Wealth accumulation is critical for advancing women's and men's economic opportunities, and yet is understudied in developing countries. Leveraging new, nationally-representative, cross-country comparable surveys where men and women self-reported on their personal asset ownership, we show that individual-level wealth inequality is significantly higher vis-à-vis comparators based on per capita household consumption expenditure, and per capita household wealth. Intra-household wealth inequality explains about 12–30 percent of overall wealth inequality, depending on the country context. The analysis further demonstrates how survey design choices, in particular respondent selection, matter for individual wealth inequality estimates.
Over the past decade, national statistical offices in low- and middle-income countries have increasingly transitioned to computer-assisted personal interviewing and computer-assisted telephone interviewing for the implementation of household surveys. The byproducts of these types of data collection are survey paradata, which can unlock objective, module- and question-specific, actionable insights on survey respondent burden, survey costs, and interviewer effects – all of which have been understudied in low- and middle-income contexts. This study uses paradata generated by Survey Solutions, a computer-assisted personal interviewing platform used in recent national household surveys implemented by the national statistical offices of Cambodia, Ethiopia, and Tanzania. Across countries, the average household interview, based on a socioeconomic household questionnaire, ranges from 82 to 120 minutes, while the average interview with an adult household member, based on a multi-topic individual questionnaire, takes between 13 to 25 minutes. The paper further provides guidelines on the use of paradata for module-level analysis to aid in operational survey decisions, such as using interview length to estimate unit cost for budgeting purposes as well as understanding interviewer effects using a multilevel model. Our findings, particularly by module, point to where additional interviewer training, fieldwork supervision, and data quality monitoring may be needed in future surveys.