metadata: authors: 0: author: Hasanbasri,Ardina Roosiany
1: author: Kilic,Talip
2: author: Koolwal,Gayatri B.
3: author: Moylan,Heather G.
count: Africa,Malawi,Ethiopia,Tanzania
docna: 0: docna: Multidimensionality of Land Ownership among
            Men and Women in Sub-Saharan Africa
docty: Policy Research Working Paper
subtopic: Agricultural Economics,Gender and Development,Inequality,Food Security,Labor & Employment Law
teratopic: Gender,Poverty Reduction,Agriculture,Law and Development
abstracts: cdata!: Across Sub-Saharan African countries
            with customary tenure systems and low levels of documented
            land ownership, there are limited nationally representative
            insights on men and women landowners&#x2019; rights over
            land. Variations in institutions and norms governing land
            ownership further complicate cross-country comparisons.
            Using machine learning techniques and nationally
            representative, intrahousehold survey data elicited in
            private from men and women on their ownership of assets,
            this paper creates unique profiles of landowners in
            Ethiopia, Malawi, and Tanzania, anchored in a range of
            constructs related to self-reported rights and control over
            land parcels. The analysis reveals a high degree of
            cross-country consistency in the new insights. Landowners,
            particularly women, often do not have full rights and
            decision-making power over land. Multiple correspondence
            analysis demonstrates that transfer rights (rights to
            bequeath, sell, rent out, and use as collateral) contribute
            the most to the variation in the composition of the
            constructs related to rights and control over land.
            Hierarchical clustering shows 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 with no/limited
            transfer rights. Owners with transfer rights tend to have
            all other rights and measures of control. Women are
            overrepresented in the cluster of landowners with no/limited
            transfer rights, and in moving from the cluster with mostly
            joint transfer rights to the one with mostly exclusive
            transfer rights, the increase in the share of individuals
            not needing permission to exercise any right is considerably
            greater among women than men.
geo_reg_key: 517195,517191
repnme: repnme: Multidimensionality of Land Ownership among
            Men and Women in Sub-Saharan Africa
display_title: Multidimensionality of Land
            Ownership among Men and Women in Sub-Saharan Africa
listing_relative_url: /research/2022/01/33703351/multidimensionality-land-ownership-among-men-women-sub-saharan-africa
topicv3: Inequality under Law m1326002 925,Agriculture and Food Security m1326205 435,Land Tenure m1326631 888,Gender m1327621 615,Household Surveys m1504071 2761
publishtoextweb_dt: 2022-01-13T00:00:00Z
dois: 10.1596/1813-9450-9900
historic_topic: Gender,Poverty Reduction,Agriculture,Law and Development
origu: Data Production and Methods (DECPM)
no_of_pages: 44
author_keys: 0: author_key: 1512561
1: author_key: 886702
2: author_key: 788145
3: author_key: 1156566
model_extractions: Policy Research Working Paper 9900 Multidimensionality of Land Ownership among Men and Women in Sub-Saharan Africa _Ardina Hasanbasri Talip Kilic Gayatri Koolwal Heather Moylan_ Development Economics Development Data Group January 2022
Policy Research Working Paper 9900

# **Abstract**

Across Sub-Saharan African countries with customary tenure systems and low levels of documented land ownership, there are limited nationally representative insights on men and women landowners’ rights over land. Variations in institutions and norms governing land ownership further complicate cross-country comparisons. Using machine learning techniques and nationally representative, intrahousehold survey data elicited in private from men and women on their ownership of assets, this paper creates unique profiles of landowners in Ethiopia, Malawi, and Tanzania, anchored in a range of constructs related to self-reported rights and control over land parcels. The analysis reveals a high degree of cross-country consistency in the new insights. Landowners, particularly women, often do not have full rights and decision-making power over land. Multiple correspondence analysis demonstrates that transfer rights (rights to bequeath, sell, rent out, and use as collateral) contribute the most to the variation in the composition of the constructs related to rights and control over land. Hierarchical clustering shows 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 with no/limited transfer rights. Owners with transfer rights tend to have all other rights and measures of control. Women are overrepresented in the cluster of landowners with no/limited transfer rights, and in moving from the cluster with mostly joint transfer rights to the one with mostly exclusive transfer rights, the increase in the share of individuals not needing permission to exercise any right is considerably greater among women than men.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ardinah@umich.edu or tkilic@worldbank.org.

_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._ Produced by the Research Support Team
# **Multidimensionality of Land Ownership among Men and Women in Sub-Saharan Africa**

Ardina Hasanbasri<sup>†</sup> , Talip Kilic<sup>‡</sup> , Gayatri Koolwal<sup>*</sup> , and Heather Moylan<sup>⁑1</sup> JEL Codes: C81, C83, J16, Q15.

Keywords: Land Rights, Gender, Household Surveys, Machine Learning, Clustering, Malawi, Ethiopia, Tanzania, Sub-Saharan Africa.

> 1† Corresponding Author. Lecturer, Department of Economics, University of Michigan, Ann Arbor, <u>ardinah@umich.edu. ‡ Senior Economist, Development Data Group, World Bank, tkilic@worldbank.org.</u> *Consultant, Development Data Group, World Bank, gkoolwal@worldbank.org. ⁑Survey Specialist, Development Data Group, World Bank, <u>hmoylan@worldbank.org. The authors would like to thank Cheryl Doss, Mekonnen Ayano,</u> Susmita Baulia and the participants of the MAGNET research seminar and the Nordic Conference of Development Economics 2021 for their helpful comments and discussions. The research was funded by the World Bank Living Standards Measurement Study – Plus (LSMS+) program (www.worldbank.org/lsmsplus).
# **1. Introduction**

Land is a primary factor of agricultural production and land ownership has the potential to enhance welfare of households and individuals through multiple channels, including increasing access to liquidity through land rental and sales (Holden et al., 2011; Deininger et al., 2009); providing access to credit through the use of land as collateral (Zhang et al., 2020); and enhancing the wealth of future generations through land bequests (Wineman and Liverpool-Tasie, 2019). Research has also demonstrated the links between land ownership and greater socioeconomic status and participation in intrahousehold decision-making (Kishindo, 2010; Wiig, 2013). The empirical evidence on the impacts of land ownership on development outcomes has, however, revealed context-specificity in the resulting impacts,<sup>2</sup> and of particular importance to this paper, the literature has fallen short of providing representative insights regarding the profiles of the general population of landowners in low-income countries, specifically those in Sub-Saharan Africa that rely heavily on customary tenure systems. There are at least two reasons for the latter.

