Looking at Success

Kevin Lewis

July 01, 2021

Beholding Inequality: Race, Gender, and Returns to Physical Attractiveness in the United States
Ellis Monk, Michael Esposito & Hedwig Lee
American Journal of Sociology, July 2021, Pages 194-241


Physical attractiveness is an important axis of social stratification associated with educational attainment, marital patterns, earnings, and more. Still, relative to ethnoracial and gender stratification, physical attractiveness is relatively understudied. In particular, little is known about whether returns to physical attractiveness vary by race or significantly vary by race and gender combined. In this study, we use nationally representative data to examine whether (1) socially perceived physical attractiveness is unequally distributed across race/ethnicity and gender subgroups and (2) returns to physical attractiveness vary significantly across race/ethnicity and gender subgroups. Notably, the magnitude of the earnings disparities along the perceived attractiveness continuum, net of controls, rivals and/or exceeds in magnitude the black-white race gap and, among African-Americans, the black-white race gap and the gender gap in earnings. The implications of these findings for current and future research on the labor market and social inequality are discussed.

Assumptions About Algorithms' Capacity for Discrimination
Arthur Jago & Kristin Laurin
Personality and Social Psychology Bulletin, forthcoming


Although their implementation has inspired optimism in many domains, algorithms can both systematize discrimination and obscure its presence. In seven studies, we test the hypothesis that people instead tend to assume algorithms discriminate less than humans due to beliefs that algorithms tend to be both more accurate and less emotional evaluators. As a result of these assumptions, people are more interested in being evaluated by an algorithm when they anticipate that discrimination against them is possible. We finally investigate the degree to which information about how algorithms train using data sets consisting of human judgments and decisions change people's increased preferences for algorithms when they themselves anticipate discrimination. Taken together, these studies indicate that algorithms appear less discriminatory than humans, making people (potentially erroneously) more comfortable with their use.

Understanding women's wage growth using indirect inference with importance sampling
Robert Sauer & Christopher Taber
Journal of Applied Econometrics, June/July 2021, Pages 453-473


The goal of this work is to investigate the effects of time out of the labor market for childcare on women's lifecycle wage growth. We develop a dynamic lifecycle model of human capital, fertility, and labor supply for women. We estimate by indirect inference using importance sampling and formalize the use of this procedure. The results indicate a modest effect of fertility-induced non-employment spells on human capital accumulation. The difference in human capital among prime-age women would be approximately 2.4% higher at its peak if the relationship between fertility and working were eliminated, and 4.7% higher if the relationship between marriage and fertility was also eliminated.

Politics and Gender in the Executive Suite
Alma Cohen, Moshe Hazan & David Weiss
NBER Working Paper, June 2021


Are the political preferences of CEOs associated with the representation and compensation of women in the executive suite? We find that Democratic CEOs (those who contribute more to Democratic candidates) are associated with higher representation of women in the executive suite. To explore causality, we use an event study approach and show that replacing a Republican with a Democratic CEO is associated with 20%-60% in more women in the executive suite. Finally, we show that Democratic CEOs are associated with a significant reduction (or even disappearance) of the gender gap in the level and performance-sensitivity of executive pay.

Measuring the Effectiveness of the Proposal to Divest Military Commanders of Disposition Authority for Sexual Assault Cases: A Comparative Quantitative Analysis
Brian Cox
Cornell Working Paper, May 2021


As suggestions to modify the practice of the U.S. military justice system return to the fore of American political discourse, the perennial proposal to divest commanders of authority to convene courts-martial to adjudicate allegations of sexual assault is once again at the center of the debate. While reformists are adamant that the suggested revision would support efforts to end what has been characterized as an "epidemic of rape" in the U.S. military, the precise connection between the "reform" and the desired improved outcomes remains tenuous. An assessment of jurisdictions that have already divested commanders of such authority could provide persuasive support to the reformist assertion that the United States could expect improved performance - if the assessment reveals improved performance in other jurisdictions. This essay conducts a comparative quantitative analysis of four jurisdictions - Australia, the United Kingdom, Israel, and Canada - to determine whether vesting court-martial convening authority in lawyers rather than commanders has resulted in improved performance in selected criteria in relation to the issue of sexual assault in the military. The comparative quantitative analysis conducted in this essay indicates that there is no correlative relationship between the "reform" and the improved performance reformists hope to achieve, at least in the context of the jurisdictions examined. This lack of a demonstrated correlative relationship in other jurisdictions creates reason to doubt whether divesting commanders of the authority to convene courts-martial to adjudicate allegations of sexual assault would lead to improved performance related to sexual assault in the U.S. military.

