Contract No.: 53-3198-4-025

MPR Reference No.: 8243-140

FOOD STAMP PARTICIPANTS’

FOOD SECURITY AND

NUTRIENT AVAILABILITY

Final Report

July 1999

Barbara Cohen

James Ohls

Margaret Andrews

Michael Ponza

Lorenzo Moreno

Amy Zambrowski

Rhoda Cohen

Submitted to: Submitted by:

U.S. Department of Agriculture Mathematica Policy Research, Inc.

Food and Nutrition Service P.O. Box 2393

Office of Analysis and Evaluation Princeton, NJ 08543-2393

3101 Park Center Drive (609) 799-3535

Alexandria, VA 22302

Project Officers Project Director:

Patricia McKinney James C. Ohls

Margaret Andrews

Principal Investigators:

Barbara Cohen

James Ohls

Survey Director:

Rhoda Cohen

iii

ACKNOWLEDGMENTS

We would like to thank the many people who contributed to this study. At FNS, Margaret

Andrews and Patricia McKinney, the project officers, provided guidance, advice, and good ideas

throughout the project. Important input was also received from Ted Macaluso and Steven Carlson.

An external advisory panel provided helpful review and suggestions at critical points. The

members are Robert Moffitt, Pamela Haines, John Eltinge, and Suzanne Murphy.

Within MPR, Lorenzo Moreno made extensive input into the design of the study, as did its first

Survey Director, Anne Ciemnecki. Barbara Carlson designed and implemented the sampling and

computed the weights used in the analysis. Sampling advice was provided by John Hall. The

project nutritionist, Liz Lutchman, made major contributions to organizing the food coding work and

providing nutrition expertise to support that effort. Phil Gleason reviewed an earlier draft of this

report and provided valuable suggestions.

Survey staff who made important contributions in helping make the data collection successful

included Julita Milliner, Robin Most, Jim Cashion, Sally Waltman, Bea Jones, Marianne Stevenson,

Margo Salem, and Jessica Mamer. The success of the field survey effort owed much to the three

survey supervisors, Gayle Jones, Susan Drury, and Michele Waters-Hooks. Barbara Kolln

programmed the survey interview with assistance from Linda Bandeh. Mike Watts implemented the

CATI sample management program and organized the creation of analysis files from the survey data.

Typing support within the survey division was provided by Elizabeth Finnerty, Gloria Gustus, Lynne

Beres, and Denise Dunn.

Both the field interviewers and the telephone survey staff provided excellent interviewing

support for the project.

The crew in the food coding room worked long and hard, with great care, to help turn carrots

into calories. Lucy Tindall organized and supervised the coding operation. Among the coders

making important contributions were Phyllis Schanck, Trish Wurpel, Steven Bishop, Terry Silverii,

Marsha Tobias, Chris McGrath, and Leela Narayan.

Extraordinary analytical programming support was provided by Amy Zambrowski, who

designed and implemented the file structure. Important analysis programming was also provided by

Laura Hession, Courtney Carter, Elizabeth Stuart, and Jill Corcoran.

Marjorie Mitchell did an excellent and tireless job of coordinating the production of this report.

She was assisted in this production work by Cindy McClure, Monica Capizzi, Cathy Harper, Jennifer

Baskwell, and Jill Miller. Walt Brower provided strong editing support.

We also thank the many Food Stamp Program offices throughout the country who assisted us

by providing sample frame and respondent location information, as well as the respondents

themselves who patiently cooperated with the interviewing.

v

CONTENTS

Chapter Page

EXECUTIVE SUMMARY ....................................... xv

I INTRODUCTION .............................................. 1

A. AN OVERVIEW OF THE FSP .................................. 2

1. Eligibility Criteria .......................................... 2

2. Application and Recertification Procedures ...................... 4

3. Benefits ................................................. 4

B. ISSUES RELATED TO FOOD SECURITY AND 

NUTRIENT AVAILABILITY ................................... 5

1. Food Security ............................................. 6

2. Nutrient Availability Based on Seven-Day Food Use Data .......... 10

3. Research Issues .......................................... 11

C. RESEARCH QUESTIONS .................................... 14

D. OUTLINE OF REPORT ...................................... 15

II DATA AND METHODS ......................................... 17

A. SAMPLING AND DATA COLLECTION METHODS .............. 17

1. The Household Surveys .................................... 17

2. Response Rates .......................................... 21

B. ANALYSIS METHODS ...................................... 21

1. Weighting ............................................... 24

2. Calculation of Variances .................................... 25

3. Formal Hypothesis Tests ................................... 26

C. LIMITATIONS OF THE DATA AND ANALYSIS .................. 26

1. Lags Between Participant Sampling and Data Collection ............ 26

2. Lack of Nonparticipants Without Telephones .................... 27

3. Accuracy of Nonparticipant Eligibility Determination .............. 27
CONTENTS (continued)

Chapter Page

vi

II D. DESCRIPTION OF THE SAMPLES OF PARTICIPANTS 

(continued) AND NONPARTICIPANTS ................................... 28

E. COMPARISONS OF THE FOOD STAMP SAMPLE WITH 

OTHER DATA ON FOOD STAMP RECIPIENTS .................. 31

III FOOD SECURITY LEVELS AND THEIR DETERMINANTS ........... 35

A. LEVELS OF FOOD INSUFFICIENCY AND FOOD SECURITY ....... 35

1. Aspects of Food Security ................................... 38

2. Overall Levels of Food Security .............................. 41

B. HOUSEHOLD CHARACTERISTICS ASSOCIATED 

WITH FOOD INSECURITY ................................... 46

C. PERCEIVED REASONS FOR NOT HAVING ENOUGH FOOD ...... 55

D. COPING MECHANISMS USED BY HOUSEHOLDS .............. 55

E. RELATIONSHIP BETWEEN FOOD SECURITY AND FOOD 

SUFFICIENCY LEVELS ..................................... 58

F. CONCLUSIONS ............................................ 59

IV STORE ACCESS AND FOOD SECURITY .......................... 61

A. BACKGROUND ............................................ 61

B. STORE ACCESS IN THE NFSPS SAMPLE ....................... 63

C. ASSOCIATIONS OF STORE ACCESS AND FOOD SECURITY ...... 67

D. CONCLUSIONS ............................................ 71
CONTENTS (continued)

Chapter Page

vii

V NUTRIENT AVAILABILITY ..................................... 73

A. OVERVIEW OF THE DATA .................................. 73

B. NUTRITION-RELATED CHARACTERISTICS OF THE

 ANALYSIS SAMPLE ........................................ 74

C. NUTRIENT AVAILABILITY FROM HOME FOOD SUPPLIES ....... 78

1. Average Nutrient Availability ................................ 78

2. Percentages of Households with Food Use Exceeding RDA-Based 

Threshold Levels ......................................... 78

3. Subgroup Analysis ........................................ 81

D. FOOD USE BY FOOD GROUP ................................ 86

E. DETERMINANTS OF NUTRIENT AVAILABILITY ............... 86

1. Variables Used in the Regressions ............................. 86

2. Regression Results ........................................ 90

3. Summary ............................................... 92

VI CORRELATIONS BETWEEN FOOD SECURITY

AND NUTRIENT AVAILABILITY ................................ 95

A. NUTRIENT AVAILABILITY FOR DIFFERENT LEVELS OF

FOOD SECURITY .......................................... 95

B. REGRESSION ANALYSIS .................................... 97

1. Descriptive Regressions .................................... 98

2. Regressions with an Interactive Specification ................... 101

C. DISCUSSION OF FINDINGS ................................. 106

1. Relevant Past Research .................................... 107

2. The Time Periods Covered by the Alternative Measures ........... 114

3. Use of Coping Mechanisms ................................ 115

4. Possible Behavior-Based Explanations of the Patterns

in the Data ............................................. 115
CONTENTS (continued)

