Harnessing Food Demand Systems for Improved Nutrition in Tanzania ∗

Ellen McCullough†1, Soye Shin2, Joanne Arsenault3, and Chen Zhen1

1Department of Agricultural & Applied Economics, University of Georgia, USA2Duke-NUS Medical School, Singapore

3FHI 360, USA

April 2020

Abstract

Consumer preferences can be leveraged to magnify the impacts of agricultural investments andinterventions on improved diets for all consumers in an economy, not just farmers. Using aholistic approach and nationally representative panel data from Tanzania, we estimate demandfor 19 food groups using an Exact Affline Stone Index demand system, which is flexible in func-tional form, utility-theoretic, controls for location-specific unobserved heterogeneity, addressescensoring resulting from a short consumption recall period, and accounts for bias arising fromendogenous prices. We find that consumers sometimes respond to price changes in unexpectedways. For the poorest consumers, e.g., because of substitution patterns, staple grain prices are amore important determinant of protein and iron intake than are the prices of foods that containmuch higher levels of protein and iron. Some of the most pro-poor dietary interventions mightinvolve increasing the affordability of staple foods for poor consumers.

Key words – exact affline stone index demand system, income elasticity of demand, price elastic-ity of demand, diet quality, micronutrient intake, macronutrient intake, dietary energy, consumerpreferences, agricultural priority setting, Sub-Saharan Africa, Tanzania JEL: O12, D12, I15

∗The authors are grateful to the Bill & Melinda Gates Foundation and the UK Department for InternationalDevelopment for funding this research through the Harnessing Food Demand Systems for Improved Nutrition in Sub-Saharan Africa project. We thank Meichen Lu for her assistance developing figures and Yawotse Nouve for researchassistance. We thank Kate Schneider for useful discussions about assessing diet quality.

†Corresponding author: emccullough@uga.edu, Phone: +1(214)460-7377, Fax: +1(706)542-0739; Mail: 315BConner Hall, Athens, GA 30606.

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Introduction

The most important impact pathway of global agriculture’s most striking success story, the Green

Revolution, has been to lower real food prices for poor consumers in developing countries. It is

estimated that, as a result of diffusion and adoption of modern varieties associated with the Green

Revolution, crop prices are 36-66% lower and the prevalence of child malnutrition in developing

countries is 6-8% lower than would have been the case in a world without the Green Revolution

(Evenson and Gollin, 2003). Rising incomes and lower food prices are generally associated with

improved diet quality, and thus are closely linked with improved anthropometric, human health,

and cognitive outcomes (Black et al., 2008; Arimond and Ruel, 2004; Rah et al., 2010; Wengreen

et al., 2009; Busert et al., 2016). Improving consumers’ access to and ability to afford a quality diet

remains a major justification for investing in agricultural R&D and promoting technology adoption

(CGIAR, 2016). A growing consensus of nutrition experts also acknowledge the important role that

consumption of healthy foods plays in improving health outcomes, and the role of economic access

and consumer preferences in determining food consumption patterns (e.g., Haddad and Hawkes,

2016; Willett et al., 2019). With a coalescence around the role of improved diets in improving

nutritional status, experts from both the agricultural development and the global nutrition com-

munities have called for the global agricultural R&D system to reorient itself around micronutrient

rich foods. Pingali (2015), e.g., asserts that agricultural policies should reverse a longstanding bias

that favors staple grains over other nutrient rich foods.

Despite broad agreement around the importance of improving diet quality and widespread

recognition of the role of agricultural R&D in improving consumers’ access to nutrition, there has

been surprisingly little systematic effort to understand how diets mediate the impacts of agricultural

interventions and to use this understanding to compare different crops in terms of their importance

as a determinant of diet quality. This is a difficult problem analytically because consumers respond

to price changes by substituting demand for different products, which can lead to unexpected

outcomes. Furthermore, consumers’ preferences are considerably context specific, and the behavior

of poor consumers is very different from that of wealthy consumers. In this paper, we seek to develop

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a careful understanding of how demand for food in Tanzania responds to changes in consumer’s

incomes and the food prices they face, and to assess the implications for diet quality, especially for

the poor. We take a demand system approach so that we can leverage powerful consumer utility

theory to identify own- and cross-price effects among a multitude of food groups. We pay careful

attention to potential endogeneity bias arising from unobserved heterogeneity.

We find that the poorest consumers exhibit elastic demand (with respect to their total ex-

penditures) for poultry, eggs, red meat, wheat, nuts & seeds, fruit, dairy, and fish & seafood.

Expenditure elasticities of demand are lower (less than 1) for maize, pulses, vegetables, roots &

tubers, cassava, fats & oils, sugar, and other foods. Consistent with findings of other studies, for

almost all goods, higher income households exhibit lower expenditure elasticities of demand than

do poorer consumers. Among the top quartile of consumers, whose total expenditures exceed the

international $2.50/day purchasing power parity (PPP) poverty line, demand is expenditure-elastic

for only a few goods: soft drinks, roots & tubers, red meat, poultry, eggs, and dairy. We then

assess the diet quality implications of these expenditure-demand patterns, finding that, for poor

consumers whose expenditures are below the Tanzanian poverty line (the bottom quartile), rising

expenditure and hence income is most strongly associated with increased intake of fat and protein,

which increase by more than 1% when total expenditures increase by 1%. With a few exceptions

(zinc and vitamin A in the middle expenditure quartiles), demand for micro-nutrients is not very

expenditure-elastic, though wealthier consumers tend to increase their intake of micro-nutrients

more strongly with rising total expenditures than do the poorest consumers. We find that poor

households consume lower than the recommended levels of dietary energy, protein, zinc, vitamin A,

and folate. A simulated increase in total household expenditures by 10% would help to close gaps

in dietary intake of energy, protein, zinc, and vitamin A for the poor. Folate intake is insufficient

across all expenditure quartiles, and the simulated expenditure increase would help to close gaps

in folate intake for all quartiles.

Across income quartiles, consumer demand for rice, wheat and poultry are relatively elastic

with respect to their own prices. While consumers across the expenditure distribution exhibit

similar substitution patterns, the magnitude of own- and cross-price elasticities diminishes for

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higher income consumers. When we consider the diet implications of food price changes while

accounting for consumers’ substitution patterns, we find that maize prices are the most important

determinant of diet quality. For poor consumers, a 1% maize price increase leads to a decrease in

dietary energy (0.5%), protein (0.5%), fat (>0.5%), iron (>0.5%), zinc (>0.5%), and folate (0.4%).

Pulses, nuts & seeds prices are also important determinants of poor consumers’ intake of dietary

energy, protein, zinc, iron, and folate. Vitamin A intake decreases when pulse prices increase, but

increases when nut & seed prices increase. When root & tuber prices increase, consumers decrease

their intake of vitamin A, and folate. Increased sugar prices lead to increased intake of iron, zinc

and folate. Lowering staple grain and pulse & nut prices for poor consumers would help to improve

dietary intake of energy, protein, and zinc, though large gaps in sufficiency would remain. Lowering

prices of starchy staples could improve intake of vitamin A for poor consumers and folate for all

consumers.

This study builds on a large literature addressing empirical estimation of demand systems. We

estimate a two-way Exact Affline Stone Index (EASI) demand system, which is consistent with

consumer theory, and can be approximated with a linearized form (Lewbel and Pendakur, 2009).

It is more flexible in functional form than the popular Almost Ideal Demand (AID) model (Deaton

and Muellbauer, 1980) and its quadratic variant (Banks, Blundell, and Lewbel, 1997), allowing

the data to determine the shape of Engel curves and allowing price effects to vary with consumers’

expenditure levels. We follow the instrumental variables approach developed by Zhen et al. (2014) to

establish the effect of sugar-sweetened beverage taxes on diet quality in the US. With this approach,

we address censoring resulting from a short consumption recall period and account for bias arising

because prices are endogenously determined with demand (Lafrance, 1991). To control for location

specific unobserved heterogeneity in consumer preferences, we identify our model using temporal

variation in prices within a location. This strategy follows the correlated random effects approach

to censored demand system modeling developed by Meyerhoefer, Ranney, and Sahn (2005). After

estimating the demand system model parameters, we use food composition tables to establish the

relationship between total expenditures and food price changes and diet quality for Tanzanian

consumers.

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We add important new evidence to the body of demand estimation applications in Africa. We

are unaware of any EASI model applications apart from Zhen, Lazaro, and Mitchell (2016), or any

applications that allow for a larger than 2nd degree polynomial relationship between expenditures

and demand, nor any that allow price elasticities to vary with expenditures. The vast majority

(95%) of food demand elasticity estimates from Africa are derived using cross-sectional data (Colen

et al., 2018). Our estimates of expenditure elasticity of demand tend to be quite a bit larger than

those from 66 studies that are reviewed in a recent meta-analysis of food demand elasticities across

48 African countries (Colen et al., 2018). Because so few demand studies are estimated using a

flexible form with respect to total household expenditures, we are unable to compare our estimates

of poor consumers’ total expenditure elasticities of demand with those of other studies. Zhen,

Lazaro, and Mitchell (2016) use the Tanzanian Household Budget survey, a cross-sectional dataset

from 2011-12. Our demand elasticity estimates are considerably larger than those from Zhen,

Lazaro, and Mitchell (2016) for most groups.

We seek to understand demand patterns for different macro- and micro- nutrients by first

estimating consumers’ food preferences and then converting this demand into its underlying macro-

and micro- nutrient components,1 similar to the approach used by Ecker and Qaim (2011) in Malawi

and following the approach used by Sahn (1988). Our nutrient demand elasticities are very similar

to those estimated by Ecker and Qaim (2011) for the poorest segment of consumers. Our nutrient

intake elasticities with respect to the prices of different food groups vary considerably from theirs,

which could arise from differences in our population of study, our use of a panel estimator, our

different functional form, or our use of one large demand system rather than a multistage budgeting

system based on the weak separability assumption that is often empirically rejected or cannot be

powefully tested (Lewbel, 1996). Our calorie-expenditure elasticities are slightly larger than the

average of those reviewed by Colen et al. (2018). Our approach to understanding diet quality

differs from that the reduced-form approach of Abdulai and Aubert (2004b), who directly estimate

demand for dietary energy and nutrients using data that the authors collected in Tanzania. Our

approach suggests higher income elasticities of demand for nutrients than does theirs.1Dietary energy is not a macro-nutrient, of course, but we refer to it as one in the manuscript to more simply list

the dietary components that we examine.

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A growing literature uses experimental approaches to estimate consumer’s demand patterns in

response to changes in their incomes or prices. A challenge with this literature is that the price or

income changes correspond to only a few discrete values for a few commodities. Thus, it is difficult

to identify elasticity parameters as one can with a dataset representing a full country’s population

and spanning multiple years of coverage. Almås, Haushofer, and Shapiro (2019) estimate an Almost

Ideal Demand System with several food groups using an experimental dataset collected to evaluate

the impacts of a randomly assigned conditional cash transfer targeting poor households in Kenya.

They find that the income elasticity of demand for calories is 0.58, for protein is 1.30, and for fat

is 0.496. Our estimates are similar, with the exception of fat (we estimate the income elasticity of

demand is much higher than 1). Jensen and Miller (2010) explore the impacts of a randomized food

staple subsidy on food consumption patterns in China and find that the subsidies had effects on

dietary intake of energy and protein that differed between the two provinces where the experiment

was conducted, but that the effects were not statistically significant. Their evidence suggests that

consumers did diversify their diets in response to food staple subsidies, and that calorie and protein

intake increased more for poor consumers than for wealthier consumers.

Recently, a number of papers have addressed the prices of macro- and micro- nutrients with a

view towards understanding how affect access to nutritious diets for consumers around the world.

Masters et al. (2018) use linear programming to calculate the minimum cost of consumers’ access

to a diet that achieves adequate intake of 17 different macro- and micro-nutrients in Tanzania

and Ghana, allowing the diet’s content to change with price fluctuations of different food items.

They decompose the adequate diet’s costs by its food and nutrient components and evaluate the

sensitivity of the diet’s cost to its components’ prices. Our approach is complementary to theirs

in that we start with patterns of consumers’ preferences for foods and then show how different

price or income interventions might close gaps in diet quality, within the framework of consumers’

demand patterns. Headey and Alderman (2019) create a global dataset of the costs of nutrients per

calorie and then assess the association between nutrient costs and diet quality and anthropometric

indicators. They find that healthy foods, such as dark green leafy vegetables and vitamin A-rich

fruits and vegetables, are relatively expensive in low income countries, and that higher milk prices

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were associated with higher child stunting. Recognizing high prices as an important determinant

of food intake, our analysis allows us to evaluate the sensitivity of diets to these prices, with a view

towards informing how price-focused interventions could affect diet patterns.

We develop a framework for countries to use to consider the effects of agricultural interventions

on consumers’ diets. This framework is appropriate not just for examining under-nutrition, but also

for evaluating the implications of policies for over-consumption of fats, sugar, and dietary energy.

Due to data limitations, we are not able to incorporate consumption of foods away from home

into our diet quality analysis, nor are we able to examine crucial dynamics of of intra-household

food allocation or assess diet quality relative to nutrient requirements that are specific to each

individuals’ activity levels and health status. Nevertheless, we offer an important exploration of

how consumers’ preferences shape their food demand, and thus diet quality response, to changing

prices and incomes.

Data Overview

We use data on household characteristics and food consumption in the Tanzania Living Standards

Measurement Study (LSMS) program. The LSMS is a global integrated household survey program

within the World Bank’s Development Data Group, collating various social and economic variables

from nationally representative households in collaboration with national statistical offices. Beyond

the nationally representative sample and extensive survey questionnaires, the LSMS has the ad-

vantage of building household survey data in a panel structure. The survey was designed so that

original households surveyed at the first round were targeted for re-interview and any adults within

the original households who created new households were also tracked with different household ID.2

This panel structure allows us to control for unobserved time-invariant food consumption character-

istics. The longitudinal sample was refreshed in the fourth round due to changes in administrative

boundaries and demographic information, therefore, we use the first three rounds of the survey

data that span the periods of October 2008 – October 2009, October 2010 - September 2011, and

October 2012 - November 2013, respectively.2The split-off household is considered as an independent panel household apart from its original household.

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The sample comprises 11,115 households in total,3 which equates to 5,020 panel households after

tracking original and split-off households across three survey rounds. The sample was constructed

based on the National Master Sample frame which is a list of all populated enumeration areas

(EA) in the country developed from the 2002 Population and Housing Census (Basic Information

Document-National Panel Survey (NPS 2008-2009), 2009). Samples are also classified into 410

EAs, 26 regions, 8 administrative zones, and 3 urban types (Dar-es-Salaam/other urban/rural).

