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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 respondent burden, survey costs, and interviewer effects. This study does precisely that, using paradata generated by the Survey Solutions computer-assisted personal interviewing platform in recent national household surveys implemented by the national statistical offices in 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. Using a multilevel model that is estimated for each household and individual questionnaire module, the paper shows that interviewer effects on module duration are significantly larger than the estimates from high-income contexts. Food consumption, household roster, and non-farm enterprises consistently emerge among the top five household questionnaire modules in terms of total variance in duration, with 5 to 50 percent of the variability being attributable to interviewers. Similarly, labor, health, and land ownership appear among the top five individual questionnaire modules in terms of total variance in duration, with 6 to 50 percent of the variability being attributable to interviewers. These findings, particularly by module, point to where additional interviewer training, fieldwork supervision, and data quality monitoring may be needed in future surveys.

Household surveys.

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A better understanding of how individual wealth and time use are linked-across paid, unpaid, and leisure activities-is important for targeting widespread gender inequalities in time allocation, as well as in accessing economic opportunities. The lack of reliable, individual-level data on asset ownership across different subpopulations, however, has limited discussions of these issues in the literature. Using a unique nationally representative survey from Cambodia, this paper shows that individual wealth, as measured through self-reported ownership of physical and financial assets, is significantly associated with time allocation to different activities. The role of asset ownership in time use is also stronger, particularly among women, vis-a-vis the competing proxies for socioeconomic status. Ownership of financial accounts, motorized vehicles, and mobile phones-all of which can improve access to networks, markets, and services-is associated with less time in unpaid work, and in some cases greater time in paid work, specifically among women in off-farm jobs. There are also distinct gender differences in how men and women shift their time away from leisure and childcare, highlighting the importance of social norms in choices over time use. The analysis highlights the utility of integrated, intra-household, individual-disaggregated data collection on asset ownership, time use, and employment in lower-income contexts.

Asset Ownership --- Employment --- Employment and Unemployment --- Gender --- Gender and Development --- Household Survey --- ICT Economics --- Inequality --- Information and Communication Technologies --- Labor --- Poverty Reduction --- Time Allocation --- Time Use --- Wealth

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Established in 2016, the World Bank living standards measurement study - plus (LSMS+) program works to enhance the availability and quality of intra-household, self-reported, individual-disaggregated survey data collected in low- and middle-income countries on key dimensions of men's and women's economic opportunities and welfare. This report presents findings on gender differences in labor market outcomes and ownership of physical and financial assets in Cambodia, based on a national survey that was implemented by the National Institute of Statistics (NIS) in 2019, with support from the LSMS+ program.

Employment --- Employment and Unemployment --- Gender --- Gender and Economics --- Labor Markets --- Living Standards --- Poverty Monitoring and Analysis --- Poverty Reduction --- Social Protections and Labor