Chapter Page

viii

REFERENCES ............................................... 119

APPENDIX A: DATA COLLECTION METHODS .................. A.1

APPENDIX B: WEIGHTING ................................... B.1

APPENDIX C: VARIANCES ................................... C.1

APPENDIX D: CONVERSION OF FOOD USE DATA INTO

NUTRIENT AVAILABILITY ESTIMATES ........... D.1

APPENDIX E: GEOCODING .................................. E.1

APPENDIX F: COMPARISON OF NFSPS SEVEN-DAY

FOOD USE DATA WITH OTHER SOURCES ......... F.1

APPENDIX G: COMPARISON OF SEVEN-DAY AND FOUR-DAY

FOOD USE INTERVIEW RESULTS ................ G.1

APPENDIX H: DETAILED REGRESSION RESULTS ............... H.1

APPENDIX I: RESPONSES ON INDIVIDUAL FOOD SECURITY

ITEMS, BY PARTICIPATION STATUS .............. I.1

APPENDIX J: FOOD USE SHARES BY FOOD GROUP FOR

FOOD STAMP HOUSEHOLDS,

BY HUNGER CLASSIFICATION STATUS ........... J.1

APPENDIX K: ANALYSIS OF THE ROBUSTNESS OF THE FINDINGS

IN CHAPTER VI ................................ K.1
ix

TABLES

Table Page

II.1 SURVEY RESPONSE RATES ...................................... 22

II.2 CHARACTERISTICS OF FOOD STAMP PARTICIPANT AND 

NONPARTICIPANT HOUSEHOLD SAMPLES ......................... 29

II.3 CHARACTERISTICS OF HOUSEHOLDS RECEIVING FOOD STAMPS ..... 32

II.4 AVERAGE MONTHLY INCOME AND FOOD STAMP BENEFITS FOR

FOOD STAMP HOUSEHOLDS, BY SELECTED CHARACTERISTICS ...... 34

III.1 SELECTED INDICATORS OF FOOD SUFFICIENCY AMONG 

PARTICIPANTS AND NONPARTICIPANTS .......................... 39

III.2 SELECTED INDICATORS OF FOOD INSECURITY AMONG 

PARTICIPANTS AND NONPARTICIPANTS WITH CHILDREN 

UNDER 18 YEARS OF AGE ........................................ 40

III.3 FREQUENCY OF CUTTING/SKIPPING MEALS OR GOING 

WITHOUT FOOD FOR A WHOLE DAY BECAUSE OF LACK OF 

FOOD OR MONEY ............................................... 42

III.4 FOOD SECURITY AMONG PARTICIPANTS AND 

NONPARTICIPANTS ............................................. 44

III.5 COMPARISON WITH FOOD INSECURITY LEVEL FOR OTHER 

POPULATIONS .................................................. 45

III.6 FOOD SECURITY LEVELS OF FOOD STAMP PROGRAM 

PARTICIPANTS, BY HOUSEHOLD CHARACTERISTICS ............... 47

III.7 MULTIVARIATE LOGIT ANALYSIS OF THE DETERMINANTS 

OF FOOD INSECURITY AMONG PARTICIPANTS ..................... 49

III.8 FOOD SECURITY LEVELS FOR ELIGIBLE NONPARTICIPANTS,

BY HOUSEHOLD CHARACTERISTICS .............................. 51

III.9 MULTIVARIATE LOGIT ANALYSIS OF THE DETERMINANTS 

OF FOOD INSECURITY AMONG ELIGIBLE NONPARTICIPANTS ........ 52
TABLES (continued)

Table Page

x

III.10 FOOD SECURITY LEVELS FOR NEAR-ELIGIBLE

NONPARTICIPANTS, BY HOUSEHOLD 

CHARACTERISTICS ............................................. 53

III.11 MULTIVARIATE LOGIT ANALYSIS OF THE DETERMINANTS 

OF FOOD INSECURITY AMONG NEAR-ELIGIBLE 

PARTICIPANTS ................................................. 54

III.12 AMONG THOSE WHO DO NOT HAVE ENOUGH FOOD TO 

EAT IN THEIR HOUSEHOLD, REASONS FOR NOT HAVING 

ENOUGH TO EAT ............................................... 56

III.13 COPING MECHANISMS AND FOOD SECURITY AMONG 

PARTICIPANTS ................................................. 57

III.14 FOOD SUFFICIENCY AND FOOD SECURITY ......................... 60

IV.1 ACCESS TO STORES ............................................. 64

IV.2 FOOD SECURITY AND ACCESS TO STORES ......................... 68

V.1 SELECTED CHARACTERISTICS OF THE SAMPLE AVAILABLE

FOR THE NUTRIENT AVAILABILITY ANALYSIS ..................... 75

V.2 NUTRIENT AVAILABILITY MEASURES FOR SELECTED

NUTRIENTS .................................................... 79

V.3 NUTRIENT AVAILABILITY AS PROPORTION OF RDA, 

BY HOUSEHOLD CHARACTERISTICS .............................. 82

V.4 PROPORTION OF HOUSEHOLDS WITH NUTRIENT 

AVAILABILITY = 100 PERCENT OF RDA, BY HOUSEHOLD

CHARACTERISTIC .............................................. 84

V.5 PROPORTION OF HOUSEHOLDS WITH NUTRIENT

AVAILABILITY $75 PERCENT OF RDA, BY HOUSEHOLD

CHARACTERISTIC .............................................. 85

V.6 SHARE OF VARIOUS TYPES OF FOODS IN HOUSEHOLD

FOOD USE ..................................................... 87
TABLES (continued)

Table Page

xi

V.7 REGRESSION ANALYSIS OF RELATIONSHIPS BETWEEN

NUTRIENT AVAILABILITY AND HOUSEHOLD

CHARACTERISTICS ............................................. 91

VI.1 HOUSEHOLD NUTRIENT AVAILABILITY, BY FOOD SECURITY

INDEX LEVELS ................................................. 96

VI.2 REGRESSION ANALYSIS OF RELATIONSHIPS BETWEEN NUTRIENT

AVAILABILITY, HOUSEHOLD CHARACTERISTICS, AND FOOD

SECURITY ..................................................... 99

VI.3 REGRESSION ANALYSIS OF RELATIONSHIPS BETWEEN NUTRIENT

AVAILABILITY AND HOUSEHOLD CHARACTERISTICS ............. 104

VI.4 AVAILABILITY OF FOOD ENERGY PER WEEK PER ENU, BY LEVELS

OF FOOD SUFFICIENCY ......................................... 109

VI.5 NUTRIENT AVAILABILITY BY LEVELS OF FOOD SUFFICIENCY ...... 111

VI.6 MEAN INTAKES FOR WOMEN 19 TO 50, EXPRESSED AS 

PERCENTAGE OF RDAs ......................................... 112

VI.7 STANDARD DEVIATIONS OF NUTRIENT AVAILABILITY 

VARIABLES, BY FOOD SECURITY LEVEL ......................... 118

xiii

EXHIBITS

Exhibit Page

III.1 FOOD SECURITY QUESTIONS .................................... 36

xv

EXECUTIVE SUMMARY

The Food Stamp Program (FSP), administered by the Food and Nutrition Service (FNS) of the

U.S. Department of Agriculture (USDA), provides financially needy households with benefits that

are used for the purchase of food from authorized retailers. To receive food stamps, households

must meet eligibility requirements (primarily related to income and assets). In 1997, the program

provided more than $22 billion in benefits to 22 million people in 9 million households.

