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Classifying Human Development with Latent Class Analysis

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  • Ann L. Owen

    (Hamilton College)

  • Julio Videras

    (Hamilton College)

Abstract

We use latent class analysis to create an indicator of human development that integrates a broad range of country characteristics including per capita income, health, inequality, environmental performance, and life satisfaction. We show that each of these characteristics is important in distinguishing across development experiences. Because latent class analysis is model-based and allows us to test the significance of each aspect of development and the number and size of the groupings, this approach is superior to an index that divide countries into ad hoc equally-sized groups. Our results suggest that income per capita does not, by itself, capture all of the important dimensions of development and that a rank-ordering of development experiences may not always be appropriate.

Suggested Citation

  • Ann L. Owen & Julio Videras, 2016. "Classifying Human Development with Latent Class Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 127(3), pages 959-981, July.
    • Handle: RePEc:spr:soinre:v:127:y:2016:i:3:d:10.1007_s11205-015-0992-8
    DOI: 10.1007/s11205-015-0992-8
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    1. Nolan, Anne & Smyth, Emer, 2020. "Clusters of health behaviours among young adults in Ireland," Research Series, Economic and Social Research Institute (ESRI), number RS101.

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