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A simulation-based assessment of the bias produced when using averages from small DHS clusters as contextual variables in multilevel models

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  • Øystein Kravdal

    (Universitetet i Oslo)

Abstract

There is much interest these days in the importance of community institutions and resources for individual mortality and fertility. DHS data may seem to be a valuable source for such multilevel analysis. For example, researchers may consider including in their models the average education within the sample (cluster) of approximately 25 women interviewed in each primary sampling unit (PSU). However, this is only a proxy for the theoretically more interesting average among all women in the PSU, and, in principle, the estimated effect of the sample mean may differ markedly from the effect of the latter variable. Fortunately, simulation experiments show that the bias actually is fairly small - less than 14% - when education effects on first birth timing are estimated from DHS surveys in sub-Saharan Africa. If other data are used, or if the focus is turned to other independent variables than education, the bias may, of course, be very different. In some situations, it may be even smaller; in others, it may be unacceptably large. That depends on the size of the clusters, and on how the independent variables are distributed within and across communities. Some general advice is provided.

Suggested Citation

  • Øystein Kravdal, 2006. "A simulation-based assessment of the bias produced when using averages from small DHS clusters as contextual variables in multilevel models," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 15(1), pages 1-20.
    • Handle: RePEc:dem:demres:v:15:y:2006:i:1
    DOI: 10.4054/DemRes.2006.15.1
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    1. Øystein Kravdal, 2002. "Education and fertility in sub-Saharan africa: Individual and community effects," Demography, Springer;Population Association of America (PAA), vol. 39(2), pages 233-250, May.
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    Cited by:

    1. Øystein Kravdal, 2012. "Further evidence of community education effects on fertility in sub-Saharan Africa," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(22), pages 645-680.
    2. Filippo Temporin, 2020. "How Does Deprivation Affect Early-Age Mortality? Patterns of Socioeconomic Determinants of Neonatal and Postneonatal Mortality in Bolivia," Demography, Springer;Population Association of America (PAA), vol. 57(5), pages 1681-1704, October.
    3. Agee, Mark D., 2010. "Reducing child malnutrition in Nigeria: Combined effects of income growth and provision of information about mothers' access to health care services," Social Science & Medicine, Elsevier, vol. 71(11), pages 1973-1980, December.
    4. Micklewright, John & Schnepf, Sylke V. & Silva, Pedro N., 2012. "Peer effects and measurement error: The impact of sampling variation in school survey data (evidence from PISA)," Economics of Education Review, Elsevier, vol. 31(6), pages 1136-1142.
    5. Esther O Lamidi, 2015. "State Variations in Women’s Socioeconomic Status and Use of Modern Contraceptives in Nigeria," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-15, August.
    6. Jet Wildeman & Jeroen Smits & Sandor Schrijner, 2023. "Ethnic Variation in Fertility Preferences in Sub-Saharan Africa," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(4), pages 1-23, August.
    7. Lei, Lei, 2017. "The impact of community context on children's health and nutritional status in China," Social Science & Medicine, Elsevier, vol. 179(C), pages 172-181.
    8. Uthman, Olalekan Abdulrahman & Moradi, Tahereh & Lawoko, Stephen, 2009. "The independent contribution of individual-, neighbourhood-, and country-level socioeconomic position on attitudes towards intimate partner violence against women in sub-Saharan Africa: A multilevel m," Social Science & Medicine, Elsevier, vol. 68(10), pages 1801-1809, May.
    9. Pedro N. Silva & John Micklewright & Sylke V. Schnepf, 2012. "The impact of sampling variation on peer measures: a comment on a proposal to adjust estimates for measurement error," DoQSS Working Papers 12-12, Quantitative Social Science - UCL Social Research Institute, University College London.
    10. Garikayi Bernard Chemhaka & Clifford Odimegwu, 2020. "Individual and community factors associated with lifetime fertility in Eswatini: an application of the Easterlin–Crimmins model," Journal of Population Research, Springer, vol. 37(3), pages 291-322, September.
    11. Delprato, Marcos & Chudgar, Amita & Frola, Alessia, 2024. "Spatial education inequality for attainment indicators in sub-saharan Africa and spillovers effects," World Development, Elsevier, vol. 176(C).

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    More about this item

    Keywords

    simulation; bias; clustering; Demographic and Health Surveys (DHS); measurement error; multilevel model;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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