Job mismatch refers to the inability of people to secure quality employment within their local labor market that matches their skills. Job mismatch is indirectly related to mobility through employment and earnings. Better spatial, skills, and requisite earnings matches may improve employment and take-home earnings, thus contributing to mobility from poverty.

Evidence of the Relationship between Predictor and Related Outcomes

• Many researchers cite the “spatial mismatch hypothesis” and its negative impacts on access to job opportunities for low-income communities, particularly for minority communities. Namely, literature points to the mismatch between where growing job centers are located and where job seekers’ homes are located. Without jobs close to home, low-wage earners may be forced to bear the burden of long or expensive commutes at costs that do not justify that commute (Fernandez and Su 2004). In fact, a 2008 study reported that 70 percent of entry-level jobs (in manufacturing, retail, or wholesale sectors, for example) were in the suburbs, not in low-income neighborhoods, and only 32 percent were within a quarter mile of a transit stop. Further, entry-level jobs often have non-peak-hour shifts, such as evenings or weekends, when public transportation services may be limited or unavailable, further complicating job accessibility (Sanchez 2008).
• Studies find that proximity to jobs increases the probability of employment, reduces time being jobless, and reduces job search times (Andersson et al. 2018; Ihlandfeldt and Sjoquist 1993).
• Although quantitative studies suggest that living in a lower-poverty neighborhood can improve job accessibility, the results of one randomized mobility experiment did not reveal any significant effect on employment in the short-term. The Moving to Opportunity (MTO) program provided vouchers for single mothers living in high-poverty, inner-city public housing projects to move closer to lower-poverty suburban job centers (thereby addressing job mismatch and creating opportunity for upward mobility from poverty). The moves had positive impacts on health and safety but no short-term impacts on employment or earnings. Explanations offered for the ambiguous results were related to disruptions caused by the relocations, such as the loss of social support networks that are a source of informal child care, job referrals, and labor market information. The study authors speculated that employment outcomes would become more positive over time as movers form new social networks (Katz, Kling, and Liebman 2001). But the final evaluation of the program, released 10 years after the evaluation by Katz and colleagues (2001), did not reveal better employment or income outcomes for the group that received vouchers for relocation either (National Bureau of Economic Research 2011).
• Physical distance is only one aspect of spatial mismatch. Even modest distances can be a barrier if there is little access to transportation. Rising poverty rates in the suburbs make it more challenging for low-income families to have access to affordable transportation. With public transit more limited in the suburbs, low-income households in those areas need to have a car to get around (Smart Growth America 2019). Many studies have found that vehicle ownership improves job accessibility (Andersson et al. 2018).
• Kasinitz and Rosenberg (1996) found that race, not just spatial access to jobs, is an important factor in labor markets because of the moderating factor of social networks that can exclude or include workers. A study of the Red Hook neighborhood of Brooklyn found that blue-collar jobs in the neighborhood were filled through social networks that excluded local residents, particularly Black people. The research led to questions about whether bringing jobs closer to where people experiencing poverty live necessarily improves their employment opportunities, given racial discrimination and educational requirement barriers (Kasinitz and Rosenberg 1996). Similarly, Hellerstein, Neumark, and McInerney titled their 2008 research “Spatial Mismatch or Racial Mismatch” and asserted that the problem is not necessarily that jobs do not exist where Black people live, but rather that there is a lack of jobs into which Black people are hired. Space alone played a minor role in employment rates for low-skilled Black men (Hellerstein, Neumark, and McInerney 2008).
• Measuring skill mismatch is difficult because of the lack of direct information regarding workers’ skills and job requirements. Nevertheless, a 2017 study using data collected from over 40 countries through the Programme for the International Assessment of Adult Competencies assessed the minimum and maximum skills required for each occupation and then classified workers to assess who is overskilled, well matched, and underskilled for their jobs along the skill domains of literacy and numeracy. It asserts that only well-matched workers can fully deploy their skill sets, and having better matches would improve employee well-being by reducing stress and improve the overall productivity of the local economy (Pellizzari and Fichen 2017). Mismatches between jobs and education or skills are known to affect labor turnover (or intention to quit) and job satisfaction. Skill mismatches are even more influential than education mismatches (Allen and van der Valden, 2001).
• Some researchers point to a lack of evidence for the skills mismatch hypothesis, whereby employers claim that job seekers do not have the mix of skills needed to fill positions. Some researchers also speculate that hiring methods may need to be improved so employers can more efficiently identity applicants to fill vacancies (Burtless 2014).

