United States 

Department of 

Agriculture

Economic

Research

Service

Sharad Tandon

Maurice R. Landes

Estimating the Range of 

Food-Insecure Households 

in India

Economic 

Research 

Report 

Number 133

May 2012
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Recommended citation format for this publication: 

Tandon, Sharad and Maurice R. Landes. Estimating the Range of 

Food-Insecure Households in India, ERR-133, U.S. Department of 

Agriculture, Economic Research Service, May 2012.

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A Report from the Economic Research Service

Abstract

This study provides a quantitative assessment of food security using a large householdlevel expenditure survey conducted by the Government of India during 2004/05. The 

analysis tests the impact of several key assumptions required to estimate actual calories 

consumed from the expenditure data. The authors found significant differences in the 

estimates of calories consumed and the number of food-insecure people under alternative plausible assumptions for computing the calorie content of nonprocessed foods, 

processed foods, and meals eaten outside the household. The measurement errors were 

largest in accounting for calories consumed by the highest and lowest income households. Overall, the difference between the highest and lowest estimate of the number of 

people consuming an average of less than 2,100 calories per day was equivalent to about 

17 percent of India’s population, or 173 million people in 2004/05. Given the significant 

measurement error in estimating calories consumed, it is important to consider not only 

consumption surveys, but also aggregate food availability studies and survey data on 

anthropometric measures that accompany undernourishment—such as growth stunting—

in assessing food insecurity. 

Keywords: Food insecurity, calorie consumption, nutrition, household survey, development, India

Acknowledgments

The authors thank audience members who attended seminars of the USDA Economic 

Research Service and the Food and Agriculture Organization of the United Nations for 

providing valuable comments, as well as four anonymous reviewers for their formal 

comments. We also thank Courtney Knauth and Wynnice Pointer-Napper for editorial and 

design assistance.

Estimating the Range of 

Food-Insecure Households 

in India

Economic 

Research 

Report 

Number 133

May 2012

Sharad Tandon, standon@ers.usda.gov

Maurice R. Landes, mlandes@ers.usda.gov
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Contents

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

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

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Estimation of Calorie Consumption From Household Expenditures . . 4

Baseline Estimates of Calorie Consumption . . . . . . . . . . . . . . . . . . . . . . . 5

Calories From Processed Foods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Calories From Whole Meals Received or Given . . . . . . . . . . . . . . . . . . . 7

Total Calories Purchased per Household Member . . . . . . . . . . . . . . . . . . 9

Results Using Alternate Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Adding It All Up: Implications for Counting 

the Food-Insecure Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Comparison With Direct Response Method Estimates of 

Household Food Security Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Appendix 1. Conversion of household size to 

effective number of young adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Appendix 2. Processed food items for which 

calorie counts could not be determined . . . . . . . . . . . . . . . . . . . . . . . . . . 26
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Summary

What Is the Issue?

Policymakers and researchers are increasingly concerned with assessing 

the worldwide food-insecure population and the ways it may be changing. 

A common denominator in different approaches to assessing food insecurity is the measurement of calories consumed. The ability to measure 

food consumption is basic not only to gauging food insecurity, but also for 

targeting and evaluating policies aimed at alleviating it, such as the U.S. 

Government’s Feed the Future initiative. This report examines problems in 

measuring calorie consumption and the food-insecure population, even when 

extensive household survey data are available, and finds that the extent of 

food insecurity varies markedly according to how it is measured. The analysis focuses on India, the country with the largest food-insecure population 

in the world, using a large household data set compiled by the Government of 

India for tracking household food security.

What Did the Study Find? 

The authors found significant differences in estimates of the size of India’s 

food-insecure population—comprising people who consumed less than 2,100 

calories per day—across three major assessment methodologies: (1) The aggregate production and consumption approach used in the annual global food 

insecurity assessment by the U.S. Department of Agriculture (USDA); (2) the 

household expenditure survey approach; and (3) the survey of direct responses 

to questions on household food security status. Each of the methods, summarized briefly, describes a different but important aspect of food security: 

•	Aggregate production and consumption approach—Food production 

and trade statistics are used to determine a country’s total food availability, which can then be used to estimate the number of food-insecure 

households through data on income distribution. Based on this approach, 

USDA’s 2005 global assessment estimated India’s food-insecure population at 217 million people.

•	Household expenditure survey approach—Household responses to the 

Indian Government’s National Sample Survey allow estimation of calories 

derived from food purchased or produced by households and also provide 

information on the characteristics of these households. Using these data and 

appropriate weighting factors to expand to the entire population, the authors 

found a baseline estimate of 508 million food-insecure people in India for 

2004/05. However, the estimates ranged from 404 million to 577 million, 

depending on alternative plausible assumptions on the calorie content of 

foods and of meals consumed outside the home.

•	Survey of direct responses to questions on household food security—

This approach relies on survey questions that ask respondents about the 

adequacy of food consumption for household members. The authors found 

that estimates based on a specific question on household food security 

in India’s National Sample Survey gave an estimate of 19 million foodinsecure people in 2004/05, sharply lower than for the alternative methods. 

The authors note, however, that the Indian survey instrument differs 
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significantly from the carefully designed, multi-question modules used in 

the United States and elsewhere. 

India’s household expenditure survey data may have the potential to provide 

the most accurate assessment of food insecurity, because they contain the 

most detail on household food availability. In analyzing the data, the authors 

found a large spread in estimates of the food-insecure population in India 

when they used alternative assumptions for estimating calories consumed. 

The highest and lowest counts of calorie intake resulted in an estimated 

difference of about 173 million food-insecure people in 2004/05. Specific 

measurement issues raised by the analysis are:

•	Difficulty in determining calories in processed foods, an increasingly important component of diets across Indian households. Because of the wide range 

of nutritional content within various categories of processed foods, it is not 

possible to reliably discern calorie content from the survey data.

•	Errors in estimating the calorie content of meals consumed outside the

home and meals provided to nonhousehold members—growing trends in 

Indian diets—for which calories cannot be precisely estimated from the 

survey data. Miscounting calories from these sources, as well as those 

from processed foods, can distort estimates of food insecurity rates across 

income groups and survey years.

•	Potential errors associated with estimating consumption of processed

foods and meals outside the home, which vary with household characteristics such as income and, therefore, are nonrandom sources of error. In 

this analysis, we find that the errors are largest when accounting for calories consumed by the highest and lowest income households.

•	Conflicting sources of information on the calorie content of unprocessed

foods. The calorie conversion factors used by the Government of India 

and FAO differ substantially for some foods.

How Was the Study Conducted?

 Household data collected in the 2004/05 round of India’s National Sample 

Survey, a survey of approximately 125,000 households conducted every 

5 years, provide quantity and expenditure information for approximately 

152 different food items for each household. Baseline estimates of calories 

purchased in the form of nonprocessed foods were calculated by multiplying 

quantities purchased by the average amount of calories per unit of quantity, 

using calorie conversion information from a source used by the Government 

of India. The baseline estimates of consumption of nonprocessed food calories were adjusted to account for household purchases of processed foods, 

calories included in the number of meals eaten outside the home, and meals 

given to nonhousehold members (such as to guests). The sensitivity of these 

baseline estimates of calorie consumption was then tested by perturbing the 

baseline assumptions, including (1) using an alternate source of information 

on the calorie content of foods from the Food and Agriculture Organization 

of the United Nations, (2) using alternate assumptions on the cost of calories from processed foods, and (3) accounting for the error involved in estimating the calories included in meals consumed outside the home or given to 

nonhousehold members. 
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Introduction

Despite India’s rapid income growth over the past two decades, current estimates of the number of food-insecure people derived from aggregate production, trade, and income distribution data suggest that the country accounts 

for nearly 40 percent of the world’s food-insecure population (e.g., Shapouri 

et al., 2011). This estimate is corroborated by a high prevalence of anthropometric indicators that accompany undernourishment, such as being underweight (e.g., Deaton and Dreze, 2008). Even though there is overwhelming 

evidence that a large share of the Indian population does not have adequate 

access to food, quantifying the extent of the problem and its change over time 

remains problematic not only in India, but also in other developed and developing countries (Nord et al., 2008; Barrett, 2010).

