2. Seasonality in Calorie Consumption: Evidence from Mozambique. Channing Arndt, Mikkel Barslund, Jose Sulemane Support from the African Economic Research Consortium is gratefully acknowledged.

3. 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.

4. Seasonality in Maize Prices

5. 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

6. Absolute Deviations from Year-Mean in the Logarithm of Calorie, 2002-03

7. Approach (existence of seasonality) Seasonality Predicted (January) calorie intake. calpc i – natural logarithm of per capita calorie consumption pcons i – household non-food consumption (proxy for income) other i – i.e. Gender, age, education of head, household size, household demographics etc. NOTE: Separate regressions by region (North, Center, South)

8. 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.

9. 8/XX Value of Monthly Dummies: Centre Region

10. 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

11. Role of Sin function Permits estimation of factors that expand or contract seasonal tendencies. Potential Seasonality profiles Jan 1 st Apr 1 st Jul 1 st Oct 1 st

12. 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.