First, the research has disproportionately focused on a particular definition of ownership: whether individuals are documented owners<sup>3</sup> or reported owners.<sup>4</sup> _Documented land ownership_ is a relatively uncommon event in much of Sub-Saharan Africa where customary tenure systems continue to exert significant influence on individuals’ use and ownership of land <mark>(Chauveau et al.,</mark> 2007; Doss et al., 2014; Chimhowu, 2019) and many countries face a common struggle in integrating contemporary statutory laws with customary laws (Berge et al., 2014; Ayano, 2018; Dancer, 2017). In customary tenure systems, individuals’ _reported land ownership_ can entail different sets of rights across localities and population groups, including men versus women; multiple people can be assigned different rights for the same parcel of land; and the scope of individuals’ land rights can evolve over time (Ayano, 2018; Witten, 2007). As such, simply knowing reported landowners without information regarding their land rights can underlie 2 Carter and Olinto (2003) discuss how the links between land ownership and access credit depend on landholding size and Higgins et al. (2018) argue that land ownership interventions should ensure that lending institutions in targeted localities would have services to help support these objectives. Besley and Burgess (2000) examine different land reforms across stats in India — including policies conferring ownership on tenants after certain periods of time — and find that poverty reduction stemming from specific land reforms depends on the distribution of landownership and local incentives. The links between women’s land ownership and intra-household bargaining power and empowerment also vary across countries, depending on local context and other economic opportunities (Meinzen-Dick et al., 2019). Yokying and Lambrecht (2019) discuss, in a study from northern Ghana, how substantial gender gaps continue to persist among landowners in terms of decision-making over cultivation and the use of earnings. Nuances of ownership, including the security of property rights, and joint versus exclusive ownership, also matter in understanding the links between land ownership and development outcomes (Kilic et al., 2021a; Place et al., 1994).

3 Documented owners are individuals who can claim ownership of an asset through the use of a document that is recognized by the government of that country and that lists the individual as an owner of the specific asset. For land, the type of documentation conferring ownership (and the associated rights) will vary according to the laws and tenure systems in a given country (United Nations, 2019).

4 Reported owners are individuals who consider themselves to be owners of an asset, irrespective of whether they possess documented ownership of the asset. “Reported ownership measures people’s self-perceptions about their ownership status, it need not—and cannot—be objectively verified.” (United Nations, 2019: 16) 2
misleading insights regarding the profiles of landowners and gender differences in land rights in a given country.<sup>5</sup> In fact, Slavchevska et al. (2021) show how different land rights, such as rights to sell, rent, or bequeath, do not necessarily overlap in Sub-Saharan Africa, and should not be used interchangeably. Relatedly, Kilic et al. (2021b) document that having rights to bequeath and sell land - but not rights to rent out or use as collateral - is significantly linked with investment and cash crop adoption in Malawi, with especially important effects for women. Individuals who are joint owners of the same parcel may also have different decision-making roles as it relates to crop choice, timing of cultivation and input use (Acosta et al., 2020; Twyman et al., 2015). Thus, land ownership should be understood as multidimensional and as a bundle consisting of rights and decision-making in customary tenure systems. In fact, the idea that property ownership contains a bundle of rights has been widely acknowledged in economics (Demsetz, 1967; Alchian and Demsetz, 1973; Schaleger and Ostrom, 1992; and Sikor et al., 2017).

The second reason for the knowledge gaps regarding the profiles of landowners in low-income countries is the fact that the available evidence has been informed by data collected in small-scale studies and the representative insights on land ownership and rights in customary tenure systems have been out of reach due to the gaps in the availability and quality of individual disaggregated survey data. Even if landowners and right holders are individuals, nationally representative survey data on landownership has traditionally been collected at the household-level. And even when the surveys attempted to identify sole or joint landowners were identified within households (and often for each parcel owned by any household member), there have been two major deficiencies in largescale survey data collection: (a) surveys have not systematically sought to identify exclusive and joint holders of specific rights, alongside reported and documented owners, and (b) individual household members have not been interviewed personally and it has been commonplace for selfidentified most knowledgeable household members to identify intra-household landowners and right holders, which resulted in biased measurements of men’s and women’s land ownership and rights (Kilic et al. 2021b).

Against this background, the first contribution of our paper is to provide cross-country, nationally representative insights regarding land rights among men and women in Sub-Saharan Africa, with a focus on non-residential (primarily agricultural) landowners. The analysis is informed by the nationally-representative household surveys that have been conducted in Ethiopia, Malawi, and Tanzania over the period of 2016-2020 and that have interviewed adult household members in private and collected self-reported, parcel-level information on each respondent’s _reported_ , _documented,_ and _economic_<sup>_6_</sup> ownership of land, as well as his/her right to invest in, use as collateral, rent out, bequeath, and sell that piece of land. Across these different ownership and rights constructs, respondents were asked whether they had exclusive ownership and rights or held them jointly with other individuals. Additionally, respondents were asked to identify individuals

> 5 Doss et al. (2015) provide a discussion on the nuances of landownership measures and their importance for genderdisaggregated statistics on land.

> 6 Economic owners are individuals that report themselves as having control over the proceeds of a land sale in the hypothetical event that a given  parcel would be sold.

3
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
the one with mostly exclusive transfer rights, the increase in the share of individuals not needing permission to exercise any right is much greater among women than men.

Moreover, the cluster definitions are robust to feeding into the algorithm a larger or a smaller set of ownership, rights, and decision-making variables. The profiles of owners across the clusters indicate that owners who have transfer rights tend to have other decision-making and rights. These findings, together with the MCA results, imply that household survey questions on land rights can be trimmed down to only include those on transfer rights, without any loss in our ability to cluster landowners. Documented ownership (whose incidence is very low in the three countries) or economic ownership do not appear to be influential in determining rights and decision-making in our sample. This finding is also consistent with the findings of Doss et al. (2014) who have shown that having a land title in Uganda did not guarantee other land rights.

Furthermore, the differentiation of exclusive versus joint ownership and rights variables as part of the hierarchical clustering exercise creates more variation in clusters relative to simpler approaches (as expected) but results in clusters that are much more consistent across countries, which eases cross-country comparisons. This finding reveals not only the importance of eliciting self-reported data on exclusive versus joint ownership and rights arrangements, but also the utility of machine learning techniques in accommodating the high-dimensional survey data and efficiently creating clusters of landowners that facilitate cross-country comparable profiling, particularly across contexts with significant variation in institutions and norms governing land ownership.

The profiling of landowners based on the clustering results provides an entryway to explore further gaps in our knowledge of landownership.<sup>9</sup> In later sections, we analyze gender differences in ownership bundles; the rigidity of bundles (defined as the variation in bundles within a cluster) across countries; how ownership bundles interact with permission structures and decision making; and whether individuals with land use rights are different from landowners in terms of their rights bundles (in a subset of countries where information on land rights are collected also for individuals with land use rights).

The paper is structured as follows. Section 2 of the paper provides a description of the data followed by a discussion of methodology and clustering results in Section 3. Section 4 discusses the variation within a cluster and cross-country differences. With the resulting landowner clusters, Section 5 analyzes each clusters’ overall level of ownership, decision-making, and permission status in detail when it comes to their land. Section 6 discusses further sensitivity analysis and Section 7 concludes.

> 9 The importance of transfer rights in distinguishing landowners points to the need of further research on how behavior and outcomes may change depending on whether the landowner has the ability to transfer or not. While the investigation of this research question is outside the scope of this paper, Deininger and Jin (2006) have found that the ability to transfer land, more so than tenure security, provide higher incentive for landowners to invest in their land.

