Wheat Flour Price Shocks 

and Household Food Security 

in Afghanistan

Anna D’Souza

Economic

Research

Report

Number 121

July 2011

United States 

Department of 

Agriculture

Economic

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Recommended citation format for this publication: 

D’Souza, Anna, Wheat Flour Price Shocks and Household Food

Security in Afghanistan, ERR-121, U.S. Department of Agriculture,

Economic Research Service, July 2011.

Photos: Bread in window and man with vegetable cart in Afghanistan, 

used by permission of Dean Jolliffe, World Bank; background photo: 

wheat fields in Afghanistan, Shutterstock.

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A Report from the Economic Research Service

Abstract

To investigate the impact of rising staple food prices on household food security, we use 

a unique nationally representative household survey from Afghanistan, one of the world’s 

poorest, most food-insecure countries. The econometric framework allows us to control 

for household, district, and province factors to isolate the effects of the 2008 sudden rise 

in wheat flour prices on several measures of household well-being related to food security. 

The results show large declines in food consumption and dietary diversity, but smaller 

declines in calories consumed. The evidence suggests that households choose to trade 

off quality for quantity, as they moved toward staple foods and away from micronutrientrich foods, such as meat and vegetables. Additionally, for urban areas, the evidence 

suggests that wheat is a Giffen good; that is, as the price of wheat flour increases, demand 

for wheat products also increases. These findings may provide useful information for 

domestic and international policymakers and development agencies as they continue to 

confront the challenges of improving food security in this conflict-affected country.

Keywords: food security, high food prices, nutrition, poverty, Afghanistan

Acknowledgments

The author is grateful to Dean Jolliffe, World Bank, and to the Government of 

Afghanistan’s Central Statistics Organization for granting access to the data for this 

research as background for poverty assessment done in conjunction with the World 

Bank. The author also wishes to thank ERS colleagues Cheryl Christensen, Mark Nord, 

John Pender, and Shahla Shapouri as well as seminar participants at ERS and at the 

2010 Agricultural and Applied Economics Association Annual Meeting for comments. 

This report draws on research first presented as a selected paper, coauthored with Dean 

Jolliffe, at the 2010 AAEA Annual Meeting. Thanks also to our editor, Priscilla Smith, 

and our designer, Wynnice Pointer-Napper. 

Anna D’Souza, adsouza@ers.usda.gov

Wheat Flour Price Shocks 

and Household Food Security 

in Afghanistan

Economic 

Research 

Report 

Number 121

July 2011
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Contents

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

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

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Household and Price Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Estimating Changes in Household Well-Being as a Result 

of Wheat Flour Price Increases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Estimating the Differential Impact of Wheat Flour Price Increases . . . . 11

Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

The Impact of Higher Wheat Flour Prices on Afghan Households . . . . 14

Differential Impact of Higher Wheat Flour Prices . . . . . . . . . . . . . . . . . 16

Demand for Wheat Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Appendix 1—Survey Design, Sample Selection and Construction 

of Key Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Appendix 2—Robustness Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
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Summary 

What Is the Issue?

With a long history of political instability and conflict, as well as weak infrastructure and mountainous terrain, Afghanistan is particularly vulnerable to 

economic and natural shocks. During the 2007-08 period of high global food 

prices, the country experienced rapid increases in the prices of staple foods 

and other commodities due to a confluence of international and domestic 

factors. For households that spend the majority of their budgets on food, the 

high prices led to a severe erosion of purchasing power, disproportionally 

affecting poor households. In this study, we investigate how increases in 

wheat flour prices affect measures of household well-being associated with 

food security in Afghanistan. Identifying food-insecure populations and their 

coping mechanisms can help national and local governments and aid agencies 

working in Afghanistan in designing interventions and responding to local 

needs during future periods of high food prices.

What Were the Study Findings?

• Afghan households coped with the sudden rise in food prices by cutting 

back on overall food consumption and, to a lesser extent, on calories 

consumed. 

• Households were able to buffer the effects of the wheat flour price shocks 

on calories consumed by changing the composition of their diets, moving 

away from micronutrient-rich foods, such as meat, fruits, and vegetables, 

toward grains. 

• The decline in household food security was felt across both rural and 

urban areas. Urban households made changes that led to large declines in 

food consumption, but were able to maintain calories by greatly reducing 

the diversity of their diets and buying cheaper foods. Rural households 

made changes that led to smaller declines in their food consumption and 

in the variety of foods they consumed, but relatively larger declines in 

calories. 

• As the price of wheat flour increased, demand for wheat products was 

relatively steady in rural areas, but rose in urban areas. 

The results of this study may be used to inform current policy discussions 

on food security within Afghanistan and, more generally, within the international development community. The dearth of data and analysis available 

on consumption patterns and nutrition in Afghanistan poses challenges to 

political leaders, lawmakers, and humanitarian organizations interested in 

creating programs and policies to alleviate poverty and food insecurity. Such 

analysis is particularly crucial in areas of ongoing conflict, which are susceptible to shocks but for which high-quality quantitative data are rare. 

How Was the Study Conducted?

We used a unique cross-sectional, nationally representative survey collected 

by the Government of Afghanistan prior to and during the 2007-08 period 

of high food and commodity price inflation. The 2007-08 National Risk and 
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Vulnerability Assessment (NRVA) is a sample of over 20,000 households 

from all 34 provinces of Afghanistan. It was the first nationally representative 

household survey in Afghanistan conducted across a 13-month time period 

and designed to account for seasonal variations in consumption. 

The most important implication of the design is that the NRVA provides a 

comprehensive and representative portrayal of consumption patterns prior to 

and after the onset of the food price shock, allowing us to calculate measures 

related to household food security and providing substantial variation in 

prices for the analysis. Using the household and price data, an ordinary least 

squares model was used to estimate changes in household well-being related 

to food security that result from increases in the price of wheat flour, controlling for household and environmental factors. 
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Introduction

The case of Afghanistan during the 2007-08 period of high food price 

inflation is used to illustrate the impact of staple food price increases on 

household food security. Years of political instability and war have led to 

high rates of poverty and food insecurity in this landlocked nation.1 From 

2007 to 2008, the average price of wheat flour, the staple food, increased 

by over 100 percent in Afghanistan; we examine the resulting changes 

in several measures of household well-being related to food security. We 

also examine differences in the impact of the price shock across households based on location and based on whether a household owned and/or 

operated agricultural land. 

The USDA defines food security as “access by all people at all times to enough 

food for an active, healthy life” (Nord et al., 2010, p. 2). The United Nations 

Food and Agriculture Organization uses the following definition: “Food security exists when all people, at all times, have physical and economic access 

to sufficient, safe and nutritious food that meets their dietary needs and food 

preferences for an active and healthy life” (UN-FAO, 2006, p. 1). These broad 

definitions have been linked to four main dimensions: availability, access, 

utilization, and stability (UN-FAO, 2006). Availability refers to the physical 

existence of food, which relates to production, stocks, and trade. Access refers 

to a household’s ability to obtain food, which depends on income, prices, and 

distance to local food markets. Utilization refers to an individual’s ability 

to process nutrients and energy from food, which depends on many factors 

including dietary diversity and nutrient absorption, intrahousehold allocation of 

food, and hygienic preparation. The last dimension refers to the stability of the 

other three dimensions over time. 

In this analysis, we concentrate on two dimensions of food security: access 

and utilization. We use two measures of a household’s access to food—the 

real value of monthly per capita food consumption and per capita daily 

calorie availability.2 We also use two measures related to utilization—household dietary diversity and the availability of protein (a key macronutrient). 

The relationship between these measures and rising food prices is examined 

using a unique cross-sectional, nationally representative survey collected by 

the Government of Afghanistan prior to and during the 2007-08 period of high 

food prices. The data come from the 2007-08 National Risk and Vulnerability 

Assessment (NRVA), a sample of over 20,000 households from all 34 provinces of Afghanistan.3 It is the first nationally representative household survey 

in Afghanistan conducted across a 13-month time period. It was designed to 

account for seasonal variations in consumption and well-being and is therefore seasonally representative. The most important feature of the design for 

this analysis is that the NRVA provides a comprehensive and representative 

portrayal of consumption patterns prior to and after the onset of the food price 

shock, allowing us to calculate measures related to household food security and 

providing substantial variation in prices for the analysis.

From August 2007 to September 2008, the consumer food price index in 

Afghanistan increased by 40 percent. While the average nominal monetary 

value of household food consumption remained roughly constant throughout 

1According to Shapouri et al. (2009), 

in 2008 Afghanistan had a nutrition 

gap—the difference between available 

food and food needed to support a 2,100 

per capita calorie intake—of 2 million 

tons. Only North Korea was estimated 

to have a larger nutrition gap in that 

year.

2The real value of monthly per capita 

food consumption is the monetary 

value of the food acquired by the 

household—per capita per month—after the value has been deflated using 

a price index that takes into account 

geographic and seasonal differences in 

prices. Calorie availability refers to the 

calories associated with food acquired 

by a household; it does not measure 

actual intake levels.

