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Seasonality in Calorie Consumption: Evidence from Mozambique . Channing Arndt, Mikkel Barslund, Jose Sulemane. Support from the African Economic Research Consortium is gratefully acknowledged. Introduction & Motivation.
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Seasonality in Calorie Consumption: Evidence from Mozambique.Channing Arndt, Mikkel Barslund, Jose Sulemane Support from the African Economic Research Consortium is gratefully acknowledged.
Introduction & Motivation • Around 70 percent of Mozambicans live in rural areas – economic outcomes related to agriculture • Agriculture is inherently subject to seasonality • Prices on agricultural products are subject to seasonality. Maize prices, for example • Peak in January-February and trough in May • Large differences between peak and trough • Maize is a very important staple food • How does these observations influence calorie consumption ? • Our analysis considers the existence of seasonality and determinants of the magnitude of seasonality.
Data – Household Consumption 2002/03 • 4695 rural households with interviews equally spread over a 12 month time span. • The time span, July 2002 – June 2003, corresponds essentially to an agricultural season. • Representative by province • Data appears to be of good quality • Rich module for daily consumption of food • Conclude: data very well suited to the analysis of seasonality
Absolute Deviations from Year-Mean in the Logarithm of Calorie, 2002-03
Approach (existence of seasonality) Seasonality Predicted (January) calorie intake. calpci – natural logarithm of per capita calorie consumption pconsi – household non-food consumption (proxy for income) otheri – i.e. Gender, age, education of head, household size, household demographics etc. NOTE: Separate regressions by region (North, Center, South)
Results: Existence of Seasonality • Immediate post-harvest period calorie intake significantly larger than hungry season trough (January). • Seasonal patterns most strongly evident in the central region. • While the peak differs significantly from the trough, it is more difficult to establish significant differences from the annual average. • Elevated calorie consumption also observed during the planting season.
Determinants of seasonality Estimate: Explanatory variables: Level Regional dummies Income / income squared Household size Gender of Head Literate female Magnitude Regional dummies Income Household size Gender of Head Age of HH head Literate adult female Existence of a market Existence of road
Role of Sin function Permits estimation of factors that expand or contract seasonal tendencies. Potential Seasonality profiles Jan 1st Apr 1st Jul 1st Oct 1st
Results: Determinants of Seasonality • Few statistically significant results. • Most interesting results generated in the central region where seasonal factors appeared most clearly. • For the central region: weak evidence that seasonality tends to shrink with an income proxy (non-food expenditure in this particular case) and increase with the dependency ratio.