5
# **2. LSMS+: Land Rights and Decision-Making Measures in Defining Ownership**

# **2.1 Multidimensionality of Ownership**

This paper uses three nationally representative, multi-topic household surveys supported by the LSMS+ program, namely the Tanzania National Panel Survey (NPS4 2018/2019), Ethiopia Socioeconomic Survey (ESS4 2018/2019), and the Malawi Integrated Household Panel Survey (IHPS 2016). The LSMS+ modules on asset ownership and rights are comparable across countries and were administered directly to all household members 18 and older on different asset classes, including ownership and rights to residential and non-residential land (Hasanbasri et al., 2021).<sup>10</sup> A specific emphasis of the survey data collection was on self-reporting and interviewing respondents privately — and hence conducting interviews within the household simultaneously, when possible.<sup>11</sup> This paper focuses on characterizing landowners’ rights and decision-making over non-residential land parcels. A respondent is defined as the reported landowner if they answer upfront that he/she owns the parcel. A _parcel_ is defined as a continuous piece of land which can have more than one parcel. Parcels were first identified and rostered through the household questionnaire and then carried forward to individual interviews. Non-residential land in these contexts is mainly used for agriculture. In Ethiopia, for example, 87 percent of non-residential parcels have been used in agriculture in the last 12 months.<sup>12</sup> Rights and decision-making questions were asked directly to individuals who reported themselves as owners or as individuals with use rights on the land. Since the paper aims to analyze rights associated with ownership, we restrict our sample to those who report themselves as landowners. Later in the paper, however, we address sensitivity of our findings to the inclusion of non-owners with use rights (which is only possible to do in Ethiopia and Tanzania). Most individuals who provided answers on rights and decision making were owners instead of users of land.

For each parcel, respondents are asked about different types of ownership (reported, economic, and documented); rights (to sell, bequeath, use as collateral, rent out, and make improvements/invest); as well as decision-making in the case of agricultural parcels.<sup>13</sup> A detailed list of land rights and ownership variables in the LSMS+ surveys, as well as additional decisionmaking variables, are presented in Table 1. All countries had the same questionnaire structure and implementation, except for Ethiopia where the right to sell was not asked because land is legally

> 10 Other types of assets were also covered, including financial accounts, and mobile phones. Ownership of livestock was covered in Ethiopia.

> 11 The module on land specifically covers all land owned or accessed via use rights and follows the recommendations of FAO, World Bank and UN Habitat (2019).

> 12 Ethiopia and Malawi looked at all non-residential land used for agriculture. Tanzania was not restricted to agricultural use only due to missing data on current use. 13 Along with rights/ownership, respondents reported on how each parcel was acquired; identified the individuals from whom the asset was inherited or received as a gift, as applicable; and provided the current hypothetical sales value for each asset (and the construction costs specifically for the dwelling) and limited information on their knowledge of asset transactions in their communities.

6
owned by the state and is prohibited to be sold.<sup>14</sup> The scope of rights included in the questionnaire was influenced by Schlager and Ostrom’s (1992) theoretical framework which focuses, in the context of natural resources, on issues related to access, withdrawal, management, exclusion and alienation while defining a bundle of rights.

**Table 1: LSMS+ Survey Questions on Rights and Decision-Making**

|**Rights over land parcels:**|
|---|
|1) **Sell:**Are you among the individuals who have the right to sell the land, even if you need permission or<br>consent from someone else?|
|2) **Bequeath:**Are you among the individuals who have the right to bequeath the land, even if you need<br>permission or consent from someone else?|
|3) **Collateral:**Are you among the individuals who have the right to use land as collateral, even if you need<br>permission or consent from someone else?|
|4) **Rent:**Are you among the individuals who have the right to rent out the land, even if you need permission<br>or consent from someone else?|
|5) **Improvement**: Are you among the individuals who have the right to make improvements/invest in the<br>land, even if you need permission or consent from someone else?|
|**Additional ownership and decision-making variables:**<br>1)**Documented ownership**: Is your name among the names listed as owners on the document for this land?<br>2)**Economic ownership**: If the parcel were to be sold today, would you be among the individuals that<br>would decide how the money would be used**?**|
|3)**Parcel decision-making:**Are you among the decision-makers about the parcel, regarding the timing of<br>crop activities, crop choice, and input use?|

Notes: In Ethiopia, the question on selling right was not asked. Questions were asked to individuals who report themselves as the owner or have use rights. In Malawi, questions were asked only to owners.

Additionally, the LSMS+ modules further asked landowners to identify whether ownership is joint or exclusive, and separately whether permission is needed to exercise rights. Up to three household members and two non-household members can be listed who share ownership/give permission. The design and implementation of these modules therefore acknowledges that there are varying

> 14 Ethiopia’s constitution established that the right to ownership of land belongs “exclusively to the State and the peoples of Ethiopia.” Individuals have the right to use land allocated to them; however, they do not have the right to sell the land. Additional background information on land tenure in Ethiopia can be found in Crewett et al. (2008a), Crewett et al. (2008b), and Dokken (2015).

7
degrees of ownership which has implication on how decisions are made and bargaining power (Doss et al., 2020; Doss et al., 2011).<sup>15</sup> One could argue that these variables can already be categorized into bundles of rights depending on the question of interest. Under the Evidence and Data for Gender Equality (EDGE) initiative, for example, the United Nations has emphasized the concept of _SDG ownership_ : having either documented ownership, right to bequest, or right to sell.<sup>16</sup> Under Schlager and Ostrom’s (1992) classification, on the other hand, rights are designated into three categories: management, alienation, and exclusion. Management rights are related to patterns of use and ability to improve on the land. For example, decision-making and improvement rights over land in the LSMS+ survey modules can be classified into the management category. Exclusion rights are those related to determining who has access or the ability to transfer land, while alienation rights relate to whether the land can be sold or rented. Economic ownership measure, as captured in the LSMS+ survey modules, does not fit into this standard classification. However, economic ownership is still of interest because it provides a more tangible idea of who would receive the valuation of the land when a land transfer occurs. Documented ownership, on the other hand, might increase the land’s ability to be transferred or used as collateral as well as an official recognition that would improve its tenure security (see, for example, Sitko et al., 2014; Jacoby and Minten, 2007). Our methodology will help us analyze whether we empirically see these theoretical bundles and categorization in the data. We can contrast how these bundles compare with what the owners view as enough rights to report themselves as landowners.

# **2.2 What Do the Data Tell Us about Ownership?**

Table 2 presents the share of individuals owning non-residential (primarily agricultural) land across the three countries. About a third or more of rural men and women own non-residential land, with a slightly higher share of men than women owning land. While urban respondents are less likely to own non-residential land, the shares are still substantial in urban Malawi and Tanzania — spanning about 19 percent of urban women and men in Tanzania, and 14 and 10 percent of women and men, respectively, in Malawi. Reported ownership masks exclusive and joint ownership which varies by country context. For example, Ethiopia’s reported owners are more likely to be joint owners while Tanzania and Malawi have a more equal distribution among the two types of owners.