Employment Lapses and Subsequent Hiring Disadvantages: An Experimental Approach Examining Types of Discrimination and Mechanisms
Katherine Weisshaar
Socius: Sociological Research for a Dynamic World, June 2021


Employment interruption is a common experience in today's labor market, most frequently due to unemployment from job loss and temporary lapses to care for family or children. Although existing research shows that employment lapses cause disadvantages at the hiring interface compared to individuals with no employment disruptions, competing theories predict different mechanisms explaining these hiring penalties. In this study, the author uses an original conjoint survey experiment to causally assess perceptions of fictitious job applicants, focusing on a comparison of unemployed applicants and nonemployed caregiver applicants, who left work to care for family, to currently employed applicants. The author examines whether disadvantages for job applicants with employment gaps are receptive to positive information (and therefore represent a form of "informational bias") or are resistant to information (reflecting "cognitive bias") and further assesses which types of information affect or do not affect levels of bias in fictitious hiring decisions. Results show that positive information on past job performance and social skills essentially eliminates disadvantages faced by unemployed job applicants, but nonemployed caregiver applicants remain disadvantaged even with multiple types of positive information. These findings suggest that unemployed applicants face informational biases but that nonemployed caregiver applicants face cognitive biases that are rigid even with rich forms of positive or counter-stereotypical information. This study has implications for understanding the career consequences of employment disruption, which is especially relevant to consider in light of labor market disruptions during the recent pandemic.

Assessing the Employment Effects of California's Paid Family Leave Program
Samantha Marie Schenck
Eastern Economic Journal, June 2021, Pages 406-429


Until recently, it was impossible to study how paid family leave mandates would impact employment in the USA. This changed in 2004 when California implemented the USA's first paid family leave legislation. California's program provides us with a quasi-natural experiment to study how the implementation of paid family leave affected employment in the state. This paper uses several difference-in-difference, fixed-effects regressions on a large panel of business establishments to study the impact California's program had on establishment-level employment. Most model specifications show that the law is correlated with an increase in employment in firm establishments in the state.

Women at Work in the United States Since 1860: An Analysis of Unreported Family Workers
Barry Chiswick & RaeAnn Halenda Robinson
Explorations in Economic History, forthcoming


Estimated labor force participation rates among free women in the pre-Civil War period were exceedingly low. This is due, in part, to cultural or societal expectations of the role of women and the lack of thorough enumeration by Census takers. This paper develops an augmented labor force participation rate for free women in 1860 and compares it with the augmented rate for 1920 and today. Our methodology identifies women who are likely providing informal and unenumerated labor for market production in support of a family business, that is, unreported family workers. These individuals are not coded in the original data as formally working, but are likely to be engaged in the labor force on the basis of the self-employment of other relatives in their household. Unreported family workers are classified into four categories: farm, merchant, craft, and boardinghouse keepers. Using microdata, the inclusion of these workers more than triples the free female labor force participation rate in the 1860 Census from 16 percent to 57 percent, more than doubles the participation rate in the 1920 Census from 24 percent to 50 percent, and has a trivial effect on the currently measured rate of 56 percent (2015-2019 American Community Survey). This suggests that rather than a steep rise from a very low level in the female labor force participation rate since 1860, it has in fact always been high and fairly stable over time. In contrast, the effect of including unreported family workers in the male augmented labor force participation rate is relatively small.

Up to No Good? Gender, Social Impact Work, and Employee Promotions
Christiane Bode, Michelle Rogan & Jasjit Singh
Administrative Science Quarterly, forthcoming


Firms increasingly offer employees the opportunity to participate in firm-sponsored social impact initiatives expected to benefit the firm and employees. We argue that participation in such initiatives hinders employees' advancement in their firms by reducing others' perceptions of their fit and commitment. Because social impact work is more congruent with female than male gender role stereotypes, promotion rates will be lower for participating men, and male evaluators will be less likely than female evaluators to recommend promotion for male participants. Using panel data on 1,379 employees of a consulting firm, we find significantly lower promotion rates for male participants relative to female participants, female non-participants, and male non-participants. A vignette experiment involving 893 managers shows that lower promotion rates are due to lower perceptions of fit, but not commitment, and greater bias against male participants by male evaluators. Taken together, the results of the two studies suggest that the negative effect of participation on promotion is conditional upon participant and evaluator gender, underscoring the role of gender in evaluation of social impact work. In settings in which decision makers are predominately male, gender beliefs may limit male employees' latitude to contribute to the firm's social impact agenda.

