The Non-Linear Relationship Between Land Ownership and Child Labor

Authors

DOI:

https://doi.org/10.15294/edaj.v14i1.21758

Keywords:

Child labor , land ownership, agricultural households, IFLS data, family members

Abstract

This study examines the relationship between household land ownership and the number of hours children spend working. This assumption is based on previous research suggesting that children from households with large landholdings are more likely to be engaged in child labor than those from land-poor households. This phenomenon arises from the fact that land is a crucial asset for agricultural households, often requiring family members, including children, to participate in farm-related activities. This study employs a random effects method using panel data from the Indonesia Family Life Survey (IFLS) for the years 2000, 2007, and 2014. The findings reveal a distinct pattern, particularly in the Indonesian context, where land size exhibits a non-linear relationship with children's working hours. As land ownership increases, children's working hours tend to decrease; however, beyond a certain threshold, children's working hours begin to rise with increasing land size. Heterogeneity analysis further indicates that non-food farmland has a greater impact on the increase in children's working hours. This may be due to the higher demand for additional labor in larger-scale agricultural production, which often relies on family members for support.

Author Biographies

  • Faiz Abdullah Wafi, Department of Economics, Faculty of Economics and Business, Universitas Indonesia

    Master Student in Department Economics, Faculty of Economics and Business. Specialisation: Primary Data/Survey Design, Secondary Data Management & Analysis, Impact Evaluation Research, Labor Economics, Political Economy, Monitoring and Evaluation.

  • I Dewa Gede Karma Wisana, Department of Economics, Faculty of Economics and Business, Universitas Indonesia

    Lecturer in economics and researcher in demography, population and human resources field. Specialisation: Primary Data/Survey Design, Secondary Data Management & Analysis, Impact Evaluation Research, Randomised Experiments, Monitoring and Evaluation.

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Published

2025-02-25

Article ID

21758