Abstract

The analysis presented here suggests that low-, middle-, and high-income countries all

respond differently to changes in income and food prices and, furthermore that low-income

countries are more responsive than high-income countries to such changes. These conclusions are based on a two-stage, cross-country demand system fit to the 1996 International

Comparison Project (ICP) data for nine broad categories and eight food sub-categories of

goods across 114 countries. The broad consumption groups include: food, beverage, and

tobacco; clothing and footwear; education; gross rent, fuel, and power; house furnishings

and operations; medical care; recreation; transport and communications; and other items.

The food sub-groups include bread and cereals, meat, fish, dairy products, oils and fats,

fruit and vegetables, beverages and tobacco, and other food products. The country data

exhibit group heteroskedasticity, and a maximum likelihood procedure that corrects for

group heteroskedasticity is developed and used to estimate the model. Theil’s information

inaccuracy measures are calculated to measure the goodness of fit of the system of equations, while Strobel measures are calculated to measure the goodness of fit on an individual equation basis. Using the estimated parameters, income and price elasticities are calculated for the broad consumption categories and the food sub-categories. 

Keywords: Consumption, cross-country demand, complete demand system, food

demand, elasticity, heteroskedasticity, maximum likelihood.

Acknowledgments

The authors express their deep appreciation to Yonas Biru and Yuri Dikhanov, World Bank,

for making the data available. Without their assistance, the study would not be possible.

Appreciation is also extended to Mary Bohman, John Dunmore, and Shahla Shapouri for

their support of the project and their review comments, as well as to Tom Hertel (Purdue

University), Jeffrey Reimer (University of Wisconsin), and Terry Roe (University of

Minnesota) who provided review comments. Partial support for this project was provided

by the U.S. Department of Agriculture under Agreement No. 43-3AEK-2-80029 and the

International Agriculture Trade Center, IFAS, University of Florida. Special thanks are

extended to Sharon Lee for editorial advice and Wynnice Pointer-Napper for the final document layout and charts.

United States

Department

of Agriculture

www.ers.usda.gov

Electronic Report from the Economic Research Service

October 2003

Technical

Bulletin

Number 1904

International Evidence on Food

Consumption Patterns

James Seale, Jr., Anita Regmi, and 

Jason Bernstein
Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Chapter 1—Background on Demand Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .3

Restrictions on Demand Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Choice of Demand Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Preference Independence, Block Independence, and 

Stepwise Budgeting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Chapter 2—International Comparison Project Data . . . . . . . . . . . . . . . . . . . . .8

Aggregating Data at National Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Scaling Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Budget Share and Volume of Aggregate Categories and 

Food Sub-Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Chapter 3—Two-State Cross-Country Demand Model . . . . . . . . . . . . . . . . . .16

Florida-Preference Independence Model . . . . . . . . . . . . . . . . . . . . . . . . . . .16

Florida-Slutsky Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

Conditional Florida-Slutsky Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

Chapter 4—Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . . . . . . . .20

Homoskedasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Group Heteroskedasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Chapter 5—Parameter Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

Aggregate Model Elasticities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

Food Subgroup Elasticities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48

Appendix: Goodness-of-Fit and Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50

ii ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

The data in the technical bulletin are available at 

http://www.ers.usda.gov/data/internationalfooddemand
Summary

A number of forces in the developing and the developed countries are driving changes in

global food consumption patterns, with income growth being one of the most important. As

food purchasing power increased among most consumers in the world during the past few

decades, consumers shifted to more expensive forms of nutrients. This study presents information on consumer responsiveness to changes in food prices and income. Such information can be used to predict future food needs as well as enhance the ability to assess the

need for different types of food among different countries.

The findings of this study suggest that low-, middle-, and high-income countries have

distinct demand responses; low-income countries are more responsive to income changes

than middle- and high-income countries. Additionally, the magnitude of demand responses

varies among consumption items, with smaller responses to income changes for staple

consumption categories such as food and clothing, and larger responses for rent, medical

care, and other luxury items such as recreation.

Consumers make their food purchase decisions based on a budget that also must cover

expenses for clothing, housing, and other goods and services. The overall budget available

for food depends on the amount of total household budget spent on other goods and services. Therefore, an in-depth study on food demand requires an understanding of the

complete demand patterns of consumers. Using a two-stage demand model, this study

provides such a complete analysis of demand patterns, while focusing on food demand in

the second stage of the model.

Expenditure and consumption volume data were obtained for 114 countries from the 1996

International Comparison Project (ICP) for nine broad categories of goods and eight subcategories of food. The ICP provides the only available consumption data that are consistent and comparable across a large number of countries. 

The 1996 ICP data indicate that low-income countries spend a greater portion of their budget

on necessities such as food than wealthier countries do. On the other hand, relatively larger

shares of the total household budget in the wealthier countries are spent on rent and fuel,

medical care, transport and communication, and recreation. Low-value staples, such as

cereals, account for a larger share of the food budget in poorer countries, while higher-valued

food items, such as dairy and meat, are a larger share of the food budget in richer countries. 

Demand responses to price changes are also generally the largest for poorer countries and

decrease with affluence. However, when food price changes are accompanied by equivalent

income changes, the wealthier low-income and middle-income countries make the largest

adjustments to their total food budget. Like the response to income change, price change

responses are larger for items such as medical care and recreation than for necessities like

food and clothing. 

Individual food subgroup responses to income changes are the largest for the poorest country

and decline in magnitude with affluence. Staple food like cereals have smaller demand

responses to income changes than higher-valued food items, such as meat, fish, and dairy.

Demand response to food subgroup price changes are also the largest for the poorest

country and decline in magnitude with affluence. As with income changes, responses to

price changes are larger for higher valued food products, such as beverages, meat, and

dairy, than for staple food products, such as breads and cereals.

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ iii
Introduction

Recent shifts in food trade reveal dramatic changes in demand for food around the world that likely will

continue well into the future (Regmi, May 2001). A number of forces in both developing and developed

countries are driving these changes, the most important being income growth. In the past two decades,

average inflation-adjusted per capita income levels more than doubled in many countries, and food-purchasing power increased among most consumers in the world. Increased caloric intake and population

growth in developing countries have continued to maintain significant demand for grains and oilseeds.

Although the food budget may grow at a relatively slow rate among high-income consumers, global food

consumption patterns are rapidly changing—with growing demands for quality, variety, and convenience. Globalization, improved transportation, and increased purchasing power have generally increased

the demand for higher value food products—such as fruit and vegetables, meats, and processed food

products—across all countries.

With the globalization of food markets, consumer demand for food in a country is of interest to producers in all countries. Farmers in countries with large agricultural exports are starting to rely more on global markets for a significant part of their income. Trade in agricultural products also affects the livelihoods of individuals along the entire farm-to-table chain in both rural and urban sectors of the world.

Knowing how consumers respond to rises and falls in income can help policymakers assess future food

and trade needs as well as the demand for associated transportation infrastructure and facilities. An

understanding of food demand and food trends across countries and the ability to predict potential shifts

in demand for different food products will be an invaluable tool for all individuals involved in the agricultural sector.

This study analyzes demand across 114 countries and presents results that can be used to predict future

food needs for these countries. Consumers make their food purchase decisions based on a budget that

also must cover their expenses for clothing, housing, and other goods and services. The overall budget

available for food is dependent on the amount of total household budget spent on other goods and services. Therefore, an in-depth study on food demand requires an understanding of the complete demand

patterns of consumers. In this study, we present such a complete demand analysis where household decisionmaking is examined in two steps. In the first step, a consumer is assumed to make budgetary decisions across broad consumption categories, such as food, clothing, education, and other goods. In the

second step, the total food budget is further allocated to different food items. In conducting such a complete analysis, we provide results on nine broad consumption categories in addition to results on eight

food sub-categories. The results of this study not only enhance the ability to predict future food needs,

but can also be used to predict the need for clothing, transportation, medical care, and other broad consumption categories for the countries included in this study.

We developed a complete demand system consistent with the economic theory of constrained utility

maximization to study the demand patterns across 114 countries. It is a major challenge to develop a

complete demand system that appropriately reflects consumer behavior across countries spanning a wide

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 1

International Evidence on Food

Consumption Patterns

James Seale, Jr., University of Florida

Anita Regmi, MTED/ERS

Jason Bernstein, MTED/ERS
range of income levels. Many economic cross-country models fail to account for the fact that consumer

responsiveness to income changes is dependent on the initial income endowment. In other words, the

actual utility functions are not homothetic with identically shaped functions at every income level.

Rather, the shape of the utility function (the demand responsiveness) differs between consumers at low

income levels and those at high income levels. In this study, we used a non-homothetic complete

demand system satisfying constrained utility maximization—especially designed by Theil, Chung, and

Seale (1989) to analyze cross-country demand. In addition to examining the demand and consumer

responsiveness to income and price changes for aggregate groups of goods and services, this report

extends the work of Theil, Chung, and Seale to examine the demand for food sub-groups. 

2 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA
Chapter 1

Background on Demand Analysis

Demand analysis can be described as a science of consumer choice or preferences among different

goods and services. Since the demand for any one good or group of goods is dependent on the prices

and availability of other products,1 analyzing consumer demand is essentially the act of analyzing consumer preferences, that is, how consumers choose to allocate their income among different products.

Economists use the concept of utility to define the level of satisfaction or welfare that comes from a specific allocation of income among different products. The basis of demand analysis is the problem of how

to maximize utility subject to a given level of income, the latter also being known as the budget constraint. This can be expressed as:

Maximize u=v(q1, q2,...qn) subject to Σpkqk = x, (1)

where u is a utility function of the quantities of goods consumed, x is total income, and p and q are

prices and quantities, respectively. Solving this maximization problem by setting up the Langrangean

function will lead to a set of demand equations that express the quantity demanded for each good as a

function of the price and total income,

qi = gi

(x,P) , (2)

where P is the vector of commodity prices. This type of demand function, based on utility maximization,

is known as a Marshallian or uncompensated demand function. For a logarithmic utility function, both

income and price elasticities can be calculated by taking the derivative of the Lagrangean function,

resulting in the following equation,

where ηi is the income elasticity and μij are the uncompensated price elasticities.2 So that changes in

prices and total expenditure do not violate the budget constraint in the demand function, the following

conditions on the elasticities must hold,

where w is the budget share. These two conditions are known as Engel and Cournot aggregation, respectively, and together are sometimes referred to as the adding-up restriction.

The Marshallian demand function is the solution to the consumer’s problem of maximizing utility subject to the budget constraint. However, the consumer’s problem can also be expressed as one of minimizing total expenditures or costs subject to a predetermined utility level or,

Minimize x = Σpkqk subject to v(q1, q2,...qn)= u . (5)

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 3

dq d d p i i ij

j

n

j log log log (3) = + ∑

=

η μ x 

1

,

w ww j

j

n

j i

j

n

ij j = =

∑ = ∑

1 1

η μ 1 and + = 0, (4)

1 Assuming a relatively free-market economy.

2 The compensated price elasticity measures demand response when price changes are compensated by equivalent income

changes such that the real income (and utility) remains unchanged. By contrast, the uncompensated price elasticity represents

demand response when price changes are not compensated by income change—depicting the case where real income and total

utility changes—while monetary income remains unchanged.
The solution to this problem is the Hicksian demand function, which is equivalent to the Marshallian

demand function when evaluated at the optimal utility level,

qi = hi

(u,P)= gi(x,P). (6)

The Hicksian demand function is also known as the compensated demand function, since it represents

demand when utility is held constant. Price elasticities derived from the Hicksian demand function are

called “compensated” or “Slutsky” price elasticities and are equal to the uncompensated price elasticity

(also called “Cournot” price elasticities) plus the product of the income elasticity and the budget share, or,

εij = uij + ηiwj , (7)

where εij is the Slutsky price elasticity. 

Restrictions on Demand Equations

Besides the adding-up restriction, there are three basic restrictions on demand equations. These can be

expressed in terms of the compensated price elasticities as follows:

The homogeneity restriction implies that a proportionate change in income and prices of all goods will

leave consumption of any one good unchanged. The symmetry restriction means the increase in the price

of any good i will cause an increase in the compensated quantity demanded of j equal to the increase in

the compensated quantity demanded of i caused by an increase in the price of j. Without this restriction,

inconsistent choices between products would be made and there would be no substitute or complement

products. The negativity restriction comes from the convexity of the utility function, which is due to the

fact that the utility is maximized in the Marshallian demand function, or alternatively, that costs are minimized in the Hicksian demand function.

The adding-up, homogeneity, symmetry, and negativity restrictions represent the basic restrictions

imposed on all demand functions. Determining income and price elasticities that meet these restrictions

is the primary aim of demand analysis. Of course, it is possible to determine income and price elasticities without using demand equations derived from utility maximization or cost minimization. A logarithmic-demand model directly specifies the logarithmic quantity demanded as a function of logarithmic

income and prices with income and price elasticities acting as coefficients. Such coefficients can be easily estimated by applying ordinary least squares (OLS) to cross-sectional or time series data. However, in

order to maximize utility and satisfy the necessary restrictions, this model would require constant budget

shares and constant elasticities, which is inconsistent with observations that budget shares change when

income changes (Deaton and Muellbauer, 1984).

Choice of Demand Models

Both Marshallian and Hicksian demand equations satisfy the restrictions imposed by demand theory. An

example of a Marshallian demand equation is the Linear Expenditure System (LES), first estimated by

Stone and widely applied using individual country data. Although easy to use, the LES assumes additive

4 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Homogeneity: (8)

Symmetry: (9)

Negativity: for all and that are not constants. (10) 

ε

ε ε

ε

ij

j

n

ij ji

i j ij j i

j

n

i

n

j xw x x x

=

= =

∑ =

=

∑ ∑ <

1

1 1

0

0,
preferences, severely restricting substitution possibilities, and it also rules out inferior goods.3 Another

major weakness of the model is that marginal budget shares obtained from estimation are constant with

income changes. This property, known as “homotheticity,” can lead to estimations where the income

elasticity for necessities actually increases when income rises (Theil and Clements, 1987).

The Rotterdam model, first proposed by Barten (1964) and Theil (1965), uses both Marshallian and

Hicksian demand functions. Unlike the LES, where restrictions are maintained or imposed algebraically

within the model, restrictions must be imposed on the Rotterdam model and can be statistically tested.

The Rotterdam model also allows the estimation of substitutes and complements. Moreover, the

Rotterdam model allows for the separability of preferences, a desirable and useful property in demand

analysis. If separability holds, total expenditure can be partitioned into groups of goods, making it possible to analyze the preferences in one group independent of the quantities in other groups. However, the

Rotterdam model has a strong disadvantage in that it produces, like the LES model, constant marginal

shares, leading to counterintuitive results, particularly with cross-country analysis, in terms of changes in

income (Theil and Clements, 1987).

The problem of constant marginal budget shares is avoided, using a popular Hicksian demand function

called the Almost Ideal Demand System (AIDS) model. The AIDS model can be used to generate systems

of demand equations that can be estimated over broadly defined groups of commodities. Since budget shares

are not constant, income elasticities change with income changes. However, the AIDS model has several disadvantages: parameters in the AIDS model are non-linear and are difficult to estimate, negativity is not satisfied at all data points, and separability is not nested in the general specification. However, Theil, Chung, and

Seale (1989) overcame these problems by combining the core of the AIDS model, called “the Working’s

model,” with the differential approach and separability attributes of the Rotterdam model. 

The Working’s model (1943) expresses budget shares as a linear function of total real expenditures. In its

general form, Working’s (1943) model states that, for n goods, i = 1,...,n,

wi = αi + βi log E + εi , (11)

parameters to be estimated. Since the budget shares across all consumption groups sum to 1, the α’s and

β’s are subject to the adding-up conditions,

The marginal budget share, θi

, is not constant but varies by affluence, and it exceeds the budget shares

by βi ;

Accordingly, when income changes, wi changes as does the marginal share. The income elasticity is the

ratio of the marginal share to the budget share, given as follows:

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 5

3 Inferior goods are goods for which the quantity demanded falls as income rises; that is, the income effect is negative.

where equals the budget share for good , and represent the price of and the expenditure 

on good respectively. = is total real expenditure, is a residual term, and the and are

w

PE

E

iP E

i EE

i

i i

i i

i i n i ii

=

∑ = , 1 ε αβ

α β i

i

n

i

i

n

= =

∑ = ∑

1 1

1 and = 0. (12)

θ αβ β i

i

ii ii

dE

dE = = + (1+ log = (13) E w ) . +

θi

i

i

i

i

w

dE

dE

E

E

d E

d E = = (log (log (14) ))

.
Dividing θi in equation 13 by wi results in:

Equation 15 shows that βi is greater than zero for a luxury good (has an income elasticity greater than 1),

while it is less than zero for a necessity (has an income elasticity less than 1). If βi = 0, then the good has

unitary elasticity. Equation 15 also shows that, if the good is a luxury or a necessity (whether the βs are less

than or greater than zero), the income elasticity of good i will decline as income increases. This is due to the

fact that the budget shares (wi

) of necessities decline as income increases whereas the budget shares of luxuries increase as income increases. In the case of unitary elasticities, income elasticities remain unchanged

with income changes. This is because the budget shares do not change for unitary elastic goods as income

levels change.

Using the differential approach in the Rotterdam model, Theil, Chung, and Seale extended the model to

incorporate prices adding a non-linear substitution term to the basic linear function. This model, known

as the Working Preference Independence (PI) or the Florida model,4 allows for separability and has

fewer parameters to be estimated than in the AIDS model. This model is further discussed in chapter 3.

Preference Independence, Block Independence,

and Stepwise Budgeting

Preference independence or strong separability implies that the preference ordering among goods is not

dependent on the quantities consumed of any other goods. Block independence applies this idea to broad

groups or categories of goods. Block independence or strong group-separability implies that the preference ordering among items within one broad consumption group is not dependent on the quantities of

items consumed in other groups. This enables stepwise demand analysis, which assumes that consumers

spend their income in steps or stages. In the first stage, consumers allocate their incomes among broad

categories of goods, like food, shelter, and entertainment. In the second stage, consumers spend their

budget on goods within these broad categories. Figure 1 depicts the stepwise budgeting procedure

assumed in our analysis.

6 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

4 The model, developed by Theil, Chung, and Seale (1989), was originally named the “Working PI” (Preference Independence)

model but was renamed the Florida model by Seale, Walker, and Kim (1991). In later writings, Theil (1996) also referred to it

as the “Florida model.”

Stepwise (two-stage) budgeting procedure

Figure 1

Food

Clothing Education Medical care Recreation

Cereals Fish Oils/fats Fruits/

vegetables

Housing Transport

Meat Dairy Beverages/

tobacco

θ β i

i

i

w wi

= 1+ (15) . 
While it is reasonable to assume that expenditures among broad consumption categories in the first stage

of the budgeting process might be independent of each other, demand for goods within the food categories may not be independent. For example, many food products are substitutes or complements and

have cross-price effects. Therefore, when estimating the second stage of the model, preference independence is generally replaced by the weak separability assumption. In our analysis of food sub-categories,

the Florida-Slutsky model using the weak-separability assumption replaces the Florida-PI model. Both

the Florida-PI and Florida Slutsky model are further discussed in Chapter 3.

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 7
Chapter 2

International Comparison Project Data

Expenditure and price data for the two-stage cross-country demand model were obtained from the

International Comparison Project (ICP), which was originally initiated by researchers at the University

of Pennsylvania (Kravis, Heston, and Summers, 1975). The ICP is maintained by the International

Comparison Program Development Data Group of the World Bank. Over the years, data collected by the

ICP have increased from 10 countries in Phase I (1970) to 115 countries in 1996 (table 1). Theil, Chung,

and Seale (1989) used the data from the first four phases, covering a total of 60 countries (seven countries from the earlier phases were excluded in Phase IV.) The 1996 data includes an additional 65 countries (additional to the 60 countries from Phase IV), but 10 countries present in the earlier three phases

are excluded, resulting in a total of 115 countries. The current analysis uses 114 of the 115 countries,

excluding Herzegovina, for which the 1996 population data were unavailable. Figure 2 shows a world

map highlighting the countries that were covered in the 1996 ICP.

Problems are encountered when comparing expenditures across countries since the expenditures are

expressed in different currencies. Using exchange rates to convert the expenditures to a single currency

may not always be desirable since exchange rates fail to account for the fact that services are cheaper in

developing countries. The creators of the ICP data developed a special, country-product-dummy (CPD)

approach to consider this problem. This method begins at a highly disaggregated level, with a country’s

gross domestic product divided into a large number of detailed consumption categories. For example, the

fresh vegetables category consisted of 20 individual items in Phase I (Theil, Chung, and Seale, 1989).

Using the CPD approach, the natural logarithm of the price of item i is the sum of the item effect (Ai

)

and the country effect (Bc). If there are no missing observations and if this relation is fitted by least

squares, the coefficient Bc is the mean over all items of the log price of item i in country c. Then the

8 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Countries covered by the 1996 International Comparison Project

Figure 2

Low-income countries (< 15 percent of U.S. income level)

Middle-income countries (15 - 44 percent of U.S. income level)

High-income countries (= > 45 percent of U.S. income level) 
antilog of Bc equals the geometric mean price (say for vegetables in Phase I) over the i=1,..., 20 items in

country c. In the case of missing observations, a weighted least squares approach is used, where the

weight of each country is selected so as to correct for the number of items with missing price data in

each country. The antilog of the calculated Bc is essentially estimated purchasing power parity (PPP).

For example, in the case of fresh vegetables, when the parities are calculated using the United States as

the base country, the estimated PPPs reflect the values for each country in national currencies required to

purchase the same amount of fresh vegetables as purchased with $1 in the United States. For example,

the same amount of vegetables may be purchased by 1.5 Euros in France, or 80 rupees in Pakistan. 

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 9

Table 1—Countries represented in the International Comparison Project

Africa America Asia Europe Africa America Asia/Oceania Europe

Countries represented in Phase I Additional countries in 1996

Kenya Colombia India France Benin Antigua and Armenia Albania

United States Japan Germany Congo Barbuda Australia Belarus

Hungary Egypt Bahamas Azerbaijan Bulgaria

Italy Gabon Barbados Bahrain Czech Republ

United Kingdom Guinea Belize Bangladesh Estonia

Mauritius Bermuda Fiji Herzegovina

Countries added in Phase II Sierra Leone Dominica Georgia Hungary

Swaziland Grenada Iran Iceland

Iran Belgium Jamaica Jordan Latvia

South Korea Netherlands Mexico Kazakhstan Lithuania

Malaysia Trinidad and Kyrgyzstan Macedonia

Philippines Tobago Lebanon Moldova

Mongolia Romaina

Countries added in Phase III St. Kitts and Nepal Russia

 Nevis New Zealand Slovakia

Malawi Brazil Pakistan Austria St. Lucia Oman Slovenia

Zambia Jamaica Sri Lanka Denmark St. Vincent & Qatar Sweden

Mexico Syria Ireland the Grenadines Singapore Switzerland

Uruguay Thailand Luxembourg Syria Turkey

Poland Tajikistan Ukraine

Romania Thailand

Spain Turkmenistan

Yugoslavia Uzbekistan

Vietnam

Countries added in Phase IV Yemen

Botswana Argentina Hong Kong Finland Countries excluded in 1996

Cameroon Bolivia Indonesia Greece

Ethiopia Canada Israel Norway Ethiopia Colombia India Yugoslavia

Cote d'Ivoire Chile Portugal Costa Rica

Madagascar Costa Rica Dominican Republic

Mali Dominican Rep. El Salvador

Morocco Ecuador Guatemala

Nigeria El Salvador Honduras

Senegal Guatemala Panama

Tanzania Honduras

Tunisia Panama

Zimbabwe Paraguay

Peru

Venezuela

Countries excluded in Phase IV

Jamaica Iran Romania

Mexico Malaysia

Syria

Thailand
Aggregating Data at National Levels

As mentioned earlier, difficulties are encountered when trying to incorporate raw price data from crosscountry surveys into demand models. Prices that maintain additivity5 of the data at various levels of

aggregation need to be calculated for each consumption category in the model. Prices denominated in

national currencies cannot be used since this compares two different units of measure, such as the

Japanese yen and the U.S dollar. One solution to convert expenditures into a single currency would be to

use official exchange rates. Again, exchange rates do not account for the fact that services are cheaper in

less developed countries, and these tend to overstate the poverty of poorer countries. Another solution is

to use an appropriate purchasing power parity (PPP) measure to convert the prices to a single currency.

However, mere conversion of the prices may not maintain additivity across different levels of aggregation of the data. The Geary-Khamis procedure addresses both problems by expressing expenditures in

international dollars, which can be added across categories (World Bank, 1993). This method involves

two steps. The first is to express expenditures in international dollars by dividing countries into specific

regions and then distributing regional income among such countries according to the regional price

structure. The overall purchasing power parity for a country c (πc), is defined as:

where Eic is the per capita expenditure in national currency on category i in country c and Vic is the per

capita expenditures expressed in U.S. dollars. Then, prices for each country c are obtained by dividing

expenditures in national currencies by those in international dollars or,

In our analysis, the program ICP ToolPak developed by Yuri Dikhanov of the World Bank is used to

obtain Geary-Khamis estimates of prices and expenditures.

Scaling Problems

The 1996 ICP data were collected between 1993 and 1996 by six different agencies contracted by the

United Nations for countries in Asia, Africa, the Middle East, the Caribbean, Latin America, the

Organization of Economic Cooperation and Development (OECD), and the Commonwealth of

Independent States (CIS) region. Each of the agencies was responsible for collecting data for a particular

region, for which the data were collected at a very disaggregate level and then aggregated upward using

the Geary-Khamis methodology to express PPPs in terms of base country currency, which in most cases

was the 1996 U.S. dollars. However, not all PPPs were expressed relative to the United States. Data for

Asia were expressed relative to Hong Kong, and data for Latin America were expressed relative to

Mexico. Since Mexico was also represented in the OECD data, merging Latin America with the rest of

the data was relatively easy. Merging the data from Asia was more challenging. The Asian data were

first transformed to the U.S. base by making Japan, which was represented in the OECD data, the base

country instead of Hong Kong. However, the transformed Asian data had scaling problems. For example,

Singapore appeared to be poorer than most sub-Saharan African countries (fig. 3). 

10 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

πc

ic

i

n

c ic

i

n

E

P V

= 

∑

∑

=

=

1

1

, (16)

P

E

V

 c

ic c c

n

ic

i

n =

∑

∑

=

=

( /)

.

π

1

1

 (17)

5 Additivity implies that expenditures of individual items in a consumption group add to the total group expenditure.
By comparing the PPPs from our data with those from the World Bank’s World Development Indicators

(WDI), we noticed a close match for all countries except for those from Asia. Therefore, we used the

WDI rankings as a new scale for the Asian data. For example, Hong Kong’s PPP in 1996, according to

the WDI, was about 79.8 percent of the U.S. level. Therefore, for Asian countries, per capita expenditures

of broad consumption categories were multiplied by 0.798 to get expenditures relative to the U.S. level,

which was normalized to equal one. Figure 4 shows the effects of the re-scaling of the Asian data. A similar exercise was done for expenditure data for each of the food sub-groups. Total food sub-group expenditures were multiplied by the newly re-scaled PPPs for Asia to get relative expenditures in real terms. 

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 11

Scatter of log of income and food, beverage, and tobacco budget share

Figure 3

0

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

-7 -6 -5 -4 -3 -2 -1 0

Budget share

Asian countries

Hong Kong

Scatter of income and food budget share, with Asia re-scaled

Figure 4

Budget share

Hong Kong

0

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

-4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0
Budget Shares and Volumes of Aggregate Categories 

and Food Sub-Categories6

To facilitate analysis, the 114 countries covered by the 1996 ICP are divided into low-, middle-, and highincome countries, based on their income relative to that of the United States.7 Low-income countries represent

those with real per capita income less than 15 percent of the U.S. level, middle-income countries with real per

capita income between 15 and 45 percent of the U.S. level, and high-income countries with per capita income

equal to or greater than 45 percent of the U.S. level. This criterion for grouping indicates that the majority of

Sub-Saharan African countries, poor transition economies such as Mongolia and Turkmenistan, and lowincome Middle Eastern and Asian countries such as Yemen and Nepal fall within the first group. High-income

countries include most Western European countries, and others such as Australia, New Zealand, Canada and

the United States; while the middle-income countries include better-off transition economies such as Estonia,

Hungary, Slovenia, North African countries, and many Latin American countries. 

The simple average budget shares for the nine aggregate consumption categories for each of the three country groups are presented in table 2. It should be noted that the food grouping includes food prepared and

consumed at home plus beverage and tobacco. It does not include food consumed away from home. As

expected, the budget shares for food tend to decrease as income rises, ranging from over 73 percent of the

total budget in Tanzania to just below 10 percent in the United States. Although the range for clothing and

footwear is not as large as in the case of food, the simple average budget shares are also higher for lowincome countries compared with the other two groups. The budget share for education is higher for highincome countries compared with the middle-income and low-income countries. This could be due to the fact

that education is not homogeneous and quality variations may exist across countries, causing expenditures to

rise in wealthier countries. Similarly, the budget shares for gross-rent, fuel and power; house furnishings and

operations; medical care; transport and communication; and other items generally increase as income levels

increase. On average, consumers in low-income countries spend less than 4 percent of their total budget on

medical care, while consumers in high-income countries spend over 10 percent. As income increases, budget

shares for recreation, a clear luxury item, also tend to increase. On average, low-income countries spend less

than 2 percent of their total budget on recreation while high-income countries spend up to 8 percent of their

total budget on recreation. 

The conditional budget shares for the eight food sub-categories are presented in table 3. Cereals, fats and

oils, and fruits and vegetables account for a larger share of the total food budget in low-income countries

compared with high-income countries. The fruits and vegetable category also includes data on roots and

12 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

6 For detailed data on volume of consumption, nominal expenditures and budget shares for each country, contact Yonas Biru,

International Comparison Program Development Data Group, World Bank, (202) 473-1268. 

7 Note that this classification is merely done to facilitate analysis in this publication and is not based on any generally accepted

criteria for classification. Since the classification is based on the ICP data used in this analysis, some countries may be in a

group with which they normally would not be associated.