3The Government of Afghanistan 

has published initial findings in the National Risk and Vulnerability Assessment 2007-08: a profile of Afghanistan

(2009). The report is being used to 

monitor progress toward the United 

Nations Millennium Development 

Goals (MDG). The reduction of hunger—part of MDG1—is a priority for 

the Government of Afghanistan and is 

a target under the Economic and Social 

Development Pillar of the Afghanistan 

National Development Strategy (IMF, 

2008).
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the year, the average real (deflated) monetary value dropped from 1,200 

Afghani (the Afghan currency) in fall 2007 to 798 Afghani in summer 2008, 

reflecting a large decline in household well-being. In other words, the Afghan 

households surveyed spent approximately the same amount of Afghanis 

on food during each quarter of the survey, but due to the price increases, 

the money allowed them to purchase less and less food over the year.4

The decline in well-being is further reflected in the large increase in the 

percentage of households with per capita daily calorie availability less than 

2,100 calories (commonly used minimum daily energy requirements); over 

the survey year, this measure increased by 10 percentage points to a striking 

34 percent. 

Identifying populations who are food insecure or who are vulnerable to food 

insecurity is an important step in designing well-targeted short-term and 

long-term interventions (Barrett, 2010). Moreover, declines in overall food 

security can have potentially serious implications. In particular, low levels of 

dietary diversity have been statistically linked to poor diet quality and inadequate nutrient availability (Arimond and Ruel, 2004). Micronutrient deficiencies have been linked to multiple negative outcomes for children, as well 

as adults; these include impaired cognitive development, physical and mental 

disabilities, child and maternal deaths, and lower productivity (UNICEF/ 

Micronutrient Initiative, 2009).5 Young children, lactating and pregnant 

women, and the chronically ill are particularly susceptible to decreases 

in nutrient intakes due to their high nutrient requirements (UN-WFP and 

UNICEF, 2008). Moreover, undernutrition is the leading cause of child 

mortality worldwide (Caulfield et al., 2004). 

This report complements recent studies on the impact of food price shocks 

on household well-being. The majority of recent studies use household survey 

data from preshock periods to simulate the short-run effects of price increases 

on poverty.6 Ivanic and Martin (2008) and Robles and Torero (2010) analyze 

data from several low-income countries and find that on average food price 

shocks increase urban poverty rates more than rural poverty rates. Ul Haq 

et al. (2008) find similar results using data from Afghanistan’s neighboring 

country of Pakistan. 

Bouis et al. (2011) examine consumption patterns of rural households in 

Bangladesh. They find that a 50-percent increase in the price of staple foods 

would lead to a 10-percent decrease in the quantity of staple foods consumed. 

And a 50-percent increase in the price of all foods would lead to a 15-percent 

reduction in energy intake (calories), a 30-percent reduction in iron intake, 

and a marked shift in expenditures from nonstaples to staples. 

In an exception to these simulation studies, Jensen and Miller (2008) use a 

panel of 1,300 urban poor households in two Chinese provinces to assess the 

nutritional effects of food price increases. The data were collected between 

April and December 2006. They find a limited nutritional impact of the price 

increases, as measured by calorie intake. The authors argue that this is due in 

part to government policies that stabilized grain prices and in part to household substitution toward cheaper foods.

More recently, de Brauw (2011) provides more direct evidence on the link 

between food price increases and child health. He examines the impact 

4Nominal values refer to the actual 

amount of money spent on food acquired; real or deflated values refer to 

values that have been adjusted using 

a price index that takes into account 

differences in prices over time and 

across regions so that one gets a sense 

of how much less food households are 

able to purchase as a result of the price 

increases.

5See also Grantham-McGregor et 

al. (2001) for a review of studies on 

iron deficiency and childhood cognitive development.

6See Ruel et al. (2009) for a detailed 

review of the literature on the effects of 

economic crises on household wellbeing.
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of high food prices on child anthropometric statistics using data from El 

Salvador. He finds that food price inflation is associated with a decline in 

height-for-age scores in children under 3 years old. 

This study is unique in that it provides econometric evidence from actual 

household data collected during a period of high food prices in a conflictaffected country. Our study informs current policy discussions within 

Afghanistan and, more generally, within the international development 

community. Although this study provides valuable insight into the effect of 

sudden high food prices on an economically vulnerable population, there 

remains a dearth of data and analysis available on consumption and nutrition in Afghanistan. This dearth poses acute challenges to political leaders, 

lawmakers, and humanitarian organizations interested in creating programs 

and policies to alleviate poverty and mitigate food insecurity. These new 

survey data provide a unique opportunity to calculate indicators of consumption and nutrition and to analyze changes in these indicators over time. 

Moreover, understanding household coping mechanisms better enables 

national and local governments to respond to local needs. Such analysis is 

particularly crucial in conflict areas, which may be most susceptible to food 

price shocks, but which usually have little quantitative data. The findings may 

provide valuable information to policymakers and humanitarian agencies as 

they consider strategies to respond to future periods of high food prices.
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Background

Over the past decade, the Afghan economy has experienced strong growth, 

with real gross domestic product (GDP) growth averaging approximately 

10.8 percent per year between 2003 and 2009. But after decades of war and 

political instability, landlocked Afghanistan remains one of the world’s least 

developed nations and one of the poorest countries in its region. GDP per capita 

was $350 in 2007 and $457 in 2008, in current U.S. dollars (IMF, 2009). In a 

country like Afghanistan, though, where the drug economy is large, the official National Income Accounting data are likely to significantly understate 

GDP. The United Nations Office on Drugs and Crime estimates that in 2007 

the farmgate value of opium cultivation was US$1 billion, but this dropped to 

US$730 million in 2008 (UNODC, 2008). The potential export value in 2007 

of opium, morphine, and heroin at border prices in neighboring countries was 

$4 billion (or, in per capita terms, about $160).

Based on the broader set of development indicators used in the United 

Nations Development Program (UNDP) human development index (e.g., 

health, education, living standards) Afghanistan ranks 181 out of 182 countries (UNDP, 2009). And Afghanistan has the highest prevalence of stunting 

(reduced growth primarily due to malnutrition in childhood) in the world 

among children under 5 years old (UNICEF, 2009, p.11).7

The Afghan economy is largely based on agriculture; major crops include 

wheat, rice, maize (corn), barley, vegetables, fruits, and nuts. Approximately 

70 percent of cultivated crop area is devoted to wheat and about 15 percent is 

devoted to rice, barley, and maize (Chabot and Dorosh, 2007). Due to large 

fluctuations in weather and insecurity, however, wheat production is highly 

volatile and the country is dependent on its trading partners to meet any shortfalls. In fact, Afghan wheat production has been estimated to be over five times 

as variable as that of its neighbor, Pakistan (Persaud, 2010). Pakistan is the 

major supplier of wheat to Afghanistan, mostly in the form of flour, due to close 

historical ties and a shared 1,600-kilometer border. Estimates of Pakistan’s 

share of the Afghan wheat and flour import market range from 59 percent 

(Chabot and Dorosh, 2007) to 79 percent (Maletta, 2004).

According to the United Nations World Food Programme, Afghanistan is 

among the world’s most vulnerable countries in terms of absorbing food and 

fuel price shocks; such countries are heavily dependent on food and fuel 

imports and have large populations of poor people who spend significant shares 

of their income on food and often live in a state characterized by food insecurity (Sanogo, 2009). Also, mountainous terrain and poor infrastructure, coupled 

with weak governance and ongoing conflict, have limited the Afghanistan 

Government’s ability to manage its food distribution and supply networks.

Seasonality also plays an important role in food security in Afghanistan. 

Temperatures vary dramatically across seasons, with hot summers and frigid 

winters; and the climate in the highlands varies with elevation. In many cases, 

severe winter conditions affect transportation, and in high mountainous areas 

roads are often blocked throughout the winter due to heavy snow accumulation. 

International prices of food commodities increased substantially in 2007 and 

rapidly in early 2008, peaking around May-July 2008. During this period, 

7Estimates are based on data from 

the 2004 National Nutrition Survey.
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Afghanistan experienced several shocks that led to a disruption of its food 

supply network, causing prices to soar throughout the country. Due to drought 

and early snow melt, the 2008 wheat harvest of 1.5 million metric tons was the 

worst harvest since 2000 (Persaud, 2010). (The harvest period typically falls 

in the summer months: May-August.) The price impact of the large shortfall 

in wheat production was magnified by an export ban in Pakistan and rising 

international food prices. In February 2008, the Government of Afghanistan 

eliminated import tariffs on wheat and wheat flour (tariffs had been set at 

2.5 percent), but due to export bans in Pakistan, Iran, and Kazakhstan, there 

was little downward effect on prices. Between fall 2007 and summer 2008, 

the prices of domestic wheat and domestic wheat flour increased by over 100 

percent. Figure 1 displays the retail price of wheat flour in four major markets 

from January 2006 to December 2009. 

Total inflation was largely driven by the surge in food prices. Figure 2 presents the Government of Afghanistan’s consumer price indices (CPI) for food 

and nonfood items based on prices from six major urban areas from 2006 to 

2009. During the NRVA survey timeframe (August 2007-September 2008), 

the urban food CPI increased by nearly 60 percent, while the nonfood CPI 

increased by only 10 percent. Our calculations using the NRVA district price 

survey data produced similar results. We find a 40-percent national increase 

in food prices, with a 60-percent increase in urban areas. 

Figure 1

Retail wheat flour prices in Afghanistan, 2006-09

Afghani per kilogram

Source: UN Food and Agriculture Organization, Global Information and Early Warning System.

Jan. 2006 Jan. 07 Jan. 08 Jan. 09

0

10

20

30

40

50 Herat Jalalabad Kabul Kandahar

Figure 2

Consumer Price Index in Afghanistan, 2006-09

March 2004 = 100

Source: Government of Afghanistan, Central Statistics Organization.

