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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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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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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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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Immigrant Diversity and Complex Problem Solving
January 2016
Working Paper Number:
CES-16-04
In the growing literature exploring the links between immigrant diversity and worker productivity, recent evidence strongly suggests that diversity generates productivity improvements. However, even the most careful extant empirical work remains at some remove from the mechanisms that theory says underlie this relationship: interpersonal interaction in the service of complex problem solving. This paper aims to `stress-test' these theoretical foundations, by observing how the relationship between diversity and productivity varies across workers differently engaged in complex problem solving and interaction. Using a uniquely comprehensive matched employer-employee dataset for the United States between 1991 and 2008, this paper shows that growing immigrant diversity inside cities and workplaces offers much stronger benefits for workers intensively engaged in various forms of complex problem solving, including tasks involving high levels of innovation, creativity, and STEM. Moreover, such effects are considerably stronger for those whose work requires high levels of both problem solving and interaction.
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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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How Does Geography Matter in Ethnic Labor Market Segmentation Process? A Case Study of Chinese Immigrants in the San Francisco CMSA
March 2007
Working Paper Number:
CES-07-09
In the context of continuing influxes of large numbers of immigrants to the United States, urban labor market segmentation along the lines of race/ethnicity, gender, and class has drawn considerable growing attention. Using a confidential dataset extracted from the United States Decennial Long Form Data 2000 and a multilevel regression modeling strategy, this paper presents a case study of Chinese immigrants in the San Francisco metropolitan area. Correspondent with the highly segregated nature of the labor market as between Chinese immigrant men and women, different socioeconomic characteristics at the census tract level are significantly related to their occupational segregation. This suggests the social process of labor market segmentation is contingent on the immigrant geography of residence and workplace. With different direction and magnitude of the spatial contingency between men and women in the labor market, residency in Chinese immigrant concentrated areas is perpetuating the gender occupational segregation by skill level. Whereas abundant ethnic resources may exist in ethnic neighborhoods and enclaves for certain types of employment opportunities, these resources do not necessarily help Chinese immigrant workers, especially women, to move upward along the labor market hierarchy.
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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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A Dynamic Structural Model of Contraceptive Use and Employment Sector Choice for Women in Indonesia
September 2010
Working Paper Number:
CES-10-28
This research investigates the impact of the Indonesian family planning program on the labor force participation decisions and contraceptive choices of women. I develop a discrete choice dynamic structural model, where each married woman in every period makes joint choices regarding the method of contraceptive used and the sector of employment in which to work in order to maximize her expected discounted lifetime utility function. Each woman obtains utility from pecuniary sources, nonpecuniary sources, and choice-specific time shocks. In addition to the random shocks, there is uncertainty in the model as a woman can only imperfectly control her fertility. Dynamics in the model are captured by several forms of state and duration dependence. Women in this model make different choices due to different preferences, differences in observable characteristics, and realization of uncertainty. The choices made by a woman depend on the compatibility between raising children and the sector of employment (including wages). While making decisions regarding contraceptive use, a woman considers the trade-off between costs (monetary and nonmonetary) of having a child and the benefits from having one. The primary source of data for this study is the first wave of the Indonesia Family Life Survey (IFLS 1), a retrospective panel. In my research, I use the geographic expansion and the changing nature of the Indonesian family planning program as sources of exogenous variation to identify the parameters of the structural model. I estimate the model using maximum likelihood techniques with data from IFLS 1 for the periods 1979-1993. Structural model estimates indicate that informal sector jobs offer greater compatibility between work and childcare. Parameter estimates indicate that choices of contraception method and employment sector vary by exogenous characteristics.
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