Table 2—Average budget shares for aggregate consumption categories, 114 countries in 19961

Food Gross Transport &

Country groups beverages Clothing & rent, fuel House Medical communica-

& tobacco footwear & power operations care Education tion Recreation Other

Percent of total expenditures

Low-income 52.58 7.67 9.65 4.99 3.80 5.40 7.94 1.67 6.30

Middle-income 34.69 7.01 14.55 6.70 7.36 6.88 10.72 3.91 8.18

High-income 16.97 5.65 17.37 6.60 10.16 7.72 12.36 7.84 15.32

1For detailed data on volume of consumption, nominal expenditures and the budget shares for each country, 

please contact Yonas Biru, International Comparison Program Development Data Group, World Bank, (202) 473-1268.
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 13

Table 3—Conditional budget shares for food sub-categories, 114 countries in 1996

Total Total

Country Beverages, Breads, Fats, Fruits, Other food expenditures 

 tobacco cereals Meat Fish Dairy oils vegetables foods expenditure (US =1)

Percent of total food expenditures % total

expenditures

Low-income countries

Tanzania 4.74 39.55 9.60 6.38 3.56 3.30 24.22 8.65 73.24 0.020

Nigeria 2.73 34.08 12.88 15.22 5.61 5.15 15.44 8.89 72.97 0.029

Tajikistan 1.19 46.89 5.25 0.37 1.75 10.80 31.62 2.14 68.94 0.034

Zambia 12.98 18.45 24.38 12.28 6.02 6.55 13.12 6.22 60.81 0.035

Yemen 22.89 26.11 11.83 6.13 5.70 5.53 10.75 11.06 61.13 0.035

Malawi 4.86 40.44 17.48 12.84 3.23 3.11 13.21 4.83 53.35 0.038

Madagascar 5.92 44.47 9.65 3.79 2.09 2.35 26.18 5.56 65.88 0.038

Mali 6.76 34.39 14.10 3.01 3.81 8.11 9.89 19.93 53.27 0.039

Mongolia 6.13 30.38 31.21 0.03 18.11 3.46 3.78 6.91 58.74 0.039

Benin 9.45 23.57 14.27 7.56 4.13 4.48 33.24 3.29 55.40 0.049

Kenya 15.49 32.49 5.13 0.43 15.08 2.64 17.57 11.17 45.82 0.053

Sierra Leone 5.29 34.94 4.38 12.73 1.13 12.24 16.47 12.82 62.09 0.058

Nepal 9.79 57.61 3.29 0.63 5.36 4.33 14.58 4.41 57.88 0.058

Turkmenistan 6.45 24.81 23.12 1.04 9.91 6.19 20.23 8.25 50.82 0.060

Congo 9.53 10.67 9.23 14.50 3.86 2.50 44.85 4.87 46.92 0.065

Senegal 6.54 26.51 13.93 13.12 4.40 13.95 13.08 8.47 53.35 0.069

Vietnam 7.85 35.65 21.93 10.34 2.81 1.51 9.44 10.48 64.75 0.071

Bangladesh 4.12 50.17 4.38 9.21 3.21 3.88 9.58 15.46 56.05 0.072

Pakistan 4.38 21.20 7.68 0.71 26.84 10.06 17.32 11.80 46.99 0.082

Azerbaijan 2.89 39.02 14.40 1.07 5.64 10.22 13.04 13.72 73.51 0.088

Cote d'Ivoire 19.52 19.60 14.38 2.16 4.42 1.49 23.26 15.18 44.32 0.090

Paraguay 11.05 15.45 33.66 3.09 9.96 4.12 12.71 9.96 27.27 0.091

Uzbekistan 4.80 27.32 10.67 0.15 12.00 5.05 19.30 20.71 48.33 0.095

Kyrgyzstan 10.84 21.08 9.64 0.35 8.11 6.10 33.86 10.01 47.15 0.096

Cameroon 19.14 16.07 16.22 4.66 1.25 3.79 31.21 7.65 43.80 0.096

Moldova 7.06 19.78 15.86 1.69 16.96 6.05 24.04 8.57 43.45 0.096

Bolivia 13.40 21.91 23.90 0.89 5.95 3.16 22.17 8.62 42.52 0.101

Ecuador 9.78 14.80 19.51 5.48 12.92 5.90 21.09 10.52 29.09 0.103

Armenia 5.20 18.86 8.18 1.57 6.23 9.58 34.36 16.02 69.66 0.107

Sri Lanka 14.93 21.81 1.78 12.45 6.67 1.44 26.42 14.51 63.55 0.108

Jordan 10.05 8.42 27.56 1.68 11.33 9.04 15.47 16.44 37.67 0.114

Albania 5.13 20.64 18.99 0.31 17.37 9.54 22.64 5.39 69.26 0.123

Indonesia 11.32 33.47 5.13 8.70 5.70 4.74 23.73 7.20 50.62 0.126

Jamaica 11.50 18.92 24.72 6.81 11.87 3.16 14.12 8.90 34.78 0.126

Zimbabwe 13.91 23.70 22.04 2.61 8.99 6.68 10.02 12.04 25.58 0.127

Guinea 19.14 16.07 16.22 4.66 1.25 3.79 31.21 7.65 43.69 0.130

Syria 10.32 8.50 16.01 0.99 12.25 13.02 27.85 11.07 47.92 0.138

Georgia 4.39 27.10 12.86 1.52 14.45 6.99 21.99 10.70 47.39 0.139

Middle-income countries

Ukraine 9.38 17.82 21.63 2.50 13.99 4.21 19.87 10.60 45.03 0.153

Philippines 11.91 29.73 14.49 14.50 6.71 1.76 11.10 9.81 48.35 0.163

Peru 9.23 21.30 22.18 4.65 9.62 3.70 21.37 7.95 30.31 0.168

Botswana 36.43 24.23 11.86 0.73 4.70 2.25 6.23 13.58 32.80 0.168

Thailand 28.57 16.11 18.64 3.31 5.23 2.76 16.38 8.99 28.56 0.170

Morocco 11.85 20.15 19.91 1.92 6.55 8.60 18.41 12.60 45.61 0.176

Venezuela 7.18 26.93 22.37 3.77 10.04 3.87 17.08 8.76 29.47 0.177

Macedonia 15.61 18.10 19.35 2.11 12.36 5.31 18.45 8.70 34.73 0.185

Continued--
14 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 3—Conditional budget shares for food sub-categories, 114 countries in 1996--Continued

Total Total

Country Beverages, Breads, Fats, Fruits, Other food expenditures 

 tobacco cereals Meat Fish Dairy oils vegetables foods expenditure (US =1)

Percent of total food expenditures % total

expenditures

Middle-income countries--continued

Belize 15.00 10.89 6.47 0.88 10.29 4.41 7.35 44.70 31.17 0.185

Egypt 9.25 24.65 23.62 4.56 10.10 8.36 12.53 6.92 48.08 0.186

St. Vincent 

 & Grenadines 8.73 25.78 7.63 10.36 11.05 4.68 16.62 15.15 35.87 0.187

Swaziland 11.95 25.25 22.87 2.28 9.42 4.36 11.33 12.53 27.48 0.197

Lebanon 13.33 10.47 18.84 1.68 9.45 4.21 35.14 6.89 39.33 0.201

Belarus 13.00 14.98 21.67 4.09 18.06 6.31 14.79 7.10 50.45 0.203

Kazakhstan 11.17 30.77 17.76 1.61 9.20 7.12 9.32 13.05 51.82 0.203

Dominica 6.01 16.86 11.52 9.57 8.75 2.15 29.69 15.46 38.27 0.203

Latvia 18.92 12.89 18.87 3.05 14.89 4.29 17.80 9.30 41.76 0.214

St. Lucia 7.62 14.39 21.23 7.38 11.50 2.84 30.32 4.71 46.62 0.216

Brazil 12.32 16.80 24.54 2.31 14.04 3.62 14.83 11.55 22.71 0.217

Bulgaria 12.35 17.07 19.68 0.81 13.94 3.49 24.78 7.89 30.70 0.218

Russia 15.46 14.26 22.92 4.13 13.27 4.26 16.24 9.47 34.35 0.225

Fiji 18.20 15.72 12.75 11.85 6.99 3.96 21.16 9.37 36.28 0.232

Grenada 8.73 25.78 7.63 10.36 11.05 4.68 16.62 15.15 40.99 0.233

Turkey 9.47 20.34 13.55 1.01 12.84 8.42 23.23 11.14 32.60 0.236

Lithuania 19.88 12.92 20.67 3.47 14.10 4.83 11.98 12.15 40.42 0.243

Romania 13.47 14.62 24.34 0.80 12.82 5.71 20.61 7.63 45.26 0.248

Iran 4.79 24.80 23.88 1.66 11.17 6.96 18.62 8.12 32.55 0.258

Mexico 18.88 21.67 17.33 3.12 10.88 2.30 13.00 12.82 26.63 0.263

Bahrain 7.91 13.13 13.46 9.45 10.38 3.13 25.55 16.98 28.55 0.269

Chile 13.41 21.48 21.79 2.06 11.19 4.60 17.33 8.13 22.96 0.273

Antigua 

 & Barbuda 8.73 25.78 7.63 10.36 11.05 4.68 16.62 15.15 36.12 0.273

Poland 26.53 10.33 21.24 1.55 8.35 3.44 14.49 14.07 30.65 0.283

Trinidad 

 & Tobago 17.00 14.22 16.00 5.73 9.36 5.14 14.64 17.91 22.06 0.291

Estonia 21.39 16.08 20.26 2.97 13.17 4.73 10.18 11.21 33.45 0.299

Gabon 9.53 10.67 9.23 14.50 3.86 2.50 44.85 4.87 47.94 0.301

Tunisia 13.66 13.83 13.56 5.02 10.58 4.32 28.19 10.84 35.95 0.307

St. Kitts

 & Nevis 8.73 25.78 7.63 10.36 11.05 4.68 16.62 15.15 36.33 0.311

Uruguay 19.90 21.46 20.14 1.69 10.17 1.97 15.25 9.43 25.25 0.314

Slovakia 25.44 10.04 20.56 1.68 13.86 4.57 13.43 10.40 32.06 0.319

Hungary 23.58 10.91 20.48 0.77 12.76 4.73 12.67 14.09 22.54 0.346

Argentina 15.02 14.59 26.13 1.39 12.67 3.46 17.22 9.51 32.79 0.385

Oman 4.65 16.84 16.38 7.72 11.21 4.64 22.25 16.31 24.14 0.403

Qatar 7.32 10.63 23.15 5.61 10.36 2.72 20.99 19.21 26.22 0.426

Slovenia 24.13 10.08 22.13 1.90 11.41 2.98 17.21 10.16 21.34 0.437

High-income countries

Czech Republic 28.09 10.25 21.27 1.76 11.63 4.03 12.38 10.59 25.00 0.451

Greece 24.56 7.25 16.03 4.53 13.58 5.37 17.27 11.40 21.17 0.485

Korea 17.82 20.70 12.69 11.69 5.02 0.88 21.23 9.97 31.64 0.494

Portugal 21.49 13.06 22.40 12.18 8.53 3.65 14.50 4.18 23.23 0.505

Spain 17.70 12.47 23.98 10.32 11.60 4.77 13.82 5.35 17.52 0.508

Ireland 37.33 9.51 16.38 1.97 10.09 2.74 13.42 8.56 16.59 0.522

Continued--
tubers, a cheap source of calories in many poor countries.8 Therefore, a large portion of the food budget in

Sub-Saharan countries is spent on fruits and vegetables, for example, 45 percent in Congo and 33 percent in

Benin. On the other hand, the budget shares of meat, dairy, and beverages and tobacco are greater for highincome countries compared with both low- and middle-income countries. 

The total food expenditures column in table 3 presents the food share of total household budget. The

nominal expenditures in national currency for each food sub-category can be obtained by multiplying

total food expenditures in national currencies by the respective budget shares. Per capita real volume of

consumption of the aggregate and food sub-categories is given by the per capita expenditure in international dollars obtained from the Geary-Khamis procedure. Prices for each country are, as earlier

explained, obtained by dividing the expenditures in national currencies by these volumes (equation 17).

The value of total volumes consumed is the total real expenditure in international dollars and can be

used as an income proxy. The final column in table 3 provides per capita national income (total real

expenditure in international dollars) relative to that of the United States, which is normalized to one. The

normalized total expenditure values indicate the wealth of a country relative to the United States.

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 15

8 Ideally, one would prefer to disaggregate roots and tubers for the fruits and vegetables category; however, the 1996 ICP data

do not allow for this possibility.

Table 3—Conditional budget shares for food sub-categories, 114 countries in 1996--Continued

Total Total

Country Beverages, Breads, Fats, Fruits, Other food expenditures 

 tobacco cereals Meat Fish Dairy oils vegetables foods expenditure (US =1)

Percent of total food expenditures % total

expenditures

High-income countries--continued

Singapore 25.21 10.29 13.29 14.99 4.97 1.82 18.14 11.28 13.04 0.536

Mauritius 24.69 10.06 15.55 8.36 10.47 5.22 17.86 7.79 28.12 0.558

Israel 18.59 14.45 14.11 2.51 12.97 1.86 19.37 16.15 17.70 0.577

New Zealand 32.93 12.62 13.87 1.74 9.20 2.28 16.85 10.51 15.19 0.585

Finland 31.45 11.44 15.16 2.85 12.57 1.96 13.45 11.12 14.67 0.587

Bahamas 21.89 14.06 23.27 6.22 11.06 5.53 11.29 6.68 35.73 0.593

Sweden 27.47 11.42 15.18 4.37 11.71 2.29 14.44 13.12 13.26 0.638

Netherlands 24.00 12.36 18.67 2.16 12.61 2.21 15.72 12.27 13.29 0.646

France 21.36 10.89 24.92 4.75 11.80 2.85 12.39 11.05 15.34 0.682

United Kingdom 47.53 8.31 12.57 2.25 6.88 1.27 12.02 9.16 16.37 0.686

Belgium 21.06 10.78 24.72 6.06 10.96 3.87 12.38 10.16 14.36 0.693

Norway 29.99 7.70 16.35 4.85 12.79 1.52 11.06 15.73 15.98 0.695

Italy 16.18 11.32 23.58 5.40 13.90 3.86 19.14 6.61 16.59 0.701

Austria 23.72 13.45 20.98 1.64 11.29 3.80 14.10 11.01 13.53 0.715

Germany 28.25 14.87 20.30 1.87 7.11 2.27 8.28 17.05 13.09 0.718

Australia 25.24 13.50 16.91 3.11 9.67 1.65 18.34 11.56 15.07 0.732

Japan 23.15 22.28 7.82 17.02 4.79 0.66 12.79 11.49 14.88 0.741

Canada 29.48 11.43 16.46 2.65 11.19 2.11 18.12 8.57 11.68 0.754

Bermuda 20.28 10.06 11.55 2.97 7.84 4.34 13.52 29.43 14.23 0.782

Switzerland 26.18 10.73 16.52 1.81 15.16 1.97 17.02 10.60 14.57 0.794

Barbados 18.00 13.12 22.14 4.92 9.02 3.28 18.04 11.48 11.10 0.796

Hong Kong 17.87 9.04 22.67 19.66 3.44 3.33 11.81 12.19 10.28 0.799

Iceland 27.41 11.87 16.45 5.05 11.56 1.62 10.83 15.23 18.90 0.801

Denmark 28.81 8.93 20.38 2.04 11.12 2.16 11.93 14.65 14.02 0.808

Luxembourg 43.12 8.88 18.30 2.26 7.83 1.88 11.64 6.09 17.08 0.972

United States 28.71 11.39 19.58 1.19 8.59 1.77 14.66 14.11 9.73 1.000

Low-income 

 average 9.25 26.97 14.62 5.03 7.89 5.90 20.34 10.00 52.58 0.080

Middle-income

 average 14.22 17.98 17.91 4.57 10.78 4.36 18.24 11.94 34.69 0.249

High-income

 average 25.92 11.83 17.94 5.47 10.03 2.78 14.62 11.41 16.97 0.674
Chapter 3

Two-Stage Cross-Country Demand Model

As explained in chapter 1, our analysis of international food consumption patterns is conducted in two

stages (fig. 1). The first stage involves estimating an aggregate demand system using the Florida-PI

model across nine broad consumption categories: food—which includes food prepared and consumed at

home plus beverages and tobacco—clothing and footwear, education, gross rent and fuel, house furnishings and operations, medical care, transport and communications, recreation, and other expenditures.

The second stage of the analysis involves a second demand system using the Florida-Slutsky model

across eight food sub-categories: bread and cereals, meat, fish, dairy products, oils and fats, fruits and

vegetables, beverages and tobacco, and other food products. This section will discuss the actual models

estimated in each stage and their results. 

Florida-Preference Independence (PI) Model

The Working-Preference Independence model (PI) or the Florida-PI model, used in the first stage of our

analysis, is an extension of Working’s original 1948 model, which was briefly described in chapter 1.

Using the differential approach of consumer demand, Theil, Chung, and Seale (1989) incorporated

prices into Working’s model. The resulting model can be divided into a linear, quadratic, and a cubic

component as indicated below:

wi

=LINEAR + QUADRATIC + CUBIC + εic . (18)

LINEAR = Real-income term 

= αi + βi qc . (18a)

QUADRATIC = Pure price term

CUBIC = Substitution term

where qc is the natural logarithm of Qc, the measure of total real per capita income, qc

*=(1+qc), Pi is the

geometric mean price of Pi over all countries, and φ represents the income flexibility—the inverse of the

income elasticity of the marginal utility of income—which is assumed constant in the model.

The linear term in the model represents the effect of a change in real income—that is, the volume of

total expenditure—on budget share. Since the quadratic and cubic terms vanish at geometric mean

prices, the linear term is also the budget share at geometric mean prices. The quadratic term—quadratic

because it contains products of the αs and the βs—is the pure price term showing how the increase in

price results in a higher budget share on good i, even if the volume of expenditures goes down or stays

the same. The cubic term—cubic because it involves φ—is a substitution term reflecting how higher

prices may cause lower budget shares for good i due to substitution of good i for other goods.

16 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

=+ − + ∑

⎡

⎣

⎢

⎢

⎤

⎦

⎥ = ⎥

( ) log ( )log . αβ αβ i i ic

ic

i

j jc

jc

j j

n

q

P

P q

P

P 1

 (18b)

=+ − + ∑

⎡

⎣

⎢

⎢

⎤

⎦

⎥ = ⎥

φα β α β ( ) log ( )log . * * i i c

ic

i

j j c

jc

j j

n

q

P

P q

P

P 1

 (18c)
The expenditure elasticity for the Florida-PI model is calculated by the ratio of the marginal share to the

budget share as follows (Theil, Chung, and Seale, 1989, pp. 110-111 for derivation):

where, in this case, wi represents the budget share of good i at geometric mean prices, c represents the

country, θic is the marginal share of good i in c, and βi is the estimated coefficient on qc in the ith good

equation. Three types of own-price elasticities of demand can be calculated from the parameter estimates. The first of these, the Frisch-deflated own-price elasticity of good i, is the own-price elasticity

when own-price changes and income is compensated to keep the marginal utility of income constant. It

is given by:

where φ is the money flexibility and is estimated from the model. The Slutsky (compensated) own-price

elasticity measures the change in demand for good i when the price of i changes, while real income

remains unchanged. It is calculated from the following:

The Cournot (uncompensated) own-price elasticity refers to the situation when own-price changes while

nominal income remains constant but real income changes, and is calculated from:

Florida-Slutsky Model

The Florida-Slutsky model, which assumes weak separability, is used to estimate the second stage of the

model, the food subcategories. Similar to the Florida-PI model, the Florida-Slutsky model has three

components: a linear real-income term; a quadratic pure-price term; and a linear substitution term

replacing the cubic term in the former model, that is,

wic = (αi + βi

qc) (23a)

The πijs represent Slutsky price coefficients, a matrix of compensated price responses. The compensated

(Slutsky) price elasticities may be estimated by the ratio, πij / wic while the uncompensated (Cournot)

own-price elasticity is given by the difference between the compensated elasticity and the income effect,

that is πij / wic – (wic+βi

). Similar to the Florida-PI model, the expenditure elasticity for the FloridaSlutsky model can be calculated at geometric mean prices using the formula given by equation 19. 

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 17

θ β ic

ic

i

w wic

= +1 , (19) 

F w

w ic i

ic

= + φ β , (20)

S w w

w

F w ic i ic i

ic

= ic i

+ −− φ = −− β β β ( )( ) ( ). 1 1 (21)

++ − + ∑

⎡

⎣

⎢

⎢

⎤

⎦

⎥ = ⎥

( ) log ( )log αβ αβ i i ic

ic

i

j jc

jc

j j

n

q

P

P q

P

1 P (23b)

+

⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥ ∑

=

πij

jc

j j

n P

P log

1

 . (23c)

C w w

w

w Sw ic i ic i

ic

= ic i ic i

+ −− φ − + =− + β β β β ( )( ) ( ) ( ). 1 (22)
Conditional Florida-Slutsky Model

The Florida Sltusky model can be written in terms of a conditional demand system. That is, the demand

for good i contained in group Sg is conditional on group expenditure. The conditional Florida-Slutsky

model is,

The unconditional Frisch own-price elasticity is given by:

The unconditional Slutsky price elasticity is given by:

18 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

w q

q

P

P q

P

P

P

P

ic

* i i* gc

i i* gc

i Sg c

i Sg

j Sg j j* gc

jc

j

j Sg ij

jc

j

= + (24a)

+ ( + + (24b)

+ , (24c)

α β

αβ αβ

π

*

* , *

*

) log ( )log

log

∈

∈

∈

∈

− ∑

⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥

∑

η ηη ic

U

Fc ic* g = . (25) i S ∈

F

W w i

u gc

gc

ic

ic

= = gc i

φ θ φη η

Θ *

*

* , (26)

ε

π

ijc ε ηη η ij

ic

ijc gc ic jc jc gc gc w

= =+ − w W * ** * Φ ( ) 1 , (27)

where, = , is the (unconditional) budget share of good , is the budget share of 

group S in country c, is the geometric mean price of good , is the log of real expenditures 

on group S , and the and s are conditional parameters to be estimated. In particular, the s 

are the conditional Slutsky (compensated) price parameters.

g

g

w

w

W

w iSW

P iSq

ic

* ic

gc

ic g gc

i g gc

i

* i ij* ij*

∈

∈

αβ π π , *

Income and price elasticities estimated from the conditional Florida - Slutsky model are conditional on 

given food expenditures. The unconditional demand elasticities can then be obtained using the 

parameters estimated in the first stage of the analysis. For example, the unconditional expenditure 

elasticity ( ) is simply the conditional expenditure elasticity ( =1+ where ) 

multiplied by the income elasticity of demand for food as a group ( ) obtained from the Florida - PI 

model (equation 19), or:

η η β αβ

η

ic

U

ic

* i* ic ic i i* gc

Fc

ww q * ** = +

where is the marginal share for group in country , = is the conditional marginal share of 

good , is the unconditional marginal share of good , and is the income flexibility parameter 

estimated in stage one by the Florida - PI model.

Θ

Θ gc g ic* i

g

g i

S c

iS i

θ θ

∈ θ φ
All parameters of the Florida-PI and the Florida-Slutsky model were estimated by maximum likelihood

(ML) using the scoring method (Harvey, 1990, pp. 133-135) and the GAUSS software.

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 19

where = is the conditional Slutsky price elasticity in country for good with regard to good j, 

 is the conditional Slutsky price parameter, is the conditional fitted budget share of S in 

country c, is the marginal share of group S in country c, is the income flexibility 

term estimated from the first stage, is the share of group S in country at geometric mean (group) 

prices, and are conditional expenditure elasticities of i and j, respectively, in country c, and

 is the unconditional expenditure elasticity of group g (food in our c

ij

*

g

ε

π

π

φ φ

η η

η

ijc

* ij

ic

ic

* g

gc

gc

gc

gc

gc g

ic jc

gc

w

c i

w i

W

W c

*

*

* *

,

∈

Φ = ΘΘ

ase) in country c. The 

unconditional Cournot price elasticity can be estimated using the unconditional Slutsky elasticity 

as given by equation 28.

C w W ijc ijc ic ic* gc gc = - (28) εη η * × .
Chapter 4

Maximum Likelihood Estimation

The Florida-PI and Florida-Slutsky models can be estimated with maximum likelihood (ML). If all

countries or groups of countries have identical covariance matrices, the model can be estimated under

the condition of homoskedasticity. If countries or groups of countries have covariance matrices of differing magnitudes, the system exhibits heteroskedasticity. If so, the ML estimator should explicitly take

heteroskedasticity into account. In the two sections that follow, we describe the ML estimator under the

conditions of both homoskedasticity and group heteroskedasticity. 

Homoskedasticity

Let a system of demand equations be given by:

wic = f

ic (θi

) + εic , (29)

where εic∼N(0, Σ*), i (=1, ..., n) represents good i, n being the total number of goods, c (=1, ..., N) 

represents country c, and N is the total number of countries. Because of adding-up restrictions, Σ* is 

an n x n singular covariance matrix. Accordingly, we drop one equation for estimation purposes (Barten,

1969). Barten (1969) shows that parameter estimates are invariant to the equation dropped and the

dropped parameters can be calculated from the adding-up restrictions of the model.

Define Σ to be the (n-1) x (n-1) resulting covariance matrix of the n-1 equations. Rewriting equation 29

in vector and matrix notation where wc and εc are n-1 vectors, and wic and εic are the i

th elements,

respectively, such that:

wc = f

c (θ) + εc , (30)

and θ is a parameter vector to be estimated. For the Florida-PI, define f

c (θ) to be:

20 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

f q X q qx q

 q q x q 

c c c c ccT c

c c ccT c

() ( ) ( ) ( )

( ) ( ) ( ), * **

θα β α β α β α β

φ α β φα β α β

=+ + + − + +

 + X (31a) + −+ +

f q X q q x q x c gc c gc gc c

T

gc c () ( ) ( ) ( ) . * * * ** * * * θα β α β α β α β π =+ + + − + + + (31b) *

where = [ ] and = [ ] are vectors of parameters, is a diagonal matrix of order with 

as the diagonal element, is a column vector ( is its transpose) with as its element ( = , , 

), and = log where = / is the geometric mean price of good 

over all countries. For the conditional Florida -Slutsky model, define ( ) to be:

th th

α α ββ

θ

i i c ic

c cTic

ic

ic

i

nc

i

i c ic

c

n-1 X n- x

i x x xi i

n- x

P

P

P

P P PN i

N f

1

1

1 ⎛

⎝

⎜ ⎞

⎠

⎟ − ⎛

⎝

⎜ ⎞

⎠ log log ⎟ ∑

where = [ ] and = [ ] are vectors of parameters ( ), is an symmetric 

matrix of (homogeneity and symmetry imposed), is a diagonal matrix of order with as 

the diagonal element ( ), is a column vector ( is its transpose) with as its element 

( ) , and = log where =

* α α ββ π

π

* **

*

log log

i

*

i g

ij c ic

th

gc c

T

ic

th

g ic

ic

i

nc

i

i c ic

n-1 i S n-1 n-1

s X n-1 x

i iS x x x i

i = 1, , n-1 and i S x P

P

P

P P P

∈

∈

∈ ⎛

⎝

⎜ ⎞

⎠

⎟ − ⎛

⎝

⎜ ⎞

⎠

⎟

x

K ∑

∈

/ is the geometric 

mean price of good over all countries.

N

iS N g
The log-likelihood function of equation 31a or 31b is:

and must be maximized with respect to both unknown parameter matrices, Σ−1 and θ. Maximizing L

with respect to Σ−1 yields:

Substituting R for Σ into equation 32 yields the concentrated log-likelihood function,

For the Florida-PI model, the first-order derivatives of L* with respect to θ (=α,β,φ) is:

For the Florida-Slutsky model, the first-order derivatives of L* with respect to θ (=α∗,β∗,π∗) is:

The expectation of the second-order derivatives of L* (or the expectation of the Hessian) with respect to

θ (=α,β,φ) or to θ (=α∗,β∗,π∗) is:

where E( ) is the expectations operator. The asymptotic covariance matrix of the ML estimator of θ is

–M-1 (Theil, Chung, and Seale, 1989, p. 47). The maximum likelihood estimates of θ can be obtained

through the method of scoring (Harvey, 1990, pp. 133-135), and the asymptotic standard errors are computed from the square root of the diagonal of –M-1 where M is defined above.

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 21

L N w f w f c c T

c

N

= −− ∑ c c − − −

=

constant +

1

2 log 1

2

Σ [ , (32) Σ 1 1

1

( )] [ ( )] θ θ

dL

d

df

d R w f 

* c

T

T

c c c

N

θ

θ

θ = , (35a) θ ( ) [ ( )] ⎡

⎣

⎢ ⎤

⎦

⎥ ∑ − −

=

1

1

dL

d

df

d R w f 

* c

T

T

c c c

N

θ

θ

θ = , (35b) θ ( ) [ ( )] ⎡

⎣

⎢ ⎤

⎦

⎥ ∑ − −

=

1

1

M E d L

d d

df

d

R df

d T

c

T c

N T

c

T = ⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥ − ⎡

⎣

⎢ ⎤

⎦ ∑ ⎥ ⎡

⎣

⎢ ⎤

⎦

⎥ =

− 2

1

1 * ( ) ( ) ( )

θ θ

θ

θ

θ θ

θ

 = , (36)

L NR * = constant +

1

2 log . (34) -1

R

N w f w f c c c

N

c c T ( ) [ ( )] ( )] θ θθ = − ∑ − =

1 [ . (33)

1

where consists of three submatrices: + (square of order - ), 

(same order), a 

column vector of - elements), = 

=

c

c c

df d df d A B n 1

df d q A q B df d X q q x q

n1 A X q x q x I

B X

T c T

T c c c c ccT c

c ccT c T c n

c

() ()

( ) ( ) ( ) ( ) ( )(

( ) [( ) ] ,

** *

θθ θα

θβ θφ α β α β α β

αβ αβ

φ φ

=

= + = + −+ +

− + +− +

−

1 −1

( ) [( ) ] , * * α β φα β + −+ q x q xI I − −

n 1

c c

T

c

T cn n and are the identity matrix of dimension 

- (Theil, Chung, and Seale, p. 46 - 47).

1 1

where consists of three submatrices: (square of order - ), 

(same order), an - - matrix with each row equal 

to A = and is the identity matrix of 

dimension - .

c

c c

*

df d df d A n 1

df d q A df d X n 1 n 1

x X q x q xI I

n 1

T c T

T

gc

c

T c gc cT

c

T cn n

() ()

() () (

), ( ) [ ( ) ] ,

* *

** *

* * **

θθ θα

θβ θπ

α β αβ

=

= =

− + +− + − −

x

1 1 1
Group Heteroskedasticity

Let there be G groups of countries and assume heteroskedasticity across the covariance matrices of the

individual groups such that the covariance matrix of g (=1, ...,G) is KgΣ and Kg is a scalar. In this case,

the log-likelihood function is:

where N = Σg Ng , and the concentrated log-likelihood function is:

The second-order derivatives of L* with respect to θ and Kg have zero expectations. Define M to be the

matrix of the expectations of the second-order derivatives of L* with respect to all θ and Kg. Thus,

where 

22 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

L n- N K N

K wf wf gg g g

g

c Ng c c T = + constant - log log (37) ∑ − ∑ ∑ ∈ − c c − 1 -1 -1

2 2

1

2

Σ [ ( )] [ ( )], θ θ Σ

L n- N K N RK gg g * = + ∑ (, ) , − constant - log log (38) 1

2 2

1 θ

dL

d K

df

d

R w f T g

g

c Ng

c

T

T

c c

* ( ) [ ( )],

θ

θ

θ = ∑ θ ⎡

⎣

⎢ ⎤

⎦ ∑ ⎥ ∈ − 1 −1 (39)

dL

dK

n N

K K w f R w f 

g

g

g g

c Ng c c T c c

* ( ) = − [ ( )] [ ( )]. − − ∑ ∈ − − − 1

2

1

2

1 θ θ (40)

M

d L

d d

d L

dKdK

 T

T

=

⎡

⎣

⎢

⎢

⎢

⎢

⎤

⎦

⎥

⎥

⎥

⎥

2

2

*

*

θ θ

 0

 0 

 (41)

d L

d d K

df

d

df

d T g

g

c Ng c

T

c

T

2 1 * () ()

θ θ

θ

θ

θ

θ = ∑ ∑ ⎡

⎣

⎢ ⎤

⎦

⎥ ⎡

⎣

⎢ ⎤

⎦ ∈ ⎥ R , (42)

T

-1

d L

dK

n N

K K w f R w f 

g

g

g g

c Ng c c T c c

2

2 23

1 1

2

1 * ( ) = − [ ( )] [ ( )] − − ∑ ∈ − − − θ θ , (43)

and is a diagonal matrix with the diagonal element equal to: th d L dKdK G G g 2 * T x

and and is defined as a vector of all .