Three alternative approaches are commonly used to assess national food 

security status:

1. Computing average calorie consumption from aggregate food supply 

and use balances.

2. Using household survey data on food expenditures to estimate household-level food consumption and food security status.

3. Using survey data on household members’ assessments of the 

adequacy of food availability to determine their food security status.

Policymakers often focus on estimates of calories consumed in the first 

two approaches. Each of these methods describes a different but important 

aspect of food security. Aggregate production and trade statistics better 

describe food availability, which can then be used to derive estimates of the 

number of food-insecure households by using information on the distribution of incomes (Shapouri et al., 2011). Household-level consumption survey 

data, by comparison, generally do not provide information on the sources of 

consumption (e.g., domestic versus imported food), but allow policymakers 

to identify which individual households are food insecure, along with the 

location and other characteristics of those households. Household survey data 

also generally allow for a more complete and detailed accounting of food 

items purchased, resulting in somewhat more precise estimates of the nutritional content of food consumed by each individual household (Deaton and 

Subramanian, 1996). 

The third approach for assessing food consumption and food insecurity 

involves using a carefully designed and administered survey module to ask 

respondents directly about the adequacy of food consumption for household 

members (National Research Council, 2006). This is the approach used 

by USDA for assessing food security in the United States. It has also been 

used in local or national surveys in other countries, including Argentina, 

Bangladesh, Brazil, India, and Uganda, and is being developed for use by 

the U.S. Agency for International Development to track food security conditions in developing countries (Nord et al., 2002; Deitchler et al., 2011). This 

method bypasses the data problems of estimating the nutritional content 

of foods consumed, but it does not provide information on food sources or 
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diet composition and may yield different results in different cultural settings 

(Saxena, 2011).

The Government of India regularly generates estimates of food security 

status for policymakers, using survey-based estimates of caloric intake. This 

report, taking advantage of these data, focuses on the calorie consumption 

of individual Indian households using a household-level survey. There have 

been a number of estimates of calorie purchases in India using similar data 

(e.g., Deaton and Subramanian, 1996; Deaton and Dreze, 2008; National 

Sample Survey Organization, 2007; Tandon and Landes, 2011). While estimates of calorie purchases are based on the sum of calories in nonprocessed 

and processed food items, adjusted for calories contained in meals given to 

nonhousehold members and consumed in meals outside the home, these estimates require a number of important assumptions that, according to our findings, can significantly affect the precision of the results.

Next, we briefly describe the survey used to estimate calorie consumption 

throughout this report. We then explain the methods we used to estimate 

calorie consumption from the survey and calories consumed by each household. Subsequently, we examine the sensitivity of the baseline estimates to the 

use of alternative assumptions for three key components of the estimates: (1) 

the calorie content of nonprocessed foods, (2) the calorie content of processed 

foods, and (3) the calories in meals consumed outside the home and provided 

to nonhousehold members. Finally, we assess the overall findings and provide 

comparisons with food insecurity estimates from alternative approaches. 
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Data

Household-level expenditure data are collected annually by India’s National 

Sample Survey Organization (NSSO). The NSSO conducts annual surveys 

on a range of topics and conducts a more detailed survey of both consumer 

expenditures and employment every 5 years. These surveys are the official 

source of household data used by the Indian Government to monitor and 

study poverty and food security in the country. For the consumer expenditure surveys, a sample of approximately 125,000 households is surveyed in 

each round, and the data collected include quantity and value of purchases of 

approximately 152 separate food items, along with the sources of each item 

(e.g., home-produced, purchased, etc.). The survey also reports the number 

of meals consumed outside the household and provided to nonhousehold 

members. In addition to household consumption, the survey reports a range of 

household and individual characteristics, including the number of household 

members, the household location, and the education and age of household 

members. The 61st round of the NSSO Consumer Expenditure Survey used in 

this study reports detailed expenditure data for 124,536 households collected 

during 2004-05.1

The NSSO Consumer Expenditure Survey is not a random sample of Indian 

households. Rather, the sample is geographically dispersed and stratified into 

rural and urban portions and further stratified based on measures of income. 

Portions of rural villages and urban towns are randomly selected to sample 

households and, within these regions, households of particular income groups 

and sectors (e.g., rural or urban) are randomly sampled. Thus, the sample 

becomes representative of the entire country only when the data for the 

households in the survey are properly weighted based on their prevalence in 

the entire country.2

1Surveys for 124,624 households were 

reported, but only 124,584 could be 

matched to probability weights, which 

are needed to estimate purchases for the 

entire Indian population from the sample. 

Furthermore, 48 households were 

excluded where household size could not 

be calculated. We also conducted robustness checks to exclude households with 

possible recording errors. In particular, 

the expenditure on processed food items 

listed in the appendix, which is a subset 

of total food expenditure, is greater than 

the total food expenditure for 1,041 

households. These households have been 

excluded from the analysis. We also 

exclude sample households with implausibly high consumption and households 

where expenditures on nonprocessed 

food were equal to zero but consumption 

of nonprocessed calories was positive. In 

all, these adjustments exclude 1,648 of 

the original 124,536 households, leaving 

a total of 122,888 households. 

2Probability weights are calculated 

using the multipliers provided by 

the NSSO, which are the number of 

households in the entire population 

represented by the household in the 

sample. We first multiply the multiplier 

by the size of the household, which 

gives us the number of people that are 

represented by the household members, 

and then divide by the total population 

to get the weights used in the calculations. Results are qualitatively identical 

if households are treated as the level of 

observation, with weights equal to the 

multiplier divided by the total number 

of Indian households estimated in the 

2001 Indian census.
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Estimation of Calorie Consumption 

From Household Expenditures

We first estimate household calorie consumption using an approach developed by Deaton and Subramanian (1996) for estimating calorie consumption from household expenditure data. It is important to note that the calorie 

consumption estimates are derived from the NSSO data that include both 

expenditures on purchases outside the household and food produced at home. 

The estimates do not, however, account for food losses that may occur at the 

household level. The estimation strategy involves three steps: 

•	Estimation of calories consumed from nonprocessed foods, such as

particular types of rice, fruits, or vegetables. In order to estimate calories 

consumed from each source, we follow Deaton and Subramanian and 

use the estimated calories contained in quantities of each food, based 

on a nutritional study of a broad array of Indian foods conducted by the 

Government of India (Gopalan et al., 1989). 

•	Estimation of calories consumed from processed foods such as salted

snacks, cakes, and pastries. Because of the wide variety of items in this 

category, it is difficult to estimate the actual number of calories in each 

item.3 Given this variety, Deaton and Subramanian based their estimates 

on the value of expenditures on processed foods and used the simplifying assumption that calories from processed foods are purchased at 

a premium over nonprocessed foods. Specifically, they calculated the 

average calories purchased per rupee spent for each household and 

assumed that households received 50 percent fewer calories per rupee 

spent on processed foods than they did from nonprocessed foods.

•	Estimation of calories consumed in meals outside the household and

in meals given to nonhousehold members.4 The NSSO survey provides 

information on the number of such meals for each household, but it is 

necessary to impute the number of calories associated with these meals. 

Deaton and Subramanian accomplished this by econometrically estimating the relationship between the total nonprocessed and processed 

calories purchased by each household, the numbers of home meals, 

meals given, and meals outside the home. Those results are then used to 

derive estimates of the calories associated with each type of meal. Under 

restrictive assumptions, this methodology provides an estimate of calories 

contained in each type of meal.

Once the baseline estimates of total calorie consumption are calculated, 

calorie intake totals for individuals are computed to permit comparisons with 

individual consumption benchmarks. This computation must account for 

both size and composition of households. To achieve this, we use the age and 

gender of children and adults to adjust total household size to “young adult 

equivalents,” for which the benchmark consumption level—used in the 2005 

USDA Food Security Assessment to define a food-insecure person—is 2,100 

calories per day.5,6

3See appendix 2 for a complete list 

of food items for which calorie information was too difficult to infer.