The National Food Stamp Program Survey (NFSPS) was conducted for FNS by Mathematica

Policy Research, Inc. (MPR) between June 1996 and January 1997, to obtain information with which

to assess how well the FSP is serving its current and potential clients. One of the main objectives

of the survey was to examine the food security of FSP participants, in terms of the adequacy of the

food available to them and their risk of hunger. The study also examined the amount of nutrients

FSP participants used from home food supplies. Both of these are important measures of the

program’s success in improving the nutritional status of its participants. Key research questions

included: 

C What levels of food security are experienced by program participants and

nonparticipants?

C How do food security levels vary by household characteristics and selected program

design parameters?

C What are households’ perceived reasons for food insecurity?

C What are the main mechanisms for coping with food security problems?

C What is the relationship between food security and nutrient availability?

PRINCIPAL FINDINGS

Nationally, 50 percent of FSP participants experience some level of food insecurity. Using

a measure of food security based on 18 survey questions (Hamilton et al. 1997), 28 percent of FSP

participants were classified as food insecure without hunger, 17 percent were classified as food

insecure with moderate hunger, and 5 percent were classified as food insecure with severe hunger.

Virtually all respondents experiencing food insecurity indicated that the main reason for their food

insecurity was lack of financial resources. Food security is lower among FSP participants than

among the two nonparticipant groups examined, both of which had higher average incomes.
xvi

The average levels of nutrients available to respondents, based on a seven-day food use

survey, exceed Recommended Dietary Allowances (RDAs) for all of the key nutrients

examined. Nutrient availability as a percentage of RDA ranged from 114 for calcium and 117 for

zinc to 256 for vitamin C. 

However, behind the averages, substantial percentages of households have nutrient

availability below their RDAs. For instance, the percentage of households meeting 100 percent

of the RDA for iron is 69 percent, while that for the folate is 79 percent. When assessing these

results, one must recognize that the RDAs are set conservatively, and most people do not require the

full RDAs. When the analysis is repeated using 75 percent of the RDA as the criterion, considerably

more households meet the threshold, but not all. The percentage meeting 75 percent of the RDA for

iron is 85 percent; that for folate is 88 percent.

The relationships between food security and variables measuring store access are quite

weak. The strongest such relationship was with the respondent having access to a car, which was

clearly associated with higher levels of food security. No definite correlation was observed between

food security and proximity to a supermarket.

There is an unexpected positive correlation between the level of food insecurity and the

availability of nutrients--households classified as experiencing hunger tend to have higher

levels of nutrient availability than those who are not classified as experiencing hunger. This

positive association exists for all eight nutrients studied and is statistically significant for four of

them, in an analysis of variance framework. When the analysis is placed in a multivariate regression

context that controls for the effects of other factors, the estimated association between the variables

remains positive, and it is statistically significant for some nutrients. The reason for these findings

is unclear, but the body of the report cites a number of past studies and behavioral hypotheses that

could provide at least partial explanations. It is possible, for instance, that there is an association

between the likelihood of a family experiencing food insecurity due to lack of resources at certain

times and its propensity to consume relatively large amounts of food at other times, when resources

to obtain food are available.

DATA AND METHODS

The analysis was based on household surveys conducted using two sample frames: (1) a list

frame consisting of administrative lists of FSP participants, which yielded a sample of FSP

participants, and (2) a random-digit-dialing (RDD) frame, which yielded samples of FSP-eligible and

near-eligible nonparticipants, as well as some FSP participants. Overall, MPR completed surveys

of 2,454 FSP participants, 450 FSP-eligible nonparticipants, and 405 near-eligible nonparticipants.

The data have been weighted to make them nationally representative of these populations. 

Since most of the research questions addressed in this report are descriptive, most findings are

based on tabular and cross-tabular analysis. In some instances, however, multivariate techniques

were used to examine relationships between the roles of various factors when other factors are held

constant.
xvii

The data assembled for the study represent a solid basis for examining the research questions

highlighted above. As with all survey data, however, they have limitations that should be noted in

interpreting the analysis, the three most important being: 

1. Lags between participant sampling and data collection meant that many participants

had dropped off food stamps by the time they were contacted. Since most of the

research questions were relevant only for active participants, these dropouts were not

interviewed. As a result, the sample tends to have too many long-term food stamp

participants and too few short-term participants.

2. The sample lacks nonparticipants without telephones, since the RDD sampling

methodology effectively limited the nonparticipant sample to households with

telephones. While the sample has been post-stratified in an attempt to correct for this,

the correction is probably not complete. To the extent that nonparticipants without

phones are different from those with phones, the non-telephone households are not

reflected in our analysis.

3. The accuracy of nonparticipant eligibility determinations is only approximate, since

nonparticipant eligibility was determined with a short screening instrument that could

not fully replicate all the complex eligibility criteria the FSP uses in assessing applicant

eligibility. Furthermore, even for the full interviews, in which more-detailed data on

income, household expenses, and living arrangements were obtained, the data were not

sufficient to fully replicate the information obtained during an FSP application. As a

result, the determinations of “FSP-eligible” and “FSP-near-eligible” used in the analysis

must be taken as approximations; some households were undoubtedly misclassified.

1

I. INTRODUCTION

The Food Stamp Program (FSP), the largest of the 15 nutrition assistance programs administered

by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), is the

cornerstone of America’s strategy for ensuring that all Americans have enough to eat. Households

participating in the FSP receive benefits that are used to purchase food from authorized retailers.

To receive food stamps, households must meet eligibility requirements, primarily related to income

and assets. In 1997, the program provided more than $22 billion in benefits to more than 22 million

people in 9 million households.

Because the FSP is such an important part of the nation’s policy for providing assistance to lowincome households, it is essential that the program be assessed periodically to see how well it is

achieving its objectives. The National Food Stamp Program Survey (NFSPS), conducted in 1996

by Mathematica Policy Research, Inc. (MPR) was designed to obtain and analyze survey information

from program participants and eligible nonparticipants to assess key aspects of how well the program

is meeting the needs of low-income households requiring food assistance. Three areas of the FSP

structure and operations are of particular interest in the NFSPS:

1. Customer service

2. Access to authorized food retailers

3. Food security and nutrient availability

This report summarizes the findings on food security and nutrient availability. It assesses

whether, under the structure of the current FSP, program participants are getting appropriate amounts
2

of food. Two other reports, Ponza et al. 1998 and Ohls et al. 1998, examine questions of customer

service and retailer access, respectively.

