the LFS- report to own non-residential agricultural property.<sup>22</sup> While this could explain part of the above findings,<sup>23</sup> it raises concerns about a large number of agricultural properties having been omitted. If correct, this may compromise the ability of Prindex data to provide information on any issues involving agricultural land, including reporting of statistics on SDG indicator 5.a.1.

A second concern emerging from Zambian data is that, rather than obtain property information from the most knowledgeable household member and then possibly cross-check responses against information supplied by other members with legitimate interests, Prindex obtains information on tenure, the type of formal documents, and other land characteristics from an adult member who is randomly selected.<sup>24</sup> In the case of Zambia, more than half (53%) of information on formal land documents comes from owners’ uncles, aunts, sons, daughters, brothers, sisters, cousins, grandchildren, grandparents, nieces, nephews, fathers, mothers or in-laws. As reliable information on the type of formal documents is difficult to obtain without physical inspection of relevant documents even from owners, this may introduce measurement error.<sup>25</sup> As respondents who are not owners also provide the majority of responses on perceived tenure security, any analysis of such data would need to be preceded by a discussion of the extent to which responses to the same question by owners and non-owners are comparable or if, as tenure security perceived by non-owners will be affected by factors entirely different from those relevant for owners, they need to be analyzed separately. At the same time, co-owners are not asked to provide information on owners’ gender and in cases where a document exists no information is requested on gender of individuals listed on documents. This limits the scope for gender analysis, e.g. by checking the extent to which documented and perceived rights match.

# **5. Conclusion and policy implications**

This paper aimed to advance the debate on land data collection by going beyond conceptual issues and exploring if cost-effective implementation of a household-level SDG compliant module is possible and can offer insights as a basis for actionable policy advice and tracking progress on implementation. Zambia’s experience suggests that piggy-backing a slightly expanded SDG module on existing surveys, such as the

> 22 This is combined with a failure to document land use or tenure type for residential land for which the Prindex questionnaire only asks if a property has any land attached to it or not (Q36). Published data suggest that 27% of households access only a residence with no land attached, a figure more than double what emerges from LFS data.

> 23 Especially if located on state land, titled agricultural properties may be more easily transferred and used for collateral than the primary residence. 24 As only (co)owners can decide on land transfers or use of land as collateral, this reduces the sample size for any regressions involving these variables, as illustrated by the limited number of observations in table 8.

> 25 The literature documenting the presence and adverse impacts of intra-household informational imperfections even among monogamously married spouses reinforces this (Udry 1996). Recent experimental evidence supports the notion that spouses do not always communicate information about resources (Ashraf 2009) even if such strategic exploitation of informational advantages results in efficiency losses (Castilla 2019).

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