Jan. 2006 Jan. 07 Jan. 08 Jan. 09

0

50

100

150

200

250

Food

Nonfood
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Household and Price Data

The primary data come from the 2007-08 National Risk and Vulnerability 

Assessment (NRVA), conducted by the Government of Afghanistan’s 

Central Statistics Organization and the Ministry of Rural Rehabilitation 

and Development. The survey was administered between August 2007 and 

September 2008 and covered over 20,000 households (about 150,000 individuals) in 2,572 communities in all 34 provinces of Afghanistan. 

The survey was stratified spatially and temporally to ensure that the samples 

for each quarter reflect the overall composition of the country. The stratification over time is critical to identifying the effects of the wheat flour price 

increases since the econometric identification hinges on having nationally 

representative price and household data before and after the price shock. (See 

appendix 1 for details on survey design.) 

The yearlong fieldwork allowed coverage of insecure areas. It is extremely 

difficult to obtain high-quality household data in conflict-affected countries. 

The NRVA was able to achieve this task through a process of informally 

securing permission from local leaders in insecure areas, as well as using 

a flexible design for field work. In particular, when an area (specifically, a 

primary sampling unit) was considered too dangerous to interview at the 

scheduled time, it would not be replaced immediately, but would be reconsidered at a later date within the quarter.

The household data include extensive information on consumption, expenditures, income, assets and credit, and housing characteristics, as well as health 

and education. A key component is the food consumption section, which asks 

female respondents about the frequency and amount of consumption of 91 food 

items over the previous week. The broad coverage of foods, including seasonal 

varieties, allows for better calculation of calorie and nutrient availability than 

the smaller number of food items typically included in household surveys.

Another key component of the NRVA is the district market price survey. 

Data were collected on the prevailing prices of the food items included in 

the consumption section, as well as domestic and imported grains and fuel. 

Given Afghanistan’s mountainous terrain and poor infrastructure, transportation costs most likely vary greatly across the country, and in particular in 

remote and insecure areas. In order to create an accurate measure of the value 

of food consumption, it is necessary to obtain data on prices that households 

face in their local markets.

The final sample for our analysis consists of 20,491 households in 394 

districts in 34 Afghan provinces. Approximately 80 percent of households 

reside in rural areas; of these households, 6.3 percent are Kuchi or nomadic 

pastoralists. On average, households have 8.6 members living in about 3.6 

rooms, or, in the case of Kuchi populations, tents. The typical household 

consists of 2.1 men, 2 females, and 4.5 children (under 16). Heads of households are about 45 years old, mostly illiterate, and almost all married.

We create several measures of household well-being associated with food security: real value of monthly per capita food consumption (a key factor used in 

the assessment of poverty); per capita daily calorie availability; per capita daily 
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protein availability; and household dietary diversity. We also examine shares of 

calories and expenditures devoted to various food groups. Here we provide a brief 

description of the variables used in the analysis. (See appendix 1 for details.)

To calculate the value of total monthly food consumption (in Afghani), we 

map district price data to quantity data from the household consumption 

module. Households are asked for the quantity of foods consumed over the 

past 7 days; these quantities are multiplied by 4.2 to get monthly values. The 

survey includes foods consumed, regardless of their source (i.e., food bought 

on the market as well as food produced or obtained through other methods 

such as food aid and gifts).8 We convert nominal values to real values using a 

consumer food price index based on NRVA price data.9

Daily per capita calorie availability (in kilocalories) and protein availability 

(in grams) are calculated by dividing daily total household calorie and 

protein availability by the effective household size. The effective number 

of household members incorporates guests eating meals within the home.10

Food quantities were converted to kilocalories and nutrients using the United 

Nations Food and Agriculture Organization (FAO) Food Composition Tables 

for the Near East.11

To measure household dietary diversity, we use the food consumption score 

(FCS), an indicator developed by the UN World Food Programme (WFP).12

The FCS has been used in field assessments to gauge short-term changes in 

food security during periods of high food-price inflation (Sanogo, 2009). It is 

constructed by taking the weighted sum of the frequencies with which households consumed foods within eight food groups over the previous week.13 The 

food groups include staples, pulses, vegetables, fruit, meat/fish, milk/dairy, 

sugar, and oil/fat. The national average is 61. In a country like Afghanistan, 

where most households consume staples and oil every day, the WFP uses a 

cutoff of 48 for an acceptable diet. Under this categorization, approximately 

80 percent of the population has acceptable diets; however recent work 

suggests that the cutoff points for the FCS classifications may be too low 

(Weismann et al., 2009).

Table 1 presents weighted estimates for key variables for the nation, by location and based on whether a household owned and/or operated agricultural land 

(referred to hereafter as “agricultural household”). In the empirical work, we 

explore differences between rural and urban areas, as well as differences between 

households based on access to agricultural land. These categorizations address 

theoretical distinctions drawn in the literature. (See section, “Estimating the 

Differential Impact of Wheat Flour Price Increases,” in the chapter “Estimating 

Changes in Household Well-Being as a Result of Wheat Flour Price Increases.”) 

Average monthly nominal per capita total consumption is 1,925 Afghani. About 

60 percent of total consumption is spent on food. (By contrast, U.S. households 

spend about 13 percent of disposable income on food.14) 

As in many developing countries, poverty in Afghanistan is more prevalent 

in rural areas (World Bank, 2007). Total monthly per capita consumption in 

rural areas is 44 percent lower than that in urban areas; also rural households 

spend a larger share (63 percent) of their total consumption on food than do 

urban households (44 percent). Although average per capita calorie availability is similar across rural and urban areas, rural households have lower 

8Ideally one would use shadow 

prices to calculate the value of food 

produced at home. The survey instrument does not provide enough information to create shadow prices; therefore 

we use market prices, as is common in 

the literature. Since not all food items 

were available in all district markets 

at all times of the year, we impute the 

missing elements to obtain a complete 

price matrix.

9We use a Laspeyres price index 

estimated by quarter for each region. 

Real consumption is relative to the 

chosen base: urban areas in the Central 

region in Quarter 1. The capital, Kabul, 

is located in the Central region.

10Some studies use household size 

to calculate per capita amounts. In 

Afghanistan the custom of sharing 

meals with guests makes it important 

to account for guests eating meals from 

the household cooking pot. The effective household size also incorporates 

information on household members 

eating outside the home.

11Spices, water, and “other” foods do 

not contribute to total calories. USDA 

sources were used for a few items that 

were not available in the FAO tables.

12See Weismann et al. (2009) for an 

evaluation of the effectiveness of the 

food consumption score in measuring 

household food security. See World 

Food Programme (2008) for a general 

discussion of FCS analysis, as well as 

recent evidence from several developing countries.

13Weights for the food groups range 

from 0.5 to 4 based on nutrient density. 

Condiments receive 0 nutritional 

weight. Frequencies are truncated at 

7 for each food group. The measure 

ranges from 0 to 112.

14U.S. Department of Labor, Bureau 

of Labor Statistics (2009) Available at 

http://www.bls.gov/news.release/cesan.

nr0.htm/.
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Table 1

Population statistics by area and household type

National Rural1 Urban1 Agricultural 

households2

Nonagricultural 

households2

Nominal value of monthly per capita total 

consumption (Afghani)

1,925.58 1,675.54 2,931.47 1,752.19 2,159.44 

(1,158.22) (822.96) (1,658.50) (934.17) (1,370.65)

Nominal value of monthly per capita food 

consumption (Afghani)

1,157.57 1,104.57 1,370.82 1,133.96 1,189.42 

(583.11) (532.26) (715.17) (528.39) (648.32)

Per capita daily calorie availability

(kilocalories)

2,600.71 2,577.04 2,695.95 2,586.81 2,619.47 

(973.68) (952.85) (1,048.05) (976.91) (969.05)

Per capita daily protein availability

(grams)

141.97 133.67 175.34 140.20 144.35 

(298.27) (282.72) (352.08) (290.08) (308.98)

Food consumption score 60.95 59.58 66.46 61.59 60.07 

(20.03) (19.57) (20.89) (19.48) (20.71)

Price of domestic wheat flour (Afghani per kg) 25.32 25.52 24.50 25.77 24.70 

(7.40) (7.57) (6.60) (7.57) (7.12)

Price of vegetable oil (Afghani per kg) 4.38 4.39 4.32 4.40 4.36 

(0.18) (0.18) (0.16) (0.18) (0.18)

Price of domestic rice (Afghani per kg) 3.72 3.72 3.69 3.73 3.70 

(0.27) (0.28) (0.24) (0.28) (0.27)

Price of lamb (Afghani per kg) 5.21 5.19 5.27 5.20 5.22 

(0.13) (0.14) (0.09) (0.13) (0.13)

Price of milk (Afghani per liter) 3.27 3.25 3.34 3.25 3.29 

(0.22) (0.22) (0.24) (0.23) (0.22)

Price of kerosene (Afghani per liter) 3.86 3.87 3.84 3.88 3.84 

(0.14) (0.14) (0.13) (0.14) (0.14)

Age of household head 44.87 44.43 46.63 45.37 44.19 

(13.78) (13.73) (13.87) (13.68) (13.89)

Dummy for married household heads 0.95 0.95 0.94 0.95 0.94 

Dummy for literate household heads 0.32 0.27 0.52 0.30 0.34 

Dummy for plateau areas 0.22 0.26 0.07 0.27 0.16 

Dummy for mountainous areas 0.39 0.48 0.04 0.49 0.27 

Total observations 20,491 16,411 4,080 11,768 8,723

Percentage of full sample 100 80 20 57 43 

Note: Estimates are population-weighted means, with standard deviations in parentheses. 

