Land ownership is multidimensional, spanning various rights and decision-making roles. Although theoretical frameworks exist in categorizing bundles of property rights, in contexts characterized by a mix of formal and customary tenure systems, the lack of individual-level data on specific rights and different forms of ownership has made it difficult to empirically study bundles of land rights and how they vary across population groups, including by gender. Using nationally representative survey data that was collected on individual-level ownership and rights of agricultural land over the period of 2016-2020 in Malawi, Tanzania, and Ethiopia, we use a machine learning clustering algorithm to distinguish types of land ownership categories and break down the bundles of land rights (i.e., rights to bequeath, sell, rent, invest, and use as collateral) and/or decision-making variables that these landowners have. A multiple correspondence analysis (MCA) is used to understand how rights or decision-making variables correlate with each other, while the hierarchical clustering algorithm finds patterns and assigns landowners with similar bundles to the same cluster. The analysis then compares how the bundles of rights that empirically emerge differ from the property rights framework put forth in the theoretical literature. One key result is that rights related to the ability of transfer land is key in differentiating landowner clusters. Using the resulting clusters, our analysis further highlights cross country differences in land ownership status as well as patterns by gender.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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