How the 2020 Census Found No Black Disadvantage in Mexico: The Effects of State Ethnoracial Constructions on Inequality
Christina Sue, Fernando Riosmena, Edward Telles | April, 2021
A central goal of modern ethnoracial statistics is to measure population size and inequality and an increasing number of countries are including ethnoracial questions on their censuses. Although scholarship has examined how states “make” racial categories and identities via official classification systems, much less attention has been paid in the literatures on stratification and the politics of official ethnoracial classification, to how these classifications affect portraits of population size and, especially, inequality. The Mexican government recently introduced questions on several major surveys and the 2020 Census to measure the black population, generally defining blackness and indigeneity in cultural terms but once in racial terms. We leverage these new data along with an ongoing nationally representative academic survey that has measured race-ethnicity more “neutrally” and consistently to ascertain the implications of these different framings in ethnoracial population size and inequality. After accounting for identification growth over time, we find that cultural frameworks produce a portrait of no black disadvantage, while a racial framework produces substantial black disadvantage, with similar and sizable indigenous disadvantage emerging from both racial and cultural framings. Overall, our study shows how estimates of population size and inequality can be highly dependent on states’ conceptualization of ethnoracial racial categories and has broad implications for the literatures on state ethnoracial classification, stratification, race and ethnicity, as well as for public policy.
Combining Rules and Discretion in Economic Development Policy: Evidence on the Impacts of the California Competes Tax Credit
Matthew Freedman, David Neumark, Shantanu Khanna | March, 2021
We evaluate the effects of one of a new generation of economic development programs, the California Competes Tax Credit (CCTC), on local job creation. Incorporating perceived best practices from previous initiatives, the CCTC combines explicit eligibility thresholds with some discretion on the part of program officials to select tax credit recipients. The structure and implementation of the program facilitates rigorous evaluation. We exploit detailed data on accepted and rejected applicants to the CCTC, including information on scoring of applicants with regard to program goals and funding decisions, together with restricted access American Community Survey (ACS) data on local economic conditions. Using a difference-in-differences approach, we find that each CCTC-incentivized job in a census tract increases the number of individuals working in that tract by over two – a significant local multiplier. We also explore the program’s distributional implications and impacts by industry. We find that CCTC awards increase employment among workers residing in both high income and low income communities, and that the local multipliers are larger for non-manufacturing awards than for manufacturing awards.
The Impacts of Opportunity Zones on Zone Residents
Matthew Freedman, Shantanu Khanna, David Neumark | March, 2021
Created by the Tax Cuts and Jobs Act in 2017, the Opportunity Zone program was designed to encourage investment in distressed communities across the U.S. We examine the early impacts of the Opportunity Zone program on residents of targeted areas. We leverage restricted-access microdata from the American Community Survey and employ difference-in-differences and matching approaches to estimate causal reduced-form effects of the program. Our results point to modest, if any, positive effects of the Opportunity Zone program on the employment, earnings, or poverty of zone residents.
Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment
Ian Burn, Daniel Firoozi, Daniel Ladd, David Neumark | January, 2021
We explore whether ageist stereotypes in job ads are detectable using machine learning methods measuring the linguistic similarity of job-ad language to ageist stereotypes identified by industrial psychologists. We then conduct an experiment to evaluate whether this language is perceived as biased against older workers. We find that language classified by the machine learning algorithm as closely related to ageist stereotypes is perceived as ageist by experimental subjects. The scores assigned to the language related to ageist stereotypes are larger when responses are incentivized by rewarding participants for guessing how other respondents rated the language. These methods could potentially help enforce anti-discrimination laws by using job ads to predict or identify employers more likely to be engaging in age discrimination.
The Career Evolution of the Sex Gap in Wages: Discrimination vs. Human Capital Investment
David Neumark, Giannina Vaccaro | December, 2020
Several studies find that there is little sex gap in wages at labor market entry, and that the sex gap in wages emerges (and grows) with time in the labor market. This evidence is consistent with (i) there is little or no sex discrimination in wages at labor market entry, and (ii) the emergence of the sex gap in wages with time in the labor market reflects differences between men and women in human capital investment (and other decisions), with women investing less early in their careers. Indeed, some economists explicitly interpret the evidence this way. We show that this interpretation ignores two fundamental implications of the human capital model, and that differences in investment can complicate the interpretation of both the starting sex gap in wages (or absence of a gap), and the differences in “returns” to experience. We then estimate stylized structural models of human capital investment and wage growth to identify the effects of discrimination and differences in human capital investment, and find evidence more consistent with discrimination reducing women’s wages at labor market entry.
Happy Together? The Peer Effects of Dual Enrollment Students on Community College Student Outcomes
Vivian Yuen Ting Liu, Di Xu | November, 2020
Nationally, 15% of first-time community college students were high school dual enrollment (DE) students, which raises concerns about how high school peers might influence college enrollees. Using administrative data from a large state community college system, we examine whether being exposed to a higher percentage of DE peers in entry-level (gateway) math and English courses influences non-DE enrollees’ performance. Using a two-way fixed effects model, our results indicate that college enrollees exposed to a higher proportion of DE peers had lower pass rates and grades in gateway courses, and higher course repetition rates. Supplemental student-level analysis suggests that greater exposure to DE peers during a student’s initial semester in college reduces next-term college persistence.