First - order derivatives of with respect to (= , , ) or (= , , ) are: *

R K

N K wf wf K K

L

g

g

c Ng cc cc Tg

*

( , ) [ ( )][ ( )] ,

* *

θ θθ

θ αβφ θ α β π

= ∑ ∑ ∈ − − 1 1

with defined in equations 35a and 35b for the Florida - PI and Florida - Slutsky models, 

respectively. The first - order derivative of with respect to is: 

df d

L K

c T

* g

( ) θ θ

and all off - diagonal elements equal zero (i.e., = 0 for g h). The method of scoring 

(Harvey, 1990, pp. 133 -135) is used to estimate all and all elements of . Asymptotic standard errors 

are calculated from the square root of the diagonal of .

d L dK dK

K

M

g h

-

2

1

* ≠

−

θ
Chapter 5

Parameter Estimates

Theil, Chung, and Seale (1989) divide the Phase IV data into two groups, countries in either Phases II or

III and those not in either. Fitting the Florida-PI model to the group data individually, they find that the

group covariance matrices are not equal; the covariance matrix of the group of newly added countries is

almost twice as large as that of the group of countries in Phases II or III (Theil, Chung, and Seale, 1989,

pp. 74-75). Given this difference, they infer that the covariance matrices of these two groups are heteroskedastic. 

We extend this approach and allow for heteroskedasticity among three separate groups of countries:

Group 1, those included in Theil, Chung, and Seale’s (1989) estimation from the first three phases of the

ICP; Group 2, those added in Phase IV; and Group 3, those countries first appearing in the 1996 ICP

data (and not in the first four phases). Group 1 has 26 countries, Group 2 has 23 countries, and Group 3

has 65 countries (table 4). 

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 23

We normalize = for Group 1 countries (equation 37). As such, we estimate two heteroskedastic 

parameters. Income is normalized so that the per capita real income of the United States equals one, 

and all other country per capita real incomes are relative to that of the United States. These and other 

parameters and their associated asymptotic standard errors are estimated with the heteroskedastic -

corrected ML procedure for the 114 countries. Additionally, symmetry is imposed in the Florida -

Slutsky model (i.e., = ).

K 1

ij S

g

π π ij ji g

* * ∀ ∈ ,

Table 4–Classification of 114 countries for correction for heteroskedasticity

Africa America Asia Europe Africa America Asia/Oceania Europe

Group I. Countries included from the first 3 phases Group 3. Additional countries in 1996

Malawi Brazil Japan Austria Benin Antigua & Armenia Albania

Zambia United States Pakistan Belgium Cameroon Barbuda Australia Belarus

Uruguay Philippines Denmark Congo Bahamas Azerbaijan Bulgaria

South Korea France Cote d'Ivoire Barbados Bahrain Czech Republic

Sri Lanka Germany Egypt Belize Bangladesh Estonia

Syria Hungary Gabon Bermuda Fiji Iceland

Thailand Ireland Guinea Dominica Georgia Latvia

Italy Kenya Grenada Iran Lithuania

Luxembourg Mali Jamaica Jordan Macedonia

Netherlands Mauritius Mexico Kazakhstan Moldova

Poland Sierra Leone Trinidad & Kyrgyzstan Russia

Romania Swaziland Tobago Lebanon Slovakia

Spain St. Kitts & Mongolia Slovenia

United Nevis Nepal Sweden

Kingdom St. Lucia New Zealand Switzerland

St.Vincent & Oman Turkey

the Grenadines Qatar Ukraine

Singapore

Group 2. Countries added in Phase IV Tajikistan

Turkmenistan

Botswana Argentina Hong Kong Finland Uzbekistan

Madagascar Bolivia Indonesia Greece Vietnam

Morocco Canada Israel Norway Yemen

Nigeria Chile Portugal

Senegal Ecuador

Tanzania Paraguay

Tunisia Peru

Zimbabwe Venezuela
Table 5 presents the estimated parameters for the first stage, the aggregate model. The estimated two Kgs

exceed 1 confirming the presence of heteroskedasticity. As indicated by the negative βs, only food, beverage and tobacco, and clothing and footwear are necessities; all other consumption categories except

education are luxuries. The categories clothing and footwear and education have near zero βi and, hence,

have near-unitary income elasticity. The β parameter for food, beverages, and tobacco is by far the

largest β in absolute value. Its estimate of -0.135 (with an asymptotic standard error of 0.006) is comparable to the value, -0.134, obtained by Theil, Chung, and Seale (1989, table 5-4, p. 105) for the 1980

normalization of their extended and pooled data.9 This parameter estimate retains the property of the

strong version of Engel’s law: when income doubles, the budget share of food declines by approximately

0.1 (Theil, Chung, and Seale, 1989, p. 44). The αs from this study and those of Theil, Chung, and Seale

(1989) are not comparable since their data are normalized on 1980 geometric-mean prices while the current data are in 1996 prices.

Table 6 presents the estimated parameters

for the second-stage model, the food subgroups. Similar to the aggregate model, the

estimated two Kgs exceed 1 confirming the

presence of heteroskedasticity. As indicated by the negative sign of β∗s, bread and

cereals, fats, and oils, and fruits and vegetables are (conditionally) inelastic food

items, while the remaining five are conditionally elastic items.10 The negative β* for

fruits and vegetables can be explained by

the fact that the data for this food sub-category also include expenditures on roots

and tubers, a staple among poor consumers. The πijs in the table present the

compensated own-price effects, the diagonal of the Slutsky matrix. 

The most prominent measures of income

and price sensitivities for a good are

income and own-price elasticities. These

measures are not constant but should vary

with different levels of affluence. For

example, the income elasticity of demand

for a necessity such as food, beverage, and

tobacco should be larger for a low-income

county than for a high-income country.

Own-price elasticities of demand should

also be larger in absolute value for lowincome countries than for high-income

countries (Timmer, 1981).

24 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 5—Maximum likelihood estimates of the 

aggregate model, 114 countries in 1996

Parameter Standard error

Income flexibility -0.809 0.021

Beta:

Food, beverages & tobacco -0.135 0.006

Clothing and footwear -0.006 0.002

Gross rent, fuel & power 0.027 0.004

House operations 0.012 0.002

Medical care 0.024 0.003

Education 0.005 0.002

Transport & communication 0.021 0.003

Recreation 0.020 0.002

Other 0.032 0.003

Alpha:

Food, beverages & tobacco 0.151 0.011

Clothing and footwear 0.059 0.004

Gross rent, fuel & power 0.179 0.008

House operations 0.077 0.004

Medical care 0.106 0.004

Education 0.074 0.004

Transport 0.133 0.006

Recreation 0.074 0.004

Other 0.147 0.006

K:

K1 1.089 0.114

K2 1.294 0.080

9 The estimate of -0.134 for food, beverages, and tobacco is obtained by simply adding the parameter estimate of food, -.135, to

that of beverages and tobacco, 0.001.

10 Remember the parameter estimates are conditional on total per capita food expenditures, not total per capita expenditures.

*
Aggregate Model

The expenditure and own-price elasticities based on the aggregate Florida-PI model have the desired properties discussed earlier (tables 7-10). From equation 19, we note that a luxury good (with an income elasticity

greater than 1) is associated with a positive βi

, while βi is negative for a necessity (income elasticity less than

1). If βi equals zero, the good has unitary elasticity. Table 7 presents the expenditure elasticities calculated at

geometric mean prices for the 114 countries. These country-specific income-elasticity values represent the

estimated percentage change in demand for a particular good if total income increases by 1 percent. As the

estimated budget shares for recreation in Tanzania, Nigeria, Yemen, and Malawi (countries with very small

actual budget shares ranging from 0.64 to 0.72 percent) were negative, the elasticities for recreation in these

countries are estimated using their actual budget shares. 

The income (expenditure) elasticity of demand for food, beverages, and tobacco varies greatly among

countries and is highest among low-income countries; it varies from 0.80 for Tanzania to 0.68 for

Georgia. It ranges between 0.67 to 0.49 for middle-income countries and from 0.48 to 0.10 for highincome countries. The average income elasticity for the low-income group of countries is 0.73, and it is

over twice the size of the average, 0.34, of high-income countries. Another feature to note is that, for

high-income countries, the income elasticity of demand for food, beverages, and tobacco gradually

decreases from 0.48 for the Czech Republic, with an income level 45 percent that of the United States,

to 0.25 for Denmark whose income level is 81 percent that of the United States. Thereafter, the elasticity

measure decreases rapidly to 0.13 for Luxembourg and 0.10 for the United States.

The income elasticity for clothing and footwear, another necessity, also decreases in value from lowincome to high-income countries. However, given the estimated absolute βi value is close to zero, the

elasticity values are close to one for all countries. The income elasticities for clothing and footwear

range in the low-income countries from 0.93 in Tanzania to 0.92 in Georgia, in the middle-income countries from 0.92 in Ukraine to 0.91 in Slovenia, and in the high-income countries from 0.91 in the Czech

Republic to 0.90 in the United States. 

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 25

Table 6—Maximum likelihood estimates of the food sub-group model, 114 countries in 1996

Parameter Standard error Parameter Standard error

Beta: Diagonal of the Slutsky Matrix:

Beverages and tobacco 0.065 0.007 π11 -0.037 0.016

Breads and cereals -0.068 0.008 π22 -0.145 0.019

Meat 0.011 0.007 π33 -0.198 0.012

Fish 0.010 0.005 π44 -0.067 0.011

Dairy 0.015 0.004 π55 -0.081 0.009

Fats & oils -0.014 0.003 π66 -0.031 0.013

Fruits & vegetables -0.025 0.008 π77 -0.183 0.005

Other foods 0.007 0.006 π88 -0.135 0.024

Alpha: K:

Beverages and tobacco 0.217 0.009 K1 1.294 0.010

Breads and cereals 0.133 0.009 K2 1.373 0.011

Meat 0.178 0.008

Fish 0.057 0.005

Dairy 0.111 0.005

Fats & oils 0.031 0.003

Fruits & vegetables 0.155 0.009

Other foods 0.118 0.007
26 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 7—Income elasticity for aggregate consumption categories, 114 countries in 19961

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

Low-income countries:

Tanzania 0.800 0.929 1.370 1.358 2.949 1.091 1.415 3.7782 2.618

Nigeria 0.786 0.927 1.328 1.319 2.166 1.088 1.363 4.0302 2.038

Tajikistan 0.778 0.927 1.312 1.304 1.986 1.087 1.344 3.969 1.893

Zambia 0.777 0.926 1.309 1.301 1.960 1.087 1.341 3.746 1.872

Yemen 0.777 0.926 1.309 1.301 1.958 1.087 1.340 4.0772 1.871

Malawi 0.772 0.926 1.300 1.293 1.879 1.086 1.330 4.1252 1.805

Madagascar 0.772 0.926 1.300 1.293 1.878 1.086 1.330 3.166 1.804

Mali 0.770 0.926 1.298 1.290 1.858 1.086 1.327 3.052 1.787

Mongolia 0.770 0.926 1.297 1.290 1.854 1.086 1.326 3.024 1.783

Benin 0.758 0.925 1.280 1.273 1.722 1.084 1.305 2.412 1.671

Kenya 0.753 0.924 1.273 1.266 1.677 1.084 1.297 2.250 1.632

Sierra Leone 0.748 0.924 1.267 1.261 1.641 1.083 1.290 2.134 1.601

Nepal 0.747 0.924 1.266 1.260 1.641 1.083 1.289 2.131 1.600

Turkmenistan 0.745 0.923 1.264 1.258 1.625 1.083 1.286 2.085 1.587

Congo 0.740 0.923 1.259 1.253 1.597 1.082 1.280 2.003 1.562

Senegal 0.736 0.922 1.254 1.249 1.575 1.082 1.275 1.943 1.542

Vietnam 0.734 0.922 1.253 1.248 1.568 1.082 1.274 1.923 1.536

Bangladesh 0.733 0.922 1.252 1.247 1.565 1.082 1.273 1.916 1.533

Pakistan 0.723 0.921 1.244 1.239 1.524 1.081 1.263 1.812 1.497

Azerbaijan 0.718 0.921 1.240 1.235 1.507 1.080 1.259 1.773 1.482

Cote d'Ivoire 0.716 0.921 1.239 1.234 1.501 1.080 1.257 1.758 1.476

Paraguay 0.715 0.921 1.238 1.233 1.497 1.080 1.256 1.748 1.472

Uzbekistan 0.711 0.921 1.235 1.231 1.486 1.080 1.253 1.725 1.463

Kyrgyzstan 0.711 0.921 1.235 1.231 1.486 1.080 1.253 1.724 1.462

Cameroon 0.711 0.920 1.235 1.230 1.484 1.080 1.252 1.720 1.460

Moldova 0.710 0.920 1.235 1.230 1.484 1.080 1.252 1.719 1.460

Bolivia 0.707 0.920 1.232 1.228 1.473 1.080 1.250 1.697 1.451

Ecuador 0.705 0.920 1.231 1.227 1.470 1.079 1.248 1.689 1.447

Armenia 0.701 0.920 1.229 1.225 1.460 1.079 1.246 1.668 1.439

Sri Lanka 0.701 0.920 1.229 1.224 1.459 1.079 1.245 1.666 1.438

Jordan 0.696 0.919 1.226 1.222 1.448 1.079 1.242 1.643 1.428

Albania 0.689 0.919 1.222 1.218 1.434 1.078 1.238 1.614 1.415

Indonesia 0.686 0.919 1.221 1.217 1.429 1.078 1.237 1.605 1.410

Jamaica 0.686 0.919 1.221 1.217 1.428 1.078 1.236 1.603 1.409

Zimbabwe 0.685 0.919 1.221 1.216 1.427 1.078 1.236 1.600 1.408

Guinea 0.683 0.919 1.219 1.215 1.422 1.078 1.235 1.591 1.404

Syria 0.677 0.918 1.217 1.213 1.412 1.078 1.232 1.572 1.395

Georgia 0.676 0.918 1.216 1.212 1.411 1.078 1.231 1.569 1.394

Middle-income countries:

Ukraine 0.666 0.917 1.212 1.208 1.396 1.077 1.226 1.541 1.380

Philippines 0.658 0.917 1.209 1.205 1.386 1.077 1.223 1.522 1.371

Peru 0.655 0.917 1.208 1.204 1.382 1.076 1.222 1.515 1.367

Botswana 0.655 0.917 1.208 1.204 1.381 1.076 1.221 1.514 1.367

Thailand 0.653 0.917 1.207 1.203 1.379 1.076 1.221 1.511 1.365

Morocco 0.650 0.916 1.206 1.202 1.375 1.076 1.219 1.503 1.361

Venezuela 0.649 0.916 1.206 1.202 1.374 1.076 1.219 1.501 1.360

See notes at end of page. Continued--

Country
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 27

Table 7—Income elasticity for aggregate consumption categories, 114 countries 

in 19961

--Continued

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

Middle-income countries--continued:

Macedonia 0.643 0.916 1.204 1.200 1.368 1.076 1.217 1.490 1.354

Belize 0.643 0.916 1.204 1.200 1.367 1.076 1.217 1.489 1.354

Egypt 0.643 0.916 1.203 1.200 1.367 1.076 1.217 1.489 1.354

St.Vincent & Grenadines 0.642 0.916 1.203 1.200 1.366 1.076 1.216 1.487 1.353

Swaziland 0.635 0.916 1.201 1.198 1.360 1.076 1.214 1.476 1.347

Lebanon 0.632 0.916 1.200 1.197 1.357 1.075 1.213 1.471 1.344

Belarus 0.631 0.915 1.200 1.197 1.356 1.075 1.213 1.469 1.343

Kazakhstan 0.631 0.915 1.200 1.196 1.356 1.075 1.212 1.468 1.343

Dominica 0.631 0.915 1.200 1.196 1.356 1.075 1.212 1.468 1.343

Latvia 0.623 0.915 1.198 1.194 1.349 1.075 1.210 1.457 1.337

St. Lucia 0.623 0.915 1.198 1.194 1.348 1.075 1.210 1.456 1.336

Brazil 0.622 0.915 1.197 1.194 1.348 1.075 1.210 1.455 1.335

Bulgaria 0.621 0.915 1.197 1.194 1.347 1.075 1.209 1.453 1.335

Russia 0.617 0.915 1.196 1.193 1.343 1.075 1.208 1.447 1.331

Fiji 0.612 0.914 1.195 1.191 1.339 1.075 1.207 1.441 1.328

Grenada 0.611 0.914 1.195 1.191 1.339 1.075 1.207 1.440 1.328

Turkey 0.609 0.914 1.194 1.191 1.337 1.075 1.206 1.437 1.326

Lithuania 0.605 0.914 1.193 1.190 1.334 1.074 1.205 1.432 1.323

Romania 0.602 0.914 1.192 1.189 1.332 1.074 1.204 1.428 1.321

Iran 0.595 0.914 1.191 1.188 1.328 1.074 1.202 1.421 1.317

Mexico 0.592 0.914 1.190 1.187 1.326 1.074 1.201 1.418 1.315

Bahrain 0.588 0.913 1.189 1.186 1.323 1.074 1.200 1.414 1.313

Chile 0.586 0.913 1.189 1.186 1.322 1.074 1.200 1.412 1.311

Antigua & Barbuda 0.585 0.913 1.189 1.186 1.322 1.074 1.200 1.411 1.311

Poland 0.580 0.913 1.187 1.184 1.318 1.074 1.199 1.406 1.308

Trinidad & Tobago 0.575 0.913 1.187 1.184 1.315 1.073 1.197 1.401 1.305

Estonia 0.570 0.913 1.186 1.183 1.313 1.073 1.196 1.397 1.303

Gabon 0.568 0.913 1.185 1.182 1.312 1.073 1.196 1.396 1.302

Tunisia 0.564 0.912 1.185 1.182 1.310 1.073 1.195 1.392 1.300

St. Kitts & Nevis 0.562 0.912 1.184 1.181 1.309 1.073 1.195 1.390 1.299

Uruguay 0.560 0.912 1.184 1.181 1.308 1.073 1.194 1.389 1.298

Slovakia 0.557 0.912 1.183 1.180 1.307 1.073 1.194 1.387 1.297

Hungary 0.541 0.911 1.181 1.178 1.299 1.072 1.191 1.375 1.290

Argentina 0.517 0.911 1.177 1.175 1.290 1.072 1.187 1.361 1.281

Oman 0.506 0.910 1.176 1.173 1.286 1.072 1.186 1.355 1.278

Qatar 0.492 0.910 1.174 1.171 1.282 1.071 1.184 1.348 1.273

Slovenia 0.486 0.910 1.173 1.171 1.280 1.071 1.183 1.345 1.272

High-income countries:

Czech Republic 0.477 0.909 1.172 1.170 1.277 1.071 1.182 1.341 1.269

Greece 0.456 0.909 1.170 1.168 1.272 1.071 1.179 1.333 1.264

Korea 0.450 0.909 1.170 1.167 1.270 1.071 1.179 1.331 1.263

Portugal 0.444 0.908 1.169 1.167 1.269 1.071 1.178 1.328 1.261

Spain 0.442 0.908 1.169 1.166 1.268 1.071 1.178 1.328 1.261

Ireland 0.434 0.908 1.168 1.166 1.266 1.070 1.177 1.325 1.259

Singapore 0.425 0.908 1.167 1.165 1.264 1.070 1.176 1.322 1.257

Mauritius 0.411 0.908 1.166 1.164 1.262 1.070 1.175 1.318 1.255

See notes at end of page. Continued--

Country
28 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 7—Income elasticity for aggregate consumption categories, 114 countries 

in 19961

--Continued

Food, 

beverages

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

High-income countries--continued:

Israel 0.399 0.907 1.165 1.163 1.259 1.070 1.174 1.315 1.252

New Zealand 0.394 0.907 1.165 1.163 1.258 1.070 1.173 1.313 1.252

Finland 0.393 0.907 1.165 1.163 1.258 1.070 1.173 1.313 1.251

Bahamas 0.389 0.907 1.165 1.162 1.257 1.070 1.173 1.312 1.251

Sweden 0.361 0.906 1.163 1.160 1.253 1.069 1.171 1.305 1.246

Netherlands 0.356 0.906 1.162 1.160 1.252 1.069 1.171 1.304 1.245

France 0.332 0.906 1.161 1.159 1.249 1.069 1.169 1.299 1.242

United Kingdom 0.330 0.906 1.161 1.159 1.248 1.069 1.169 1.298 1.242

Belgium 0.325 0.906 1.160 1.158 1.248 1.069 1.169 1.297 1.241

Norway 0.324 0.906 1.160 1.158 1.247 1.069 1.168 1.297 1.241

Italy 0.320 0.906 1.160 1.158 1.247 1.069 1.168 1.296 1.241

Austria 0.311 0.905 1.160 1.157 1.246 1.069 1.168 1.295 1.239

Germany 0.309 0.905 1.160 1.157 1.245 1.069 1.168 1.294 1.239

Australia 0.300 0.905 1.159 1.157 1.244 1.069 1.167 1.293 1.238

Japan 0.293 0.905 1.159 1.157 1.244 1.069 1.167 1.292 1.237

Canada 0.284 0.905 1.158 1.156 1.243 1.069 1.166 1.290 1.236

Bermuda 0.265 0.905 1.157 1.155 1.240 1.068 1.165 1.287 1.234

Switzerland 0.257 0.904 1.157 1.155 1.240 1.068 1.165 1.286 1.234

Barbados 0.256 0.904 1.157 1.155 1.239 1.068 1.165 1.286 1.234

Hong Kong 0.254 0.904 1.157 1.155 1.239 1.068 1.165 1.285 1.233

Iceland 0.252 0.904 1.157 1.155 1.239 1.068 1.165 1.285 1.233

Denmark 0.247 0.904 1.157 1.155 1.238 1.068 1.164 1.284 1.233

Luxembourg 0.126 0.903 1.152 1.150 1.228 1.067 1.159 1.270 1.223

United States 0.103 0.902 1.152 1.150 1.227 1.067 1.159 1.268 1.222

Low-income average 0.729 0.922 1.257 1.252 1.641 1.082 1.279 2.042 1.593

Middle-income average 0.602 0.914 1.194 1.191 1.338 1.074 1.206 1.440 1.327

High-income average 0.335 0.906 1.162 1.160 1.251 1.069 1.170 1.303 1.245 1

Countries are reported based on ascending per capita real income levels.

2

As the estimated budget shares for recreation were negative, these values are calculated using the actual budget shares.

Country
All other consumption categories are luxuries with expenditure elasticities greater than 1. The elasticity

values are higher for less affluent countries and span a wide range. Recreation is by far the most luxurious good with an income elasticity of demand ranging from 4.12 for Malawi to 1.27 for the United

States. The goods, medical care and other items, are the next most luxurious goods followed by transportation and communication, gross rent, fuel and power, and home furnishings and operations.

Although education is a luxury good, it is the least luxurious. With the estimated absolute βi value close

to zero, the estimated elasticities for education are close to 1 and range from 1.09 in Tanzania to 1.07 in

the United States. 

As presented by equations 20 to 22 in chapter 3, three types of own-price elasticities of demand for a good

can be calculated from the parameter estimates of the Florida-PI model. Tables 8-10 present the estimated

Frisch, Slutsky, and Cournot own-price elasticity for the nine aggregate commodity groups, across 114

countries. In the tables, countries are listed in ascending order of affluence. The elasticity measures perform in accordance with Timmer’s proposition: own-price elasticities of demand are larger in absolute values for low-income countries than for high-income ones. The values of the Cournot and Frisch own-price

elasticities decline monotonically in absolute value when traveling from poor to rich countries. 

The Slutsky own-price elasticity of demand for food, beverages, and tobacco begins at -0.30 for

Tanzania, increases (absolutely) to -0.39 for Thailand, and declines thereafter (absolutely) to 

-0.08 for the United States. To clarify the reason for this, take the logarithmic derivative of equation 21,

using equation 11 and suppressing the error term:

House furnishings and operations and transport and communications have Slutsky own-price elasticity values greater than unity in absolute values for a few of the countries with the lowest income levels. Recreation, medical care and other goods have Slutsky own-price elasticity measures greater

than unity in absolute values for all or most of the low-income countries and many of the middleincome countries. For Tanzania, the Slutsky own-price elasticities of demand for recreation, medical

care, and other items are –3.05, –2.39, and –2.12, respectively. For the United States, they are –0.93,

–0.86, and –0.81, respectively.

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 29

d S

Q

w

ww w 

c

i ic i i

ic ic i ic i

log( ) [ ( )]

( )( ) . φ β βββ β = − +−

+ −−

2 1

1 (44)

If good is a luxury, > 0, and the derivative is negative; as real per capita income increases, the 

Slutsky elasticity of the good decreases. If good is a necessity, < 0 so that > 0. 

If the term in brackets on the right side of equation 44 is positive, then both the numerator and the 

derivative are positive. This is the case for food, beverages, and tobacco. When is sufficiently large, 

that is, when is sufficiently small, the derivative for this good is positive for the poorest countries. 

Eventually, however, becomes sufficiently small so that the derivative becomes negative. In this

case, the Slutsky own-price elasticity becomes smaller in absolute value.

ic

ic

i

i

w

Q

w

i

i i

c

β

own-price β β −

 The turning point, when the 

Slutsky own-price elasticity starts declining with increasing per capita income, is at the per capita 

income level of Lithuania or at 25 percent of the per capita income level of the United States. 

The Cournot and Frisch elasticity values are all larger than the corresponding Slutsky elasticities. The 

Frisch values are between the corresponding Cournot and Slutsky ones for food, beverage and tobacco

clothing and footwear, and education, while they are larger than both the corresponding Cournot and 

Slutsky elasticities for the other three goods. To see the reason for this result, recall that in equation 

21 and 22, = + ) and = (1 ). By manipulation, = + ) + ).

Noting that in all the cases, and + ) > 0, if |F|< |- | then |C| >

i i ii

i

C S w S F w C F Fw w

F< w 1.00

ic ic ic ic

ic

− −− − − ( ((

(

β β ββ

0 | |, if | |>|- |, then β

| | < | |, and if | | |- |, then = . 

F F 1.00

C F F 1.00 C F ≈
30 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 8—Frisch own-price elasticity for aggregate consumption categories, 114 countries 

in 19961

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

Low-income countries:

Tanzania -0.647 -0.751 -1.108 -1.098 -2.385 -0.882 -1.145 -3.0562 -2.117

Nigeria -0.635 -0.750 -1.074 -1.066 -1.751 -0.880 -1.102 -3.2612 -1.648

Tajikistan -0.629 -0.749 -1.061 -1.054 -1.606 -0.879 -1.087 -3.209 -1.531

Zambia -0.628 -0.749 -1.059 -1.052 -1.585 -0.879 -1.084 -3.030 -1.514

Yemen -0.628 -0.749 -1.059 -1.052 -1.584 -0.879 -1.084 -3.2982 -1.513

Malawi -0.624 -0.749 -1.052 -1.045 -1.520 -0.878 -1.075 -3.3372 -1.460

Madagascar -0.624 -0.749 -1.052 -1.045 -1.519 -0.878 -1.075 -2.560 -1.459

Mali -0.623 -0.749 -1.050 -1.044 -1.503 -0.878 -1.073 -2.468 -1.445

Mongolia -0.623 -0.749 -1.049 -1.043 -1.499 -0.878 -1.072 -2.445 -1.442

Benin -0.613 -0.748 -1.035 -1.029 -1.392 -0.877 -1.055 -1.951 -1.351

Kenya -0.609 -0.747 -1.029 -1.024 -1.356 -0.876 -1.049 -1.820 -1.320

Sierra Leone -0.605 -0.747 -1.024 -1.019 -1.327 -0.876 -1.043 -1.726 -1.295

Nepal -0.604 -0.747 -1.024 -1.019 -1.327 -0.876 -1.043 -1.724 -1.294

Turkmenistan -0.602 -0.747 -1.022 -1.017 -1.314 -0.876 -1.040 -1.686 -1.283

Congo -0.598 -0.746 -1.018 -1.013 -1.292 -0.875 -1.035 -1.620 -1.263

Senegal -0.595 -0.746 -1.015 -1.010 -1.274 -0.875 -1.031 -1.571 -1.247

Vietnam -0.594 -0.746 -1.013 -1.009 -1.268 -0.875 -1.030 -1.555 -1.242

Bangladesh -0.593 -0.746 -1.013 -1.008 -1.266 -0.875 -1.029 -1.549 -1.240

Pakistan -0.585 -0.745 -1.006 -1.002 -1.232 -0.874 -1.021 -1.466 -1.210

Azerbaijan -0.581 -0.745 -1.003 -0.999 -1.219 -0.874 -1.018 -1.434 -1.198

Cote d'Ivoire -0.579 -0.745 -1.002 -0.998 -1.214 -0.874 -1.017 -1.422 -1.194

Paraguay -0.578 -0.745 -1.001 -0.997 -1.210 -0.874 -1.016 -1.414 -1.190

Uzbekistan -0.575 -0.744 -0.999 -0.995 -1.202 -0.873 -1.013 -1.395 -1.183

Kyrgyzstan -0.575 -0.744 -0.999 -0.995 -1.201 -0.873 -1.013 -1.394 -1.182

Cameroon -0.575 -0.744 -0.999 -0.995 -1.200 -0.873 -1.013 -1.391 -1.181

Moldova -0.575 -0.744 -0.999 -0.995 -1.200 -0.873 -1.013 -1.390 -1.181

Bolivia -0.571 -0.744 -0.997 -0.993 -1.191 -0.873 -1.010 -1.372 -1.173

Ecuador -0.570 -0.744 -0.996 -0.992 -1.188 -0.873 -1.010 -1.366 -1.171

Armenia -0.567 -0.744 -0.994 -0.990 -1.180 -0.873 -1.007 -1.349 -1.163

Sri Lanka -0.567 -0.744 -0.994 -0.990 -1.180 -0.873 -1.007 -1.347 -1.163

Jordan -0.563 -0.744 -0.992 -0.988 -1.171 -0.872 -1.005 -1.329 -1.155

Albania -0.557 -0.743 -0.989 -0.985 -1.159 -0.872 -1.001 -1.305 -1.144

Indonesia -0.555 -0.743 -0.988 -0.984 -1.156 -0.872 -1.000 -1.298 -1.141

Jamaica -0.555 -0.743 -0.987 -0.984 -1.155 -0.872 -1.000 -1.296 -1.140

Zimbabwe -0.554 -0.743 -0.987 -0.984 -1.154 -0.872 -1.000 -1.294 -1.139

Guinea -0.552 -0.743 -0.986 -0.983 -1.150 -0.872 -0.998 -1.287 -1.136

Syria -0.548 -0.742 -0.984 -0.981 -1.142 -0.872 -0.996 -1.272 -1.128

Georgia -0.547 -0.742 -0.984 -0.980 -1.141 -0.871 -0.996 -1.269 -1.127

Middle-income countries:

Ukraine -0.539 -0.742 -0.980 -0.977 -1.129 -0.871 -0.992 -1.246 -1.116

Philippines -0.533 -0.742 -0.978 -0.975 -1.121 -0.871 -0.989 -1.231 -1.108

Peru -0.530 -0.741 -0.977 -0.974 -1.117 -0.871 -0.988 -1.225 -1.105

Botswana -0.530 -0.741 -0.977 -0.974 -1.117 -0.871 -0.988 -1.224 -1.105

Thailand -0.528 -0.741 -0.976 -0.973 -1.116 -0.870 -0.987 -1.222 -1.104

See notes at end of page. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 31

Table 8—Frisch own-price elasticity for aggregate consumption categories, 114 countries 

in 19961--Continued

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

Middle-income countries--continued:

Morocco -0.525 -0.741 -0.975 -0.972 -1.112 -0.870 -0.986 -1.215 -1.101

Venezuela -0.525 -0.741 -0.975 -0.972 -1.111 -0.870 -0.986 -1.214 -1.100

Macedonia -0.520 -0.741 -0.973 -0.970 -1.106 -0.870 -0.984 -1.205 -1.095

Belize -0.520 -0.741 -0.973 -0.970 -1.106 -0.870 -0.984 -1.204 -1.095

Egypt -0.520 -0.741 -0.973 -0.970 -1.106 -0.870 -0.984 -1.204 -1.095

St.Vincent & Grenadines -0.519 -0.741 -0.973 -0.970 -1.105 -0.870 -0.984 -1.203 -1.094

Swaziland -0.514 -0.741 -0.971 -0.969 -1.100 -0.870 -0.982 -1.193 -1.089

Lebanon -0.511 -0.740 -0.971 -0.968 -1.098 -0.870 -0.981 -1.190 -1.087

Belarus -0.511 -0.740 -0.970 -0.968 -1.097 -0.870 -0.981 -1.188 -1.086

Kazakhstan -0.510 -0.740 -0.970 -0.968 -1.096 -0.870 -0.981 -1.187 -1.086

Dominica -0.510 -0.740 -0.970 -0.968 -1.096 -0.870 -0.980 -1.187 -1.086

Latvia -0.504 -0.740 -0.969 -0.966 -1.091 -0.869 -0.979 -1.178 -1.081

St. Lucia -0.504 -0.740 -0.968 -0.966 -1.090 -0.869 -0.978 -1.177 -1.080

Brazil -0.503 -0.740 -0.968 -0.966 -1.090 -0.869 -0.978 -1.176 -1.080

Bulgaria -0.502 -0.740 -0.968 -0.965 -1.089 -0.869 -0.978 -1.175 -1.079

Russia -0.499 -0.740 -0.967 -0.964 -1.086 -0.869 -0.977 -1.170 -1.077

Fiji -0.495 -0.740 -0.966 -0.963 -1.083 -0.869 -0.976 -1.165 -1.074

Grenada -0.494 -0.740 -0.966 -0.963 -1.083 -0.869 -0.976 -1.165 -1.074

Turkey -0.492 -0.739 -0.966 -0.963 -1.082 -0.869 -0.975 -1.162 -1.072

Lithuania -0.489 -0.739 -0.965 -0.962 -1.079 -0.869 -0.974 -1.158 -1.070

Romania -0.486 -0.739 -0.964 -0.962 -1.077 -0.869 -0.974 -1.155 -1.068

Iran -0.481 -0.739 -0.963 -0.960 -1.074 -0.869 -0.972 -1.149 -1.065

Mexico -0.479 -0.739 -0.962 -0.960 -1.072 -0.869 -0.972 -1.147 -1.063

Bahrain -0.476 -0.739 -0.962 -0.959 -1.070 -0.868 -0.971 -1.143 -1.062

Chile -0.474 -0.739 -0.961 -0.959 -1.069 -0.868 -0.970 -1.142 -1.061

Antigua & Barbuda -0.473 -0.739 -0.961 -0.959 -1.069 -0.868 -0.970 -1.141 -1.060

Poland -0.469 -0.738 -0.960 -0.958 -1.066 -0.868 -0.969 -1.137 -1.058

Trinidad & Tobago -0.465 -0.738 -0.960 -0.957 -1.064 -0.868 -0.968 -1.133 -1.056

Estonia -0.461 -0.738 -0.959 -0.956 -1.062 -0.868 -0.968 -1.130 -1.054

Gabon -0.460 -0.738 -0.959 -0.956 -1.061 -0.868 -0.967 -1.129 -1.053

Tunisia -0.456 -0.738 -0.958 -0.956 -1.059 -0.868 -0.967 -1.126 -1.052

St. Kitts & Nevis -0.454 -0.738 -0.958 -0.955 -1.058 -0.868 -0.966 -1.124 -1.051

Uruguay -0.453 -0.738 -0.957 -0.955 -1.058 -0.868 -0.966 -1.123 -1.050

Slovakia -0.451 -0.738 -0.957 -0.955 -1.057 -0.868 -0.966 -1.122 -1.049

Hungary -0.437 -0.737 -0.955 -0.953 -1.051 -0.867 -0.963 -1.112 -1.043

Argentina -0.418 -0.736 -0.952 -0.950 -1.043 -0.867 -0.960 -1.100 -1.036

Oman -0.409 -0.736 -0.951 -0.949 -1.040 -0.867 -0.959 -1.096 -1.033

Qatar -0.398 -0.736 -0.950 -0.947 -1.036 -0.866 -0.957 -1.090 -1.030

Slovenia -0.393 -0.736 -0.949 -0.947 -1.035 -0.866 -0.956 -1.088 -1.028

High-income countries:

Czech Republic -0.386 -0.735 -0.948 -0.946 -1.033 -0.866 -0.956 -1.085 -1.026

Greece -0.369 -0.735 -0.946 -0.944 -1.028 -0.866 -0.954 -1.078 -1.022

Korea -0.364 -0.735 -0.946 -0.944 -1.027 -0.866 -0.953 -1.076 -1.021

Portugal -0.359 -0.735 -0.945 -0.943 -1.026 -0.866 -0.953 -1.074 -1.020

Spain -0.357 -0.735 -0.945 -0.943 -1.026 -0.866 -0.952 -1.074 -1.020

See notes at end of page. Continued--
32 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 8—Frisch own-price elasticity for aggregate consumption categories, 114 countries 

in 19961

--Continued

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

High-income countries--continued:

Ireland -0.351 -0.734 -0.945 -0.943 -1.024 -0.866 -0.952 -1.071 -1.018

Singapore -0.343 -0.734 -0.944 -0.942 -1.022 -0.866 -0.951 -1.069 -1.017

Mauritius -0.333 -0.734 -0.943 -0.941 -1.020 -0.865 -0.950 -1.066 -1.015

Israel -0.323 -0.734 -0.942 -0.941 -1.018 -0.865 -0.949 -1.063 -1.013

New Zealand -0.319 -0.734 -0.942 -0.940 -1.018 -0.865 -0.949 -1.062 -1.012

Finland -0.318 -0.734 -0.942 -0.940 -1.017 -0.865 -0.949 -1.062 -1.012

Bahamas -0.315 -0.734 -0.942 -0.940 -1.017 -0.865 -0.949 -1.061 -1.011

Sweden -0.292 -0.733 -0.940 -0.938 -1.013 -0.865 -0.947 -1.055 -1.008

Netherlands -0.288 -0.733 -0.940 -0.938 -1.012 -0.865 -0.947 -1.054 -1.007

France -0.269 -0.733 -0.939 -0.937 -1.010 -0.865 -0.945 -1.050 -1.005

United Kingdom -0.267 -0.732 -0.939 -0.937 -1.009 -0.865 -0.945 -1.050 -1.004

Belgium -0.263 -0.732 -0.938 -0.937 -1.009 -0.865 -0.945 -1.049 -1.004

Norway -0.262 -0.732 -0.938 -0.937 -1.009 -0.864 -0.945 -1.049 -1.004

Italy -0.259 -0.732 -0.938 -0.936 -1.008 -0.864 -0.945 -1.048 -1.003

Austria -0.251 -0.732 -0.938 -0.936 -1.007 -0.864 -0.944 -1.047 -1.002

Germany -0.250 -0.732 -0.938 -0.936 -1.007 -0.864 -0.944 -1.047 -1.002

Australia -0.242 -0.732 -0.937 -0.936 -1.006 -0.864 -0.944 -1.045 -1.001

Japan -0.237 -0.732 -0.937 -0.935 -1.006 -0.864 -0.943 -1.045 -1.001

Canada -0.230 -0.732 -0.937 -0.935 -1.005 -0.864 -0.943 -1.043 -1.000

Bermuda -0.214 -0.732 -0.936 -0.934 -1.003 -0.864 -0.942 -1.041 -0.998

Switzerland -0.208 -0.731 -0.936 -0.934 -1.002 -0.864 -0.942 -1.040 -0.998

Barbados -0.207 -0.731 -0.936 -0.934 -1.002 -0.864 -0.942 -1.040 -0.998

Hong Kong -0.205 -0.731 -0.936 -0.934 -1.002 -0.864 -0.942 -1.040 -0.997

Iceland -0.204 -0.731 -0.936 -0.934 -1.002 -0.864 -0.942 -1.039 -0.997

Denmark -0.200 -0.731 -0.935 -0.934 -1.002 -0.864 -0.942 -1.039 -0.997

Luxembourg -0.102 -0.730 -0.932 -0.930 -0.993 -0.863 -0.938 -1.027 -0.989

United States -0.084 -0.730 -0.931 -0.930 -0.992 -0.863 -0.937 -1.026 -0.988

Low-income average -0.590 -0.746 -1.017 -1.012 -1.327 -0.875 -1.034 -1.652 -1.288

Middle-income average -0.487 -0.739 -0.966 -0.963 -1.082 -0.869 -0.975 -1.165 -1.073

High-income average -0.271 -0.733 -0.940 -0.938 -1.012 -0.865 -0.946 -1.054 -1.007

1

Countries are reported based on ascending per capita real income levels.

2

As the estimated budget shares for recreation were negative, these values are calculated using the actual budget shares.
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 33

Table 9—Slutsky own-price elasticity for aggregate consumption categories, 

114 countries in 19961

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

Low-income countries:

Tanzania -0.296 -0.694 -0.996 -1.050 -2.298 -0.830 -1.062 -2.9732 -2.006

Nigeria -0.320 -0.694 -0.956 -1.016 -1.673 -0.826 -1.015 -3.1742 -1.543

Tajikistan -0.330 -0.695 -0.940 -1.002 -1.528 -0.825 -0.997 -3.124 -1.426

Zambia -0.332 -0.695 -0.937 -1.000 -1.507 -0.825 -0.994 -2.947 -1.408

Yemen -0.332 -0.695 -0.937 -1.000 -1.506 -0.825 -0.994 -3.2112 -1.407

Malawi -0.338 -0.695 -0.928 -0.992 -1.442 -0.824 -0.984 -3.2492 -1.353

Madagascar -0.338 -0.695 -0.928 -0.992 -1.440 -0.824 -0.984 -2.485 -1.352

Mali -0.340 -0.695 -0.926 -0.990 -1.425 -0.823 -0.981 -2.395 -1.339

Mongolia -0.340 -0.695 -0.925 -0.989 -1.421 -0.823 -0.980 -2.373 -1.336

Benin -0.353 -0.695 -0.906 -0.974 -1.312 -0.821 -0.960 -1.884 -1.242

Kenya -0.358 -0.695 -0.899 -0.968 -1.275 -0.820 -0.952 -1.754 -1.209

Sierra Leone -0.362 -0.695 -0.892 -0.962 -1.246 -0.819 -0.945 -1.661 -1.183

Nepal -0.362 -0.695 -0.892 -0.962 -1.245 -0.819 -0.945 -1.659 -1.182

Turkmenistan -0.364 -0.695 -0.889 -0.960 -1.232 -0.819 -0.941 -1.621 -1.170

Congo -0.368 -0.695 -0.884 -0.955 -1.209 -0.818 -0.935 -1.555 -1.149

Senegal -0.371 -0.695 -0.879 -0.952 -1.190 -0.818 -0.930 -1.507 -1.132

Vietnam -0.372 -0.695 -0.877 -0.950 -1.184 -0.818 -0.929 -1.490 -1.126

Bangladesh -0.372 -0.695 -0.877 -0.950 -1.181 -0.817 -0.928 -1.485 -1.124

Pakistan -0.378 -0.695 -0.867 -0.942 -1.146 -0.816 -0.918 -1.401 -1.092

Azerbaijan -0.380 -0.694 -0.863 -0.938 -1.132 -0.816 -0.913 -1.368 -1.078

Cote d'Ivoire -0.381 -0.694 -0.861 -0.937 -1.126 -0.815 -0.911 -1.356 -1.073

Paraguay -0.382 -0.694 -0.860 -0.936 -1.123 -0.815 -0.910 -1.348 -1.070

Uzbekistan -0.383 -0.694 -0.857 -0.934 -1.114 -0.815 -0.907 -1.329 -1.061

Kyrgyzstan -0.383 -0.694 -0.857 -0.934 -1.113 -0.815 -0.907 -1.328 -1.061

Cameroon -0.384 -0.694 -0.856 -0.933 -1.111 -0.815 -0.907 -1.325 -1.059

Moldova -0.384 -0.694 -0.856 -0.933 -1.111 -0.815 -0.907 -1.324 -1.059

Bolivia -0.385 -0.694 -0.853 -0.931 -1.102 -0.814 -0.904 -1.306 -1.051

Ecuador -0.386 -0.694 -0.852 -0.930 -1.099 -0.814 -0.902 -1.299 -1.048

Armenia -0.387 -0.694 -0.849 -0.928 -1.090 -0.814 -0.899 -1.282 -1.039

Sri Lanka -0.387 -0.694 -0.849 -0.928 -1.089 -0.814 -0.899 -1.280 -1.039

Jordan -0.388 -0.694 -0.846 -0.925 -1.080 -0.813 -0.896 -1.261 -1.030

Albania -0.390 -0.694 -0.841 -0.922 -1.067 -0.813 -0.891 -1.237 -1.017

Indonesia -0.391 -0.694 -0.840 -0.920 -1.063 -0.812 -0.890 -1.229 -1.013

Jamaica -0.391 -0.694 -0.839 -0.920 -1.062 -0.812 -0.889 -1.227 -1.012

Zimbabwe -0.391 -0.694 -0.839 -0.920 -1.061 -0.812 -0.889 -1.225 -1.011

Guinea -0.391 -0.694 -0.837 -0.919 -1.057 -0.812 -0.887 -1.218 -1.008

Syria -0.392 -0.694 -0.834 -0.916 -1.048 -0.811 -0.884 -1.202 -0.999

Georgia -0.392 -0.694 -0.834 -0.916 -1.047 -0.811 -0.883 -1.199 -0.998

Middle-income countries:

Ukraine -0.393 -0.694 -0.828 -0.911 -1.033 -0.811 -0.878 -1.175 -0.984

Philippines -0.394 -0.694 -0.824 -0.909 -1.024 -0.810 -0.874 -1.159 -0.975

Peru -0.394 -0.694 -0.823 -0.907 -1.020 -0.810 -0.873 -1.153 -0.972

Botswana -0.394 -0.694 -0.823 -0.907 -1.020 -0.810 -0.873 -1.153 -0.971

Thailand -0.394 -0.694 -0.822 -0.907 -1.018 -0.810 -0.872 -1.150 -0.970

See notes at end of table. Continued--
34 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 9—Slutsky own-price elasticity for aggregate consumption categories, 

114 countries in 19961

--Continued

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

Middle-income countries--continued:

Morocco -0.393 -0.694 -0.820 -0.906 -1.014 -0.809 -0.870 -1.143 -0.966

Venezuela -0.393 -0.694 -0.820 -0.905 -1.013 -0.809 -0.870 -1.141 -0.965

Macedonia -0.393 -0.694 -0.817 -0.903 -1.007 -0.809 -0.867 -1.132 -0.959

Belize -0.393 -0.694 -0.817 -0.903 -1.007 -0.809 -0.867 -1.131 -0.959

Egypt -0.393 -0.694 -0.817 -0.903 -1.007 -0.809 -0.867 -1.131 -0.959

St.Vincent & Grenadines -0.393 -0.694 -0.817 -0.903 -1.006 -0.809 -0.867 -1.130 -0.958

Swaziland -0.393 -0.694 -0.814 -0.901 -1.000 -0.808 -0.864 -1.120 -0.952

Lebanon -0.392 -0.694 -0.813 -0.900 -0.997 -0.808 -0.863 -1.116 -0.949

Belarus -0.392 -0.694 -0.813 -0.900 -0.996 -0.808 -0.862 -1.114 -0.948

Kazakhstan -0.392 -0.694 -0.812 -0.899 -0.996 -0.808 -0.862 -1.113 -0.948

Dominica -0.392 -0.694 -0.812 -0.899 -0.996 -0.808 -0.862 -1.113 -0.948

Latvia -0.391 -0.694 -0.810 -0.897 -0.989 -0.808 -0.859 -1.104 -0.942

St. Lucia -0.391 -0.694 -0.809 -0.897 -0.989 -0.808 -0.859 -1.102 -0.941

Brazil -0.391 -0.694 -0.809 -0.897 -0.988 -0.808 -0.859 -1.101 -0.940

Bulgaria -0.391 -0.694 -0.809 -0.897 -0.988 -0.807 -0.859 -1.100 -0.940

Russia -0.390 -0.694 -0.807 -0.896 -0.984 -0.807 -0.857 -1.095 -0.936

Fiji -0.389 -0.694 -0.805 -0.894 -0.980 -0.807 -0.855 -1.089 -0.932

Grenada -0.389 -0.694 -0.805 -0.894 -0.980 -0.807 -0.855 -1.089 -0.932

Turkey -0.389 -0.694 -0.804 -0.894 -0.978 -0.807 -0.854 -1.086 -0.931

Lithuania -0.388 -0.694 -0.803 -0.893 -0.975 -0.807 -0.853 -1.082 -0.928

Romania -0.387 -0.694 -0.802 -0.892 -0.973 -0.806 -0.852 -1.078 -0.925

Iran -0.385 -0.694 -0.800 -0.890 -0.969 -0.806 -0.850 -1.072 -0.921

Mexico -0.385 -0.694 -0.799 -0.890 -0.967 -0.806 -0.849 -1.069 -0.919

Bahrain -0.384 -0.693 -0.798 -0.889 -0.965 -0.806 -0.848 -1.066 -0.917

Chile -0.383 -0.693 -0.797 -0.888 -0.963 -0.806 -0.847 -1.064 -0.916

Antigua & Barbuda -0.383 -0.693 -0.797 -0.888 -0.963 -0.806 -0.847 -1.063 -0.915

Poland -0.381 -0.693 -0.795 -0.887 -0.960 -0.805 -0.846 -1.058 -0.912

Trinidad & Tobago -0.380 -0.693 -0.794 -0.886 -0.957 -0.805 -0.844 -1.054 -0.909

Estonia -0.378 -0.693 -0.793 -0.885 -0.954 -0.805 -0.843 -1.050 -0.906

Gabon -0.378 -0.693 -0.792 -0.885 -0.954 -0.805 -0.843 -1.049 -0.906

Tunisia -0.376 -0.693 -0.791 -0.884 -0.952 -0.805 -0.842 -1.047 -0.904

St. Kitts & Nevis -0.376 -0.693 -0.791 -0.883 -0.951 -0.804 -0.841 -1.045 -0.902

Uruguay -0.375 -0.693 -0.790 -0.883 -0.950 -0.804 -0.841 -1.044 -0.902

Slovakia -0.374 -0.693 -0.790 -0.883 -0.948 -0.804 -0.840 -1.042 -0.900

Hungary -0.368 -0.693 -0.786 -0.880 -0.941 -0.804 -0.836 -1.031 -0.892

Argentina -0.357 -0.693 -0.781 -0.876 -0.931 -0.803 -0.831 -1.018 -0.883

Oman -0.353 -0.693 -0.778 -0.875 -0.928 -0.802 -0.829 -1.012 -0.879

Qatar -0.346 -0.693 -0.776 -0.873 -0.923 -0.802 -0.827 -1.006 -0.874

Slovenia -0.342 -0.693 -0.775 -0.872 -0.921 -0.802 -0.826 -1.003 -0.872

High-income countries:

Czech Republic -0.338 -0.693 -0.773 -0.871 -0.918 -0.801 -0.824 -1.000 -0.869

Greece -0.327 -0.692 -0.770 -0.868 -0.913 -0.801 -0.821 -0.992 -0.863

Korea -0.324 -0.692 -0.769 -0.868 -0.911 -0.801 -0.820 -0.990 -0.862

Portugal -0.320 -0.692 -0.768 -0.867 -0.909 -0.801 -0.819 -0.988 -0.860

Spain -0.319 -0.692 -0.768 -0.867 -0.909 -0.800 -0.819 -0.987 -0.860

Ireland -0.314 -0.692 -0.767 -0.866 -0.907 -0.800 -0.818 -0.984 -0.857

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 35

Table 9—Slutsky own-price elasticity for aggregate consumption categories, 

114 countries in 19961

--Continued

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

High-income countries--continued:

Singapore -0.309 -0.692 -0.766 -0.865 -0.905 -0.800 -0.817 -0.982 -0.855

Mauritius -0.301 -0.692 -0.764 -0.864 -0.902 -0.800 -0.815 -0.978 -0.852

Israel -0.294 -0.692 -0.762 -0.863 -0.899 -0.799 -0.814 -0.975 -0.850

New Zealand -0.291 -0.692 -0.762 -0.863 -0.898 -0.799 -0.813 -0.973 -0.849

Finland -0.290 -0.692 -0.762 -0.863 -0.898 -0.799 -0.813 -0.973 -0.848

Bahamas -0.288 -0.692 -0.761 -0.862 -0.897 -0.799 -0.813 -0.972 -0.848

Sweden -0.269 -0.692 -0.758 -0.860 -0.892 -0.799 -0.810 -0.965 -0.842

Netherlands -0.266 -0.692 -0.758 -0.860 -0.891 -0.799 -0.809 -0.964 -0.841

France -0.251 -0.692 -0.755 -0.858 -0.888 -0.798 -0.807 -0.960 -0.837

United Kingdom -0.249 -0.692 -0.755 -0.858 -0.887 -0.798 -0.807 -0.959 -0.837

Belgium -0.246 -0.692 -0.755 -0.858 -0.887 -0.798 -0.807 -0.958 -0.836

Norway -0.245 -0.692 -0.754 -0.857 -0.886 -0.798 -0.806 -0.958 -0.836

Italy -0.242 -0.692 -0.754 -0.857 -0.886 -0.798 -0.806 -0.957 -0.835

Austria -0.236 -0.692 -0.753 -0.857 -0.884 -0.798 -0.805 -0.955 -0.834

Germany -0.235 -0.692 -0.753 -0.857 -0.884 -0.798 -0.805 -0.955 -0.834

Australia -0.228 -0.692 -0.752 -0.856 -0.883 -0.798 -0.804 -0.954 -0.832

Japan -0.224 -0.691 -0.752 -0.856 -0.882 -0.797 -0.804 -0.952 -0.831

Canada -0.218 -0.691 -0.751 -0.855 -0.881 -0.797 -0.803 -0.951 -0.830

Bermuda -0.204 -0.691 -0.749 -0.854 -0.879 -0.797 -0.802 -0.948 -0.828

Switzerland -0.198 -0.691 -0.749 -0.854 -0.878 -0.797 -0.801 -0.947 -0.827

Barbados -0.197 -0.691 -0.749 -0.854 -0.877 -0.797 -0.801 -0.947 -0.826

Hong Kong -0.196 -0.691 -0.749 -0.853 -0.877 -0.797 -0.801 -0.946 -0.826

Iceland -0.194 -0.691 -0.748 -0.853 -0.877 -0.797 -0.801 -0.946 -0.826

Denmark -0.191 -0.691 -0.748 -0.853 -0.876 -0.797 -0.801 -0.945 -0.825

Luxembourg -0.100 -0.691 -0.741 -0.848 -0.865 -0.795 -0.794 -0.931 -0.813

United States -0.082 -0.691 -0.739 -0.847 -0.863 -0.795 -0.792 -0.929 -0.811

Low-income average -0.368 -0.694 -0.881 -0.953 -1.241 -0.818 -0.933 -1.585 -1.171

Middle-income average -0.384 -0.694 -0.804 -0.893 -0.979 -0.807 -0.854 -1.089 -0.931

High-income average -0.250 -0.692 -0.757 -0.859 -0.890 -0.798 -0.809 -0.963 -0.840

1

Countries are reported based on ascending per capita real income levels.

2

As the estimated budget shares for recreation were negative, these values are calculated using the actual budget shares.
36 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 10—Cournot own-price elasticity for aggregate consumption categories, 

114 countries in 19961

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

Low-income countries:

Tanzania -0.839 -0.770 -1.097 -1.094 -2.335 -0.889 -1.134 -3.0002 -2.058

Nigeria -0.816 -0.769 -1.066 -1.063 -1.718 -0.888 -1.094 -3.2002 -1.607

Tajikistan -0.805 -0.768 -1.054 -1.052 -1.577 -0.887 -1.080 -3.151 -1.495

Zambia -0.803 -0.768 -1.052 -1.050 -1.556 -0.887 -1.077 -2.975 -1.478

Yemen -0.803 -0.768 -1.052 -1.049 -1.555 -0.887 -1.077 -3.2372 -1.477

Malawi -0.796 -0.767 -1.046 -1.043 -1.493 -0.886 -1.069 -3.2752 -1.426

Madagascar -0.796 -0.767 -1.045 -1.043 -1.492 -0.886 -1.069 -2.515 -1.425

Mali -0.794 -0.767 -1.044 -1.041 -1.477 -0.886 -1.067 -2.424 -1.413

Mongolia -0.794 -0.767 -1.043 -1.041 -1.473 -0.886 -1.066 -2.402 -1.409

Benin -0.777 -0.766 -1.030 -1.028 -1.370 -0.885 -1.050 -1.918 -1.323

Kenya -0.770 -0.765 -1.025 -1.023 -1.335 -0.884 -1.044 -1.790 -1.293

Sierra Leone -0.763 -0.765 -1.021 -1.018 -1.307 -0.884 -1.039 -1.698 -1.269

Nepal -0.763 -0.765 -1.021 -1.018 -1.307 -0.884 -1.039 -1.697 -1.269

Turkmenistan -0.760 -0.764 -1.019 -1.016 -1.295 -0.884 -1.036 -1.660 -1.258

Congo -0.753 -0.764 -1.016 -1.013 -1.273 -0.884 -1.032 -1.595 -1.239

Senegal -0.748 -0.764 -1.013 -1.009 -1.256 -0.883 -1.028 -1.548 -1.225

Vietnam -0.745 -0.763 -1.012 -1.008 -1.250 -0.883 -1.027 -1.532 -1.219

Bangladesh -0.745 -0.763 -1.011 -1.008 -1.248 -0.883 -1.027 -1.526 -1.218

Pakistan -0.732 -0.763 -1.005 -1.002 -1.216 -0.882 -1.019 -1.445 -1.190

Azerbaijan -0.726 -0.762 -1.003 -0.999 -1.203 -0.882 -1.016 -1.414 -1.178

Cote d'Ivoire -0.723 -0.762 -1.002 -0.998 -1.198 -0.882 -1.015 -1.402 -1.174

Paraguay -0.721 -0.762 -1.001 -0.997 -1.195 -0.882 -1.014 -1.395 -1.171

Uzbekistan -0.717 -0.762 -0.999 -0.996 -1.187 -0.882 -1.012 -1.376 -1.164

Kyrgyzstan -0.717 -0.762 -0.999 -0.995 -1.187 -0.882 -1.012 -1.375 -1.164

Cameroon -0.716 -0.762 -0.999 -0.995 -1.185 -0.882 -1.012 -1.372 -1.162

Moldova -0.716 -0.762 -0.999 -0.995 -1.185 -0.882 -1.011 -1.372 -1.162

Bolivia -0.711 -0.761 -0.997 -0.993 -1.177 -0.882 -1.009 -1.354 -1.155

Ecuador -0.709 -0.761 -0.996 -0.993 -1.174 -0.882 -1.009 -1.348 -1.153

Armenia -0.705 -0.761 -0.995 -0.991 -1.167 -0.881 -1.007 -1.331 -1.146

Sri Lanka -0.704 -0.761 -0.995 -0.991 -1.166 -0.881 -1.006 -1.330 -1.145

Jordan -0.698 -0.760 -0.993 -0.989 -1.158 -0.881 -1.004 -1.312 -1.138

Albania -0.690 -0.760 -0.990 -0.986 -1.147 -0.881 -1.001 -1.289 -1.128

Indonesia -0.687 -0.760 -0.989 -0.985 -1.143 -0.881 -1.000 -1.282 -1.125

Jamaica -0.686 -0.760 -0.989 -0.985 -1.142 -0.881 -1.000 -1.280 -1.124

Zimbabwe -0.686 -0.760 -0.989 -0.985 -1.141 -0.881 -1.000 -1.278 -1.123

Guinea -0.683 -0.760 -0.988 -0.984 -1.138 -0.881 -0.999 -1.272 -1.120

Syria -0.676 -0.759 -0.986 -0.982 -1.131 -0.880 -0.996 -1.257 -1.114

Georgia -0.675 -0.759 -0.986 -0.982 -1.129 -0.880 -0.996 -1.254 -1.113

Middle-income countries:

Ukraine -0.663 -0.759 -0.983 -0.979 -1.118 -0.880 -0.993 -1.232 -1.102

Philippines -0.655 -0.758 -0.981 -0.976 -1.110 -0.880 -0.990 -1.217 -1.095

Peru -0.651 -0.758 -0.980 -0.976 -1.107 -0.880 -0.989 -1.212 -1.093

Botswana -0.651 -0.758 -0.980 -0.975 -1.107 -0.880 -0.989 -1.211 -1.092

Thailand -0.649 -0.758 -0.980 -0.975 -1.105 -0.880 -0.989 -1.209 -1.091

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 37

Table 10—Cournot own-price elasticity for aggregate consumption categories, 

114 countries in 19961

--Continued

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

Middle-income countries--continued:

Morocco -0.645 -0.758 -0.979 -0.974 -1.102 -0.879 -0.988 -1.203 -1.088

Venezuela -0.643 -0.758 -0.979 -0.974 -1.101 -0.879 -0.987 -1.201 -1.087

Macedonia -0.637 -0.757 -0.978 -0.973 -1.097 -0.879 -0.986 -1.192 -1.083

Belize -0.637 -0.757 -0.978 -0.972 -1.096 -0.879 -0.986 -1.192 -1.083

Egypt -0.637 -0.757 -0.978 -0.972 -1.096 -0.879 -0.986 -1.192 -1.083

St.Vincent & Grenadines -0.636 -0.757 -0.977 -0.972 -1.096 -0.879 -0.986 -1.191 -1.082

