This paper studies how residential segregation by race and by education affects job search via neighbor networks. Using confidential microdata from the US Census Bureau, I measure segregation for each characteristic at both the individual level and the neighborhood level. My findings are manifold. At the individual level, future coworkership with new neighbors on the same block is less likely among segregated individuals than among integrated workers, irrespective of races and levels of schooling. The impacts are most adverse for the most socioeconomically disadvantaged demographics: Blacks and those without a high school education. At the block level, however, higher segregation along either dimension raises the likelihood of any future coworkership on the block for all racial or educational groups. My identification strategy, capitalizing on data granularity, allows a causal interpretation of these results. Together, they point to the coexistence of homophily and in-group competition for job opportunities in linking residential segregation to neighbor-based informal hiring. My subtle findings have important implications for policy-making.
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UNEMPLOYMENT DURATION AND GEOGRAPHIC MOBILITY: DO MOVERS FARE BETTER THAN STAYERS?
October 2014
Working Paper Number:
CES-14-41
This study uses a sample of unemployed workers constructed from the American
Community Survey and the LEHD database, to compare the unemployment durations of those who find subsequent employment by relocating to a metropolitan area outside of their originally observed residence, versus those who find employment in their original location. Results from a hazard analysis confirm the importance of many of the determinants of migration posited in the literature, such as age, education, and local labor market conditions. While simple averages and OLS estimates indicate that migrating for a new job reduces the probability of re-employment within a given time frame and lengthens the spell of unemployment in the aggregate, after controlling for selection into migration using an IV approach based on local house price changes, the results suggest that out-migrating for employment actually has a large and significant beneficial effect of shortening the time to re-employment. This implies that those who migrate for jobs in the data may be particularly disadvantaged in their ability to find employment and thus have a strong short-term incentive to relocate.
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FALLING HOUSE PRICES AND LABOR MOBILITY: EVIDENCE FROM MATCHED EMPLOYER-EMPLOYEE DATA
August 2013
Working Paper Number:
CES-13-43
This study uses worker-level employment data from the U.S. Census Bureau to test whether falling home prices affect a worker's propensity to take a job in a different metropolitan area from where he is currently located. Using a sample of workers from the American Community Survey, I employ a within-MSA-time estimation that compares homeowners to renters in their propensities to relocate for jobs according to data from the Longitudinal Employer Household Dynamics database. This strategy allows me to disentangle the influence of house prices from that of other time-varying, location-specific shocks. Estimates show that homeowners who have experienced declines in the nominal value of their home are approximately 20% less likely to take a new job in a location outside of the metropolitan area that they currently live and work in, relative to an equivalent renter. This evidence is consistent with the hypothesis that housing lock-in has contributed to the decreased labor mobility of homeowners during the recent housing bust.
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Job Referral Networks and the Determination of Earnings in Local Labor Markets
December 2010
Working Paper Number:
CES-10-40
Referral networks may affect the efficiency and equity of labor market outcomes, but few studies have been able to identify earnings effects empirically. To make progress, I set up a model of on-the-job search in which referral networks channel information about high-paying jobs. I evaluate the model using employer-employee matched data for the U.S. linked to the Census block of residence for each worker. The referral effect is identified by variations in the quality of local referral networks within narrowly defined neighborhoods. I find, consistent with the model, a positive and significant role for local referral networks on the full distribution of earnings outcomes from job search.
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Interactions, Neighborhood Selection, and Housing Demand
August 2002
Working Paper Number:
CES-02-19
This paper contributes to the growing literature that identifies and measures the impact of social context on individual economic behavior. We develop a model of housing demand with neighborhood e'ects and neighborhood choice. Modelling neighborhood choice is of fundamental importance in estimating and understanding endogenous and exogenous neighborhood effects. That is, to obtain unbiased estimates of neighborhood effects, it is necessary to control for non-random sorting into neighborhoods. Estimation of the model exploits a unique data set of household data that has been augmented with contextual information at two di'erent levels ('scales') of aggregation. One is at the neighborhood cluster level, of about ten neighbors, with the data coming from a special sample of the American Housing Survey. A second level is the census tract to which these dwelling units belong. Tract-level data are available in the Summary Tape Files of the decennial Census data. We merge these two data sets by gaining access to confidential data of the U.S. Bureau of the Census. We overcome some limitations of these data by implementing some significant methodological advances in estimating discrete choice models. Our results for the neighborhood choice model indicate that individuals prefer to live near others like themselves. This can perpetuate income inequality since those with the best opportunities at economic success will cluster together. The results for the housing demand equation are similar to those in our earlier work [Ioannides and Zabel (2000] where we find evidence of significant endogenous and contextual neighborhood effects.
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Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes
October 2005
Working Paper Number:
CES-05-23
We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than those in nearby blocks. We find evidence of significant social interactions operating at the block level: residing on the same versus nearby blocks increases the probability of working together by over 33 percent. The results also indicate that this referral effect is stronger when individuals are similar in sociodemographic characteristics (e.g., both have children of similar ages) and when at least one individual is well attached to the labor market. These findings are robust across various specifications intended to address concerns related to sorting and reverse causation. Further, having determined the characteristics of a pair of individuals that lead to an especially strong referral effect, we provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including employment and wages.
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The Potential for Using Combined Survey and Administrative Data Sources to Study Internal Labor Migration
January 2017
Working Paper Number:
CES-17-55
This paper introduces a novel data set combining survey data from the American Community Survey (ACS) with administrative data on employment from the Longitudinal Employer-Household Dynamics program, in order to study geographic labor mobility. With its rich set of information about individuals at the time of the migration decision, large sample size, and near-comprehensive ability to detect labor mobility, the new combined ACS-LEHD data offers several advantages over the existing data sets that are typically used in the study of migration, such as the Decennial Census, Current Population Survey, and Internal Revenue Service data. An overview of how these different data sets can be employed, and examples demonstrating the usefulness of the newly proposed data set, are provided.
