# **1. Introduction**

Household surveys serve a vital role in national statistical systems; inform official statistics on an extensive range of socioeconomic phenomena; and are required for tracking progress towards national and international development goals. Multi-topic household surveys are implemented frequently across the developing world to fill data and research gaps, and there is increasing international momentum to improve the scope of intra-household, self-reported, individualdisaggregated survey data collected on key dimensions of men’s and women’s economic wellbeing (FAO, World Bank and UN Habitat, 2019; Hasanbasri et al., 2021; ILO, 2018; UN, 2019).<sup>2</sup> Although household surveys have continued to grow in terms of topical coverage and complexity, particularly in low- and middle-income countries, there are persisting gaps in empirical evidence regarding various aspects of survey implementation, including respondent burden, survey costs and interviewer effects, which would be critical to assess for gauging data quality concerns – both during and after data collection – and for informing decisions regarding the design of future surveys.

A related promising development is that over the last decade, national statistical offices (NSOs) in low- and middle-income countries have accelerated their transition to computer-assisted personal interviewing (CAPI) for face-to-face surveys (Carletto et al., 2022) and have adopted computerassisted telephone interviewing (CATI) for phone surveys, particularly in response to the data needs brought on by the COVID-19 pandemic (Gourlay et al., 2021). As such, practitioners can address the aforementioned empirical knowledge gaps regarding survey implementation by leveraging _survey paradata_ , which are data that are generated as a by-product of computer-assisted data collection and that capture the entire process of creating a final survey dataset (Couper, 1998; Kreuter, 2013). To give an example, the Survey Solutions CAPI/CATI platform automatically provides an extensive paradata file for each survey. This ancillary dataset is a highly disaggregated account of the “life” of a survey and includes time stamped records of all “events” associated with each interview (e.g., interview record creation, interview assignment to an enumerator, answer provision, modification and comment addition in each questionnaire field, interview completion, to name a few).

> 2 Several indicators for the Sustainable Development Goals (SDGs) require individual-disaggregated survey data, including SDG 1.4.2 (the proportion of total adult population with secure tenure rights to land, with legally recognized documentation and who perceive their rights to land as secure, by sex and by type of tenure), SDG 5.a.1 (a) (the proportion of total agricultural population with ownership or secure rights over agricultural land, by sex), and SDG 5.a.1 (b) (the share of women among owners or rights-bearers of agricultural land, by type of tenure), SDG 5.b.1 (the proportion of individuals who own a mobile telephone, by sex); SDG 8.10.2 (the proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider, by sex). Research has revealed the importance of eliciting self-reported survey data collected in private interviews for the accurate measurement of these and related indicators (Kilic et al., 2021, Kilic et al., 2022, Hasanbasri et al., 2021).

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