Swaziland -0.628 -0.757 -0.976 -0.971 -1.091 -0.879 -0.984 -1.181 -1.078

Lebanon -0.625 -0.757 -0.975 -0.970 -1.089 -0.879 -0.983 -1.178 -1.076

Belarus -0.624 -0.757 -0.975 -0.970 -1.088 -0.879 -0.983 -1.176 -1.075

Kazakhstan -0.623 -0.757 -0.975 -0.970 -1.087 -0.879 -0.983 -1.176 -1.075

Dominica -0.623 -0.757 -0.975 -0.970 -1.087 -0.879 -0.983 -1.176 -1.075

Latvia -0.615 -0.756 -0.974 -0.968 -1.082 -0.879 -0.981 -1.167 -1.070

St. Lucia -0.614 -0.756 -0.974 -0.968 -1.082 -0.879 -0.981 -1.166 -1.070

Brazil -0.613 -0.756 -0.973 -0.968 -1.081 -0.879 -0.981 -1.165 -1.070

Bulgaria -0.613 -0.756 -0.973 -0.968 -1.081 -0.879 -0.981 -1.164 -1.069

Russia -0.608 -0.756 -0.973 -0.967 -1.078 -0.879 -0.980 -1.159 -1.067

Fiji -0.602 -0.756 -0.972 -0.966 -1.075 -0.878 -0.979 -1.154 -1.064

Grenada -0.602 -0.756 -0.972 -0.966 -1.075 -0.878 -0.979 -1.154 -1.064

Turkey -0.599 -0.756 -0.971 -0.966 -1.074 -0.878 -0.978 -1.152 -1.063

Lithuania -0.595 -0.755 -0.971 -0.965 -1.071 -0.878 -0.977 -1.148 -1.061

Romania -0.591 -0.755 -0.970 -0.964 -1.070 -0.878 -0.977 -1.145 -1.059

Iran -0.585 -0.755 -0.969 -0.963 -1.067 -0.878 -0.976 -1.139 -1.056

Mexico -0.581 -0.755 -0.969 -0.963 -1.065 -0.878 -0.975 -1.137 -1.055

Bahrain -0.577 -0.755 -0.968 -0.962 -1.063 -0.878 -0.975 -1.134 -1.053

Chile -0.575 -0.755 -0.968 -0.962 -1.062 -0.878 -0.974 -1.132 -1.052

Antigua & Barbuda -0.574 -0.755 -0.968 -0.962 -1.062 -0.878 -0.974 -1.132 -1.052

Poland -0.568 -0.754 -0.967 -0.961 -1.059 -0.878 -0.973 -1.127 -1.050

Trinidad & Tobago -0.563 -0.754 -0.967 -0.960 -1.057 -0.878 -0.972 -1.124 -1.048

Estonia -0.557 -0.754 -0.966 -0.960 -1.055 -0.878 -0.972 -1.121 -1.046

Gabon -0.556 -0.754 -0.966 -0.959 -1.055 -0.878 -0.972 -1.120 -1.046

Tunisia -0.552 -0.754 -0.965 -0.959 -1.053 -0.877 -0.971 -1.117 -1.044

St. Kitts & Nevis -0.549 -0.754 -0.965 -0.959 -1.052 -0.877 -0.971 -1.116 -1.043

Uruguay -0.547 -0.754 -0.965 -0.958 -1.052 -0.877 -0.970 -1.115 -1.043

Slovakia -0.544 -0.753 -0.965 -0.958 -1.051 -0.877 -0.970 -1.113 -1.042

Hungary -0.527 -0.753 -0.963 -0.956 -1.045 -0.877 -0.968 -1.104 -1.037

Argentina -0.502 -0.752 -0.961 -0.954 -1.038 -0.877 -0.965 -1.093 -1.031

Oman -0.491 -0.752 -0.960 -0.953 -1.036 -0.877 -0.964 -1.088 -1.028

Qatar -0.477 -0.751 -0.959 -0.952 -1.032 -0.876 -0.963 -1.083 -1.025

Slovenia -0.470 -0.751 -0.958 -0.951 -1.031 -0.876 -0.962 -1.081 -1.024

High-income countries:

Czech Republic -0.462 -0.751 -0.958 -0.950 -1.029 -0.876 -0.962 -1.078 -1.022

Greece -0.441 -0.750 -0.956 -0.949 -1.025 -0.876 -0.960 -1.072 -1.019

Korea -0.435 -0.750 -0.956 -0.948 -1.024 -0.876 -0.960 -1.070 -1.018

Portugal -0.428 -0.750 -0.956 -0.948 -1.023 -0.876 -0.959 -1.068 -1.017

Spain -0.426 -0.750 -0.956 -0.948 -1.023 -0.876 -0.959 -1.068 -1.017

Ireland -0.418 -0.750 -0.955 -0.947 -1.021 -0.876 -0.959 -1.066 -1.015

See notes at end of table. Continued--
38 ✺ International Evidence and Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 10—Cournot own-price elasticity for aggregate consumption categories, 

114 countries in 19961

--Continued

Country

Food, 

beverage 

& tobacco

Clothing 

& 

footwear

Gross 

rent, fuel 

& power

House 

operationsMedical 

care

Education

Transport 

& 

communicationRecreation Other

High-income countries--continued:

Singapore -0.409 -0.749 -0.955 -0.947 -1.020 -0.876 -0.958 -1.063 -1.014

Mauritius -0.396 -0.749 -0.954 -0.946 -1.018 -0.876 -0.957 -1.060 -1.012

Israel -0.384 -0.749 -0.953 -0.945 -1.016 -0.875 -0.957 -1.058 -1.011

New Zealand -0.379 -0.749 -0.953 -0.945 -1.016 -0.875 -0.956 -1.057 -1.010

Finland -0.378 -0.749 -0.953 -0.945 -1.015 -0.875 -0.956 -1.057 -1.010

Bahamas -0.374 -0.749 -0.953 -0.945 -1.015 -0.875 -0.956 -1.056 -1.010

Sweden -0.346 -0.748 -0.952 -0.944 -1.012 -0.875 -0.955 -1.051 -1.007

Netherlands -0.341 -0.748 -0.952 -0.943 -1.011 -0.875 -0.954 -1.050 -1.006

France -0.318 -0.747 -0.951 -0.942 -1.009 -0.875 -0.953 -1.046 -1.004

United Kingdom -0.316 -0.747 -0.951 -0.942 -1.008 -0.875 -0.953 -1.046 -1.004

Belgium -0.311 -0.747 -0.951 -0.942 -1.008 -0.875 -0.953 -1.045 -1.003

Norway -0.310 -0.747 -0.951 -0.942 -1.008 -0.875 -0.953 -1.045 -1.003

Italy -0.306 -0.747 -0.950 -0.942 -1.007 -0.875 -0.953 -1.044 -1.003

Austria -0.297 -0.747 -0.950 -0.941 -1.006 -0.875 -0.952 -1.043 -1.002

Germany -0.295 -0.747 -0.950 -0.941 -1.006 -0.875 -0.952 -1.043 -1.002

Australia -0.286 -0.747 -0.950 -0.941 -1.005 -0.875 -0.952 -1.041 -1.001

Japan -0.280 -0.747 -0.950 -0.941 -1.005 -0.875 -0.952 -1.041 -1.001

Canada -0.271 -0.747 -0.949 -0.941 -1.004 -0.875 -0.952 -1.040 -1.000

Bermuda -0.253 -0.746 -0.949 -0.940 -1.003 -0.875 -0.951 -1.037 -0.999

Switzerland -0.245 -0.746 -0.949 -0.940 -1.002 -0.875 -0.951 -1.036 -0.998

Barbados -0.244 -0.746 -0.949 -0.940 -1.002 -0.875 -0.951 -1.036 -0.998

Hong Kong -0.242 -0.746 -0.948 -0.940 -1.002 -0.875 -0.951 -1.036 -0.998

Iceland -0.240 -0.746 -0.948 -0.940 -1.002 -0.875 -0.950 -1.036 -0.998

Denmark -0.235 -0.746 -0.948 -0.939 -1.001 -0.874 -0.950 -1.035 -0.997

Luxembourg -0.119 -0.744 -0.946 -0.936 -0.994 -0.874 -0.947 -1.025 -0.991

United States -0.098 -0.744 -0.945 -0.936 -0.993 -0.874 -0.947 -1.023 -0.990

Low-income average -0.741 -0.763 -1.015 -1.012 -1.308 -0.883 -1.031 -1.627 -1.264

Middle-income average -0.593 -0.755 -0.971 -0.966 -1.075 -0.878 -0.978 -1.154 -1.063

High-income average -0.321 -0.748 -0.951 -0.943 -1.010 -0.875 -0.954 -1.049 -1.006

1

Countries are reported based on ascending per capita real income levels.

2

As the estimated budget shares for recreation were negative, these values are calculated using the actual budget shares.
Food Subgroup Elasticities

The expenditure and price elasticities calculated using the Florida-Slutsky model are conditional on a given

food budget. In other words, the expenditure elasticity measures the percentage change in demand for a 1-

percent change in food budget, while the price elasticity measures the percentage response to a 1-percent

change in price assuming a given food budget. However, the conditional elasticities can be converted to

unconditional elasticities using the parameters estimated from the Florida-PI model in the first stage. The

unconditional elasticities measure percentage change in demand from a 1-percent change in overall income.

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 39

With > 0 for five of the eight food subcategories, the estimated conditional income elasticities are 

greater than 1 for these five food groups, indicating these to be (conditionally) elastic food items. 

However, using equation 25 the conditional income elasticity is converted to the unconditional income

elasticity. The estimated income (expenditure) elasticities presented in table 11 are all less an 1, 

excepting for beverage and tobacco in low-income countries. This is consistent with conventional 

theory that food is a necessity and not a luxury item in household expenditures. Given the relatively 

low food budget share of beverage and tobacco in many low-income countries, this category can be 

considered a luxury item among consumers in some poorer countries.

* βi

Similar to the estimated income elasticity for aggregate consumption categories, the income elasticities 

for food sub-categories are the largest for the poorest country (Tanzania) and decline in magnitude 

with affluence, with the smallest elasticities for the United States. Across each country, staple food 

items (with negative ) have smaller elasticities than the more conditionally elastic food items such as

beverages and tobacco, and meat and dairy. For example, the income elasticity for cereals ranges from 

0.62 in Tanzania to 0.47 in Georgia, 0.28 in Slovenia, and 0.05 in the United States. In contrast, the 

elasticity for beverages and tobacco are higher across all countries, ranging from 1.70 in Tanzania,

1.00 in Georgia, 0.65 in Slovenia, and 0.13 in the United States.

βi

*

As discussed in earlier sections, the conditional Slutsky elasticity is given by = , where is 

the conditional Slutsky price parameter in country for good with respect to good , and is the 

conditional fitted budget share (at geometric mean prices) of food sub-group in country . Since for 

a given food sub-group , is invariant across countries, the conditional Slutsky elasticity for food 

sub-group will be greater for smaller budget shares. Poorer countries typically have larger budget 

shares for staple items such as breads and cereals, which form a smaller share of food budget among 

wealthier countries. Therefore

ij

*

g

ij

*

ε

π

π

π

ijc

* ij

*

i

ic

w c

c i jw

iS c

i

i

*

*

∈

, contrary to theory, the estimated conditional Slutsky own-price 

elasticities for cereals may be larger for wealthier countries. This problem of a constant continues ij

* π

The unconditional Frisch own-price elasticities are not a function of and do not suffer the problem 

encountered in calculating the unconditional Slutsky elasticities. Using the estimated parameters from 

stages one and two and using equation 26, Frisch own-price elasticities are calculated. These uncondi -

tional own-price elasticities (table 12) represent elasticities estimated at a point when the marginal 

utility of income is held constant. The values of the Frisch own-price elasticities lie between the 

values of the Slutsky own-price elasticities (when real income is held constant) and the Cournot ownprice elasticities (when nominal income is held constant) for necessities and can be considered as a 

reasonable estimate of the average own-price elasticities for the food subij

* π

categories.
40 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 11—Unconditional expenditure elasticity for food sub-categories, 

114 countries in 19961

Country

Beverages, 

tobacco

Breads, 

cereals Meat Fish Dairy Fats, oils

Fruits, 

vegetables

Other 

foods

Low-income countries:

Tanzania 1.700 0.619 0.859 1.035 0.963 0.633 0.707 0.856

Nigeria 1.693 0.608 0.843 1.018 0.946 0.622 0.694 0.840

Tajikistan 1.675 0.602 0.835 1.008 0.937 0.616 0.688 0.832

Zambia 1.513 0.594 0.833 0.994 0.930 0.608 0.685 0.830

Yemen 1.522 0.594 0.833 0.995 0.930 0.609 0.685 0.830

Malawi 1.538 0.592 0.828 0.991 0.925 0.606 0.681 0.825

Madagascar 1.372 0.579 0.827 0.975 0.917 0.596 0.678 0.824

Mali 1.656 0.596 0.827 0.998 0.928 0.610 0.681 0.824

Mongolia 1.273 0.565 0.824 0.960 0.909 0.584 0.673 0.821

Benin 1.336 0.568 0.812 0.956 0.900 0.584 0.665 0.809

Kenya 1.618 0.583 0.808 0.975 0.906 0.596 0.665 0.805

Sierra Leone 1.459 0.571 0.802 0.957 0.895 0.586 0.659 0.799

Nepal 1.102 0.513 0.798 0.909 0.869 0.537 0.646 0.795

Turkmenistan 1.414 0.567 0.799 0.950 0.890 0.581 0.656 0.796

Congo 1.466 0.567 0.794 0.949 0.887 0.581 0.653 0.791

Senegal 1.194 0.536 0.787 0.914 0.866 0.554 0.642 0.784

Vietnam 1.103 0.512 0.784 0.897 0.856 0.534 0.636 0.781

Bangladesh 1.139 0.523 0.784 0.903 0.859 0.543 0.638 0.781

Pakistan 1.084 0.504 0.772 0.883 0.843 0.526 0.626 0.770

Azerbaijan 1.059 0.493 0.767 0.874 0.835 0.517 0.621 0.764

Cote d'Ivoire 1.250 0.535 0.767 0.902 0.850 0.551 0.628 0.764

Paraguay 1.172 0.523 0.765 0.890 0.843 0.541 0.625 0.762

Uzbekistan 1.172 0.522 0.761 0.886 0.839 0.539 0.622 0.758

Kyrgyzstan 1.145 0.517 0.761 0.882 0.837 0.535 0.620 0.758

Cameroon 1.227 0.529 0.761 0.893 0.842 0.545 0.623 0.758

Moldova 1.187 0.524 0.760 0.888 0.839 0.540 0.621 0.758

Bolivia 1.031 0.480 0.754 0.857 0.820 0.504 0.610 0.751

Ecuador 1.089 0.501 0.754 0.867 0.825 0.521 0.613 0.751

Armenia 1.023 0.476 0.748 0.850 0.814 0.500 0.605 0.746

Sri Lanka 0.963 0.433 0.746 0.836 0.805 0.466 0.598 0.744

Jordan 1.024 0.477 0.743 0.846 0.809 0.500 0.601 0.740

Albania 0.951 0.431 0.734 0.823 0.792 0.462 0.588 0.731

Indonesia 0.909 0.376 0.730 0.809 0.783 0.421 0.579 0.728

Jamaica 1.009 0.470 0.732 0.834 0.797 0.492 0.593 0.730

Zimbabwe 1.217 0.514 0.734 0.865 0.814 0.529 0.602 0.731

Guinea 1.084 0.493 0.730 0.845 0.802 0.511 0.595 0.727

Syria 1.028 0.476 0.723 0.829 0.791 0.496 0.587 0.721

Georgia 1.003 0.467 0.722 0.823 0.787 0.488 0.585 0.719

Middle-income countries:

Ukraine 0.983 0.458 0.711 0.810 0.775 0.479 0.576 0.708

Philippines 0.888 0.387 0.701 0.781 0.754 0.423 0.559 0.698

Peru 0.944 0.439 0.699 0.792 0.759 0.462 0.564 0.697

Botswana 0.989 0.458 0.700 0.801 0.764 0.478 0.568 0.697

Thailand 0.922 0.425 0.697 0.785 0.755 0.451 0.561 0.694

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 41

Table 11—Unconditional expenditure elasticity for food sub-categories, 

114 countries in 19961--Continued

Country

Beverages, 

tobacco

Breads, 

cereals Meat Fish Dairy Fats, oils

Fruits, 

vegetables

Other 

foods

Middle-income countries--continued:

Morocco 0.974 0.452 0.694 0.793 0.757 0.472 0.563 0.691

Venezuela 0.905 0.414 0.691 0.777 0.748 0.442 0.555 0.689

Macedonia 0.906 0.417 0.685 0.773 0.742 0.443 0.552 0.683

Belize 0.899 0.412 0.685 0.771 0.741 0.439 0.551 0.683

Egypt 0.898 0.411 0.685 0.770 0.741 0.438 0.550 0.683

St.Vincent & Grenadines 0.889 0.404 0.684 0.767 0.739 0.433 0.549 0.681

Swaziland 1.022 0.461 0.679 0.788 0.747 0.477 0.554 0.677

Lebanon 0.859 0.380 0.673 0.752 0.725 0.413 0.538 0.671

Belarus 0.891 0.411 0.673 0.759 0.729 0.436 0.542 0.671

Kazakhstan 0.878 0.401 0.672 0.755 0.727 0.428 0.540 0.670

Dominica 0.859 0.382 0.671 0.750 0.724 0.413 0.537 0.669

Latvia 0.878 0.404 0.665 0.749 0.720 0.430 0.535 0.662

St. Lucia 0.831 0.352 0.662 0.736 0.711 0.390 0.527 0.660

Brazil 0.877 0.404 0.663 0.747 0.718 0.429 0.533 0.661

Bulgaria 0.872 0.401 0.662 0.745 0.716 0.426 0.532 0.660

Russia 0.873 0.403 0.657 0.742 0.712 0.428 0.529 0.655

Fiji 0.830 0.367 0.651 0.727 0.701 0.398 0.520 0.649

Grenada 0.825 0.360 0.651 0.725 0.700 0.393 0.519 0.649

Turkey 0.826 0.364 0.648 0.723 0.698 0.396 0.518 0.646

Lithuania 0.829 0.373 0.644 0.721 0.695 0.401 0.516 0.642

Romania 0.812 0.355 0.640 0.714 0.689 0.388 0.511 0.638

Iran 0.869 0.404 0.635 0.722 0.691 0.425 0.514 0.633

Mexico 0.807 0.360 0.630 0.704 0.679 0.389 0.504 0.628

Bahrain 0.772 0.309 0.625 0.691 0.670 0.351 0.495 0.623

Chile 0.824 0.379 0.625 0.704 0.676 0.403 0.502 0.622

Antigua & Barbuda 0.770 0.310 0.622 0.688 0.667 0.351 0.493 0.620

Poland 0.798 0.361 0.617 0.692 0.666 0.388 0.495 0.615

Trinidad & Tobago 0.778 0.342 0.612 0.682 0.658 0.372 0.488 0.610

Estonia 0.776 0.345 0.606 0.678 0.654 0.374 0.485 0.604

Gabon 0.788 0.358 0.605 0.680 0.654 0.384 0.486 0.603

Tunisia 0.816 0.379 0.602 0.683 0.654 0.399 0.486 0.600

St. Kitts & Nevis 0.734 0.286 0.597 0.659 0.639 0.328 0.472 0.595

Uruguay 0.759 0.335 0.596 0.665 0.642 0.364 0.476 0.594

Slovakia 0.759 0.338 0.593 0.663 0.639 0.366 0.474 0.591

Hungary 0.745 0.337 0.576 0.645 0.622 0.362 0.462 0.574

Argentina 0.670 0.246 0.549 0.604 0.587 0.290 0.432 0.547

Oman 0.676 0.288 0.538 0.598 0.578 0.318 0.428 0.537

Qatar 0.648 0.261 0.523 0.579 0.561 0.295 0.414 0.522

Slovenia 0.649 0.277 0.516 0.574 0.555 0.305 0.411 0.515

High-income countries:

Czech Republic 0.638 0.272 0.507 0.564 0.545 0.300 0.404 0.506

Greece 0.597 0.233 0.485 0.535 0.519 0.267 0.383 0.483

Korea 0.576 0.187 0.478 0.524 0.510 0.234 0.374 0.477

Portugal 0.577 0.217 0.471 0.519 0.504 0.253 0.371 0.470

Spain 0.580 0.232 0.470 0.519 0.503 0.263 0.372 0.468

Ireland 0.578 0.245 0.461 0.512 0.495 0.271 0.367 0.460

See notes at end of table. Continued--
42 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 11—Unconditional expenditure elasticity for food sub-categories, 

114 countries in 19961

--Continued

Country

Beverages, 

tobacco

Breads, 

cereals Meat Fish Dairy Fats, oils

Fruits, 

vegetables

Other 

foods

High-income countries--continued:

Singapore 0.556 0.218 0.451 0.498 0.483 0.249 0.356 0.450

Mauritius 0.565 0.254 0.438 0.491 0.473 0.274 0.351 0.437

Israel 0.525 0.211 0.424 0.469 0.455 0.239 0.336 0.423

New Zealand 0.523 0.217 0.419 0.465 0.450 0.242 0.333 0.418

Finland 0.521 0.217 0.418 0.464 0.448 0.242 0.332 0.417

Bahamas 0.489 0.113 0.413 0.450 0.438 0.171 0.320 0.411

Sweden 0.477 0.197 0.384 0.425 0.411 0.221 0.304 0.382

Netherlands 0.466 0.185 0.378 0.418 0.405 0.211 0.299 0.377

France 0.431 0.159 0.353 0.389 0.377 0.187 0.278 0.352

United Kingdom 0.432 0.169 0.351 0.387 0.375 0.194 0.277 0.350

Belgium 0.424 0.163 0.345 0.381 0.369 0.188 0.273 0.344

Norway 0.426 0.170 0.344 0.381 0.369 0.193 0.272 0.343

Italy 0.417 0.160 0.340 0.375 0.364 0.185 0.268 0.339

Austria 0.404 0.153 0.330 0.364 0.353 0.178 0.260 0.329

Germany 0.402 0.153 0.328 0.362 0.351 0.177 0.259 0.327

Australia 0.388 0.143 0.318 0.350 0.340 0.168 0.250 0.317

Japan 0.388 0.160 0.312 0.345 0.334 0.179 0.247 0.311

Canada 0.376 0.155 0.302 0.335 0.324 0.174 0.240 0.301

Bermuda 0.338 0.104 0.281 0.308 0.300 0.133 0.220 0.281

Switzerland 0.330 0.112 0.273 0.300 0.291 0.137 0.214 0.272

Barbados 0.375 0.175 0.273 0.311 0.297 0.183 0.221 0.272

Hong Kong 0.335 0.137 0.270 0.299 0.289 0.154 0.214 0.269

Iceland 0.326 0.118 0.268 0.294 0.286 0.140 0.210 0.267

Denmark 0.322 0.124 0.262 0.289 0.281 0.143 0.207 0.261

Luxembourg 0.159 0.040 0.133 0.146 0.142 0.057 0.104 0.133

United States 0.134 0.050 0.110 0.121 0.117 0.059 0.086 0.109

Low-income average 1.247 0.527 0.780 0.910 0.860 0.546 0.636 0.777

Middle-income average 0.836 0.373 0.641 0.720 0.693 0.402 0.514 0.639

High-income average 0.440 0.170 0.356 0.393 0.381 0.196 0.281 0.355

1

Countries are reported based on ascending per capita real income levels.
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 43

Table 12—Unconditional Frisch own-price elasticity for food sub-categories, 

114 countries in 19961

Country

Beverages, 

tobacco

Breads, 

cereals Meat Fish Dairy Fats, oils

Fruits, 

vegetables

Other 

foods

Low-income countries:

Tanzania -1.375 -0.500 -0.695 -0.837 -0.779 -0.512 -0.572 -0.692

Nigeria -1.369 -0.492 -0.682 -0.823 -0.765 -0.503 -0.562 -0.679

Tajikistan -1.355 -0.487 -0.676 -0.815 -0.758 -0.498 -0.556 -0.673

Zambia -1.223 -0.480 -0.674 -0.804 -0.752 -0.492 -0.554 -0.671

Yemen -1.231 -0.480 -0.674 -0.805 -0.752 -0.492 -0.554 -0.671

Malawi -1.243 -0.479 -0.670 -0.801 -0.748 -0.490 -0.551 -0.667

Madagascar -1.110 -0.469 -0.669 -0.788 -0.742 -0.482 -0.548 -0.666

Mali -1.339 -0.482 -0.669 -0.807 -0.750 -0.493 -0.551 -0.666

Mongolia -1.029 -0.457 -0.666 -0.777 -0.735 -0.472 -0.544 -0.664

Benin -1.080 -0.459 -0.657 -0.773 -0.728 -0.472 -0.538 -0.654

Kenya -1.309 -0.471 -0.654 -0.788 -0.733 -0.482 -0.538 -0.651

Sierra Leone -1.180 -0.462 -0.648 -0.774 -0.724 -0.474 -0.533 -0.646

Nepal -0.891 -0.415 -0.645 -0.735 -0.703 -0.435 -0.522 -0.643

Turkmenistan -1.143 -0.458 -0.646 -0.768 -0.720 -0.470 -0.530 -0.643

Congo -1.186 -0.459 -0.642 -0.768 -0.717 -0.470 -0.528 -0.639

Senegal -0.965 -0.434 -0.636 -0.739 -0.701 -0.448 -0.519 -0.634

Vietnam -0.891 -0.414 -0.633 -0.725 -0.692 -0.432 -0.514 -0.631

Bangladesh -0.922 -0.423 -0.634 -0.731 -0.695 -0.440 -0.516 -0.632

Pakistan -0.877 -0.407 -0.625 -0.714 -0.682 -0.425 -0.507 -0.622

Azerbaijan -0.857 -0.399 -0.620 -0.706 -0.676 -0.418 -0.502 -0.618

Cote d'Ivoire -1.011 -0.433 -0.620 -0.729 -0.687 -0.445 -0.508 -0.618

Paraguay -0.948 -0.423 -0.619 -0.720 -0.682 -0.437 -0.505 -0.616

Uzbekistan -0.948 -0.422 -0.616 -0.717 -0.679 -0.436 -0.503 -0.613

Kyrgyzstan -0.926 -0.418 -0.615 -0.713 -0.677 -0.432 -0.502 -0.613

Cameroon -0.993 -0.428 -0.615 -0.722 -0.681 -0.441 -0.504 -0.613

Moldova -0.960 -0.423 -0.615 -0.718 -0.679 -0.437 -0.503 -0.613

Bolivia -0.834 -0.388 -0.610 -0.693 -0.663 -0.408 -0.493 -0.608

Ecuador -0.881 -0.405 -0.609 -0.701 -0.667 -0.421 -0.496 -0.607

Armenia -0.827 -0.385 -0.605 -0.688 -0.658 -0.404 -0.489 -0.603

Sri Lanka -0.778 -0.350 -0.603 -0.676 -0.651 -0.377 -0.483 -0.602

Jordan -0.828 -0.385 -0.601 -0.684 -0.654 -0.404 -0.486 -0.599

Albania -0.769 -0.348 -0.593 -0.665 -0.641 -0.374 -0.476 -0.591

Indonesia -0.735 -0.304 -0.590 -0.654 -0.633 -0.340 -0.468 -0.588

Jamaica -0.816 -0.380 -0.592 -0.674 -0.645 -0.398 -0.479 -0.590

Zimbabwe -0.984 -0.416 -0.594 -0.700 -0.658 -0.428 -0.487 -0.591

Guinea -0.876 -0.398 -0.591 -0.683 -0.649 -0.413 -0.481 -0.588

Syria -0.831 -0.385 -0.585 -0.671 -0.639 -0.401 -0.475 -0.583

Georgia -0.811 -0.377 -0.584 -0.666 -0.636 -0.395 -0.473 -0.582

Middle-income countries:

Ukraine -0.795 -0.370 -0.575 -0.655 -0.626 -0.388 -0.466 -0.573

Philippines -0.718 -0.313 -0.567 -0.631 -0.610 -0.342 -0.452 -0.565

Peru -0.764 -0.355 -0.565 -0.641 -0.614 -0.374 -0.456 -0.563

Botswana -0.799 -0.371 -0.566 -0.648 -0.618 -0.387 -0.459 -0.564

Thailand -0.746 -0.344 -0.563 -0.635 -0.610 -0.365 -0.453 -0.561

See notes at end of table. Continued--
44 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table 12—Unconditional Frisch own-price elasticity for food sub-categories, 

114 countries in 19961--Continued

Country

Beverages, 

tobacco

Breads, 

cereals Meat Fish Dairy Fats, oils

Fruits, 

vegetables

Other 

foods

Middle-income countries--continued:

Morocco -0.787 -0.366 -0.561 -0.642 -0.613 -0.382 -0.455 -0.559

Venezuela -0.732 -0.335 -0.559 -0.628 -0.605 -0.357 -0.449 -0.557

Macedonia -0.733 -0.338 -0.554 -0.625 -0.600 -0.359 -0.446 -0.553

Belize -0.727 -0.333 -0.554 -0.623 -0.599 -0.355 -0.445 -0.552

Egypt -0.726 -0.332 -0.554 -0.623 -0.599 -0.354 -0.445 -0.552

St.Vincent & Grenadines -0.719 -0.327 -0.553 -0.620 -0.597 -0.350 -0.444 -0.551

Swaziland -0.826 -0.373 -0.549 -0.637 -0.604 -0.386 -0.448 -0.547

Lebanon -0.695 -0.307 -0.544 -0.608 -0.586 -0.334 -0.435 -0.543

Belarus -0.721 -0.332 -0.544 -0.614 -0.590 -0.353 -0.438 -0.542

Kazakhstan -0.710 -0.324 -0.544 -0.611 -0.588 -0.346 -0.436 -0.542

Dominica -0.695 -0.309 -0.543 -0.607 -0.585 -0.334 -0.434 -0.541

Latvia -0.710 -0.327 -0.537 -0.606 -0.582 -0.348 -0.432 -0.536

St. Lucia -0.672 -0.285 -0.536 -0.595 -0.575 -0.315 -0.426 -0.534

Brazil -0.709 -0.327 -0.536 -0.604 -0.581 -0.347 -0.431 -0.534

Bulgaria -0.705 -0.324 -0.535 -0.603 -0.579 -0.345 -0.430 -0.533

Russia -0.706 -0.326 -0.532 -0.600 -0.576 -0.346 -0.428 -0.530

Fiji -0.671 -0.297 -0.526 -0.587 -0.567 -0.322 -0.420 -0.525

Grenada -0.667 -0.291 -0.526 -0.586 -0.566 -0.318 -0.420 -0.525

Turkey -0.668 -0.294 -0.524 -0.585 -0.564 -0.320 -0.419 -0.522

Lithuania -0.671 -0.301 -0.521 -0.583 -0.562 -0.325 -0.417 -0.519

Romania -0.657 -0.287 -0.518 -0.577 -0.557 -0.314 -0.413 -0.516

Iran -0.702 -0.327 -0.514 -0.584 -0.559 -0.343 -0.415 -0.512

Mexico -0.653 -0.291 -0.510 -0.570 -0.549 -0.315 -0.408 -0.508

Bahrain -0.625 -0.250 -0.505 -0.559 -0.541 -0.284 -0.400 -0.504

Chile -0.666 -0.307 -0.505 -0.569 -0.547 -0.326 -0.406 -0.503

Antigua & Barbuda -0.623 -0.251 -0.503 -0.557 -0.539 -0.284 -0.398 -0.502

Poland -0.646 -0.292 -0.499 -0.559 -0.539 -0.313 -0.400 -0.498

Trinidad & Tobago -0.629 -0.276 -0.495 -0.552 -0.532 -0.301 -0.395 -0.493

Estonia -0.628 -0.279 -0.490 -0.548 -0.529 -0.302 -0.392 -0.489

Gabon -0.637 -0.290 -0.490 -0.550 -0.529 -0.310 -0.393 -0.488

Tunisia -0.660 -0.307 -0.487 -0.552 -0.529 -0.323 -0.393 -0.485

St. Kitts & Nevis -0.594 -0.231 -0.483 -0.533 -0.517 -0.266 -0.381 -0.481

Uruguay -0.614 -0.271 -0.482 -0.538 -0.519 -0.294 -0.385 -0.481

Slovakia -0.614 -0.273 -0.480 -0.536 -0.517 -0.296 -0.384 -0.478

Hungary -0.603 -0.272 -0.466 -0.522 -0.503 -0.293 -0.373 -0.464

Argentina -0.542 -0.199 -0.444 -0.489 -0.474 -0.235 -0.349 -0.443

Oman -0.547 -0.233 -0.435 -0.484 -0.468 -0.257 -0.346 -0.434

Qatar -0.524 -0.211 -0.423 -0.468 -0.453 -0.239 -0.335 -0.422

Slovenia -0.525 -0.224 -0.418 -0.464 -0.449 -0.247 -0.332 -0.416

High-income countries:

