Access to Employment for Adults in Poverty in the Buffalo-Niagara Region

Abstract

This study extends the spatial mismatch literature by examining access to employment for the low-income population in Erie and Niagara Counties in western New York State. The analysis uses geographical information systems (GIS) to map residence and employment locations and calculate measures of employment and transport access. Throughout the two-county region, two-thirds of adults in poverty live within close proximity to a reasonable number of low-wage jobs. The ratio of the number of jobs accessible within a 30-minute commute by automobile versus public transit varies only slightly across neighbourhoods with high poverty rates. The close proximity of the suburbs to the central city and the network of grid and radial streets connecting the two places make suburban job access reasonable. Thus, despite higher numbers of low-wage jobs in the suburbs, job access in the city is superior to job access in the suburbs due to higher densities of employment opportunities and the existence of developed transport networks. While poverty is highly centralised in the cities of Buffalo and Niagara Falls, the analysis suggests that, based on the spatial distribution of low-wage employment and concentrations of poverty, central-city locations have significant advantages in terms of proximity to jobs. However, Black/African American adults in poverty have poorer access to automobiles than Whites, and, as a result, they may be able to search for jobs only within a smaller area. This study recommends enhancements to public transit in places with large concentrations of low-wage jobs and increased access to reliable automobiles in places with small concentrations of low-wage jobs. Above all, unemployment rates in low-income neighbourhoods suggest a need to enhance programmes to improve job readiness, placement and support services.

Access to Public Transit and Its Influence on Ridership for Older Adults in Two U.S. Cities

“…(Hess 2005a), but San José has a greater percentage of housing units built in recent decades, re ecting the city's growth throughout the 1970s, 1980s, and 1990s. Buffalo possesses new, lower-density residential environments similar to those found in San José, but in Buffalo they are located in suburban municipalities (outside the study area for this research), while in San José they are located within the boundaries of the region's central city.…”

Public transport versus private car GIS-based estimation of accessibility applied to the Tel Aviv metropolitan area

“…A more accurate assessment of travel time by public transport will thus generate a more realistic picture of the accessibility gaps between car-less versus car-owning households. This may well shed a different light on the relatively small car-transit disparities found by, e.g., Hess (2005) and therefore result in different conclusions regarding the size of the accessibility gap. In the following, we will develop a method to comparatively assess transit and car travel times.…”

“…For instance, Hess (2005) finds a car/transit job accessibility ratio of 1.7-8.2. In our terms, this amounts to an Access Area value of 12-59%.…”

“…When car accessibility is substantially higher than public transport accessibility, an area can be defined as car dependent and the car share in the modal split of an area is expected to be high, suggesting that transport-related negative externalities can be reduced through an improvement of accessibility by alternative modes of transport (Shen 1998;Blumenberg and Ong 2001;Hess 2005;Kawabata and Shen 2006;Kawabata 2009;Kwok and Yeh 2004). The relevant comparison is thus car versus public transport accessibility at the level of an area (e.g.…”

“…Studies that apply location-based measures to assess the differential accessibility by car and transit include Shen (1998), Blumenberg and Ong (2001), Hess (2005), Kawabata and Shen (2006) and Kawabata (2009). These and comparable studies focus on accessibility to employment and apply relatively simple estimates of mode-based travel time to compare accessibility levels.…”