1Statistical tests of differences in means between rural and urban households show significant differences at a 5-percent level of significance for 

all variables, with the exceptions of per capita protein availability and per capita calories availability (which is significant at 10 percent). 

2Statistical tests of differences in means between agricultural and nonagricultural households show significant differences at a 5-percent level of 

significance for all variables, with the exception of the price of lamb.

Source: 2007-08 National Risk and Vulnerability Assessment.
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levels of dietary diversity and protein availability, suggesting varying degrees 

of micronutrient intake.15 The rural-urban differences are also observed—

though to a lesser degree—between agricultural and nonagricultural households. Approximately 67 percent of rural households and 15 percent of 

urban households have access to agricultural land. And the vast majority (95 

percent) of agricultural households is situated in rural areas.

In the empirical analysis, we include the prices of milk, lamb, rice (domestic), 

and vegetable oil, as well as the price of kerosene, which is commonly used 

in cooking. These four food products plus wheat flour make up 80 percent of 

monthly household expenditure for the relatively poor (20th to 50th percentile of the total consumption distribution). The prices of food and fuel, while 

statistically different across subpopulations, do not differ by more than 4 

percent. The variation in prices over the survey year is driven largely by 

temporal differences, rather than spatial ones. For example, using ANOVA 

(analysis of variance analysis), we find that 75-85 percent of the total variation in wheat flour prices is explained by variation over the four quarters of 

the survey year; in contrast, approximately 5 percent of the total variation can 

be explained by variation at the province level.16

Table 2 displays key variables by quarter for the national sample. With the 

exception of lamb prices, we observe substantial increases in food and fuel 

prices. The price of wheat flour more than doubles at the national level, with 

an increase of 111 percent in rural areas and 80 percent in urban areas. 

The repercussions of these price increases can be seen in the inflationadjusted values of monthly per capita total consumption and food consumption; in contrast, the nominal values show little change.17 The real value of 

15Means of per capita calorie availability for rural and urban areas are not 

statistically different at the 5-percent 

level of significance.

16Prices are aggregated to the stratum level in order to mitigate potential 

measurement error in district-level 

prices. Strata are based on urban and 

rural designation within provinces.

17Quarter 1-Central region urban 

prices are used as the base for deflating 

nominal values.

Table 2

Population statistics by quarter for full sample

Quarter 1

(Fall)

Quarter 2 

(Winter)

Quarter 3 

(Spring)

Quarter 4 

(Summer)

Nominal value of per capita total consumption (Afghani) 2,017.79 1,902.86 1,876.92 1,914.64

Real value of per capita total consumption (Afghani) 2,022.00 1,718.27 1,519.12 1,477.56

Nominal value of per capita food consumption (Afghani) 1,196.98 1,123.25 1,129.01 1,182.97

Real value of per capita food consumption (Afghani) 1,201.19 961.26 789.41 797.60

Per capita daily calorie availability (kilocalories) 2,884.92 2,725.03 2,445.83 2,387.33

Per capita daily protein availability (grams) 188.08 219.80 91.60 74.74

Food consumption score 67.88 61.28 57.86 57.69

Price of domestic wheat flour (Afghani per kg) 16.16 20.51 30.19 33.05

Price of vegetable oil (Afghani per kg) 64.81 76.93 88.90 91.70

Price of domestic rice (Afghani per kg) 33.93 33.99 46.16 55.29

Price of lamb (Afghani per kg) 182.34 186.20 189.28 180.27

Price of milk (Afghani per liter) 23.44 25.66 27.23 30.75

Price of kerosene (Afghani per liter) 43.15 45.78 46.83 55.48

Note: Estimates are population-weighted means. Real values reflect adjustments for spatial and temporal price differences, 

covering 13 months of field work.

Source: 2007-08 National Risk and Vulnerability Assessment.
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monthly per capita food consumption drops by a third, from about 1,200 

Afghani to about 800 Afghani. Calorie and protein availability also decline, 

as does the average level of dietary diversity. 

The majority of the Afghan diet is comprised of grains and over 10 

percent of calories come from oil or fat, patterns that are typical of lowincome countries (UN-WFP, 2008; Bouis et al., 2011). Grains represent 

48 percent of food expenditure and 70 percent of calories consumed for 

the national sample. Wheat is the main staple, with few close substitutes. 

Most wheat is consumed in the form of naan, local unleavened bread that 

is prepared by households after purchasing refined wheat flour or whole 

wheat (Chabot and Dorosh, 2007). Annual per capita wheat consumption is estimated to be about 160 kilograms (FEWSNET, 2007). And the 

data show that wheat flour contributes 54 percent of total calories to the 

Afghan diet.

Table 3 displays calorie and food expenditure shares. Urban households 

devote fewer resources to grains and dairy and more resources to the other 

food groups than do rural households; shares of total calories follow a similar 

pattern. The distinctions between agricultural and nonagricultural households 

are generally similar to those of rural and urban households. 

Table 3

Calorie and food expenditure composition

National Rural1 Urban1 Agricultural 

households2

Nonagricultural 

households2

Calorie shares

Grain 0.702 0.712 0.663 0.708 0.695

Meat/fish 0.023 0.023 0.022 0.023 0.022

Dairy 0.053 0.058 0.032 0.062 0.041

Oil/fat 0.117 0.110 0.147 0.107 0.131

Vegetable 0.037 0.035 0.047 0.034 0.041

Fruit 0.020 0.017 0.032 0.018 0.022

Sugar 0.046 0.044 0.053 0.045 0.047

Expenditure shares

Grain 0.476 0.484 0.446 0.476 0.477

Meat/fish 0.119 0.119 0.120 0.122 0.116

Dairy 0.098 0.108 0.060 0.114 0.077

Oil/fat 0.078 0.078 0.080 0.074 0.083

Vegetable 0.087 0.080 0.116 0.079 0.099

Fruit 0.060 0.051 0.097 0.055 0.067

Sugar 0.036 0.036 0.038 0.036 0.036

Note: Estimates are population-weighted means. 

1Statistical tests of differences in means between rural and urban households show significant 

differences at a 1-percent level of significance for all calorie and expenditure shares, with the 

exception of meat/fish. 

2Statistical tests of differences in means between agricultural and nonagricultural households 

show significant differences at a 1-percent level of significance for all calorie shares and all 

expenditure shares, with the exceptions of grain and sugar. 

Source: 2007-08 National Risk and Vulnerability Assessment.
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Estimating Changes in Household Well-Being 

as a Result of Wheat Flour Price Increases 

The expectation is that the large increases in wheat flour prices will be associated with reductions in well-being in terms of the value of food consumption, calorie availability, and dietary diversity. In terms of per capita protein 

availability, it is not clear whether the income or substitution effects will 

dominate, and thus if the effects of the price increases will be positive or 

negative. (See box, “The Paradox of Giffen Goods,” for a discussion of 

income and substitution effects.) It is also expected that households will 

change the composition of their diets, with a general movement toward 

cheaper, lower quality foods, which should be reflected in the changes in 

expenditure and calorie shares devoted to various food groups. 

The econometric analysis uses ordinary least squares (OLS) estimation techniques.18 The basic specification is as follows: 

(1) log(xh) = 0 + 1 log(price wheat flourapq) + log (pricesapq) 

 + HHh + DISTdq + p + h

where x is a measure of household well-being for household h. Prices denotes 

a vector of commodity prices for area a (urban or rural), province p, and 

quarter q. HH denotes household-level variables; DIST denotes district-level 

variables for district d and  denotes province dummy variables.19 These 

control variables are described in detail below.  denotes a Huber-White 

robust error term, clustered to take into account the survey stratification. 

The coefficient of interest is 1, which can be interpreted as the price elasticity of the dependent variable with respect to changes in the price of 

domestic wheat flour.20 It is not possible to identify separately the price 

increases due to high international food prices, the domestic drought, or 

normal patterns of seasonality; therefore the results should be interpreted as 

elasticities due to overall price changes. 

We use the price of wheat flour since households often purchase wheat in the 

form of flour; the results are robust to using the prices of imported wheat flour, 

and domestic and imported wheat. Average prices are calculated for rural and 

urban areas within each province for each quarter in order to minimize potential biases caused by measurement error in prices at the district level. 

Estimating the Differential Impact 

of Wheat Flour Price Increases

During the 2007-08 period of high food-price inflation, governments and 

international organizations emphasized the vulnerability of urban households, whose members often do not have means to produce their own food 

(UN-FAO, 2008). Urban households rarely have access to agricultural land 

and are thus unable to produce their own food from crops or animals, are 

more dependent on cash income, and are more likely to consume internationally traded staple foods than are rural households (Ruel et al., 2009). Also, 

as described earlier, several simulation studies have found that poverty rates 

increased disproportionately in urban areas as food prices rose rapidly. 

18In this report, we use a reducedform OLS estimation approach. 

However, price elasticities can also be 

estimated using a system of equations, such as the almost ideal demand 

system used in Banks et al. (1997). 