Czech Republic -0.516 -0.220 -0.410 -0.456 -0.441 -0.243 -0.326 -0.409

Greece -0.482 -0.188 -0.392 -0.433 -0.420 -0.216 -0.310 -0.391

Korea -0.466 -0.151 -0.387 -0.424 -0.412 -0.189 -0.302 -0.385

Portugal -0.466 -0.176 -0.381 -0.420 -0.408 -0.205 -0.300 -0.380

Spain -0.469 -0.187 -0.380 -0.420 -0.407 -0.213 -0.300 -0.379

Ireland -0.468 -0.198 -0.373 -0.414 -0.401 -0.219 -0.297 -0.372

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 45

Table 12—Unconditional Frisch own-price elasticity for food sub-categories, 

114 countries in 19961

--Continued

Country

Beverages, 

tobacco

Breads, 

cereals Meat Fish Dairy Fats, oils

Fruits, 

vegetables

Other 

foods

High-income countries--continued:

Singapore -0.449 -0.176 -0.365 -0.403 -0.391 -0.202 -0.288 -0.364

Mauritius -0.457 -0.206 -0.354 -0.397 -0.382 -0.221 -0.284 -0.353

Israel -0.424 -0.170 -0.343 -0.380 -0.368 -0.193 -0.272 -0.342

New Zealand -0.423 -0.176 -0.339 -0.376 -0.364 -0.196 -0.269 -0.338

Finland -0.422 -0.176 -0.338 -0.375 -0.363 -0.196 -0.268 -0.337

Bahamas -0.396 -0.092 -0.334 -0.364 -0.354 -0.138 -0.258 -0.333

Sweden -0.386 -0.159 -0.310 -0.344 -0.333 -0.178 -0.246 -0.309

Netherlands -0.377 -0.149 -0.306 -0.338 -0.327 -0.170 -0.242 -0.305

France -0.348 -0.129 -0.286 -0.314 -0.305 -0.152 -0.225 -0.285

United Kingdom -0.349 -0.137 -0.284 -0.313 -0.304 -0.157 -0.224 -0.283

Belgium -0.343 -0.132 -0.279 -0.308 -0.299 -0.152 -0.220 -0.278

Norway -0.344 -0.138 -0.278 -0.308 -0.298 -0.156 -0.220 -0.277

Italy -0.337 -0.129 -0.275 -0.303 -0.294 -0.150 -0.217 -0.274

Austria -0.327 -0.124 -0.267 -0.294 -0.285 -0.144 -0.210 -0.266

Germany -0.325 -0.124 -0.265 -0.292 -0.284 -0.143 -0.209 -0.264

Australia -0.314 -0.115 -0.257 -0.283 -0.275 -0.136 -0.202 -0.256

Japan -0.314 -0.129 -0.252 -0.279 -0.270 -0.145 -0.200 -0.251

Canada -0.304 -0.125 -0.245 -0.271 -0.262 -0.140 -0.194 -0.244

Bermuda -0.273 -0.084 -0.228 -0.249 -0.242 -0.108 -0.178 -0.227

Switzerland -0.267 -0.091 -0.221 -0.242 -0.236 -0.111 -0.173 -0.220

Barbados -0.303 -0.141 -0.221 -0.251 -0.240 -0.148 -0.179 -0.220

Hong Kong -0.271 -0.110 -0.218 -0.242 -0.234 -0.124 -0.173 -0.218

Iceland -0.264 -0.096 -0.216 -0.238 -0.231 -0.114 -0.170 -0.216

Denmark -0.260 -0.100 -0.212 -0.234 -0.227 -0.116 -0.167 -0.211

Luxembourg -0.128 -0.032 -0.108 -0.118 -0.115 -0.046 -0.084 -0.108

United States -0.108 -0.040 -0.089 -0.098 -0.095 -0.047 -0.070 -0.088

Low-income average -1.009 -0.426 -0.631 -0.736 -0.695 -0.442 -0.514 -0.628

Middle-income average -0.676 -0.302 -0.519 -0.582 -0.560 -0.325 -0.416 -0.517

High-income average -0.356 -0.138 -0.288 -0.318 -0.308 -0.158 -0.227 -0.287

1

Countries are reported based on ascending per capita real income levels.
The Frisch own-price elasticities for the food sub-categories vary by affluence according to economic

theory; low-income countries are more responsive to price changes compared with higher-income countries (table 12). The absolute values of the own-price elasticities are also less for the conditionally inelastic food groups, such as breads and cereals, fats and oils, and fruits and vegetables. These values for

breads and cereals are -0.50 for Tanzania, -0.38 for Georgia, -0.22 for Slovenia, and -0.04 for the United

States. Similarly, the Frisch own-price elasticities for fats and oils, and for fruits and vegetables range

from -0.51 and -0.57, respectively, for Tanzania, to -0.05 and -0.07, respectively, for the United States.

The absolute values of the Frisch own-price elasticities for the conditionally elastic food items are higher, and for the poorest country, Tanzania, these range from -1.37 for beverage and tobacco, to -0.70 for

meat, -0.83 for fish, -0.78 for dairy, and -0.69 for other food products. The values of these elasticities

decline in absolute terms with affluence and are -0.11 for beverage and tobacco, -0.09 for meat, -0.10 for

fish, -0.10 for dairy, and -0.09 for other food products for the wealthiest country, the United States.

46 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA
Concluding Remarks

This report accomplishes two major goals. The first is presenting a two-stage cross-country demand

model that enables estimating unconditional income and price elasticities for disaggregate consumption

sub-groups, while the second is providing income and price elasticities across 114 countries for nine

aggregate consumption categories and eight food sub-categories. While previous research presented

multi-stage demand estimation models, there have been no empirical works conducted across as many

countries and consumption categories. Additionally, this report also presents a new methodology for calculating unconditional price elasticities in a two-stage demand model. 

The results of this research confirm many of the results obtained and established by earlier studies. Lowincome countries spend a greater portion of their budget on necessities, such as food, while richer countries spend a greater proportion of their income on luxuries, such as recreation. Low-value staples, such

as cereals, account for a larger share of the food budget in poorer countries, while high-value food items,

such as dairy and meat, are a larger share of the food budget in richer countries. Low-income countries

are also more responsive to changes in income and food prices and, therefore, make larger adjustments

to their food consumption pattern when incomes and prices change. However, our study illustrates that

adjustments to price and income changes are not made uniformly across all food categories. Staple food

consumption changes the least, while consumption of higher-value food items such as dairy and meat

changes the most. Additionally, our results indicate that when price changes are accompanied by equivalent income changes, wealthier low-income countries and middle-income countries make the most

adjustments to their food demand.

Finally, this research provides estimates for income and own-price elasticities for nine aggregate consumption groups and eight food subgroups across 114 countries. These elasticities can be used as inputs

in future work designed to forecast future food demand and supply and in projects designed to simulate

the effects of different government policy options. In addition to the actual elasticity estimates, parameters estimated from our models can be used (with the latest expenditure data) to estimate new elasticities

for recent years for the countries included in our analysis as well as for other countries. 

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 47
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1980.

Theil, H. Studies in Global Econometrics. Kluwer Academic Press, Boston, MA, 1996.

Theil, Henri, and Kenneth W. Clements. Applied Demand Analysis: Results from System-Wide

Approaches. Ballinger Publishing Company, Cambridge, MA, 1987.

Theil H., C. F. Chung, and J.L. Seale, Jr. International Evidence on Consumption Patterns. JAI Press,

Inc., Greenwich, CT, 1989.

Timmer, C.P. “Is There Curvature in the Slutsky Matrix?” Review of Economics and Statistics. Vol. 63,

1981, pp. 395-402.

48 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA
United Nations. World Comparisons of Purchasing Power and Real Product for 1980: Phase IV of the

International Comparison Project. Two Parts, United Nations Publication, New York, 1986-87.

Working, H. “Statistical Laws of Family Expenditure,” Journal of the American Statistical Association.

Vol. 38, 1943, pp. 43-56.

World Bank. World Development Indicators, Washington, DC, 2001.

World Bank. Purchasing Power of Currencies: Comparing National Incomes Using ICP Data. SocioEconomic Data Division, International Economics Department, Washington, DC, 1993.

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 49
Appendix: Goodness-of-Fit and Outliers

In figure 4, the budget shares of food, beverages, and tobacco for each of the 114 countries is plotted

against the natural log of per capita income. Obviously, the budget shares decline as per capita income

increases, and the relationship behaves according to Engel’s law. Evident from figure 4, the 1996 ICP

data contain several outlying observations. As discussed by Theil, Chung, and Seale (1989), the earlier

phases of the ICP data also contained several outliers, especially African countries where the data are

quite unreliable.1

To identify the outliers, we follow Theil, Chung, and Seale and calculate the information inaccuracy

measure from statistical information theory. Specifically, the information inaccuracy measure is:

Information inaccuracy measures can be decomposed into Strobel (1982) measures, an indication of the

goodness-of-fit of the model for each good in the Florida system. The Strobel measure is defined as:

Information inaccuracies and Strobel measures for the Florida-PI model are calculated for all 114 countries based on the parameters estimated using 114 countries as reported in table 5 in chapter 5 and the

predicted budget shares from equation 18. These measures are reported in table A1 for all countries as

well as the overall group averages for low-, middle-, and high-income countries.

The high-income countries have the smallest average value of information inaccuracy measures (second

column in table A1). Its average information inaccuracy measure, 0.038, is only 66 percent the size of

the middle-income country group’s average of 0.058 and 44 percent of the low-income country’s average

of 0.086. Average Strobel measures (columns 3-11 in table A1) are also the smallest for the high-income

group, and range from 0.002 to 0.006, compared with a higher range from 0.004 to 0.010 for middleincome countries and from 0.006 to 0.011 for low-income countries. 

50 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

I w w

w c ic

ic

i= ic

n

 = log (A.1) $ , 1

∑

I ww w w

w ic ic ic ic

ic

ic

 = + log (A.2) $ $ − ,

and I = I . Strobel measures, like the I , have a lower bound of zero and no upper bound. If 

= , then I = 0; otherwise, the measure is positive.

c

ic

c i i c

w w ic ic

∑

$

1

2

This is true even for population data. Seale and Theil (1986) note that, according to , July 20, 1985:30, the 

1984 population estimate of the Ethiopian government based on prior censuses differed from the actual mid -1984 census by 

no less than 9 million out of a population of 43 million.

 Note that is not the same as , is the fitted budget share based on country 's observed prices, while is the fitted 

budget share at geometric mean prices.

The Economist

wic wic wic c wic $ $

where is the observed budget share of good in country , and is the fitted budget share of good 

 in country based on equation 29 in chapter 4. When the model fits perfectly, and the

value of is zero. The value is positive when, for some or , is non-zero. Let the difference 

equal the residual . A Taylor expansion shows that when these residuals are sufficiently small,

1

2 . This illustrates how increases when the residuals become larger in absolute values.

2

w i cw

i c w w ic

I i cw w

e

I e

w

I

ic ic

ic ic

c ic ic

ic

c ic

i= ic

n

c

$

$ , ,

$

= ∀

−

≈ ∑

2

1
Gross rent, fuel, and power has the highest Strobel measures across all three country groups. Food, beverages, and tobacco and recreation have the next highest Strobel measures for the middle-income and

high-income country groups. For the low-income country group, although the average Strobel measure

on recreation is smaller compared with other consumption categories, the actual goodness-of-fit was particularly bad. The average Strobel measure presented in table A1 excludes the undefined values for the

four countries whose predicted budget shares for recreation were negative. 

Smaller inaccuracy and Strobel measures indicate a better goodness-of-fit. Following Theil, Chung, and

Seale, countries with Ic>0.10 (at two decimal places) are declared to be outliers. Of the original 114

countries, 23 countries are identified as outliers by this criterion. Of the 23 countries, 17 fall within the

low-income country group, 4 within the middle-income country group, and only two (Bahamas and

Hong Kong) within the high-income country group. The large number of outliers identified within the

low-income country group explains the observed poorer average goodness-of-fit for this group. 

Seven of the identified outliers are from Africa (Cole d’Ivoire, Egypt, Madagascar, Malawi, Nigeria,

Tanzania, and Zimbabwe), three from America (Bahamas, Ecuador, and Paraguay), one (Albania) from

Europe, six from Central-Asian-Transition countries (Armenia, Azerbaijan, Georgia, Mongolia,

Tajikistan, and Turkmenistan), and six others from Asia (Bahrain, Hong Kong, Iran, Philippines, Sri

Lanka, and Yemen).3 It is also of interest to note that the number of outliers appears to be related with

when the countries first appeared in the ICP study. For example, there are only three outliers among the

countries in the first three ICP phases. Of the 33 countries introduced in Phase IV, eight are outliers, five

of which are poor African countries. Of the 60 countries introduced in 1996, 12 are outliers, seven of

which are transitional economies, four of which are Middle Eastern, and the last of which is the

Bahamas.

Having deleted these outliers, the model was re-estimated with 91 countries. As it turns out, the parameters

were hardly changed by the deletion of the outliers from the sample. This was not the case in the earlier

phases of the ICP when the total number of countries was much lower than in the 1996 sample. Based on

our finding, we calculated all elasticities from the 114 country parameter estimates. We do, however, report

the 91 country parameter estimates and elasticities here in the appendix (app. tables A2-A9).

Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 51

3 Theil, Chung, and Seale provide some rationale as to why many poorer countries tend to be outliers. These include underreporting of home-produced food, and distortions of consumption data due to a large presence of tourists in the country

(Bahamas in our case and Jamaica in the earlier study).
52 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table A-1—Information Inaccuracies and Strobel measures, 114 countries in 19961

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicationRecreation Other

Low-income countries:

Tanzania i 0.003 0.001 0.001 0.003 0.003 0.011 0.018 i 0.002

Nigeria i 0.004 0.005 0.008 0.000 0.001 0.022 0.017 i 0.003

Tajikistan 0.078 0.001 0.001 0.047 0.001 0.000 0.000 0.001 0.002 0.025

Zambia 0.039 0.000 0.000 0.001 0.009 0.019 0.002 0.002 0.003 0.002

Yemen i 0.001 0.003 0.002 0.004 0.003 0.000 0.006 i 0.011

Malawi i 0.003 0.009 0.000 0.010 0.002 0.002 0.000 i 0.000

Madagascar 0.073 0.002 0.000 0.005 0.000 0.000 0.006 0.006 0.052 0.001

Mali 0.072 0.003 0.014 0.000 0.000 0.006 0.017 0.030 0.000 0.000

Mongolia 0.143 0.000 0.012 0.001 0.007 0.017 0.029 0.015 0.021 0.042

Benin 0.049 0.000 0.002 0.025 0.002 0.000 0.007 0.003 0.004 0.007

Kenya 0.058 0.014 0.000 0.004 0.021 0.001 0.011 0.001 0.004 0.002

Sierra Leone 0.032 0.003 0.008 0.004 0.000 0.000 0.003 0.003 0.005 0.006

Nepal 0.060 0.003 0.002 0.002 0.000 0.000 0.001 0.042 0.005 0.004

Turkmenistan 0.095 0.004 0.004 0.038 0.002 0.034 0.002 0.005 0.001 0.004

Congo 0.071 0.005 0.018 0.000 0.005 0.004 0.003 0.014 0.002 0.021

Senegal 0.022 0.000 0.009 0.001 0.000 0.006 0.001 0.004 0.001 0.000

Vietnam 0.037 0.001 0.002 0.004 0.002 0.000 0.000 0.004 0.011 0.013

Bangladesh 0.093 0.006 0.005 0.023 0.010 0.005 0.000 0.028 0.010 0.005

Pakistan 0.059 0.001 0.000 0.038 0.002 0.001 0.001 0.011 0.004 0.001

Azerbaijan 0.115 0.034 0.003 0.046 0.013 0.012 0.003 0.000 0.001 0.003

Cote d'Ivoire 0.127 0.004 0.001 0.024 0.013 0.010 0.044 0.000 0.012 0.019

Paraguay 0.125 0.052 0.004 0.018 0.006 0.000 0.014 0.011 0.008 0.012

Uzbekistan 0.077 0.001 0.015 0.006 0.010 0.002 0.026 0.000 0.001 0.016

Kyrgyzstan 0.065 0.002 0.003 0.008 0.001 0.001 0.000 0.043 0.005 0.001

Cameroon 0.041 0.003 0.030 0.004 0.002 0.000 0.000 0.000 0.001 0.000

Moldova 0.090 0.005 0.000 0.019 0.004 0.019 0.038 0.002 0.000 0.001

Bolivia 0.032 0.001 0.000 0.001 0.000 0.003 0.008 0.002 0.001 0.015

Ecuador 0.113 0.034 0.004 0.000 0.010 0.004 0.007 0.004 0.031 0.020

Armenia 0.104 0.036 0.008 0.010 0.014 0.013 0.007 0.016 0.000 0.001

Sri Lanka 0.170 0.019 0.060 0.023 0.002 0.011 0.007 0.040 0.004 0.004

Jordan 0.036 0.005 0.000 0.018 0.000 0.003 0.002 0.001 0.001 0.005

Albania 0.172 0.052 0.020 0.010 0.005 0.009 0.017 0.019 0.003 0.037

Indonesia 0.090 0.012 0.014 0.007 0.000 0.013 0.027 0.012 0.003 0.002

Jamaica 0.104 0.006 0.004 0.027 0.004 0.000 0.001 0.046 0.013 0.003

Zimbabwe 0.145 0.052 0.006 0.018 0.014 0.001 0.049 0.000 0.000 0.005

Guinea 0.058 0.000 0.042 0.007 0.001 0.001 0.000 0.000 0.005 0.001

Syria 0.075 0.000 0.000 0.011 0.023 0.004 0.000 0.023 0.003 0.011

Georgia 0.150 0.000 0.003 0.027 0.011 0.001 0.008 0.000 0.001 0.098

Middle-income countries:

Ukraine 0.018 0.000 0.001 0.001 0.004 0.001 0.001 0.000 0.001 0.010

Philippines 0.133 0.005 0.008 0.023 0.020 0.030 0.024 0.013 0.008 0.003

Peru 0.065 0.010 0.000 0.001 0.003 0.000 0.000 0.001 0.044 0.005

Botswana 0.108 0.006 0.003 0.005 0.025 0.000 0.047 0.001 0.001 0.019

Thailand 0.059 0.018 0.007 0.008 0.001 0.000 0.000 0.011 0.009 0.004

Morocco 0.020 0.001 0.001 0.001 0.000 0.001 0.002 0.000 0.001 0.014

Venezuela 0.051 0.006 0.000 0.006 0.000 0.000 0.019 0.013 0.002 0.003

See notes at end of page. Continued--

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Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 53

Table A-1—Information Inaccuracies and Strobel measures, 114 countries in 1996 1--Continued

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicati

on

Recreation Other

Middle-income countries--continued:

Macedonia 0.022 0.001 0.003 0.002 0.008 0.005 0.000 0.002 0.000 0.000

Belize 0.037 0.003 0.002 0.008 0.000 0.005 0.001 0.006 0.001 0.011

Egypt 0.114 0.013 0.005 0.019 0.036 0.002 0.008 0.012 0.017 0.001

St.Vincent & Grenadines 0.038 0.000 0.003 0.000 0.001 0.019 0.002 0.002 0.006 0.005

Swaziland 0.095 0.018 0.002 0.001 0.049 0.000 0.016 0.001 0.000 0.007

Lebanon 0.079 0.003 0.023 0.003 0.005 0.000 0.013 0.001 0.025 0.006

Belarus 0.048 0.011 0.000 0.008 0.008 0.002 0.006 0.008 0.002 0.003

Kazakhstan 0.080 0.017 0.010 0.003 0.013 0.002 0.003 0.011 0.004 0.017

Dominica 0.049 0.001 0.000 0.001 0.001 0.003 0.006 0.012 0.001 0.025

Latvia 0.023 0.002 0.000 0.001 0.011 0.002 0.005 0.001 0.000 0.001

St. Lucia 0.055 0.017 0.000 0.004 0.001 0.000 0.003 0.006 0.012 0.010

Brazil 0.067 0.020 0.013 0.010 0.003 0.001 0.016 0.002 0.001 0.001

Bulgaria 0.053 0.004 0.001 0.029 0.006 0.006 0.004 0.002 0.000 0.001

Russia 0.027 0.001 0.014 0.008 0.001 0.001 0.002 0.000 0.000 0.000

Fiji 0.040 0.002 0.001 0.000 0.005 0.002 0.005 0.000 0.007 0.018

Grenada 0.037 0.003 0.003 0.000 0.001 0.012 0.001 0.004 0.007 0.007

Turkey 0.057 0.000 0.000 0.013 0.013 0.012 0.004 0.000 0.009 0.006

Lithuania 0.013 0.004 0.002 0.000 0.004 0.000 0.001 0.001 0.000 0.001

Romania 0.053 0.008 0.001 0.032 0.001 0.000 0.001 0.001 0.008 0.000

Iran 0.184 0.010 0.009 0.096 0.000 0.001 0.001 0.009 0.013 0.045

Mexico 0.036 0.002 0.006 0.002 0.001 0.001 0.001 0.006 0.005 0.013

Bahrain 0.124 0.003 0.001 0.003 0.001 0.000 0.013 0.010 0.007 0.086

Chile 0.064 0.016 0.007 0.004 0.005 0.000 0.023 0.001 0.000 0.007

Antigua & Barbuda 0.061 0.013 0.006 0.001 0.000 0.015 0.000 0.006 0.011 0.010

Poland 0.014 0.000 0.004 0.001 0.003 0.003 0.002 0.000 0.001 0.000

Trinidad & Tobago 0.100 0.004 0.005 0.016 0.000 0.002 0.000 0.009 0.009 0.055

Estonia 0.022 0.000 0.003 0.002 0.003 0.002 0.003 0.000 0.006 0.004

Gabon 0.093 0.029 0.013 0.008 0.012 0.002 0.000 0.004 0.026 0.000

Tunisia 0.059 0.000 0.005 0.022 0.015 0.005 0.000 0.006 0.000 0.005

St. Kitts & Nevis 0.052 0.015 0.003 0.001 0.000 0.007 0.000 0.006 0.011 0.010

Uruguay 0.034 0.002 0.000 0.002 0.000 0.019 0.006 0.003 0.000 0.000

Slovakia 0.024 0.000 0.001 0.004 0.005 0.007 0.002 0.003 0.002 0.000

Hungary 0.030 0.007 0.005 0.001 0.002 0.000 0.000 0.000 0.014 0.001

Argentina 0.049 0.011 0.004 0.000 0.001 0.003 0.027 0.001 0.002 0.001

Oman 0.091 0.000 0.001 0.024 0.001 0.014 0.000 0.013 0.018 0.019

Qatar 0.075 0.001 0.012 0.008 0.012 0.001 0.001 0.012 0.015 0.015

Slovenia 0.018 0.002 0.001 0.001 0.005 0.000 0.001 0.006 0.001 0.001

High-income countries:

Czech Republic 0.017 0.000 0.001 0.000 0.001 0.001 0.000 0.003 0.010 0.000

Greece 0.051 0.000 0.004 0.002 0.001 0.003 0.006 0.012 0.004 0.019

Korea 0.040 0.009 0.005 0.002 0.000 0.002 0.009 0.005 0.003 0.005

Portugal 0.029 0.002 0.000 0.019 0.001 0.000 0.002 0.001 0.001 0.003

Spain 0.051 0.001 0.000 0.008 0.002 0.000 0.001 0.001 0.004 0.034

Ireland 0.034 0.005 0.000 0.005 0.003 0.002 0.001 0.000 0.002 0.016

Singapore 0.082 0.011 0.000 0.007 0.000 0.013 0.001 0.012 0.036 0.002

Mauritius 0.055 0.001 0.000 0.005 0.021 0.005 0.007 0.004 0.003 0.008

See notes at end of page. Continued--

I

c
54 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table A-1—Information Inaccuracies and Strobel measures, 114 countries in 19961--Continued

Country

Food, 

beverages 

& tobacco

Clothing 

& 

footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicati

on

Recreation Other

High-income countries--continued:

Israel 0.021 0.001 0.004 0.002 0.000 0.000 0.008 0.001 0.000 0.004

New Zealand 0.016 0.003 0.005 0.003 0.000 0.000 0.001 0.001 0.002 0.000

Finland 0.030 0.004 0.008 0.001 0.008 0.001 0.001 0.000 0.002 0.006

Bahamas 0.166 0.068 0.003 0.043 0.000 0.001 0.006 0.003 0.042 0.000

Sweden 0.027 0.003 0.005 0.010 0.006 0.000 0.000 0.000 0.001 0.001

Netherlands 0.010 0.001 0.000 0.000 0.002 0.001 0.000 0.001 0.001 0.003

France 0.013 0.000 0.002 0.000 0.001 0.008 0.000 0.000 0.000 0.001

United Kingdom 0.055 0.000 0.003 0.001 0.006 0.017 0.013 0.008 0.001 0.007

Belgium 0.005 0.000 0.000 0.000 0.001 0.001 0.000 0.001 0.002 0.000

Norway 0.011 0.001 0.001 0.000 0.003 0.000 0.000 0.000 0.001 0.004

Italy 0.009 0.000 0.002 0.001 0.000 0.000 0.001 0.002 0.000 0.003

Austria 0.008 0.000 0.000 0.001 0.001 0.001 0.000 0.000 0.001 0.004

Germany 0.008 0.000 0.000 0.001 0.001 0.003 0.003 0.000 0.000 0.000

Australia 0.019 0.000 0.006 0.000 0.001 0.000 0.001 0.000 0.010 0.000

Japan 0.025 0.002 0.000 0.001 0.004 0.008 0.001 0.004 0.005 0.000

Canada 0.014 0.003 0.003 0.002 0.003 0.001 0.001 0.000 0.002 0.000

Bermuda 0.089 0.008 0.001 0.014 0.025 0.005 0.015 0.001 0.006 0.015

Switzerland 0.031 0.003 0.003 0.001 0.011 0.002 0.001 0.006 0.000 0.004

Barbados 0.090 0.019 0.000 0.053 0.000 0.005 0.002 0.000 0.005 0.005

Hong Kong 0.123 0.015 0.049 0.005 0.004 0.006 0.001 0.006 0.031 0.007

Iceland 0.011 0.001 0.000 0.003 0.001 0.000 0.000 0.000 0.004 0.000

Denmark 0.027 0.000 0.004 0.000 0.009 0.003 0.001 0.000 0.006 0.004

Luxembourg 0.038 0.025 0.001 0.005 0.000 0.006 0.000 0.000 0.001 0.000

United States 0.024 0.001 0.000 0.002 0.007 0.014 0.000 0.000 0.000 0.000

Low-income average 0.086 0.011 0.009 0.013 0.006 0.006 0.011 0.012 0.007 0.011

Middle-income average 0.058 0.007 0.004 0.009 0.007 0.004 0.006 0.005 0.007 0.010

High-income average 0.038 0.006 0.003 0.006 0.004 0.003 0.003 0.002 0.006 0.005

1

Countries are reported based on ascending per capita real income levels.

Note: i = imaginary number resulting from negative predicted budget shares.

 Low-income average Strobel measures exclude imaginary values.

