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Racial Disparity in an Era of Increasing Income Inequality
January 2017
Working Paper Number:
carra-2017-01
Using unique linked data, we examine income inequality and mobility across racial and ethnic groups in the United States. Our data encompass the universe of tax filers in the U.S. for the period 2000 to 2014, matched with individual-level race and ethnicity information from multiple censuses and American Community Survey data. We document both income inequality and mobility trends over the period. We find significant stratification in terms of average incomes by race and ethnic group and distinct differences in within-group income inequality. The groups with the highest incomes - Whites and Asians - also have the highest levels of within-group inequality and the lowest levels of within-group mobility. The reverse is true for the lowest-income groups: Blacks, American Indians, and Hispanics have lower within-group inequality and immobility. On the other hand, our low-income groups are also highly immobile when looking at overall, rather than within-group, mobility. These same groups also have a higher probability of experiencing downward mobility compared with Whites and Asians. We also find that within-group income inequality increased for all groups between 2000 and 2014, and the increase was especially large for Whites. In regression analyses using individual-level panel data, we find persistent differences by race and ethnicity in incomes over time. We also examine young tax filers (ages 25-35) and investigate the long-term effects of local economic and racial residential segregation conditions at the start of their careers. We find persistent long-run effects of racial residential segregation at career entry on the incomes of certain groups. The picture that emerges from our analysis is of a rigid income structure, with mainly Whites and Asians confined to the top and Blacks, American Indians, and Hispanics confined to the bottom.
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Response Error & the Medicaid undercount in the CPS
December 2016
Working Paper Number:
carra-2016-11
The Current Population Survey Annual Social and Economic Supplement (CPS ASEC) is an important source for estimates of the uninsured population. Previous research has shown that survey estimates produce an undercount of beneficiaries compared to Medicaid enrollment records. We extend past work by examining the Medicaid undercount in the 2007-2011 CPS ASEC compared to enrollment data from the Medicaid Statistical Information System for calendar years 2006-2010. By linking individuals across datasets, we analyze two types of response error regarding Medicaid enrollment - false negative error and false positive error. We use regression analysis to identify factors associated with these two types of response error in the 2011 CPS ASEC. We find that the Medicaid undercount was between 22 and 31 percent from 2007 to 2011. In 2011, the false negative rate was 40 percent, and 27 percent of Medicaid reports in CPS ASEC were false positives. False negative error is associated with the duration of enrollment in Medicaid, enrollment in Medicare and private insurance, and Medicaid enrollment in the survey year. False positive error is associated with enrollment in Medicare and shared Medicaid coverage in the household. We discuss implications for survey reports of health insurance coverage and for estimating the uninsured population.
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Local Labor Demand and Program Participation Dynamics
November 2016
Working Paper Number:
carra-2016-10
Estimates the effect of fluctuations in local labor conditions on the likelihood that existing participants are able to transition out of the Supplemental Nutrition Assistance Program (SNAP). Our primary data are SNAP administrative records from New York (2007-2012) linked to the 2010 Census at the person-level. We further augment these data by linking to industry-specific labor market indicators at the county-level. We find that local labor markets matter for the length of time individuals spend on SNAP, but there is substantial heterogeneity in estimated effects across local industries. While employment growth in industries with small shares of SNAP participants has no impact on SNAP exits, growth in local industries with creases the likelihood that recipients exit the program. We also observe corresponding increases in entries when these industries experience localized contractions. Notably, estimated industry effects vary across race groups and parental status, with Black Alone non-Hispanic, Hispanic, and mothers benefiting the least from improvements in local labor market conditions.
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Differences in Self-employment Duration by Year of Entry & Pre-entry
November 2016
Working Paper Number:
carra-2016-09
Self-employment is associated with entrepreneurship and a motivation to pursue an opportunity. Previous research indicates that people also become self-employed because of limited opportunities in the wage sector. Using a unique set of data that links the American Community Survey to Form 1040 and W-2 records, this paper extends the existing literature by examining self-employment duration for five consecutive entry cohorts, including two cohorts who entered self-employment during the Great Recession. Severely limited labor market opportunities may have driven many in the recession cohorts to enter self-employment, while those entering self-employment during the boom may have been pursuing opportunities under favorable market conditions. To more explicitly test the concept of "necessity" versus "opportunity" self-employment, we also examine the pre-entry wage labor attachment of entrants. Specifically, we ask whether an association exists between wage labor attachment and the duration of self-employment. We also explore whether the demographic/socio-economic characteristics and self-employment exit behavior of the cohorts are different, and if so, how. We find evidence consistent with the existence of "necessity" vs. "opportunity" self-employment types.
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Playing with Matches: An Assessment of Accuracy in Linked Historical Data
June 2016
Working Paper Number:
carra-2016-05
This paper evaluates linkage quality achieved by various record linkage techniques used in historical demography. I create benchmark, or truth, data by linking the 2005 Current Population Survey Annual Social and Economic Supplement to the Social Security Administration's Numeric Identification System by Social Security Number. By comparing simulated linkages to the benchmark data, I examine the value added (in terms of number and quality of links) from incorporating text-string comparators, adjusting age, and using a probabilistic matching algorithm. I find that text-string comparators and probabilistic approaches are useful for increasing the linkage rate, but use of text-string comparators may decrease accuracy in some cases. Overall, probabilistic matching offers the best balance between linkage rates and accuracy.