Initial Host-Society/Migrant Relations: Implications for U.S. Refugee Integration
Thoa V. Khuu, Frank D. Bean | October, 2020
Research into factors affecting immigrant integration carries important implications for immigration scholars and policymakers. By immigrant integration we mean the nature and extent of temporal and generational convergence between newcomers and natives in sociocultural patterns and socioeconomic attainment (Brown and Bean 2006; Jimenez 2016). Although many studies have investigated the extent to which immigrants and natives come to resemble one another (Waters and Pineau 2015), fewer have devoted attention to whether newcomers arriving under various entry auspices exhibit different integration dynamics and outcomes. A notable exception involves research assessing the degree to which unauthorized entrants incur substantial handicaps compared to those entering with legal status. Because the United States has largely failed to extend official societal membership to unauthorized migrants, their families have been deprived of access to opportunities for achieving socioeconomic mobility (Brown and Bean 2016). Research shows that this has negatively affected migrants, their migrating children, and even their children born in the United States (e.g., Bean, Brown and Bachmeier 2015; Gonzales 2015). Although numerous studies provide striking examples of how this kind of host-society/migrant relationship strongly affects migrant integration, little investigation has delved into the nature and degree to which immigrants arriving under alternative forms of legal entry undergo different integration experiences.
The Causes and Consequences of the 1986 Immigration Reform and Control Act (IRCA)
Frank D. Bean, Thoa V. Khuu | October, 2020
The United States often views itself as a nation of immigrants. Because of this, in part since the beginning of the twentieth century, it has only rarely adopted major changes in its immigration policy. Until the reforms of 1986, only the 1924 National Origins Quota Act and its modification in 1965 (through amendments to the 1952 McCarran Walter Act) involved substantial reform. This changed with the passage of the 1986 Immigration Reform and Control Act (IRCA) and its derivative sequel, the 1990 Immigration Act. And as of this writing in 2020, no other substantial pieces of immigration legislation have been passed by Congress. IRCA emerged from and followed in considerable measure the recommendations of the Select Commission on Immigration and Refugee Policy (1979-1981). That body sought to reconcile two competing political constituencies, one favoring greater immigration restriction and the other an expansion of family-based and work-related migration. The IRCA legislation contained something for each side: the passage of employer sanctions, or serious penalties on employers for hiring unauthorized workers, for the restriction side; and the provision of a legalization program, which outlined a pathway for certain unauthorized entrants to obtain green cards and eventually citizenship, for the reform side. The complete legislative package also included other provisions: including criteria for the admission of agricultural workers, a measure providing financial assistance to states for the costs they would incur from migrants legalizing, a requirement that states develop ways to verify that migrants were eligible for welfare benefits, and a provision providing substantial boosts in funding for border enforcement activities. In the years after the enactment of IRCA, research has revealed that the two major compromise provisions plus the agricultural provision have generated mixed results. Employer sanctions failed to curtail unauthorized migration much, in all likelihood because of minimal funding for enforcement, while legalization and the agricultural measure resulted in widespread enrollment, with almost all of the unauthorized migrants who qualified for it coming forward to take advantage of the opportunity to become U.S. legalized permanent residents (LPRs). In general, however, IRCA can be interpreted in political historical terms as exemplifying contradictory parts. On the one hand, its somewhat expansionist and its legalization elements reflect the inclusive/pluralistic tendencies of much of 18th century immigration, but its employer sanction provisions contained the seeds for the subsequent development of restrictive/exclusive socio-political tendencies.
Finding and Keeping Friends in College and their Influence on Alcohol Use: A Network
Analysis
David R. Schaefer, Irene van Woerden, Daniel Hruschka, Meg Bruening | September, 2020
Objective. We investigate how alcohol use and friendship co-evolve during students’ transition to university. We discern effects of peer influence from friend selection based on alcohol use, whether such effects vary in strength across the school year, and whether alcohol has different effects on friendship formation versus friendship maintenance. Method. We gathered data on friendships, alcohol use, and binge drinking from 300 residence hall students (71% female) at a large, public U.S. university. Surveys were conducted at four time points during the 2015-16 academic year. We used a stochastic actor-oriented model (SAOM) to test whether alcohol use was influenced by one’s friends, while simultaneously testing for friend selection based on alcohol use and related network processes. Results. Students were 7.0 times more likely to drink alcohol weekly if all vs. none of their friends drank weekly, and 6.8 times more likely to binge drink when all vs. none of their friends engaged in binge drinking, after controlling for friend selection. Alcohol use differentially affected friendship creation and maintenance in a complex manner (1) weekly drinkers were more likely to form new friendships and dissolve existing friendships than non-drinkers; and (2) similarity on drinking fostered new friendships, but had no effect on friendship persistence. Conclusions. Friends influence one another’s weekly drinking and binge drinking, while conversely, alcohol use contributes to both friendship formation and friendship instability.