The rest of this introductory chapter provides a context for the report. Section A provides a brief

overview of the FSP. Section B discusses current issues regarding food security and nutrient

availability. Section C examines the research questions, and Section D describes the organization

of the rest of the report.

A. AN OVERVIEW OF THE FSP 

The objective of the FSP, as stated in its authorizing legislation, is to “permit low-income

households to obtain a more nutritious diet through normal channels of trade by increasing food

purchasing power for all eligible households who apply for participation” (see Food Stamp Act of

1977, Section 2). To accomplish this, the USDA administers a multibillion-dollar program that

provides services throughout the United States. 

Eligibility standards and benefit levels for the program are set by Congress. Broad policy

guidance in implementing these standards is provided by FNS, through its headquarters in

Alexandria, Virginia, and through regional offices in various parts of the country. FSP benefits are

federally funded, and program administrative costs are shared by federal, state, and local

governments. Direct day-to-day administration of the program is carried out by the states (or, in

some areas, by counties, under state supervision).

1. Eligibility Criteria

Households must meet eligibility requirements to receive food stamps. Households may have

no more than $2,000 in countable resources, such as a bank account ($3,000 if the household

contains at least one person age 60 or older). Certain resources (such as a house and lot) are not
3

counted. Households have to meet at least one, and usually two, income tests unless all members

are receiving Temporary Assistance for Needy Families (TANF), Supplemental Security Income

(SSI), or, in some places, General Assistance (GA). The gross income test assesses whether the

household’s gross income exceeds 130 percent of the poverty level for its household size. The net

income test is based on gross income minus certain deductions for expenses and other factors. To

be eligible, a household must have net income below the poverty level. Most households must meet

both the gross and net income tests, but a household with an elderly person or a person who is

receiving certain types of disability payments has to meet only the net income test. Except for those

exceptions noted, households with income over the limits for their size are not eligible to receive

food stamps.

The welfare reform act of 1996 ended eligibility for many immigrants and placed time limits

on benefits for able-bodied, childless adults. Most noncitizens are ineligible. Exceptions are certain

refugees, people seeking asylum, immigrants with credit for 10 years of work in this country, and

veterans or active-duty military personnel and their families. If citizenship is in doubt, proof is

required. Alien status must be verified. With some exceptions, able-bodied adults between age 16

and 60 must register for work, accept suitable employment, and take part in an employment and

training program to which they are referred by the food stamp office. Failure to comply with these

requirements can result in disqualification from the program. In addition, able-bodied adults between

age 18 and 50 who do not have any dependent children can get food stamps for only 3 months in a

36-month period if they do not work or participate in a workfare or employment and training

program other than job search. However, this requirement can be waived in some locations. 
4

2. Application and Recertification Procedures 

Households that may be eligible for food stamps can apply at local offices, which are usually

located at the county level (in rural areas) and at the subcounty level (in more densely populated

urban areas). Most applicants are required to appear in person at their local office. However, elderly

or disabled people and anyone who has difficulty getting to the office may be interviewed by

telephone or in their homes. During the application process, households are required to supply

detailed information about household composition, income, assets, and certain expenses to allow the

eligibility staff to determine whether or not they are eligible. In many instances, they are also

required to verify the accuracy of the information they have supplied. Because of the verification

requirements, as well as office scheduling constraints and other factors, the application process

frequently requires two or more trips to the food stamp office.

Households participating in the FSP must periodically be recertified for eligibility. Although

local offices exercise some discretion about the length of the certification period, it tends to be six

months to a year, except that households with incomes judged to be particularly volatile are

recertified more frequently. In general, the recertification process parallels the initial application

process, although recertification can be more expeditious, since the basic information about the case

is available and the focus is on determining whether any key household circumstances have changed,

rather than on obtaining extensive new information.

3. Benefits

Applicant households that meet the legislated income and asset standards are certified as eligible

for the program. Once certified, households receive monthly benefits, with the amount based on

their income (net of certain deductions) and household size. Benefit levels are determined through
5

formulas derived from the “Thrifty Food Plan,” a set of estimated expenditure levels needed to

maintain adequate diets. 

Households have traditionally received benefits in the form of food coupons. Depending on

local procedures and household circumstances, these coupons are issued in one of several ways.

They may be sent to clients through the mail, issued directly over the counter at welfare offices, or

provided through intermediaries (such as banks or check-cashing establishments) when participants

show an Authorization-to-Participate (ATP) card. Except in a few relatively uncommon

circumstances, these coupons can be exchanged only for food at authorized food retailers, of which

there are about 180,000 throughout the country.

The majority of food stamp households receive their benefits through electronic benefit transfer

(EBT) systems, debit-card type mechanisms that debit food stamp accounts electronically after food

is purchased at participating retailers. All states are required by law to set up EBT systems by the

year 2002. It is anticipated that this will have several effects, including making it harder for food

stamp trafficking (selling food stamps for cash) to occur, streamlining retail check-out operations,

and reducing the stigma felt by some participants when using food coupons. Fifty-one percent of

households, receiving 52 percent of total benefits, were using EBT issuances as of October 1998.

Approximately nine percent of the participant sample in the NFSPS received food stamp benefits

through EBT.

B. ISSUES RELATED TO FOOD SECURITY AND NUTRIENT AVAILABILITY

A major objective of the FSP is to ensure the availability of adequate nutritious food to lowincome households. Therefore, this report on the findings of the NFSPS project focuses on assessing

the extent of the program clientele’s access to safe, nutritious food. In addition, it examines such
6

matters as the determinants of food security, and the degree to which food security and food

availability are correlated.

The report uses mainly two methodological constructs to assess the adequacy of households’

access to food. One is “food security,” which is measured through 18 survey questions, on a scale

which has recently been developed under a different FNS-sponsored project (Hamilton et al. 1997).

The other approach is a more traditional “nutrient availability” measure, which has been used on

periodic USDA Nationwide Food Consumption Surveys (NFCSs) since the mid-1960s (Peterkin et

al. 1988). 

1. Food Security

Concern about hunger among Americans has for many decades been an important factor in

shaping United States nutrition policy (see Ohls and Beebout 1993). However, attempts to measure

hunger systematically have posed major challenges to advocates and policy analysts alike. Early

attempts were to define hunger in the medical sense by equating it with the physical manifestations

of malnutrition. It is now recognized that, while associated with it, hunger is not identical to

malnutrition. It is more likely to be a condition preceding the medical or psychological aspects of

malnutrition. Malnutrition is only one of the possible outcomes of hunger. Hunger has also been

defined in the social sense as the inability to obtain an adequate amount of food, even if the shortage

is not prolonged enough to cause health problems. This has historically been measured by food

shortages, missed meals, and inadequate nutrient availability or intake.

The late 1970s and early 1980s marked growing interest in broadening the concept of hunger

to the more general construct of income-induced “food insecurity.” This broader concept came to
This discussion draws heavily on material in Bickel et al. 1996. 1

7

be defined in terms of various phenomena and experiences associated with being at risk of hunger

as well as experiencing hunger directly.1

In an attempt to bridge the gap between the social and medical definitions of hunger, several

efforts were initiated in the late 1980s and early 1990s to define a concept of food insecurity. An

expert working group of the American Institute of Nutrition developed conceptual definitions, which

were published by the Life Sciences Research Office (LSRO) of the Federation of American

Societies for Experimental Biology (Anderson 1990). They are as follows:

C Food Security. “Access by all people at all times to enough food for an active, healthy

life. Food security includes at a minimum: (1) the ready availability of nutritionally

adequate and safe foods, and (2) an assured ability to acquire acceptable foods in

socially acceptable ways (e.g., without resorting to emergency food supplies,

scavenging, stealing, or other coping strategies).”