Alternatively, one can estimate the 

price elasticities using quantile regression estimation. OLS estimation 

provides an estimate of the partial derivative of the dependent variable with 

respect to wheat flour prices evaluated 

at the mean of the dependent variable. By contrast, quantile regression 

estimation allows the derivative to 

be estimated at various points of the 

distribution of the dependent variable. Such analysis is the subject of 

ongoing research, and may be useful 

for policymakers who are interested in 

the impact of price increases on more 

vulnerable households.

19A dummy variable refers to an 

indicator function that is equal to one 

when certain criteria are met, and 

equal to zero otherwise. In this case, 

a dummy variable is created for each 

province. For example, the dummy 

variable for Kabul province would 

equal one for all households that are 

located in the Kabul province and zero 

for all households that are located in 

other provinces.

20Price elasticity is defined as the 

percentage change in the dependent 

variable for a given percentage change 

in prices.
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Broadly, economic theory and empirical data suggest that urban or landless 

rural households are more adversely affected by increases in food prices 

than agricultural rural households are. More specifically, households can be 

categorized into net buyers and net sellers; the former depend on the market 

for food, while the latter produce enough food to consume as well as to sell 

on the market. When food prices increase, purchasing power declines for all 

households, however net sellers of food are able to benefit from higher selling 

prices and thus suffer a smaller decline in overall well-being due to the price 

shock. Since the survey does not include questions on quantities of food 

produced or sold on the market, we are unable to distinguish directly between 

net sellers and net buyers. Therefore, we use two proxies for a household’s 

potential to be a net seller. As a broad categorization, we use a dummy for 

rural households, as most net sellers are situated in rural areas and, additionally, policymakers may be interested in knowing how households in different 

areas cope with price shocks. As a finer categorization, we use a dummy 

for access to agricultural land, defined as households that report owning or 

operating agricultural land. These households are the most likely to engage in 

home production of food and thus are potential net sellers of food. 

The concept of a Giffen good dates back to the Principles of Economics, by 

Alfred Marshall (1895), and is named after Robert Giffen (1837-1910), a Scottish 

statistician and economist. In contrast to the basic “Law of Demand,” which states 

that quantity demanded decreases as price increases, for a Giffen good, quantity 

demanded increases as price increases. This paradox is driven by the fact that a 

Giffen good is an inferior good, rather than a normal good. Holding prices constant, 

as income increases, the demand for a normal good increases but the demand for an 

inferior good decreases. This characteristic defines the demand curve for a Giffen 

good through the income effect associated with the price increase. Specifically, the 

consumer response to a price increase can be decomposed into an income effect 

and a substitution effect. The income effect refers to the decrease in consumer 

purchasing power, which, in turn, increases the demand for inferior goods. The 

substitution effect refers to the change in the relative attractiveness of the good after 

its price increases; that is, consumers are induced to substitute away from the good 

whose price increased since it has become relatively more expensive. In the case of 

a Giffen good, the income effect is larger than the substitution effect; consumers 

feel poorer and so they purchase more of the inferior good despite the fact that it is 

relatively less attractive after the price increase. As a result, the demand curve for a 

Giffen good is upward sloping.

While the conventional example of the Irish potato blight has been discredited 

(Dwyer and Lindsay, 1984), recent research (Jensen and Miller, 2008) provides 

real-world evidence of Giffen goods. Jensen and Miller run a field experiment in 

China in which they provide and then take away subsidies to purchase staple foods. 

They find strong evidence that rice is a Giffen good among the urban poor in Hunan 

province. They find weaker evidence that wheat is a Giffen good among the urban 

poor in Gansu province. However, when looking at the subset of the population 

among whom such behavior is theorized to be more likely (i.e., poor households that 

are well off enough to consume more than just staples), they find stronger evidence 

that wheat is a Giffen good for some households in Gansu province. 

The Paradox of Giffen Goods
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To identify differences in the effects of price shocks across rural and urban 

areas, we add an interaction of the urban dummy and the log of wheat flour 

prices to the basic specification:

(2) log(xh) = 0 + 1 log(price wheat flourapq) + 2[log (price wheat flourapq) 

 X urbana] + (log(pricesapq) + HHh + DISTdq + p + h

The effect of wheat flour prices on household well-being for rural households 

is 1 and for urban households it is 1 + 2. The specification of agricultural 

and nonagricultural households is analogous; the urban dummy is simply 

replaced with the nonagricultural household dummy.

Control Variables

To isolate the effect of changes in wheat flour prices on household well-being, 

it is important to control for simultaneous price changes in other important 

commodities since household decisionmaking is based on relative prices. We 

include the average prices of milk, lamb, rice, vegetable oil, and kerosene. 

Average prices are calculated for rural and urban areas within each province 

for each quarter. 

The household-level controls include the following: dummies for consumption quintiles (bottom quintile is excluded); age of household head; dummy 

for households in which heads are literate; dummy for households in which 

heads are married. The consumption quintile dummies are intended to proxy 

for socio-economic status; they are constructed based on real monthly per 

capita total consumption.21 As a robustness test, described in appendix 2, 

household composition variables are included in the regression.

District-level variables include dummies for topography—plateau and mountainous areas (plains areas are excluded)—and a dummy for urban areas. In the 

regressions examining the differential effects by access to agricultural land, we 

include a dummy for nonagricultural households instead of the urban dummy; 

the results are robust to the simultaneous inclusion of both dummy variables. 

Lastly, we include province dummies to control for observable and unobservable time-invariant province-level factors that could confound the results, for 

example, differences in the degree of conflict and instability that are present 

throughout the survey year in certain provinces.

21Total household consumption 

(Afghani per month) consists of expenditures on food, nonfood, durables 

and rent, following guidelines in 

Deaton and Zaidi (2002). For nonfood 

items, we use a nonfood price index 

developed by the Afghanistan Central 

Statistics Organization; it accounts for 

temporal, but not spatial, differences 

in prices. 
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The Impact of Higher Wheat Flour Prices 

on Afghan Households

Increases in the price of wheat flour are associated with declines in several 

dimensions of well-being related to food security for Afghan households 

(table 4). A 1-percent increase in the price of domestic wheat flour is associated with a 0.20-percent decline in the real value of monthly per capita food 

consumption. Given that prices were observed to more than double from 

2007 to 2008, the magnitude of this effect is large. The estimates based on 

calories show, however, that households were able to buffer the impact of 

the large shock in prices to their energy levels to a large extent. The calorie 

elasticity is less than half the size of the food consumption elasticity, with a 

decline in per capita daily calorie availability of 0.07 percent for the same 

1-percent increase in prices. This relatively smaller effect on calories reflects 

changes in dietary composition. A 1-percent increase in the price of wheat 

flour is associated with a 0.10-percent decline in the food consumption score 

and a 0.25-percent decline in daily per capita protein availability. 

The findings suggest that households were trading off quality for quantity. In 

particular, they shifted toward lower quality (less micronutrient-rich), cheaper 

foods, thus allowing them to acquire more food to maintain their calories—to 

the extent possible—in the face of large declines in purchasing power. 

These findings are consistent with Bouis et al. (2011), who use demand 

estimation techniques to simulate changes in energy and nutrient intake 

in Bangladesh households resulting from an increase in food prices. Their 

results demonstrate that in the face of food price increases, expenditure on 

staple foods increases and expenditure on nonstaple foods decreases. Further, 

households are forced to sacrifice mineral and vitamins in order to maintain 

basic energy requirements. 

The parameter estimates for the control variables mostly follow a logical 

pattern. Households with higher levels of overall consumption display higher 

levels of well-being as measured by the four dependent variables. Urban 

households, on average, are relatively worse off than rural households; this 

result is surprising since urban areas are usually richer than rural areas. As 

we will see below, once we account for differential price effects between 

these areas, the sign of the coefficient on the urban dummy variable becomes 

positive for three of the four dependent variables, suggesting that these initial 

coefficients on the urban dummy suffer from omitted variable bias. The 

coefficients on the price variables are not consistent across models for the 

four dependent variables, but, in most cases, the coefficients are negative, as 

we would expect.22 The remaining control variables play a smaller role in 

explaining the dependent variables; most are statistically insignificant or are 

inconsistent in sign and magnitude across models. 

Tables 5 and 6 display the coefficient of interest from regressions that examine 

the impact of higher prices on expenditure and calorie shares for seven food 

groups, respectively. The changes in expenditure shares are stark and are 

consistent with a story of substitution across food groups. Households moved 

away from higher quality, micronutrient-dense food groups toward grains. 