4Previous research suggests that 

meals consumed outside the household 

might be particularly important to 

poorer households and thus significantly affect estimates of calorie consumption (e.g., Minhas, 1991).

5See appendix 1 for a table describing 

how each household member translates 

into the effective number of young 

adults requiring 2,100 calories per day. 

In the sample of 122,888 households, 

the effective household size was 4.68. 

On the other hand, some studies report 

per capita consumption (e.g., Deaton 

and Subramanian, 1996; Deaton and 

Dreze, 2008), while others try to 

account for differing calorie requirements by counting each child as half a 

household member (e.g., Hicks, 2009). 

The household size in each of these 

scenarios is 4.94 and 4.88 respectively, 

which would result in a slightly smaller 

per capita consumption and higher foodinsecure population when this consumption is compared with a consumption 

benchmark of 2,100 calories.

6The benchmark daily calorie consumption used in this analysis (2,100 

calories per day) differs from NSSO 

(2007) estimates. NSSO uses different 

benchmarks for rural and urban households—2,400 calories/day for rural, 

2,100 calories/day for urban. Previous 

research has suggested that the actual benchmark chosen significantly 

changes estimates of food insecurity 

(e.g., Meenakshi and Vishwanathan, 

2003). However, the primary point 

of this analysis is to demonstrate the 

changes in estimates of food security 

in response to changing assumptions. 

When different benchmarks are used 

for rural and urban areas, the main 

result stands that there is a large difference in the estimated number of 

food-insecure individuals.
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Baseline Estimates of Calorie Consumption

The first step in generating the baseline estimates is computation of calories 

derived from the nonprocessed foods itemized in the survey household data, 

using the conversion factor found in Gopalan et al. (1989). The results for 

calories from nonprocessed foods are shown in table 1. 

Table 1 also summarizes information on expenditures on processed foods, the 

number of meals consumed outside the home, and meals given to nonhousehold members, all of which will be accounted for to derive an estimate of total 

calories purchased or home-produced. In the case of processed foods, for which 

only value information is available in the NSSO data, household members 

spent an average of 3.79 rupees per day. This is equivalent to about 9 percent 

of daily food expenditures, although it is substantially larger for some households. Meals consumed outside the home—including meals paid for, received 

from employers, consumed at school, and a residual category of “other meals 

received” (including meals at weddings, birthdays, and other ceremonies)—

do not appear to constitute a large category, on average, but some households 

do consume a large number of these types of meals. The number of meals 

provided to nonhousehold members also appears small for the average household, but again, the values become large for some households. 

The results for nonprocessed calories purchased can also be broken out both 

by food category and the income groups identified in the NSSO survey (table 

2). Column 1 in table 2 shows summary statistics for households with income 

in the top 10th percentile, and column 2 presents summary statistics for all 

other households, labeled as “middle income and poor,” with results pooled 

across both urban and rural sectors.7 The differences in column 3 indicate 

that poorer households purchase an average of 315 fewer calories than their 

more affluent counterparts and also purchase significantly less expensive 

calories, as indicated by the 51 fewer calories received per rupee spent. 

Regardless of income level, the majority of nonprocessed calories purchased 

are derived from grain products. Despite the difference in total nonprocessed 

calories purchased between poorer and richer households, richer households 

are shown to purchase nearly identical amounts of grains—statistically, the 

hypothesis that grain purchases are equal between the two groups cannot be 

rejected at the 10-percent significance level. However, more affluent households purchased significantly more of every other food category, with the 

exception of calories from alcohol. This difference in purchases of foods that 

7

A rural household is classified as 

affluent by the NSSO if it owns a motor 

vehicle, mechanized farm equipment, 

or electrical appliances, if any family 

member is a highly paid professional 

such as a doctor, if the household owns 

a sufficient amount of land, or if it owns 

at least 10 head of cattle and/or buffalo.

Table 1

Summary of data used in constructing estimates of household calorie consumption

Variable Average Std. deviation Minimum Maximum Observations

Nonprocessed calories purchased per household 

member 2,107 845.4 0 68,993 122,888

Rupees spent by entire household on processed food 

items per day 3.79 4.46 0 181.3 122,888

Meals eaten outside by the entire household per day .529 .984 0 21.3 122,888

Meals given by household to nonmembers per day .021 .366 0 32.2 122,888

Source: Estimates calculated by authors using the 61st round of the National Sample Survey.
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are more expensive partially accounts for the substantial difference between 

rich and poor households in rupees spent per calorie purchased.8,9

Calories From Processed Foods

Processed foods comprise a broad array of products of varying nutritional 

content for which the NSSO survey provides only expenditure—but not 

quantity—data. Processed food categories in the survey include items such as 

“salted refreshments,” “cake/pastry,” and residual categories such as “other 

milk products” and “other processed food,” for which it is difficult to assign 

calorie values.10 For our analysis, rather than attaching a calorie value to 

a quantity purchased, it is necessary to attach a calorie value to household 

expenditures on these processed foods. The baseline estimates follow Deation 

and Subramanian (1996) by assuming that the cost of calories from processed 

foods is twice the cost of calories from nonprocessed foods.

8It is interesting to note that, although richer households consumed 

more meat, the number of meat 

calories is much smaller than any other 

food category and has a very small 

standard error. Thus, richer households 

generally substitute more towards 

nonmeat food groups.

9These estimates of sources of 

nonprocessed foods are similar to estimates presented in the NSSO (2007).

10See appendix 2 for a complete 

list of food items that are included as 

“Processed Foods.”

Table 2

Summary statistics of nonprocessed calories purchased 

per day by income group

Variable Affluent 

households

Middle-income 

and poor 

households

Difference 

(column 1–

column 2)

1 2 3

Calories purchased per household 

member per day

2,356

(6.29)

2,041

(2.51)

315.1***

(9.52)

Calories per rupee spent 228.8

(.841)

279.8

(.480)

-51.0***

(1.51)

Source of nonprocessed calories

Calories from grains 1,420

(3.77)

1,424

(1.83)

-4.96

(6.80)

Calories from pulses 116.6

(.597)

87.6

(.205)

29.0***

(1.05)

Calories from butter/oils/sugar 333.8

(1.95)

239.9

(.566)

93.9***

(2.87)

Calories from meat 25.5

 (.308)

18.3

(.157)

7.18***

(.653)

Calories from vegetables 104.4

(.504)

89.3

(.252)

15.1***

(.775)

Calories from fruits and nuts 79.1

(1.09)

39.9

(.352)

39.2***

(1.69)

Calories from eggs 7.64

(.091)

4.97

(.03)

2.67***

(.172)

Calories from dairy 267.3

(1.61)

133.7

(.638)

133.5***

(3.61)

Calories from alcohol 2.28

(.186)

2.88

(.108)

-.594***

(.221)

Observations 25,730 97,158

Standard errors clustered by district are reported in parentheses.

***Denotes significance at the 1% level; **Denotes significance at the 5% level;

 *Denotes significance at the 10% level.

Source: Estimates calculated using the 61st round of the National Sample Survey.
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Following this approach, the cost of calories from nonprocessed foods is 

computed for each household and then doubled to convert each household’s 

expenditures on processed foods to a calorie equivalent. 

Although each household has different values for both calories purchased 

per rupee and rupees spent on processed foods, the sample averages are 

3.79 rupees per household spent on processed foods per day and a cost of 

269 calories of nonprocessed foods per rupee. Applying the assumption that 

processed calories cost twice as much as nonprocessed calories, the average 

household purchases approximately 508 processed calories per day, or 114 

calories per household member per day. It is important to note, however, 

that there does not appear to be a strong basis for assuming that the average 

processed calorie costs twice as much as the average nonprocessed calorie. It 

is possible that processed foods vary widely in caloric content and cost and 

that the composition of purchases of processed foods varies across households based on such factors as occupation (agricultural versus nonagricultural), location (rural versus urban), and income level.

Calories From Whole Meals Received or Given

The NSSO data provide information on the number of meals consumed 

outside the household and meals provided to nonhousehold members, so it 

is necessary to impute estimates of the calories associated with those meals 

to make the required additions and subtractions to household calorie totals. 

