Abstract
This study targets the empirical space between cross-country analyses exploring links between income and nutrition without insights on micro-level determinants, and relevant microeconomic studies hindered by small sample size and/or incomplete data. We use the rural samples of the three waves of the Uganda National Panel Survey, and estimate panel regressions of child height-for-age z-scores (HAZ) controlling for time-invariant child-level heterogeneity. On the whole, we find no impact of short-term changes in total gross income on HAZ but document small positive correlations for younger children. Sector-differentiated analyses indicate that compared to wage earnings, only share of income from non-farm self-employment correlates positively with HAZ. Within agriculture, shares of income from consumption of own crop production and from low-protein crop production underlie the negative effect of share of income from crop production. While we cannot claim causal relationships, our findings suggest the possibility of "stickiness" of crop production to own consumption.
Highlights
In the quest for widespread and sustainable welfare gains, not all income may have equal effects
The factors that are commonly understood to interact to that hinder nutrition are (1) household food insecurity, which encompasses food availability as well as quality, (2) inadequate care, and (3) unhealthy environment (Behrman & Deolalikar, 1988; UNICEF, 1990).4. The direction of these biologically based impacts is well established in the literature, and we take them as given: any positive or negative impacts of agriculture on nutrition must act through these channels
We report initially two alternative crop income calculations that include the estimated value of household crop production that was consumed at home either from (1) the agriculture questionnaire or (2) the food consumption section of the household questionnaire
Summary
In the quest for widespread and sustainable welfare gains, not all income may have equal effects. While some differences could be due to context-specific dynamics, numerous reviews in recent years express concerns regarding (i) the validity of the empirical methods used for impact estimation, and (ii) the inconsistency in the types of data used across studies which often lack information on income and have information on only consumption or anthropometry but not both (Arimond et al, 2011; Leroy, Ruel, Verhofstadt, & Olney D., 2008; World Bank, 2007) Despite these challenges, the sheer number of studies conducted over the last few decades speaks to the long-standing and urgent demand for insights into how to effectively leverage growth for nutritional improvement.
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