Particularly, the combination of the administrative zones and urban/rural provides 16 strata at

which sampling weights for nationally representative statistics are calculated.

The household food consumption module assembles data on consumed quantities for 56 food

items (excluding alcoholic beverages) using a 7-day recall. Households’ total quantity consumed

for each food item is then decomposed according to three different sources: market purchase, own

production, and food gifts. If some of the consumed quantities are from market purchase, the

survey reports expenditures for purchased foods in Tanzanian Shilling (TSH). We aggregate these

56 food items into 19 food-at-home (hereafter, “food”) groups based on the typical composition of

Tanzanian meals and the nutritional characteristics of foods. The 19 food groups are rice; maize;

wheat and other cereals; cassava; roots, tubers and other starches; sugar; pulses; nuts and seeds;

vegetables; fruit; red meat; poultry; eggs; fish and seafood; dairy; fats and oils; coffee, tea, and

cocoa; soft drink and juice; and other food (salt and other spices).

Given the absence of disaggregated product-level prices (e.g., at the barcode level for packaged

foods), we calculate unit values by dividing market purchase expenditures on a food item by the

quantity purchased (either in kilograms, litres, or pieces4). Before constructing price indexes, we

first convert consumption and unit values to a common measurement unit at the food item level,

using the most commonly reported unit for that item. We standardize the unit at the food item

level, as opposed to the food group level, because, for five out of 56 food items, the item’s most

common unit differs from its corresponding group’s most common unit.5 This practice has no3The initial total number of households is 12,199 but we remove households whose non-food expenditure is zero

and daily per capita kcal, calculated based on household composition and adult-equivalent scales, is less than 1% orgreater than 95% of the distribution of daily per capita kcal in a corresponding survey round.

4The food group “eggs” is the only one reported in pieces.5The food items whose most common units differ from their food group’s most common unit include honey, syrup,

jams, marmalade and jellies (food group “sugar”); canned milk and milk powder (food group “dairy”); cooking oil

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adverse impact on our price indexes, which are the price variables in the demand system, because

these indexes normalize food cost to the food item-specific base value. We identify and top-code

price outliers using the interquartile range method with a factor of 1.5.

The food-item level unit values are missing whenever a household did not purchase that item

during the 7-day recall period prior to the interview date. Following the approach of Deaton (1988)

and Cox and Wohlgenant (1986), we impute unit values for these households using median unit

values at the food item, unit, and location level, starting with the most disaggregated location

specification (EA) and proceeding with more aggregated locations (ward, district, region, and

urban/rural) until all missing values are filled. There are a few observations (two to six depending

on survey waves) for which non-missing price does not exist because of their uncommon units at

which the consumption was reported (e.g., goat consumption reported in pieces not in kg). For

these cases, we impute prices by extrapolating from the median expenditure of the same items in

locations that are the closest to the locations in which the observations with no non-missing prices

are observed. We address unit value bias arising from households’ cost minimizing behavior in the

section addressing estimation strategy.

The LSMS data also provides data on households’ non-food expenditures, from which we cal-

culate households’ total expenditures (i.e., expenditures on food and non-food items) and budget

share on foods on a weekly level. We then obtain per-capita total expenditure for each household on

a weekly level by dividing the households’ total expenditures by household size (the total number

of household members). Figure 1 presents households’ average budget share on foods by per-capita

total expenditure quartile. As expected, households’ food budget shares decrease with rising per-

capita total expenditures. The bottom quartile households spend 76% of their total expenditures

on foods while the top quartile households only spend 46% on foods.6

We report descriptive statistics for each food group’s average share in the total food budget and

(food group “fats and oils”); other raw materials for drinks (food group “soft drink and juice”); and prepared tea andcoffee (food group “coffee, tea, and cocoa”).

6The cutoff between the first and second expenditure quartiles corresponds with US$ 1.27 per person per day,2011 PPP, which is roughly similar to the Tanzanian national poverty line, which is US$ 1.79 per person per day,2011 PPP. The cutoff between the second and third expenditure quartiles is US$ 2.06 per person per day, 2011 PPP,which is very close to the international, “dollar a day” poverty line when corrected for purchasing power parity, whichis US$1.91. The cutoff between the third and fourth expenditure quartiles is US$ 3.58 per person per day, 2011 PPP,which is similar to the “two dollar a day” poverty line when corrected for purchasing power parity (US$3.20).

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average unit value by expenditure quartile in table 1.7 Maize accounts for the largest food budget

share for all expenditure quartiles except the top quartile, which is expected for the country’s most

important staple food. Households in Tanzania follow common patterns of diet diversification as

incomes increase. Households in higher expenditure quartiles spend a larger share of their food

budgets on rice, wheat, vegetables, fruits, red meat, poultry, eggs, fish, dairy, fats & oils, and

coffee, tea & cocoa. Wealthier households spend a smaller share of their food budgets on maize,

pulses, and nuts & seeds. Tanzanian households also follow common patterns of consuming higher

quality food items as expenditures increase. Across all food groups, the average unit value increases

in higher expenditure quartiles, implying that wealthier households increasingly consume higher

quality products.

We convert food consumption quantities at the food item level into its nutritional components –

dietary energy, macronutrient profile (protein and fat) and micronutrient profile (iron, zinc, vitamin

A, and total folate) in order to better understand the relationship between diet quality and food

demand, which responds to food price and income changes. Using the consumed quantities for

each food item, we calculate nutrient values per kilogram of that food item by applying conversion

factors taken from the nutrition literature that adjust for edible portions and nutrient losses during

cooking.8

Model

We characterize Tanzania household food preferences in an EASI demand system with 19 food

groups and a composite numéraire good that incorporates all other consumption goods and services.

This is called an incomplete demand system by LaFrance and Hanemann (1989). The authors

showed that inclusion of the numéraire good is necessary for providing correct welfare impact

estimates. Another reason for preferring an incomplete demand system is that its expenditure

elasticities are close to the policy-relevant income elasticities. In contrast, expenditure elasticities7For the sake of showing overall unit price differences across food groups and expenditure quartiles, we convert

non-kilogram food items to kilograms using the conversion rates that we use for nutrient intake calculation. Thus,all unit prices are measured in in terms of Tanzanian Shillings (TSH) per kilogram.

8We obtain conversion factors for each of 56 food items from the following sources: 1) Tanzania Food CompositionTables (Lukmanji et al., 2008); 2) WorldFood Dietary Assessment System International Mini-list (Calloway andMurphy, 2006); and 3) USDA National Nutrient Database for Standard Reference (USDA, 2016).

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generated by a conditional food demand system reflect how demand changes with respect to a

change in food expenditures, which are likely endogenous with food prices. Zhen et al. (2014)

found that a conditional demand system underestimates the degree of substitution among foods

and beverages.

We choose the EASI functional form, as opposed to the popular AID model and its variants, for

several reasons. First, the EASI model allows the Engel curves to take any shape as determined by

data. This feature is especially important in the context of developing economies because household

income ranges are often wide and therefore, expenditure elasticities for foods can vary by income

groups (e.g., the lowest 10% vs. the highest 10% of the household income distribution). By contrast,

the AID model is limited to a quadratic relationship between demand and total real expenditures

(Banks, Blundell, and Lewbel, 1997). Second, by including interaction terms between log prices

and real total expenditure in the model, for which it is known as the two-way EASI, the model

allows Hicksian demand to vary with total expenditures, which is consistent with utility theory.

The AID models only allow Marshallian demand to vary with income through the income effect

in the Slutsky equation. Like the AID model, the EASI model also can be approximated using

an estimation equation that is linear in parameters. Without this property, it would be extremely

difficult econometrically to accommodate simultaneously both censoring of demand (which is quite

common in the data due to the 7-day recall period) and non-linear estimation equations.

The two-way approximate EASI demand system is specified as

w∗hit = µi +J∑j=1

αij log phjt +L∑r=1

βiryrht +

J∑j=1

αijy(yht × log phjt) +K∑k=1

γikzhkt + uhit,

(h = 1, ...,H; i = 1, ..., J − 1; t = 1, 2, 3).

(1)

In Equation 1, w∗hit is household h’s latent budget share for the i th food group in survey wave

t. The observed budget share, whit, equals the latent share w∗hit according to whit ≡ max{0, w∗hit}.

The price index for household h and food group j at wave t is denoted by phjt, with J being the

number of demand groups (J = 20 in this case, with 19 food groups plus a numéraire). We discuss

the price index variable and demand censoring further in the next section.

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The variable yht represents household h’s real total expenditures in period t, with L being

the highest degree of total expenditure polynomial included in the specification. Selection of L is

discussed in the results section. Following Lewbel and Pendakur (2009), we construct yht as the

Stone price-deflated total household expenditure: log xht−∑Jj=1whjt log phjt, where xht is nominal

total household expenditures on food and other goods and services. We also include interaction

terms between the real total household expenditure and the price indexes to examine how Hicksian

demand changes conditional on total expenditure.

Lastly, the vector zhkt represents K demand shifters (including a constant) that control for

observed taste differences among households. These demand shifters are comprised of household

demographic variables, which include log household head age, log household size, a martial status

dummy (married vs. all other), household head’s education level, maximum years of schooling

within a household, and nine variables for the proportion of household members within each of ten

gender-age groups: 0-14, 15-29, 30-44, 45-64, and 65+, with the female 65+ age group set as the

reference group.

Within-community Model

Our main specification extends equation (1) to a panel structure, through which we control for time-

invariant unobserved community level heterogeneity in food preferences. Following Meyerhoefer,

Ranney, and Sahn (2005), we employ a correlated random effects specification by adding EA level

means across survey waves of the price index (phjtc) and EA level means of the interactions between

the price index and the real total household expenditure (denoted by yht × log phjtc). Our main

results are focused on this correlated random effects specification, which we refer to hereafter as

the “within-community" model. We also present results from a pooled cross-sectional model as a

robustness check.

One might argue that a household level panel specification would better capture relevant un-

observed heterogeneity. Such a model is very difficult to estimate, however, due to the number of

correlated random effects and price collinearity. We associate each household with corresponding

EA in the first wave of the survey. For households that split and move, we include their original

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EA to control for the tastes and preferences of their source community. By selecting this approach,

we posit that split-off households continue to share similar characteristics (e.g., tastes) with their

mother households (Atkin, 2013). These EA level mean variables are considered as additional de-

mand shifters contained in zhkt. Therefore, the number of demand shifters K increases by 40 in

the within-community model compared to the cross-sectional model, due to the inclusion of mean

price and price-expenditure interactions for all 19 food groups plus the numéraire.

Estimation Strategy

Following the vast literature on censored demand (Perali and Chavas, 2000; Meyerhoefer, Ranney,

and Sahn, 2005; Kasteridis, Yen, and Fang, 2011), we use the Tobit model to characterize censor-

ing. To estimate the Tobit demand system from Equation (1), we use the extended Amemiya’s

generalized least squares (AGLS) estimator developed by Zhen et al. (2014). The extended AGLS

estimator builds on the standard AGLS estimator for single-equation limited dependent variable

models (Amemiya, 1979; Newey, 1987), extending it to a system of limited dependent variable

equations with cross-equation restrictions.

Endogeneous Regressors

There are two sources of endogeneity in the demand system (Equation 1). First, log xht is deflated

by a Stone price index, which introduces budget shares into log real total expenditure yht. We

correct this form of endogeneity by creating an instrument for yht constructed as log xht deflated by

a modified Stone index where wit, the sample-average budget share for food group i, replaces whit.

The second form of endogeneity arises when deriving prices from unit values. Cost minimization

strategies such as choosing lower quality products at time of high market prices and price search

may cause unit values to be endogenously determined with demand.

Unit values contain information on both market prices and quality, which is demonstrated by the

evidence of higher unit values for the top quartile of households (table 1). If one estimates demand

using unit values without accounting for quality, the results will be biased (Cox and Wohlgenant,

1986; Deaton, 1988). We also account for household cost minimization behavior, which arises

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when households who actively search for lower prices are likely to exhibit different characteristics,

unobserved to the econometrician, from those who do not search. If the econometrician does not

correct for correlation between household cost minimization behavior and unobserved heterogeneity,

demand parameters could be biased.

We address unit value bias and potential biases from household price search in two ways. First,

we construct household Fisher Ideal price indexes at the food group level using food-item level

unit values as elements. Specifically, the Fisher Ideal price indexes for household h, food group j

(j = 1, . . . , J − 1), in time period t is calculated as

phjt =√∑

pkhqk0∑pk0qk0

∑pkhqkh∑pk0qkh

. (2)

where pkh and qkh are the unit value and physical quantity of food-item code k in food group j that

household h reports, respectively. The base unit value, pk0, and quantity, qk0, of k are calculated

as averages of the 7 day-recall values across sample households.

The Fisher Ideal price index is superlative in that it provides a second-order approximation to

a linearly homogeneous expenditure function. Compared with using food group level unit values as

the price variables for the demand system described by Equation 1, the Fisher Ideal index addresses

unit value bias arising from substitution between food items within a food group. However, to the

extent that the Fisher Ideal price index is still constructed using item-level unit values, it does not

account for unit value bias arising from within-item substitution nor endogeneity from price search.

To address price search endogeneity, our second bias reduction strategy creates instrument

variables for each price index. For price index phjt, we calculate a mean price index using the price

index values of other households (donor households) in the same and survey window (month and

year). If a household has no such donor household, we use a mean price index calculated over

other households in the same region and survey wave for the household. This approach to creating

price instruments originated from Hausman (1996) and was popularized by Nevo (2001). Common

supply shocks ensure strong correlation between the instrument and the endogenous price. The

identification assumption is that idiosyncratic demand shocks experienced by household h are not

correlated with those of the donor households.

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The price index for the numéraire good is the Tanzanian consumer price index (CPI) less food,

alcoholic beverages, tobacco, and narcotics. The instrument for the numéraire good is the CPI

lagged by two months.9

Diet Quality Assessment

Using the food demand system parameters, we calculate nutrient demand elasticities following

Huang (1996). We report the derivation of nutrient demand elasticities in Appendix B. The fun-

damental premise is that nutrient intake largely depends on food consumption, and that food

consumption is affected by individuals’ preferences for foods, their expenditures, and the prices

they face. Leveraging the latent responsiveness of nutrient demand in response to price and ex-

penditure shocks, we predict changes in Tanzanian households’ overall diet quality under different

price and income shocks for consumers across the wealth distribution.

