5. Food Demand Analysis

5.1 Data Collection at Household Level

5.2 Determinants of Food Demand

5.3 Nutritional Economics

M4902-430 Food and Nutrition SecurityUniversity of Hohenheim

Olivier Ecker

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Theoretical framework of food demand

Consumer Theory:• Basic assumption: Consumers choose a consumption bundle x,

so as to maximize utility u(x) subject to their budget constraint m = px. → Maximization of overall utility!

• The optimal consumption bundle x* depends on the prices of goods p and the available income m.

• Marshallian demand function for good i: xi* = Di(p,m)• Consumers’ expenditure (cost) function: m(u,p)→ Minimum expenditure required to attain a specific utility level

at given prices

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What does food demand analysis do?It describes and explains the level of demand for the food commodities an individual consumes, given the structure of relative prices faced, real income, and a set of individual characteristics. → A set of elasticities is an important result of food demand analysis.

Why is this important?• Utility is determined by the quantity of goods an individual

consumes. For the poor, food accounts for the largest share of the consumption bundle.

• The nutrition status of individuals is to a large extent determined by the level of food consumption. Therefore, knowledge about consumption patterns and how they change through exogenous shocks is important for food and nutrition security.

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5.1 Data Collection at Household Level

Data collection approaches1. Food expenditure data

Households interviewed report on their food expenditure (purchased and from other sources) for the past 7 days (periods can also be longer)

2. 24-hour recall dataHouseholds interviewed report on their food intake for the last 24 hours, usually meal by meal.

3. Food weighing (dietary record)A surveyor is present in the household during meal preparation times and weighs all ingredients used.

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Example: food expenditure survey form

Code Item Quantity (kg) Value ($)1 Rice2 Wheat…8 Red lentils

9 Chickpeas…20 Milk21 Cheese…30 Beef31 Chicken meat

The disaggregation of food items varies by survey (typically 30-120).

How much of the following food items did your household consume in the past one week (purchases, own production, received in-kind)?

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Example: 24h-recall survey form

Item Code a Quantity (kg) Value ($)Breakfast

Lunch

Dinner

Snacks

a 1 = rice, 2 = wheat, …. , 8 = red lentils etc.

What type of food and how much did your household consume duringthe past 24 hours?

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Pros and cons of survey approaches…Food expenditure data• Food expenditures are usually part of living standard

measurement studies (LSMS). Together with non-food expenditures, they are preferred over monthly income as a measure of permanent income and thus living standard.

• But, households might forget to mention certain items because of relatively long recall period.

• It is not really food intake what is assessed, because food waste, spoilage, portions given to guests or fed to animals etc. can hardly be captured.

• Shorter recall periods can improve on some disadvantages (e.g., 3- or 7-day recall).

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…pros and cons of survey approaches24-hour recall data• For an assessment of household food intake, 24-h recall data

are better and often include more details.• Recall period is short, so that more accuracy.• Disaggregation by meal helps determine inhibiting and

enhancing factors for micronutrient absorption.• But, 24-h recall is only snapshot; day-to-day diversity neglected.Food weighing• In terms of recording, weighing is still more accurate than 24-

h recall, but the presence of the surveyor might cause a bias.

Problems of all approaches• Single round of data collection cannot capture seasonality

effects (optimum: two or more rounds in different seasons).• Mostly no information on intra-household distribution

(surveys at individual level are possible but rare).

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5.2 Determinants of Food DemandWhat determines household food consumption?

• Income (→ expenditure data)• Prices: own- & cross-prices

• Household characteristics:– Household size, sex & age structure, decision maker,– Education & nutritional knowledge,– Taste, food habits, religious taboos

• Food availability & accessibility:– Farming & self-sufficiency, cash liquidity– Market integration & infrastructural endowment– Agro-ecological production conditions– Seasonality of food crops

→ Which variables reflect these determinants best?→ Which variables are available from the data?

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Food consumption in Russia

• 712 households were interviewed in 1995 in three oblasts.• Summer and winter interview rounds to account for

seasonality effects.• Food expenditure over last 30 days was recorded.• Consumption from own production is included in total

expenditure.• Household consumption was converted into per-capita

consumption, assuming that food distribution follows standard adult-equivalent weights.

• Households were stratified in four quartiles according to per-capita total expenditure.

Study by Qaim, von Braun, tho Seeth (1997)

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Food expenditure patterns in Russia

0.30Gini coefficient

69.7Food expend. share (%)

2,277Food expend.(Thsd. Rbl.)

