We make use of predicted social and civic activities (social capital) to account for selection into "social" occupations. Individual selection accounts for more than the total difference in wages observed between social and non-social occupations. The role that individual social capital plays in selecting into these occupations and the importance of selection in explaining wage differences across occupations is similar for both men and women. We make use of restricted 2000 Decennial Census and 2000 Social Capital Community Benchmark Survey. Individual social capital is instrumented by distance weighted surrounding census tract characteristics.
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Individual Social Capital and Migration
March 2018
Working Paper Number:
CES-18-14
This paper determines how individual, relative to community social capital affects individual migration decisions. We make use of non-public data from the Social Capital Community Benchmark Survey to predict multi-dimensional social capital for observations in the Current Population Survey. We find evidence that individuals are much less likely to have moved to a community with average social capital levels lower than their own and that higher levels of community social capital act as positive pull-factor amenities. The importance of that amenity differs across urban/rural locations. We also confirm that higher individual social capital is a negative predictor of migration.
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In-migration and Dilution of Community Social Capital
June 2018
Working Paper Number:
CES-18-32
Consistent with predictions from the literature, we find that higher levels of in-migration dilute multiple dimensions of a community's level of social capital. The analysis employs a 2SLS
methodology to account for potential endogeneity of migration.
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Decennial Census Return Rates: The Role of Social Capital
January 2017
Working Paper Number:
CES-17-39
This paper explores how useful information about social and civic engagement (social capital)
might be to the U.S. Census Bureau in their efforts to improve predictions of mail return rates for the Decennial Census (DC) at the census tract level. Through construction of Hard-to-count (HRC) scores and multivariate analysis, we find that if information about social capital were available, predictions of response rates would be marginally improved.
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An Evaluation of the Gender Wage Gap Using Linked Survey and Administrative Data
November 2020
Working Paper Number:
CES-20-34
The narrowing of the gender wage gap has slowed in recent decades. However, current estimates show that, among full-time year-round workers, women earn approximately 18 to 20 percent less than men at the median. Women's human capital and labor force characteristics that drive wages increasingly resemble men's, so remaining differences in these characteristics explain less of the gender wage gap now than in the past. As these factors wane in importance, studies show that others like occupational and industrial segregation explain larger portions of the gender wage gap. However, a major limitation of these studies is that the large datasets required to analyze occupation and industry effectively lack measures of labor force experience. This study combines survey and administrative data to analyze and improve estimates of the gender wage gap within detailed occupations, while also accounting for gender differences in work experience. We find a gender wage gap of 18 percent among full-time, year-round workers across 316 detailed occupation categories. We show the wage gap varies significantly by occupation: while wages are at parity in some occupations, gaps are as large as 45 percent in others. More competitive and hazardous occupations, occupations that reward longer hours of work, and those that have a larger proportion of women workers have larger gender wage gaps. The models explain less of the wage gap in occupations with these attributes. Occupational characteristics shape the conditions under which men and women work and we show these characteristics can make for environments that are more or less conducive to gender parity in earnings.
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A Task-based Approach to Constructing Occupational Categories
with Implications for Empirical Research in Labor Economics
September 2019
Working Paper Number:
CES-19-27
Most applied research in labor economics that examines returns to worker skills or differences in earnings across subgroups of workers typically accounts for the role of occupations by controlling for occupational categories. Researchers often aggregate detailed occupations into categories based on the Standard Occupation Classification (SOC) coding scheme, which is based largely on narratives or qualitative measures of workers' tasks. Alternatively, we propose two quantitative task-based approaches to constructing occupational categories by using factor analysis with O*NET job descriptors that provide a rich set of continuous measures of job tasks across all occupations. We find that our task-based approach outperforms the SOC-based approach in terms of lower occupation distance measures. We show that our task-based approach provides an intuitive, nuanced interpretation for grouping occupations and permits quantitative assessments of similarities in task compositions across occupations. We also replicate a recent analysis and find that our task-based occupational categories explain more of the gender wage gap than the SOC-based approaches explain. Our study enhances the Federal Statistical System's understanding of the SOC codes, investigates ways to use third-party data to construct useful research variables that can potentially be added to Census Bureau data products to improve their quality and versatility, and sheds light on how the use of alternative occupational categories in economics research may lead to different empirical results and deeper understanding in the analysis of labor market outcomes.