We assess adequacy of each macro and micro nutrient relative to the household’s requirements.

Each household’s daily nutrient requirements for energy (FAO/WHO/UNU, 2004) and protein

(WHO and UNU, 2007) are calculated according to weight and gender, assuming moderate activity

and an average body weight (e.g., 65 kg for adult males and 55 kg for adult females). The daily

micronutrient requirements, which are also age and gender specific, are derived from the Institute

of Medicine Dietary Estimated Average Requirements (EAR) (Institute of Medicine, 2006), with

the exception of zinc which is from the International Zinc Nutrition Consultative Group (IZiNCG,

2004). The EAR is the intake level at which half the population meets its requirement for a

particular nutrient. Because actual nutrient requirements of individuals are unknown and 50% of

the population has requirement less than the EAR, the EAR is typically used to assess diet adequacy

of a group.10 For each household, we assign the appropriate age and gender nutrient requirement

to each individual in the household and sum these values across the household members to obtain

a total household EAR.

We assess diet adequacy for each household and nutrient in two ways. Similar to Foster Greer9As Tanzanian CPI is not available from September 2008 to Septermber 2009, we use the CPI of the month from

which data is available for the missing instrument variable for the price index for the numéraire good and two monthsahead of that month for the price index for the numéraire good.

10We discuss requirements for each nutrient further in Appendix C.

15

Thorbecke - 0 (FGT-0) poverty gap index, we first calculate the share of households whose household

daily intake of each nutrient (which we calculate from predicted food group budget shares) is below

the total household EAR for corresponding nutrients.11 Second, we obtain an intake adequacy index

for each nutrient by dividing the household’s daily total nutrient intake by the total household EAR

for that nutrient. To calculate total nutrient intake, we sum predicted intake for each food group,

conditional on the household’s total expenditures, prices faced, and demand shifters. Within each

food group and each household, we use that household’s consumption share of each food item, and

item-specific nutrient and conversion factors, to determine the nutrient intake associated with that

group.12 For the intake adequacy index, we cap each household’s nutrient adequacy at 1 so that

one household’s over-consumption cannot offset another household’s under-consumption.

To evaluate the impacts of total expenditure and food price changes on consumers’ diet quality,

we also predict household consumption of food items after simulating changes in prices and ex-

penditures. While we could simulate any number of policies that might affect diet quality through

changes in food demand, in this paper we focus on a hypothetical 10% increase in total expenditure

and a hypothetical 10% decrease in the price of selected food groups. For food price changes,

we aggregate the 19 food groups into five broader commodity groups (i.e., staple grains, starchy

staples, pulses & nuts, fruits and vegetables, and animal source foods).13 We apply household-

specific nutrient demand elasticities to the “actual” household nutrient intakes and calculate the

post-shock household daily nutrient intakes. Using the calculated household nutrient intakes, the

household-specific post-shock diet adequacy is then assessed relative to the total household EARs.

With this simulation approach, we assess the partial equilibrium response to the simulated food

price change. This could be considered an instantaneous elasticity of diet quality with respect to

food price changes, allowing consumers to substitute between food and non-food consumption, and

to substitute between food groups within food consumption. In our food price simulations, we do

not account for any changes in farmer income that might arise as a result of food price changes. A11We obtain a predicted food budget share for each food group using the EASI demand system parameters.12We impute food group level unit values non-consuming households by multiplying household-specific price indexes

by the corresponding food group level mean unit values.13The food commodity ‘staple grains’ includes rice, maize, wheat and other cereals; ‘starchy staples’ includes

cassava, roots, tubers, and other starches; ‘pulses and nuts’ includes pulses, nuts and seeds; ‘fruits and vegetables’includes vegetables and fruits; and ‘animal source foods’ includes red meat, poultry, eggs, fish and seafood, and dairy.

16

a decrease in maize prices would likely decrease the incomes of maize producers who are net sellers

of maize, for example.

Results

We estimate the system of J−1 Tobit equations (Equation 1) using the extended AGLS estimator.

After estimating the demand equations, we recover the parameters of the budget share equation

for the numéraire good, which is not censored, using the homogeneity, symmetry, and adding-up

(∑Ji=1 µj = 1 and

∑Jj=1 βjr = 0) restrictions.14 We first determine which Engel-curve, between L =

1 and L = 5, best fits the data by testing the joint significance of coefficients βiL (i = 1, . . . , J − 1)

using a minimum distance estimator. We find that L = 3, or a 3rd degree of polynomial on real total

household expenditure, results in the best fit.15 When conducting this test, we do not impose the

homogeneity and symmetry conditions on the demand system. Otherwise, the test would become

a joint test of βiL and the theoretical restrictions of consumer demand.

We also test for the joint significance of coefficients on the interaction between log price and

real total expenditures (αijy). Without imposing the symmetry and homogeneity conditions, the

test produces a test statistic of 2,662 (p-value < 0.000) with 380 degrees of freedom. This result

suggests that Hicksian demand indeed varies with total expenditures. We explain the derivation of

food demand elasticities in Appendix A.

Given that the within-community EASI model controls for unobserved heterogeneity at the EA

level but the cross-sectional EASI model does not, our main results presented below are based on

the within-community model. Qualitatively, the within-community results are similar to the cross-

sectional results. Quantitatively, they differ in two notable ways. First, the absolute magnitudes

of own-price elasticities estimated from the within-community model tend to be smaller than those

from the cross-sectional model. Figure 2 shows this finding more clearly. Neglecting the effects of14Adding-up restrictions ensure that the estimated budget shares sum to one. Satisfying the above three parametric

restrictions, a set of demand functions are integrable.15Under the null that yLh can be excluded from the demand system, the test statistic is asymptotically distributed

as χ2(J − 1). When L = 3, the test statistic is 199, whereas it is 63.4 when L = 4. Although the null with the teststatistics for L = 4 is not rejected, we find that adding additional polynomial term only creates numerical challengesdue to increasing collinearity. Under the model with L = 4, the signs and magnitudes of the expenditure elasticitieson some food groups (including staple foods) are unreasonably large and some are negative even for the pooresthouseholds. Also, gaps between mean and median elasticities are substantially large.

17

regional food tastes on price variations could lead the econometrician to over-estimate the magni-

tude of own-price elasticities. For example, suppose region A, where households have traditionally

allocated a large portion of their expenditure on rice, tends to have lower rice prices than region

B, where households do not exhibit strong preferences for rice.16 The pooled cross-sectional model

relies on between-community as well as within-community price variation to identify the price ef-

fects, while the within-community model relies only on within-community price variations. When

the lower price at community A is not entirely exogenous to demand, the pooled cross-sectional

model tends to overestimate the price response by associating the entire spatial price difference

between communities A and B with the consumption difference between the two communities. The

within-community model corrects for this omitted variable bias by using community-specific aver-

age prices as control variables. If we fail to address the contribution of community preferences, we

are likely to overestimate the causal effects of prices on demand. The second difference between

the within-community model and cross-sectional model is that the cross-sectional model reports

more cross-price elasticity pairs that are statistically significant, implying that ignoring unobserved

community heterogeneity is likely to lead to over-rejection of the null effect of variations in relative

food prices on consumption.

Food Demand Expenditure Elasticities

As for the total expenditure elasticities at the sample median, the last column in table 2 presents

the demand elasticities with respect to total expenditures for each food group. Table 3 shows

the same results for the pooled cross-sectional model. Expenditure elasticity estimates are similar

between models, suggesting that unobserved location-specific heterogeneity is not an important

determinant of the sensitivity of food consumption to changes in total expenditures. For both

models, food demand is more responsive to expenditure changes than to food price changes (we

discuss these in the subsection on food demand price elasticities). These findings are consistent

with previous research showing that households’ food consumption patterns are more sensitive to16Over the generations, households tend to evolve their preferences for local foods that are readily available in their

region and easy to grow. Easy access to local foods results in low prices of these foods, thus reinforcing households’strong tastes for these foods (see, e.g., Atkin, 2013).

18

income effects than price effects (Weliwita, Nyange, and Tsujii, 2002; Abdulai and Aubert, 2004a).

Our results show that total expenditure elasticities for inferior staple foods, such as maize and

cassava, are relatively small and insignificant. This suggests that households tend to shift away from

these less-preferred food groups with additional income, which is consistent with our expectation.

By contrast, total expenditure elasticities for red meat, poultry, and eggs are over two, implying

that the quantities consumed of these animal source products almost double with a 1% increase

in total expenditures. These demand patters are consistent with those resulting from another

Tanzanian nutrient study (Abdulai and Aubert, 2004a). Our expenditure elasticities for animal

sourced foods, however, differ from those estimated by Weliwita, Nyange, and Tsujii (2002), who

show that demand for meat is inelastic with respect to total food expenditures using a conditional

demand system. Among non-staple foods, we characterize vegetables, fish & seafood, and fats &

oils as normal goods, in that an increase in total expenditure leads to a less than proportionate

increase in demand.

Figure 3 shows quartile-specific elasticities of food demand with respect to total expenditure

changes. We observe notable differences in the effects of total expenditure on food consumption

across quartiles. The most striking pattern is that food consumption becomes less sensitive to total

expenditure changes as per-capita total expenditures increase. For instance, there are seven food

groups with an expenditure elasticity greater than two in the bottom quartile, and none in the top

quartile. As expected, fish & seafood are luxury goods (with total expenditure elasticity of 1.42) for

households below the national poverty line, but not for households above it. Expenditure elasticity

for fruit is 2.21 among the poorest households but it drops to 0.94 in the second quartile. Moreover,

the lowest quartile households exhibit very strong income elasticity of demand for both poultry and

eggs. With a 1% increase in total expenditure, their consumption of these food groups increases

by 4.36% and 3.77%, respectively. Lastly, although statistically insignificant, the total expenditure

elasticity for cassava is negative for the top quartile (-0.05). Combined with the relatively large

own-price elasticities for cassava among these households, this result provides suggestive evidence

that relatively richer households could perceive cassava as inferior food.

19

Nutrient Expenditure Elasticities

In this subsection, we first report the elasticities of households’ dietary intake of macro- and micro-

nutrients with respect to total expenditures that are implied by the estimated demand system

parameters. Then we evaluate the effects of a simulated 10% increase in total expenditure on diet

quality of Tanzanian households across income quartiles. Sample-wide median nutrient demand

elasticities with respect to total household expenditures are presented in the last column of table

4. For most nutrients, total expenditure elasticities are fairly large and all significant at the 1%

level. These findings underscore the role that income growth plays in improving dietary quality

(Ecker and Qaim, 2011). We also report nutrient total expenditure elasticities separately by income

quartile in figure 4. For zinc, vitamin A, folate, protein, dietary energy, and iron, nutrient intake

elasticities with respect to total expenditure are generally larger in magnitude for the wealthier

quartiles than for the poorest quartile. The only exceptions are fat and sugar, which show the

largest expenditure elasticities in the poorest quartile. The expenditure elasticity of demand for

vitamin A is 0.76% in the poorest quartile, 1.26% in the third quartile, and 1.25% for non-poor

households. For folate, the expenditure elasticity is 0.82% for the first quartile, 0.94% for the third

quartile, and 1.01% for non-poor households.

Using these nutrient expenditure elasticities, we simulate the impact of a 10% increase in total

expenditure on 1) the share of households whose intake is adequate (i.e. it exceeds the EAR, figure

5) and 2) continuous nutrient intake adequacy (figure 6). In the status-quo, few households in the

poorest quartile consume enough dietary energy to exceed the EAR (only 7%). The results are

similar for protein (only 31%), iron (54%), zinc (18%), vitamin A (7%) or folate (4%). Even in the

highest quartile, sufficient intake is not universal, with only 72% of households consuming dietary

energy above the EAR, only 79% consuming adequate zinc, and only 41% consuming adequate

folate. A simulated 10% increase in expenditure raises the portion of households in each quartile

whose intake exceeds the EAR, as depicted in figure 5, though intake for many households remains

inadequate. The share of households in the poorest quartile who transition from insufficient to

sufficient intake is 4.6% for dietary energy, 12% for protein, 7% for iron and zinc, 5% for vitamin

A, and 2.8% for folate.

20

As for nutrient intake levels as a share of the EAR (intake gaps), we find that median intake

gaps are largest for poor households before the simulated increase in total expenditures. Consumers

in the bottom two quartiles’ average dietary energy intake only 64% of EAR in the first quartile

and 85% of EAR in the next quartile. A simulated 10% increase in expenditure raises dietary

energy intake to 72% of EAR in the first quartile and to 0.94% in the second quartile. For protein,

the bottom quartile is the only segment showing insufficiency (at 85% of EAR), and a projected

increase in expenditures would bring these poor consumers to a 94% adequacy level. Iron intake

appears to be sufficient for median households in the sample across poverty quartiles. Zinc intake

by households in the bottom quartile increases from 73% of the EAR to 80% after the simulated

increase in expenditures. For vitamin A, the poorest quartile consumes 60% of EAR, and the

second quartile consumes 94% of EAR. After the income simulation, the poorest quartile increases

to 64%, and the median household in the 2nd quartile achieves intake adequacy of vitamin A. Folate

intake is insufficient across the income distribution, at 58% of EAR for the poorest quartile and

0.92% of EAR for the wealthiest. After the income simulation, adequacy increases to 64% in the

poorest quartile and to sufficiency in the wealthiest quartile. Altogether this analysis suggests that

interventions that raise the incomes of very poor households could be effective in reducing gaps in

intake of dietary energy, protein, zinc, vitamin A, and folate for poor consumers.

Food Demand Price Elasticities

We report the median Marshallian price elasticities of demand for the whole sample in table 2 for

the within-community model and table 3 for the cross-sectional model. Both model specifications

show that households’ food consumption is more sensitive to own-price variations compared to

cross-price changes. Demand for rice, wheat and other cereals, roots, tubers, and other starches,

nuts and seeds, and poultry is highly responsive to own-price changes, with elasticities near or

below −2. Demand for sugars, vegetables, eggs, and fats is relatively less responsive to own-price

variations.

Quartile-specific food demand own-price elasticities are reported in table 5. Figures 7 to 10

illustrate own- and cross-price elasticities separately for each of the four expenditure quartiles.