Modifiable Kindergarten Factors that Predict Being a Bully, Victim, or Bully-victim
by the Upper Elementary Grades
Paul L. Morgan, Adrienne D. Woods, Yangyang Wang, George Farkas, Marianne M. Hillemeier,
Yoonkyung Oh | September, 2020
The investigators analyzed a population-based cohort (N range=7,182-8,210; kindergarten Mage =67.5 months) to identify modifiable factors by kindergarten predictive of being a bully, victim, or bully-victim during third, fourth, or fifth grade. Chi-squared analyses supported the bully-victim subtype. Greater academic achievement lowered children’s risk for being bullies (odds ratio [OR] range = .66 to .75), victims (OR =.83 to .85), and bully-victims (OR = .72 to .76). Externalizing problem behaviors increased children’s risk for being bullies (OR = 1.84 to 2.16), victims (OR = 1.35 to 1.43), and bully-victims (OR = 1.90 to 2.17). Internalizing problem behaviors and parenting did not consistently predict children’s bullying victimization. Achievement and behavior but not parenting constitute modifiable targets of early bullying victimization interventions.
Disease, Downturns, and Wellbeing: Economic History and the Long-Run Impacts of COVID-19
Vellore Arthi, John Parman | September, 2020
How might COVID-19 affect human capital and wellbeing in the long run? The COVID-19 pandemic has already imposed a heavy human cost—taken together, this public health crisis and its attendant economic downturn appear poised to dwarf the scope, scale, and disruptiveness of most modern pandemics. What evidence we do have about other modern pandemics is largely limited to short-run impacts. Consequently, recent experience can do little to help us anticipate and respond to COVID-19’s potential long-run impact on individuals over decades and even generations. History, however, offers a solution. Historical crises offer closer analogues to COVID-19 in each of its key dimensions—as a global pandemic, as a global recession—and offer the runway necessary to study the life-course and intergenerational outcomes. In this paper, we review the evidence on the long-run effects on health, labor, and human capital of both historical pandemics (with a focus on the 1918 Influenza Pandemic) and historical recessions (with a focus on the Great Depression). We conclude by discussing how past crises can inform our approach to COVID-19—helping tell us what to look for, what to prepare for, and what data we ought to collect now.
How Valuable are Civil Liberties? Evidence from Gang Injunctions and Housing Prices
in Southern California
Emily Owens, Michelle D. Mioduszewski, Christopher J. Bates | August, 2020
Place-based and proactive policing strategies can reduce crime. However, the broader net impacts of these policies on targeted communities has yet to be quantified, meaning there is little empirical evidence on if, or when, policing is socially beneficial. Using a spatial discontinuity in constraints on police actions created by civil gang injunctions and temporal variation in when injunctions are enacted, we find that aggressive policing can reduce, rather than increase, people’s desire to live in affected neighborhoods. Mover demographics suggest that homebuyers perceive injunction areas as safe places, but where negative police encounters are common. Dividing our sample by pre-injunction crime rates suggest that net willingness-to-pay to avoid aggressive police encounters falls as the possible expected benefit from crime reduction increases.
Understanding Socioeconomic Disparities in Travel Behavior during the COVID-19 Pandemic
Rebecca Brough, Matthew Freedman, and David C. Phillips | June, 2020
We document the magnitudes of and mechanisms behind socioeconomic differences in travel behavior during the COVID-19 pandemic. We focus on King County, Washington, one of the rst places in the U.S. where COVID-19 was detected. We leverage novel and rich administrative and survey data on travel volumes, modes, and preferences for different demographic groups. Large average declines in travel, and in public transit use in particular, due to the pandemic and related policy responses mask substantial heterogeneity across socioeconomic groups. Travel intensity declined considerably less among less-educated and lower-income individuals, even after accounting for mode substitution and variation across neighborhoods in the impacts of public transit service reductions. The relative inability of less-educated and lower-income individuals to cease commuting explains at least half of the difference in travel responses across groups.
Older Workers Need Not Apply? Ageist Language in Job Ads and Age Discrimination in
Hiring
Ian Burn, Patrick Button, Luis Munguia Corella, and David Neumark | May, 2020
We study the relationships between ageist stereotypes – as reflected in the language used in job ads – and age discrimination in hiring, exploiting the text of job ads and differences in callbacks to older and younger job applicants from a resume (correspondence study) field experiment (Neumark, Burn, and Button, 2019). Our analysis uses methods from computational linguistics and machine learning to directly identify, in a field-experiment setting, ageist stereotypes that underlie age discrimination in hiring. The methods we develop provide a framework for applied researchers analyzing textual data, highlighting the usefulness of various computer science techniques for empirical economics research. We find evidence that language related to stereotypes of older workers sometimes predicts discrimination against older workers. For men, our evidence points to age stereotypes about all three categories we consider – health, personality, and skill – predicting age discrimination, and for women, age stereotypes about personality. In general, the evidence is much stronger for men, and our results for men are quite consistent with the industrial psychology literature on age stereotypes.