> 15 A number of papers have discussed the effects of joint ownership and joint titling of land on welfare measures (Wiig, 2013; Newman et al., 2015; Kabumbuli, 2016; Agarwal, 2003).

> 16 This is related to the Sustainable Development Goals (SDGs); guidelines can be accessed at https://unstats.un.org/edge/publications/docs/Guidelines_final.pdf

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**Table 2: Share of Non-Residential Landowners Across Countries**

|||**Et**<br>|**hiopia**<br>|**Tanz**<br>|**ania**<br>|**Ma**<br>|**lawi**<br>|
|---|---|---|---|---|---|---|---|
|||Urban|Rural|Urban|Rural|Urban|Rural|
|Share of in|dividual landowners|||||||
|Men|Reported owner|0.08*|0.33***|0.19|0.37|0.10***|0.30**|
||Exclusive owner|0.02|0.06|0.07|0.20|0.04**|0.15***|
||Joint owner|0.06|0.29***|0.13|0.20|0.06|0.17|
|Women|Reported owner|0.06*|0.30***|0.19|0.33|0.14***|0.36**|
||Exclusive owner|0.01|0.05|0.08|0.16|0.07**|0.21***|
||Joint owner|0.05|0.25***|0.11|0.19|0.07|0.17|
|Share of ho|usehold with any land|0.09|0.44|0.23|0.55|0.19|0.50|
|# of obs (la|ndowner + non-|||||||
|landowners|)|8073|7315|1199|1785|1307|3428|
|# ofobs (ho|useholds)|3655|3115|502|683|649|1799|

**Notes:** Landowners are individuals who self-report as the owner of the land. Significant difference across men and women (conducted within urban and rural areas) are indicated by asterisks: ***p<0.01, **p<0.05, *p<0.1. Some individuals have both exclusive and joint ownership of land which resulted in a higher share of combined exclusive and joint owners relative to reported owners.

Within the sample of non-residential landowners, Figures 1 and 2 illustrate that despite selfreporting as an owner, respondents often do not have full rights and decision-making power over their land parcels. Figure 1 reports the distribution of rights and decision-making variables among landowners across the three countries. Figure 2 shows the number of rights held per parcel by landowners (an individual can own multiple parcels). Consistent with the findings of Slavchevska et. al. (2021), rights tend to not overlap with each other. In Tanzania, for example, the majority of landowners (53 percent) do not have the right to bequeath, while in Malawi, 52 percent of landowners do not have the right to use their land as collateral. Across countries, men are more likely to have these rights than women.

Across countries, less than half of landowners reported having all rights, with the exception of Ethiopia, with men more likely to than women to hold all rights. Figure 2 focuses specifically on rights to bequeath, sell, rent, improve, and use as collateral and reported the number of rights a landowner holds. In Ethiopia, most landowners have all four rights (right to sell is not allowed by the state), covering nearly three-quarters of landowners. In Tanzania and Malawi, on the other hand, more than half of landowners do not have all rights to land.

Additional differences arise with documented ownership (Figure 1), which is about 80 percent among landowners in Ethiopia, but significantly more limited in Malawi and Tanzania. Starting in 1996, Ethiopia pursued a land certification program which is one of the most ambitious among Sub-Saharan Countries. Holden et al. (2011) discuss how the land certification program helped enhance rental land market participation in Ethiopia. Women landowners were especially affected by the certification program. Since men tend to be the ones who cultivate land, women have less 9
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unclear what exactly one is measuring with the proxy. To achieve a more objective categorization, this paper uses a clustering exercise to find which variables differentiate landowners the most with the aim of finding an aggregate categorization of ownership.

# **3. Methodology**

In this section, we briefly discuss the two main methodologies used in this paper: Multiple Correspondence Analysis (MCA) and Hierarchical Clustering with Principal Components (HCPC).<sup>17</sup>

# **3.1 Multiple Correspondence Analysis Using Land Rights Data**

Factor Analysis methods, such as Principal Component Analysis (PCA) and MCA are dimensionality reduction techniques. These methods can be used in exploratory data analysis to identify important patterns in the data, particularly when there are a large number of correlated variables. MCA is an extension of PCA that is used when all variables are categorical with multiple levels without necessarily an order. The discussion below will focus solely on MCA given the type of data that we use.

Factor analysis methods have a long history of being used in other disciplines (Abdi and Williams, 2010) and in a number of economic studies to reduce the dimensionality of data. In labor economics, a single measure of skill can be derived using PCA when multiple skill variables are available (see Autor et al., 2003; Antonovics and Golan, 2012). In the macro literature, factor analysis is useful since multiple economic measures are highly correlated. Gregory and Head (1999) used dynamic factor analysis to find a single measure of common economic activity among G7 countries. Factor analysis can also be used in modeling asset returns and economic forecasting (for example Ludvigson and Ng, 2009). In the development context, PCA has been used to create a wealth index. The Demographic and Health Survey (DHS) uses PCA on household durables data to create a single wealth index measure separately for urban and rural households. Most studies that use PCA typically assign the first principal component as the index measure that summarizes the data.

For our purposes, MCA helps in better understanding land ownership in two ways. First, we are able to analyze which variables (based on their variance) distinguish different types of right bundles/profiles. Second, since MCA provides a transformation of the data from categorical to continuous variables, we can use the continuous variables for the hierarchical clustering method that will categorize individuals into groups with a similar rights profile. Common clustering methods, such as hierarchical clustering and k-means clustering, rely on continuous variables instead of categorical.

> 17 For a more theoretical discussion on these methodologies, please refer to Abdi and Valentin (2007), Husson et al. (2017) and Pagès (2014).

13
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# **3.2 Hierarchical Clustering**

Using the resulting components from MCA, we conduct Hierarchical Clustering on Principal Components (HCPC). The hierarchical clustering algorithm is quite intuitive and relies on grouping together observations that are the most similar. Our measure of dissimilarity would be the Euclidean distance on the components from the MCA. The dissimilarity between landownerparcel _i_ and landowner-parcel _l_ is equal to where _m_ is a component. Each landowner-parcel observation has value i for component _m_ . Hierarchical clustering identifies the main groupings by building a tree-like structure. We will use xx Hierarchical clustering identifies the main groupings by building a tree-like structure. We will use an agglomerative algorithm to build the tree. The algorithm starts by treating all observations as a single cluster, at the roots of the tree. First, the two observations with the least Euclidean distance are identified and connected side-by-side. These two then are grouped together in a new cluster. For this new cluster and all the remaining observations, the Euclidean distance is recalculated again to find the two most similar groups, which are then linked as well.<sup>20</sup> The algorithm keeps repeating this step until all observations/clusters are linked, creating a hierarchical tree.

Once a dendrogram is created, one needs to partition the tree into smaller clusters. The HCPC method chooses the highest quality partition to determine the final clusters. A good quality partition ensures the minimum variance within a cluster (landowners within a cluster are the most similar) and maximizes the variance across clusters (landowners belonging to differing clusters are very different).