22The price of kerosene is an exception; its coefficient is positive and 

statistically significant for three out 

of four key dependent variables. This 

result is counterintuitive and difficult 

to explain.
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Table 4

The impact of higher food prices on household well-being

Log real value 

of per capita 

monthly food 

consumption

Log 

per capita 

daily calorie 

availability

Log 

food consumption 

score

Log 

per capita 

daily protein 

availability

Log wheat flour price -0.202*** -0.070*** -0.102*** -0.249***

[0.021] [0.020] [0.024] [0.069]

Urban -0.163*** -0.153*** -0.043*** -0.181***

[0.012] [0.011] [0.011] [0.032]

Consumption quintile 2 0.345*** 0.222*** 0.165*** 0.290***

[0.008] [0.008] [0.009] [0.016]

Consumption quintile 3 0.544*** 0.331*** 0.248*** 0.454***

[0.008] [0.008] [0.009] [0.020]

Consumption quintile 4 0.756*** 0.440*** 0.342*** 0.695***

[0.010] [0.010] [0.010] [0.025]

Consumption quintile 5 1.124*** 0.596*** 0.484*** 1.140***

[0.012] [0.011] [0.011] [0.031]

Log vegetable oil price -0.058* 0.056 0.019 0.324***

[0.034] [0.034] [0.039] [0.105]

Log domestic rice price 0.032 -0.085*** 0.086*** -0.432***

[0.022] [0.021] [0.023] [0.097]

Log lamb price -0.117** -0.044 -0.029 0.007

[0.054] [0.050] [0.057] [0.136]

Log milk price -0.003 -0.074*** -0.018 -0.353***

[0.026] [0.025] [0.025] [0.063]

Log kerosene price 0.114*** 0.118*** -0.098** 0.545***

[0.043] [0.042] [0.045] [0.119]

Head age 0.051*** 0.032* 0.184*** 0.206***

[0.018] [0.019] [0.017] [0.049]

Head married 0.015 -0.040*** 0.093*** -0.005

[0.010] [0.009] [0.009] [0.026]

Head literate 0.011* 0.036*** -0.042*** -0.015

[0.006] [0.005] [0.005] [0.017]

Plateau 0.003 0.001 0.005 0.022

[0.013] [0.011] [0.014] [0.028]

Mountainous 0.003 0.002 0.010 0.050*

[0.012] [0.012] [0.013] [0.029]

Observations 20,491 20,491 20,491 20,491

R2 0.663 0.421 0.439 0.306

Notes: Each column represents a separate regression; OLS estimates are population weighted. 

Robust standard errors -in brackets- are clustered by stratum and adjusted for survey design. 

Real values reflect adjustments for spatial and temporal price differences, covering 13 months of 

field work. Consumption quintile 1 is excluded. Plains is excluded topography category. 

 *, **, and *** denote significance at 10 percent, 5 percent, and 1 percent, respectively.

Source: USDA, Economic Research Service.
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The calorie shares results support this story as well, though to a lesser extent, 

than the expenditure shares results. The price increases are associated with 

a smaller share of calories coming from fruit and oil/fat and a larger share 

coming from grains; we observe no significant decline in calorie shares 

coming from meat/fish or vegetables. The decline in calories coming from 

fruit is suggestive of a lower intake of micronutrients and vitamins. 

Broadly, the findings suggest that households experienced overall reductions 

in micronutrient availability in addition to the reduction in macronutrient 

(protein) availability. As mentioned above, such nutritional declines can have 

serious implications particularly for vulnerable groups like children under 2 

years old, pregnant and lactating mothers, and the elderly. 

Differential Impact 

of Higher Wheat Flour Prices

While the repercussions of rising food prices are observed at the national 

level, the effects vary greatly across rural and urban areas (table 7). Urban 

areas experienced a much greater decline in the real value of food consumption than rural areas did. For a 1-percent increase in wheat flour prices, the 

value of real monthly per capita food consumption in rural areas declines by 

approximately 0.19 percent, while the decline in urban areas is nearly double, 

at 0.37 percent. (Recall that the effect for urban households is the sum of 

coefficients on the interaction term and the base term. An F-test of the joint 

significance of both coefficients is statistically significant at 1 percent.) This 

evidence is in line with the literature on the food-price inflation of 2007-08 

that demonstrated the disproportionate impact on urban areas, in terms of 

poverty and total consumption. 

Table 5

Changes in expenditure shares by food group

Grain Meat/fish Dairy Oil/fat Vegetable Fruit Sugar

Log wheat flour price 0.191*** -0.032*** -0.019*** -0.031*** -0.028*** -0.070*** -0.012***

[0.011] [0.009] [0.007] [0.004] [0.004] [0.007] [0.002]

Observations 20,491 20,491 20,491 20,491 20,491 20,491 20,491

R2 0.474 0.319 0.261 0.27 0.268 0.285 0.143

See notes for table 4. Each column represents a separate regression; dependent variable is the household expenditure share devoted to the food 

group listed at top of column. All control variables listed in table 4 are included in the regressions.

Source: USDA, Economic Research Service.

Table 6

Changes in calorie shares by food group

Grain Meat/fish Dairy Oil/fat Vegetable Fruit Sugar

Log wheat flour price 0.042*** 0 -0.004 -0.017*** 0 -0.020*** -0.002

[0.008] [0.002] [0.004] [0.004] [0.002] [0.003] [0.002]

Observations 20,491 20,491 20,491 20,491 20,491 20,491 20,491

R2 0.324 0.289 0.287 0.241 0.260 0.233 0.234

See notes for table 4. Each column represents a separate regression; dependent variable is the household calorie share from the food group listed 

at top of column. All control variables listed in table 4 are included in the regressions.

Source: USDA, Economic Research Service.
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Table 7

The differential impact of higher food prices on household well-being, 

by area

Log real value 

of per capita 

monthly food 

consumption

Log 

per capita 

daily calorie 

availability

Log 

food consumption 

score

Log 

per capita 

daily protein 

availability

Log wheat flour price -0.188*** -0.075*** -0.094*** -0.233***

[0.021] [0.020] [0.024] [0.070]

Log wheat flour price x 

urban dummy

-0.178*** 0.057** -0.112*** -0.218**

[0.034] [0.028] [0.029] [0.100]

Urban 0.425*** -0.340*** 0.326*** 0.538

[0.113] [0.094] [0.096] [0.344]

Consumption quintile 2 0.348*** 0.221*** 0.167*** 0.293***

[0.008] [0.008] [0.009] [0.016]

Consumption quintile 3 0.549*** 0.329*** 0.251*** 0.460***

[0.008] [0.008] [0.009] [0.020]

Consumption quintile 4 0.763*** 0.438*** 0.346*** 0.703***

[0.010] [0.010] [0.010] [0.024]

Consumption quintile 5 1.129*** 0.594*** 0.488*** 1.147***

[0.012] [0.011] [0.011] [0.032]

Log vegetable oil price -0.037 0.05 0.033 0.350***

[0.033] [0.034] [0.039] [0.105]

Log domestic rice price 0.033 -0.085*** 0.086*** -0.432***

[0.022] [0.021] [0.023] [0.097]

Log lamb price -0.109** -0.047 -0.023 0.017

[0.053] [0.050] [0.057] [0.137]

Log milk price 0.01 -0.078*** -0.009 -0.337***

[0.026] [0.025] [0.025] [0.063]

Log kerosene price 0.127*** 0.113*** -0.089** 0.561***

[0.043] [0.042] [0.045] [0.119]

Head age 0.050*** 0.032* 0.184*** 0.205***

[0.018] [0.019] [0.017] [0.049]

Head married 0.015 -0.040*** 0.093*** -0.005

[0.010] [0.009] [0.009] [0.026]

Head literate 0.012** 0.036*** -0.041*** -0.013

[0.006] [0.005] [0.005] [0.016]

Plateau 0.004 0.001 0.005 0.023

[0.013] [0.011] [0.014] [0.028]

Mountainous 0.004 0.002 0.010 0.050*

[0.012] [0.012] [0.013] [0.029]

Observations 20,491 20,491 20,491 20,491

R2 0.665 0.421 0.441 0.306

P-value of F-statistic of 

joint significance 0.000 0.574 0.000 0.000

See notes for table 4. The last row displays the p-value of the F-statistic of the joint significance 

of both coefficients related to the log of wheat flour prices.

Source: USDA, Economic Research Service.
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The per capita calorie availability results paint a different story. Rural households experienced a small decline in per capita calorie availability; urban 

households did not. The coefficients on the log of wheat flour price and its 

interaction with the urban dummy are not jointly significant at conventional 

levels. Urban households maintain energy levels, but at the expense of their 

dietary quality, as measured by dietary diversity. 

It is also interesting to note the change in the coefficients on the urban 

dummy once the interaction variables have been included in the models. 

Specifically the coefficients in the food consumption, dietary diversity, 

and protein models become positive, as we would expect. These results are 

consistent with evidence on the relative wealth of urban areas. 

The regression models of dietary diversity, protein availability, and shares of 

expenditure and calories highlight some differences between rural and urban 

households. Urban households experienced much larger declines in dietary 

diversity and protein availability than rural households did, reflecting potentially serious reductions in micronutrient and macronutrient intake. They also 

exhibited a greater movement, in terms of calorie and expenditure shares, 

out of higher quality food groups into grains (tables 8 and 9). Urban households reduced calorie shares coming from dairy, vegetables, fruit and sugar; 

Table 8

Differential changes in expenditure shares by area

Grain Meat/fish Dairy Oil/fat Vegetable Fruit Sugar

Log wheat flour price 0.187*** -0.032*** -0.018** -0.032*** -0.029*** -0.068*** -0.011***

[0.012] [0.009] [0.008] [0.004] [0.004] [0.007] [0.002]

Log wheat flour price X 

urban dummy

0.051*** -0.003 -0.016** 0.010** 0.01 -0.032*** -0.013***

[0.015] [0.009] [0.007] [0.004] [0.008] [0.011] [0.003]

Observations 20,491 20,491 20,491 20,491 20,491 20,491 20,491

R2 0.476 0.319 0.261 0.271 0.268 0.287 0.146

P-value of F-statistic of 

joint significance 0.000 0.002 0.000 0.000 0.031 0.000 0.000

See notes for table 4. The last row displays the p-value of the F-statistic of the joint significance of both coefficients related to the log of wheat 

flour prices.

Source: USDA, Economic Research Service.

