they need _permission_ from before exercising their land rights and the degree of tenure security over the land. Finally, each respondent was asked whether he/she had any _decision-making power_ over planting time and crop choice on each parcel. These surveys have been conducted under the World Bank Living Standards Measurement Study-Plus (LSMS+) program,<sup>7</sup> using cross-country comparable survey methods and tools, and in accordance with the international guidelines (United Nations, 2019) for individual-disaggregated survey data collection on asset ownership and control.<sup>8</sup> Compared to the work by Slavchevska et al. (2021) based on household survey data on land ownership and rights as reported by most knowledgeable household members, our analysis relies on self-reported survey data and a wider range of land ownership and rights constructs.

The second contribution of our paper is to introduce the use of unsupervised machine learning (ML) techniques in the analysis of high-dimensional survey data on land ownership and rights – elicited from individuals, at the parcel-level, and based on survey questions on a range of constructs related to self-reported rights to and control over land. The cross-country comparability in questionnaires and respondent selection protocols permits a robust analysis of how machine learning techniques perform in different countries with different institutions and norms around land ownership and rights. The unsupervised ML algorithms are useful for analyzing data without a definite label – in our case, the true “land ownership” type or label is unknown to the researcher. We use multiple correspondence analysis (MCA) and hierarchical clustering to understand how much certain land rights variables matter in distinguishing between different types of reported ownership. Once clusters of different types of reported ownership are identified based on selfreported land rights, we conduct additional analyses that profile landowners within each cluster and across countries as a function of their individual and household attributes. Our methods facilitate a more convenient and data-driven, as opposed to subjective, profiling of landowners based on their self-reported rights (the latter of which often involves, for example, picking one or two rights or decision-making roles to reflect nuances of ownership).

On the whole, the findings show a high degree of consistency across Ethiopia, Malawi, and Tanzania. Despite self-reporting themselves as owners, survey respondents often do not have full rights and decision-making power over their land parcels. There are significant gender gaps in land rights among landowners, in favor of men. Using MCA, we calculate a measure of variance that distinguishes landowners based on the composition of self-reported rights and control over land. Among different land rights and control variables across countries, transfer rights consistently contribute most to this variance. Hierarchical clustering further demonstrates that landowners can effectively be clustered into three categories: (1) owners with mostly exclusive transfer rights, (2) owners with mostly joint transfer rights, and (3) owners who claimed to not have transfer rights or only a limited number of them. Women are over-represented in the cluster of landowners without or with limited transfer rights, and in moving from the cluster with mostly joint transfer rights to

> 7 Please visit <u>www.worldbank.org/lsmsplus</u> for more information on the LSMS+ program. LSMS+ supported surveys over the period of 2016-2021 did collect self-reported, intra-household information on individuals’ ownership of and rights to a broad range of physical and financial assets, beyond land.

> 8 Compared to the work of Slavchevska et al. (2021), who used household survey data on land ownership and rights as reported by most knowledgeable household members.

4