# **3.3 Clustering Results and Robustness Checks**

The MCA analysis has shown that the transfer rights contribute the most to the variation in the composition of a wider body of constructs related to self-reported rights to and control over land. Consequently, we further restrict our specification for hierarchical clustering to use only the five rights variables (bequeath, sell, rent, improve, and use as collateral). Focusing on these rights also improves the conceptualization and interpretation of the resulting clusters.

As a robustness check, we compare how the results change when we base the clusters on all landrelated variables versus only rights variables. The resulting tabulations are presented in Table 4. Very similar clusters result in either approach, with about less than 3 percent of individuals assigned to different clusters depending on the method. This confirms that rights variables are what drives the algorithm in choosing the cluster categories, even when other variables are fed into the algorithm. MCA results using only rights variables are consistent with MCA discussed in the previous section and are provided in the appendix.

> 20 For the previously new created cluster that encompasses two observations, one can use the mean of the two observation and find the Euclidian distance between this mean and the other observations.

18
**Table 4: Cross-Tabulations of Clusters Based on All Variables vs. Only Variables on Rights**

||**E**<br>Cluster w/|**thiopia**<br>All Variabl|es|**T**<br>Cluster w/|**anzania**<br>All Variab|les|||Cluster w/ A|**Malawi**<br>ll Variabl|es||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Cluster w/|1|2|3|Cluster w/<br>1|2|3||Cluster w/|1|2|3||
|Right Variables||||Right Variables||||Right Variables|||||
|1|33.95|0.26|0.13|1<br>46.84|0.00|1.20||1|44.64|0.00|0.15||
|2|0.32|19.34|0.16|2<br>0.24|17.69|0.00||2|0.00|14.28|0.00||
|3|1.26|0.63|43.95|3<br>0.08|0.08|33.87||3|1.24|0.21|39.48||
||S|um of Diag|onal|97.24<br>S|um of Dia|gonal|98.40||S|um of Dia|gonal|98.40|

<u>Notes: The table reports the cluster in which the landowner is assigned to. Those assigned in the diagonal are the proportion of</u> landowners that were grouped to the same people in both clustering algorithms.

As an additional check, we further analyze different combinations of rights variables in creating clusters. Given that the MCA analysis shows that the right to make improvements on land does not exhibit similar patterns as the other rights, one could potentially take out the right to improve from the clustering algorithm without changing the resulting cluster categorization significantly. When comparing clustering results with all variables and transfer rights (right to use as collateral, bequeath, rent, and sell), and dropping improvement rights, we found that 94 percent of landowners in Malawi), 97 percent in Ethiopia, and 99 percent in Tanzania were groups in the same three clusters.<sup>21</sup> For the rest of the paper, our preferred specification for hierarchical clustering is based on the four transfer rights (without the right to improve).

In Table 5, we experiment with taking out each transfer right one by one, to see how the results change. Again, the clusters created by different combinations of rights are quite robust, with more than 90 percent of individuals generally being grouped the same way. One exception is Ethiopia, where clustering across two rights were effectively compared after dropping one (the right to sell was not available, given how land markets operate in the country, and improvement was automatically taken out in this exercise) and the algorithm created more than 3 clusters. Fortunately, there were only 3 clusters with the most observations, which is what was counted in Table 5, but this resulted in a lower percentage of matches in Ethiopia. For Ethiopia and Tanzania, taking out the right to use as collateral from the algorithm created the least percentage of matches but still fairly large. For Malawi, taking out the rights to bequest, sell or collateral provides a similar percentage of matches.

> 21 Interestingly, neither documented nor economic ownership in Ethiopia followed similar patterns as the right to transfer variables in the MCA. These two variables have to do with land transfers; however, the results indicate that they are separate from the rights to transfer in the country.

19
**Table 5: Extent of Identical Cluster Assignment After Omitting a Specific Right**

|Clustering w/ 3<br>rights variables|Su<br>|m of diagonal<br>|<br>|
|---|---|---|---|
|instead of 4|**Ethiopia**|**Tanzania**|**Malawi**|
|w/o bequest|89.66|98.64|94.23|
|w/o sell|N/A|96.96|94.02|
|w/o collateral|**85.42**|**95.44**|94.18|
|w/o rent|86.16|97.28|96.34|

<u>Notes: In Ethiopia, the right to sell was not asked.</u> In Table 6, we compare the clustering results based on only one right, as opposed to the set of rights together. The results are consistent with findings in Table 5 which shows that the right to use land as collateral has one of the most important roles in Ethiopia and Tanzania. The right to sell, on the other hand, was more closely aligned to the clustering result for Malawi.

**Table 6: Extent of Identical Cluster Assignment After Omitting All but One Right**

|Comparing<br>cluster using all<br>rights with|S|um of diagona|l|
|---|---|---|---|
|<br>individual right<br>variable_x_|**Ethiopia**|**Tanzania**|**Malawi**|
|bequest|92.66|91.27|86.96|
|Sell|N/A|**94.64**|**90.82**|
|collateral|**94.92**|**94.08**|85.41|
|Rent|**94.50**|92.79|88.97|
|improvement|84.10|57.00|63.40|

<u>Notes:</u> The table reports the cluster that the landowner is assigned to. Those assigned in the diagonal are the proportion of landowners that were grouped to the same people in both clustering algorithms.

The issue with using only one right, as seen in Table 6, is that there are landowners who only have one or two rights and not fully all. Using an aggregate land ownership variable from a clustering algorithm will capture this but using an individual variable as a proxy would not. However, for certain countries such as Ethiopia and Tanzania, using transfer one right variable would give a similar result as the cluster with limited loss of information (approximately 94% of information is consistent with clustering results).

Given the importance of the four transfer rights, in the following analysis we maintain the clusters based on these rights and further explore cross-country differences in clusters, as well as in relation to landowner characteristics. It is worth keeping in mind that the clustering results separates landowners in terms of transfer rights when interpreting results in the next section.

20
<!-- Start of picture text --> 0.5<br>0.4 0.21<br>0.24 0.11<br>;§ 03 0.29 gender<br>5S osmn<br>Boo 0.14 Bw<br>0.1 0.13 0.06 aa<br>Exclusive Joint Nothold TOExclusive Joint Nothold Exclusive Joint Not hold<br>cluster type<br><!-- End of picture text --> <!-- Start of picture text --> 0.4 011<br>031 ee<br>§ 0.33 gender<br>2S 0.28 F<br>Bo2 Bw<br>on 0.08<br>Exclusive Joint Not hold psExclusive Joint Not hold<br>cluster type<br><!-- End of picture text -->
At a glance, Malawi’s cluster distribution for exclusive rights is quite balanced. Country level statistics, however, mask differences between matrilineal and patrilineal communities. When parsing the Malawi data into matrilineal and patrilineal marriages (Figure 8b), one observes stark gender differences in the compositions of clusters. Women landowners in matrilineal marriages are more likely to be in exclusive clusters (31 percent, compared to 16 percent for men) and vice versa for patrilineal marriages (12 percent, compared to 35 percent for men). For the “not hold” cluster, the majority in both customs are female. There are more joint landowners in patrilineal marriages as well.