21

Demand for almost all food groups is elastic, except for sugar, vegetables, dairy, fats and oils,

coffee, tea, and cocoa, and other foods. For households below the domestic poverty line, the own-

price elasticities for rice, wheat and other cereals, roots, tubers, and other starches, nuts and seeds,

and poultry are substantially large. Among all quartiles, the own-price elasticity for maize (the

primary staple food in Tanzania) is statistically significant with the exception of the top quartile

households. As expected, we first find that own-price elasticities generally fall in absolute terms with

rising per-capita total expenditure. This trend is prominent among rice, wheat & other cereals,

nuts & seeds, fruit, and poultry. For instance, the poorest 25% of consumers respond to a 1%

increase in poultry price by reducing poultry consumption by 2.90%. Non-poor consumers in the

top income quartile do not decrease their demand for poultry (i.e., the price elasticity of demand is

-0.19 and not statistically different from zero). We also observe that demand becomes more price

elastic in a few groups as income rises. This is the case for vegetables, dairy, and in coffee, tea &

cocoa. A possible explanation for this is that higher income households may have satisfied their

subsistence (or pre-committed) demand for these foods so that demand is more responsive to price

changes at the margin (Piggott, 2003).

Comparing cross-price elasticities across quartiles, we also find that poorer households demon-

strate stronger substitution patterns between food groups in response to price changes in one group.

Specifically, there are 27 statistically significant substitute pairs between food groups among the

poorest households whereas there are only 12 significant substitute pairs found for the richest house-

holds. Consumers at different income levels exhibit different substitution patterns. For instance,

for the bottom quartile households, rice, roots & tubers, and pulses are statistically significant

substitutes for vegetables. Top quartiles do not show any substitutes for vegetables, by contrast.

For poor households, pulses are a good substitute for not only vegetables but also for animal prod-

ucts and fish and seafood. On the contrary, pulses are not substituted with any other food groups

among the richest households.

22

Nutrient Price Elasticities

For each food group, we calculate the elasticity of micro- and macro-nutrient intake with respect

to a change in that food price, which accounts for both the own-price effect on consumption of

that food and also for all of the substitutions that the consumers are expected to make when a

food changes price. For most individual food groups, the sample-wide elasticity of nutrient intake

with respect to a price change in that food group is small and not significantly different from

zero (table 4). Maize is an important exception. When maize prices increase by 1%, the median

response among Tanzanian consumers is a 0.34% decrease in dietary energy intake, 0.36% decrease

in protein intake, 0.24% decrease in fat intake, 0.49% decrease in iron intake, 0.44% decrease in

zinc intake, and 0.29% decrease in folate intake. The same maize price increase is also associated

with an increase in sugar intake (0.46%) and vitamin A intake (0.30%). When pulses increase in

price by 1%, this is associated with a median sample-wide decrease of dietary energy by 0.10%,

protein by 0.15%, iron by 0.21%, zinc by 0.11%, vitamin A by 0.24%, and folate by 0.09%. We find

that vitamin A intake is most responsive to the price of roots & tubers, with a 1% price increase in

that group associated with a median 0.65% decrease in vitamin A intake. The substitution effects

between maize and roots & tubers explains why vitamin A intake increases when maize prices

increase. Several other substitution patterns lead to counterintuitive results. For example, the

increase in sugar intake that accompanies maize price increases is driven by substitution between

maize and sugar consumption, as well as between maize and coffee & tea consumption (as shown in

table 4). Overall, we find that an increase in coffee & tea prices drives an increase in consumption

of most macro- and micro-nutrients.

We further explore the sensitivity of macro- and micro-nutrient intake within each total expen-

diture quartile in Figures 11 to 13. The stars denote combined nutrient effects that are statistically

significant at the 5% level. We first find that the nutrient intake of poor households is more strongly

affected by food price changes than is the nutrient intake of non-poor or near-poor households. For

instance, the price of maize, pulses, and nuts & seeds affect the macro- and micro-nutrient consump-

tion of the first quartile of households most strongly. The effects of these food groups, however,

diminish with rising total expenditure.

23

Consistent with the result from the whole sample, we find that the price of roots & tubers is

important for vitamin A intake, especially for the poorest households. A 1% increase in the price of

this food group results in a drop in vitamin A intake by 1.03% with the magnitude of the elasticity

decreasing in absolute terms with rising income. This food group accounts for only 6.2% of total

food expenditure for the poorest households. This suggests that encouraging consumption of roots,

tubers, and other starches by decreasing its price is critical for promoting vitamin A intake among

the poor. For the top 50% of households, the price of vegetables is an important determinant of

vitamin A intake. In addition, our finding that poultry prices do not affect intake adequacy of

macro- or micro- nutrients is interesting because households, especially poor households, exhibit

demand patterns that are quite responsive to the price of poultry. Because households substitute

poultry with other, less expensive foods that are nutrient-dense, poultry price increases do not

necessarily reduce overall diet quality of poor households.

Next, we simulate the effects of stylized agricultural interventions targeting five different food

groups that would seek to raise productivity and thus lower food prices for consumers (staple grains,

starchy staples, pulses & nuts, fruits & vegetables, and animal source foods). For consumers in

the poorest expenditure quartile, Figures 14 and 15 we report the share of households who achieve

adequate intake and the median adequacy index across micro- and macro- nutrients after a simulated

10% decrease in each food group’s price, respectively. In these simulations, we include the effects

of consumer substitutions between food groups as relative food prices change.

We find that the staple grains strategy results in an additional 2.5% of poor households achieving

dietary intake adequacy, while also improving adequacy of protein (6.1%), iron (4.7%) and zinc

(3.8%). Cheaper staple food prices result in a smaller share of households achieving Vitamin A

adequacy (1%), and the effect on folate adequacy is marginal (0.4%). Targeting starchy staple

foods for price lowering would result in smaller increases in the share of poor households achieving

adequate intake of dietary energy, protein, iron, and zinc. However, it would lead to vitamin

A intake adequacy among an additional 5.3% of poor households, and folate intake would be

adequate for an additional 2.6% of households. A strategy targeting starchy staples is the only

strategy evaluated that would increase intake across all macro- and micro- nutrients included in

24

this analysis. It would increase the share of poor households who have adequate intake of dietary

energy (by 2.7%), protein (by 5.9%), iron (by 6.7%), zinc (by 4.2%), vitamin A (by 1.2%), and

folate (by 3.0%). A strategy targeting fruits and vegetables would actually decrease the share of

households who achieve sufficient intake of dietary energy (by 0.6%), protein (by 0.9%), iron (by

1.9%), and zinc (by 0.5%), while marginally increasing the share of poor households with adequate

intake of vitamin A (0.7%) and folate (by 0.1%). An animal sourced foods strategy would increase

the share of poor households with sufficient intake of protein (by 1.5%) and vitamin A (by 0.4%),

while lowering the share of poor households with sufficient intake of dietary energy (by 0.8%), iron

(by 3.8%), zinc (by 0.3%) and folate (by 1.3%).

The impacts of the interventions resulting in lower food prices have a very similar effect on

macro- and micro- nutrient intake gaps, as shown in figure 15, in that the only food group to

improve intake across the board is pulses & nuts. Staple grain interventions improve the adequacy

ratio for all nutrients except for vitamin A, which is best improved via interventions targeting

starchy staples. Strategies lowering consumer prices of fruit & vegetables and animal source foods

improve the adequacy ratio with respect to some macro- and micro- nutrients while decreasing the

adequacy ratio with respect to other macro- and micro- nutrients. Results of this simulated food

price change suggests that the diet quality of different households across the income distribution

would respond differently to different strategies to lower food prices, with the strongest impacts

felt by households below the poverty line.

Discussion and Conclusions

Our demand systems estimation approach allows us to examine very rich patterns of dietary change

associated with consumer income growth as well as the substitution patterns that cause price

changes of individual foods to alter overall demand patterns in sometimes counter-intuitive ways.

Generally, growth in total expenditures is associated with diet diversification, with poor consumers

exhibiting especially large expenditure-elastic demand for poultry, eggs, red meet, nuts & seeds,

fruit, dairy, rice and wheat. However, given the overall nutrition profile of consumers’ preferences,

only fat and protein behave as “luxury” macro-nutrients for poor consumers, with intake increasing

25

by more than 1% when expenditures grow by 1%. The expenditure-intake elasticities of iron, zinc,

vitamin A, and folate are all lower than 1.

Many poor households consume inadequate quantities of dietary energy, protein, zinc, vitamin

A and folate. A simulated increase in total expenditures by 10% suggests that expenditure growth

would help to close dietary income gaps for many consumers, but that gaps would persist. Overall,

the evidence is consistent with general improvements in diet quality as expenditures increase, and

much lower prevalence of intake inadequacy among higher income households than poor households.

Cash transfers and other income-boosting interventions targeting the poor are likely to result in

improved intake of protein, dietary energy, and key micro- nutrients.

By examining consumers’ own- and cross-price elasticity patterns in the context of a large

demand system, we gain several insights. First, the dominance of one single food staple, maize, in

Tanzanian diets, results in its price becoming an important determinant not just of poor consumers’

dietary energy intake, but also their intake of most macro- and micro- nutrients. Second, food

price changes can affect overall diet quality indirectly through substitution patterns. When these

indirect substitution patterns induce consumers to substitute away from more nutritious foods and

towards less nutritious foods when one food’s price increases, then consumers’ preference patterns

can exacerbate the diet quality impact of an increase in the price of that food.

Because maize consumption accounts for such a large budget share, small percentage changes

in demand can result in large nutrient intake changes simply because the underlying quantitites

demanded are large. By the same token, intake of key macro- and micro- nutrients is not very

responsive to changes in the prices of animal sourced foods like poultry, eggs, and dairy because

these items are consumed in small quantities by poor households. Red meat prices are an important

determinant of fat intake, however, for all consumers, and for protein intake of wealthier consumers.

The elasticities of intake of protein, fat, iron, zinc, and folate with respect to maize prices are

negative and statistically significant. When maize prices increase, consumers exhibit very strong

substiutions away from nuts and seeds and away from poultry and cassava. They substitute to-

wards rice, wheat, sugar, roots & tubers, fats & oils, coffee, tea & cocoa and soft drinks. Ironically,

increased coffee, tea & cocoa prices lead to improved intake of energy, protein, iron and zinc. Con-

26

sumers substitute towards fats and oils, eggs, red meat, and pulses when the price of coffee, tea &

cocoa changes. So interventions to lower the prices of these food items could have negative health

impacts by discouraging such substitutions and decreasing intake of more healthful substitutes.

Researchers seeking to target productivity growth efforts towards those that will most strongly

improve the profile of poor consumers’ diets through market-wide price effects should not underes-

timate the importance of affordable pulses and nuts and staple foods. Lowering the prices of these

items would improve the overall macro- and micro- nutrient profile.

This research of course comes with caveats. First, because we measure food intake at the

household level rather than the individual level, we do not know that food is shared equitably among

household members according to their dietary requirements. If distribution is indeed unequal, then

fewer individuals will achieve dietary intake adequacy at any given total expenditure level. Studies

that carefully measure food distribution within households have not found that intra-household

distribution is inequitable (e.g., Coates et al., 2018). Another empirical challenge is that we cannot

reconstruct the nutritional profile of food items that are consumed away from the home given the

data available. Generally, food consumption away from home is not likely to be a major determinant

of diet quality because it is still quite small. However, it is growing in urban areas and could be

an important source of intra-household differences in food intake that might have diet quality

implications.

An improved understanding of consumer preferences in Tanzania could inform priority-setting

within the country as well as in the international agricultural R&D system. Ultimately, it will

be important to consider the impacts of agricultural interventions that affect relative food prices

on both producers and consumers. Producer and consumer effects are usually at odds with each

other, though Bel (2018) found that global increases in prices of quinoa, an important food staple

in Peru, generally raised welfare without harming consumers. In order to evaluate welfare impacts

of different agricultural interventions, it will be important to consider these new consumer demand

elasticities within a general equilibrium framework that also accounts for producer responses to

price changes.

27

References

2018. “Foods and Fads: The Welfare Impacts of Rising Quinoa Prices in Peru.” World Development

112:163–179.

Abdulai, A., and D. Aubert. 2004a. “A cross-section analysis of household demand for food and

nutrients in Tanzania.” Agricultural Economics 31:67–79.

—. 2004b. “Nonparametric and parametric analysis of calorie consumption in Tanzania.” Food

Policy 29:113–129.

Almås, I., J. Haushofer, and J.P. Shapiro. 2019. “The income elasticity for nutrition: Evidence

from unconditional cash transfers in Kenya.” NBER Working Paper 25711.

Amemiya, T. 1979. “The estimation of a simultaneous-equation Tobit model.” International Eco-

nomic Review 20:169–181.

Arimond, M., and M.T. Ruel. 2004. “Dietary diversity is associated with child nutritional status:

Evidence from 11 demographic and health surveys.” The Journal of Nutrition 134:2579–2585.

Atkin, D. 2013. “Trade, tastes, and nutrition in India.” American Economic Review 103:1629–63.

Banks, J., R. Blundell, and A. Lewbel. 1997. “Quadratic Engel curves and consumer demand.”

Review of Economics and Statistics 79:527–539.

Black, R.E., L.H. Allen, Z.A. Bhutta, L.E. Caulfi, M.D. Onis, M. Ezzati, C. Mathers, and J. Rivera.

2008. “Maternal and child undernutrition: global and regional exposures and health conse-

quences.” The Lancet 371:243–260.

Busert, L.K., M. Neuman, E.A. Rehfuess, S. Dulal, J. Harthan, S.S. Chaube, B. Bhandari,

H. Costello, A. Costello, D.S. Manandhar, and N.M. Saville. 2016. “Dietary diversity is posi-

tively associated with deviation from expected height in rural Nepal.” The Journal of Nutrition

146:1387–1393.

28

Calloway, D., and S. Murphy. 2006. WorldFood Dietary Assessment System, 2nd ed. Berkeley,

California: University of California at Berkeley.

CGIAR. 2016. “CGIAR Strategy and Results Framework, 2016-2030.” Working paper, CGIAR.

Coates, J., B.N. Patenaude, B.L. Rogers, A.C. Roba, Y. Kidane, A. Fikre, K.L. Spielman, J. Gerald,

D.R. Friedman, N. Science, and H. Avenue. 2018. “Intra-household nutrient inequity in rural

Ethiopia.” Food Policy 81:82–94.

Colen, L., P.C. Melo, Y. Abdul-salam, D. Roberts, S. Mary, and S.G.Y. Paloma. 2018. “Income

elasticities for food, calories and nutrients across Africa: A meta-analysis.” Food Policy 77:116–

132.

Cox, T.L., and M.K. Wohlgenant. 1986. “Prices and quality effects in cross-sectional demand anal-

ysis.” American Journal of Agricultural Economics 68:908–919.