C Food Insecurity. “Limited or uncertain availability of nutritionally adequate and safe

foods or limited or uncertain ability to acquire acceptable foods in socially acceptable

ways.”

C Hunger. “The uneasy or painful sensation caused by a lack of food. The recurrent and

involuntary lack of access to food. Hunger may produce malnutrition over time . . . .

Hunger . . . . is a potential, although not necessary, consequence of food insecurity.”

In part, the broadening of the relevant concepts took place within the government, with the

inclusion in the NFCS of a set of questions related to whether households perceived themselves as

having adequate food. However, two private research efforts also gave substantial impetus to the

evolving focus on food insecurity. First, the Community Childhood Hunger Identification Project

(CCHIP), using funding from local and national business and philanthropic organizations,

demonstrated that reasonable and consistent answers could be obtained to a set of survey questions

about the broad concept of food insecurity (Wehler 1991 and 1995). This CCHIP project involved
8

the administration of a common survey instrument designed to assess food security in a loosely

coordinated set of local community surveys around the country.

Second, work by Katherine Radimer, Christine Olson, and others at Cornell University

provided additional theoretical support for the growing body of literature on the food security

concept (Radimer et al. 1992; and Olson et al. 1995). Their work also made advances in the use of

measurement scales in assessing the answers to food insecurity-related survey questions.

Beginning in 1992, FNS began a systematic effort to develop a common battery of questions on

food insecurity that could be administered regularly on government-conducted national surveys.

Drawing on the body of research findings on food security that had developed to that point, together

with additional research commissioned from outside researchers, FNS staff assembled the full range

of instrumentation on food security that had been used up to then and identified sets of items that

appeared to have significant promise as indicators of food security. They were assisted in this work

by a panel of experts that included many of the leading researchers in the area.

The culmination of the FNS work on designing a detailed approach for monitoring food security

was a module of questions that appeared for the first time as a supplement to the Current Population

Survey (CPS) in April 1995. These questions obtained information about respondent households’

shopping patterns and about various aspects of food security, as experienced both in the previous 30

days and in the previous 12 months.

Using the April 1995 CPS data, FNS and cooperating federal agencies developed a food security

scale based on 18 of the CPS questions. This was done using an Item Response Theory (IRT) model,

which posits an underlying latent variable (in the present context, food insecurity and hunger) that

cannot be observed directly but can be estimated from respondent answers to a set of instrument

items. The food security model assigns a relative “severity” to each of the 18 survey questions on
9

which it is based, ranging from such low-severity items as whether the respondent “worried whether

our food would run out” to very severe items, such as a child skipping a meal because no food was

available. The household’s food security scale score is computed on the basis of the total number

of affirmative answers to the 18 questions (or 10 questions, if no children are present in the

household). This scale is then divided into ranges of severity using cutpoints that allow

classification of households to the following categories:

C Food secure

C Food insecure; no hunger evident

C Food insecure with hunger evident

C Food insecure with severe hunger evident

Food security scales have been computed based on two time periods: the 12 months previous

to the interview and the 30 days previous to the interview (Hamilton 1997). Based on reliability tests

and related analysis, it was concluded that the 12-month scale is the stronger of the two, and

subsequent attention has focused principally on it.

The food security scale currently in use has been found to perform quite well on standard tests

of statistical reliability, and the model-based ranking of food security questions on which it is based

has strong intuitive face validity. In addition, as compared to methods involving observing nutrient

intake or nutrient availability, the food security scale represents an inexpensive approach to assessing

food security issues for large populations, and it provides an assessment tool which covers a longer

period than just a day or a week.

A disadvantage of the scale is that so far it has been estimated with only one year of data, and

its stability across years has not been verified. (FNS-sponsored work to examine this is currently
However, research undertaken at the Economic Research Service, including that done by Rose 2

and Oliveira (1997a and 1997b) using intake data on food sufficiency, suggests possible associations.

This is discussed in a later chapter.

10

under way.) Also, the scale’s relationship to direct nutrient-based observation data has not

previously been examined, because of lack of suitable data sets.2

2. Nutrient Availability Based on Seven-Day Food Use Data

The other main measurement construct used in this report to assess households’ access to food

is “nutrient availability,” as measured through seven-day food use data. (In parts of the discussion,

the terms “nutrient availability” and “food use” are used interchangeably. From a formal point of

view, nutrient availability is regarded as an outcome variable that is measured through data collection

on “food use.”) The objective of the seven-day food use data collection technique is to record all

the foods the household used from the home food supply during the seven-day period covered by the

data collection. This includes foods used within the home, as well as foods (such as lunches taken

to school) prepared in the home but then taken elsewhere for consumption. Both purchased food and

food obtained at no cost, such as home-grown produce, are included. The food covered in the food

use concept also includes any food wastage occurring within the home, including plate waste and

food gone bad during storage. For each food used, information is obtained on the type of food, the

form of the food as brought into the home (for example, fresh, frozen, canned), the amount used, the

amount purchased, and the cost.

In understanding this technique, it is important to note that “food use,” as the term is used here,

differs significantly from “food eaten” (usually measured by 24-hour-intake interviews). In

particular, food use does not include foods bought and eaten outside the home. On the other hand,

it does include foods that were in the home food supplies but were then wasted (not eaten). Further,
11

it focuses specifically on food used from home supplies, not supplies themselves. Food that is in the

home food inventory at the start of the week but is not used and remains in the home’s food

inventory at the end of the week is not counted.

An obvious analytic disadvantage to focusing on “food use” rather than “food eaten” is that

there is no direct link to actual nutrient consumption. On the other hand, advantages include the

following: (1) unlike 24-hour recall data, the food use data can be used to examine food

expenditures; (2) the food use information focuses on foods from home food supplies, which are the

foods the FSP seeks to influence; and (3) given the techniques available for collecting food use data,

as discussed below, it is usually possible to cover a longer period (for example, 7 days) for a food

use data collection than can, in practical terms, be covered through intake data.

3. Research Issues

While much of the analysis of food security done to date has focused on the overall U.S.

population, the NFSPS affords an opportunity to focus specifically on the food security levels of a

group of households of particular policy interest: FSP participants. The study estimates the

distribution of food security levels among participants and assesses the household characteristics

associated with alternative food security statuses.

The NFSPS data, which include eligible and near-eligible nonparticipants, also make it possible

to examine food security levels for nonparticipants. For eligible nonparticipants, it is possible that

their decisions not to participate may reflect their having adequate levels of food security without

FSP assistance, and it is important to assess whether this is the case. For “near eligible” respondents,

it is of interest to examine whether significant numbers of near-eligibles appear to need help from

the program, as evidenced by their having low levels of food security. 
12

An important challenge in examining such issues is that households with low levels of food

security may be more likely to participate in the FSP and other nutrition programs. This selfselection can make it very difficult to assess direct program impacts with cross-section survey data.