# **4.2 Rigidity of Rights Bundles within a Cluster**

Within a cluster, we find substantial variation in bundles of rights (Table 7). In Malawi, for example, the cluster in the first column reflects about 60 percent of observations (landowner-parcel level) that do not have the right, in the following order, to bequest, collateral, rent, and sell (“NNNN”). The second most commonly recurring bundle type is ENNN (bequeath is exclusively held and no other rights held), reflecting about 7 percent of observations.

Cluster bundles vary in terms of their rigidity, or how much rights vary within a cluster. Ethiopia has the least variation in rights bundles. The majority in the “exclusive” cluster (approx. 84 percent) hold exclusive rights for all four rights, while 88 percent in the “joint” cluster hold all joint rights. When one randomly chooses a parcel-landowner combination from Ethiopia who has a right to bequeath, for example, there is a high probability that this landowner will also have the other two transfer rights. Within Ethiopia, for the cluster where rights are mostly not held, 65 percent of landowners hold no rights at all (NNNN) — a substantial number but nevertheless reflecting less rigidity than Tanzania.

For Tanzania and Malawi, the “joint” cluster has more variation of bundles; 52 percent of landowners have all joint transfer rights in Tanzania and 36 percent in Malawi. As discussed earlier, the joint cluster does have fewer observations in these two countries compared to other clusters. In Malawi, especially, those with joint rights tend to also have some exclusive or no rights in their bundles.

Given the variation in rights bundles observed in the data, having a machine learning algorithm prevents researcher bias when assigning individuals to a cluster and is more efficient in doing so compared to assigning individuals manually.

22
# **Table 7: Bundles of Rights Belonging to a Cluster by Country (Agricultural Land)**

|**Malawi**||||||**Tanzania**||||||**Ethiopia**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Type of right|s in order: be|quest, collatera|l, rent, sell|||Type of right|s in order: be|quest, collatera|l, rent, sell|||Type of right|s in order: beq|uest, collateral|, rent|||
|Not Hold**(N)**|, Jointly Hold|**(J)**, Exclusively H|old**(E)**|||Not Hold**(N)**|, Jointly Hold**(**|**J)**, Exclusively|Hold**(E)**|||Not Hold**(N)**|, Jointly Hold**(**|**J)**, Exclusively H|old**(E)**|||
|Cluster 1:<br>Ho|Mostly Not<br>ld|Cluster 2:|Mostly Joint|Cluster 3<br>Exclu|: Mostly<br>sive|Cluster 1:<br>Ho|Mostly Not<br>ld|Cluster 2:|Mostly Joint|Cluster<br>Excl|3: Mostly<br>usive|Cluster 1:<br>Ho|Mostly Not<br>ld|Cluster 2: M|ostly Joint|Cluster<br>Exc|3: Mostly<br>lusive|
|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|
|NNNN|61.22|JJJJ|37.96|EEEE|75.67|NNNN|87.35|JJJJ|51.35|EEEE|82.21|NNN|65.42|JJJ|87.93|EEE|84.78|
|ENNN|6.72|JNJN|10.7|ENEE|11.12|NNJN|4.28|NJJJ|10.91|EEJE|4.99|ENN|14.39|JJE|3.16|JEE|4.72|
|JNNN|5.09|JNJJ|8.2|EEJE|2.15|NNEN|1.75|NNJJ|7.71|EEEN|3.64|JNN|10.75|JEJ|2.12|EEN|2.96|
|NNEN|4.99|EJJJ|5.35|EEEN|1.89|NNNE|1.56|NJJN|6.37|NEEE|3.13|NNE|6.12|EJJ|2.05|ENE|2.64|
|ENNE|4.02|JEEJ|4.43|EENE|1.63|ENNN|1.31|EJJE|6.37|ENEE|2.42|NNJ|1.97|JJN|1.90|NEE|1.27|
|ENEN|3.52|NNJJ|3.95|NEEE|1.58|NJNN|1.25|EEJJ|4.12|NNEE|1.61|NEN|0.68|NJJ|1.65|EEJ|1.27|
|NNNE|3.33|EEJJ|3.69|EEEJ|1.54|NENN|0.85|EJJJ|4.11|ENJE|0.44|NJN|0.67|JNJ|1.19|EJE|1.25|
|NNJN|2.54|JNNJ|3.52|JEEE|1.17|NNNJ|0.62|JEJJ|3.92|ENEN|0.35|||||JEN|0.52|
|NNEE|1.78|JJJE|2.65|ENJE|0.98|NNEJ|0.32|JEEJ|1.81|EJEE|0.35|||||ENJ|0.23|
|NNNJ|1.1|JEJJ|2.5|EJEE|0.97|ENNE|0.28|JJJN|1.03|NEEN|0.32|||||JNE|0.17|
|NJNN|1.03|JJEJ|2.3|JNEE|0.54|JNNN|0.20|JEJE|0.87|EEEJ|0.26|||||EJN|0.11|
|EENN|0.89|JEJE|2.08|ENEJ|0.33|NEJN|0.16|JJEJ|0.56|NENE|0.13|||||NJE|0.05|
|NEEN|0.8|JNJE|1.9|EEJN|0.22|NJEN|0.06|EJJN|0.48|EEJN|0.07|||||NEJ|0.02|
|JNEN|0.78|NJJN|1.79|NEJE|0.11|JNEN|0.02|JNJJ|0.20|NJEE|0.07|||||||
|ENJN|0.76|JJJN|1.74|JEEN|0.1|||NJNJ|0.14|JEEE|0.02|obs|746|obs|1,338|obs|1,844|
|NENN|0.33|EJJE|1.49|||||JJJE|0.06|||||||||
|JNNE|0.32|EJEJ|1.13|||||||||||||||
|ENNJ|0.24|ENJJ|0.79|||obs|612|obs|225|obs|472|||||||
|EJNN|0.21|JJNN|0.66|||||||||||||||
|NENE|0.11|NJJJ|0.59|||||||||||||||
|NNJE|0.1|JJEN|0.41|||||||||||||||
|NEJN|0.1|EJJN|0.36|||||||||||||||
|||JJEE|0.35|||||||||||||||
|||JNEJ<br>JJNJ<br>NJJE|0.3<br>0.29<br>0.29|||||||||||||||
|||JENJ|0.29|||||||||||||||
|||NEJJ|0.28|||||||||||||||
|obs|932|obs|303|obs|914|||||||||||||

<u>Notes: The table reports rights bundle for a parcel-individual level observation that is assigned into a cluster.</u> 23
# **5. Ownership Bundles, Decision-Making, and Permission Structures by Cluster**

