This paper reports an investigation of the validity and reliability of a set of predictors of the survival of small, start-up companies. Having a bank loan was a significant positive predictor of survival . The use of the model as a predictor of survival was investigated on an hold-out sample. One group of companies in the hold-out sample had high predicted probabilities of survival, in spite of note having bank loans. This group had a survival rate that was slightly better than that of companies in the hold-out sample that had obtained bank loans. The group with high survival rate, but without bank loans, made greater use of other forms of loans. The group of companies with a high survival rate, but without bank loans, accounted for 22% of the hold-out.
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Who Moves to Mixed-Income Neighborhoods?
August 2010
Working Paper Number:
CES-10-18
This paper uses confidential Census data, specifically the 1990 and 2000 Census Long Form data, to study the income dispersion of recent cohorts of migrants to mixed-income neighborhoods. If recent in-migrants to mixed-income neighborhoods exhibit high levels of income heterogeneity, this is consistent with stable mixed-income neighborhoods. If, however, mixed-income neighborhoods are comprised of older homogeneous lower-income (higher income) cohorts combined with newer homogeneous higher-income (lower-income) cohorts, this is consistent with neighborhood transition. Our results indicate that neighborhoods with high levels of income dispersion do in fact attract a much more heterogeneous set of in-migrants, particularly from the tails of the income distribution, but that income heterogeneity does tend to erode over time. Our results also suggest that the residents of mixed-income neighborhoods may be less heterogeneous with respect to lifetime income.
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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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Disentangling Labor Supply and Demand Shifts Using Spatial Wage Dispersion: The Case of Oil Price Shocks
November 2013
Working Paper Number:
CES-13-57
We separate changes in labor supply and demand through changes in higher-order moments of the wage distribution. We illustrate this idea in a study of the effects of oil price shocks, which generate a predictable labor demand adjustment across regions. Empirically, oil price shocks decrease average wages, particularly skilled wages, and increase wage dispersion, particularly unskilled wage dispersion. In a model with spatial energy intensity differences and nontradables, labor demand shifts, while explaining the response of average wages to oil price shocks, have counterfactual implications for the response of wage dispersion. Only shifts in labor supply can explain this latter fact.
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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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Migration and Dispersal of Hispanic and Asian Groups: An Analysis of the 2006-2008 Multiyear American Community Survey
October 2011
Working Paper Number:
CES-11-33
This report seeks to evaluate selective migration processes of Hispanic and Asian nationality groups in the US from established settlement areas, using recent migration data from the American Community Survey. The underlying goal is to detect migration tendencies leading toward an increased dispersion of these groups associated with their migration processes. Using descriptive statistics, maps, and migration models, we assess how migration processes in the 2006-8 period are leading to the dispersal of Hispanic and Asian race ethnic groups across metropolitan areas, with special attention to the roles of co-ethnic communities and spatial assimilation. These analyses employ migration data available from the 3-year 2006-8 American Community Survey using restricted data from the US Census Bureau's Research Data Centers. This use of the restricted ACS files permitted the first post 2000 analysis of inter-metropolitan migration for Hispanic groups (Mexicans, Puerto Ricans, Cubans, Salvadorans, Dominicans) and Asian groups (Chinese, Indians, Filipinos, Vietnamese, Koreans) using the detailed demographic and geographic attributes available with these files. The data and analysis presented here provide a benchmark for further research of this kind with the American Community Survey in light of the fact that migration data will no longer be available from the US decennial census. The study examines migration from these groups' major settlement areas to other metropolitan area destinations as they are affected by the attraction of co-ethnic communities and by a migrant selectivity pattern consistent with the perspective of spatial assimilation. The migration processes themselves were evaluated in terms of two components: the out--migration rates of residents, and the destination selection of movers. From the perspective of co-ethnic community attraction, it was hypothesized that the outmigration rates from high co-ethnic settlement areas would be lower than those from areas where the group had a smaller overall presence and that the destination selections of out-migrants would be positively affected by the presence of high co-ethnic population shares in destination areas. From the spatial assimilation perspective, it was hypothesized that out-migration from high coethnic areas would least likely occur for group members with lowest education, poor facility with English, and recently arrived in the US; whereas the selection of destinations with large coethnic population shares would be most likely to occur for these same population categories. The results strongly confirm that co-ethnic community attraction continues to reduce outmigration of groups from major settlement origins and positively influences their destination selections. A series of multivariate migrant destination selection models confirm a consistent draw of ethnically similar destinations across individual Hispanic and Asian groups when other economic, demographic and structural metropolitan attributes are taken into account. In contrast, results regarding spatial assimilation are typically mixed or nonexistent in characterizing both out-migration and mover destination selectivity patterns. In fact, we find contrary evidence for some Asian groups for whom it is the most educated, and native born migrants who show a penchant for selecting destinations with greater co-ethnic population shares. Among the greatest destinations for Indians, for example, are Philadelphia, Seattle, Dallas, Boston and Atlanta- areas with higher than average Indian population shares, and areas that also house knowledge-based industries. The selection of co-ethnic destinations among Hispanic group migrants appears somewhat impervious to education attainment and Hispanic and Mexican group movers, who are foreign born and who arrived since 2000, are least, rather than most, prone to select co-ethnic destinations. The mover destination models make plain that employment growth at destination provides a strong draw for all Hispanic groups. This suggests that recent growth in low skilled jobs in parts of the country with small Hispanic populations are nonetheless attracting newly arrived, and less skilled Mexicans and other Hispanics who might have previously been especially lured to destinations with large co-ethnic population shares.
