This paper examines the relationship between job sprawl and the spatial mismatch between blacks and jobs. Using data from a variety of sources including the U.S. Census and U.S. Department of Commerce’s ZIP Code Business Patterns, I control extensively for metropolitan area characteristics and other factors. In addition, I use metropolitan area physical geography characteristics as instruments for job sprawl to address the problem of simultaneity bias. I find a significant and positive effect of job sprawl on mismatch conditions faced by blacks that remains evident across a variety of model specifications. This effect is particularly important in the Midwest and West, and in metropolitan areas where blacks’ share of the population is not large and where blacks’ population growth rate is relatively low. Among others, the results also reveal that the measure of mismatch used in this analysis is highly correlated across metropolitan areas with blacks’ employment outcomes in the expected direction.

In this paper, we assess whether boosting minority car-ownership rates would narrow inter-racial employment rate differentials. We pursue two empirical strategies. First, we explore whether the effect of auto ownership on the probability of being employed is greater for more segregated groups of workers. Exploiting the fact that African-Americans are considerably more segregated from whites than are Latinos, we estimate car-employment effects for blacks, Latinos, and whites and test whether these effects are largest for more segregated groups. Second, we use data at the level of the metropolitan area to test whether the car-employment effect for blacks relative to that for whites increases with the degree of black relative isolation from employment opportunities. We find the strongest car effects for blacks, followed by Latinos, and then whites. Moreover, this ordering is statistically significant. We also find that the relative car-employment effect for blacks is largest in metropolitan areas where the relative isolation of blacks from employment opportunities is the most severe. Our empirical estimates indicate that raising minority car-ownership rates to the white car ownership rate would eliminate 45 percent of the black-white employment rate differential and 17 percent of the comparable Latinbo-white differential.

In this paper, we assess whether boosting minority car-ownership rates would narrow inter-racial employment rate differentials. We pursue two empirical strategies. First, we explore whether the effect of auto ownership on the probability of being employed is greater for more segregated groups of workers. Exploiting the fact that African-Americans are considerably more segregated from whites than are Latinos, we estimate car-employment effects for blacks, Latinos, and whites and test whether these effects are largest for more segregated groups. Second, we use data at the level of the metropolitan area to test whether the car-employment effect for blacks relative to that for whites increases with the degree of black relative isolation from employment opportunities. We find the strongest car effects for blacks, followed by Latinos, and then whites. Moreover, this ordering is statistically significant. We also find that the relative car-employment effect for blacks is largest in metropolitan areas where the relative isolation of blacks from employment opportunities is the most severe. Our empirical estimates indicate that raising minority car-ownership rates to the white car ownership rate would eliminate 45 percent of the black-white employment rate differential and 17 percent of the comparable Latinbo-white differential.

Auto insurance rates can vary dramatically, with much higher premiums in poor and minority areas than elsewhere, even after accounting for individual characteristics, driving history and coverage. This project used a unique data set to examine the relative influence of place-based socioeconomic characteristics (or redlining) and place-based risk factors on the place-based component of automobile insurance premiums. We used a novel approach of combining tract-level census data and car insurance rate quotes from multiple companies for sub-areas within the city of Los Angeles. The quotes are for a hypothetical individual with identical demographic and auto characteristics, driving records and insurance coverage. This method allowed the individual demographic and driving record to be fixed. Multivariate models are then used to estimate the independent contributions of these risk and redlining factors to the place-based component of the car insurance premium. We find that both risk and redlining factors are associated with variations in insurance costs in the place-based component, with black and poor neighborhoods being adversely affected, although risk factors are stronger predictors. However, even after risk factors are taken into account in the model specification, SES factors remain statistically significant. Moreover, simulations show that redlining factors explain more of the gap in auto insurance premiums between black (and Latino) and white neighborhoods and between poor and nonpoor neighborhoods. The findings do not appear sensitive to the individual characteristics of the hypothetical driver.

In this paper, we analyze employer demand for ex-offenders. We use data from a recent survey of employers to analyze not only employer preferences for offenders, but also the extent to which they check criminal backgrounds in the presence of very imperfect information about the job applicants whom they consider. We investigate the firm and job characteristics that correlate with these measures of employer demand. We also consider the extent to which such demand changed during the 1990's, in response to tighter labor market conditions, using data from surveys administered at different points in time. Finally, we consider the quantities of demand for ex-offenders relative to their supply, based on a variety of estimates of total stocks and annual flows of offenders back to the civilian population.

This paper presents a test of the hypothesis that employers in suburban locations are more likely to discriminate against African-Americans than employers located in central cities. Using a difference-in-difference framework, we compare central city-suburban differences in racial hiring outcomes for firms where a white person is in charge of hiring (white employers, for short) to similar geographic differences in outcomes for firms where a black person is in charge of hiring (black employer). We find that both suburban black and white employers hire fewer blacks than their central-city counterparts. Moreover, the suburban/central city hiring gap among black employers is as large as, or larger than, that of white employers. Suburban black employers, however, receive many more applications from blacks and hire more blacks than do white firms in either location.