Another set of research issues involves examining what household characteristics and other

factors are associated with low levels of food security. This can aid in understanding the causes of

food insecurity and in devising ways of targeting resources to alleviate the problem. Of particular

interest are factors related to the design parameters of the FSP benefit schedules, such as household

size and shelter costs.

Additional insight can also be gained through examination of households’ perceived reasons for

their lack of food security. One relevant question, for instance, is whether households’ lack of food

security is due principally to lack of general financial resources or to specific elements of their

housing situations or household characteristics. The answers to this and similar questions have

important implications for food assistance policy choices. Closely related insight can be gained

through understanding the “coping” mechanisms that households use to deal with food insecurity.

Additional sets of research issues related to food security revolve around how other sets of

policy-relevant variables affect food security. One set of variables of particular interest involves

measures of access to food stores. As discussed in Ohls et al. (1998), Kaufman et al. (1997), and

Montovani et al. (1997), there has been considerable concern that access of low-income people to

food stores with low prices is significantly limited, a factor that could decrease their ability to obtain

nutritious foods. While recent evidence suggests that this may not be as serious a problem as had

been thought (see each of the above studies), it is nevertheless of interest to assess possible

relationships between food security and store access.
13

So far, the discussion has highlighted research issues focusing on the recently developed food

security concept. There are also important issues relating to nutrient availability as measured by

NFSPS seven-day food use data. Previous research has shown that on average the nutrient

availability for food stamp recipients exceeds the Recommended Dietary Allowances (RDAs), often

by substantial margins (USDA, Human Nutrition Information Service, 1982, Table 11). For

instance, in that previous work, the average nutrient availability as a percentage of RDA was

estimated to range from 114 percent for calcium to 274 percent for vitamin C, with most of the

percentages ranging from 120 to 190. However, these averages masked the fact that substantial

numbers of program participants had nutrient availabilities below 100 percent of their RDAs. For

instance 23 percent of households failed to meet their RDA for food energy and 38 percent failed

to meet the calcium RDA. The current study affords an opportunity to update these estimates.

Since 1977, food sufficiency has been measured through a single question or a short sequence

of questions designed to classify households as having: “enough of the kinds of food you want to

eat,” “enough but not always the kinds of food you want to eat,” “sometimes not enough to eat,” or

“often not enough to eat” (U.S. Bureau of Census, 1995 April Current Population Survey, data

collection instrumentation). Another issue is the degree to which this question taps into the food

security construct or other dimensions of food inadequacy.

Finally, an important methodological research issue examined in this report involves the degree

to which food security levels and nutrient availability are correlated. Since both of these measures

attempt to provide indications of food or nutrient sufficiency, one might expect them to be quite

highly correlated. However, it is possible that the two constructs tap different dimensions of

sufficiency. Also, a number of technical issues could reduce the degree of correlation. For instance,
These other surveys use 24-hour intake observations rather than seven-day food use for their 3

nutrient-based data.

14

the principal food security index that has been developed covers a 12-month period, while the sevenday food use data collection is limited to seven days. 

It is thus of considerable interest to examine the relationship between the two measures. In the

future, both food security data and nutrient-based information will be available in several national

datasets, including the Continuing Survey of Food Intake by Individuals (CSFII) and the National

Health and Nutrient Examination Survey (NHANES), both of which plan to include the food security

module on their next administrations. However, the NFSPS is the first data set that includes both 3

food security information and nutrient-based observations, and it thus provides an early opportunity

to examine relationships between the two types of data.

C. RESEARCH QUESTIONS

The research issues discussed above can be summarized in the following key research questions

that have guided the development of this report:

C What levels of food security are experienced by program participants and

nonparticipants?

C How do food security levels vary by household characteristics and program design

parameters?

C What are households’ perceived reasons for food insecurity?

C What are the main coping mechanisms used to deal with food security problems?

C What are people’s level of dietary knowledge?

C What is the relationship between food security and nutrient availability?
15

D. OUTLINE OF REPORT

The rest of this report is organized into five chapters. Chapter II describes the NFSPS and

presents the characteristics of the participant and nonparticipant samples. Chapter III presents

information on the food security levels of survey respondents, both for the sample as a whole and

also selected subgroups. A multivariate logit analysis of the determinants of food security is also

presented. Chapter IV examines associations between food security and access to food stores.

Chapter V presents information from the seven-day food use data collection, and Chapter VI

examines correlations between the nutrient availability data and the food security data.

17

II. DATA AND METHODS

This chapter describes the data collection methodology underlying the NFSPS and the

characteristics of the participant and nonparticipant samples analyzed in this report. In addition, it

describes analysis methods, including the weights that were constructed to make the participant and

nonparticipant data nationally representative. Limitations of the data and analyses, as well as how

they may affect the findings, are also discussed. 

A. SAMPLING AND DATA COLLECTION METHODS 

Addressing the research objectives highlighted in Chapter I, as well as those of the other reports,

required obtaining nationally representative data from three different sets of households:

1. A sample of FSP participants, who could provide information about their experiences

with the program, their access to stores, their food security, and their food use

2. A sample of FSP-eligible nonparticipants, who could provide information about their

reasons for nonparticipation, their levels of food security and need for food stamp

assistance, and their access to food stores

3. A sample of “near-eligible” nonparticipants with which to examine the potential need

for food stamp benefits by households who were just above the established eligibility

limits, as well as about their access to foodstores

Efficiently obtaining data from all three of these groups required a multifaceted data collection

design as described below. (See Appendix A for a detailed discussion of the methods used to select

the sample, conduct the survey, and process the data.)

1. The Household Surveys

The household surveys, conducted between June 1996 and January 1997, were organized and

directed from MPR’s main survey facilities near Princeton, New Jersey, and were based on samples
18

obtained from two frames: (1) a random-digit-dialing (RDD) frame of all American households with

telephones, and (2) a list frame consisting of administrative lists of FSP participants. 

a. Nonparticipant Household Surveys

For identification of eligible and near-eligible nonparticipants for the data collection, randomly

drawn U.S. telephone numbers were called and given a short screening interview to determine

(1) whether they belonged to a household, and (2) whether the household appeared to meet (eligible)

or almost meet (near-eligible) criteria for food stamps. Households who passed this screen, who

were not FSP participants, and were willing to participate in the survey were then given a full

nonparticipant household interview. Information collected included experiences with the FSP, if

any, data on access to food stores, and food security information. The number of nonparticipant

completions from the RDD frame was 450 eligible nonparticipants and 405 near-eligible

nonparticipants. 

In implementing this approach for the RDD sample, RDD respondents were first asked whether

they were receiving food stamps and what their household size was. Then they were asked whether

the household’s monthly income was greater than or less than “X,” where “X” was set at 150 percent

of the poverty level for a household of that size. Households that passed this initial screen and were

not receiving food stamps were then tracked into the full nonparticipant interview, which obtained

detailed income, asset, and shelter information. Using these detailed data, gross and net income and

deductions, as defined by the FSP, were calculated, as well as countable household assets.

Households whose reported income and assets were under the applicable program limits were then
All households that got this far in the assignment process had reported gross incomes less than 1

150 percent of the poverty level, since otherwise they would have been screened out during the initial

part of the RDD screener interview.

Sample sizes were based on targets set during the design stage of the project, based on trade- 2

offs between precision requirements and costs.

