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(Trying to) catch up with the higher-skilled Joneses: student loans in a segmented educational market from a post-Keynesian perspective

Gustavo Pereira Serra

Journal of Post Keynesian Economics2025https://doi.org/10.1080/01603477.2025.2460794article
AJG 2ABDC B
Weight
0.37

Abstract

This paper analyzes the economic effects of student loans in a segmented educational market. The motivation here draws upon some studies that verify differences in labor income returns and repayment difficulties depending on the characteristics of the institution attended by the student. I put forward a neo-Kaleckian model that considers three types of households: rentiers (RH), lower-skilled workers (LSW), and higher-skilled workers (HSW). Moreover, a cost-minimizing representative firm combines physical capital and labor in effective units in the production process, which also features some labor skill substitution, thus generating a bargaining process between the different worker groups and firms that determines the wage gap. The main result is that, for a debt-financed human capital investment, the conditions that drive long-term economic growth do not necessarily align with those that reduce the wage gap and household debt. In fact, in some cases, widening the wage gap may be a necessary condition for boosting economic activity and human capital accumulation. However, this investment might not reduce the wage gap and could raise concerns about household debt.

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https://doi.org/https://doi.org/10.1080/01603477.2025.2460794

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@article{gustavo2025,
  title        = {{(Trying to) catch up with the higher-skilled Joneses: student loans in a segmented educational market from a post-Keynesian perspective}},
  author       = {Gustavo Pereira Serra},
  journal      = {Journal of Post Keynesian Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/01603477.2025.2460794},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.