1) The document discusses changes in consumption patterns using data from Malawi. It analyzes the proportions of calories derived from different food groups across household expenditure quartiles. 2) ANOVA tests show the shares of calories from maize, roots/tubers, and meat/fish/eggs differ significantly across expenditure quartiles, while shares of other foods do not. 3) Poorer households derive most calories from staples like maize, while higher-income households consume more vegetables, milk, and animal products, indicating greater dietary diversity with more income.

1. Changes in Consumption
Patterns: ANOVA
Source: Babu and Sanyal (2009) 1

2. Consumption Patterns: Importance
1. Total consumption accounts for more than two-thirds
of national income in many countries, largest
component having implications for the state of the
economy.
2. Important to know changes in pattern of
consumption since it reveals changes in economic
welfare and living standards – a measure of economic
performance. Finally,
3. Facilitates understanding the price responsiveness of
consumption required for microeconomic policy
issues, which include the measurement of
distortions, optimal taxation and the treatment of
externalities.
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3. Consumption Pattern & Food Security
1. Food security = f(Per capita availability of food , Changes
in the composition of diet)
• Causes of changes in diet:
 Demographic and epidemiological transitions
 Demographic transition occurs due to a shift from a pattern of
high fertility and mortality to one of low fertility and mortality,
 Epidemiological transition occurs due to a shift from a pattern
of high prevalence of infectious disease associated with
malnutrition, to one of high prevalence of chronic and
degenerative diseases associated with urban lifestyles.
 Nutritional outcomes of such transitions: Changes in body
composition and morbidity.
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4. Food Consumption Patterns: Determinants
• Food consumption:
Quantity and quality of food intake by
households/individuals
Good proxy: calorie or nutrient intake
Economic Devt & associated changes in CP
 In addition to income & expenditure, rural–
urban migration, changes in demographic
structures and improvements in
education, transport and communications, and
marketing infrastructure would affect CPs.
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5. Percentage distribution of calorie
source by level of per capita GNP
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6. ANOVA
• ANOVA: To test if differences exist between two or
more population means.
• Populations pertain to interval data.
• Procedure: Analyze sample variance.
• Two variables: 1 Nominal , 1 Quantitative.
• Suppose categorical variable has only 2 values, then
we use 2-sample t-test.
• Superiority of ANOVA: allows for 3 or more groups.
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7. Descriptive Approach
• Graphical investigation:
• Juxtaposed box plots
• Multiple histograms
• How far the differences between groups are
significant depends on
• the difference in the means
• the standard deviations of each group
• the sample sizes
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8. ANOVA
ANOVA: Test of hypotheses about more than two
population mean parameters:
H0: The means of all the groups are equal.
Ha: Not all the means are equal
It doesn’t say how or which ones differ.
It can also be extended to multiple comparisons
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9. Assumptions
• Each population follows normal distribution
Verify by drawing histograms and/or normal
quantile plots, or use assumptions.
Valid even with non-normal populations but not
so with outliers.
• Standard deviations of each population are
approximately equal
Verify if the ratio of largest to smallest sample st.
dev. is greater than 2:1.
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10. Normality Verification
• Options for verification:
• assumptions about population
• histograms for each group
• normal quantile plot for each group
• No robust method when it comes to small
samples; hence, assume normality.
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11. Table 5.1 Food group shares (%) of household calorie
intake for Malawi
Food group Mean share Standard deviation
Maize 63.66 38.71
Other grains 9.76 21.91
Roots/tubers 16.54 27.01
Meat, fish & eggs 4.91 16.43
Milk 1.56 7.73
Vegetables 1.98 9.92
Pulses 0.76 5.24
Note: The mean share of calories does not sum to 100 per cent since the calories from other food are not
available.
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12. Descriptive Statistics
• Statistics: Mean and standard deviations of the food
shares from the various groups.
• As a proportion of the total household calorie
intake, the mean share of maize was almost 64 per
cent - maize is the dominant staple food crop in
Malawi.
• Roots and tubers (cassava, plantains and sweet
potato) (16.5 per cent of total calorie intake).
• Last: grains such as rice and sorghum.
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13. Table 5.2 Mean share of calories from various food
groups by expenditure brackets
Per capita
Roots/ Meat, fish
expenditure Maize Other grains Milk Vegetables Pulses
tubers and eggs
quartiles
1 (Lowest) 62.12 11.1 15.03 8.64 1.51 1.05 0.42
2 (Lower
72.17 6.75 13.52 3.64 0.64 1.73 0.36
middle)
3 (Upper
64.8 8.11 15.45 4.43 2.75 1.88 1.58
middle)
4 (Highest) 57.3 12.3 21.97 1.97 1.27 3.4 0.72
Total 63.67 9.77 16.55 4.91 1.57 1.98 0.76
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14. Descriptive Statistics
• Maize: Major source of calories followed by roots
and tubers, and other grains (such as rice, sorghum).
• Consumption preferences similar across quartiles.
• For the higher expenditure groups, the share of
calories from maize, which is a staple diet, declines
while the share of calories from animal
products, vegetables and dairy products increases;
because with increase in incomes, households
substitute a better variety of food (in calorie and
dietary content) compared to maize, which usually
has lower dietary value.
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15. Table 5.3 One-way ANOVA for share of calories across
expenditure quartiles
F Sig.
Maize 3.565 0.014
Other grains 1.97 0.117
Roots/tubers 2.837 0.037
Meat/fish/eggs 5.09 0.002
Milk 1.82 0.142
Vegetables 1.59 0.191
Pulses 1.66 0.174
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16. Test of Hypothesis
• Reject the null hypothesis that the shares of
calories from maize for the four expenditure groups
are the same at the 5 per cent level.
• Reject the null hypothesis that calorie shares of
roots and tubers and meat, fish and eggs for
households in all the expenditure groups are the
same at the 5 and 1 per cent levels respectively.
• The groups for which the null hypothesis cannot be
rejected are other grains, milk, vegetables and
pulses.
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17. Inference
• Malawi:
Most of the households consume maize as part of a
staple diet in order to derive calories.
ANOVA: Calorie shares of maize for households in
different expenditure groups are not the same.
Poorer households derive most of the calories from
cereals such as maize.
As income of the households increases, there is
greater substitution towards vegetables, milk and
meat, fish and eggs. Thus, there is a tendency
towards greater dietary diversity as income
increases.
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