Job Proximity and the Urban Employment Problem: Do Suitable Nearby Jobs Improve Neighbourhood Employment Rates?

Abstract

Due to a scarcity of small-area jobs data, much of the spatial mismatch literature has not directly addressed the impact of nearby jobs on neighbourhood employment rates. Such analysis is particularly needed when considering the probable effects of neighbourhood-targeted economic development. Moreover, the occupational mix of jobs and their match with resident skills have not been dealt with adequately. A consistent measure of job proximity is found to have a significant but modest effect on neighbourhood employment and unemployment rates, with a standard deviation increase resulting in an increase in the employment rate of approximately six-tenths of a percentage point and a reduction in unemployment of approximately three-tenths of a percentage point. When considering occupational match and the average occupational level of nearby jobs, the effect of nearby jobs is larger. Race and educational attainment are found to have the largest effects on employment rates.

Exploring the connections among job accessibility, employment, income, and auto ownership using structural equation modeling

“…He concluded that residential segregation plus the barrier of transportation to work led to a high unemployment rate among the blacks living in the central cities. Kain's findings were called the spatial mismatch hypothesis (SMH) and were empirically tested with aggregate and disaggregate data (Cooke 1997;Ellwood 1986;Holzer et al 1994;Hughes and Madden 1991;Ihlanfeldt and Sjoquist 1990;Immergluck 1998;McLafferty and Preston 1996;Ong and Miller 2003;Raphael 1998). These studies produced inconsistent conclusions on the correlations between job accessibility and unemployment rates among blacks living in central cities.…”

Recipes for neighborhood development: A machine learning approach toward understanding the impact of mixing in neighborhoods

“…A mixing of land uses can increase social interaction and decrease the need Machine learning and household income appreciation for long-distance transportation and thus cut carbon emissions. By putting jobs and housing close to each other, mixing land uses can also lead to better job outcomes, and hence economic dynamism; indeed, a study of Chicago found that a greater number of jobs within two miles of neighborhoods led to higher employment and lower unemployment rates for residents (Immergluck 1998).…”

The Mechanisms of Spatial Mismatch

“…This is why some authors prefer the expression spatial/skill mismatch to depict more accurately the spatial disconnection between the residential locations of inner-city minorities and the locations of the low-skilled suburban jobs they could occupy (see Ong and Blumenberg, 1998;Immergluck, 1998).…”