Deaton, A. 1988. “Quality, quantity, and spatial variation of price.” American Economic Review

78:418–430.

Deaton, A., and J. Muellbauer. 1980. “An Almost Ideal Demand System.” American Economic

Review 70:312–326.

Ecker, O., and M. Qaim. 2011. “Analyzing nutritional impacts of policies: An empirical study for

Malawi.” World Development 39:412–428.

Evenson, R.E., and D. Gollin. 2003. “Assessing the impact of the Green Revolution, 1960 to 2000.”

Science 300:758–762.

FAO/WHO/UNU. 2004. “Human Energy Requirements.” Report of a joint FAO/WHO/UNU expert

consultation. Rome (Italy): UNU/WHO/FAO.

Haddad, L., and C. Hawkes. 2016. “A new global research agenda for food.” Nature 540:30–32.

Hausman, J.A. 1996. “Valuation of new goods under perfect and imperfect competition.” In T. F.

Bresnahan and R. J. Gordon, eds. The Economics of New Goods. Chicago: University of Chicago

Press, chap. 5, pp. 207–248.

29

Headey, D.D., and H.H. Alderman. 2019. “The relative caloric prices of healthy and unhealthy foods

differ systematically across income levels and continents.” The Journal of Nutrition 149:2020–

2033.

Huang, K.S. 1996. “Nutrient elasticities in a complete food demand system.” American Journal of

Agricultural Economics 78:21–29.

Huang, K.S., and B.H. Lin. 2000. “Estimation of food demand and nutrient elasticities from house-

hold survey data.” Working paper.

Institute of Medicine. 2006. Dietary Reference Intakes: the Essential Guide to Nutrient Require-

ments, J. J. Otten, J. P. Hellwig, and L. D. Meyers, eds. Washington, DC: National Academies

Press.

Institute of Medicine (US) Panel on Micronutrients. 2002. Dietary Reference Intakes for Vitamin A,

Vitamin K, Arsenic, Boron, Chromium, Copper, Iodine, Iron, Manganese, Molybdenum, Nickel,

Silicon, Vanadium, and Zinc. Washington, DC: National Academies Press.

IZiNCG, K. Brown, J. Rivera, Z. Bhutta, R. Gibson, J. King, B. Lonnerdal, M. Ruel, B. Sandtrom,

E. Wasantwisut, and C. Hotz. 2004. “International Zinc Nutrition Consultative Group (IZiNCG)

Technical Document# 1. Assessment of the risk of zinc deficiency in populations and options for

its control.” Food Nutrition Bulletin 25:S99–S203.

Jensen, R.T., and N.H. Miller. 2010. “Do consumer price subsidies really raise nutrition.” Review

of Economics and Statistics 93:1205–1223.

Kasteridis, P., S.T. Yen, and C. Fang. 2011. “Bayesian estimation of a censored linear Almost

Ideal Demand System: Food demand in Pakistan.” American Journal of Agricultural Economics

93:1374–1390.

Lafrance, J.T. 1991. “When is expenditure "exogenous" in separable demand models?” Western

Journal of Agricultural Economics 16:49–62.

30

LaFrance, J.T., and W.M. Hanemann. 1989. “The dual structure of incomplete demand systems.”

American Journal of Agricultural Economics 71:262–274.

Lewbel, A. 1996. “Aggregation without separability: A generalized composite commodity theorem.”

American Economic Review 86:524–543.

Lewbel, A., and K. Pendakur. 2009. “Tricks with Hicks: The EASI demand system.” American

Economic Review 99:827–63.

Lukmanji, Z., E. Hertzmark, N. Mlingi, V. Assey, G. Ndossi, and W. Fawzi. 2008. “Tanzania Food

Composition Tables.” MUHAS-TFNC, HSPH, Dar es Salaam, Tanzania.

Masters, W.A., Y. Bai, A. Herforth, D. Sarpong, F. Mishili, J. Kinabo, and J. Coates. 2018.

“Measuring the Affordability of Nutritious Diets in Africa: Price Indexes for Diet Diversity and

the Cost of Nutrient Adequacy.” American Journal of Agricultural Economics 100:1285–1301.

Meyerhoefer, C.D., C.K. Ranney, and D.E. Sahn. 2005. “Consistent Estimation of Censored De-

mand Systems using Panel Data.” American Journal of Agricultural Economics 87:660–672.

Nevo, A. 2001. “Measuring market power in the ready-to-eat cereal industry.” Econometrica 69:307–

342.

Newey, W.K. 1987. “Efficient Estimation of Limited Dependent Variable Models with Endogenous

Explanatory Variables.” Journal of Econometrics 36:231–250.

Perali, F., and J.P. Chavas. 2000. “Estimation of Censored Demand Equations from Large Cross-

section Data.” American Journal of Agricultural Economics 82:1022–1037.

Piggott, N.E. 2003. “The Nested PIGLOGModel: An Application to U.S. Food Demand.” American

Journal of Agricultural Economics 85:1–15.

Pingali, P. 2015. “Agricultural policy and nutrition outcomes – getting beyond the preoccupation

with staple grains.” Food Security 7:583–591.

31

Rah, J.H., N. Akhter, R.D. Semba, S.D. Pee, M.W. Bloem, A.A. Campbell, K. Sun, J. Badham, and

K. Kraemer. 2010. “Low dietary diversity is a predictor of child stunting in rural Bangladesh.”

European Journal of Clinical Nutrition 64:1393–1398.

Sahn, D.E. 1988. “The effect of price and income changes on food-energy intake in Sri Lanka.”

Economic Development and Cultural Change 36:315–340.

Tanzania National Bureau of Statistics. 2009. Basic Information Document-National Panel Survey

(NPS 2008-2009). United Republic of Tanzania.

USDA. 2016. USDA National Nutrient Database for Standard Reference, Release 28 . Washington,

DC: US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory.

Weliwita, A., D. Nyange, and H. Tsujii. 2002. “Food Demand Patterns in Tanzania: A Censored

Regression Analysis of Microdata.” Sri Lankan Journal of Agricultural Economics 5:9.

Wengreen, H.J., C. Neilson, R. Munger, and C. Corcoran. 2009. “Diet Quality Is Associated with

Better Cognitive Test Performance among Aging Men and Women.” The Journal of Nutrition

139.

WHO and FAO. 2004. Vitamin and Mineral Requirements in Human Nutrition, 2nd ed.. World

Health Organization.

WHO and UNU. 2007. Protein and Amino Acid Requirements in Human Nutrition, vol. 935. World

Health Organization.

Willett, W., J. Rockström, B. Loken, M. Springmann, T. Lang, S. Vermeulen, T. Garnett,

D. Tilman, F. DeClerck, A. Wood, M. Jonell, M. Clark, L.J. Gordon, J. Fanzo, C. Hawkes,

R. Zurayk, J.A. Rivera, W. De Vries, L. Majele Sibanda, A. Afshin, A. Chaudhary, M. Herrero,

R. Agustina, F. Branca, A. Lartey, S. Fan, B. Crona, E. Fox, V. Bignet, M. Troell, T. Lindahl,

S. Singh, S.E. Cornell, K. Srinath Reddy, S. Narain, S. Nishtar, and C.J. Murray. 2019. “Food in

the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems.”

The Lancet 393:447–492.

32

Zhen, C., E.A. Finkelstein, J.M. Nonnemaker, S.A. Karns, and J.E. Todd. 2014. “Predicting the ef-

fects of sugar-sweetened beverage taxes on food and beverage demand in a large demand system.”

American Journal of Agricultural Economics 96:1–25.

Zhen, C., E. Lazaro, and D. Mitchell. 2016. “Cross-Sectional Estimation of Food and Nutrient

Demand in Tanzania Using a Large Demand System.” Unpublished, Working paper.

33

Figures

Figure 1: Households’ food vs non-food budget shares, by per-capita total expenditure quartile.Q1 denotes the poorest households and Q4 the wealthiest. The median actual food budget sharewithin each total expenditure quartile is depicted.

34

Figure 2: Comparison of own-price elasticity of demand estimates for the 19 food groups betweenthe main model specification (the within-community model) and the alternate specification (apooled cross-sectional model).

35

Cassava

Coffee, tea & cocoa

Dairy

Eggs

Fats & oils

Fish & seafood

Fruit

Maize

Nuts and seeds

Other food

Poultry

Pulses

Red meat

Rice

Roots ,Tubers & other starches

Soft drink & juice

Sugar

Vegetables

Wheat & other cereals

q1 q2 q3 q4quartile

food

gro

up

0

1

2

3

4

elasticity

Elasticity of food expenditure intake with respect to total household expenditures

Figure 3: Elasticities of food demand with respect to changes in total expenditures, by total expen-diture quartile. This figure shows the median % change in quantity demanded in each food group(for each total expenditure quartile) in response to a 1% increase in total household expenditures.

36

fat

iron

kcal

protein

sugar

total_folate

vitamin_A

zinc

q1 q2 q3 q4household per capita total expenditure quartile

nutri

ent

0.70.80.91.01.11.2

elasticity

Elasticity of nutrient intake with respect to total household expenditures

Figure 4: Elasticity of macro- and micro-nutrient intake with respect to total household expen-ditures, by total per capita expenditure quartile. The figure shows the median % change in thequantity consumed of each macro- and micro-nutrient in response to a 1% increase in total house-hold expenditures.

37

Figure 5: The predicted share of households with macro- and micro- nutrient intake exceedingthe EAR before and after a 10% simulated increase in total household expenditures. A value of 1would indicate sufficient dietary intake for all households. The figure shows the share of householdswithin each quartile whose predicted intake surpasses the EAR level using actual total expendi-tures (“before”) and after imposing a simulated 10% increase in the household’s total expenditures(“after”).

38

Figure 6: Predicted response in macro- and micro- nutrient intake adequacy gaps in response toa 10% simulated increase in total household expenditures. The figure shows the median ratiobetween predicted household intake and recommended household intake for each quartile usingactual total expenditures (“before”) and after imposing a simulated 10% increase in the household’stotal expenditures (“after”). The intake ratio is capped at 1 for households whose intake exceedsEAR. In this case, those who consume more than the EAR do not offset households who consumebelow the EAR.

39

Figure 7: Graphical depiction of all own-price demand elasticities (along the diagonal) and cross-price demand elasticities for the poorest quartile of consumers. The cross-price elasticities depictthe % change in quantity demanded of the column’s food group in response to a 1% increase inthe price of the row’s food group. The first quartile of consumers correspond with those below theTanzanian domestic poverty line.

40

Figure 8: Graphical depiction of all own-price demand elasticities (along the diagonal) and cross-price demand elasticities for the second quartile of consumers. The cross-price elasticities depictthe % change in quantity demanded of the column’s food group in response to a 1% increase in theprice of the row’s food group. The second quartile of consumers corresponds with those above theTanzanian domestic poverty line but below the international, $1.25/day PPP poverty line.

41

Figure 9: Graphical depiction of all own-price demand elasticities (along the diagonal) and cross-price demand elasticities for the third quartile of consumers. The cross-price elasticities depict the% change in quantity demanded of the column’s food group in response to a 1% increase in theprice of the row’s food group. The third quartile of consumers corresponds with those above theinternational, $1.25/day PPP poverty line but below the international, $2.50/day PPP povertyline.

42

Figure 10: Graphical depiction of all own-price demand elasticities (along the diagonal) and cross-price demand elasticities for the fourth quartile of consumers. The cross-price elasticities depictthe % change in quantity demanded of the column’s food group in response to a 1% increase inthe price of the row’s food group. The fourth quartile of consumers corresponds with those aboveinternational, $2.50/day PPP poverty line.

43

Figure 11: Elasticities of dietary energy, protein and fat intake with respect to food group prices,by expenditure quartile. The top row shows the median % change in dietary energy intake wheneach food group’s price increases by 1% for each total expenditure quartile (from left to right). Themiddle row shows the median % change in protein intake when each food group’s price increases by1% for each total expenditure quartile (from left to right). The bottom row shows the median %change in protein intake when each food group’s price increases by 1% for each total expenditurequartile (from left to right).

44

Figure 12: Elasticities of iron and zinc intake with respect to food group prices, by expenditurequartile. The top row shows the median % change in iron intake when each food group’s priceincreases by 1% for each total expenditure quartile (from left to right). The bottom row showsthe median % change in zinc intake when each food group’s price increases by 1% for each totalexpenditure quartile (from left to right).

45

Figure 13: Elasticities of vitamin A and folate intake with respect to food group prices, by ex-penditure quartile. The top row shows the median % change in vitamin A intake when each foodgroup’s price increases by 1% for each total expenditure quartile (from left to right). The bottomrow shows the median % change in folate intake when each food group’s price increases by 1% foreach total expenditure quartile (from left to right).

46

Figure 14: The predicted share of poor households (in the first quartile) whose macro- and micro-nutrient intakes exceed the EAR before and after a 10% simulated decrease in the price of a set offood groups. A value of 1 would indicate sufficient dietary intake of that macro- or micro- nutrientfor all households in the quartile. The horizontal line labeled “before” shows the share of householdsin the first quartile whose predicted intake of each macro- or micro- nutrient exceeds the EAR atcurrent food prices. Each dot represents the share of households whose dietary intake exceeds theEAR after simulating a 10% decrease the corresponding food prices. In the staple grains group,we simulate a 10% decrease in the price of rice, maize, wheat & other cereals. The starchy staplesgroup includes cassava, roots, tubers & other starches. The pulses & nuts group includes pulses,nuts & seeds. The fruits & vegetables group is self explanatory. The animal sourced foods groupincludes red meat, poultry, eggs, fish & seafood, and dairy.

47

Figure 15: The predicted change in macro- and micro- nutrient intake adequacy gaps among poorhouseholds (in the first quartile) in response to a 10% simulated decrease in the price of a group offood groups. The horizontal line labeled “before” shows the median intake adequacy (as a share ofEAR) of households in the first quartile at current food prices. Each dot represents the predictedintake adequacy after simulating a 10% decrease the corresponding food prices. In the staple grainsgroup, we simulate a 10% decrease in the price of rice, maize, wheat & other cereals. The starchystaples group includes cassava, roots, tubers & other starches. The pulses & nuts group includespulses, nuts & seeds. The fruits & vegetables group is self explanatory. The animal sourced foodsgroup includes red meat, poultry, eggs, fish & seafood, and dairy. The intake ratio is capped at 1for households whose intake exceeds EAR. In this case, those who consume more than the EAR donot offset households who consume below the EAR.

