This study uses experimental methods to investigate covert racial discrimination in “roommate wanted” ads on Craigslist. Roommate relationships include significant social dimensions, and are an important site through which segregation may be reproduced or broken down, but have received very little attention by researchers. We develop fictitious racially-coded female names and identities for white, black, Hispanic, Chinese, and Indian room-seekers, along with Hispanic, Chinese, and Indian room-seekers with “Americanized” first names. We implement a field experiment and respond to over 1,500 “roommate wanted” advertisements on Craigslist across three metropolitan areas. Our emails express interest in the roommate-wanted ad, and mention that the sender is college-educated and employed full-time. We monitor response rates in the aggregate and within Census tracts of varying racial and economic characteristics. We find severe discrimination against African Americans, Hispanics, and Chinese-origin individuals. Asians with Americanized first names are treated equally to whites, while traditional Indian names and Americanized Latina names face moderate levels of discrimination. Patterns of discrimination by neighborhood race and class characteristics yield better access to upward mobility for Asian Americans than for underrepresented minority group members. Our findings reveal an important social mechanism that constricts integration and opportunity, shed new light on Asians’ and Latinas’ place in the US race system, reveal important interactions of race and presumed nativity, and show the ongoing relevance of race.
Evidence of racial and ethnic discrimination stems mostly from experiments in the U.S., Europe, and elsewhere that use names to signal race/ethnicity. Although recent work has examined individual racial perceptions of names in the U.S., no research has examined how names might convey immigrant generational status – an important signal for discrimination experiments across the world. I conduct a survey experiment that presents respondents with a series of first and last names to examine perceptions of immigrant generational status in the U.S. In total, 1,659 respondents provide information on 56 different names. I find that when presented with both traditional first and last Hispanic, Indian, or Chinese names, respondents most often believe that person was not born in the U.S. When presented with traditional white or Anglo first names combined with Hispanic or Asian last names, respondents most often believe that person was born in the U.S. but their parents were not. Individual names provide some variation within these results and some groups have stronger results than others. These findings have important implications for discrimination experiments in the U.S. and open the door for future research to distinguish between discrimination based on race/ethnicity and discrimination based on immigration status.
Scholars suggest that public mental health stigma operates at a meso-level and is associated with severity of symptoms, disclosure, self-esteem, and treatment-seeking behavior. However, the operationalization of public stigma nearly always comes from an individual-level generalization of what others believe. Using data from over 60,000 students on 75 U.S. college and university campuses between 2009 and 2015, we contextualize public stigma by creating a school-level measure of students’ individual-level endorsed mental health treatment stigma. We present multilevel logistic regression models for 21 different dependent variables. We find that even after controlling for individual-level stigma scores, school-level stigma is negatively associated with self-reports of suicidal ideation and self-injury, although not associated with screens for depression or anxiety. Moreover, school-level stigma is negatively associated with medication use, counseling and therapy visits, and to a lesser degree, informal support. We suggest that future research should continue to examine the contextual environment of public stigma, while policymakers may be able to implement changes to significantly reduce stigma at this level.
This book offers practical instruction on the use of audit studies in the social sciences. It features essays from sociologists, economists, and other experts who have employed this powerful and flexible tool. Readers will learn how to implement an audit study to examine a variety of questions in their own research. The essays first discuss situations where audit studies are the most effective. These tools allow researchers to make strong causal claims and explore questions that are often difficult to answer with observational data. Audit studies also stand as the single best way to conduct research on discrimination. The authors highlight what these studies have uncovered about labor market processes in the past decade. The next section gives some guidance on how to design an audit study. The essays cover the difficult task of getting a study through an institutional review board, the technical setup of matching procedures, and statistical power and analysis techniques.
The last part focuses on more advanced aspects. Coverage includes understanding context, what variables may signal, and the use of technology. The book concludes with a discussion of challenges and limitations with an eye towards the future of audit studies.
“Field experiments studying and testing for housing and labor market discrimination have, rightly, become the dominant mode of discrimination-related research in economics and sociology. This book brings together a number of interesting and useful perspectives on these field experiments. Many different kinds of readers will find it valuable, ranging from those interested in getting an overview of the evidence, to researchers looking for guidance on the nuts and bolts of conducting these complex experiments.”
David Neumark, Chancellor’s Professor of Economics at the University of California – Irvine
“For decades, researchers have used experimental audit studies to uncover discrimination in a variety of markets. Although this approach has become more popular in recent years, few publications provide detailed information on the design and implementation of the method. This volume provides the first deep examination of the audit method, with details on the practical, political, analytical, and theoretical considerations of this research. Social scientists interested in consuming or contributing to this literature will find this volume immensely useful.”
Devah Pager, Professor of Sociology and Public Policy at Harvard University
An audit study is a specific type of field experiment primarily used to test for discriminatory behavior when survey and interview questions induce social desirability bias. In this chapter, I first review the language and definitions related to audit studies and encourage adoption of a common language. I then discuss why researchers use the audit method as well as when researchers can and should use this method. Next, I give an overview of the history of audit studies, focusing on major developments and changes in the overall body of work. Finally, I discuss the limitations of correspondence audits and provide some thoughts on future directions.
