Job Accessibility and Racial Differences in Youth Employment Rates

1. Job Accessibility and Racial Differences in Youth Employment Rates Keith R. Ihlanfeldt, David L. Sjoquist The American Economic Review Volume 80, Issue 1 (Mar.,1990), 267-276. By MDM

2. Introduction:Over the past thirty years, the trend in black youth employment rates has been downward relative to employment rates for white youth. Despite considerable research effort, much of the trend as well as the existing gap in employment among black and white youth remains unexplained.This problem makes it difficult to formulate policies that would effectively mitigate this important social problem.

3. Employment rates for whites (blacks) in November 1998 conducted by US Department of Labor16 - 19 years old enrolled in school 0.42 (0.21)16 - 19 years old not enrolled in school 0.68 (0.45)20 - 24 years old enrolled 0.58 (0.43)20 - 24 years old not enrolled 0.79 (0.66)

4. Hypothesis:One hypothesis that may help explain both the trend and the racial gap in youth employment is that the sub-urbanizationof low-skill jobs and continued housing market segregation have acted together to reduce the job opportunities of black youth in comparison to those available to white youth. The origins of this hypothesis can be traced back to John Kain (1968) who argued that housing segregation reduces the employment opportunities of all blacks.

5. First Test:The first test of the “job access” hypothesis as it relates to youth was conducted by David Ellwood (1986) using data from the Chicago metropolitan area. He, therefore, concluded that the problem isn’t space related but race relate. The same conclusion was also reached by Jonathan Leonard (1986a), who conducted a study similar to Ellwood’s using data for the Los Angeles metropolitan area. His results suggested that young workers are sufficiently “fluid” in their commuting patterns to overcome any problems arising from an absence of nearby jobs.

6. Problemswith previous papers:Leonard has suggested that Ellwood’s measures of job accessibility may have been unreliable since their construction was based on small samples. Measurement error likely also affects this variable since the overwhelming majority of youth do not work in blue-collar jobs and the population of the commuting zone may poorly represent the number of workers competing for jobs typically held by youth. Leonard’s own measure of job access was the number of blue-collar jobs within a 15-minute commute of each census tract divided by the population 16 years of age and older of the commuting zone.

7. This paper:In this paper, the relationship between the nearness of jobs and youth job probability is explored using measures of job access that do not suffer from the limitations of those employed in previous work. They chose Philly because Philly has a large number of blacks living in the suburbs and this is useful in giving them a reliable estimate. Ihlanfeldt and Sjoquist also used a richer set of control variables and the estimation of separate equations for black and white youth broken down by age and enrollment status, and for different metropolitan areas. Philadelphia, Chicago, and Los Angeles.

8. The Estimating Equation:Assumptions:- full information- wage gradient is relatively flat - Most frequently used methods of job search are checking with friends, relatives and direct applications without referrals.Information on available job opportunities may decline rapidly with distance from home  Net wage min wage distance

9. Job access measures:They looked at the mean travel time of workers living within the youth’s community. To control for other factors that might effect the probability of employment, they selected individual and family background variables that have been found to be important in prior studies of youth employment. • Ellwood: travel time is a good job access measure as it reflects actual worker behavior • if jobs are nearby, commuting time will be low • if jobs can’t be found nearby, travel time will be high

10. Individual and family background variables Separate equations for white and black youth were estimated for the following four groups:  (1) 16-19, home, school (2) 16-19, home, not in school (3) 20-24, home, not enrolled and less than a college education (4) 20-24, not home, not enrolled, not in the military, less than a college education  • age, sex, years of education, health status, • marital status  • family income net of the youth’s earnings • characteristics of the household head: • sex, educational level, employment status

11. Results for Philadelphia Black youth have definitely worse access to employment opportunities 3. A portion of the difference in black and white employment rates can therefore be attributed to differential job access  • The sizes of the partial derivatives are similar among groups except for the relatively larger effect estimated for older white youth no longer living at home. • More mobile • Select better quality neighbourhoods

12. Other Metropolitan Areas  : Variation in times was as large in CHI as in PHI. This suggests that in CHI, poor job access is more easily overcome by white youth in comparison to black youth. • White mean travel time in LA had less variation. (var: PHI: 4.84 min, LAX: 1.69 min) • intra-metropolitan differences in job access are too small for white youth living in LA to have much of an effect on their job probability

13. Conclusion  : • Albert Rees (1986) listed three hypotheses that might explain the high rate of jobless-ness of black youth relative to white youth:  • It arises from certain aspects of the culture of the black ghetto • result of employer discrimination in hiring against young blacks • due to jobs moving away from the inner city, leaving blacks with poor access to employment opportunities

14. Conclusion  : This paper showed evidence supporting the validity of the job access hypothesis. The nearness of jobs was found to have a strong effect on the job probability of both white and black youth living in PHI and differential job access was found to explain a large portion of the racial difference in youth employment rates.