The previous section highlighted the importance of transfer rights for profiling landowners. The descriptive statistics in Tables 8-10 show that in all countries, those having transfer rights, either jointly or exclusively, will almost always have roles in decision making, economic ownership, and the right to make improvements. In Tanzania, for example, women in the joint and exclusive right clusters all reported having the right to make investments in/improve the parcel. Those not having transfer rights still enjoy these decision-making and management powers but to a lower extent. In some cases, there are large gaps — in Ethiopia, for example, only 27 percent of non-landowners indicated they had the right to make improvements, even though a high share in this group had economic ownership (79 of men and 62 percent of women) as well as decision-making power (89 percent of men and 82 percent of women). Despite having low levels of documented ownership, most reported landowners do have tenure security in Ethiopia and Tanzania, with higher reported rates of secure tenure among women having mostly exclusive rights.<sup>22</sup> Furthermore, the LSMS+ surveys included unique questions on whether permissions are needed for respondents to exercise each right, revealing some interesting differences across clusters. While landowners having exclusive transfer rights exhibit similar patterns on decision-making roles visà-vis those having joint transfer rights, permission structures look very different across these groups, and more likely to affect landowners in mostly joint clusters. The statistics on needing permission at the bottom of Table 8 through Table 10 are provided just for joint and exclusive clusters in columns (2) and (3), since the number of individuals with rights for the not-hold clusters (column (1)) is small. In some countries, we find that a high share of landowners need permission to exercise rights. In Ethiopia, for example, men and women with mostly joint rights reported always needing permission, as well as 75 percent of men and 84 percent of women in the exclusive cluster. The comparable levels are lower in Malawi and Tanzania. Permission is not necessarily needed to exercise all rights (this applied to about 60 percent of mostly exclusive landowners in Ethiopia, and only around 20 percent in Malawi and Tanzania). Again, the numbers are much higher for the joint clusters. We also find that the increase in the share of respondents not needing permission to exercise any right over land (when moving from those who have mostly joint rights to mostly exclusive rights) is much greater for women as compared to men.

> 22 Data on tenure security for Malawi was not available.  In Ethiopia and Tanzania, respondents were asked “on a scale from 1 to 5, where 1 is not at all likely and 5 is extremely likely, how likely are you to involuntarily lose ownership or use rights to this parcel in the next 5 years?” Our secure tenure variable takes the value 1 for the response “not at all likely”, and the value 0 for other responses (i.e. some likelihood of losing tenure).

24
**Table 8: Land Rights, Decision-Making and Permissions Variables by Cluster (Malawi)**

||Male La<br>|ndowners by<br>|Cluster<br>|Female L<br>|andowners b<br>|y Cluster<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(6)|(7)|(8)|
||Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|
|Document Owner|0.04|0.06|0.07|0.03|0.02|0.02|
||(0.20)|(0.23)|(0.25)|(0.17)|(0.15)|(0.15)|
|Plot Decision Making|0.78|0.93***|0.92***|0.86|0.90|0.90|
||(0.41)|(0.26)|(0.27)|(0.35)|(0.30)|(0.30)|
|Economic Owner|0.76|0.98***|0.97***|0.85|0.96***|0.96***|
||(0.43)|(0.15)|(0.17)|(0.36)|(0.19)|(0.21)|
|Right to Improve|0.71|0.98***|0.98***|0.57|0.9***|0.98***|
||(0.45)|(0.14)|(0.13)|(0.50)|(0.31)|(0.15)|
|Need permission for at least 1 right#||0.83***|0.42***||0.98***|0.44***|
|||(0.38)|(0.49)||(0.15)|(0.50)|
|Need permission for some rights (at||0.77***|0.32||0.88***|0.33|
|least 3 out of 5 inc. right to improve)||(0.42)|(0.47)||(0.33)|(0.47)|
|Need permission for all rights||0.67***|0.22***||0.75***|0.22***|
|||(0.47)|(0.41)||(0.44)|(0.42)|
|Perc. of rights needing permission||0.77***|0.32***||0.87***|0.35***|
|||(0.38)|(0.42)||(0.24)|(0.43)|
|Observations<br>Notes:Permission statistics for non-h|165<br>olders were n|90<br>ot provide|289<br>d as very fe|416<br>w individuals|76<br>in this cate|319<br>gory held ri|

<u>Notes: Permission statistics for non-holders were not provided as very few individuals in this category held rights.</u> 25
**Table 9: Land Rights, Decision-Making and Permissions Variables by Cluster (Tanzania)**

||Male L<br>|andowners by<br>|Cluster<br>|Female L<br>|andowners b<br>|y Cluster<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(6)|(7)|(8)|
||Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|
|Document Owner|0.01|0.36**|0.10***|0.01|0.08*|0.06|
||(0.10)|(0.48)|(0.30)|(0.12)|(0.28)|(0.25)|
|Plot Decision Making|0.77|0.95***|0.99***|0.82|0.96**|0.98**|
||(0.42)|(0.22)|(0.11)|(0.38)|(0.19)|(0.14)|
|Economic Owner|0.27|0.89***|1.00***|0.51|0.95***|0.99***|
||(0.45)|(0.32)|(0.03)|(0.50)|(0.21)|(0.09)|
|Right to Improve|0.68|1.00***|0.96***|0.73|1.00***|1.00***|
||(0.47)|(0.00)|(0.20)|(0.44)|(0.00)|(0.00)|
|Tenure Security|0.46|0.73|0.87***|0.72|0.9**|0.92***|
||(0.50)|(0.45)|(0.34)|(0.45)|(0.31)|(0.27)|
|Need permission for at least 1 right#||0.95***|0.42*||0.98***|0.31|
|||(0.21)|(0.49)||(0.14)|(0.46)|
|Need permission for some rights (at||0.93***|0.35***||0.91***|0.24***|
|least 3 out of 5 inc. right to improve)||(0.25)|(0.48)||(0.28)|(0.43)|
|Need permission for all rights||0.32***|0.19***||0.51***|0.19***|
|||(0.47)|(0.39)||(0.50)|(0.39)|
|Perc. of rights needing permission||0.82***|0.34||0.92***|0.26***|
|||(0.22)|(0.44)||(0.18)|(0.41)|
|Observations<br>Notes:Permission statistics for non-ho|117<br>lders were no|55<br>t provided|223<br>as very few|235<br>individuals i|86<br>n this categ|88<br>ory held righ|

<u>Notes: Permission statistics for non-holders were not provided as very few individuals in this category held rights.</u> 26
**Table 10: Land Rights, Decision-Making and Permissions Variables by Cluster (Ethiopia)**

||Male La|ndowners by|Cluster|Female L|andowners b|y Cluster|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(6)|(7)|(8)|
||Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|
|Document Owner|0.49|0.85***|0.76***|0.55|0.86***|0.76***|
||(0.50)|(0.35)|(0.43)|(0.50)|(0.35)|(0.43)|
|Plot Decision Making|0.79|1.00***|0.97***|0.62|0.89***|0.89***|
||(0.41)|(0.05)|(0.16)|(0.49)|(0.31)|(0.32)|
|Economic Decision Making if Plot|0.89|1***|0.98**|0.82|0.98***|0.96***|
|Sold|(0.31)|(0.00)|(0.14)|(0.38)|(0.13)|(0.19)|
|Right to Improve|0.27|0.92***|0.91***|0.28|0.89***|0.91***|
||(0.44)|(0.27)|(0.29)|(0.45)|(0.31)|(0.28)|
|Tenure Security|0.57|0.59|0.63|0.55|0.65|0.68*|
||(0.50)|(0.49)|(0.48)|(0.50)|(0.48)|(0.47)|
|Need permission for at least 1 right#||1.00***|0.84***||1.00***|0.75***|
|||(0.01)|(0.37)||(0.00)|(0.43)|
|Need permission for some rights (at||1.00***|0.79***||1.00***|0.72***|
|least 3 out of 5 inc. right to improve)||(0.01)|(0.40)||(0.00)|(0.45)|
|Need permission for all rights||0.88***|0.63***||0.85***|0.59***|
|||(0.33)|(0.48)||(0.36)|(0.49)|
|Perc. of rights needing permission||0.99***|0.78||1.00**|0.72***|
|||(0.06)|(0.38)||(0.03)|(0.43)|
|Observations<br>Notes:Permission statistics for non-ho|153<br>lders were no|286<br>t provided|609<br>as very few|253<br>individuals i|283<br>n this categ|430<br>ory held righ|