Table 9

Differential changes in calorie shares by area

Grain Meat/fish Dairy Oil/fat Vegetable Fruit Sugar

Log wheat flour price 0.037*** 0 -0.003 -0.016*** 0 -0.019*** -0.001

[0.008] [0.002] [0.004] [0.004] [0.002] [0.003] [0.002]

Log wheat flour price X 

urban dummy

0.066*** -0.004* -0.014*** -0.008 -0.005* -0.019*** -0.015***

[0.011] [0.002] [0.004] [0.006] [0.003] [0.004] [0.003]

Observations 20,491 20,491 20,491 20,491 20,491 20,491 20,491

R2 0.328 0.289 0.287 0.241 0.261 0.236 0.237

P-value of F-statistic of 

joint significance 0.000 0.195 0.001 0.000 0.084 0.000 0.000

See notes for table 4. The last row displays the p-value of the F-statistic of the joint significance of both coefficients related to the log of wheat 

flour prices.

Source: USDA, Economic Research Service.
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whereas rural households only reduced calorie shares coming from oil/fat 

and fruit. Also, urban households experienced a greater movement into staple 

foods than rural households did; for a 1-percent increase in the price of wheat 

flour, urban households increased expenditure and calorie shares on grain by 

0.24 percent and 0.10 percent, respectively. The large increase in the expenditure share on grain suggests that the decline in purchasing power for urban 

households may have pushed them to buy more wheat products despite the 

large price increases. Below we test this hypothesis by examining the grams 

of wheat products consumed per capita. 

There are a few potential explanations for the observed differences in 

behavior between rural and urban households. The differences may be driven 

by preferences for dietary diversity; that is, rural households may have a 

preference for maintaining the quality of their diet and thus are willing to 

cope with a small reduction in calorie availability in lieu of a larger reduction in dietary diversity. Alternatively, rural households that engage in home 

food production may have greater access to an assortment of foods during 

the period of rising prices, which would make it relatively easier for them to 

maintain a more diverse diet. 

We next turn to the more specific proxy for the net seller/net buyer relationship—whether households have access to agricultural land (tables 10-12). 

The observed differences are not as stark as those for the rural/urban proxy, 

though the general patterns are similar. Agricultural households did not experience as large a decline in the real value of food consumption or in dietary 

diversity as their counterparts, but did experience a slightly larger decline 

in calorie availability. The impact on protein availability was similar across 

groups. In addition, changes in dietary composition, based on expenditure 

and calorie shares, exhibit patterns similar to the national sample, with a 

general movement out of higher quality food groups toward staples. For some 

food groups, we observe small statistical differences between households 

based on access to agricultural land. The main results of this analysis are 

robust to several empirical tests. (See appendix 2 for details.) 

Demand for Wheat Products

Given the importance of wheat in the Afghan diet, we examine changes in 

the demand for wheat products, as measured by the daily quantity of wheat 

consumed in grams per capita (table 13).23 At the national level, we observe 

no statistically significant change in the demand for wheat products, though 

the coefficient on wheat flour prices is negative. Considering the price effects 

based on area, we find, on average, a large positive change in urban areas and 

no statistically significant change in rural areas. Considering the price effects 

based on access to agricultural land, we find a decline for agricultural households, but no statistically significant change for nonagricultural households. 

The decrease in wheat consumption associated with the price increases 

for agricultural households is in line with the “Law of Demand”—as price 

increases, quantity demanded decreases. The effect in urban areas stands in 

contrast to this fundamental principle, but is in line with the economic incentives and preferences of households. As their purchasing power declined, 

urban households were forced to make budgetary adjustments. They shifted 

their consumption toward cheap foods in order to maintain energy levels and 

23Wheat products include wheat, 

wheat flour, naan (Afghan bread), and 

other items derived from wheat.
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Table 10

The differential impact of higher food prices on household well-being, 

by access to agricultural land

Log real value 

of per capita 

monthly food 

consumption

Log 

per capita 

daily calorie 

availability

Log 

food consumption 

score

Log 

per capita 

daily protein 

availability

Log wheat flour price -0.195*** -0.105*** -0.073*** -0.272***

[0.022] [0.022] [0.025] [0.074]

Log wheat flour price X 

nonagricultural HH

-0.074*** 0.033* -0.089*** -0.006

[0.020] [0.017] [0.018] [0.051]

Nonagricultural HH 0.184*** -0.141** 0.203*** -0.068

[0.067] [0.059] [0.059] [0.175]

Consumption quintile 2 0.341*** 0.218*** 0.163*** 0.284***

[0.008] [0.008] [0.009] [0.016]

Consumption quintile 3 0.537*** 0.323*** 0.244*** 0.444***

[0.008] [0.009] [0.009] [0.020]

Consumption quintile 4 0.738*** 0.421*** 0.336*** 0.672***

[0.010] [0.010] [0.010] [0.024]

Consumption quintile 5 1.093*** 0.565*** 0.476*** 1.104***

[0.012] [0.011] [0.011] [0.031]

Log vegetable oil price -0.024 0.094*** 0.015 0.360***

[0.035] [0.035] [0.039] [0.105]

Log domestic rice price 0.003 -0.112*** 0.084*** -0.460***

[0.023] [0.021] [0.022] [0.097]

Log lamb price -0.242*** -0.169*** -0.042 -0.128

[0.053] [0.051] [0.056] [0.133]

Log milk price 0.006 -0.064** -0.024 -0.347***

[0.027] [0.025] [0.024] [0.064]

Log kerosene price 0.159*** 0.148*** -0.081* 0.584***

[0.043] [0.042] [0.043] [0.119]

Head age 0.023 0.014 0.156*** 0.172***

[0.018] [0.019] [0.017] [0.049]

Head married 0.016 -0.039*** 0.092*** -0.005

[0.011] [0.009] [0.009] [0.025]

Head literate 0.023*** 0.048*** -0.037*** -0.001

[0.006] [0.005] [0.005] [0.017]

Plateau 0.025* 0.028** -0.007 0.043

[0.013] [0.012] [0.014] [0.028]

Mountainous 0.035*** 0.038*** -0.001 0.080***

[0.012] [0.012] [0.012] [0.028]

Observations 20,491 20,491 20,491 20,491

R2 0.659 0.409 0.451 0.304

P-value of F-statistic of 

joint significance 0.000 0.001 0.000 0.000

See notes for table 4. Nonagricultural HH dummy is an indicator for households that do not 

report owning or operating agricultural land. The last row displays the p-value of the F-statistic 

of the joint significance of both coefficients related to the log of wheat flour prices.

Source: USDA, Economic Research Service.
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Table 11

Differential changes in expenditure shares by access to agricultural land

Grain Meat/fish Dairy Oil/fat Vegetable Fruit Sugar

Log wheat flour price 0.184*** -0.043*** -0.012 -0.032*** -0.024*** -0.065*** -0.012***

[0.012] [0.009] [0.008] [0.004] [0.005] [0.007] [0.002]

Log wheat flour price X 

urban dummy

0.021** 0.020*** -0.028*** 0.005* -0.003 -0.012** 0

[0.009] [0.007] [0.007] [0.003] [0.004] [0.006] [0.002]

Observations 20,491 20,491 20,491 20,491 20,491 20,491 20,491

R2 0.478 0.319 0.265 0.27 0.263 0.286 0.142

P-value of F-statistic of 

joint significance 0.000 0.01 0.000 0.000 0.000 0.000 0.000

See notes for table 4. The last row displays the p-value of the F-statistic of the joint significance of both coefficients related to the log of wheat 

flour prices.

Source: USDA, Economic Research Service.

Table 12

Differential changes in calorie shares by access to agricultural land

Grain Meat/fish Dairy Oil/fat Vegetable Fruit Sugar

Log wheat flour price 0.029*** -0.002 0 -0.013*** 0.002 -0.017*** 0

[0.008] [0.002] [0.005] [0.004] [0.002] [0.003] [0.002]

Log wheat flour price X 

urban dummy

0.029*** 0.004*** -0.015*** -0.002 -0.005*** -0.008*** -0.004***

[0.006] [0.002] [0.004] [0.004] [0.002] [0.002] [0.002]

Observations 20,491 20,491 20,491 20,491 20,491 20,491 20,491

R2 0.326 0.289 0.289 0.235 0.259 0.235 0.234

P-value of F-statistic of 

joint significance 0.000 0.389 0.001 0.002 0.236 0.000 0.022

See notes for table 4. The last row displays the p-value of the F-statistic of the joint significance of both coefficients related to the log of wheat 

flour prices.

Source: USDA, Economic Research Service.

Table 13

Changes in wheat consumption

Log per capita daily wheat consumption

Log wheat flour price -0.039 -0.05 -0.100***

[0.034] [0.035] [0.036]

Log wheat flour price X urban dummy 0.203***

[0.048]

Log wheat flour price X nonagricultural HH 0.097***

[0.027]

Observations 19,325 19,325 19,325

R2 0.215 0.218 0.203

P-value of F-statistic of joint significance 0.005 0.932

See notes for table 4. The sample includes all households that report positive consumption of 

wheat products. The last row displays the p-value of the F-statistic of the joint significance of 

both coefficients related to the log of wheat flour prices.

Source: USDA, Economic Research Service.
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wheat products provide the largest number of calories per Afghani. Data on 

calories per Afghani spent for several commonly consumed foods support 

this story (table 14). 

