Quantile Regression and Decomposition Techniques: An Application to Nutritional Demand in India

Kompal Sinha ()
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Kompal Sinha: Macquarie University

Chapter Chapter 4 in Applied Econometric Analysis Using Cross Section and Panel Data, 2023, pp 93-133 from Springer

Abstract: Abstract This chapter discusses the quantile regression technique and elaborates on the computation of parameter estimates in the context of cross-section quantile regression using rich nutrition data from rural India. The unique feature of estimating parameters at various quantiles of the distribution has several advantages: (a) it allows analyzing the relationship between variables beyond the mean; (b) it allows analyzing variables that are not independently and identically distributed; (c) it allows understanding the relationship between variables with non-linear relationships with the dependent variable. With India facing the double burden of overweight and malnutrition, using data for nutrition demand allows us to understand the benefits of using the quantile regression technique to study the distributional heterogeneity in the responsiveness of nutritional demand to the same covariates. The analysis finds the drivers of nutritional status to vary at various points of the nutritional demand highlighting heterogeneity in drivers of nutritional demand by nutritional status of households. Furthermore, to highlight the importance of a distributional analysis, we decompose the calorie consumption differential across quantiles to find tastes and preferences to dominate the differential for well-nourished households during first-generation reforms and undernourished households over second-generation reforms in India.

Keywords: Quantile regression; Oaxaca-blinder; Counterfactual; Decomposition; Calorie demand (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-981-99-4902-1_4

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DOI: 10.1007/978-981-99-4902-1_4

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