Estimating Calorie Poverty Rates Through Regression

Manoranjan Pal () and Premananda Bharati ()
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Manoranjan Pal: Indian Statistical Institute, Economic Research Unit
Premananda Bharati: Indian Statistical Institute, Biological Anthropology Unit

Chapter Chapter 4 in Applications of Regression Techniques, 2019, pp 59-84 from Springer

Abstract: Abstract In this paper we assume a tri-variate distribution of the nutrient intake (y), say calorie intake, the income (x) and the nutrient norm (z) of the households, which leads to linear or log-linear regression equations depending on the type of joint distribution assumed for the purpose of estimation. Nutrient norm takes care of age-sex composition of a household. The probability that the household consumes less than the prescribed norm can be computed from the regression result. This probability can be regarded as the estimated value of the calorie-poverty rate when taken in aggregate. In practice, since income data are not available, the per-capita total expenditure of the household is taken as a proxy to per-capita income and regression is run for different expenditure groups. We have applied this technique to the 61st round data collected by National Sample Survey Organization (NSSO), India, on calorie intakes. The estimates of the poverty rates found by this method are unbelievably high and call for further investigations. The reasons for getting such high estimates are discussed and a modification of the estimates is suggested in the paper. The modification leads to reasonable estimates of the poverty rates.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-9314-3_4

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DOI: 10.1007/978-981-13-9314-3_4

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