19

placed in the “eligible nonparticipant” sample. Households that were not under these limits but that

had assets less than $15,000 were placed in the “near-eligible nonparticipant” sample.1

b. Participant Household Surveys 

MPR completed 2,454 interviews with FSP participants. Of these, 2,150 were sampled from

the participant list frame (lists of FSP households provided by states or local food stamp offices),

and 304 came from the RDD frame. 

2

In-Person Participant Household Survey from List Frame. A total of 1,109 in-person

interviews were completed with FSP participants from the list frame. These interviews were

conducted in person to obtain data on participant households’ seven-day food use and shopping

behaviors. The in-person participant survey was clustered in a limited number of locations, both to

allow efficiencies in obtaining the samples (see below) and to limit interviewer travel costs. Thirtyfive “primary sampling units” (PSUs), usually counties, were randomly selected from throughout

the country, with probabilities of selection proportional to size. Next, machine-readable lists of FSP

participants were obtained from state or local programs for each of these PSUs, and random samples

of participants were drawn and then interviewed.

This data collection was conducted in respondents’ homes through computer-assisted personal

interviewing (CAPI) on laptop computers. In general, it consisted of two main parts. First, after

setting up an appointment by telephone, the data collector visited the respondent’s home and

conducted an interview of about one to one-and-one-half hours, which covered all the survey topics
20

other than those related to the household’s food use. At the end of the first appointment, the

household was given instructions about how to maintain food use records for the coming week, and

a repeat appointment was scheduled for seven days later. During this second interview, which

typically took between 90 and 150 minutes, information about the household’s food use for the

previous week was recorded through a paper and pencil data collection instrument. 

Telephone Participant Household Surveys from List Frame. An additional 1,041 participant

interviews were completed by telephone with computer-assisted telephone interviewing (CATI)

using an additional sample from the FSP participant list frame. It was efficient to conduct some of

the participant interviews over the telephone rather than in person, because the questions about food

use and detailed shopping behaviors were not administered to all participants. Therefore, a second

sample of participants was drawn from the same set of 35 PSUs discussed in the previous section.

While clustering was not necessary for the actual data collection with this second sample, there were

still considerable costs in assembling the sample frames of participants, so at least some clustering

was still efficient. As a result, it was decided that using exactly the same PSUs for the telephone

participant survey as for the in-person survey would yield maximum efficiencies. 

Telephone Participant Household Surveys from the RDD Frame. While the main purpose

of the RDD sample frame was to identify nonparticipants, a number of FSP participants were also

identified. To supplement the list frame sample, these households were administered a slightly

modified version of the list frame participant interview. A total of 304 completed FSP household

telephone interviews were obtained from the RDD frame. 
21

2. Response Rates

Table II.1 summarizes the response rates that were obtained in the various parts of the data

collection. With the field list sample, 1,109 (1,070 + 39) laptop CAPI interviews were obtained out

of 2,200 sample points released. However, 596 of the sample points proved to be ineligible for the

survey by the time they were contacted, usually because they were no longer receiving food stamps.

When these ineligibles are removed from the base, the response rate is 69 percent. A small number

of the in-person cases completing the first part of the interview failed to complete the food-based

second part a week later, leading to a response rate for the food use data of 67 percent.

In the telephone sample, 1,041 responses were obtained out of a total eligible sample of 1,535,

a 68 percent response rate.

For the RDD sample, 14,514 numbers were released, of which 5,219 were determined ineligible

for the screener, mostly because they were either nonworking or business numbers. Another 1,807

could not be determined. Of the remainder, 6,429 completed the screener, for a completion rate of

75 percent. At the next stage of this interviewing, 1,159 households completed full interviews out

of a total of 1,456 (1,159 + 297) that had passed the screen, yielding a response rate of 80 percent

for the full interview, conditional upon passing the screen. The combined overall response rate for

this sample is 60 percent. 

B. ANALYSIS METHODS

The research questions for this study are largely descriptive. Such issues as average levels of

food security, the types of coping mechanisms that households use to deal with food security

problems, and levels of nutrient availability can be addressed directly from the relevant data. 
22

TABLE II.1

SURVEY RESPONSE RATES

Field List Sample

Total Released 2,200

Eligible Completes with Food Use 1,070

Eligible Completes with No Food Use 39

Eligible Noncompletes 495

Ineligibles 596

CAPI Response Rate .69a

Food Use Response Rate (if CAPI portion completed) .96b

Combined CAPI-Food Use Response Rate .67c

Phone List Sample

Total Released 2,121

Eligible Completes 1,041

Eligible Noncompletes 494

Ineligibles 586

Response Rate .68d

RDD Samplee

Total Released 14,514

Screener

Eligible completes 6,429

Eligible noncompletes 1,059

Ineligible 5,219

Undetermined 1,807

Screener response rate .75f
12,707

14,514

@

6,429

7,488

'

6,429

6,429 % 1,059 % 1,807@ER

' .7517

6,429 % 1,059

6,429 % 1,059 % 5,219

' .5894

1,159

1,159 % 297

' .7960

12,707

14,514

@

6,429

7,488

@

1,159

1,456

'

1,159

1,159 % 297 % 1,509@ER@ER2

' .5984

1,159 % 297

1,159 % 297 % 4,973

' .2265

TABLE II.1 (continued)

23

Interview

Eligible completes 1,159

Eligible noncompletes 297

Ineligible 4,973

Interview response rate .80g

Overall Response Rate .60h

Computed as 1,109/(1,109 + 495). a

Computed as 1,070/1,109. b

Product of previous two rates. c

Computed as 1,041/(1,041 + 494). d

The RDD response rates are adjusted to account for (1) inability to determine whether some of the e

telephone numbers in the original sample were eligible for the screener; and (2) of those eligible for

the screener, inability to determine whether households were eligible for the full survey. The

derivation of these response rates, taking these factors into account, is displayed below:

Screener response rate: f

where screener eligibility rate adjustment ER equals:

Interview response rate: g

Combined screener-interview response rate: h

where interview eligibility rate ER2 equals:
24

Therefore the principal analysis method was to conduct interpretive tabular and cross-tabular analysis

of the relevant data. However, in some instances an attempt was made to separate the associations

of several different variables with an outcome of interest, such as food security. In these types of

analyses, multivariate logit analysis has been used. The sections below highlight a number of issues

that have been addressed in implementing the overall analytic approach.

1. Weighting

The survey was designed to achieve a nationally representative sample by obtaining essentially

the same number of list frame interviews in each PSU, except for “certainty” PSUs, where the target

sample sizes were adjusted upward to reflect their relative sizes appropriately. However, because

of a variety of practical considerations, the goal of equal sample sizes was not always fully

achievable, and, as a result, households in different PSUs effectively had somewhat different

probabilities of selection. Response rates also varied by location. Weighting was used to adjust for

these factors and make the sample representative of the national caseload. The weights used were

based on the inverses of the probabilities of selection.

Weighting was also used when combining the three participant samples (list frame in-person,

list frame phone, and RDD). Each of these samples was self-representing (except for the issues

discussed in the previous paragraph), but because of their different sample sizes, combining the three

directly by weighting observations from each equally was not statistically efficient in terms of

minimizing variances. As a result, weights were constructed that reflected the different variances

implicit in the different sample sizes. (See Appendix B.)