Gender Differences in Peer Recognition by Economists
David Card et al.
NBER Working Paper, June 2021


We study the selection of Fellows of the Econometric Society, using a new data set of publications and citations for over 40,000 actively publishing economists since the early 1900s. Conditional on achievement, we document a large negative gap in the probability that women were selected as Fellows in the 1933-1979 period. This gap became positive (though not statistically significant) from 1980 to 2010, and in the past decade has become large and highly significant, with over a 100% increase in the probability of selection for female authors relative to males with similar publications and citations. The positive boost affects highly qualified female candidates (in the top 10% of authors) with no effect for the bottom 90%. Using nomination data for the past 30 years, we find a key proximate role for the Society's Nominating Committee in this shift. Since 2012 the Committee has had an explicit mandate to nominate highly qualified women, and its nominees enjoy above-average election success (controlling for achievement). Looking beyond gender, we document similar shifts in the premium for geographic diversity: in the mid-2000s, both the Fellows and the Nominating Committee became significantly more likely to nominate and elect candidates from outside the US. Finally, we examine gender gaps in several other major awards for US economists. We show that the gaps in the probability of selection of new fellows of the American Academy of Arts and Sciences and the National Academy of Sciences closely parallel those of the Econometric Society, with historically negative penalties for women turning to positive premiums in recent years.

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis
Martin Schweinsberg et al.
Organizational Behavior and Human Decision Processes, forthcoming


In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists' gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed.

Performance Information, Racial Bias, and Citizen Evaluations of Government: Evidence from Two Studies
Gregory Porumbescu, Suzanne Piotrowski & Vincent Mabillard
Journal of Public Administration Research and Theory, July 2021, Pages 523-541


Social accountability reforms emphasize expanding performance information disclosure and incorporating citizen feedback into performance evaluations of public organizations. However, social accountability scholarship has largely ignored possible discriminatory implications of performance information use despite calls for more social equity research. We look to bridge these two literatures, arguing that increasing exposure to performance information can actually activate racial bias in citizen feedback. Using two samples of White MTurk participants residing in the United States, we test this argument in a Negative Performance Information Study (n = 800) and a Positive Performance Information Study (n = 800). In the Negative Performance Information Study, we find increased exposure to negative performance information triggers more negative performance evaluations of public organizations led by Black public managers, but not White public managers, and strengthens preferences to fire Black public managers, but not White public managers. In the Positive Performance Information Study, we find increased exposure to positive performance information has no impact on performance evaluations of Black, nor White public managers but strengthens preferences to reappoint White, but not Black public managers. These findings suggest increasing exposure to performance information triggers racial bias in performance evaluations and preferences for holding public managers accountable.

Supply- and Demand-Side Effects in Performance Appraisals: The Role of Gender and Race
Iris Bohnet, Oliver Hauser & Ariella Kristal
Harvard Working Paper, May 2021


Performance reviews in firms are common but controversial. Managers' subjective appraisals of their employees' performance and employees' self-evaluations might be affected by demographic characteristics, interact with each other as self-evaluations are typically shared with managers before they decide ("anchoring"), and these supply-side and demand-side dynamics may contribute to gender or race differences in performance ratings. Analyzing the data of a multi-national financial services firm, we find that supply-side effects were mostly driven by gender: women (particularly, women of color) gave themselves lower self-ratings. Demand-side effects were shaped by gender and race: holding self-evaluations constant, managers lowered the ratings of female and White employees less, reversing the gender gap in ratings induced by the supply side for Whites but introducing a race gap. The race-based demand-side effects were particularly pronounced in the US, negatively affecting Black, Asian and Latinx employees. Counterfactual simulations suggest that 22-28% of Black employees' ratings would have to be increased for this race gap to disappear. Finally, we evaluate a potential intervention. In 2016, a quasi-exogenous shock led to self-evaluations not being shared with managers before they appraised employees. While this disruption of supply-side influences led to "de-anchoring" with lower average manager ratings, it generally did not change any gender or race dynamics, as these were mostly shaped by demand-side factors. A possible exception were employees of color hired in 2016: when managers were not anchored by self-ratings (and were unaffected by previous years), the race gap disappeared for women (but not for men) of color.

Do women give up competing more easily? Evidence from speedcubers
Chao Fang, Ernest Zhang & Junfu Zhang
Economics Letters, forthcoming


We analyze a large sample of participants in mixed-gender Rubik's Cube competitions. Focusing on participants who barely made or missed the cut for the second round in a competition, we examine their likelihood of joining another competition in the future. We find a significant gender difference: Whereas boys are slightly discouraged by failing to qualify for the second round, girls are affected more and are more likely to give up forever. Furthermore, we find that this gender difference is most significant in countries with larger gender gaps in labor market outcomes.


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