3,267Tot. pc expend. (Thsd. Rbl)

Total

0.160.060.070.16

67.869.069.972.1

3,9642,3541,6851,009

5,8473,4112,4101,399

4321Expenditure quartile

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Food consumption patterns in Russia

22.1Meat exp. share (%) a45.6Meat (kg/year)14.7Veg. exp. share (%) a77.9Vegetables (kg/year)10.1Pot. exp. share (%) a141.8Potatoes (kg/year)11.3Bread exp. share (%) a114.8Bread (kg/year)Total

24.423.621.519.0

78.851.333.418.8

15.316.515.112.1

126.588.960.934.0

9.410.111.59.5

217.2156.9123.669.4

6.98.910.918.3

142.9116.5104.895.1

4321

Expenditure quartile

a Share in total household food expenditure.

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Income as determinant of demand

( )zyqq ii ,=Can be estimated with Engel functions:

qybybaq ⋅

=⇒⋅+= η

Linear functional form

bybaq =⇒⋅+= ηlnln

Double-log functional form

i

ii qy

yq⋅∂⋅∂

=ηIncome elasticity of demand

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Values of income/expenditure elasticities

Essential goods:(most food items)

Luxury goods:

Inferior goods: 0<η

richpoorand ηηη ><1

1>η

staplesnonstaples −<ηηEngel’s law:With increasing income levels, people tend to spend a decreasing share of their income on food.

0>ηNormal goods:

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Food expenditure shares (examples)

Germany 11%

Czech Republic 25%

Argentina 33%

Indonesia 50%

Sri Lanka 63%

Ghana 73%

Rwanda 79%

Burkina Faso 84%

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Income/expenditure elasticitiesAverage Poor Non-poor

Food, Sierra Leone 0.93 1.07 0.85

Sorghum, Niger 0.45 0.45 0.40

Rice, Mali 0.61 0.64 0.59

Cassava, Nigeria 0.78 1.42 0.20

Cereals, Brazil 0.32 0.65 0.19

Legumes, Brazil -0.20 -0.25 -0.18

Cassava, Brazil -0.50 -0.70 0.00

Source: Teklu (1996), Gray (1982).

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Prices as determinants of demand

ii

iiii qp

pqE⋅∂⋅∂

=Own-price elasticity:

(usually negative)

ij

jiij qp

pqE

⋅∂

⋅∂=Cross-price elasticity:

(can be negative or positive)

Can be estimated econometrically from demand equation:

( )zppyqq nii ,..., 1=

Price elasticities are more difficult to estimate from cross-section data than income elasticities (why?).

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Selected demand equations in Russia

Bread Potato Milk Vegetable Meat

Constant 3.27** 2.70** 3.50** 0.04 -1.13

Expenditure 0.20** 0.39** 0.33** 0.61** 0.84**

Own price -0.25** -0.09 -0.63** -0.37** -0.83**

HH members -0.05** -0.09** -0.15** -0.10** 0.01

Education -0.11* 0.10 0.27* 0.14 -0.15

Urban -0.14** -0.77** -1.44** -0.70** -0.21**

Adjusted R2 0.27 0.38 0.53 0.44 0.39

( ) ( ) ( ) εδγβα ++++= Zpricepcexpendpccons lnlnln

*, ** Coefficient is statistically significant at 10% and 5% level, respectively.

Source: Qaim et al. (1997).

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Theory of consumer behaviorEstimation of single demand equations is not fully consistent with economic theory.

Solution is a set of n demand equations, qi(p, y, z), with n income elasticities and n2 price elasticities.

That is, n + n2 parameters to be estimated.

( ) ( )qpyzquLagrangean ⋅−+= λ,

Number of independent parameters is reduced through different constraints.

( ) yqptosubjectzquMax =⋅, (budget constraint)

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Constraints from first-order conditions

∑ ∑ ===∂∂

i

ii

iiii

ii y

qpwwhereworyqp 11 η

1. The Engel equation

∑ ∑ =−=−=∂∂

i ijijij

j

ii njforwEworq

pqp ,...,1

2. The n Cournot equations

∑ ==+j

iij niE ,...,10η2’. The n Euler equations

( ) .,...,1 njiforwEww

E ijjjii

jij =≠−+= ηη

3. The n(n-1)/2 Slutsky equations

( ) ( ) ( )221

211 22 −+=

−−−−+ nnnnnnnIndependent parameters

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Estimation of complete demand systemsComplete systems are needed to be consistent with theory, but trade-off between “cost” of estimation and theoretical foundation.Complete systems are particularly important when used in general equilibrium models, and complex simulations exercises.

Different systems approaches can be used that differ in their specification of the utility function and additional assumptions:

• Linear expenditure system (LES) (restrictive assumptions)• Almost ideal demand system (AIDS) (more flexible)

Policy implicationsIncome and price elasticities of food demand, whether derived through estimation of single equations or complete systems, are key to assess food consumption impacts of policies and other external shocks.