Following the methodology introduced by Deation and Subramanian (1996), 

the baseline of calories contained in various types of meals is estimated with 

following specification:

Caloriesir = τr + β1HomeMealsir + β2MealsGivenir + β3MealsReceivedir

+ ControlVarsir + εi

Caloriesir denotes the total amount of calories purchased over the past 30 

days, both processed and nonprocessed, by household i in district r; τr denotes 

a district fixed effect to help absorb unobserved characteristics shared by all 

households within a district; HomeMeals denotes the total number of meals 

eaten by household members at home over the past 30 days; MealsGiven

denotes the total number of meals the household served to nonhousehold 

members over the past 30 days; MealsReceived denotes the total number of 

meals consumed by household members outside of the home over the past 

30 days, which is the sum of meals received from school, employers, other 

sources, and on payment; and ControlVars denotes a number of household 

characteristics that are used as control variables to help absorb unobserved 

variation in calories purchased.11,12

Although this approach requires strong assumptions, the coefficients from 

this specification can be used to adjust calories purchased to account for 

each different type of meal. The coefficient β2 provides an estimate of the 

number of the total calories purchased that were consumed by nonhousehold 

members for each meal, and β3 provides an estimate of the number of calories 

consumed by household members in meals they ate outside the household. 

Estimates of the specification are presented in table 3. Column 1 provides the 

estimates for a sparse specification, excluding the district fixed effects and 

11Control variables include the 

amount of money spent on paan (a nut 

and spice mixture wrapped in betel 

leaf, for chewing), tobacco, intoxicants; 

fuel; the monthly per capita expenditure for the household; the effective number of young adults in the 

household; an indicator for whether the 

household is rural, and an indicator for 

whether the household is reporting insufficient food sources during the year.

12This approach differs from the 

methodology reported by NSSO 

(2007), which provides the number of 

calories used to estimate the calories 

contained in meals eaten outside of the 

household, meals given to nonhousehold members, and calories contained 

in processed foods. However, NSSO 

does not provide an explanation of the 

source of these values.
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control variables. Relative to all other estimated coefficients, the estimated 

coefficient for meals consumed at home has a narrow 95-percent confidence 

interval of approximately 11.0 calories; the coefficient for Meals Given is 

also statistically significant, but has a much larger 95-percent confidence 

interval.13 On the other hand, Meals Received is not statistically significant 

and has an implausibly small magnitude for calories consumed in a meal. 

To investigate whether this is true for all types of meals received outside the 

household, column 2 shows the variable broken down into the number of 

meals received at school, from employers, and from other sources and meals 

received for payment. The coefficients on meals eaten at home and meals 

given to nonhousehold members are essentially unchanged. On the other 

hand, coefficients on meals from school, received for payment, and received 

from other sources are statistically significant. However, the signs on the 

coefficients differ. Meals from school have the expected sign and suggest that 

each meal consumed at school could contain approximately 400 calories, 

which is the resulting decrease in calorie purchases for the household per 

school meal. 

The signs on the coefficients of meals received for payment and meals from 

other sources are the opposite of what would be expected when holding all 

other factors fixed, and the coefficient is likely picking up the effects of some 

omitted variables on the total calories purchased. For example, given that 

these meals include meals at ceremonies such as weddings, it could be that 

primarily richer households receive these meals at ceremonies, and thus the 

coefficient is partially capturing the effects of income on calories purchased. 

Or perhaps receiving a meal from another household requires the household 

to provide a meal to that guest in return on some other occasion. 

The results in columns 3 and 4 try to account for some unobserved heterogeneity by estimating specifications, including fixed effects and control 

variables. The coefficients on types of meals received vary significantly in 

magnitude and, in one instance, even change signs. Given these changes, it 

is difficult to identify the number of calories contained in meals received 

outside the home. However, the estimates of the amount of calories associated 

with meals given by households seem to be stable in magnitude and statistically significant across specifications. 

Thus, when adjusting the calories purchased by individual households in 

response to meals eaten outside the home and meals given to nonhousehold 

members, we use the estimated coefficient on meals given in column 4 (664 

calories per meal). However, it should be noted that, in that estimation, one 

cannot reject the hypothesis that true calorie content of a meal is anything 

between 221 and 1,107 calories at the 5-percent significance level.14,15

13The bounds of the confidence 

interval were calculated by multiplying the estimated standard error of the 

coefficient estimate (7.84) by 1.96 and 

adding and subtracting the value from 

the coefficient estimate.

14The bounds of the confidence interval are again calculated by multiplying 

the estimated standard error of the 

coefficient estimate (225.6) by 1.96 and 

adding and subtracting the value from 

the coefficient estimate.

15These estimations suggest 

considerable uncertainty in the 

calorie content of whole meals. This 

is especially true of meals received 

outside the home. However, this pattern 

suggests that the confidence interval 

should be even larger than suggested 

above and bolsters the argument that 

uncertainty in calorie content of these 

types of meals leads to uncertainty in 

calories available for consumption by 

households.
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Total Calories Purchased per Household Member

Based on the estimation strategies described above, table 4 presents summary 

statistics of household calorie consumption by source. The vast majority of 

calories are obtained from nonprocessed foods. Other sources contribute to 

overall calorie consumption but constitute a much smaller share. However, 

some households consume a large number of calories from these other 

sources, suggesting that calories from processed foods, meals eaten outside 

the household, and given in meals to nonhousehold members are important to 

any estimate of food insecurity.

Accounting for calories consumed from all sources in table 4, table 5 

provides estimates of total calorie consumption (column 1) and the share of 

all households in the sample consuming below 2,100 calories per capita per 

day (column 2). Columns 3 and 4 estimate the same parameters for the entire 

Indian population using probability weights. The estimate of average daily 

calories consumed per household member in the sample is approximately 

2,303 (column 1). Of this total, approximately 114 calories on average were 

obtained from processed foods and approximately 85 calories from meals 

eaten outside the household. 

According to these baseline estimates, approximately 47.2 percent of the households in the sample consumed less than 2,100 calories per person day, the 

consumption level used to define a food-insecure person in the USDA’s Food 

Table 3

Calories purchased per meal consumed

Type of meal Dependent variable: Total household calories 

purchased

1 2 3 4

Meals at home 694.6***

(7.84)

701.2***

(8.03)

696.2***

(6.98)

370.1***

(7.93)

Meals given 827.0***

(269.9)

818.3***

(268.8)

818.8***

(265.1)

664.7***

(225.6)

Meals received 49.2

(34.3)

Meals received from school -401.2***

(65.1)

-327.9***

(37.2)

-158.0***

(35.9)

Meals received from 

employer

-135.5

(96.7)

-188.4***

(50.1)

-178.8***

(52.7)

Meals received for payment 207.0**

(99.8

378.8***

(52.8)

-111.4*

(67.6)

Meals received from other 

sources

349.9***

(42.1)

365.9***

(27.3)

49.8**

(22.4)

District fixed-effects N N Y Y

Control variables N N N Y

Observations 122,888 122,888 122,888 122,888

Notes: * Denotes significance at the 10% level; ** Denotes significance at the 5% level; 

*** Denotes significance at the 1% level. Standard errors are reported in parentheses. 

Source: Estimates calculated using the 61st round of the National Sample Survey.
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Security Assessment for 2005 (Meade et al., 2006).16 If we extrapolate this 

result to all of India, the average person consumes approximately 2,225 calories (column 3), and approximately 49.5 percent of households consume fewer 

than 2,100 calories per person per day. This share of households translates to 

approximately 508 million people according to the 2001 Indian Census.17 The 

differing results for the sample households (columns 1 and 2) and the country 

totals (columns 3 and 4) reflect the use of weights in the national totals to 

compensate for the NSSO sampling procedure, in which richer households are 

oversampled relative to their prevalence in the entire population.18

Figure 1 provides the histogram of average daily per capita calorie purchases 

and describes the entire distribution of the estimated calorie purchases. 