We argue that these patterns, though not conclusive, are broadly consistent with 

the paradox of Giffen goods—for which quantity demanded increases rather 

than falls as price increases. (See box, “The Paradox of Giffen Goods,” for 

details.) The findings suggest that wheat products are both inferior goods, for 

which quantity demanded increases as household income decreases, and Giffen 

goods. The increase in wheat prices induces households to buy other relatively 

cheaper goods; however, since household purchasing power is lower due to 

the price increase of a key household necessity, households are induced to buy 

more of the inferior wheat products. In urban areas, the latter effect outweighs 

the former effect and, overall, households purchase more wheat products.

These findings are consistent with Jensen and Miller (2008), who find 

evidence of Giffen behavior related to rice consumption among impoverished 

urban households in the Hunan province of China using a randomized experiment. Jensen and Miller argue—theoretically and empirically—that a good 

may exhibit Giffen properties for certain subpopulations under specific conditions, but may not necessarily exhibit such properties for all populations. 

Table 14

Calories per Afghani by food item

Wheat flour 184

Lentils 107

Mung (beans) 105

Purchased naan (Afghan bread) 103

Chickpeas 91

Rice 78

Lamb 13

Beef 9

Goat 9

Notes: Calculations use the food poverty bundle for relatively poor households (20th-50th percentiles). 

Source: 2007-08 National Risk and Vulnerability Assessment.
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Conclusions

With a long history of political instability and conflict, as well as weak infrastructure and mountainous terrain, Afghanistan is particularly vulnerable to 

economic and natural shocks. From 2007 to 2008 the price of wheat flour, 

the Afghan staple, rose dramatically due to a confluence of international and 

domestic factors. We use nationally representative household survey data from 

the 2007-08 National Risk and Vulnerability Assessment (collected by the 

Government of Afghanistan) to assess the impact of this price shock on household well-being. The survey provides a unique opportunity to study household 

responses using data collected during the price shocks, highlighting the benefits 

of such data collection initiatives in low-income, conflict countries. 

We find evidence that the increases in staple food prices led to a decline 

in several measures of household well-being related to food security. At 

the national level, we observe reductions in food consumption, calorie and 

protein levels, and dietary diversity. We also find differences in the impact of 

the price increases across rural and urban areas, as well as based on whether 

a household owned and/or operated agricultural land. 

Recent wildfires and export bans (Russia), flooding (Pakistan), and political 

instability (Middle East) have added to volatility in international commodity 

prices and are raising serious concerns about potential increases in food insecurity and global poverty. Our findings on the short-term household responses 

to high food prices suggest that if price levels were to increase again, many 

households would resort to cutting back on micronutrient-rich foods, as well 

as overall calories. Such episodes can exacerbate chronically low levels of 

nutrient intake in countries with large shares of the population living in 

poverty with generally poor diets. Even if there is only a short-term episode 

of low micronutrient and protein intake, there may be long-term repercussions 

for young children, whose development can be stunted or otherwise affected 

negatively. Recent literature highlights the links between early childhood 

nutrition and cognitive development (Glewwe and King, 2001), as well as 

long-term educational attainment (Alderman et al., 2006). Potential policy 

interventions could include micronutrient supplementation programs based 

on food inflation indicators, targeted food distribution programs, and wheat 

fortification programs.
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Appendix 1—Survey Design, Sample Selection 

and Construction of Key Variables

The primary data are drawn from the 2007-08 National Risk and 

Vulnerability Assessment (NRVA). The survey was administered between 

August 2007 and September 2008 and covered over 20,000 households 

(about 150,000 individuals) in 2,572 communities in all 34 provinces of 

Afghanistan. The frame used for drawing the sample was the 2003-05 

national household listing—a listing of every house in the country; the 

sample was selected following a stratified, multistage design. The population frame was stratified into 46 domains or strata. The 11 provinces with the 

most populous provincial centers were each stratified into urban and rural 

areas, producing 22 strata. Each of the remaining 23 provinces was then 

also treated as a separate stratum (and all were identified as rural areas), 

and finally the nomadic Kuchi population was treated as a separate stratum. 

There were 2,441 primary sampling units (PSUs) from urban and rural 

settled populations and 131 PSUs from Kuchi populations. In the second and 

final stage, households were selected. 

The effective sample size for our analysis is 20,491 households in 394 

districts. The household response rate was 99.8 percent, and the primary 

sampling unit replacement rate was 3 percent. Thirty-two households were 

dropped due to missing female questionnaire modules; all of these households were located in four communities, suggesting systematic errors in field 

operations. Fifty-two households were dropped due to missing consumption 

data. One household was missing data on household size and was dropped 

because per capita measures of consumption and food security could not be 

calculated. 

The survey was implicitly stratified over time, a key element to accounting for 

the seasonality associated with household well-being. Implicit stratification 

means that the frame was sorted both spatially and temporally to ensure that 

(with a systemic interval selection) the selected sample would be seasonally 

representative. (See Kish, 1965, p. 235-6, for a discussion.) Thus, each quarterly sample of the NRVA survey is representative at the national level. 

The NRVA consists of three components: household and community questionnaires and a district market price survey. The household questionnaire 

includes 20 sections—6 administered by female interviewers to female 

household members and 14 administered by male interviewers to the male 

household head. Households were asked about consumption, demography, 

housing infrastructure and access, maternal and child health, education, 

income sources, agriculture and livestock, migration and remittances, assets, 

and credit. 

Price data were collected at the district level based on interviews within 

each community that helped to identify the relevant market. District prices 

were used to calculate the total value of food consumption, described below. 

District prices were aggregated to the stratum level—i.e., urban and rural 

areas within provinces—by quarter in order to mitigate potential measurement error in estimating the coefficient on the log of domestic wheat flour 
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prices. The prices of other goods included in the regressions were constructed 

in a similar manner. 

To calculate the total value of food consumption, prices were matched by 

month, item, and district. Since not all food items were available in all district 

markets at all times of the year, we imputed the missing elements to obtain a 

complete price matrix, which provides prices for those items that households 

may have been producing at home, as well as goods that households may have 

obtained from more distant markets. The imputation process filled in missing 

values using the first feasible methodology according to the following order: 

(1) median of the 20 nearest neighbors (weighted by inverse distance)

(2) province median of that month

(3)national median of that month

(4) median price of 20 neighboring districts of the quarter (weighted by 

inverse distance)

(5) province median of that quarter

(6) national median of that quarter 

In order to account for the significant differences in price and quality 

between domestic and imported wheat and rice, we calculated price averages 

for domestic and imported varieties separately. The survey includes questions on the percentages of imported wheat and rice the household consumes; 

these percentages were used to calculate total expenditure for these items. 

The value of expenditure on food away from home was included in the calculation of the value of food consumption, but not included in the calculation 

of calorie availability since quantity data on such food were not collected. 

Expenditure on food away from home accounts for approximately 2 percent 

of household food expenditure. 

Per capita total consumption is the sum of the total value of goods and 

services utilized divided by the total number of household members. Total 

household consumption (Afghani per month) consists of expenditures on 

food, nonfood, durables, and rent. Nonfood expenditure consists of expenditures on medicine and health, education, clothing, housing and utilities, and 

transportation. The value of durable goods was imputed based on a detailed 

inventory of household assets and takes into account the time depreciation 

of the good, as well as the opportunity cost of the funds tied up in the good. 

Expenditure on rent was calculated using a Hedonic pricing model. 
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Appendix 2—Robustness Tests

We discuss several tests to evaluate the robustness of our coefficient of 

interest—the log of the price of domestic wheat flour. Results are available 

upon request from the author.

We replace our variable of interest with the price of imported wheat flour 

to see whether households respond differently to changes in the price 

of imported versus domestic flour; the main results are qualitatively the 

same. We also replace the price of domestic wheat flour with the prices 

of domestic wheat and imported wheat, with similar results. Although 

the magnitudes of some coefficients differ across these models, the basic 

picture remains the same. 

We include extra controls for household demographics (i.e., the number of 

males, females, and children in the household). There is no change in the 

main results. The coefficient on number of children in a household is often 

statistically significant; it slightly increases the real value of monthly per 

capita food consumption and decreases per capita daily calorie availability. 

We also include indicators of other coping mechanisms that a household may 

employ during the year to examine whether controlling for such differences 

across households change the results in a substantive manner. We include 

dummies for households that sold off livestock in the previous year or households that borrowed money to purchase food; the results are robust to such 

changes. In ongoing research, we directly examine such short-term nonfoodbased strategies, which can have long-term negative effects. For example, if 

households borrow money at very high interest rates, they may enter into a 

situation of perpetual debt, increasing their vulnerability to future shocks. 

Furthermore, the sale of livestock, especially those used as productive assets 

on farms, could reduce the future earning potential of the household. 

In a country like Afghanistan, we might be concerned that food aid is an 

important component of a household’s budget and that households receiving 

food assistance may respond differently to price shocks. We test this hypothesis by excluding 7.03 percent of households that report receiving some form 

of food assistance over the past year. The results do not change.

Finally, we estimate separate regressions for each subpopulation that we 

examine: rural, urban, agricultural, and nonagricultural households; this 

allows full flexibility for all the coefficients included in the model. That is, 

instead of including the interaction term between the urban dummy and the 

price of wheat flour, we run separate regressions for each subpopulation, 

including only the price of wheat flour and the control variables. In all of the 

cases, the coefficients of interest are substantively the same; in some cases, 

allowing more flexibility for the control variables affected the magnitude and 

significance of their coefficients.