48

Table1:

Descriptiv

estatist

ics

Averag

eFo

odBud

getSh

are

Averag

eUnitVa

lue(T

SH/k

g)

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Rice

0.07

20.11

30.12

70.12

512

3513

0513

7014

93

Maize

0.25

90.21

10.16

60.10

374

282

187

893

3

Whe

atan

dothe

rcereals

0.04

20.04

70.06

20.07

714

4215

3715

8616

43

Cassava

0.09

30.05

30.03

30.01

539

249

056

968

2

Roo

ts,tub

ers,

andothe

rstarches

0.06

20.07

20.07

30.06

157

265

574

085

3

Suga

r0.03

30.04

20.04

60.04

816

4017

6818

1517

97

Pulse

s0.07

90.06

10.05

30.03

912

5613

7114

6216

15

Nutsan

dseed

s0.02

50.01

90.01

40.01

114

5317

6619

9621

94

Vegetables

0.11

20.09

30.09

10.09

492

410

2911

0311

54

Fruit

0.03

90.05

30.06

50.07

272

081

287

899

2

Red

meat

0.02

90.05

10.07

10.10

738

0644

1549

0453

84

Poultry

0.01

80.03

10.02

90.04

239

0144

9647

7851

74

Eggs

0.00

30.00

40.00

60.01

130

9534

5637

3140

00

Fish

andseafoo

d0.05

50.06

30.07

10.07

426

3729

9732

6839

27

Dairy

0.02

00.02

50.02

60.03

585

111

0513

3116

59

Fats

andoils

0.03

30.03

80.03

90.04

427

7630

7931

5732

32

Coff

ee,te

a,an

dcocoa

0.00

50.00

70.00

80.00

991

0596

1899

2710

240

Soft

drinkan

djuice

0.00

70.00

80.01

20.02

680

594

610

2811

61

Other

food

0.01

30.00

80.00

80.00

781

579

883

386

6N

otes:Thistableshow

sthemeanvalueof

each

food

grou

p’sbu

dget

share(asashareof

totalfoo

dexpe

nditu

res)

and

each

food

grou

p’sun

itprice(in

TSH

/kg)

bype

r-capita

totale

xpen

dituresqu

artile.

49

Table2:

Food

deman

delastic

ities

with

respectto

food

prices

andtotalh

ouseho

ldexpe

nditu

res,

With

in-C

ommun

ityMod

el

Food

Group

Expe

nditu

re

12

34

56

78

910

1112

1314

1516

1718

19

1.Rice

-2.25∗

0.40

-0.09

0.53∗

-0.40

-0.04

0.32∗

-0.18

0.11

0.09

0.32∗

-0.19

0.04

-0.34∗

0.24

-0.21∗

0.04

-0.06

-0.03∗

1.34∗

(-5.22

)(1.45)

(-1.10

)(1.98)

(-1.84

)(-0.78

)(2.20)

(-1.54

)(1.64)

(0.55)

(2.08)

(-1.28

)(0.83)

(-2.39

)(1.94)

(-2.26

)(1.65)

(-0.91

)(-2.02

)(4.51)

2.Maize

0.26

-0.91∗

0.29

-0.33

0.20

0.18∗

-0.00

-0.58∗

-0.06

0.14

0.01

-0.08

0.01

0.03

0.07

0.10

0.07∗

0.10

0.02∗

0.64

(1.39)

(-2.92

)(1.78)

(-1.38

)(1.26)

(2.43)

(0.04)

(-2.05

)(-1.24

)(0.96)

(0.38)

(-0.04

)(0.23)

(0.54)

(1.43)

(1.88)

(2.28)

(1.49)

(2.22)

(1.16)

3.W

heat†

-0.13

0.73∗

-2.69∗

0.43∗

0.82∗

-0.12

-0.10

0.56∗

0.03

0.13

-0.65∗

0.16

0.01

0.22

-0.12

-0.04

0.07∗

-0.37∗

-0.05∗

1.35∗

(-1.17

)(2.40)

(-6.47

)(1.99)

(3.06)

(-1.76

)(-0.91

)(2.54)

(0.15)

(0.85)

(-3.14

)(0.59)

(0.09)

(1.88)

(-1.59

)(-0.69

)(2.27)

(-2.89

)(-2.60

)(5.19)

4.Cassava

0.66∗

-0.59

0.37∗

-1.24∗

-0.43

-0.04

-0.37∗

0.02

0.02

0.14

0.59∗

0.28

0.13

0.27

0.32

0.05

-0.03

-0.27∗

0.02

0.23

(2.28)

(-1.64

)(2.14)

(-3.92

)(-1.57

)(-0.92

)(-2.29

)(0.50)

(0.31)

(1.05)

(2.09)

(1.00)

(1.63)

(1.78)

(1.90)

(0.80)

(-1.29

)(-2.07

)(1.67)

(1.24)

5.Roo

ts†

-0.42

0.37

0.60∗

-0.47

-2.21∗

-0.05

-0.75∗

0.20

0.27∗

-0.24

0.17

0.11

-0.18

-0.24

0.41∗

0.08

-0.05

-0.02

-0.01

1.22∗

(-1.63

)(1.15)

(2.43)

(-1.65

)(-4.55

)(-1.03

)(-2.71

)(1.08)

(2.49)

(-1.24

)(1.18)

(0.25)

(-1.73

)(-1.59

)(2.39)

(1.31)

(-1.93

)(-0.20

)(-1.00

)(2.71)

6.Su

gar

-0.08

0.82∗

-0.21∗

-0.15

-0.12

-0.72∗

0.37∗

-0.06

0.10

-0.13

-0.01

-0.26

-0.13

-0.02

0.16∗

0.16∗

-0.06

0.16∗

-0.03

1.04∗

(-0.66

)(4.50)

(-1.99

)(-1.62

)(-1.12

)(-4.49

)(2.66)

(-0.71

)(1.39)

(-1.46

)(0.03)

(-1.85

)(-1.62

)(-0.42

)(2.49)

(2.09)

(-1.67

)(2.25)

(-1.22

)(6.24)

7.Pu

lses

0.47∗

-0.02

-0.09

-0.51∗

-0.96∗

0.20∗

-1.54∗

-0.09

0.17∗

-0.11

0.25∗

-0.12

0.32∗

0.52∗

0.19

-0.08

0.11∗

-0.16

-0.03

0.74∗

(2.48)

(0.01)

(-0.56

)(-2.61

)(-3.22

)(2.50)

(-5.69

)(-0.60

)(2.21)

(-0.75

)(2.13)

(-0.55

)(2.79)

(3.04)

(1.79)

(-1.15

)(2.53)

(-1.75

)(-1.75

)(3.00)

8.Nuts†

-0.37

-2.32∗

0.75∗

0.01

0.37

-0.06

-0.16

-1.79∗

-0.04

0.68

-0.65

-0.91

-0.31

0.34

-0.30

0.02

-0.01

-0.16

0.08∗

1.48∗

(-1.34

)(-2.40

)(2.03)

(-0.06

)(0.95)

(-0.75

)(-0.98

)(-3.86

)(-0.53

)(1.68)

(-1.80

)(-1.59

)(-1.78

)(1.44)

(-1.51

)(0.09)

(-0.44

)(-0.99

)(2.34)

(2.80)

9.Ve

getables

0.21∗

-0.16

0.07

-0.02

0.37∗

0.06

0.16∗

0.01

-0.65∗

-0.24∗

0.08

0.04

-0.08∗

0.22∗

-0.12∗

-0.02

0.07∗

0.15∗

-0.04∗

0.65∗

(3.13)

(-1.84

)(1.44)

(0.16)

(3.92)

(1.65)

(2.55)

(0.41)

(-9.85

)(-3.25

)(1.27)

(0.75)

(-2.30

)(4.54)

(-3.05

)(-0.62

)(4.24)

(3.34)

(-3.59

)(5.17)

10.Fruit

0.19

0.42

0.17

0.18

-0.36

-0.08

-0.13

0.61

-0.33∗

-0.87∗

0.51

0.04

-0.01

0.27

0.16

-0.08

-0.03

0.11

-0.02

1.00∗

(0.72)

(0.76)

(0.83)

(0.60)

(-1.12

)(-1.14

)(-0.88

)(1.55)

(-2.10

)(-3.40

)(1.61)

(0.07)

(-0.14

)(1.18)

(1.29)

(-0.89

)(-0.84

)(0.99)

(-1.06

)(2.37)

11.Red

meat

0.30

-0.15

-0.53∗

0.52∗

0.14

-0.03

0.14

-0.38∗

-0.01

0.27

-1.41∗

-0.61∗

0.22∗

-0.12

-0.80∗

-0.20∗

0.08∗

-0.31∗

-0.04∗

2.06∗

(1.29)

(-0.94

)(-3.63

)(2.12)

(0.91)

(-0.45

)(1.23)

(-2.54

)(-0.19

)(1.40)

(-5.86

)(-2.55

)(2.56)

(-1.54

)(-4.35

)(-3.61

)(3.20)

(-3.03

)(-2.59

)(6.69)

12.Po

ultry

-0.24

-0.41

0.08

0.19

0.03

-0.12

-0.17

-0.46∗

-0.05

-0.01

-0.55∗

-1.61∗

-0.26∗

0.20

-0.20

0.04

-0.05

0.15

0.01

2.21∗

(-1.74

)(-1.17

)(0.19)

(0.47)

(-0.25

)(-1.80

)(-1.28

)(-2.23

)(-0.77

)(-0.50

)(-2.12

)(-3.68

)(-2.47

)(1.19)

(-1.48

)(0.39)

(-1.66

)(1.29)

(1.44)

(3.77)

13.Eg

gs0.16

-0.12

-0.01

0.54

-0.93∗

-0.29

1.19∗

-0.86∗

-0.40∗

-0.07

1.01∗

-1.44∗

-0.82∗

0.41

0.08

-0.57∗

0.08

0.10

-0.03

2.23∗

(0.42)

(-0.14

)(-0.14

)(1.34)

(-2.09

)(-1.65

)(2.94)

(-2.53

)(-2.33

)(-0.72

)(2.45)

(-3.27

)(-2.35

)(1.48)

(0.22)

(-2.49

)(1.00)

(0.43)

(-0.73

)(4.58)

14.Fish†

-0.48∗

0.06

0.25∗

0.33

-0.31

-0.01

0.56∗

0.27

0.25∗

0.24

-0.09

0.39

0.13

-1.29∗

0.04

0.00

-0.03

-0.18∗

-0.05∗

0.93∗

(-2.14

)(0.21)

(1.97)

(1.60)

(-1.71

)(-0.30

)(2.70)

(1.81)

(2.63)

(1.58)

(-0.74

)(1.77)

(1.83)

(-8.79

)(0.77)

(0.07)

(-1.23

)(-1.98

)(-2.60

)(4.17)

15.Dairy

0.38∗

0.12

-0.15∗

0.42∗

0.64∗

0.09∗

0.18

-0.26∗

-0.20∗

0.13

-1.20∗

-0.33

0.03

0.02

-1.10∗

-0.11∗

0.02

0.04

-0.03∗

1.46∗

(2.12)

(1.02)

(-2.19

)(2.30)

(3.46)

(2.38)

(1.75)

(-2.35

)(-3.80

)(0.91)

(-5.97

)(-2.03

)(0.16)

(0.11)

(-5.70

)(-2.40

)(1.07)

(0.34)

(-3.01

)(7.32)

16.Fa

ts†

-0.54

0.50

-0.06

0.08

0.23

0.17

-0.16

0.05

-0.07

-0.12

-0.41∗

0.24

-0.28

-0.01

-0.16

-0.50

0.25∗

0.01

0.08

0.94∗

(-1.81

)(1.80)

(-0.45

)(0.53)

(1.34)

(1.62)

(-1.05

)(0.44)

(-0.96

)(-0.78

)(-1.99

)(1.16)

(-1.85

)(-0.05

)(-1.56

)(-0.80

)(2.32)

(0.27)

(1.78)

(2.26)

17.Coff

ee†

0.34

1.02∗

0.41∗

-0.25

-0.43∗

-0.21

0.64∗

-0.06

0.42∗

-0.15

0.67∗

-0.38

0.13

-0.17

0.09

0.75∗

-0.93∗

0.43∗

0.05

1.16∗

(1.85)

(4.09)

(2.52)

(-1.80

)(-2.42

)(-1.73

)(2.78)

(-0.45

)(3.25)

(-1.03

)(3.26)

(-1.75

)(1.08)

(-1.47

)(1.19)

(3.95)

(-11

.30)

(3.34)

(1.34)

(4.69)

18.So

ftdrink†

-0.26

0.50

-0.91∗

-0.87∗

-0.09

0.18

-0.43

-0.29

0.34∗

0.18

-0.98∗

0.55

0.07

-0.45∗

0.07

-0.01

0.17∗

-1.11∗

0.02

1.73∗

(-0.98

)(1.32)

(-2.73

)(-2.33

)(-0.36

)(1.88)

(-1.89

)(-1.20

)(2.38)

(0.78)

(-2.62

)(1.29)

(0.47)

(-2.13

)(0.37)

(-0.00

)(2.60)

(-4.08

)(0.80)

(3.54)

19.Other

food

-0.28

0.46∗

-0.37∗

0.20∗

-0.10

-0.09

-0.24

0.47∗

-0.35∗

-0.10

-0.28∗

0.29∗

-0.05

-0.32∗

-0.17∗

0.33∗

0.08

0.08

-0.89∗

0.62∗

(-1.84

)(3.79)

(-2.97

)(1.99)

(-0.66

)(-1.10

)(-1.91

)(4.56)

(-3.34

)(-0.72

)(-2.06

)(2.48)

(-0.38

)(-3.30

)(-2.50

)(3.26)

(1.46)

(1.24)

(-10

.13)

(3.05)

Not

es:Thistableshow

sthesample-widemed

ianelastic

ityof

food

deman

d(qua

ntity

consum

ed)with

respectto

food

prices

(colum

ns1thru

19)an

dtotalh

ouseho

ldexpe

nditu

res(the

last

column).The

med

iant-valueisalso

show

nin

parenthe

sis(∗p<

0.05

)below

each

med

ianelastic

ityestim

ate.

Weestim

atetheseelastic

ities

usingthewho

lesample.

Food

grou

pswho

sena

mes

have

been

shortene

daremarked

with† ;thefullfood

grou

pna

mes

arelistedhe

re:1.

Rice2.

Maize

3.W

heat

andothe

rcereals4.

Cassava

5.Roo

ts,T

ubersan

d,othe

rstarches

6.Su

gar7.

Pulse

s8.

Nutsan

dseed

s9.