I

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Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 55

Table A-2—Maximum likelihood estimates of the aggregate model, 91 countries in 1996

Parameter Standard error

Income flexibility -0.839 0.022

Beta:

Food, beverages & tobacco -0.132 0.006

Clothing and footwear -0.010 0.003

Education 0.001 0.003

Gross rent, fuel & power 0.027 0.005

House operations 0.009 0.003

Medical care 0.027 0.003

Other 0.038 0.004

Recreation 0.022 0.002

Transport 0.019 0.004

Alpha:

Food, beverages & tobacco 0.145 0.009

Clothing and footwear 0.054 0.004

Education 0.071 0.004

Gross rent, fuel & power 0.181 0.008

House operations 0.073 0.004

Medical care 0.112 0.005

Other 0.154 0.006

Recreation 0.076 0.004

Transport 0.134 0.006

K:

K1 1.310 0.159

K2 1.540 0.108

Table A-3—Maximum likelihood estimates of the food sub-group model, 91 countries in 1996

Parameter Standard error Parameter Standard error

Beta: Diagonal of the Slutsky Matrix:

Beverages and tobacco 0.067 0.010 11 -0.069 0.015

Breads and cereals -0.054 0.009 22 -0.153 0.024

Meat 0.011 0.007 33 -0.178 0.026

Fish 0.007 0.005 44 -0.068 0.009

Dairy 0.010 0.006 55 -0.086 0.013

Fats & oils -0.017 0.004 66 -0.032 0.008

Fruits & vegetables -0.030 0.010 77 -0.152 0.031

Other foods 0.007 0.008 88 -0.175 0.025

Alpha:

Beverages and tobacco 0.227 0.010 K:

Breads and cereals 0.134 0.009 K1 1.359 0.176

Meat 0.177 0.007 K2 1.533 0.115

Fish 0.052 0.005

Dairy 0.108 0.006

Fats & oils 0.028 0.004

Fruits & vegetables 0.153 0.010

Other foods 0.120 0.007

π

π

π

π

π

π

π

π
56 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table A-4—Expenditure elasticity for aggregate consumption categories, 91 countries in 19961

Country

Food,

beverages

& tobacco

Clothing 

& footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicationRecreation Other

Low-income countries:

Zambia 0.775 0.887 1.304 1.201 2.185 1.014 1.257 6.348 2.380

Mali 0.769 0.885 1.294 1.196 2.040 1.014 1.249 4.283 2.187

Benin 0.756 0.882 1.275 1.188 1.840 1.014 1.236 2.874 1.933

Kenya 0.747 0.880 1.264 1.183 1.747 1.014 1.228 2.466 1.820

Sierra Leone 0.745 0.880 1.262 1.182 1.727 1.014 1.226 2.391 1.796

Nepal 0.750 0.881 1.268 1.185 1.776 1.014 1.230 2.586 1.856

Congo 0.733 0.877 1.250 1.176 1.646 1.014 1.217 2.122 1.700

Senegal 0.733 0.877 1.250 1.176 1.643 1.014 1.217 2.115 1.697

Vietnam 0.737 0.878 1.254 1.178 1.670 1.014 1.220 2.197 1.728

Bangladesh 0.733 0.877 1.251 1.176 1.647 1.014 1.217 2.127 1.702

Pakistan 0.722 0.875 1.242 1.172 1.590 1.014 1.211 1.963 1.635

Uzbekistan 0.711 0.873 1.233 1.167 1.543 1.014 1.204 1.845 1.581

Kyrgyzstan 0.710 0.873 1.233 1.167 1.542 1.014 1.204 1.841 1.579

Cameroon 0.709 0.872 1.233 1.167 1.539 1.014 1.204 1.835 1.576

Moldova 0.707 0.872 1.231 1.166 1.531 1.014 1.203 1.817 1.567

Bolivia 0.706 0.872 1.230 1.166 1.527 1.014 1.202 1.805 1.562

Jordan 0.686 0.868 1.219 1.160 1.473 1.014 1.193 1.686 1.501

Indonesia 0.695 0.870 1.224 1.163 1.496 1.014 1.197 1.735 1.527

Jamaica 0.684 0.868 1.218 1.160 1.469 1.014 1.193 1.678 1.497

Guinea 0.679 0.867 1.216 1.158 1.459 1.014 1.191 1.656 1.485

Syria 0.676 0.866 1.215 1.158 1.452 1.014 1.190 1.643 1.478

Middle-income countries:

Ukraine 0.654 0.863 1.206 1.153 1.416 1.014 1.183 1.572 1.437

Peru 0.651 0.862 1.205 1.152 1.412 1.014 1.182 1.565 1.433

Botswana 0.651 0.862 1.205 1.152 1.411 1.014 1.182 1.563 1.432

Thailand 0.665 0.864 1.210 1.155 1.432 1.014 1.186 1.604 1.456

Morocco 0.648 0.862 1.204 1.152 1.407 1.014 1.181 1.555 1.428

Venezuela 0.645 0.861 1.203 1.151 1.404 1.014 1.181 1.550 1.424

Macedonia 0.642 0.861 1.202 1.151 1.399 1.014 1.180 1.541 1.419

Belize 0.641 0.861 1.202 1.151 1.398 1.014 1.180 1.539 1.418

St.Vincent & Grenadines 0.640 0.861 1.202 1.150 1.397 1.014 1.180 1.538 1.417

Swaziland 0.631 0.859 1.199 1.149 1.387 1.014 1.177 1.518 1.405

Lebanon 0.630 0.859 1.198 1.149 1.386 1.014 1.177 1.516 1.404

Belarus 0.630 0.859 1.198 1.149 1.385 1.014 1.177 1.516 1.404

Kazakhstan 0.629 0.859 1.198 1.149 1.385 1.014 1.177 1.515 1.403

Dominica 0.629 0.859 1.198 1.149 1.384 1.014 1.177 1.514 1.403

Latvia 0.621 0.858 1.196 1.147 1.376 1.014 1.175 1.500 1.394

St. Lucia 0.620 0.858 1.196 1.147 1.375 1.014 1.175 1.498 1.393

Brazil 0.619 0.858 1.195 1.147 1.374 1.014 1.175 1.497 1.392

Bulgaria 0.615 0.857 1.194 1.146 1.370 1.014 1.174 1.490 1.388

Russia 0.610 0.856 1.193 1.146 1.366 1.014 1.173 1.482 1.382

Fiji 0.622 0.858 1.196 1.147 1.377 1.014 1.175 1.501 1.395

Grenada 0.608 0.856 1.193 1.145 1.365 1.014 1.173 1.480 1.381

Turkey 0.607 0.856 1.192 1.145 1.363 1.014 1.172 1.477 1.380

Lithuania 0.603 0.855 1.191 1.145 1.360 1.014 1.171 1.472 1.376

Romania 0.600 0.855 1.191 1.144 1.358 1.014 1.171 1.467 1.374

Mexico 0.589 0.854 1.188 1.143 1.349 1.014 1.169 1.453 1.364

Chile 0.583 0.853 1.187 1.142 1.345 1.014 1.168 1.447 1.360

Antigua & Barbuda 0.583 0.853 1.187 1.142 1.345 1.014 1.168 1.447 1.360

Poland 0.577 0.852 1.186 1.142 1.341 1.014 1.167 1.439 1.355

Trinidad & Tobago 0.571 0.851 1.185 1.141 1.337 1.014 1.166 1.433 1.351

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 57

Table A-4—Expenditure elasticity for aggregate consumption categories, 91 countries in 19961--Continued

Country

Food,

beverages

& tobacco

Clothing 

& footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicationRecreation Other

Middle-income countries--continued:

Estonia 0.567 0.851 1.184 1.141 1.335 1.014 1.166 1.429 1.349

Gabon 0.560 0.850 1.183 1.140 1.331 1.014 1.165 1.422 1.344

Tunisia 0.559 0.850 1.183 1.140 1.330 1.014 1.164 1.422 1.344

St. Kitts & Nevis 0.558 0.850 1.182 1.140 1.329 1.014 1.164 1.421 1.343

Uruguay 0.555 0.850 1.182 1.139 1.328 1.014 1.164 1.419 1.342

Slovakia 0.555 0.849 1.182 1.139 1.328 1.014 1.164 1.418 1.341

Hungary 0.538 0.848 1.179 1.138 1.319 1.013 1.162 1.404 1.332

Argentina 0.515 0.845 1.176 1.136 1.309 1.013 1.159 1.388 1.321

Oman 0.502 0.844 1.174 1.135 1.304 1.013 1.158 1.380 1.315

Qatar 0.486 0.842 1.173 1.134 1.299 1.013 1.156 1.371 1.310

Slovenia 0.481 0.842 1.172 1.133 1.297 1.013 1.156 1.369 1.308

High-income countries:

Czech Republic 0.451 0.839 1.169 1.131 1.288 1.013 1.153 1.355 1.298

Greece 0.439 0.838 1.168 1.131 1.285 1.013 1.152 1.350 1.295

Korea 0.473 0.841 1.171 1.133 1.294 1.013 1.155 1.365 1.305

Portugal 0.437 0.838 1.168 1.131 1.284 1.013 1.152 1.349 1.294

Spain 0.428 0.837 1.167 1.130 1.282 1.013 1.152 1.346 1.292

Ireland 0.418 0.836 1.166 1.130 1.280 1.013 1.151 1.342 1.289

Singapore 0.446 0.839 1.168 1.131 1.286 1.013 1.153 1.353 1.297

Mauritius 0.395 0.835 1.164 1.129 1.274 1.013 1.149 1.335 1.284

Israel 0.392 0.834 1.164 1.129 1.274 1.013 1.149 1.334 1.283

New Zealand 0.387 0.834 1.164 1.128 1.273 1.013 1.149 1.333 1.282

Finland 0.387 0.834 1.164 1.128 1.273 1.013 1.149 1.332 1.282

Sweden 0.354 0.832 1.161 1.127 1.267 1.013 1.147 1.323 1.276

Netherlands 0.349 0.831 1.161 1.127 1.266 1.013 1.147 1.322 1.275

France 0.324 0.830 1.160 1.126 1.262 1.013 1.146 1.317 1.271

United Kingdom 0.322 0.830 1.160 1.126 1.262 1.013 1.145 1.316 1.270

Belgium 0.317 0.829 1.159 1.126 1.261 1.013 1.145 1.315 1.269

Norway 0.316 0.829 1.159 1.126 1.261 1.013 1.145 1.315 1.269

Italy 0.312 0.829 1.159 1.125 1.260 1.013 1.145 1.314 1.269

Austria 0.302 0.828 1.159 1.125 1.259 1.013 1.145 1.312 1.267

Germany 0.300 0.828 1.158 1.125 1.259 1.013 1.145 1.312 1.267

Australia 0.291 0.828 1.158 1.125 1.257 1.013 1.144 1.310 1.266

Japan 0.284 0.827 1.158 1.125 1.257 1.013 1.144 1.309 1.265

Canada 0.275 0.827 1.157 1.124 1.255 1.013 1.143 1.307 1.263

Bermuda 0.252 0.826 1.156 1.124 1.253 1.013 1.143 1.303 1.260

Switzerland 0.248 0.825 1.156 1.123 1.252 1.013 1.142 1.302 1.260

Barbados 0.243 0.825 1.156 1.123 1.252 1.013 1.142 1.301 1.259

Iceland 0.242 0.825 1.156 1.123 1.251 1.013 1.142 1.301 1.259

Denmark 0.237 0.825 1.156 1.123 1.251 1.013 1.142 1.300 1.259

Luxembourg 0.112 0.819 1.151 1.120 1.240 1.013 1.138 1.285 1.247

United States 0.089 0.818 1.151 1.120 1.238 1.013 1.138 1.282 1.245

Low-income average 0.722 0.875 1.246 1.174 1.645 1.014 1.214 2.334 1.704

Middle-income average 0.597 0.855 1.192 1.145 1.363 1.014 1.172 1.478 1.379

High-income average 0.327 0.831 1.161 1.126 1.265 1.013 1.146 1.321 1.274

1

Countries are reported based on ascending per capita real income levels.
58 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table A-5—Frisch own-price elasticity for aggregate consumption categories, 91 countries in 19961

Country

Food,

beverages

& tobacco

Clothing 

& footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicationRecreation Other

Low-income countries:

Zambia -0.650 -0.744 -1.094 -1.008 -1.833 -0.851 -1.054 -5.325 -1.996

Mali -0.645 -0.743 -1.085 -1.004 -1.711 -0.851 -1.048 -3.593 -1.835

Benin -0.635 -0.740 -1.070 -0.997 -1.543 -0.851 -1.037 -2.411 -1.622

Kenya -0.627 -0.738 -1.061 -0.992 -1.465 -0.851 -1.030 -2.069 -1.527

Sierra Leone -0.625 -0.738 -1.058 -0.991 -1.449 -0.851 -1.028 -2.006 -1.506

Nepal -0.630 -0.739 -1.064 -0.994 -1.490 -0.851 -1.032 -2.169 -1.557

Congo -0.615 -0.736 -1.049 -0.987 -1.381 -0.850 -1.021 -1.780 -1.426

Senegal -0.615 -0.736 -1.049 -0.986 -1.379 -0.850 -1.021 -1.774 -1.424

Vietnam -0.618 -0.737 -1.052 -0.988 -1.401 -0.850 -1.023 -1.843 -1.450

Bangladesh -0.615 -0.736 -1.049 -0.987 -1.382 -0.850 -1.021 -1.784 -1.427

Pakistan -0.606 -0.734 -1.042 -0.983 -1.334 -0.850 -1.016 -1.647 -1.371

Uzbekistan -0.596 -0.732 -1.035 -0.979 -1.295 -0.850 -1.010 -1.547 -1.326

Kyrgyzstan -0.596 -0.732 -1.034 -0.979 -1.293 -0.850 -1.010 -1.545 -1.325

Cameroon -0.595 -0.732 -1.034 -0.979 -1.291 -0.850 -1.010 -1.539 -1.322

Moldova -0.593 -0.731 -1.033 -0.978 -1.285 -0.850 -1.009 -1.524 -1.315

Bolivia -0.592 -0.731 -1.032 -0.978 -1.281 -0.850 -1.008 -1.515 -1.310

Jordan -0.575 -0.728 -1.023 -0.973 -1.236 -0.850 -1.001 -1.415 -1.259

Indonesia -0.583 -0.730 -1.027 -0.975 -1.255 -0.850 -1.004 -1.456 -1.281

Jamaica -0.574 -0.728 -1.022 -0.973 -1.232 -0.850 -1.001 -1.407 -1.256

Guinea -0.570 -0.727 -1.020 -0.972 -1.224 -0.850 -0.999 -1.390 -1.246

Syria -0.567 -0.727 -1.019 -0.971 -1.218 -0.850 -0.998 -1.378 -1.240

Middle-income countries:

Ukraine -0.549 -0.724 -1.012 -0.967 -1.188 -0.850 -0.993 -1.318 -1.206

Peru -0.546 -0.723 -1.011 -0.967 -1.184 -0.850 -0.992 -1.313 -1.202

Botswana -0.546 -0.723 -1.011 -0.967 -1.184 -0.850 -0.992 -1.312 -1.202

Thailand -0.558 -0.725 -1.015 -0.969 -1.202 -0.850 -0.995 -1.346 -1.221

Morocco -0.543 -0.723 -1.010 -0.966 -1.180 -0.850 -0.991 -1.305 -1.198

Venezuela -0.541 -0.723 -1.009 -0.966 -1.178 -0.850 -0.991 -1.300 -1.195

Macedonia -0.538 -0.722 -1.008 -0.965 -1.174 -0.850 -0.990 -1.293 -1.191

Belize -0.538 -0.722 -1.008 -0.965 -1.173 -0.850 -0.990 -1.292 -1.190

St.Vincent & Grenadines -0.537 -0.722 -1.008 -0.965 -1.172 -0.850 -0.990 -1.290 -1.189

Swaziland -0.529 -0.721 -1.006 -0.964 -1.163 -0.850 -0.988 -1.274 -1.179

Lebanon -0.528 -0.721 -1.005 -0.964 -1.162 -0.850 -0.987 -1.272 -1.178

Belarus -0.528 -0.721 -1.005 -0.964 -1.162 -0.850 -0.987 -1.272 -1.178

Kazakhstan -0.528 -0.721 -1.005 -0.964 -1.162 -0.850 -0.987 -1.271 -1.177

Dominica -0.528 -0.721 -1.005 -0.964 -1.161 -0.850 -0.987 -1.270 -1.177

Latvia -0.521 -0.720 -1.003 -0.962 -1.155 -0.850 -0.986 -1.258 -1.169

St. Lucia -0.520 -0.719 -1.003 -0.962 -1.154 -0.850 -0.986 -1.256 -1.168

Brazil -0.519 -0.719 -1.003 -0.962 -1.153 -0.850 -0.985 -1.256 -1.168

Bulgaria -0.516 -0.719 -1.002 -0.962 -1.150 -0.850 -0.985 -1.250 -1.164

Russia -0.511 -0.718 -1.001 -0.961 -1.146 -0.850 -0.984 -1.243 -1.160

Fiji -0.522 -0.720 -1.004 -0.963 -1.155 -0.850 -0.986 -1.259 -1.170

Grenada -0.510 -0.718 -1.001 -0.961 -1.145 -0.850 -0.984 -1.241 -1.159

Turkey -0.509 -0.718 -1.000 -0.961 -1.144 -0.850 -0.983 -1.239 -1.158

Lithuania -0.506 -0.718 -1.000 -0.960 -1.141 -0.850 -0.983 -1.235 -1.154

Romania -0.503 -0.717 -0.999 -0.960 -1.139 -0.850 -0.982 -1.231 -1.152

Mexico -0.494 -0.716 -0.997 -0.959 -1.132 -0.850 -0.981 -1.219 -1.145

Chile -0.489 -0.715 -0.996 -0.958 -1.129 -0.850 -0.980 -1.214 -1.141

Antigua & Barbuda -0.489 -0.715 -0.996 -0.958 -1.129 -0.850 -0.980 -1.214 -1.141

Poland -0.484 -0.715 -0.995 -0.958 -1.125 -0.850 -0.979 -1.208 -1.137

Trinidad & Tobago -0.479 -0.714 -0.994 -0.957 -1.122 -0.850 -0.978 -1.202 -1.134

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 59

Table A-5—Frisch own-price elasticity for aggregate consumption categories, 91 countries in 19961--Continued

Country

Food,

beverages

& tobacco

Clothing 

& footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicationRecreation Other

Middle-income countries--continued:

Estonia -0.476 -0.714 -0.993 -0.957 -1.120 -0.850 -0.978 -1.199 -1.132

Gabon -0.470 -0.713 -0.992 -0.956 -1.116 -0.850 -0.977 -1.193 -1.128

Tunisia -0.469 -0.713 -0.992 -0.956 -1.116 -0.850 -0.977 -1.193 -1.127

St. Kitts & Nevis -0.468 -0.713 -0.992 -0.956 -1.115 -0.850 -0.977 -1.192 -1.127

Uruguay -0.466 -0.713 -0.992 -0.956 -1.114 -0.850 -0.976 -1.190 -1.126

Slovakia -0.465 -0.713 -0.992 -0.956 -1.114 -0.850 -0.976 -1.189 -1.125

Hungary -0.451 -0.711 -0.989 -0.954 -1.107 -0.850 -0.975 -1.178 -1.117

Argentina -0.432 -0.709 -0.987 -0.953 -1.098 -0.850 -0.972 -1.164 -1.108

Oman -0.421 -0.708 -0.985 -0.952 -1.094 -0.850 -0.971 -1.158 -1.104

Qatar -0.408 -0.707 -0.984 -0.951 -1.089 -0.850 -0.970 -1.150 -1.099

Slovenia -0.404 -0.706 -0.983 -0.951 -1.088 -0.850 -0.970 -1.148 -1.097

High-income countries:

Czech Republic -0.379 -0.704 -0.981 -0.949 -1.080 -0.850 -0.967 -1.137 -1.089

Greece -0.368 -0.703 -0.980 -0.949 -1.078 -0.850 -0.967 -1.133 -1.086

Korea -0.397 -0.706 -0.983 -0.950 -1.086 -0.850 -0.969 -1.145 -1.095

Portugal -0.367 -0.703 -0.980 -0.949 -1.077 -0.850 -0.967 -1.132 -1.086

Spain -0.359 -0.702 -0.979 -0.948 -1.075 -0.850 -0.966 -1.129 -1.084

Ireland -0.351 -0.702 -0.978 -0.948 -1.073 -0.850 -0.965 -1.126 -1.082

Singapore -0.374 -0.704 -0.980 -0.949 -1.079 -0.850 -0.967 -1.135 -1.088

Mauritius -0.331 -0.700 -0.977 -0.947 -1.069 -0.850 -0.964 -1.120 -1.077

Israel -0.329 -0.700 -0.977 -0.947 -1.069 -0.850 -0.964 -1.119 -1.077

New Zealand -0.325 -0.700 -0.976 -0.947 -1.068 -0.850 -0.964 -1.118 -1.076

Finland -0.324 -0.700 -0.976 -0.947 -1.068 -0.850 -0.964 -1.118 -1.076

Sweden -0.297 -0.698 -0.974 -0.945 -1.063 -0.850 -0.962 -1.110 -1.070

Netherlands -0.293 -0.697 -0.974 -0.945 -1.062 -0.850 -0.962 -1.109 -1.069

France -0.272 -0.696 -0.973 -0.944 -1.059 -0.850 -0.961 -1.105 -1.066

United Kingdom -0.270 -0.696 -0.973 -0.944 -1.058 -0.850 -0.961 -1.104 -1.066

Belgium -0.266 -0.696 -0.973 -0.944 -1.058 -0.850 -0.961 -1.103 -1.065

Norway -0.265 -0.696 -0.973 -0.944 -1.058 -0.850 -0.961 -1.103 -1.065

Italy -0.262 -0.695 -0.972 -0.944 -1.057 -0.850 -0.961 -1.102 -1.064

Austria -0.253 -0.695 -0.972 -0.944 -1.056 -0.850 -0.960 -1.101 -1.063

Germany -0.252 -0.695 -0.972 -0.944 -1.056 -0.850 -0.960 -1.100 -1.063

Australia -0.244 -0.694 -0.971 -0.944 -1.055 -0.850 -0.960 -1.099 -1.062

Japan -0.238 -0.694 -0.971 -0.943 -1.054 -0.850 -0.960 -1.098 -1.061

Canada -0.231 -0.694 -0.971 -0.943 -1.053 -0.850 -0.959 -1.096 -1.060

Bermuda -0.211 -0.693 -0.970 -0.943 -1.051 -0.850 -0.959 -1.093 -1.057

Switzerland -0.208 -0.692 -0.970 -0.943 -1.050 -0.850 -0.958 -1.092 -1.057

Barbados -0.204 -0.692 -0.970 -0.942 -1.050 -0.850 -0.958 -1.092 -1.056

Iceland -0.203 -0.692 -0.970 -0.942 -1.050 -0.850 -0.958 -1.092 -1.056

Denmark -0.199 -0.692 -0.969 -0.942 -1.049 -0.850 -0.958 -1.091 -1.056

Luxembourg -0.094 -0.687 -0.966 -0.940 -1.040 -0.850 -0.955 -1.078 -1.046

United States -0.075 -0.686 -0.965 -0.940 -1.039 -0.850 -0.955 -1.076 -1.045

Low-income average -0.606 -0.734 -1.045 -0.984 -1.380 -0.850 -1.018 -1.958 -1.430

Middle-income average -0.501 -0.717 -1.000 -0.960 -1.143 -0.850 -0.983 -1.240 -1.157

High-income average -0.275 -0.697 -0.974 -0.945 -1.061 -0.850 -0.962 -1.109 -1.069

1

Countries are reported based on ascending per capita real income levels.
60 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table A-6—Slutsky own-price elasticity for aggregate consumption categories, 91 countries in 19961

Country

Food,

beverages

& tobacco

Clothing 

& footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicationRecreation Other

Low-income countries:

Zambia -0.355 -0.687 -0.966 -0.955 -1.743 -0.792 -0.959 -5.189 -1.866

Mali -0.362 -0.686 -0.955 -0.950 -1.622 -0.792 -0.951 -3.492 -1.707

Benin -0.375 -0.686 -0.934 -0.942 -1.453 -0.792 -0.936 -2.331 -1.495

Kenya -0.383 -0.685 -0.922 -0.936 -1.374 -0.792 -0.927 -1.993 -1.398

Sierra Leone -0.385 -0.685 -0.919 -0.935 -1.357 -0.792 -0.925 -1.931 -1.378

Nepal -0.380 -0.685 -0.926 -0.938 -1.399 -0.792 -0.930 -2.093 -1.429

Congo -0.393 -0.684 -0.906 -0.929 -1.287 -0.792 -0.915 -1.708 -1.295

Senegal -0.393 -0.684 -0.905 -0.929 -1.285 -0.792 -0.915 -1.702 -1.293

Vietnam -0.390 -0.684 -0.910 -0.931 -1.308 -0.792 -0.918 -1.770 -1.320

Bangladesh -0.392 -0.684 -0.906 -0.929 -1.288 -0.792 -0.916 -1.711 -1.297

Pakistan -0.398 -0.684 -0.895 -0.924 -1.238 -0.792 -0.908 -1.575 -1.238

Uzbekistan -0.403 -0.683 -0.885 -0.920 -1.197 -0.791 -0.900 -1.474 -1.190

Kyrgyzstan -0.404 -0.683 -0.885 -0.920 -1.195 -0.791 -0.900 -1.472 -1.188

Cameroon -0.404 -0.683 -0.884 -0.919 -1.193 -0.791 -0.899 -1.466 -1.185

Moldova -0.405 -0.683 -0.883 -0.919 -1.186 -0.791 -0.898 -1.451 -1.178

Bolivia -0.405 -0.682 -0.881 -0.918 -1.182 -0.791 -0.897 -1.441 -1.173

Jordan -0.410 -0.681 -0.868 -0.912 -1.133 -0.791 -0.887 -1.340 -1.117

Indonesia -0.408 -0.682 -0.874 -0.914 -1.154 -0.791 -0.892 -1.381 -1.141

Jamaica -0.410 -0.681 -0.866 -0.911 -1.129 -0.791 -0.886 -1.332 -1.113

Guinea -0.411 -0.681 -0.863 -0.910 -1.120 -0.791 -0.884 -1.314 -1.102

Syria -0.411 -0.680 -0.862 -0.909 -1.114 -0.791 -0.882 -1.302 -1.095

Middle-income countries:

Ukraine -0.412 -0.679 -0.850 -0.904 -1.080 -0.791 -0.874 -1.240 -1.056

Peru -0.412 -0.679 -0.849 -0.903 -1.076 -0.791 -0.873 -1.234 -1.052

Botswana -0.412 -0.679 -0.849 -0.903 -1.076 -0.791 -0.873 -1.233 -1.051

Thailand -0.412 -0.680 -0.856 -0.906 -1.096 -0.791 -0.878 -1.269 -1.074

Morocco -0.412 -0.679 -0.847 -0.902 -1.071 -0.791 -0.872 -1.226 -1.046

Venezuela -0.412 -0.679 -0.846 -0.902 -1.069 -0.791 -0.871 -1.221 -1.043

Macedonia -0.411 -0.678 -0.844 -0.901 -1.064 -0.791 -0.870 -1.213 -1.038

Belize -0.411 -0.678 -0.844 -0.901 -1.063 -0.791 -0.869 -1.212 -1.037

St.Vincent & Grenadines -0.411 -0.678 -0.844 -0.901 -1.063 -0.791 -0.869 -1.211 -1.036

Swaziland -0.410 -0.678 -0.840 -0.899 -1.052 -0.791 -0.866 -1.193 -1.024

Lebanon -0.410 -0.678 -0.840 -0.899 -1.051 -0.791 -0.866 -1.191 -1.023

Belarus -0.410 -0.678 -0.839 -0.899 -1.051 -0.791 -0.866 -1.191 -1.023

Kazakhstan -0.410 -0.677 -0.839 -0.899 -1.050 -0.791 -0.866 -1.190 -1.022

Dominica -0.410 -0.677 -0.839 -0.899 -1.050 -0.791 -0.866 -1.190 -1.022

Latvia -0.409 -0.677 -0.836 -0.897 -1.042 -0.791 -0.863 -1.177 -1.013

St. Lucia -0.408 -0.677 -0.836 -0.897 -1.041 -0.791 -0.863 -1.175 -1.012

Brazil -0.408 -0.677 -0.835 -0.897 -1.040 -0.791 -0.863 -1.174 -1.011

Bulgaria -0.407 -0.677 -0.834 -0.896 -1.036 -0.791 -0.862 -1.168 -1.006

Russia -0.406 -0.676 -0.832 -0.895 -1.032 -0.791 -0.860 -1.160 -1.001

Fiji -0.409 -0.677 -0.836 -0.897 -1.043 -0.791 -0.864 -1.178 -1.014

Grenada -0.406 -0.676 -0.832 -0.895 -1.031 -0.791 -0.860 -1.159 -1.000

Turkey -0.406 -0.676 -0.831 -0.895 -1.030 -0.791 -0.860 -1.157 -0.999

Lithuania -0.405 -0.676 -0.830 -0.894 -1.026 -0.791 -0.859 -1.151 -0.995

Romania -0.404 -0.676 -0.829 -0.894 -1.024 -0.791 -0.858 -1.148 -0.992

Mexico -0.401 -0.675 -0.825 -0.892 -1.015 -0.790 -0.855 -1.135 -0.983

Chile -0.399 -0.675 -0.824 -0.891 -1.012 -0.790 -0.854 -1.129 -0.978

Antigua & Barbuda -0.399 -0.675 -0.823 -0.891 -1.011 -0.790 -0.854 -1.129 -0.978

Poland -0.397 -0.674 -0.822 -0.890 -1.007 -0.790 -0.852 -1.122 -0.973

Trinidad & Tobago -0.395 -0.674 -0.820 -0.889 -1.003 -0.790 -0.851 -1.116 -0.969

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 61

Table A-6—Slutsky own-price elasticity for aggregate consumption categories, 91 countries in 19961--Continued

Country

Food,

beverages

& tobacco

Clothing 

& footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicationRecreation Other

Middle-income countries--continued:

Estonia -0.394 -0.674 -0.819 -0.889 -1.001 -0.790 -0.850 -1.113 -0.966

Gabon -0.391 -0.673 -0.817 -0.888 -0.997 -0.790 -0.849 -1.107 -0.961

Tunisia -0.391 -0.673 -0.817 -0.888 -0.996 -0.790 -0.849 -1.106 -0.961

St. Kitts & Nevis -0.390 -0.673 -0.816 -0.888 -0.995 -0.790 -0.849 -1.105 -0.960

Uruguay -0.389 -0.673 -0.816 -0.888 -0.994 -0.790 -0.848 -1.103 -0.958

Slovakia -0.389 -0.673 -0.816 -0.887 -0.994 -0.790 -0.848 -1.102 -0.958

Hungary -0.382 -0.672 -0.812 -0.886 -0.985 -0.790 -0.845 -1.089 -0.948

Argentina -0.371 -0.671 -0.807 -0.883 -0.974 -0.790 -0.841 -1.074 -0.936

Oman -0.365 -0.670 -0.804 -0.882 -0.969 -0.790 -0.839 -1.067 -0.930

Qatar -0.357 -0.670 -0.801 -0.881 -0.963 -0.790 -0.837 -1.059 -0.923

Slovenia -0.354 -0.669 -0.800 -0.880 -0.961 -0.790 -0.836 -1.056 -0.921

High-income countries:

Czech Republic -0.338 -0.668 -0.795 -0.878 -0.952 -0.790 -0.833 -1.043 -0.910

Greece -0.330 -0.667 -0.794 -0.877 -0.948 -0.790 -0.831 -1.038 -0.906

Korea -0.350 -0.669 -0.799 -0.880 -0.959 -0.790 -0.835 -1.053 -0.918

Portugal -0.329 -0.667 -0.793 -0.877 -0.948 -0.790 -0.831 -1.038 -0.905

Spain -0.324 -0.667 -0.792 -0.876 -0.945 -0.790 -0.830 -1.034 -0.902

Ireland -0.318 -0.666 -0.791 -0.876 -0.943 -0.790 -0.829 -1.031 -0.899

Singapore -0.335 -0.668 -0.795 -0.878 -0.950 -0.790 -0.832 -1.041 -0.908

Mauritius -0.303 -0.665 -0.788 -0.874 -0.937 -0.790 -0.827 -1.023 -0.893

Israel -0.301 -0.665 -0.787 -0.874 -0.936 -0.790 -0.827 -1.023 -0.892

New Zealand -0.298 -0.665 -0.787 -0.874 -0.935 -0.790 -0.826 -1.021 -0.891

Finland -0.297 -0.665 -0.787 -0.874 -0.935 -0.790 -0.826 -1.021 -0.891

Sweden -0.275 -0.664 -0.783 -0.872 -0.928 -0.790 -0.823 -1.012 -0.883

Netherlands -0.272 -0.664 -0.782 -0.872 -0.927 -0.790 -0.823 -1.011 -0.882

France -0.255 -0.663 -0.780 -0.871 -0.923 -0.790 -0.821 -1.005 -0.877

United Kingdom -0.253 -0.663 -0.780 -0.871 -0.923 -0.790 -0.821 -1.005 -0.876

Belgium -0.249 -0.663 -0.779 -0.870 -0.922 -0.790 -0.821 -1.004 -0.875

Norway -0.249 -0.662 -0.779 -0.870 -0.922 -0.790 -0.820 -1.004 -0.875

Italy -0.246 -0.662 -0.779 -0.870 -0.921 -0.790 -0.820 -1.003 -0.874

Austria -0.239 -0.662 -0.778 -0.870 -0.919 -0.790 -0.820 -1.001 -0.872

Germany -0.238 -0.662 -0.778 -0.870 -0.919 -0.790 -0.819 -1.000 -0.872

Australia -0.231 -0.662 -0.777 -0.869 -0.918 -0.789 -0.819 -0.998 -0.870

Japan -0.226 -0.661 -0.776 -0.869 -0.917 -0.789 -0.818 -0.997 -0.869

Canada -0.219 -0.661 -0.776 -0.869 -0.915 -0.789 -0.818 -0.996 -0.868

Bermuda -0.202 -0.660 -0.774 -0.868 -0.912 -0.789 -0.816 -0.992 -0.864

Switzerland -0.199 -0.660 -0.773 -0.868 -0.912 -0.789 -0.816 -0.991 -0.863

Barbados -0.195 -0.660 -0.773 -0.868 -0.911 -0.789 -0.816 -0.990 -0.863

Iceland -0.194 -0.660 -0.773 -0.867 -0.911 -0.789 -0.816 -0.990 -0.862

Denmark -0.191 -0.660 -0.773 -0.867 -0.910 -0.789 -0.816 -0.989 -0.862

Luxembourg -0.093 -0.656 -0.765 -0.864 -0.897 -0.789 -0.810 -0.973 -0.846

United States -0.074 -0.656 -0.764 -0.863 -0.895 -0.789 -0.809 -0.970 -0.844

Low-income average -0.394 -0.683 -0.900 -0.926 -1.284 -0.792 -0.911 -1.879 -1.295

Middle-income average -0.400 -0.676 -0.830 -0.894 -1.028 -0.791 -0.859 -1.157 -0.997

High-income average -0.254 -0.663 -0.782 -0.871 -0.926 -0.790 -0.822 -1.010 -0.880

1

Countries are reported based on ascending per capita real income levels.
62 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table A-7—Cournot own-price elasticity for aggregate consumption categories, 91 countries in 19961

Country

Food,

beverages

& tobacco

Clothing 

& footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicationRecreation Other

Low-income countries:

Zambia -0.809 -0.764 -1.083 -1.007 -1.792 -0.861 -1.049 -5.214 -1.931

Mali -0.801 -0.762 -1.075 -1.003 -1.674 -0.861 -1.043 -3.520 -1.777

Benin -0.784 -0.759 -1.061 -0.997 -1.512 -0.861 -1.033 -2.364 -1.573

Kenya -0.772 -0.757 -1.053 -0.993 -1.436 -0.861 -1.027 -2.030 -1.482

Sierra Leone -0.769 -0.757 -1.051 -0.992 -1.420 -0.861 -1.025 -1.968 -1.463

Nepal -0.776 -0.758 -1.055 -0.994 -1.460 -0.861 -1.029 -2.128 -1.511

Congo -0.754 -0.754 -1.042 -0.987 -1.355 -0.861 -1.019 -1.748 -1.387

Senegal -0.754 -0.754 -1.042 -0.987 -1.353 -0.861 -1.019 -1.743 -1.385

Vietnam -0.759 -0.755 -1.045 -0.989 -1.374 -0.861 -1.021 -1.809 -1.409

Bangladesh -0.755 -0.754 -1.042 -0.987 -1.356 -0.861 -1.019 -1.752 -1.388

Pakistan -0.741 -0.752 -1.036 -0.984 -1.310 -0.861 -1.014 -1.619 -1.335

Uzbekistan -0.727 -0.750 -1.030 -0.981 -1.272 -0.861 -1.009 -1.522 -1.293

Kyrgyzstan -0.726 -0.750 -1.029 -0.981 -1.271 -0.861 -1.009 -1.519 -1.291

Cameroon -0.725 -0.750 -1.029 -0.980 -1.269 -0.861 -1.009 -1.514 -1.289

Moldova -0.723 -0.749 -1.028 -0.980 -1.263 -0.861 -1.008 -1.499 -1.282

Bolivia -0.721 -0.749 -1.027 -0.979 -1.259 -0.861 -1.007 -1.490 -1.278

Jordan -0.697 -0.746 -1.019 -0.975 -1.216 -0.861 -1.001 -1.393 -1.230

Indonesia -0.708 -0.747 -1.023 -0.977 -1.234 -0.861 -1.004 -1.432 -1.250

Jamaica -0.695 -0.745 -1.019 -0.975 -1.213 -0.861 -1.001 -1.386 -1.226

Guinea -0.690 -0.745 -1.017 -0.974 -1.205 -0.861 -0.999 -1.368 -1.217

Syria -0.686 -0.744 -1.016 -0.973 -1.199 -0.861 -0.998 -1.357 -1.212

Middle-income countries:

Ukraine -0.661 -0.741 -1.010 -0.969 -1.170 -0.861 -0.993 -1.300 -1.180

Peru -0.658 -0.740 -1.009 -0.969 -1.168 -0.861 -0.993 -1.294 -1.177

Botswana -0.657 -0.740 -1.009 -0.969 -1.167 -0.861 -0.993 -1.293 -1.176

Thailand -0.673 -0.742 -1.013 -0.971 -1.184 -0.861 -0.996 -1.326 -1.195

Morocco -0.654 -0.740 -1.008 -0.968 -1.164 -0.861 -0.992 -1.286 -1.173

Venezuela -0.651 -0.740 -1.008 -0.968 -1.161 -0.861 -0.992 -1.282 -1.170

Macedonia -0.647 -0.739 -1.007 -0.968 -1.158 -0.861 -0.991 -1.275 -1.166

Belize -0.646 -0.739 -1.007 -0.968 -1.157 -0.861 -0.991 -1.274 -1.165

St.Vincent & Grenadines -0.646 -0.739 -1.007 -0.967 -1.156 -0.861 -0.991 -1.272 -1.165

Swaziland -0.635 -0.738 -1.005 -0.966 -1.148 -0.861 -0.989 -1.257 -1.156

Lebanon -0.634 -0.737 -1.005 -0.966 -1.147 -0.861 -0.989 -1.255 -1.155

Belarus -0.634 -0.737 -1.004 -0.966 -1.147 -0.861 -0.989 -1.255 -1.154

Kazakhstan -0.633 -0.737 -1.004 -0.966 -1.146 -0.861 -0.989 -1.254 -1.154

Dominica -0.633 -0.737 -1.004 -0.966 -1.146 -0.861 -0.989 -1.253 -1.154

Latvia -0.624 -0.736 -1.003 -0.965 -1.140 -0.861 -0.988 -1.242 -1.147

St. Lucia -0.623 -0.736 -1.003 -0.965 -1.139 -0.861 -0.987 -1.240 -1.146

Brazil -0.622 -0.736 -1.002 -0.965 -1.138 -0.861 -0.987 -1.239 -1.145

Bulgaria -0.618 -0.735 -1.002 -0.964 -1.135 -0.861 -0.987 -1.233 -1.142

Russia -0.612 -0.735 -1.001 -0.964 -1.131 -0.861 -0.986 -1.227 -1.138

Fiji -0.625 -0.736 -1.003 -0.965 -1.140 -0.861 -0.988 -1.243 -1.147

Grenada -0.611 -0.735 -1.001 -0.964 -1.130 -0.861 -0.986 -1.225 -1.137

Turkey -0.609 -0.734 -1.000 -0.963 -1.129 -0.861 -0.986 -1.223 -1.136

Lithuania -0.605 -0.734 -1.000 -0.963 -1.127 -0.861 -0.985 -1.219 -1.133

Romania -0.601 -0.734 -0.999 -0.963 -1.125 -0.861 -0.985 -1.215 -1.131

Mexico -0.590 -0.732 -0.997 -0.962 -1.118 -0.861 -0.983 -1.204 -1.124

Chile -0.584 -0.732 -0.997 -0.961 -1.115 -0.861 -0.983 -1.199 -1.121

Antigua & Barbuda -0.583 -0.732 -0.997 -0.961 -1.115 -0.861 -0.982 -1.199 -1.121

Poland -0.577 -0.731 -0.996 -0.961 -1.112 -0.861 -0.982 -1.193 -1.117

Trinidad & Tobago -0.570 -0.730 -0.995 -0.960 -1.109 -0.861 -0.981 -1.188 -1.114

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 63

Table A-7—Cournot own-price elasticity for aggregate consumption categories, 91 countries in 19961--Continued

Country

Food,

beverages

& tobacco

Clothing 

& footwear

Gross rent, 

fuel & 

power

House 

operationsMedical 

care

Education

Transport & 

communicationRecreation Other

Middle-income countries--continued:

Estonia -0.566 -0.730 -0.995 -0.960 -1.107 -0.861 -0.981 -1.185 -1.112

Gabon -0.558 -0.729 -0.994 -0.959 -1.104 -0.861 -0.980 -1.179 -1.109

Tunisia -0.558 -0.729 -0.994 -0.959 -1.104 -0.861 -0.980 -1.179 -1.109

St. Kitts & Nevis -0.556 -0.729 -0.993 -0.959 -1.103 -0.861 -0.980 -1.178 -1.108

Uruguay -0.554 -0.729 -0.993 -0.959 -1.102 -0.861 -0.980 -1.176 -1.107

Slovakia -0.553 -0.729 -0.993 -0.959 -1.102 -0.861 -0.979 -1.176 -1.107

Hungary -0.535 -0.727 -0.991 -0.958 -1.095 -0.861 -0.978 -1.164 -1.099

Argentina -0.511 -0.725 -0.989 -0.956 -1.087 -0.861 -0.976 -1.152 -1.091

Oman -0.498 -0.723 -0.988 -0.955 -1.083 -0.861 -0.975 -1.145 -1.087

Qatar -0.482 -0.722 -0.987 -0.955 -1.079 -0.861 -0.974 -1.138 -1.083

Slovenia -0.477 -0.722 -0.986 -0.954 -1.078 -0.861 -0.974 -1.137 -1.082

High-income countries:

Czech Republic -0.446 -0.719 -0.984 -0.953 -1.071 -0.861 -0.972 -1.126 -1.074

Greece -0.434 -0.718 -0.984 -0.953 -1.068 -0.861 -0.971 -1.122 -1.072

Korea -0.468 -0.721 -0.986 -0.954 -1.076 -0.861 -0.973 -1.133 -1.079

Portugal -0.431 -0.718 -0.983 -0.952 -1.068 -0.861 -0.971 -1.121 -1.071

Spain -0.422 -0.717 -0.983 -0.952 -1.066 -0.861 -0.971 -1.118 -1.070

Ireland -0.412 -0.717 -0.982 -0.952 -1.064 -0.861 -0.970 -1.116 -1.068

Singapore -0.441 -0.719 -0.984 -0.953 -1.070 -0.861 -0.972 -1.124 -1.073

Mauritius -0.389 -0.715 -0.981 -0.951 -1.061 -0.861 -0.969 -1.110 -1.064

Israel -0.386 -0.715 -0.981 -0.951 -1.060 -0.861 -0.969 -1.109 -1.063

New Zealand -0.381 -0.715 -0.981 -0.951 -1.059 -0.861 -0.969 -1.108 -1.063

Finland -0.381 -0.715 -0.981 -0.951 -1.059 -0.861 -0.969 -1.108 -1.063

Sweden -0.347 -0.712 -0.979 -0.950 -1.055 -0.861 -0.968 -1.101 -1.058

Netherlands -0.343 -0.712 -0.979 -0.949 -1.054 -0.861 -0.968 -1.100 -1.057

France -0.318 -0.711 -0.978 -0.949 -1.051 -0.861 -0.967 -1.095 -1.054

United Kingdom -0.316 -0.711 -0.978 -0.949 -1.051 -0.861 -0.967 -1.095 -1.054

Belgium -0.310 -0.710 -0.978 -0.949 -1.050 -0.861 -0.966 -1.094 -1.053

Norway -0.309 -0.710 -0.978 -0.949 -1.050 -0.861 -0.966 -1.094 -1.053

Italy -0.306 -0.710 -0.978 -0.949 -1.050 -0.861 -0.966 -1.093 -1.053

Austria -0.296 -0.709 -0.978 -0.948 -1.049 -0.861 -0.966 -1.091 -1.052

Germany -0.294 -0.709 -0.977 -0.948 -1.049 -0.861 -0.966 -1.091 -1.052

Australia -0.285 -0.709 -0.977 -0.948 -1.048 -0.861 -0.966 -1.090 -1.051

Japan -0.278 -0.708 -0.977 -0.948 -1.047 -0.861 -0.966 -1.089 -1.050

Canada -0.269 -0.708 -0.977 -0.948 -1.046 -0.861 -0.965 -1.087 -1.049

Bermuda -0.246 -0.707 -0.976 -0.947 -1.044 -0.861 -0.965 -1.084 -1.047

Switzerland -0.243 -0.707 -0.976 -0.947 -1.044 -0.861 -0.965 -1.084 -1.047

Barbados -0.237 -0.706 -0.976 -0.947 -1.043 -0.861 -0.964 -1.083 -1.046

Iceland -0.236 -0.706 -0.976 -0.947 -1.043 -0.861 -0.964 -1.083 -1.046

Denmark -0.232 -0.706 -0.976 -0.947 -1.043 -0.861 -0.964 -1.083 -1.046

Luxembourg -0.109 -0.701 -0.973 -0.945 -1.035 -0.861 -0.962 -1.070 -1.037

United States -0.087 -0.700 -0.972 -0.945 -1.033 -0.861 -0.962 -1.068 -1.036

Low-income average -0.742 -0.753 -1.039 -0.985 -1.354 -0.861 -1.016 -1.923 -1.391

Middle-income average -0.599 -0.734 -1.000 -0.963 -1.129 -0.861 -0.985 -1.224 -1.136

High-income average -0.322 -0.711 -0.979 -0.949 -1.054 -0.861 -0.967 -1.099 -1.057

1

Countries are reported based on ascending per capita real income levels.
64 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table A-8—Unconditional expenditure elasticity for food sub-categories, 91 countries in 19961 

Country

Beverages & 

tobacco Cereal Meat Fish Dairy Oils & Fats

Fruits & 

vegetables Other food

Low-income countries:

Zambia 1.483 0.618 0.835 0.927 0.874 0.581 0.674 0.832

Mali 1.610 0.616 0.825 0.920 0.865 0.582 0.668 0.822

Benin 1.317 0.593 0.813 0.896 0.848 0.552 0.653 0.810

Kenya 1.097 0.550 0.800 0.868 0.830 0.488 0.631 0.797

Sierra Leone 1.411 0.586 0.793 0.879 0.829 0.551 0.640 0.790

Nepal 1.568 0.600 0.804 0.896 0.842 0.567 0.651 0.801

Congo 1.169 0.558 0.780 0.854 0.812 0.513 0.623 0.777

Senegal 1.087 0.542 0.779 0.847 0.809 0.485 0.616 0.776

Vietnam 1.425 0.587 0.793 0.880 0.829 0.552 0.640 0.790

Bangladesh 1.124 0.550 0.779 0.850 0.810 0.499 0.620 0.777

Pakistan 1.071 0.534 0.768 0.835 0.798 0.478 0.608 0.766

Uzbekistan 1.157 0.546 0.759 0.832 0.791 0.503 0.607 0.756

Kyrgyzstan 1.132 0.542 0.758 0.831 0.790 0.497 0.605 0.756

Cameroon 1.172 0.548 0.759 0.834 0.791 0.506 0.608 0.756

Moldova 1.207 0.552 0.759 0.836 0.792 0.512 0.609 0.757

Bolivia 1.030 0.518 0.757 0.820 0.785 0.456 0.596 0.754

Jordan 0.910 0.442 0.733 0.784 0.757 0.323 0.560 0.731

Indonesia 1.007 0.506 0.736 0.798 0.763 0.447 0.580 0.733

Jamaica 0.992 0.498 0.725 0.786 0.752 0.441 0.572 0.723

Guinea 1.067 0.517 0.726 0.794 0.756 0.472 0.579 0.724

Syria 1.023 0.507 0.725 0.790 0.754 0.456 0.575 0.723

Middle-income countries:

Ukraine 0.931 0.470 0.693 0.750 0.718 0.410 0.544 0.690

Peru 0.911 0.460 0.692 0.747 0.717 0.393 0.541 0.690

Botswana 0.971 0.483 0.693 0.754 0.720 0.433 0.549 0.691

Thailand 0.968 0.485 0.704 0.764 0.730 0.431 0.556 0.701

Morocco 0.902 0.455 0.692 0.746 0.716 0.384 0.540 0.690

Venezuela 0.966 0.482 0.693 0.754 0.720 0.431 0.549 0.691

Macedonia 0.897 0.453 0.681 0.735 0.706 0.387 0.533 0.679

Belize 0.889 0.449 0.681 0.734 0.705 0.379 0.531 0.679

St.Vincent, Grenadines 0.881 0.444 0.681 0.733 0.705 0.369 0.530 0.679

Swaziland 0.852 0.425 0.670 0.719 0.693 0.341 0.518 0.668

Lebanon 0.884 0.447 0.671 0.724 0.695 0.382 0.525 0.669

Belarus 0.873 0.440 0.670 0.723 0.694 0.370 0.523 0.669

Kazakhstan 1.002 0.481 0.673 0.737 0.701 0.441 0.537 0.671

Dominica 0.853 0.426 0.670 0.720 0.693 0.343 0.518 0.668

Latvia 0.823 0.404 0.659 0.705 0.680 0.306 0.506 0.657

St. Lucia 0.870 0.439 0.660 0.713 0.684 0.376 0.516 0.658

Brazil 0.866 0.437 0.660 0.712 0.684 0.372 0.516 0.658

Bulgaria 0.859 0.434 0.650 0.702 0.673 0.372 0.509 0.648

Russia 0.819 0.406 0.648 0.696 0.670 0.319 0.500 0.647

Fiji 0.868 0.439 0.660 0.712 0.684 0.374 0.516 0.658

Grenada 0.824 0.411 0.649 0.696 0.670 0.328 0.501 0.647

Turkey 0.824 0.411 0.649 0.696 0.670 0.328 0.501 0.647

Lithuania 0.819 0.411 0.638 0.686 0.660 0.337 0.495 0.636

Romania 0.807 0.401 0.638 0.684 0.659 0.317 0.492 0.636

Mexico 0.801 0.401 0.627 0.674 0.649 0.325 0.486 0.626

Chile 0.759 0.364 0.616 0.658 0.635 0.256 0.469 0.614

Antigua & Barbuda 0.811 0.410 0.617 0.666 0.640 0.349 0.483 0.616

Poland 0.795 0.400 0.617 0.664 0.638 0.330 0.479 0.615

Trinidad & Tobago 0.767 0.381 0.606 0.650 0.626 0.302 0.468 0.604

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 65

Table A-8—Unconditional expenditure elasticity for food sub-categories, 91 countries in 19961--Continued

Country

Beverages & 

tobacco Cereal Meat Fish Dairy Oils & Fats

Fruits & 

vegetables Other food

Middle-income countries--continued:

Estonia 0.773 0.387 0.606 0.651 0.627 0.312 0.469 0.604

Gabon 0.729 0.344 0.594 0.634 0.613 0.230 0.451 0.593

Tunisia 0.771 0.388 0.596 0.642 0.617 0.323 0.464 0.594

St. Kitts & Nevis 0.756 0.377 0.595 0.639 0.616 0.301 0.460 0.594

Uruguay 0.804 0.405 0.597 0.646 0.619 0.355 0.469 0.595

Slovakia 0.746 0.373 0.585 0.628 0.605 0.301 0.453 0.583

Hungary 0.740 0.372 0.574 0.618 0.594 0.308 0.446 0.573

Argentina 0.659 0.305 0.541 0.577 0.558 0.189 0.409 0.540

Oman 0.665 0.327 0.531 0.569 0.549 0.248 0.408 0.530

Qatar 0.642 0.308 0.520 0.556 0.537 0.218 0.397 0.519

Slovenia 0.639 0.314 0.510 0.546 0.527 0.239 0.392 0.509

High-income countries:

Czech Republic 0.586 0.278 0.478 0.510 0.493 0.188 0.363 0.476

Greece 0.570 0.266 0.467 0.498 0.481 0.170 0.353 0.466

Korea 0.626 0.308 0.499 0.535 0.516 0.235 0.384 0.498

Portugal 0.575 0.275 0.467 0.499 0.482 0.192 0.356 0.466

Spain 0.571 0.280 0.457 0.489 0.472 0.212 0.351 0.456

Ireland 0.547 0.259 0.446 0.476 0.460 0.175 0.339 0.445

Singapore 0.573 0.252 0.477 0.507 0.491 0.123 0.357 0.476

Mauritius 0.532 0.267 0.415 0.446 0.429 0.219 0.322 0.414

Israel 0.511 0.245 0.414 0.442 0.427 0.172 0.316 0.413

New Zealand 0.515 0.250 0.414 0.443 0.428 0.185 0.317 0.413

Finland 0.515 0.251 0.414 0.443 0.428 0.185 0.317 0.413

Sweden 0.461 0.224 0.372 0.398 0.384 0.163 0.284 0.371

Netherlands 0.457 0.218 0.372 0.397 0.383 0.150 0.283 0.371

France 0.413 0.191 0.340 0.362 0.350 0.119 0.256 0.339

United Kingdom 0.417 0.197 0.340 0.363 0.350 0.133 0.258 0.339

Belgium 0.416 0.196 0.340 0.362 0.350 0.129 0.258 0.339

Norway 0.419 0.200 0.340 0.363 0.351 0.140 0.259 0.339

Italy 0.403 0.189 0.329 0.351 0.339 0.125 0.249 0.328

Austria 0.389 0.182 0.318 0.340 0.328 0.118 0.241 0.318

Germany 0.389 0.182 0.318 0.340 0.328 0.118 0.241 0.318

Australia 0.374 0.173 0.308 0.328 0.317 0.107 0.232 0.307

Japan 0.369 0.179 0.297 0.318 0.307 0.130 0.227 0.297

Canada 0.356 0.172 0.287 0.307 0.296 0.126 0.219 0.286

Bermuda 0.317 0.136 0.265 0.282 0.273 0.059 0.197 0.264

Switzerland 0.320 0.143 0.265 0.282 0.273 0.077 0.199 0.264

Barbados 0.349 0.175 0.256 0.277 0.265 0.155 0.202 0.255

Iceland 0.309 0.142 0.255 0.271 0.262 0.086 0.192 0.254

Denmark 0.312 0.147 0.255 0.272 0.263 0.097 0.193 0.254

Luxembourg 0.138 0.056 0.117 0.124 0.120 0.013 0.086 0.116

United States 0.116 0.054 0.095 0.102 0.098 0.034 0.072 0.095

Low-income average 1.193 0.548 0.772 0.846 0.804 0.498 0.615 0.769

Middle-income average 0.825 0.411 0.635 0.684 0.657 0.338 0.494 0.633

High-income average 0.428 0.203 0.347 0.371 0.358 0.138 0.264 0.346

1

Countries are reported based on ascending per capita real income levels.
66 ✺ International Evidence on Food Consumption Patterns / TB-1904 Economic Research Service/USDA

Table A-9—Unconditional Frisch own-price elasticity for food sub-categories, 91 countries in 19961

Country

Beverages & 

tobacco Cereal Meat Fish Dairy Oils & Fats

Fruits & 

vegetables Other food

Low-income countries:

Zambia -1.236 -0.515 -0.696 -0.772 -0.728 -0.484 -0.562 -0.694

Mali -1.359 -0.520 -0.697 -0.777 -0.730 -0.492 -0.564 -0.694

Benin -1.092 -0.492 -0.674 -0.743 -0.703 -0.458 -0.541 -0.672

Kenya -0.921 -0.462 -0.672 -0.729 -0.697 -0.410 -0.530 -0.670

Sierra Leone -1.182 -0.491 -0.664 -0.737 -0.694 -0.462 -0.536 -0.662

Nepal -1.317 -0.504 -0.675 -0.753 -0.707 -0.477 -0.547 -0.673

Congo -0.993 -0.474 -0.662 -0.726 -0.690 -0.435 -0.529 -0.660

Senegal -0.909 -0.453 -0.651 -0.708 -0.676 -0.405 -0.515 -0.649

Vietnam -1.194 -0.492 -0.664 -0.737 -0.695 -0.463 -0.536 -0.662

Bangladesh -0.954 -0.467 -0.662 -0.722 -0.688 -0.424 -0.526 -0.660

Pakistan -0.907 -0.452 -0.651 -0.707 -0.676 -0.405 -0.515 -0.649

Uzbekistan -0.978 -0.461 -0.641 -0.704 -0.668 -0.425 -0.513 -0.639

Kyrgyzstan -0.957 -0.458 -0.641 -0.702 -0.667 -0.420 -0.512 -0.639

Cameroon -0.990 -0.463 -0.641 -0.704 -0.668 -0.428 -0.513 -0.639

Moldova -1.003 -0.459 -0.631 -0.695 -0.658 -0.426 -0.506 -0.629

Bolivia -0.856 -0.430 -0.629 -0.682 -0.652 -0.379 -0.496 -0.627

Jordan -0.765 -0.371 -0.616 -0.659 -0.636 -0.272 -0.471 -0.614

Indonesia -0.847 -0.425 -0.618 -0.671 -0.642 -0.376 -0.488 -0.616

Jamaica -0.831 -0.417 -0.608 -0.659 -0.631 -0.369 -0.479 -0.606

Guinea -0.894 -0.433 -0.609 -0.666 -0.633 -0.395 -0.485 -0.607

Syria -0.858 -0.425 -0.608 -0.662 -0.632 -0.383 -0.482 -0.606

Middle-income countries:

Ukraine -0.788 -0.397 -0.586 -0.634 -0.608 -0.347 -0.461 -0.584

Peru -0.771 -0.390 -0.586 -0.632 -0.607 -0.333 -0.458 -0.584

Botswana -0.822 -0.409 -0.587 -0.638 -0.609 -0.367 -0.465 -0.585

Thailand -0.821 -0.412 -0.597 -0.648 -0.620 -0.365 -0.471 -0.595

Morocco -0.750 -0.378 -0.575 -0.620 -0.595 -0.319 -0.448 -0.573

Venezuela -0.803 -0.400 -0.576 -0.626 -0.598 -0.358 -0.456 -0.574

Macedonia -0.757 -0.382 -0.575 -0.621 -0.596 -0.326 -0.450 -0.573

Belize -0.750 -0.379 -0.575 -0.620 -0.595 -0.320 -0.448 -0.573

St.Vincent, Grenadines -0.743 -0.374 -0.575 -0.619 -0.595 -0.311 -0.447 -0.573

Swaziland -0.716 -0.358 -0.564 -0.605 -0.583 -0.287 -0.436 -0.562

Lebanon -0.744 -0.376 -0.564 -0.609 -0.585 -0.322 -0.441 -0.563

Belarus -0.734 -0.370 -0.564 -0.608 -0.584 -0.311 -0.440 -0.562

Kazakhstan -0.843 -0.405 -0.566 -0.620 -0.589 -0.371 -0.452 -0.564

Dominica -0.718 -0.359 -0.564 -0.605 -0.583 -0.289 -0.436 -0.562

Latvia -0.691 -0.339 -0.552 -0.592 -0.571 -0.256 -0.424 -0.551

St. Lucia -0.729 -0.369 -0.554 -0.598 -0.574 -0.315 -0.433 -0.552

Brazil -0.726 -0.367 -0.554 -0.597 -0.573 -0.312 -0.433 -0.552

Bulgaria -0.732 -0.370 -0.554 -0.598 -0.574 -0.317 -0.434 -0.552

Russia -0.684 -0.340 -0.542 -0.582 -0.560 -0.267 -0.418 -0.541

Fiji -0.728 -0.368 -0.554 -0.598 -0.573 -0.314 -0.433 -0.552

Grenada -0.689 -0.343 -0.542 -0.582 -0.561 -0.274 -0.419 -0.541

Turkey -0.689 -0.344 -0.542 -0.582 -0.561 -0.275 -0.419 -0.541

Lithuania -0.696 -0.349 -0.542 -0.583 -0.561 -0.286 -0.421 -0.541

Romania -0.672 -0.334 -0.531 -0.570 -0.549 -0.264 -0.410 -0.530

Mexico -0.665 -0.333 -0.521 -0.560 -0.539 -0.270 -0.404 -0.520

Chile -0.642 -0.307 -0.520 -0.556 -0.537 -0.216 -0.396 -0.519

Antigua & Barbuda -0.685 -0.346 -0.522 -0.563 -0.540 -0.295 -0.408 -0.520

Poland -0.658 -0.331 -0.511 -0.549 -0.528 -0.273 -0.397 -0.509

Trinidad & Tobago -0.646 -0.321 -0.510 -0.548 -0.527 -0.254 -0.394 -0.509

See notes at end of table. Continued--
Economic Research Service/USDA International Evidence on Food Consumption Patterns / TB-1904 ✺ 67

Table A-9—Unconditional Frisch own-price elasticity for food sub-categories, 91 countries in 19961--Continued

Country

Beverages & 

tobacco Cereal Meat Fish Dairy Oils & Fats

Fruits & 

vegetables Other food

Middle-income countries--continued:

Estonia -0.651 -0.326 -0.510 -0.548 -0.528 -0.263 -0.395 -0.509

Gabon -0.612 -0.289 -0.499 -0.532 -0.515 -0.193 -0.379 -0.498

Tunisia -0.647 -0.326 -0.500 -0.538 -0.518 -0.271 -0.389 -0.499

St. Kitts & Nevis -0.634 -0.316 -0.500 -0.536 -0.517 -0.252 -0.386 -0.498

Uruguay -0.675 -0.340 -0.501 -0.542 -0.519 -0.298 -0.394 -0.499

Slovakia -0.637 -0.319 -0.500 -0.537 -0.517 -0.257 -0.387 -0.498

Hungary -0.617 -0.310 -0.479 -0.515 -0.495 -0.256 -0.372 -0.477

Argentina -0.555 -0.257 -0.456 -0.486 -0.470 -0.159 -0.345 -0.455

Oman -0.558 -0.274 -0.446 -0.478 -0.461 -0.208 -0.343 -0.445

Qatar -0.537 -0.258 -0.435 -0.465 -0.449 -0.182 -0.332 -0.434

Slovenia -0.532 -0.262 -0.425 -0.455 -0.439 -0.199 -0.326 -0.424

High-income countries:

Czech Republic -0.495 -0.235 -0.403 -0.431 -0.416 -0.158 -0.306 -0.402

Greece -0.479 -0.224 -0.393 -0.419 -0.405 -0.143 -0.297 -0.392

Korea -0.533 -0.262 -0.425 -0.455 -0.439 -0.200 -0.327 -0.424

Portugal -0.484 -0.231 -0.393 -0.420 -0.405 -0.161 -0.299 -0.392

Spain -0.478 -0.235 -0.382 -0.410 -0.395 -0.178 -0.294 -0.381

Ireland -0.456 -0.216 -0.372 -0.397 -0.383 -0.146 -0.282 -0.371

Singapore -0.471 -0.207 -0.392 -0.417 -0.404 -0.101 -0.293 -0.391

Mauritius -0.451 -0.226 -0.351 -0.378 -0.363 -0.185 -0.272 -0.350

Israel -0.432 -0.207 -0.350 -0.374 -0.362 -0.146 -0.267 -0.349

New Zealand -0.422 -0.205 -0.340 -0.364 -0.351 -0.151 -0.260 -0.339

Finland -0.423 -0.206 -0.340 -0.364 -0.351 -0.152 -0.260 -0.339

Sweden -0.395 -0.192 -0.319 -0.341 -0.329 -0.140 -0.244 -0.318

Netherlands -0.379 -0.181 -0.308 -0.329 -0.318 -0.125 -0.234 -0.307

France -0.349 -0.161 -0.286 -0.305 -0.295 -0.100 -0.216 -0.286

United Kingdom -0.352 -0.167 -0.287 -0.306 -0.296 -0.113 -0.218 -0.286

Belgium -0.351 -0.165 -0.287 -0.306 -0.296 -0.109 -0.217 -0.286

Norway -0.340 -0.163 -0.276 -0.295 -0.285 -0.114 -0.210 -0.275

Italy -0.338 -0.159 -0.276 -0.294 -0.285 -0.105 -0.209 -0.275

Austria -0.324 -0.152 -0.265 -0.283 -0.274 -0.098 -0.201 -0.265

Germany -0.324 -0.152 -0.265 -0.283 -0.274 -0.098 -0.201 -0.265

Australia -0.310 -0.143 -0.255 -0.271 -0.263 -0.089 -0.192 -0.254

Japan -0.316 -0.153 -0.255 -0.273 -0.263 -0.112 -0.195 -0.254

Canada -0.303 -0.147 -0.244 -0.261 -0.252 -0.107 -0.187 -0.244

Bermuda -0.267 -0.115 -0.223 -0.237 -0.229 -0.049 -0.166 -0.222

Switzerland -0.269 -0.120 -0.223 -0.237 -0.229 -0.065 -0.167 -0.222

Barbados -0.290 -0.146 -0.213 -0.231 -0.221 -0.129 -0.168 -0.213

Iceland -0.258 -0.118 -0.212 -0.226 -0.219 -0.072 -0.160 -0.212

Denmark -0.260 -0.122 -0.212 -0.227 -0.219 -0.081 -0.161 -0.212

Luxembourg -0.113 -0.046 -0.095 -0.101 -0.098 -0.011 -0.070 -0.095

United States -0.091 -0.042 -0.074 -0.079 -0.077 -0.027 -0.056 -0.074

Low-income average -1.002 -0.460 -0.648 -0.710 -0.675 -0.418 -0.516 -0.646

Middle-income average -0.694 -0.346 -0.534 -0.575 -0.553 -0.284 -0.415 -0.532

High-income average -0.358 -0.170 -0.291 -0.310 -0.300 -0.115 -0.221 -0.290

1

Countries are reported based on ascending per capita real income levels.