Can Certificate Programs Solve the Skills and Spatial Mismatch Problem? Job Portability and Residential Mobility in a Coding and Apprenticeship Program

Nondegree certificate programs that are free, short, and located in urban areas may pose implications for the skills and spatial match between urban job-seekers and employers. Leveraging a large-scale survey of applicants to a unique technology certificate and apprenticeship program, we apply machine-learning generated propensity score weights, finding that apprenticeship completers were more likely to see a significant reduction in the distance between their homes and jobs when compared to similar non-admitted applicants. Additionally, through descriptive analyses, we see that a greater proportion of apprenticeship completers changed both their residential and employment locations. Finally, our geospatial depictions further highlighted these residential and employment relocations often clustered in areas marked by distinct racial, economic, and employment characteristics. Our findings suggest that these types of education programs may solve the spatial mismatch problem for individuals but have limited implications for residential attainment.

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Publication Details

Journal
Urban Education
Published
2025-03-19
DOI
https://doi.org/10.1177/00420859251315480
Primary Topic
Labor market dynamics and wage inequality
Type
article
Field-Weighted Citation Impact
0.00

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article

Can Certificate Programs Solve the Skills and Spatial Mismatch Problem? Job Portability and Residential Mobility in a Coding and Apprenticeship Program

Jason Jabbari, Odis Johnson, Wenrui Huang, Yung Chun
Urban Education
Labor market dynamics and wage inequality
article

Can Certificate Programs Solve the Skills and Spatial Mismatch Problem? Job Portability and Residential Mobility in a Coding and Apprenticeship Program

Jason Jabbari, Odis Johnson, Wenrui Huang, Yung Chun
article en

Abstract

Nondegree certificate programs that are free, short, and located in urban areas may pose implications for the skills and spatial match between urban job-seekers and employers. Leveraging a large-scale survey of applicants to a unique technology certificate and apprenticeship program, we apply machine-learning generated propensity score weights, finding that apprenticeship completers were more likely to see a significant reduction in the distance between their homes and jobs when compared to similar non-admitted applicants. Additionally, through descriptive analyses, we see that a greater proportion of apprenticeship completers changed both their residential and employment locations. Finally, our geospatial depictions further highlighted these residential and employment relocations often clustered in areas marked by distinct racial, economic, and employment characteristics. Our findings suggest that these types of education programs may solve the spatial mismatch problem for individuals but have limited implications for residential attainment.

Urban EducationVol. 61(3)
Johns Hopkins University (US), John Brown University (US), Washington University in St. Louis (US)
Smith Richardson Foundation
Openalex Percentile: Top 92%
Labor market dynamics and wage inequality
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