The distribution is centered at the mean calorie purchases of 2,303 calories 

16There is some uncertainty as to 

what level of calorie purchases will 

actually result in a consumption of 

2,100 calories. Wastage in calories 

purchased would imply that purchasing 2,100 calories would result in a 

lower level of consumption. However, 

the point of this analysis is to show 

how the assessment changes when 

assumptions change rather than simply 

reporting the raw numbers of the food 

insecure. Furthermore, the choice of 

2,100 calories purchased can therefore 

be interpreted as more severe food insecurity than 2,100 calories consumed.

17This estimate is derived by multiplying the estimate of the share of 

all Indian households purchasing less 

than 2,100 calories by the number of 

households in the 2001 Indian Census, 

multiplied by the average household 

size in the 2001 Indian Census.

18There are no significant differences 

in the amount of measurement error in 

rural and urban households, and thus 

for simplicity, we report estimates for 

the sample pooled across rural and 

urban sectors.

Table 4

Summary statistics of calories consumed per day by source

Variable Average Std. deviation Minimum Maximum Observations

Nonprocessed 

calories per 

household 

member

2,107 845 0 68,993 122,888

Processed 

calories per 

household 

member

114 302 0 76,730 122,888

Calories 

consumed in 

meals outside 

the household 

per household 

member

85 198 0 3,988 122,888

Calories given 

to nonhousehold members 

per household 

member

3.6 80 0 16,817 122,888

Source: Estimates calculated by authors using the 61st round of the National Sample Survey. 

Each cell reports unweighted summary statistics from the entire sample of 122,888 households.

Table 5

Baseline estimate of average calories consumed

All sample households Total country estimates

 1 2 3 4

Variable Avg. per capita 

cal./day

Share of sample 

that is food 

insecure 

Avg. 

per capita

cal./day

Share of 

food-insecure 

households

Total calories 

consumed

2,303

(2.59)

.472

(.001)

2,225

(4.27)

.495

(.003)

Number of 

households 122,888

Note: Standard errors are reported in parentheses. 

Source: Estimates calculated using the 61st round of the National Sample Survey. Population estimates are constructed using National Sample Survey Organization (NSSO) multipliers to calculate population weights. 
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per day presented in table 4 and is skewed slightly to the right-hand side, 

implying that there are more people consuming well above the average calories than well below the average.

Figure 1

Distribution of daily per capita calories purchased

Source: Estimates calculated using the 61st round of the National Sample Survey.

0 1,000 2,000 3,000 4,000 5,000

Baseline calories per capita
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Results Using Alternate Assumptions

The next step in the analysis is to evaluate the impacts on the individual 

baseline caloric intake estimates of using alternative plausible assumptions 

for deriving those estimates. Each step in computing the baseline calorie estimates required assumptions or methods that are potential sources of error in 

the results. In this section, the baseline assumptions are replaced with other 

plausible assumptions, one by one, to examine the impact on the estimates of 

calories purchased and the food-insecure population. The assumptions and 

methods used in each of the three steps of the baseline calculations are examined by:

•	Using alternative sources of data on the calorie content of nonprocessed

foods.

•	Using alternative assumptions on the costs of calories from processed

foods.

•	Accounting for the large confidence intervals around the econometric

estimates of calories associated with meals eaten outside the household 

and given to nonhousehold members. 

Adjustment 1. The effect of alternative sources of data on the calorie 

content of nonprocessed food.

Estimating calorie consumption from household survey data requires the use 

of conversion factors for converting food quantity data into calorie equivalents. 

The baseline estimates of nonprocessed calories consumed per household 

member are based on the nutritional information for Indian foods contained in 

the Nutritive Value of Indian Food (NVIF) (Gopalan et al., 1989). To evaluate 

the sensitivity of these results to the choice of conversion factors, calorie 

consumption was also calculated using the conversion factors employed by the 

Food and Agriculture Organization of the United Nations (FAO) for estimating 

aggregate calorie availability for South Asian countries.19

The two sources differ in the types of food categories for which nutritional 

information is provided. NVIF provides nutritional information for more 

disaggregated food items than the FAO source, which applies average calorie 

information for a number of different food items.20 Neither source will yield 

unambiguously higher estimates of total calories purchased. NVIF lists 

higher calorie information for 59 food items, FAO provides higher calorie 

information for 40 food items, and 53 items have the same calorie value. The 

net effect of using one source over the other depends on which food items are 

more common in a household’s food purchases.

Although NVIF is somewhat more detailed, the comparison with FAO 

sources is informative for two reasons. First, FAO estimates the number of 

food-insecure people in India (e.g., FAO, 2011), and it is important to assess 

the possible reasons why estimates might differ between sources. Second, 

the comparison can demonstrate whether averaging across food items might 

obscure the numbers of food-insecure households in a country.

19This concordance is available 

online at http://faostat.fao.org/site/368/

DesktopDefault.aspx?PageID=368; we 

accessed the site in August 2010. For 

food items in the NSSO survey that have 

no link to the food items in the FAO 

list, the calorie value from NVIF was 

used to make the two sources of calorie 

information as similar as possible.

20To illustrate the difference between 

the two sources: The NSSO survey 

records purchases of 9 different types 

of pulses, NVIF reports nutritional 

information for 19 distinct types of 

pulses, and FAO reports nutritional 

information for only 3 types. Thus, 9 

of the 19 pulse items reported in NVIF 

are matched to the NSSO categories, 

while 1 of the 3 FAO calorie values are 

matched to each of the 9 different food 

categories. In both instances, the calorie counts are relatively close, with the 

average calories in a kilogram of pulses 

from the NVIF and FAO measured at 

3,418 and 3,511.4, respectively.
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To examine the sensitivity of the results to the alternative sets of conversion 

factors, the total nonprocessed calories consumed are computed using the 

two sets of calorie information. We then estimate the following empirical 

specification:

NonprocessedCaloriesir = τr + βFAONonProcessedCaloriesir 

+ εControlsir + εi

where NonprocessedCaloriesir denotes the total number of nonprocessed 

calories consumed in household i and district r when calories are calculated 

using NVIF conversions, and FAONonprocessedCalories denotes the same 

measure with calories calculated using the nutritional information provided 

by FAO. The symbol τ denotes fixed effects, introduced to account for timeinvariant differences between districts, and Controls denotes a number of 

household variables that are used to control for the effects of observable 

household characteristics on calories consumed.21 With this approach, the 

resulting estimates of the coefficient β will describe how nonprocessed calories consumed differ between the two sources. Estimates of calorie consumption using the nutritional information in NVIF will be higher if the value of β

is greater than 1 (β>1) and, conversely, will be lower if β is less than 1(β<1). 

The results are shown in table 6. Column 1 presents estimates for a sparse 

specification of the model with no fixed effects or control variables; column 

2 presents estimates when the district fixed effects are added; and column 

3 presents estimates when both district fixed effects and household control 

variables are included. Because the estimates of β are greater than 1 and the 

p-value from the hypothesis test of β=1 is essentially 0 in all specifications, 

the results indicate that using the FAO conversion factors yields lower estimates of nonprocessed calories consumed than those derived using the NVIF 

factors. The results in column 3, the most complete estimation model, suggest 

that increasing the FAO calories consumed by one calorie is associated with 

an increase in NVIF calories by 1.031, a difference of 3.1 percent. 

21Control variables include the 

amount of money spent on paan, 

tobacco, intoxicants, and fuel, the 

monthly per capita expenditure for 

the household, the effective number 

of young adults in the household, an 

indicator for whether the household is 

rural, and an indicator for whether the 

household is reporting insufficient food 

sources during the year.

Table 6

Correlation between calories consumed using NVIF nutritional 

information and FAO nutritional information

Dependent variable: Nonprocessed calories 

purchased using NVIF

 1 2 3

Nonprocessed FAO calories 1.034***

(.003)

1.033***

(.003)

1.031***

(.006)

Fixed effects N Y Y

Control variables N N Y

P-value of test coefficient on 

FAOCal=1 .0000 .0000 .0000

Observations 122,888 122,888 122,888

NVIF=Nutritive Value of Indian Foods (Gopalan et al., 1989); FAO=Foreign Agriculture 

Organization.

 *** Denotes significance at the 1% level; **Denotes significance at the 5% level; * Denotes 

significance at the 10% level. Standard errors are reported in parentheses.

