Identifying modules with high total variance estimates as well as high ICC-I measures is a first step towards instituting measures to minimize interviewer effects, including additional interviewer training and fieldwork supervision. Among the household questionnaire modules, food consumption, household roster and non-farm enterprises consistently emerge among the top 5 modules in terms of total variance in duration. In the case of the food consumption module, 22 to 50 percent of the variability is attributable to interviewers, depending on the country, and the comparable ranges are 7 to 27 percent for the household roster module and 5 to 17 percent for the non-farm enterprises module. Country-specific findings reveal additional modules that rank in the top 5 in terms of total variance and that are also associated with ICC-I estimates of at least 10 percent. These include housing, non-food consumption, consumer durables and livestock, with interviewer effects explaining 14 to 40 percent of total variance in module duration, depending on the module.

Likewise, labor, health and land ownership rank in the top 5 individual questionnaire modules in terms of total variance in duration in the majority of the countries. The ICC-I estimates range from 10 to 25 percent for the labor module, 11 to 15 percent for the health module and 6 to 50 percent for the land module. Additional modules emerge in specific countries with high total variance and ICC-I estimates (of at least 10 percent), including time use and subjective wellbeing, and modules that aim to capture the ownership of and rights to livestock and financial assets. In these cases, depending on the module, interviewer effects explain 11 to 29 percent of total variance in module duration.

On the whole, the module duration estimates, the survey unit costs, and multi-level hierarchical model insights presented in our paper constitute operationally relevant and previously undocumented reference points for the NSOs and survey practitioners in low- and middle-income countries that may adopt (a) questionnaires and fieldwork protocols similar to ours, including the goal of minimizing the use of proxy respondents while collecting personal information on adults; and (b) the use of paradata and empirical methods presented in this paper to improve further efficiency and quality of future surveys.<sup>3</sup> The paper is thus structured as follows. Section 2 discusses the LSMS+ data as well as how the paradata was extracted and cleaned. Section 3 provides descriptive statistics related to time costs of individual modules in the LSMS+ in comparison to household modules and a land module example of using interview length and budgets to make design decisions. Section 4 conducts a multi-level model analysis to understand how the interviewer effect contributes to variation in interview length. Lastly, Section 5 concludes.

> 3 Since the implementation of the surveys that inform our analyses, Survey Solutions issued multiple updates that also have a bearing on the paradata file structure. While researchers may not be able to leverage our syntax files with ease for the purpose of analyzing their own Survey Solutions paradata, our syntax files can be made available upon request. Our paradata, however, cannot be shared, as they contain confidential information that is excluded from the public use survey datasets.

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