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Combining insights from quantile and ordinal regression: Child malnutrition in Guatemala

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  • Sweeney, Stuart
  • Davenport, Frank
  • Grace, Kathryn

Abstract

Chronic child undernutrition is a persistent problem in developing countries and has been the focus of hundreds of studies where the primary intent is to improve targeting of public health and economic development policies. In national level cross-sectional studies undernutrition is measured as child stunting and the goal is to assess differences in prevalence among population subgroups. Several types of regression modeling frameworks have been used to study childhood stunting but the literature provides little guidance in terms of statistical properties and the ease with which the results can be communicated to the policy community. We compare the results from quantile regression and ordinal regression models. The two frameworks can be linked analytically and together yield complementary insights. We find that reflecting on interpretations from both models leads to a more thorough analysis and forces the analyst to consider the policy utility of the findings. Guatemala is used as the country focus for the study.

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  • Sweeney, Stuart & Davenport, Frank & Grace, Kathryn, 2013. "Combining insights from quantile and ordinal regression: Child malnutrition in Guatemala," Economics & Human Biology, Elsevier, vol. 11(2), pages 164-177.
    • Handle: RePEc:eee:ehbiol:v:11:y:2013:i:2:p:164-177
    DOI: 10.1016/j.ehb.2012.06.001
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    Cited by:

    1. Griffen, Andrew S., 2016. "Height and calories in early childhood," Economics & Human Biology, Elsevier, vol. 20(C), pages 55-69.
    2. Arndt, Channing & Hussain, M. Azhar & Salvucci, Vincenzo & Østerdal, Lars Peter, 2016. "Effects of food price shocks on child malnutrition: The Mozambican experience 2008/2009," Economics & Human Biology, Elsevier, vol. 22(C), pages 1-13.
    3. Sharaf, Mesbah & Rashad, Ahmed, 2016. "Economic and Socio-Demographic Determinants of Child Nutritional Status in Egypt: A Comprehensive Analysis using Quantile Regression Approach," Working Papers 2016-4, University of Alberta, Department of Economics.

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