<u>Notes: Permission statistics for non-holders were not provided as very few individuals in this category held rights.</u>

# **6. Sensitivity Analyses**

# **6.1 Comparing Reported Owners versus Individuals with Use Rights**

The analysis above is based on respondents identifying themselves as landowners. In Ethiopia and Tanzania, rights and decision-making variables were asked to both owners and users of land (even if they were not self-reported owners). This allows us to investigate whether including the useronly group will change the categorization of bundles. Table 11 shows that our findings are quite robust — incorporating individuals who are only users in the analysis still leads to three cluster categories based on the rights to transfer. Users were assigned to clusters which included the owners instead of populating their own cluster category. This suggests that transfer rights and distinguishing exclusivity/jointness determine users’ right bundles similarly to owners.

In Ethiopia, 19 percent of the sample are users who have mostly exclusive transfer rights, which amounts to 58 percent of users. Thus, research that focuses only on reported ownership would miss out a large portion of individuals who would identify as non-owners but would still be exclusive 27
holders of transfer rights over the land. These patterns can vary by country context, however—in Tanzania, for example, users tend to not have transfer rights. We also find that users are rarely in the joint cluster in comparison to owners (only 2.2 percent of users in Tanzania and 1.7 percent in Ethiopia).

**Table 11: Percentages of Land Users and Owners in Each Clusters**

**Ethiopia**

||**Cluster Categ**<br>|**ory Based on Holding Tr**<br>|**ansfer Rights**<br>|
|---|---|---|---|
||Mostly Exclusive|Mostly Joint|Mostly Not Holding|
|Using but not an owner|19.0%|1.7%|12.3%|
|Reported owner|29.8%|26.0%|11.2%|
|Total number of obs.|6189|||

Notes: Percentages sum to 100%.

## **Tanzania**

||**Cluster Categ**<br>|**ory Based on Holding T**<br>|**ransfer Rights**<br>|
|---|---|---|---|
||Mostly Exclusive|Mostly Joint|Mostly Not Holding|
|Using butnot anowner|4.6%|2.2%|16.5%|
|Reported owner|28.2%|13.9%|34.6%|
|Total number of obs.|1681|||

<u>Notes: Percentages sum to 100%.</u>

# **6.2 Clustering Using a Simpler Specification (Hold versus Not Hold)**

When narrowing the categorization of rights and decision-making roles from mostly exclusively/jointly/not held, to simply whether rights and decision-making are “held” versus “not held”, we continue to find that transfer rights tend to cluster together and explains a large part of the variation in the first dimension. Figure 9 demonstrates the MCA results for Ethiopia (using all variables). Separately, we found similar results for Tanzania and Malawi, further underscoring the importance of transfer rights in categorizing different types of landowners.

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# **7. Conclusion**

Individual land ownership can ease access to credit, allow for better consumption smoothing during economic volatility, as well as improve bargaining power within the household. Understanding these channels has been difficult, in view of cross-country differences in common forms of ownership (e.g. reported, economic versus documented), and different rights that landowners may have over their parcels particularly under customary land tenure. Leveraging unsupervised machine learning techniques and nationally-representative, intra-household survey data elicited in private from men and women, this paper creates unique profiles of landowners in Ethiopia, Malawi, and Tanzania, anchored in a range of constructs related to their self-reported rights to and control over land parcels.

The analysis reveals a high degree of cross-country consistency in new insights. Landowners, particularly women, often do not have full rights and decision-making power over land. Multiple Correspondence Analysis demonstrates that transfer rights (rights to bequeath, sell, rent out and use as collateral) contribute the most to the variation in the composition of the constructs related to rights to and control over land. These transfer rights are aligned with the alienation rights discussed by Schlager and Ostrom (1992).

Hierarchical clustering further shows 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 without any or with only limited transfer rights. Owners with transfer rights are shown to have all other decision-making and rights. Provided that transfer rights are included in the mix, the cluster definitions are also robust to the changes in the composition of the rest of the variables that are used in hierarchical clustering. This, together with the MCA findings, implies that household survey questions on land rights can be trimmed down to only include those on transfer rights, without any loss in our ability to cluster reported landowners.

Moreover, hierarchical clustering allows for a more nuanced investigation of structural and gender differences in land ownership across countries. For example, in Tanzania, within the cluster of landowners with no/limited transfer rights, 87 percent have none of the four transfer rights. In Malawi, this share is about 60 percent. And in general, women are over-represented in this cluster of landowners across the three countries. In addition, the results highlight the importance of collecting data on permissions to exercise rights over land. For example, we find that although landowners with either mostly exclusive or mostly joint rights typically have the same bundles of rights and other ownership variables, permissions to exercise these rights vary between the two clusters. In Malawi and Tanzania, landowners who need permission for at least one transfer right ranges from 31-33 percent within the exclusive cluster to 93-98 percent within the joint cluster. The analysis also reveals that in each country, moving from the cluster with mostly joint transfer rights to the one with mostly exclusive transfer rights, the increase in the share of individuals not needing permission to exercise any right is considerably greater among women than men. These findings motivate the need for further research on how joint owners exercise their rights and what restrictions they may face when permissions are involved.

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Finally, sensitivity analyses demonstrate that the findings regarding transfer rights are robust to the implementation of hierarchical clustering with (i) simpler categorization (holding versus not holding) of rights variables and (ii) an expanded sample that includes individuals with land use rights, together with landowners. The inclusion of individuals with land use rights in the analysis sample is particularly important in Ethiopia, where 19 percent of the individuals within the cluster with mostly exclusive transfer rights are those with land use rights (constituting 50 percent of the sample of individuals with land use rights). Therefore, research that focuses only on self-reported landowners would miss out on a large portion of individuals who would otherwise hold transfer rights over their land.

Overall, our findings provide a more nuanced perspective on land ownership and rights among men and women across different countries in Sub-Saharan Africa. The analysis showcases the utility of machine learning techniques that can be applied to high-dimensional, intra-household, individual-disaggregated survey data on asset ownership to (a) identify the subset of rights that is essential to the conceptualization of land ownership under customary tenure arrangements and that is consistent across Ethiopia, Malawi and Tanzania, and (b) allow for a cross-country-comparable approach to profiling landowners across contexts with significant variation in institutions and norms governing land ownership.

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