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.
95
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n-
Meticais/kg
Seasonality in Maize Prices
7000.00
6000.00
5000.00
4000.00
3000.00
2000.00
1000.00
.00
Mean National Price
Sofala
Data – Household Consumption
2002/03


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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
0.9
0.7
0.5
0.3
0.1
-0.1
-0.3
-0.5
-0.7
Jan
Feb
Mar
Apr
May Jun
Jul
Aug Sep
Oct
Nov Dec
Approach (existence of seasonality)
calpci      Monthi    pconsi    otheri   i
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



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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.
Value of Monthly Dummies:
Centre Region
Centre
0. 5
0. 45
0. 4
0. 35
0. 3
0. 25
0. 2
0. 15
0. 1
0. 05
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
M ont h ( J a n=1 )
Centre
Poly. (Centre)
8/XX
Determinants of seasonality
Estimate:
log( calpci )      leveli    magnitudei  Sin(seasoni )   i
Explanatory variables:
Level
Magnitude
Regional dummies
Regional dummies
Income / income squared
Income
Household size
Household size
Gender of Head
Gender of Head
Literate female
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


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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.