning 2017 to 2023. The empirical strategy employs a difference-in-differences design with race-by-post-2020 interaction terms, conditional on province, year-by-quarter, education, industry, and occupation fixed effects, with standard errors clustered at the survey stratum level. The results show that racial wage inequality widened sharply and persistently after 2020 across all subgroups examined. Among wage employees, African/Black workers experienced a relative wage penalty of approximately 37 percent and Colored workers approximately 52 percent. The most striking finding concerns employers: African/Black business owners suffered a relative earnings loss of approximately 59 percent, entirely reversing a modest pre-pandemic advantage, exposing the fragility of post-apartheid entrepreneurial gains. Gender-disaggregated estimates reveal larger conditional penalties for non-White men than women among the employed, though this coexists with documented severe labor force exclusion of women during the crisis. Occupational strata analysis confirms that penalties are present across all skill levels and are not explained by compositional sorting across industries or occupations. These findings are consistent with a structural amplifier interpretation, whereby the pandemic revealed and entrenched pre-existing vulnerabilities in a segmented labor market rather than generating new ones. |
| Date: | 2026–03–20 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:8xrjc_v1 |
| By: | Thomas Groll; Sharyn O'Halloran |
| Abstract: | We develop a theory of distributive competition under redistricting that explains both electoral outcomes and the equilibrium allocation of policy benefits by endogenizing voter pivotality. In a multi-district model with primaries, general elections, and group-targeted transfers, districting shapes political influence through two channels: a selection channel for descriptive representation (who wins office) and a competition channel for substantive representation (who receives policy benefits). District composition alters candidate matchups, shifting voter responsiveness and political leverage, and each channel alone yields distinct predictions about whether packing or cracking voters is optimal. For minority voters, the welfare effects of districting depend on electoral leverage, preferences over descriptive versus partisan representation, primary rules, and competitiveness. The channels align on packing when minorities are electorally weak and value descriptive representation, and align on cracking when minorities are electorally pivotal and prioritize partisan outcomes. When the channels diverge, or when endogenous feedback reshapes electoral leverage, minority welfare can be nonmonotonic in voter concentration. Our results identify when majority-minority districts enhance minority welfare and when dispersion strengthens political influence. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.01340 |
| By: | A. Lazos (Audencia Business School); R. Shneor |
| Abstract: | While immigrant entrepreneurs contribute to economies by creating employment opportunities and innovative ventures, they often represent a marginalised group facing greater challenges in access to entrepreneurial finance. Crowdfunding may help remedy some of this challenge through more democratic access to finance and investment opportunities. This study examines the effects of the presence of immigrants in entrepreneurial teams on equity crowdfunding campaigns' success and on the ventures' survival. To answer these questions, we build on risk-attitude, cognitive resource diversity, and social capital theories. Our analysis uses data about UK-based firms behind 1, 171 equity crowdfunding offerings on three platforms (Crowdcube, Seeders and SynicateRoom). The results suggest a relation following an inverted U-shape between the share of immigrants in entrepreneurial teams and both the amount raised and number of investors. Furthermore, the campaign's goal sum mediates these associations. Interestingly, the higher the share of immigrants among entrepreneurial team members, the lower the likelihood of an equity crowdfunded venture's long-term survival. However, such effects may be overturned when fundraising by majority immigrant teams involve relatively high sums, while avoiding undercapitalisation |
| Keywords: | immigrant, equity crowdfunding, success, entrepreneurial teams |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05563834 |