Vegetables

10.Fruit11

.Red

meat12

.Po

ultry13

.Eg

gs14

.Fish

andseafoo

d15

.Dairy

16.Fa

tsan

doils

17.Coff

ee,tea,a

ndcocoa18

.So

ftdrinkan

djuice19

.Other

food

s.

50

Table3:

Food

deman

delastic

ities

with

respectto

food

prices

andtotalh

ouseho

ldexpe

nditu

res,

Pooled

Cross-sectio

nalM

odel

Food

Group

Expe

nditu

re

12

34

56

78

910

1112

1314

1516

1718

19

1.Rice

-2.75∗

0.88∗

-0.19

0.29

-0.20

-0.06

0.49∗

-0.28∗

0.19∗

-0.21

0.53∗

-0.50∗

-0.06

-0.59∗

0.34∗

-0.07

0.03

0.02

-0.04∗

1.33∗

(-5.54

)(2.77)

(-1.93

)(1.70)

(-1.49

)(-1.35

)(2.76)

(-2.23

)(2.47)

(-1.87

)(2.70)

(-2.33

)(-1.35

)(-3.02

)(2.57)

(-1.38

)(1.25)

(0.21)

(-2.42

)(4.59)

2.Maize

0.51∗

-1.36∗

0.43∗

-0.15

0.03

0.18∗

-0.11

-0.36∗

-0.07

0.38

-0.06

0.26

0.04

0.15

-0.03

0.03

0.07∗

0.03

0.02∗

0.61

(2.03)

(-4.02

)(2.39)

(-0.94

)(0.35)

(2.69)

(-1.36

)(-2.11

)(-1.67

)(1.77)

(-0.84

)(1.33)

(1.41)

(1.50)

(-0.68

)(1.15)

(2.58)

(0.96)

(2.22)

(1.16)

3.W

heat†

-0.26∗

1.07∗

-2.73∗

0.24

1.03∗

-0.04

0.10

0.53∗

-0.01

-0.36∗

-0.47∗

-0.03

0.00

-0.00

-0.12

0.13∗

0.07∗

-0.39∗

-0.06∗

1.38∗

(-2.27

)(4.37)

(-8.96

)(1.78)

(4.66)

(-0.78

)(1.23)

(3.62)

(0.00)

(-3.72

)(-3.23

)(-0.31

)(0.22)

(-0.28

)(-1.80

)(2.26)

(2.49)

(-3.90

)(-2.90

)(6.74)

4.Cassava

0.40∗

-0.16

0.23∗

-1.56∗

-0.16

0.00

-0.28∗

-0.21

0.05

-0.04

0.57∗

-0.25

0.09

0.15

0.43∗

0.12

-0.01

-0.21

0.02

0.16

(2.17)

(-0.67

)(2.09)

(-4.63

)(-0.81

)(0.14)

(-2.08

)(-0.92

)(0.96)

(0.23)

(2.14)

(-0.86

)(1.36)

(1.45)

(2.34)

(1.55)

(-0.52

)(-1.92

)(1.62)

(1.47)

5.Roo

ts†

-0.19

0.06

0.77∗

-0.21

-2.51∗

-0.09

-0.79∗

0.52∗

0.29∗

0.01

0.15

0.57

-0.09

-0.10

0.29∗

0.05

-0.08∗

-0.04

-0.01

1.00∗

(-1.44

)(0.18)

(3.32)

(-1.25

)(-5.42

)(-1.86

)(-3.18

)(2.30)

(3.12)

(0.02)

(1.13)

(1.95)

(-1.65

)(-1.31

)(2.33)

(1.23)

(-2.74

)(-0.44

)(-0.72

)(2.98)

6.Su

gar

-0.14

0.78∗

-0.06

-0.05

-0.22∗

-0.78∗

0.40∗

0.12

0.16∗

-0.17∗

-0.07

-0.02

-0.08

-0.03

0.10

0.20∗

-0.07∗

0.20∗

0.01

0.96∗

(-1.37

)(5.72)

(-0.63

)(-0.57

)(-2.13

)(-6.33

)(3.06)

(1.40)

(2.43)

(-2.35

)(-0.88

)(-0.27

)(-1.07

)(-0.60

)(1.80)

(3.03)

(-2.02

)(3.48)

(0.47)

(6.93)

7.Pu

lses

0.71∗

-0.28

0.12

-0.40∗

-1.03∗

0.22∗

-1.58∗

-0.00

0.20∗

0.02

0.29∗

0.16

0.31∗

0.65∗

0.09

-0.13

0.08∗

-0.18∗

-0.02

0.67∗

(2.86)

(-1.69

)(1.47)

(-2.35

)(-3.40

)(2.61)

(-5.66

)(-0.02

)(2.52)

(0.39)

(2.40)

(1.14)

(2.73)

(3.40)

(1.19)

(-1.76

)(2.49)

(-2.23

)(-1.44

)(2.58)

8.Nuts†

-0.56

-1.51∗

0.70∗

-0.41

0.96∗

0.07

-0.04

-1.98∗

-0.06

0.29

-0.58∗

-1.65∗

-0.28∗

0.24

-0.42∗

0.10

0.02

0.00

0.09∗

1.51∗

(-1.95

)(-2.42

)(2.46)

(-1.51

)(2.08)

(1.13)

(-0.62

)(-4.75

)(-0.49

)(1.29)

(-1.98

)(-2.29

)(-2.04

)(1.44)

(-2.15

)(1.08)

(0.67)

(-0.24

)(2.72)

(3.07)

9.Ve

getables

0.30∗

-0.17∗

0.04

-0.00

0.37∗

0.09∗

0.18∗

-0.01

-0.70∗

-0.22∗

-0.04

-0.06

-0.10∗

0.25∗

-0.08∗

-0.05

0.08∗

0.14∗

-0.04∗

0.71∗

(5.24)

(-2.98

)(1.28)

(0.44)

(5.37)

(2.79)

(3.09)

(0.25)

(-12

.87)

(-3.47

)(-0.77

)(-0.51

)(-2.77

)(5.93)

(-2.49

)(-1.69

)(6.08)

(3.81)

(-3.94

)(7.40)

10.Fruit

-0.33

1.22

-0.41

-0.10

0.02

-0.11

0.03

0.26

-0.31∗

-1.32∗

0.52

-0.56

-0.16

-0.08

0.30

0.01

-0.01

0.18

-0.06∗

1.04∗

(-1.27

)(1.87)

(-1.85

)(-0.39

)(-0.02

)(-1.54

)(0.12)

(1.28)

(-2.17

)(-4.93

)(1.84)

(-1.44

)(-1.45

)(-0.64

)(1.74)

(0.08)

(-0.30

)(1.57)

(-2.09

)(2.64)

11.Red

meat

0.52∗

-0.33∗

-0.38∗

0.49

0.11

-0.06

0.16

-0.34∗

-0.12∗

0.28

-1.45∗

-0.37∗

0.21∗

-0.05

-0.73∗

-0.21∗

0.07∗

-0.33∗

-0.02∗

2.11∗

(2.80)

(-2.14

)(-3.20

)(1.88)

(0.74)

(-1.37

)(1.68)

(-2.57

)(-2.52

)(1.46)

(-6.73

)(-1.98

)(2.48)

(-0.98

)(-4.30

)(-3.64

)(2.95)

(-3.67

)(-2.01

)(6.46)

12.Po

ultry

-0.53∗

0.24

-0.04

-0.27

0.46

-0.04

0.02

-0.81∗

-0.12

-0.35∗

-0.34

-2.34

-0.35

-0.01

-0.10

0.16∗

-0.02

0.32∗

0.02∗

2.16∗

(-3.30

)(0.25)

(-0.64

)(-1.55

)(1.70)

(-0.93

)(-0.16

)(-3.60

)(-1.74

)(-2.70

)(-1.82

)(-5.34

)(-3.47

)(-0.36

)(-1.10

)(2.45)

(-1.12

)(3.08)

(2.33)

(4.85)

13.Eg

gs-0.41

0.21

-0.01

0.34

-0.51∗

-0.18

1.13∗

-0.78∗

-0.49∗

-0.53∗

1.00∗

-1.95∗

-0.72

0.41

-0.02

-0.52∗

0.08

0.33

0.02

2.14∗

(-1.88

)(1.22)

(0.08)

(0.98)

(-1.97

)(-1.19

)(3.36)

(-3.02

)(-2.83

)(-3.09

)(2.64)

(-4.51

)(-2.39

)(1.61)

(-0.15

)(-2.71

)(1.23)

(1.75)

(0.54)

(5.36)

14.Fish†

-0.85∗

0.42∗

0.02

0.16

-0.12

-0.01

0.71∗

0.19

0.29∗

-0.07

0.01

0.05

0.14

-1.56∗

0.07

0.07

-0.04

-0.11

-0.05

0.94∗

(-2.79

)(1.98)

(0.25)

(1.20)

(-1.22

)(-0.61

)(3.13)

(1.85)

(2.91)

(-0.56

)(0.17)

(0.60)

(1.76)

(-8.89

)(0.97)

(1.37)

(-1.94

)(-1.58

)(-2.64

)(4.34)

15.Dairy

0.55∗

-0.17

-0.15∗

0.58∗

0.41∗

0.05

0.07

-0.36∗

-0.14∗

0.28∗

-1.08∗

-0.16

-0.00

0.05

-1.24∗

-0.17∗

0.02

-0.00

-0.03∗

1.39∗

(3.66)

(-1.22

)(-2.13

)(3.13)

(2.68)

(1.44)

(0.93)

(-3.35

)(-2.92

)(2.51)

(-5.90

)(-1.19

)(-0.19

)(0.47)

(-6.86

)(-3.95

)(1.39)

(-0.06

)(-3.10

)(6.58)

16.Fa

ts†

-0.19

0.11

0.25

0.24

0.13

0.21∗

-0.26

0.17

-0.14

0.02

-0.44∗

0.57∗

-0.25∗

0.12

-0.26∗

-0.53

0.26∗

-0.02

0.11∗

0.92∗

(-1.20

)(0.86)

(1.60)

(1.25)

(1.20)

(2.00)

(-1.57

)(1.22)

(-1.55

)(0.11)

(-2.20

)(2.04)

(-1.97

)(0.99)

(-2.13

)(-0.63

)(2.62)

(-0.20

)(2.12)

(2.31)

17.Coff

ee†

0.21

0.92∗

0.40∗

-0.12

-0.64∗

-0.22∗

0.48∗

0.08

0.51∗

-0.05

0.61∗

-0.11

0.13

-0.26∗

0.12

0.78∗

-0.96∗

0.48∗

-0.03

1.12∗

(1.31)

(5.15)

(2.68)

(-1.26

)(-3.74

)(-2.08

)(2.98)

(0.74)

(4.53)

(-0.35

)(3.65)

(-0.97

)(1.29)

(-2.42

)(1.49)

(4.91)

(-12

.89)

(4.32)

(-1.05

)(5.68)

18.So

ftdrink†

0.01

0.07

-0.90∗

-0.64∗

-0.15

0.23∗

-0.46∗

-0.01

0.32∗

0.32

-1.03∗

1.15∗

0.21

-0.27

-0.01

-0.04

0.19∗

-1.24∗

0.01

1.59∗

(-0.22

)(0.38)

(-3.54

)(-2.30

)(-0.58

)(2.82)

(-2.53

)(-0.54

)(2.73)

(1.68)

(-3.56

)(3.06)

(1.68)

(-1.76

)(-0.07

)(-0.39

)(3.35)

(-5.91

)(0.66)

(4.02)

19.Other

food

-0.37∗

0.38∗

-0.46∗

0.15

-0.07

0.05

-0.16

0.55∗

-0.31∗

-0.36∗

-0.14

0.35∗

0.05

-0.37∗

-0.15∗

0.46∗

-0.04

0.06

-0.91∗

0.65∗

(-2.61

)(3.62)

(-2.91

)(1.82)

(-0.70

)(0.59)

(-1.44

)(5.54)

(-3.55

)(-3.34

)(-1.48

)(3.37)

(0.90)

(-3.72

)(-2.80

)(4.44)

(-0.96

)(0.92)

(-9.92

)(3.57)

Not

es:Thistableshow

sthesample-widemed

ianelastic

ityof

food

deman

d(qua

ntity

consum

ed)with

respectto

food

prices

(colum

ns1thru

19)an

dtotalh

ouseho

ldexpe

nditu

res(the

last

column).The

med

iant-valueisalso

show

nin

parenthe

sis(∗p<

0.05

)below

each

med

ianelastic

ityestim

ate.

Weestim

atetheseelastic

ities

usingthewho

lesample.

Food

grou

pswho

sena

mes

have

been

shortene

daremarked

with† ;thefullfood

grou

pna

mes

arelistedhe

re:1.

Rice2.

Maize

3.W

heat

andothe

rcereals4.

Cassava

5.Roo

ts,T

ubersan

d,othe

rstarches

6.Su

gar7.

Pulse

s8.

Nutsan

dseed

s9.

Vegetables

10.Fruit11

.Red

meat12

.Po

ultry13

.Eg

gs14

.Fish

andseafoo

d15

.Dairy

16.Fa

tsan

doils

17.Coff

ee,tea,a

ndcocoa18

.So

ftdrinkan

djuice19

.Other

food

s.