Source: Estimates calculated using the 61st round of the National Sample Survey.
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Table 7 shows the estimates of average total calorie consumption and the 

share of households purchasing less than 2,100 calories per day using both 

the NVIF (column 1) and FAO (column 2) conversion factors. Column 3 

indicates that per capita calorie consumption is approximately 63 calories 

lower when using FAO conversion factors to calculate nonprocessed calories consumed rather than the NVIF conversion factors. As a consequence, 

the estimated share of food-insecure households is about 3.6 percent higher 

when using the FAO conversion factors. These estimates from the sample are 

mirrored by estimates for the entire population in columns 4 through 6. For 

the population as a whole, the FAO calorie information increases the estimate 

of food-insecure households by 5.1 percent, which translates into approximately 53 million people according to the 2001 Indian Census (column 6).22 

Adjustment 2. Using alternate assumptions on the cost 

of calories from processed foods

Following the estimation strategy used by Deaton and Subramanian (1996), 

the baseline estimates of household calorie consumption assumed that calories from processed food in each household are twice as expensive as the 

calories from nonprocessed food. This assumption has both desirable and 

undesirable properties. In particular, the assumption implies that poorer 

households also purchase cheaper processed foods than richer households, 

which is a plausible characterization of actual consumption behavior. 

However, there does not appear to be a strong rationale supporting the 

assumption that processed calories should be exactly twice as expensive as 

nonprocessed calories, making it a source of uncertainty in the resulting estimates of household calorie purchases. 

The importance of the assumed calorie content of processed foods is assessed 

by analyzing alternate scenarios. First, we assume that calories purchased in 

processed foods are the same price as those purchased in nonprocessed foods, 

and then we assume that processed food calories are four times more expensive. Although these assumptions are simplistic, many varieties of processed 

22The primary driver of this difference appears to be the calories 

contained in certain types of cereals 

and pulses. In each food category, the 

NVIF has approximately 300 more 

calories contained in a particular type 

of cereal and approximately 200 more 

calories in a particular type of pulse. 

These two food items account for a 

large amount of calories purchased, 

and thus NVIF results in a larger estimate of calorie consumption.

Table 7

Comparison of calorie consumption using alternate sources of nutritional information

All sample households Total country estimates

1 2 3 4 5 6

Calories 

purchased 

using NVIF

Calories purchased using 

FAO

Difference

Calories 

purchased 

using NVIF

Calories 

purchased

using FAO

Difference

Average calorie consumption 2,303

(2.59)

2,240

(2.53)

63.3***

(.375)

2,225

(4.27)

2,147

(4.06)

77.7***

(.736)

Share of food-insecure 

households

.472

(.001)

.508

(.001)

-.036***

(.001)

.495

(.003)

.546

(.003)

-.051***

(.001)

NVIF=Nutritive Value of Indian Foods (Gopalan et al., 1989); FAO=Foreign Agriculture Organization.

Notes: 

1. Standard errors clustered at the district level are presented in parentheses.

2. *** Denotes significant at the 1% level; **significant at the 5% level; * significant at the 10% level.

3. Columns 1 and 4 calculate the estimate of calories purchased using NVIF nutritional totals and the share of households that purchased fewer than 2,100 calories. Columns 2 and 5 calculate the same totals using FAO nutritional tools. Columns 3 and 6 present 

the difference between the two, subtracting columns 2 and 5 from columns 1 and 4, respectively.

Source: Estimates calculated using 122,888 households from the 61st round of the National Sample Survey. Population estimates are constructed using National Sample Survey multipliers to calculate population weights.
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foods purchased by the average household are both more and less expensive 

than these bounds, and these likely represent only a fraction of the processed 

food items available to Indian consumers. The bounds chosen are intended 

to illustrate a range of possibilities rather than a best estimate of calories 

consumed from processed foods. Until household data can identify what 

processed foods households are actually purchasing and their caloric content, 

there will be a large confidence interval on any estimate of calories purchased 

from processed foods. 

The results show that the alternate assumptions on the cost of processed 

food calories have a significant impact on the estimates of per capita calorie 

consumption and the food-insecure population (table 8). The difference 

between assuming that processed calories are four times as expensive as 

those in nonprocessed foods and assuming they are the same price as for 

nonprocessed foods is an average of 171 calories per capita (column 3). That 

translates into a 9.5-percent difference in the share of the sample households 

with per capita daily purchases less than 2,100 calories (column 3). If we 

extrapolate to the entire population in column 6, the estimated difference in 

the number of people purchasing less than 2,100 calories per day is over 98 

million people based on the 2001 Indian Census.

Because of the potential errors introduced by oversimplified assumptions 

about the nutritional content of processed foods, it would be valuable for 

future studies to combine the NSSO consumption data with other data 

sources that better describe the processed foods most consumed by various 

categories of Indian households, as well as the calorie content of those foods. 

For example, Euromonitor International reports total expenditures on all 

packaged foods sold by grocery retailers, as well as totals by subcategories, such as “salted snacks,” that correspond to some of the NSSO codes of 

Table 8

Comparison of calorie consumption using alternate assumptions for the cost of processed foods

All sample households Total country estimates

1 2 3 4 5 6

Variable Consumption assuming 

processed and 

nonprocessed 

foods cost the 

same

Consumption 

assuming 

processed 

foods cost four 

times as much 

as nonprocessed foods

Difference

Consumption assuming 

processed and 

nonprocessed 

foods cost the 

same

Consumption 

assuming processed foods 

cost four times 

as much as 

nonprocessed 

foods

Difference

Average calorie consumption 2,417

(3.05)

2,246

(2.44)

171.2***

(1.29)

2,325

(4.85)

2,174

(4.12)

150.5***

(2.09)

Share of food-insecure 

households

.410

(.001)

.504

(.001)

-.095***

(.001)

.433

(.003)

.528

(.003)

-.095***

(.002)

Notes: 

1. Standard errors are presented in parentheses. 2. *** Denotes significance at the 1% level; ** denotes significance at the 5% level; 

* denotes significance at the 10% level. 3. Columns 1 and 4 estimate the average calories purchased and the share of households 

that purchased less than 2,100 calories using an estimate of calorie consumption that assumes processed foods are the same price 

as nonprocessed foods. Columns 2 and 5 calculate the same totals using an estimate of calorie purchases that assumes processed 

foods are four times as expensive as nonprocessed foods. Columns 3 and 6 present the difference between the two, subtracting columns 2 and 5 from columns 1 and 4, respectively.

 Source: Estimates calculated using 122,888 households from the 61st round of the National Sample Survey. Population estimates are 

constructed using NSS multipliers to calculate population weights.
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processed foods.23 The same source also reports the shares of each of these 

subcategories captured by particular brands, which may permit collection of 

more precise information on prices and nutritional content.

Adjustment 3. Accounting for the large confidence limits around 

estimates of calories in meals eaten outside the household or given to 

nonhousehold members

As discussed earlier, adjusting household calories purchased for meals 

consumed outside the household and meals given to nonhousehold members 

requires estimation of the calories consumed per meal. The NSSO expenditure surveys do not provide information needed to directly compute calories 

consumed in such meals, and the econometric procedures used by others 

(e.g., Deaton and Subramanian, 1996) yield imprecise estimates and introduce potential errors. 

To evaluate the importance of this problem, we assess the impacts of a range 

of alternate assumptions about the calories in meals consumed outside the 

household or given to nonhousehold members by accounting for the uncertainty in the regression techniques used to estimate the calories contained 

in both types of meals. The regression estimate of the calories included in 

both types of meal is approximately 664 calories (table 3), but if we require a 

5-percent significance level, we cannot rule out a true calorie value anywhere 

inside the 95-percent confidence limits of this estimate, or between 221 and 

1,107 calories per meal. 

Table 9 provides estimates of average calories consumed and the share of 

food-insecure individuals for the sample and population, when we use the 

upper and lower bound confidence limits around the econometric estimate 

of calories per meal. These results indicate that the difference between using 

the upper and lower bound limits has an impact on the share of food-insecure 

households similar to that from using alternate sources of caloric conversion 

23Euromonitor International provides 

a list of processed foods along with 

prices and quantities available in India. 

