Skilled labor convergence across Turkish regions: a club convergence algorithm approach
Human capital and skill differences are among the main determinants of income per capita, technology and productivity differences across regions and countries. This paper uses the Phillips and Sul convergence club algorithm to investigate convergence in skilled labor force shares across Turkish regi...
| Authors: | , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/179549 |
| Online Access: | https://hdl.handle.net/11441/179549 https://doi.org/10.1007/s00181-025-02789-y |
| Access Level: | Open access |
| Keyword: | White-Collar Skilled Labor Convergence Convergence Clubs |
| Summary: | Human capital and skill differences are among the main determinants of income per capita, technology and productivity differences across regions and countries. This paper uses the Phillips and Sul convergence club algorithm to investigate convergence in skilled labor force shares across Turkish regions between 2005 and 2022. The findings highlight that there is no overall convergence in skilled labor shares across Turkish regions and identify two convergence clubs, one consisting of regions with high shares of the skilled labor force and another with relatively low shares of the skilled labor force. The results indicate a regional heterogeneity in the convergence of skilled labor across different geographical clusters. Finally, the IV Probit (IV-GMM) analyses highlight that the likelihood of being part of a highly skilled club (skilled labor force share) significantly increases with GDP per capita, R&D investment per capita, net migration, and the percentage of higher education graduates, and decreases with the agricultural share of production. |
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