51

Table4:

Nutrie

ntintake

elastic

ities

with

respectto

food

prices

andtotalh

ouseho

ldexpe

nditu

res

Food

Group

Expe

nditu

re

12

34

56

78

910

1112

1314

1516

1718

19

kcal

-0.11

-0.34

-0.03

-0.17

-0.08

0.05

-0.10∗

-0.27

-0.01

0.07

-0.02

-0.09

0.01

0.02

0.05

-0.01

0.06∗

-0.03

0.00

0.95∗

(-0.37)

(-1.64)

(0.37)

(-0.78)

(-0.58)

(1.23)

(-2.02)

(-1.85)

(-0.86)

(1.06)

(-0.02)

(-0.38)

(0.28)

(0.81)

(1.61)

(-0.00)

(2.63)

(-0.45)

(0.45)

(2.45)

protein

-0.02

-0.36∗

-0.03

-0.05

-0.13

0.07

-0.15

-0.30∗

0.00

0.11

-0.13

-0.19

0.03

-0.01

-0.04

-0.03

0.05∗

-0.06

-0.01

1.10∗

(0.04)

(-2.05)

(0.52)

(-0.14)

(-1.13)

(1.64)

(-2.21)

(-2.22)

(-0.18)

(1.36)

(-1.54)

(-0.94)

(1.25)

(0.22)

(-0.08)

(-0.35)

(2.73)

(-1.23)

(-1.52)

(3.28)

fat

-0.10

-0.24

-0.06

0.09

0.18

0.07

-0.05

-0.35

-0.08

0.07

-0.39∗

-0.20

-0.12

0.07

-0.22

-0.17

0.11

-0.04

0.02

1.27∗

(-0.76)

(-1.03)

(0.07)

(0.48)

(1.11)

(1.15)

(-1.10)

(-1.93)

(-1.92)

(0.94)

(-3.23)

(-0.67)

(-2.18)

(1.13)

(-2.67)

(-0.90)

(2.29)

(-0.52)

(1.72)

(2.98)

sugar

0.00

0.46∗

-0.21∗

-0.16

-0.22∗

-0.36∗

0.04

-0.02

0.04

-0.11

-0.06

-0.12

-0.05

0.04

0.08∗

0.08

-0.02

0.02

-0.02

1.04∗

(0.22)

(3.20)

(-2.07)

(-1.40)

(-2.19)

(-3.78)

(0.18)

(-0.25)

(0.86)

(-1.15)

(-0.32)

(-0.97)

(-0.95)

(0.93)

(2.18)

(1.62)

(-0.93)

(0.56)

(-1.74)

(4.98)

iron

0.07

-0.49∗

0.04

-0.24

-0.09

0.10

-0.21

-0.32

-0.04

0.06

0.04

-0.10

0.02

0.09

0.07

0.03

0.06∗

-0.00

-0.00

0.87∗

(0.60)

(-2.33)

(0.92)

(-1.12)

(-0.62)

(1.89)

(-3.11)

(-1.91)

(-1.47)

(1.06)

(0.69)

(-0.44)

(1.02)

(1.42)

(1.85)

(0.74)

(2.71)

(-0.15)

(-0.30)

(2.28)

zinc

-0.08

-0.44∗

0.00

-0.10

-0.05

0.08

-0.11∗

-0.34∗

-0.03

0.10

-0.08

-0.16

0.04

0.03

-0.01

-0.01

0.06∗

-0.03

-0.01

1.01∗

(-0.20)

(-2.21)

(0.64)

(-0.38)

(-0.42)

(1.85)

(-1.97)

(-2.21)

(-1.23)

(1.31)

(-0.90)

(-0.79)

(1.26)

(0.75)

(0.78)

(-0.16)

(2.91)

(-0.61)

(-0.32)

(2.73)

vitamin

A-0.30

0.30

0.18

-0.07

-0.65∗

0.06

-0.24

0.04

-0.08

-0.18

-0.09

0.04

-0.23∗

-0.02

0.02

-0.20

0.10∗

0.03

0.01

1.13∗

(-1.36)

(1.50)

(1.18)

(-0.18)

(-3.43)

(1.16)

(-1.59)

(0.34)

(-1.38)

(-1.18)

(-1.19)

(0.23)

(-2.36)

(0.28)

(-0.01)

(-0.89)

(2.27)

(0.49)

(0.66)

(3.69)

totalfolate

0.16

-0.29

0.05

-0.28

-0.40∗

0.09∗

-0.53∗

-0.20

-0.03

-0.03

0.11

-0.10

0.06

0.21∗

0.09

-0.02

0.06∗

-0.05

-0.02

0.90∗

(1.53)

(-1.92)

(1.19)

(-1.65)

(-2.90)

(1.99)

(-4.98)

(-1.76)

(-0.69)

(0.32)

(1.60)

(-0.60)

(1.40)

(2.66)

(1.89)

(-0.24)

(2.77)

(-1.34)

(-1.75)

(3.05)

Not

es:Thistableshow

sthesample-widemed

ianelastic

ityof

macro-a

ndmicro-n

utrie

ntintake

(qua

ntity

consum

ed)with

respectto

food

prices

(colum

ns1thru

19)an

dtotalh

ouseho

ldexpe

nditu

res

(the

last

column).The

med

iant-valueis

also

show

nin

parenthe

sis(∗p<

0.05)be

low

each

med

ianelastic

ityestim

ate.

Weestim

atetheseelastic

ities

usingthewho

lesample.

The

columnnu

mbe

rscorrespo

ndwith

food

grou

psas

follo

ws:

1.Rice2.

Maize

3.W

heat

andothe

rcereals4.

Cassava

5.Roo

ts,T

ubers,

andothe

rstarches

6.Su

gar7.

Pulse

s8.

Nutsan

dseed

s9.

Vegetables

10.Fruit11.

Red

meat12.Po

ultry13.Eg

gs14.Fish

andseafoo

d15.Dairy

16.Fa

tsan

doils

17.Coff

ee,tea,a

ndcocoa18.So

ftdrinkan

djuice19.Other

food

s.

52

Table 5: Food demand elasticities with respect to own-food price, by expenditures quartile

Food Group Per-capita Total Expenditures Quartile

Q1 Q2 Q3 Q4

1. Rice -3.42∗ -2.36∗ -1.82∗ -1.22∗(-5.13) (-5.52) (-5.44) (-4.50)

2. Maize -1.01∗ -0.94∗ -0.81∗∗ -0.48(-3.74) (-3.24) (-2.46) (-0.77)

3. Wheat and other cereals -4.00∗ -2.85∗ -2.10∗ -1.21∗(-6.65) (-6.85) (-6.45) (-4.99)

4. Cassava -1.29∗ -1.24∗ -1.23∗ -1.21∗(-4.31) (-4.36) (-3.68) (-3.03)

5. Roots, Tubers, and other starches -2.54 ∗ -2.25∗ -2.09∗ -1.91∗(-4.34) (-5.00) (-4.87) (-3.93)

6. Sugar -0.89∗ -0.74∗ -0.63∗ -0.48∗(-5.19) (-5.27) (-4.45) (-1.97)

7. Pulses -1.69∗ -1.57∗ -1.47∗ -1.28∗(-6.11) (-6.24) (-5.77) (-4.09)

8. Nuts and seeds -2.30∗ -1.85∗ -1.51∗ -1.03∗(-3.99) (-4.14) (-3.73) (-2.85)

9. Vegetables -0.59∗ -0.64∗ -0.68∗ -0.77∗(-8.92) (-10.66) (-10.97) (-8.97)

10. Fruit -1.54∗ -0.93∗ -0.58∗ -0.16(-4.12) (-3.67) (-2.10) (-0.49)

11. Red meat -1.52∗ -1.43∗ -1.37∗ -1.31∗(-4.47) (-6.29) (-7.16) (-6.29)

12. Poultry -2.90∗ -1.72∗ -1.03∗ -0.19(-3.99) (-3.92) (-3.21) (-1.58)

13. Eggs -1.06∗ -0.84∗ -0.72∗ -0.60∗(-1.97) (-2.64) (-2.58) (-2.49)

14. Fish and seafood -1.51∗ -1.32∗ -1.20∗ -1.05∗(-8.24) (-9.75) (-9.05) (-7.92)

15. Dairy -0.90∗ -1.08∗ -1.18∗ -1.31∗(-3.47) (-6.05) (-6.62) (-6.03)

16. Fats and oils -0.70 -0.53 -0.40 -0.20(-1.50) (-0.87) (-0.61) (0.23)

17. Coffee, tea, and cocoa -0.67∗ -0.91∗ -1.04∗ -1.19∗(-6.88) (-12.87) (-14.05) (-11.49)

18. Soft drink and juice -1.16∗ -1.12∗ -1.09∗ -1.06∗(-3.44) (-4.44) (-4.82) (-4.04)

19. Other food -0.85∗ -0.88∗ -0.92∗ -1.00∗(-9.49) (-11.41) (-11.15) (-8.49)

Notes: This table shows the median own-price elasticities of food demand for each per-capita total expen-diture quartile. The quartile-level median t-value is shown in parenthesis below each median elasticity (∗

p < 0.05).

53

Appendix A Derivation of Food Demand Elasticities

Since the EASI demand is a Hicksian demand system, we first obtain Hicksian price elasticity

along and total expenditure elasticity. Then, we recover Marshallian price elasticity. For ease of

understanding the derivation, we display the two-way approximate EASI demand system below.

Note that, to simplify notation, we withold the household subscript.

wi = µi +J∑j=1

αij log pj +L∑r=1

βiryr +

J∑j=1

αijy(y × log pj) +K∑k=1

γikzk + ui,

(i = 1, ...J − 1) where y = log x−J∑j=1

wj log pj

(A.1)

From the above equation, we first take partial derivatives with respect to log pj . This gives the

following Hicksian semi elasticity:

∂wi∂ log pj

= αij + αijyy. (A.2)

Because wi = qiHpi/x

H where the superscript H emphasizes variables are compensated, we can

express the Hicksian semi elasticity as a function of the conventional Hicksian price elasticity:

∂wi∂ log pj

=∂(qiHpi/xH)∂ log pj

= αij + αijyy

= ∂qiH

∂ log pjpixH

+ ∂pi∂ log pj

qiH

xH− qi

Hpi

(xH)2∂xH

∂ log pj

= ∂qiH

∂ log pjqiH

qiHpixH

+ ∂pi∂ log pj

pipi

qiH

xH− qi

Hpi

(xH)2∂xH

∂pj

pj1

=∂ log qiH

∂ log pjwi + 1ijwi − wiwj .

(A.3)

In this equation, 1ij = ∂ log pi∂ log pj will be 1 when i = j and 0 otherwise. Accordingly, dividing both

sides of the last equation by wi and rearranging the terms gives the Hicksian price elasiticity when

54

i = j.

hij = αij + αijyy

wi− 1ij + wj (A.4)

The formula for the Hicksian price elasticity when i 6= j is hij = αij+αijyywi

+ wj .

Next, we take partial derivatives of a matrix formation of equation (A.1) with respect to nominal

total expenditure, log x, accounting for the budget share wi appearing on both sides of the demand

equation. This gives the following J × 1 (J equals to 20 in this study) vector of semi-expenditure

elasticities, se"

se = (IJ + TP ′)−1P. (A.5)

where IJ is the J dimension of the identity matrix, T is a J × 1 vector whose ith element equals∑Lr=1 rβiry

r−1, P is the J × 1 vector of log prices. Using the relation between semi-expenditure

elasticity and total expenditure elasticity (i.e., ei = seiwi

+ 1), we calculate the J × 1 vector of total

expenditure elasticities, e, as:

e = (diag(W ))−1[(IJ + TP ′)−1T]

+ 1J . (A.6)

where W is the J × 1 vector of observed budget shares and 1J is a J × 1 vector of ones.

Once we have the Hicksian (compensated) price and total expenditure elasticities, the Marshal-

lian (uncompensated) price elasticity can be easily calculated from the Slutsky equation:

eij = hij − wjei. (A.7)

where ei is a total expenditure elasticity for demand group i, or ith element of the total expenditure

elasticity vector, e.

We calculate predicted budget shares (i.e., conditional means of observed budget shares) and

replace the observed budget shares with the predicted in the above equations to obtain expected

demand elasticities.

55

Appendix B Derivation of Nutrient Elasticities

Based on methods in Huang (1996) and Huang and Lin (2000), we start with the following total

quantity ϕ of nutrient γ (ϕγ) from consumption of each food group i:

ϕγ =∑i

aγiQi(p1, ..., pn, y), (B.1)

where aγi is the quantity of nutrient γ per unit of food group i and Qi is the total consumption

quantity of food group i, which is a function of the prices of food groups (and the numéraire good),

along with total expenditures, y.

We then totally differentiate equation (B.1) with respect to prices and total expenditure, which

results in:

dϕγϕγ

=∑i

aγi dQi1ϕγ. (B.2)

As mentioned, we use price and total expenditure elasticities to back out nutrient demand

elasticities. The term dQi in equation (B.2) has the link between these two food demand elasticities

and nutrient demand elasticities. When the food demand equation Qi is totally differentiated with

respect to prices and total expenditures, we have the following formula:

dQiQi

=∑j

eijdpjpj

+ ηidy

y

dQi =[∑

j

eijdpjpj

+ ηidy

y

]Qi,

(B.3)

where eij indicates own- or cross-price elasticities and ηi represents total expenditure elasticities.

We substitute the formula of dQi in (B.3) into equation (B.2), which yields:

dϕγϕγ

=∑i

aγi[∑

j

eijdpjpj

+ ηidm

m

]Qiϕγ

=∑j

πγjdpjpj

+ ργdy

y,

(B.4)

where πγj =∑i aγiQi

eijϕγ

is the elasticity of demand for nutrient γ with respect to price of the j th

56

food and ργ =∑i aγiQi

ηiϕγ

is the total expenditure elasticity of demand for nutrient γ.

Appendix C Dietary Estimated Average Requirements (EARs) for Nutrients

We calculate energy requirements according to age, weight, sex and activity level, according to

FAO/WHO/UNU (2004), who assume average weight for adults (male 65 kg, female 55 kg) and

moderate activity levels.

Protein requirements are provided on a per kg of body weight basis. We convert these require-

ments to a daily per-person requirement by using average body weights for age and sex. We also

adjust for protein quality before assessing adequacy. The reason we make quality adjustments is

that African diets are typically low in animal source protein, which is high quality, although their

diets may not be low in protein. Accordingly, we adjust crude protein intakes for quality by using

an assumed factor of 0.75 by which the protein intakes are multiplied to obtain the amount of

“available” protein.

Micronutrient requirements for vitamin A, folate and iron come from the Institute of Medicine

Dietary Estimated average requirements (Institute of Medicine, 2006). Zinc requirements are taken

from the International Zinc Nutrition Consultative Group (IZiNCG), assuming low bioavailability

from an unrefined cereal-based diet (IZiNCG et al., 2004). Iron requirements are adjusted for lower

bioavailability of diets that are high in phytate and low in animal source foods as compared to the

18% bioavailability used in high meat diets (Institute of Medicine (US) Panel on Micronutrients,

2002). Two levels of iron adjustment are recommended, 5% bioavailability for diets with negligible

meat/fish and 10% for diets with low meat/fish (WHO and FAO, 2004). We use the 10% values

that are more appropriate for African countries with diets high in phytate.

We assume infants who are under the age of 12 months are non-breastfeeding and that women

in their households are not lactating, due to the lack of good data on breastfeeding. We believe

these assumptions do not significantly affect the total EARs at the household level. That is, total

household requirements of dietary energy, zinc, vitamin A, and total folate are balanced out because

of we assume that infants do not receive these nutrients from breastfeeding and that women do not

require extra nutrients due to lactation.

57

Notes

58