School-to-work transition data are an important component of labor market information systems (LMIS). Policy makers, researchers, and education providers benefit from knowing how long it takes work-seekers to find employment, how and where they search for employment, the quality of employment obtained, and how steady it is over time. In less-developed countries, these data are poorly collected, or not collected at all, a situation the International Labour Organization and other donors have attempted to change. However, LMIS reform efforts typically miss a critical part of the picture—the geospatial aspects of these transitions. Few LMIS systems fully consider or integrate geospatial school-to-work transition information, ignoring data critical to understanding and supporting successful and sustainable employment: employer locations; transportation infrastructure; commute time, distance, and cost; location of employment services; and other geographic barriers to employment. We provide recently collected geospatial school-to-work transition data from South Africa and Kenya to demonstrate the importance of these data and their implications for labor market and urban development policy.
By Eric M. Johnson, Robert Urquhart, Margaret M. O'Neil.
June 2018 Open Access Peer Reviewed
- Work seekers face a range of geographic (spatial) barriers to employment—commute time and costs, infrastructure and transportation service gaps, residential location bias, and suboptimal professional networks, among others—yet labor market information systems, especially in the developing world, do not collect and analyze geospatial data.
- Case studies from South Africa and Kenya presented in this brief show the importance of geospatial labor market data and how they can be used to improve programs and policy.
- Educators can use geospatial data to better understand their incoming students and to track and learn from the employment journey of their alumni. Employers may benefit from knowing the commute time and costs of their employees, especially new hires. Policy makers need to understand spatial barriers to employment when making infrastructure, transportation, housing, education, and other social service investments.
- Geospatial data can be difficult to collect and present in a useful way, requiring adequate technology and capacity.
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