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Sex Segregation in U.S. Manufacturing
June 1996
Working Paper Number:
CES-96-04
This paper studies interplant sex segregation in the U.S. manufacturing industry. The study differs from previous work in that we have detailed information on the characteristics of both workers and firms, and because we measure segregation in a new and better way. We report three main findings. First, there is a substantial amount of interplant sex segregation in the U.S. manufacturing industry, although segregation is far from complete. Second, we find that female managers tend to work in the same plants as female supervisees, even once we control for other plant characteristics. And finally, we find that interplant segregation can account for a substantial fraction of the male/female wage gap in the manufacturing industry, particularly among blue-collar workers.
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DO PUBLIC TUITION SUBSIDIES PROMOTE COLLEGE ENROLLMENT? EVIDENCE FROM COMMUNITY COLLEGE TAXING DISTRICTS IN TEXAS
September 2014
Working Paper Number:
CES-14-32
This paper estimates the effect of tuition rates on college enrollment using data for Texas from the 1990 and 2000 Censuses and the 2004 ' 2010 American Community Surveys and geographical data on Community College Taxing Districts. The effect of tuition on enrollment is identified by the facts that tuition rates for those living within a taxing district are lower than those living outside the taxing district and in Texas not all geographic locations are in a taxing district. While the estimated effect of tuition on enrollment depends on the sample used, it is negative and mostly statistically significant in the samples of iadults 18 and older and negative and sometimes statistically significant in the samples of traditional age students 18 to 24. The estimated effect of tuition on enrollment, however, is found to vary considerably by poverty level status with an increase in tuition rates having a statistically significant negative effect on college enrollment for those with household incomes that are at least 200% of the poverty level both for traditional aged students 18 to 24 years old and all adults 18 and older.
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Understanding Selection Processes: Organization Determinants and Performance Outcomes
October 1997
Working Paper Number:
CES-97-14
We use an establishment-level survey to examine the predictors of different types of selection practices as well as the relationship of different selection practices to organizational performance. We find that a wide range of contingencies in the organization, including job requirements, organizational size, union status, salary, and training, predict the intensity and the types of selection practices used. Further, we find that selection intensity has a significant and negative relationship with organizational sales, other things equal, that is driven by the use of less valid selection techniques.
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High Labor Force Attachment, but Few Social Ties? Life-Course Predictors of Women's Receipt of Childcare Subsidies
September 2019
Working Paper Number:
CES-19-26
The U.S. federal Child Care and Development Fund (CCDF) childcare subsidy represents the largest source of means-tested assistance for U.S. families with low incomes. The CCDF subsidy aims to help mothers with low incomes gain employment and education, with implications for women's labor force participation, and the wellbeing of their children. Because recipients of the CCDF subsidy are either already employed, or seek the subsidy with the goal of gaining employment or schooling, this group may represent the public assistance recipients who are best able to succeed in the low-wage labor market. However, existing research on the CCDF observes recipients only after they begin receiving the subsidy, thus giving an incomplete picture of whether recipients may select into subsidy receipt, and how subsidy recipiency is situated in women's broader work and family trajectories. My study links administrative records from the CCDF to the American Community Survey (ACS) to construct a longitudinal data set from 38 states that observes CCDF recipients in the 1-2 years before they first received the subsidy. I compare women who subsequently received the CCDF subsidy to other women with low incomes in the ACS who did not go on to receive the subsidy, with a total of roughly 641,000 individuals. I find that CCDF recipients are generally positively-selected on employment history and educational attainment, but appear to have lower levels of social support than non-recipients.
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Identifying Individual and Group Effects in the Presence of Sorting: A Neighborhood Effects Application
January 2007
Working Paper Number:
CES-07-03
Researchers have long recognized that the non-random sorting of individuals into groups generates correlation between individual and group attributes that is likely to bias naive estimates of both individual and group effects. This paper proposes a non-parametric strategy for identifying these effects in a model that allows for both individual and group unobservables, applying this strategy to the estimation of neighborhood effects on labor market outcomes. The first part of this strategy is guided by a robust feature of the equilibrium in the canonical vertical sorting model of Epple and Platt (1998), that there is a monotonic relationship between neighborhood housing prices and neighborhood quality. This implies that under certain conditions a non-parametric function of neighborhood housing prices serves as a suitable control function for the neighborhood unobservable in the labor market outcome regression. This control function converts the problem to a model with one unobservable so that traditional instrumental variables solutions may be applied. In our application, we instrument for each individual.s observed neighborhood attributes with the average neighborhood attributes of a set of observationally identical individuals. The neighborhood effects model is estimated using confidential microdata from the 1990 Decennial Census for the Boston MSA. The results imply that the direct effects of geographic proximity to jobs, neighborhood poverty rates, and average neighborhood education are substantially larger than the conditional correlations identified using OLS, although the net effect of neighborhood quality on labor market outcomes remains small. These findings are robust across a wide variety of specifications and robustness checks.
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