Researchers increasingly use correspondence audit studies to study racial/ethnic discrimination in employment, housing, and other domains. Although this method provides strong causal evidence of racial/ethnic discrimination, these claims depend on the signal being clearly conveyed through names. Few studies have pretested individual racial and ethnic perceptions of the names used to examine discrimination. The author conducts a survey experiment in which respondents are asked to identify the races or ethnicities they associate with a series of names. Respondents are provided with combinations of Hispanic and Anglo first and last names. Hispanic first names paired with Anglo last names are least likely to be recognized as Hispanic, while all versions of Hispanic first and last names are highly recognized (≥90 percent). The results suggest that researchers must use caution when trying to signal Hispanic ethnicity in experiments, and prior findings from correspondence audits may be biased from poor signals.
Online correspondence audit studies have emerged as the primary method to examine racial discrimination. Although audits use distinctive names to signal race, few studies scientifically examine data regarding the perception of race from names. Different names treated as black or white may be perceived in heterogeneous ways. I conduct a survey experiment that asks respondents to identify the race they associate with a series of names. I alter the first names given to each respondent and inclusion of last names. Names more commonly given by highly educated black mothers (e.g., Jalen and Nia) are less likely to be perceived as black than names given by less educated black mothers (e.g., DaShawn and Tanisha). The results suggest that a large body of social science evidence on racial discrimination operates under a misguided assumption that all black names are alike, and the findings from correspondence audits are likely sensitive to name selection.
Racial inequality in economic outcomes, particularly among the college educated, persists throughout US society. Scholars debate whether this inequality stems from racial differences in human capital (e.g., college selectivity, GPA, college major) or employer discrimination against black job candidates. However, limited measures of human capital and the inherent difficulties in measuring discrimination using observational data make determining the cause of racial differences in labor-market outcomes a difficult endeavor. In this research, I examine employment opportunities for white and black graduates of elite top-ranked universities versus high-ranked but less selective institutions. Using an audit design, I create matched candidate pairs and apply for 1,008 jobs on a national job-search website. I also exploit existing birth-record data in selecting names to control for differences across social class within racialized names. The results show that although a credential from an elite university results in more employer responses for all candidates, black candidates from elite universities only do as well as white candidates from less selective universities. Moreover, race results in a double penalty: When employers respond to black candidates, it is for jobs with lower starting salaries and lower prestige than those of white peers. These racial differences suggest that a bachelor’s degree, even one from an elite institution, cannot fully counteract the importance of race in the labor market. Thus, both discrimination and differences in human capital contribute to racial economic inequality.
This study uses a field experiment to study bias against living with Arab American women, a group whose position in the U.S. race system remains uncertain. We developed fictitious female white and Arab American identities and used the audit method to respond to 560 roommate-wanted advertisements in four metro areas: Los Angeles, New York, Detroit, and Houston. To focus on social—rather than purely economic—biases, all responses identified the sender as college-educated and employed and were written in grammatically correct English. We compare the number of replies received, finding that Arab-origin names receive about 40 percent fewer replies. We then model variation in discrimination rates by proximity to mosques, geographic concentration of mosques, and the percentage of Arabs living in a census tract so as to test ethnic competition theory and the contact hypothesis. In Los Angeles and New York, greater discrimination occurred in neighborhoods with the highest concentration of mosques.
The introductory chapter to this special issue highlights contemporary scholarship on networks, work, and inequality.Methodology – We review the last decade of research on this topic, identifying four key areas investigation: (1) networks and hiring, (2) networks and the labor process, (3) networks and outcomes at work, and (4) networks and institutional dynamics.Findings – Social networks play an important role in understanding the mechanisms by which and the conditions under which economic inequality is reproduced across gender, race, and social class distinctions. Throughout the review, we point to numerous opportunities for future research to enhance our understanding of these social processes.
After 25 years of intense scrutiny, social capital remains an important yet highly debated concept in social science research. This research uses data from youth and mentors in several chapters of Big Brothers/Big Sisters to assess the importance of different mentoring relationship characteristics in creating positive outcomes among youths. The literature on social capital suggests that key characteristics are: (1. the amount of time spent between individuals, (2. racial similarity, (3. level of trust, (4. social class difference, and (5. intergenerational closure. I examine the effects of these social capital measures on academic and deviant behavioral outcomes and run models using propensity score weights to address selection bias. The results indicate that both the amount of time spent in a relationship and the level of trust consistently have positive effects for youths. Counter to what some theory suggests, race-matching and closure between parent and mentor have limited effects, and social class difference between individuals has no significant effect on any of the examined outcomes. These findings have important implications for future work on social capital and adolescent relationships in general
The theory of action behind the No Child Left Behind Act of 2001 is that “shining a light” on subgroup performance will increase reading and math test scores for minority and disadvantaged students. Using a panel of all students in Grades 3 through 8 in North Carolina from 2000 to 2008 (N = 1.7 million students in 1,800 schools), the authors estimate double- and triple-differenced models with school fixed effects to examine whether subgroup-specific accountability threats increase high-stakes test scores. These sanctions are found to have positive effects for minority and disadvantaged students. Larger positive effects emerge for the lowest achieving schools rather than schools near the margin of passing. Some evidence of adverse effects is also found for low and high achievers in math, but not in reading, a finding attributed to the combination of increases in the rigor of state standards in math and responses to an accountability metric based on test score status rather than growth. The implications of the findings for the design of educational accountability systems are discussed.