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Poverty and Undernutrition a greater proportion of members are engaged in physically demanding oc- cupations (e.g. farming). A third source of variation is location. Location may affect calorie intake due to the inFuence of variations in price, eating habits, public policy or even climate between localities. While non-parametric regression techniques provide a good idea about the general shape of the curve in two dimensions, they quickly become cumbersome in a multivariate context. Want to run regressions of the form: ln b y n B = α + β ln b x n B + η ln ( n )+ J 1 j = 1 γ j p n j n P + δ z + u where y is calorie availability and x is total household expenditure. Demo- graphics enter both through a household size term ( n ) and through inclu- sion of the proportions of household members in the age groups 0–4, 5–14, 15–54 and 55+ strati±ed by sex ( n j n ). z is a vector of variables reFecting oc- cupation and location (e.g., village dummies).
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Unformatted text preview: OLS estimates may be biased as food consumption data may be subject to random error that feeds into the construction of both household ex-penditure and calorie availability. This form of measurement error may be corrected for using income as an instrument for expenditure if this is available and collected independently of expenditure, for e.g., non-food expenditure. (Deaton, 1997) Take Home Message Calorie availability: strongly associated with household economic welfare. Subramaniam and Deaton (1996) observe high calorie elasticities of around 0.55 for the poor in rural Maharashtra which is well outside the revisionist range. ²indings clearly refute the extreme view that, “increases in income . .. will not result in substantial improvements in nutrient intakes?” (Behrman and Deolalikar ,1987, page 505) Development Economics, LSE Summer School 2007 204...
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