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Property rights, market access and crop cultivation in Southern Rhodesia: evidence from historical satellite data

Author

Listed:
  • Tawanda Chingozha

    (Department of Economics, Stellenbosch University)

  • Dieter von Fintel

    (Department of Economics, Stellenbosch University and Institute of Labor Economics (IZA), Bonn)

Abstract

Agriculture plays a central role in the efforts to fight poverty and achieve economic growth. This is especially relevant in sub-Saharan Africa (SSA) where the majority of the population lives in rural areas. A key issue that is generally believed to unlock agriculture potential is the recognition of property rights through land titling, yet there is no overwhelming empirical evidence to support this in the case of SSA (Udry, 2011). This paper investigates access to markets as an important pre-condition for land titles to result in agricultural growth. Using the case of Southern Rhodesia, we investigate whether land titles incentivised African large-scale holders in the Native Purchase Areas (NPAs) to put more of their available land under cultivation than their counterparts in the overcrowded Tribal Trust Areas (TTAs). We create a novel dataset by applying a Support Vector Machine (SVM) learning algorithm on Landsat imagery for the period 1972 to 1984 - the period during which the debate on the nexus between land rights and agricultural production intensified. Our results indicate that land titles are only beneficial when farmers are located closer to main cities, main roads and rail stations or sidings.

Suggested Citation

  • Tawanda Chingozha & Dieter von Fintel, 2019. "Property rights, market access and crop cultivation in Southern Rhodesia: evidence from historical satellite data," Working Papers 03/2019, Stellenbosch University, Department of Economics.
  • Handle: RePEc:sza:wpaper:wpapers317
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    File URL: https://www.ekon.sun.ac.za/wpapers/2019/wp032019/wp032019.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    land titling; access to markets; machine learning; remote sensing;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • N37 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Africa; Oceania
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment

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