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How much should we trust micro-data? A comparison of the socio-demographic profile of Malawian households using census, LSMS and DHS data

How much should we trust micro-data? A comparison of the socio-demographic profile of Malawian households using census, LSMS and DHS data

Tasciotti, Luca ORCID: 0000-0003-2561-5530 and Wagner, Natascha (2017) How much should we trust micro-data? A comparison of the socio-demographic profile of Malawian households using census, LSMS and DHS data. European Journal of Development Research, 30 (4). pp. 588-612. ISSN 0957-8811 (Print), 1743-9728 (Online) (doi:https://doi.org/10.1057/s41287-017-0083-6)

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Abstract

This paper assesses the empirical representativeness of micro-data by comparing the Malawi 2008 census to two representative household surveys – ‘the Living Standard Measurement Survey’ and the ‘Demographic and Health Survey’ – both implemented in Malawi in 2010. The comparison of descriptive statistics – demographics, asset ownership, and living conditions – shows considerable similarities despite statistically identifiable differences due to the large samples. Differences mainly occur when wording, scope, and pre-defined answer categories diverge across surveys. Multivariate analyses are considerably less representative due to loss of observations with composite indicators yielding higher comparability as individual ones. Household-level fixed-effect specifications produce more similar results, yet are not suited for policy conclusions. Comparability of micro-data should not be assumed but checked on a case-by-case basis. Still, micro-data constitute reliable grounds for factually informed conclusions if design and context are appropriately considered.

Item Type: Article
Uncontrolled Keywords: Malawi, micro-data, DHS, LSMS, survey comparison, representativeness
Subjects: H Social Sciences > H Social Sciences (General)
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of International Business & Economics
Last Modified: 12 Feb 2020 12:44
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/26931

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