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What Drives Racial Segregation? New Evidence Using Census Microdata
October 2002
Working Paper Number:
CES-02-26
Residential segregation on the basis of race is widespread and has important welfare consequences. This paper sheds new light on the forces that drive observed segregation patterns. Making use of restricted micro-Census data from the San Francisco Bay Area and a new measurement framework, it assesses the extent to which the correlation of race with other household characteristics, such as income, education and immigration status, can explain a significant portion of observed racial segregation. In contrast to the findings of the previous literature, which has been hampered by serious data limitations, our analysis indicates that individual household characteristics can explain a considerable fraction of segregation by race. Taken together, we find that the correlation of race with other household attributes can explain almost 95 percent of segregation for Hispanic households, over 50 percent for Asian households, and approximately 30 percent for White and Black households. Our analysis also indicates that different factors drive the segregation of different races. Language explains a substantial proportion - more than 30 percent - of Asian and Hispanic segregation, education explains a further 20 percent of Hispanic segregation, while income is the most important non-race household characteristic for Black households, explaining around 10 percent of Black segregation.
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Foreign-Born and Native-Born Migration in the U.S.: Evidence from IRS Administrative and Census Survey Records
July 2018
Working Paper Number:
carra-2018-07
This paper details efforts to link administrative records from the Internal Revenue Service (IRS) to American Community Survey (ACS) and 2010 Census microdata for the study of migration among foreign-born and native-born populations in the United States. Specifically, we (1) document our linkage strategy and methodology for inferring migration in IRS records; (2) model selection into and survival across IRS records to determine suitability for research applications; and (3) gauge the efficacy of the IRS records by demonstrating how they can be used to validate and potentially improve migration responses for native-born and foreign-born respondents in ACS microdata. Our results show little evidence of selection or survival bias in the IRS records, suggesting broad generalizability to the nation as a whole. Moreover, we find that the combined IRS 1040, 1099, and W2 records may provide important information on populations, such as the foreign-born, that may be difficult to reach with traditional Census Bureau surveys. Finally, while preliminary, the results of our comparison of IRS and ACS migration responses shows that IRS records may be useful in improving ACS migration measurement for respondents whose migration response is proxy, allocated, or imputed. Taking these results together, we discuss the potential application of our longitudinal IRS dataset to innovations in migration research on both the native-born and foreign-born populations of the United States.
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Small Homes, Public Schools, and Property Tax Capitalization
March 2013
Working Paper Number:
CES-13-04
Efforts to estimate the degree to which local property taxes are capitalized into house values are complicated by any spurious correlation between property taxes and unobserved public services. One public service of particular interest is the provision of local public schools. Not only do public schools bulk large in the local property tax bill, but the inherent difficulty in measuring school quality has potentially undermined earlier attempts at achieving unbiased estimates of property tax capitalization. This particular problem has been of special concern since Oates' (1969) seminal paper. We sidestep the problem of omitted or misspecified measures of school quality by focusing on a segment of the housing market that likely places little-to-no value on school quality: small homes. Because few households residing in small homes have public school children, we anticipate that variations in their value does not account for differentials in public school quality. Using restricted-access microdata provided by the U.S. Census, and a quasi- experimental identification strategy, we estimate that local property taxes are nearly fully capitalized into the prices of small homes.
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The Opportunities and Challenges of Linked IRS Administrative and Census Survey Records in the Study of Migration
July 2018
Working Paper Number:
carra-2018-06
This paper details efforts to link administrative records from the Internal Revenue Service (IRS) to American Community Survey (ACS) and 2010 Census microdata for the study of migration in the United States. Specifically, we (1) document our linkage strategy and methodology for inferring migration in IRS records; (2) model selection into and survival across IRS records to determine suitability for research applications; and (3) gauge the efficacy of the IRS records by demonstrating how they can be used to validate and potentially improve migration responses in ACS microdata. Our results show little evidence of selection or survival bias in the IRS records, suggesting broad generalizability to the nation as a whole. Moreover, we find that the combined IRS 1040, 1099, and W2 records may provide important information on populations that are hard to reach with traditional Census surveys. Finally, while preliminary, the results of our comparison of IRS and ACS migration responses shows that IRS records may be useful in improving ACS migration measurement for respondents whose migration response is proxy, allocated, or imputed. Taking these results together, we discuss the potential applications of our longitudinal IRS dataset to innovations in migration research in the United States.
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File Matching with Faulty Continuous Matching Variables
January 2017
Working Paper Number:
CES-17-45
We present LFCMV, a Bayesian file linking methodology designed to link records using continuous matching variables in situations where we do not expect values of these matching variables to agree exactly across matched pairs. The method involves a linking model for the distance between the matching variables of records in one file and the matching variables of their linked records in the second. This linking model is conditional on a vector indicating the links. We specify a mixture model for the distance component of the linking model, as this latent structure allows the distance between matching variables in linked pairs to vary across types of linked pairs. Finally, we specify a model for the linking vector. We describe the Gibbs sampling algorithm for sampling from the posterior distribution of this linkage model and use artificial data to illustrate model performance. We also introduce a linking application using public survey information and data from the U.S. Census of Manufactures and use
LFCMV to link the records.
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