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5.3 Nutritional EconomicsFrom food consumption to calorie intake• For food and nutrition security analysis, not only the quantity

of food items consumed, but also the actual calorie and nutrient intake is of interest.

• Knowing the daily per-capita consumption of food items, calorie and nutrient intakes can be calculated using food composition tables.

• Food composition tables should reflect local food characteristics, whenever possible, and are generally preferable over international standard tables.

• Especially for micronutrient intakes, issues of bioavailability need to be accounted for (e.g., beta-carotene can only be converted to vitamin A if there is some fat in the diet; iron from plant sources is less bioavailable than from meat).

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Calorie intake in BrazilPoorest 30% Middle 50% Richest 20%

Total calories per capita 1,876 2,162 2,369

Of which in %

Legumes 17.0 11.0 7.1Sugar 12.4 13.6 13.1Vegetables 0.6 0.9 1.4Fruits 1.1 1.6 2.9Meat and fish 7.0 8.1 11.1Dairy products 2.8 5.2 8.8Oils and fats 6.5 11.6 14.2Beverages 0.4 0.7 1.5

Cereals 30.0 38.6 35.2Roots and tubers 22.1 8.6 4.6

Source: Gray (1982).

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Calorie elasticities…• Calorie elasticities can be estimated directly, just as demand

elasticities, when consumption as dependent variable is expressed in terms of calories.

• Alternatively, calorie elasticities can be derived as follows:

∑∑

=⋅∂⋅∂

=

jjcj

jjijcj

i

ici qa

Eqa

cppcE

Total calorie elasticity with respect to price of good i

where a is a set of technical coefficients measuring calorie contents of food items (from composition table), and E is the set of price elasticities of food demand.

Interpretation: How does overall calorie intake change, when theprice of good i changes by one percent?

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…calorie elasticities

∑

∑=

⋅∂⋅∂

=

jjcj

jjjcj

cy qa

qa

cyyc

η

η

Calorie elasticity with respect to income:

There is no consensus whether this calorie-income elasticity is significantly different from zero across income groups.

But disaggregate analysis suggests that, at least for the poor, it is positive (i.e., rising incomes reduce under-nutrition).

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Are food consumption databases giving reliable estimates?

Comparative study by Bouis (1994) for Kenya and the Philippines

24-h recall data 7-day food expenditure data

Household Adjusted for guests Household

1 1,706 1,651 1,441

2 1,948 1,839 1,759

3 2,026 1,912 2,043

4 2,232 2,045 2,293

ηcy 0.21 0.18 0.43

Expenditure quartile

Kenya: per-capita calorie consumption

ηcy is the total calorie-income elasticity.

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…reliable estimates?

24-h recall data 30-day food expenditure data

Household Adjusted for guests Household

1 1,726 1,625 1,385

2 1,877 1,762 1,684

3 2,035 1,876 2,029

4 2,196 1,983 2,540

ηcy 0.16 0.13 0.55

Expenditure quartile

Philippines: per-capita calorie consumption

• Elasticities from food expenditure data are often overestimated. People’s weight increase across groups is less than implied by elasticities.

• Reasons: Food expenditure data have bigger problems with waste, spoilage, etc., and transfers across income groups are more difficult to capture (e.g., laborers receiving meals).

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Nutrition effects of policies: example of setting research priorities in ColombiaHow should a given agricultural research budget be allocated to different commodities when the main objective is an improvement in the calorie status of the urban poor?Study by Pinstrup-Andersen et al. (1976) cited in Sadoulet and de Janvry (1995).

• Research in a given commodity will eventually increase the market supply of that commodity and lower the market price.

• According to price responsiveness of supply and demand, there will be direct and cross price effects.

• Consumption patterns will be affected.

• How does calorie intake of the poor change when research money is allocated to commodity i ?

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Impact of 10% increase in supply on daily per capita calorie intake (Colombian example)

5.30.05.33.48.10.37.8Beans

0.80.20.6-0.2-0.4-0.70.2Peas

2.60.32.40.41.0-1.62.6Vegetables

-1.1-11.210.19.823.15.817.3Cassava

20.0-2.222.216.238.80.138.2Maize

30.8-1.031.818.243.06.936.1Rice

10.50.410.11.33.1-3.06.2Milk

24.61.023.73.48.1-6.514.6Beef

Net effect a

Indir. effect a

Direct effect a

% of defi-

ciency

Net effect a

Indir. effect a

Direct effect a

Non-deficient hhDeficient households

a in kcal.