Abstract
Job–education matching drives inclusive growth through effective human capital investment. Examination of factors that promote the smooth flow of job-education is crucial in the matching process. We examined how agglomeration affects job-education mismatches among 101,748 employed graduates of vocational secondary schools in Sekolah Menengah Kejuruan (SMK). SMK graduates are the leading cause of unemployment in Indonesia. Data were obtained from the National Labor Force Survey (Sakernas) conducted between 2017 and 2019. This study revealed three different types of job-education mismatches: (1) overeducated workers (level of education exceeds the requirements of their job); (2) horizontally mismatched workers (skills do not align with the job requirements); and (3) workers who are both overeducated and horizontally mismatched, which defines a real mismatch. Employing the job-analysis approach, a 13.58 percent incidence of overeducation and a 61.58 percent incidence of horizontal mismatch among SMK graduates was determined. More than half of these graduates work in jobs where they lack the necessary skills. By assessing the two types of job-education mismatches, we determined that 10.13 percent were real mismatched workers. These workers endured major challenges as they simultaneously suffered horizontal mismatch and overeducation. Dealing with endogeneity and sample selection biases, we showed that agglomeration actively promotes the matching process between occupation and education. Adding 100 workers per square kilometer reduced the probability of overeducation by 0.15 percent, horizontal mismatch by 0.19 percent, and real mismatch by 0.1 percent. Indonesian agglomeration areas outside Java (Mebidangro and Sarbagita) are more effective for reducing risks of overeducation, horizontal and real mismatch than areas in Java (Jabodetabek, Gerbang Kertosusilo and Kedung Sepur). The presence of agglomeration economies correlates with a significant reduction in the job-education mismatch, with varying effects depending on the area..
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Notes
There are three agglomeration areas within Java (Jabodetabek, Gerbang Kertosusilo, and Kedung Sepur) and two agglomeration areas outside of Java (Mebidangro and Sarbagita).
The literature has used three methods to measure job-education mismatch: worker self-assessment, realized matches, and job analysis. The worker self-assessment method (direct and indirect) is based on the opinion of individuals assessing whether their education matches the required education level of their job. The realized matches’ method derives the required educational level from the actual distribution of educational levels within occupations. The job analysis method is based on evaluations by job analysts, who define the required education for jobs relying on occupational classification methods.
ISCO is a classification compiled by the International Labor Organization (ILO). ISCO provides information about workers and their positions.
ISCED is a classification compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO). ISCED provides information about education, including level, orientation, and majors.
The IMR or lambda (\({\lambda }_{i}\)) statistic is drawn by a ratio comparison between the values of the standard normal density function and the standard normal cumulative distribution function (Lyu and Noh 2017).
Industrial agglomeration has a strong correlation with the trade, transportation, and financial sectors at 98.8 percent, 99.5 percent, and 99.4 percent, respectively.
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The authors are grateful for the support of the PUTI Grant NKB-305/2023 of Universitas Indonesia.
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Appendix A
Appendix A
Variables definition
Variable | Definition | Operational definition | Source | |
\({Mismatc{\text{h}}}_{i,d,p,t}\) | Job–education mismatch | Overeducation, horizontal mismatch and real mismatch for individual i, who lives in regencies/city d, province p and year t, (match as a reference) | Sakernas 2017–2019 | |
\({A}_{i,d}\) | Agglomeration | Employment density in the regencies/city d in which individual i lives | Statistics Indonesia 2017–2019 | |
\({X}_{i,d}\) | Exogenous variables | |||
A. Individual characteristics | ||||
Demographic variables | ||||
Age | Age in linear form | Sakernas 2017–2019 | ||
Age squared | Age in square form, to examine the diminishing phenomenon | Sakernas 2017–2019 | ||
Gender | Dummy gender, 1 for male, 0 female | Sakernas 2017–2019 | ||
Marital status | D | ummy marital status, 1 for married, 0 unmarried | Sakernas 2017–2019 | |
\({\text{Job}}\) variables | ||||
Contract | Dummy type of contract, 1 for a permanent contract, 0 others | Sakernas 2017–2019 | ||
Hourly work | Weekly hours worked | Sakernas 2017–2019 | ||
Tenure | Length of time at current jobs (years) | Sakernas 2017–2019 | ||
Second job | Dummy having second job, 1 for yes, 0 others | Sakernas 2017–2019 | ||
Sector | Dummy sector (base: agricultural) | Sakernas 2017–2019 | ||
Education variable | ||||
Fields of study | Dummy fields of study in vocational secondary school (technology and engineering, energy and mining, information and communication technology, health and social sciences, agribusiness and agrotechnology, maritime affairs, business and management, tourism, and arts as a reference) | Sakernas 2017–2019 | ||
B. Regional characteristics | ||||
Urban status | Dummy urban status, 1 for urban, 0 rural | Sakernas 2017–2019 | ||
Unemployment rate | Unemployment rate in the regencies/city d | Statistics Indonesia 2017–2019 | ||
\({\sigma }_{p}\) | Province-fixed effect | |||
\({\omega }_{t}\) | Year-fixed effect | |||
\({\lambda }_{1}\) | Impulse Mills ratio from Heckman two-step procedure | |||
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Paramitasari, N., Khoirunurrofik, K., Mahi, B.R. et al. Charting vocational education: impact of agglomeration economies on job–education mismatch in Indonesia. Asia-Pac J Reg Sci (2024). https://doi.org/10.1007/s41685-024-00333-x
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DOI: https://doi.org/10.1007/s41685-024-00333-x