The data were accessed at http://portal.

euromonitor.com/Portal/ResultsList.

aspx on 10/12/2010. Analyzing just 

a few of many options available to 

consumers in the types of processed 

foods listed in appendix 2, we found 

that calories purchased per rupee averaged 47.9, with a minimum of 4.7 and 

a maximum of 348.8. The hypotheses 

that processed foods are the same 

price as nonprocessed foods (269 

calories per rupee, on average) and 

that processed foods are four times as 

expensive (67.25 calories per rupee, on 

average) are within this range.

Table 9

Comparison of calorie purchases using alternate assumptions for meals eaten outside the household

All sample households Total country estimates

1 2 3 4 5 6

Variable Consumption 

using upper 

bound limit for 

meal estimate

Consumption 

using lower 

bound limit for 

meal estimate

Difference

Consumption 

using upper 

bound limit for 

meal estimate

Consumption 

using lower 

bound limit for 

meal estimate

Difference

Average calorie consumption 2,357

(2.63)

2,248

(2.60)

108.9 ***

(.813)

2,279

(4.28)

2,170

(4.34)

108.8***

(1.23)

Share of food-insecure 

households

.437

(.001)

.504

(.001)

-.067***

(.001)

.458

(.003)

.532

(.003)

-.074***

(.001)

Notes: 

1. Standard errors are presented in parentheses.

2. *** Denotes significance at the 1% level; ** denotes significance at the 5% level; * denotes significance at the 10% level.

3. Columns 1 and 4 calculate the estimate of calories purchased and the share of households that purchased less than 2,100 calories 

using the upper bound of the estimate of calories contained in meals eaten outside the household. Columns 2 and 5 calculate the same 

totals using the lower bound of the estimate of calories contained in meals eaten outside the household. Columns 3 and 6 

present the difference between the two, subtracting columns 2 and 5 from columns 1 and 4, respectively.

Source: Estimates calculated using 122,888 households from the 61st round of the National Sample Survey. Population estimates are 

constructed using NSS multipliers to calculate population weights.
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information. The difference in the share of the population that is food insecure between estimates of calorie purchases using the upper and lower bound 

confidence limit is approximately 7.4 percent (column 6), which translates 

into nearly 76 million people according to the 2001 Indian Census. 
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Adding It All Up: Implications for Counting 

the Food-Insecure Population

The preceding sections provided baseline estimates of average caloric intake 

levels and the size of the food-insecure population, along with plausible 

alternative estimates for the key components of those calorie totals—nonprocessed foods, processed foods, and meals eaten or given outside the home—

that suggest scope for substantial error in the baseline estimates. Each of the 

alternative scenarios yielded outcomes that are significantly different from 

each other. Based on the scenario findings, it is possible to construct “High” 

and “Low” estimates of India’s food security status consistent with the NSSO 

data by combining the scenarios that yield the lowest and highest estimates of 

individual calories consumed. The following specific assumptions are made 

in constructing the High and Low estimates of calories consumed:

•	High-calorie estimate: Uses the calorie conversion factors provided in the 

list used by the Indian Government, the Nutritive Value of Indian Foods 

(NVIF), to estimate calories in nonprocessed food purchases; assumes 

that processed calories are the same price as nonprocessed calories; 

and uses the upper bound of the estimate of calories consumed in meals 

outside the home and given to nonhousehold members.

•	Low-calorie estimate: Uses the calorie conversion factors used by the 

Food and Agriculture Organization of the United Nations to estimate 

calories in nonprocessed food purchases, assumes that processed calories are four times the price of nonprocessed calories, and uses the lower 

bound of the estimate of calories consumed in meals outside the home 

and given to nonhousehold members. 

The results show a wide range of estimates of average calories consumed and 

the size of the food-insecure population (table 10). There is an average difference of approximately 280 calories purchased per capita between the High 

and Low estimates (column 3), which translates into a difference in the share 

Table 10

Comparison of high and low scenarios for calories consumed

All sample households Total country estimates

1 2 3 4 5 6

Variable High-calorie 

estimate

Low-calorie 

estimate Difference High-calorie estimate

Low-calorie 

estimate Difference

Average calorie consumption 2,471

(3.09)

2,191

(2.46)

280.2***

(1.52)

2,379

(4.88)

2,120

(4.20)

259.2***

(2.49)

Share of food-insecure 

households

.376

(.001)

.536

(.001)

-.160***

(.001)

.395

(.003)

.563

(.003)

-.168***

(.002)

Notes: 

1. Estimates calculated using 122,888 households from the 61st round of the National Sample Survey. Population estimates are constructed using NSS multipliers to calculate population weights. Standard errors clustered at the district-level are presented in 

parentheses.

2. *** Denotes significance at the 1% level; ** denotes significance at the 5% level; * denotes significance at the 10% level.

3. Columns 1 and 4 calculate the estimate of calories purchased using the ‘High Calorie’ estimate described above and the share of 

households that purchased less than 2,100 calories. Columns 2 and 5 calculate the same totals using the ‘Low Calorie’ estimate. 

Columns 3 and 6 present the difference between the two, subtracting columns 2 and 5 from columns 1 and 4, respectively.

Source: Authors’ calculations (see note 1).
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of food-insecure households in the Indian sample of 16 percent (column 3). 

If we extrapolate to the entire country, the difference in the share of foodinsecure households in India is 16.8 percent, which translates into 173 million 

people according to the 2001 Indian Census. The largest contributor to the 

difference between the High and Low estimates is the uncertainty about how 

many calories are contained in processed foods.

Table 11 presents the differences in the share of total households in India that 

are within various ranges of calorie purchases. The results indicate that the 

difference in average consumption between the High and Low calorie estimates in table 10 is accompanied by relatively large differences in households 

purchasing less than 1,500 and more than 2,400 calories per capita per day. 

Thus, although the primary sources of uncertainty in calorie consumption are 

largest for relatively richer households, households experiencing severe food 

insecurity are also affected.

Table 11

Changes to the depth of food security between the high and low 

scenarios for calories consumed

Share of all Indian households in the food 

security range:

1 2 3

Food security range High-calorie 

estimate

Low-calorie 

estimate

Difference 

(column1 minus 

column 2)

Cal<1,500 .064

(.001)

.151

(.002)

-.087***

(.002)

1,500<Cal<1,800 .133

(.002)

.198

(.002)

-.065***

(.002)

1,800<Cal<2,100 .198

(.002)

.213

(.002)

-.015***

(.002)

2,100<Cal<2,400 .196

(.002)

.167

(.002)

.029***

(.002)

Cal>2,400 .408

(.003)

.270

(.002)

.138***

(.001)

Notes: 

1. Estimates calculated using 122,888 households from the 61st round of the National 

Sample Survey. Estimates are constructed using NSS multipliers to calculate population 

weights. Standard errors clustered at the district level are presented in parentheses.

2. *** Denotes significance at the 1% level; ** denotes significance at the 5% level; 

* denotes significance at the 10% level.

3. The first column calculates the share of all Indian households in each range of total 

calories purchased using ‘High Calorie’ estimates described above. Column 2 calculates the share of households in each range of total calories purchased using the ‘Low 

Calorie’ estimates. Column 3 presents the difference between the two shares.

Source: Authors’ calculations (see note 1).
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Comparison With Direct Response 

Method Estimates of Household Food 

Security Status

A third methodology for assessing food security status is based on survey 

instruments that ask direct questions on food availability and food access 

for household members. This approach is used in the United States with a 

survey instrument, the Household Food Security Survey Module (HFSSM), 

which administers up to 18 questions to elicit information on food security 

conditions, with the data then used to place households on a food security 

scale (National Research Council, 2006). The U.S. Agency for International 

Development has developed a modification of this approach for assessing 

food security conditions in developing countries, using a simplified threequestion module to place households on a 3-point Household Hunger Scale 

(HHS) of “Food Secure,” “Food Insecure Without Hunger,” and “Food 

Insecure With Hunger” categories (Deitchler et al., 2011). 

