Job Accessibility of the Poor in Los Angeles

Abstract

Problem, research strategy, and findings: Kain's Spatial Mismatch Hypothesis (SMH) suggests that disadvantaged groups who reside in inner-city neighborhoods have low access to regional jobs. We continue to debate this assertion because empirical studies over decades have conflicting results. This research examines whether the poor face spatial mismatch and how suburbanization has changed their job accessibility, in the Los Angeles region between 1990 and 2007–2011. I define spatial mismatch as occurring when the poor in the inner city have lower job accessibility than their suburban counterparts. I estimate job accessibility based on the spatial distribution of jobs and job seekers traveling via private automobiles. My results present a complicated picture: Inner-city poor job seekers have higher job accessibility than their suburban counterparts because many jobs remain in the inner city; thus, the inner-city poor do not face spatial mismatch. Moreover, suburbanization has evened out the differences in the job accessibility of the poor and non-poor. However, the advantage of living in the inner city for job access declines with rapid employment suburbanization.

Takeaway for practice: My research suggests that, since the poor do not face spatial mismatch, spatial policies commonly advocated to address their employment challenges—moving people to the suburbs, bringing jobs to the inner city, or providing mobility options—will not be effective. Giving people cars can help overcome both spatial and nonspatial barriers, but is not politically feasible. Planners should develop synergetic policies to complement spatial approaches including reducing labor and housing market discrimination, providing education and training, developing better job search skills, and creating supportive social connections.

Keywords:

• poverty
• job accessibility
• suburbanization
• spatial mismatch hypothesis

Acknowledgments

I am grateful to the editor and the anonymous referees for their valuable suggestions to improve the article. I also want to thank Robert Schneider, Sammis White, and Nancy Frank, for their help and comments at various stages of this article.