Aggregate statistics and stylized facts are generated from the ACS-LEHD data which reveal many of the same features as the existing data sets, including the decline of aggregate mobility throughout the past decade, as well as many of the known demographic differences in migration propensity.
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Abandoning the Sinking Ship: The Composition of Worker Flows Prior to Displacement
August 2002
Working Paper Number:
tp-2002-11
declines experienced by workers several years before displacement occurs. Little attention, however,
has been paid to other changes in compensation and employment in firms prior to the actual
displacement event. This paper examines changes in the composition of job and worker flows
before displacement, and compares the "quality" distribution of workers leaving distressed firms to
that of all movers in general.
More specifically, we exploit a unique dataset that contains observations on all workers over
an extended period of time in a number of US states, combined with survey data, to decompose
different jobflow statistics according to skill group and number of periods before displacement.
Furthermore, we use quantile regression techniques to analyze changes in the skill profile of workers
leaving distressed firms. Throughout the paper, our measure for worker skill is derived from
person fixed effects estimated using the wage regression techniques pioneered by Abowd, Kramarz,
and Margolis (1999) in conjunction with the standard specification for displaced worker studies
(Jacobson, LaLonde, and Sullivan 1993).
We find that there are significant changes to all measures of job and worker flows prior to
displacement. In particular, churning rates increase for all skill groups, but retention rates drop
for high-skilled workers. The quantile regressions reveal a right-shift in the distribution of worker
quality at the time of displacement as compared to average firm exit flows. In the periods prior
to displacement, the patterns are consistent with both discouraged high-skilled workers leaving the
firm, and management actions to layoff low-skilled workers.
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Air Quality, Human Capital Formation and the Long-term Effects of Environmental Inequality at Birth
May 2017
Working Paper Number:
carra-2017-05
A growing body of literature suggests that pollution exposure early in life can have substantial long term effects on an individual's economic well-being as an adult, however the mechanisms for these effects remain unclear. I contribute to this literature by examining the effect of pollution exposure on several intermediate determinants of adult wages using a unique linked dataset for a large sample of individuals from two cohorts: an older cohort born around the 1970, and a younger cohort born around 1990. This dataset links responses to the American Community Survey to SSA administrative data, the universe of IRS Form 1040 tax returns, pollution concentration data from EPA air quality monitors and satellite remote sensing observations. In both OLS and IV specifications, I find that pollution exposure at birth has a large and economically significant effect on college attendance among 19-22 year olds. Using conventional estimates of the college wage premium, these effects imply that a 10 'g/m3 decrease in particulate matter exposure at birth is associated with a $190 per year increase in annual wages. This effect is smaller than the wage effects in the previous literature, which suggests that human capital acquisition associated with cognitive skills cannot fully explain the long term wage effects of pollution exposure. Indeed, I find evidence for an additional channel working through non-cognitive skill -pollution exposure at birth increases high school non-completion and incarceration among 16-24 year olds, and that these effects are concentrated within disadvantaged communities, with larger effects for non-whites and children of poor parents. I also find that pollution exposure during adolescence has statistically significant effects on high school non-completion and incarceration, but no effect on college attendance. These results suggest that the long term effects of pollution exposure on economic well-being may run through multiple channels, of which both non-cognitive skills and cognitive skills may play a role.
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Neighborhood Racial Status and White Out-Mobility
March 2026
Working Paper Number:
CES-26-19
Drawing on American Community Survey data, this study examines how whites' relative socioeconomic standing vis-'-vis nonwhite neighbors affects the association between minority presence and white out-mobility. Moving beyond the racial preferences versus racial proxy debate, we integrate group competition and contact theories with status theory to conceptualize 'racial status' as whites' first-order income rank relative to the subgroup status of Black, Hispanic, and Asian residents at the census tract level. Multilevel linear probability models show that whites lacking advantaged status are generally more likely to move. However, the positive association between Black or Asian concentration and white departure is weaker among status-disadvantaged whites, while the negative association with Hispanic concentration is stronger. These patterns lend greater support to contact theory than to group competition theory. By foregrounding relative status, the study demonstrates that racial and socioeconomic mechanisms are intertwined in shaping white residential mobility.
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The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators
January 2006
Working Paper Number:
tp-2006-01
The Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S. Census Bureau,
with the support of several national research agencies, has built a set of infrastructure files
using administrative data provided by state agencies, enhanced with information from other administrative
data sources, demographic and economic (business) surveys and censuses. The LEHD
Infrastructure Files provide a detailed and comprehensive picture of workers, employers, and their
interaction in the U.S. economy. Beginning in 2003 and building on this infrastructure, the Census
Bureau has published the Quarterly Workforce Indicators (QWI), a new collection of data series
that offers unprecedented detail on the local dynamics of labor markets. Despite the fine detail,
confidentiality is maintained due to the application of state-of-the-art confidentiality protection
methods. This article describes how the input files are compiled and combined to create the infrastructure
files. We describe the multiple imputation methods used to impute in missing data and
the statistical matching techniques used to combine and edit data when a direct identifier match
requires improvement. Both of these innovations are crucial to the success of the final product. Finally,
we pay special attention to the details of the confidentiality protection system used to protect
the identity and micro data values of the underlying entities used to form the published estimates.
We provide a brief description of public-use and restricted-access data files with pointers to further
documentation for researchers interested in using these data.
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