The NSSO survey used for our study administers a single direct question 

on household food security status: “Do all members of your household get 

enough food every day?”24 Responses to this question in the 2004/05 survey 

indicated that just 19 million people—or about 1.9 percent of the population—had inadequate access to food in at least 1 month of the previous year, 

sharply below what other information on India’s food security would suggest. 

However, given the simplicity of the NSSO instrument, this 2004/05 result 

may not be a credible application of the direct response methodology. 

Unfortunately, other Indian surveys using more complete versions of the 

HFSSM or HHS than the NSSO also do not provide results that can be 

readily compared with our national estimates based on aggregate production and consumption or household calorie consumption methods. Agarwal 

et al. (2009) surveyed 410 adult females in a Northeast Delhi slum using 

a 4-item variant of the HFSSM and found 51 percent of respondents to be 

food insecure. Gopichandran et al. (2010) studied 130 urban households in 

Vellore, Tamil Nadu using the HFSSM and found 61.5 percent of respondents to be food insecure with hunger and 13.1 percent to be food insecure 

without hunger. A survey of 282 low-income households in a backward area 

of Orissa reported by Nord et al. (2002), also using a variant of the HFSSM, 

found 92 percent of households to be food insecure and 57 percent to be food 

insecure with hunger. The narrow geographic scope, differing time periods, 

and nonrandom nature of the samples used in each of these studies make it 

impossible to compare the results using the direct response methodology with 

our results. A recent study provided validation of the internal consistency of 

results using the HHS methodology across several countries, but it did not 

provide external validation of HHS-based results against other methods and 

measures, such as household expenditure, food consumption, or nutritional 

status (Deitchler et al., 2010). 

24Response choices are: (1) Yes: 

Every month of the year; (2) Some 

months of the year; (3) No: no month 

of the year. Households choosing either 

(2) or (3) are counted as food insecure.
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Conclusions

We find that estimates of the number of households classified as food insecure vary significantly with alternate plausible assumptions. Overall, when 

accounting for the three sources of error examined in this report, the share 

of India’s population consuming less than 2,100 calories per day ranges from 

39.5 percent (404 million people) to 56.3 percent (577 million people), for 

a difference of approximately 173 million people.25 By contrast, USDA’s 

Food Security Assessment for 2004/05 (Meade et al., 2006) estimated the 

world’s total food-insecure population, using the same 2,100 calorie per day 

benchmark, at 775 million people and India’s share at 217 million. Thus, the 

potential error in one country is equivalent to about 22 percent of the total 

food-insecure population of 70 developing countries in 2004/05. Although 

the low estimates derived here suggest the possibility that India’s food situation could be better than indicated by some assessments based on household 

data, even the most conservative estimates in this analysis indicate that nearly 

40 percent of the population is food insecure based on the 2,100 calorie per 

capita daily standard. 

We are required to make a number of assumptions to estimate calories 

consumed per household member, even when using detailed household 

expenditure data. Assumptions regarding the amount of calories from each 

source, calories contained in nonprocessed and processed foods, and calories contained in meals eaten outside the household and given to nonhousehold members all have the potential to introduce error in estimates of 

calories purchased by households. In particular, the estimates of calories 

purchased are sensitive to assumptions accounting for calories contained in 

processed foods. These factors can result in estimates of calorie purchases 

that are different than the true values and that, in turn, affect the analysis of 

researchers concerned with the efficacy of programs designed to improve 

food security. 

In estimating calories consumed in India—the country with the largest 

number of food-insecure individuals in the world—we find important characteristics in the consumption patterns among India’s most vulnerable population. Although it is commonly thought that processed foods are important 

primarily for more affluent consumers in developing countries, the poorest 

households in India devote a nontrivial share of their purchases to such foods. 

Furthermore, relative to more affluent households, the poorest households 

purchase significantly cheaper sources of calories, primarily composed of 

grains, rather than a more diversified diet. This pattern is consistent with the 

persistently high rates of malnutrition in India.26

The approach in this study to estimating actual calorie consumption is in 

contrast to methods that assess food security by administering a survey 

module that asks respondents directly about the adequacy of food consumption for household members. The single question administered in the NSSO 

survey yielded a sharply lower estimate of the food-insecure population 

than other measures, but the instrument may be too simplistic to represent 

a credible application of this method. Other studies in India using the direct 

response approach all used localized, nonrandom samples in different years, 

so it is not possible to compare them with the national results using either 

25The population estimates are based 

on sample weights linked to India’s 

2001 Indian Census and are not strictly 

comparable to the USDA estimates for 

2004/05.

26Malnutrition rates estimated by the 

World Health Organization are available at http://www.who.int/nutgrowthdb/database/ countries/en/ (Accessed 

January 2011).
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the aggregate production and consumption or household consumption survey 

methods. Additional work may be needed to strengthen the direct response 

survey methodology used for national surveys in India, as well as to provide 

more external validation of results using the direct response approach against 

results using the other methods. 

In addition to obfuscating the actual number of food-insecure people in the 

sample, the potential measurement error introduced in estimating calories 

consumed has important implications for any empirical analysis of calorie 

consumption. For example, some studies have found that calorie consumption 

in India has been decreasing over time (NSSO, 2007; Deaton and Dreze, 2008). 

Researchers have also noted, however, that consumption of processed foods and 

meals outside the household have been increasing (Kumar et al., 2007). Given 

that the data do not indicate the number of calories in those sources, it is also 

possible that, if calories from those sources have been systematically underestimated, calorie consumption might not actually be decreasing and could even be 

increasing for a subset of the population. Without a better understanding of the 

nutritional content of all sources of calories, it is very difficult even to identify 

trends in calorie consumption over time.27

These issues suggest that researchers and policymakers should rely on a 

number of different indicators of calorie consumption and food security 

beyond those based on household consumption surveys. Additional measures 

could include information on observable health indicators, such as emaciation 

or stunted growth, that are associated with low calorie consumption, as well 

as studies of aggregate food availability.28 Finally, more attention might be 

given to strengthening survey enumeration methods to reduce key sources of 

error in computing calorie purchases.

27There are other concerns in using 

calories purchased per household 

member as a dependent variable in any 

empirical analysis. In particular, in the 

construction of the variable, we used 

household size, the amount of money 

used to purchase food, and a number 

of other household-level variables. 

We cannot consistently estimate the 

relationship between total calories 

purchased as the dependent variable 

and any of these variables, or even 

variables constructed using them, given 

the possibility of division bias (Borjas, 

1980). In these instances, even if the 

true correlation between the variables 

is precisely zero, the mechanics of 

constructing the variables can cause a 

spurious correlation. 

28In addition, policymakers should 

focus on the response of food security 

and calories consumed to policy interventions and income and production 

shocks that cause exogenous variation 

in the independent variables of interest.
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Appendix 1. Conversion of household size to 

effective number of young adults

We used the following conversions, which were used by NSSO (2007), when 

converting each household member into the effective number of young adults:

Age in 

completed 

years

Male Female

(1) (2) (3)

less than 1 0.43 0.43

1-3 .54 .54

4-6 .72 .72

7-9 .87 .87

10-12 1.03 .93

13-15 .97 .80

16-19 1.02 .75

20-39 1.00 1.71

40-49 .95 .68

50-59 .90 .64

60-69 .80 .51

70+ .70 .50
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Appendix 2. Processed food items for which 

calorie counts could not be determined

The following is a list of food items (NSSO code in parentheses) for which 

only values of household expenditure are given or for which it is too difficult 

to estimate the number of calories purchased:

Other Rice Products (106), Other Wheat Products (114), Other Cereals (122), 

Other Pulse Products (153), Other Milk Products (167), Other Edible Oils 

(174), Others: birds, crab, oyster, tortoise, etc. (186), Other Vegetables (224), 

Other Fresh Fruits (247), Other Dry Fruits (257), Cold Beverages (295), 

Fruit Juice and Shake (296), Other Beverages (298), Biscuits (300), Salted 

Refreshment (301), Prepared Sweets (302), Cake/Pastry (304), Sauce (306), 

Other Processed Food (308).