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A Loan by any Other Name:
How State Policies Changed Advanced Tax Refund Payments
June 2016
Working Paper Number:
carra-2016-04
In this work, I examine the impact of state-level regulation of Refund Anticipation Loans (RALs) on the increase in the use of Refund Anticipation Checks (RACs) and on taxpayer outcomes. Both RALs and RACs are products offered by tax-preparers that provide taxpayers with an earlier refund (in the case of a RAL) or a temporary bank account from which tax preparation fees can be deducted (in the case of a RAC). Each product is costly compared with the value of the refund, and they are often marketed to low-income taxpayers who may be liquidity constrained or unbanked. States have responded to the potentially predatory nature of RALs through regulation, leading to a switch to RACs. Using zip-code-level tax data, I examine the effects of various state-level policies on RAL activity and the transition of tax-preparers to RACs. I then specifically analyze New Jersey's interest rate cap on RALs, a regulation that was accompanied by greater enforcement of existing tax-preparer regulations. Employing an empirical strategy that uses variation in taxpayer location, which should be uninfluenced by tax preparers' decisions to provide these products and a state's decision to regulate them, I find increases in RAL and RAC use for taxpayers living near New Jersey's border with another state. Furthermore, I find that these same border taxpayers reported more social program use and more persons per household - a finding that is in line with the results of similar research into the effects of short-term borrowing on family finances.
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Measuring the Effects of the Tipped Minimum Wage Using W-2 Data
June 2016
Working Paper Number:
carra-2016-03
While an extensive literature exists on the effects of federal and state minimum wages, the minimum wage received by tipped workers has received less attention. Researchers have found it difficult to capture the hourly wages of tipped workers and thus assess the economic effects of the tipped minimum wage. In this paper, I present a new measure of hourly wages for tipped servers (wait staff and bartenders) using linked W-2 and survey data. I estimate the effect of tipped minimum wages on the wages and hourly tips of servers, as well as server employment and hours worked. I find that higher mandatory tipped minimum wages increase that portion of wages paid by employers, but decrease tip income by a similar percentage. I also find evidence that employment increases over lower values of the tipped minimum wage and then decreases at higher values. These results are consistent with a monopsony model of server employment. The wide variance of tipped minimum wages compared to non-tipped minimums provide insight into monopsony effects that may not be discernible over a smaller range of minimum wage values.
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The Impact of Immigration on the Labor Market Outcomes of Native Workers: Evidence using Longitudinal Data from the LEHD
January 2016
Working Paper Number:
CES-16-56
Empirical estimates of the effect of immigration on native workers that rely on spatial comparisons have generally found small effects, but have been subject to the criticism that out-migration by native workers dampens the observed effect by spreading it over a larger area. In contrast, studies that rely on variation in immigration across industries, occupations, or education-based skill-levels often report large negative effects, but rely primarily on repeated cross-sectional data sets which also cannot account for the adjustment of native workers over time. In this paper, we use a newly available data set, the Longitudinal Employer Household Data (LEHD), which provides quarterly earnings records, geographic location, and firm and industry identifiers for 97% of all privately employed workers in 29 states. We use this data to analyze the impact of immigration on earnings changes and the mobility response of native workers. Overall, we find that although immigration has a negative effect on the earnings and employment of native workers, and positive effects on their firm, industry, and cross-state mobility, the overall size of the effects is small.
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Taking the Leap: The Determinants of Entrepreneurs Hiring their First Employee
January 2016
Working Paper Number:
CES-16-48
Job creation is one of the most important aspects of entrepreneurship, but we know relatively little about the hiring patterns and decisions of startups. Longitudinal data from the Integrated Longitudinal Business Database (iLBD), Kauffman Firm Survey (KFS), and the Growing America through Entrepreneurship (GATE) experiment are used to provide some of the first evidence in the literature on the determinants of taking the leap from a non-employer to employer firm among startups. Several interesting patterns emerge regarding the dynamics of non-employer startups hiring their first employee. Hiring rates among the universe of non-employer startups are very low, but increase when the population of non-employers is focused on more growth-oriented businesses such as incorporated and EIN businesses. If non-employer startups hire, the bulk of hiring occurs in the first few years of existence. After this point in time relatively few non-employer startups hire an employee. Focusing on more growth- and employment-oriented startups in the KFS, we find that Asian-owned and Hispanic-owned startups have higher rates of hiring their first employee than white-owned startups. Female-owned startups are roughly 10 percentage points less likely to hire their first employee by the first, second and seventh years after startup. The education level of the owner, however, is not found to be associated with the probability of hiring an employee. Among business characteristics, we find evidence that business assets and intellectual property are associated with hiring the first employee. Using data from the largest random experiment providing entrepreneurship training in the United States ever conducted, we do not find evidence that entrepreneurship training increases the likelihood that non-employers hire their first employee.
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Is there an Advantage to Working? The Relationship between Maternal Employment and Intergenerational Mobility
September 2015
Working Paper Number:
CES-15-27
We investigate the question of whether investing in a child's development by having a parent stay at home when the child is young is correlated with the child's adult outcomes. Specifically, do children with stay-at-home mothers have higher adult earnings than children raised in households with a working mother? The major contribution of our study is that, unlike previous studies, we have access to rich longitudinal data that allows us to measure both the parental earnings when the child is very young and the adult earnings of the child. Our findings are consistent with previous studies that show insignificant differences between children raised by stay-at-home mothers during their early years and children with mothers working in the market. We find no impact of maternal employment during the first 5 years of a child's life on earnings, employment, or mobility measures of either sons or daughters. We do find, however, that maternal employment during children's high school years is correlated with a higher probability of employment as adults for daughters and a higher correlation between parent and daughter earnings ranks.
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