Addressing Non-response Bias in Surveys of Wealthy Households in Low- and Middle-Income Countries: Strategies and Implementation

• Published in: The Journal of development studies Vol. 59; no. 9; pp. 1427 - 1442
• Main Authors: Choumert-Nkolo, Johanna, Santana Tavera, Gabriela, Saxena, Prakhar
• Format: Journal Article
• Language: English
• Published: Abingdon Routledge 02.09.2023; Taylor & Francis Ltd; Taylor & Francis (Routledge)
• Subjects: Bias; Consumer behavior; Economic inequality; Economics and Finance; Entrepreneurship; high-income; Household survey; Households; Housing market; Humanities and Social Sciences; Income inequality; Literature reviews; Low income groups; middle-class; non-response; Polls & surveys; Response bias; Responses; Statistical inference; Strategies; survey bias; Urban schools; Wealth; middle-class; Household survey; survey bias JEL CLASSIFICATION CODES: O12; high-income; Household survey high-income middle-class non-response survey bias JEL CLASSIFICATION CODES: O12 C81 C83; non-response; C81; C83
• ISSN: 0022-0388; 1743-9140
• DOI: 10.1080/00220388.2023.2217998

Survey data is a crucial tool for understanding behaviours in low- and middle-income countries (LMICs), but it is often subject to various biases that impede statistical inference. One of these biases is the systematic non-response of wealthy populations, which poses a considerable risk to research on income and wealth inequality, entrepreneurship, consumer behaviour, housing markets, demand for urban services, education and career trajectories, and other topics relevant to these groups. This article offers a technical and methodological review of non-response bias inherent in surveys of wealthy households, encompassing unit non-response and item non-response. Based on a literature review, we suggest a range of strategies that can be used to minimize these biases, starting with strategies for implementing the survey itself and then proposing solutions for adjusting the sample after the survey. This article provides researchers and survey managers with a theoretical understanding of the issues at stake, as well as practical tools for mitigating biases specific to surveys of wealthy households in LMICs.