How Investments Can Influence the Predictor at State or Local Levels

Place-focused approaches can be used to improve opportunities locally. For example, state or local entities can incentivize businesses (such as through tax credits, rebates, grants, or job training programs) to move to areas with low employment opportunities and hire from within the community. At the same time that jobs are being brought into an area, housing should be protected for existing residents so they are not pushed out.

Addressing transportation barriers may also improve job mismatches. For example, more investment in public transportation can improve commute times. A 1998 study found a strong association between proximity to bus stops and employment levels (Sanchez 1998). Private business or the local government could subsidize transportation costs to reduce the burden of commuting to work.

Some job mismatch may be caused by factors such as technical skills or English-language proficiency. Training programs can be funded and implemented to train the local workforce for specialized jobs in the area. It may be best for employers to invest in training for specialized expertise, because certain skills may only be practical for their specific firm. But some practitioners and researchers believe there should be less focus on skills gaps and more focus on opportunity gaps, meaning more investment in diverse talent (Goger and Jackson 2020). Employers can address this by investing in improved hiring methods (Burtless 2014). Ultimately, because of the wide range of underlying causes for job mismatch, a multipronged approach is necessary to mitigate its prevalence and improve mobility.

References

The primary reference is marked with an asterisk.

Allen, Jim, and Rolf van der Velden. 2001. “Educational Mismatches versus Skill Mismatches: Effects on Wages, Job Satisfaction, and on-the-job Search.” Oxford Economic Papers 3: 434–52. Oxford: Oxford University Press.

*Andersson, Fredrik, John C. Haltiwanger, Mark J. Kutzbach, Henry O. Pollakowski, and Daniel H. Weinberg. 2018. “Job Displacement and the Duration of Joblessness: The Role of Spatial Mismatch.” Review of Economics and Statistics C (2): 203–18.

Burtless, Gary. 2014. “Unemployment and the “Skills Mismatch” Story: Overblown and Unpersuasive,” Brookings Institution op-ed, July 29.

Fernandez, Roberto M., and Celina Su. 2004. “Space in the Study of Labor Markets.” Annual Review of Sociology 30: 545–69.

Goger, Annelies, and Luther Jackson. 2020. “The Labor Market Doesn’t Have a ‘Skills Gap’—It Has an Opportunity Gap.” The Avenue (Brookings Institution blog), September 9.

Hellerstein, Judith K., David Neumark, and Melissa McInerney. 2008. “Spatial Mismatch or Racial Mismatch?” Journal of Urban Economics 64: 464–79.

Ihlanfeldt, Keith R., and David L. Sjoquist. 1991. “The Effect of Job Access on Black and White Youth Employment: A Cross-sectional Analysis.” Urban Studies 28 (2): 255–65.

Kasinitz, Philip, and Jan Rosenberg. 1996. “Missing the Connection: Social Isolation and Employment on the Brooklyn Waterfront.” Social Problems 43 (2): 180–96.

Katz, Lawrence F., Jeffrey R. Kling, and Jeffrey B. Liebman. 2001. “Moving to Opportunity in Boston: Early Results of a Randomized Mobility Experiment.” Quarterly Journal of Economics 116 (2): 607–54.

National Bureau of Economic Research. 2011. “Moving to Opportunity for Fair Housing Demonstration Program: Final Impacts Evaluation.” Cambridge, MA: National Bureau of Economic Research. 

Pellizzari, Michele, and Anne Fichen. 2017. “A New Measure of Skill Mismatch: Theory and Evidence from PIAAC.” IZA Journal of Labor Economics 6 (1).

Sanchez, Thomas W. 1998. “The Connection Between Public Transit and Employment.” Portland, OR: Portland State University, Toulan School of Urban Studies and Planning.

Sanchez, Thomas W. 2008. “Poverty, Policy, and Public Transportation.” Transportation Research Part A: Policy and Practice 42 (5): 833–41.

Smart Growth America. 2019. “The State of Transportation and Health Equity.” Washington, DC: Smart Growth America.

Sanchez, Thomas W. 2008. “Poverty, Policy, and Public Transportation.” Transportation Research Part A: Policy and Practice 42 (5): 833–41.

Smart Growth America. 2019. “The State of Transportation and Health Equity.” Washington, DC: Smart Growth America.