Analysis of Spatial Mismatch and Equity using Commuting and Labor Sheds: Trends in Indiana

Abstract

Commuting patterns are spatial interactions that result from the location decisions of firms, households, and the availability of transportation. Empirical evidence shows that these patterns differ across income groups, reflecting different levels of access to economic opportunities, such as jobs and modes of transportation. Accounting for this variability is an important aspect of paradigms and policies that strive for fair and equitable distribution of benefits and dis-benefits of transportation projects. However, measurable and widely accepted measures that could assist in the assessment of this distribution of transportation impacts are limited. This paper proposes a measure of accessibility mismatch for various income groups based on a ratio between commuting and labor sheds derived from open-access observed data. The paper demonstrates this measure using a case study of counties in Indiana between 2005 and 2007. The results show significant differences in the accessibility ratio between high-, middle-, and low-income groups as well as across metropolitan, micropolitan, and non-core counties, a commonly used geographical classification. This accessibility ratio can be used to explain the differences between commuting patterns across socioeconomic groups and their occupations as well as the identification of areas of limited accessibility and job opportunities.

Growth in Commuting Patterns and Their Impacts on Rural Workforce and Economic Development

“…In terms of monthly earnings, workers with earnings of $1,250 or less are more likely to be stretch commuters than workers with higher earnings (Figure 3). This trend may be somewhat surprising but reflects findings by Schleith and Horner (2014) and Chacon-Hurtado et al (2019), where lower-income workers were found to face higher rates of commuting friction. The greatest growth in stretch commuters since 2002, however, has occurred among individuals with earnings of $3,333 per month or more.…”

“…From a metropolitan perspective, studies often consider variations on the aforementioned measure of excess commuting while others may consider the number of jobs accessible within some distance of a given residential location (Borschman & Kwan, 2010; Cho et al, 2008). Similarly, job accessibility between urban and rural regions may rely on the distance decay between enumeration units (such as counties) containing potential employment opportunities and population centers (Chacon-Hurtado et al, 2019; Partridge et al, 2010).…”

“…Similarly, Renkow and Hoover (2000) used the U.S. Census Bureau's Journey to Work data at the county level to examine intercounty commuting flows. Chacon-Hurtado et al (2019) considered the flows between Indiana labor sheds to determine revealed job accessibility for workers of varying income levels.…”

“…Some groups may face mobility constraints that create longer commuting times. Often these barriers may disproportionately impact low-income or low-skilled workers who face a spatial mismatch in access to jobs that provide more substantial economic opportunities (Boschmann & Kwan, 2010; Carroll & Blair, 2007; Chacon-Hurtado et al, 2019; Immergluck, 1998; Schleith & Horner, 2014). In addition to income levels, other studies have documented how commuting characteristics and preferences vary by occupation, gender, and household structure (Kim et al, 2012; Rapino & Fields, 2012; van den Berg & Gorter, 1997; Wyly, 1998).…”