Weighting was used for the nonparticipant sample for a different reason. There was concern

that the sample would not be representative, both because of nonresponse and because the RDD data

collection methodology that was used meant that only households with telephones could be included
Whereas FSP participant households without phones were included in the in-person list sample 3

frame, such households were not included in either the computer-assisted telephone interviewing

(CATI) participant list frame or the RDD frame. Thus, the issue regarding coverage of households

without phones is also relevant for the participant sample. However, the number of FSP participants

identified from the RDD frame is small (304 cases, or 12 percent of the unweighted FSP sample).

In addition, some of the phone list sample cases without phones were followed up in person by field

staff using cellular phones to complete the interview. Therefore, it was decided that the statistical

gain from adjusting the participant sample for telephone coverage did not warrant the costs.

25

in the sample. To correct for this at least partially, it was decided to post-stratify the nonparticipant

sample, so that it would better reflect the population of low-income households who do not receive

food stamps. This was done by assigning weights based on household characteristics, such that the

weighted sample was similar to control data from an external survey with regard to those

characteristics. The methods used in doing this are presented in Appendix B.3

2. Calculation of Variances

Because of the clustering of the sample and the weighting factors used, the standard methods

for computing the variances of sample estimates that are applicable to simple self-weighting samples

(and are routinely generated by most statistical software programs) do not apply to most of the

tabulations presented in this report. In general, the variances of estimates from the current sample

are higher than those that would be applicable to a simple self-weighting sample. This has been

taken into account in the analysis.

Appendix C presents, for selected variables, variances that have been computed using the

STATA analysis package, which uses Taylor’s Series methods for taking into account the sample

design. As shown in that appendix, the design effects for the participant sample tend to be on the

order of “3,” meaning that variances are about three times those that would be observed in a simple

self-weighting sample of the same size. This in turn implies that confidence interval widths around
Design effects may be lower in multivariate analysis, where it is possible to control explicitly

4

for factors that vary across primary sampling units.

26

descriptive statistics are increased by a factor of about 1.76. Design effects are in general

4

considerably lower for the nonparticipant sample, since this sample was not clustered into a limited

number of PSUs.

3. Formal Hypothesis Tests

Much of the analysis in the following chapters is descriptive. Therefore, formal hypothesis tests

have not been performed to examine statistical significance for all the comparisons made concerning

the tabular results; performing a complete set of formal hypothesis tests for every tabular comparison

of potential interest would be somewhat cumbersome, and in most instances it probably would not

add significantly to meaning. However, a number of formal hypothesis tests have been conducted

for certain components of the analysis where they were particularly salient. 

C. LIMITATIONS OF THE DATA AND ANALYSIS

The data assembled for the study represent a solid basis for examining the research questions

highlighted earlier. As with all survey data, however, they have limitations that should be noted in

interpreting the analysis. The most important of these are discussed below.

1. Lags Between Participant Sampling and Data Collection

The list frame participant sample was obtained in spring 1996; however, the data collection

extended into early 1997. This means that by the end of the survey, the sample was about eight

months old, and considerable numbers of participants had dropped off food stamps by the time they

were contacted. Since many of the research questions involved active food stamp participants, these 
The sample should be interpreted as representing a cross-section of the FSP caseload at a given 5

point in time. Even without the interviewing lags, a cross-sectional sample has fewer short-term

participants than the alternative of focusing on a cohort of all participants entering the program at

a given point in time. The interviewing lags further limit the number of short-term participants in

the sample.

27

dropouts were not interviewed. As a result, the sample tends to have too many long-term food stamp

participants and too few short-term participants.5

2. Lack of Nonparticipants Without Telephones

As noted above, the sampling methodology effectively limited the nonparticipant sample to

households with telephones. While the sample has been post-stratified in an attempt to correct for

this, the correction probably is not complete. To the extent that the differences between

nonparticipants without phones and those with phones were not adjusted for, the nontelephone

households are not reflected in the analysis.

3. Accuracy of Nonparticipant Eligibility Determination

At the beginning of the interview, nonparticipant eligibility was determined with a short

screening instrument that could not fully replicate all the complex eligibility criteria the FSP used

in assessing applicant eligibility. Further, even for the full interviews, in which more-detailed data

on income, household expenses, and living arrangements were obtained, the data were not sufficient

to fully replicate the information obtained during an FSP application. As a result, the determinations

of “FSP-eligible” and “FSP-near-eligible” used in the analysis must be taken as approximations;

some households were undoubtedly misclassified.
28

D. DESCRIPTION OF THE SAMPLES OF PARTICIPANTS AND NONPARTICIPANTS

Interviews were completed with a total of 3,309 households for the NFSPS: 2,454 households

participating in the FSP and 855 households not participating (450 estimated eligible nonparticipant

households and 405 ineligible nonparticipant households). This section presents (weighted)

descriptive statistics for the samples of participants and nonparticipants. 

FSP participants, eligible nonparticipants, and near-eligible nonparticipants differ substantially

on their economic and demographic characteristics (Table II.2). FSP participant households are

more disadvantaged economically than eligible nonparticipant and near-eligible nonparticipant

households. Average annual gross income of FSP participant households is $8,468, which is about

$1,500 less than for eligible nonparticipants and more than $6,000 less than for near-eligible

nonparticipants. FSP households were substantially more likely to be on AFDC (now TANF) than

eligible nonparticipant households (30 percent versus 1 percent) or receive SSI (22 percent versus

7 percent). About one-third of households participating in the FSP have earnings, compared with

somewhat more than half of eligible nonparticipants and two-thirds of near-eligible nonparticipants.

Among the three study groups, there are also important differences in household composition.

FSP households are substantially more likely to contain children, and particularly to be single-parent

households with children. Nearly two-thirds of FSP households have children, and one-third are

headed by a single parent. Of eligible nonparticipating households, 40 percent contain children,

while only 6 percent are headed by a single parent. FSP households are less likely to contain elderly

people: about one-quarter of FSP households contain at least one elderly member, compared with

44 percent of eligible nonparticipating households. With regard to demographic characteristics of

the person responsible for the finances of the household, FSP participants are more likely than
29

TABLE II.2

CHARACTERISTICS OF FOOD STAMP PARTICIPANT AND NONPARTICIPANT HOUSEHOLD SAMPLES

(Percentage of Households, Unless Stated Otherwise)

Nonparticipants

Characteristic Participants Eligible Near-Eligible a b

Household Characteristics

Average Household Size 3.0 2.7 3.1

Household Contains:

Elderly 26.5 44.2 31.5 c

Single person 24.5 31.0 21.5 d

Children 63.5 40.4 50.4 e

Single parent with children 34.9 6.0 10.8 f

Multiple adults with children 28.6 34.4 39.6 g

Residential Location

Urban 72.1 66.2 62.4

Rural 22.0 27.4 27.7

Missing 5.9 6.4 9.9

Household Receives:

Earned income 32.5 52.7 67.0

No income 6.0 0.0 8.4

Aid to Families with Dependent Children (AFDC) 30.0 1.1 1.2

General Assistance (GA) 5.7 0.9 0.5

Supplemental Security Income (SSI) 22.3 6.8 3.9

Social Security Income 28.3 37.2 27.4

Average Annual Gross Income $8,468 $9,953 $14,906

Average Monthly Food Stamp Benefit $166 n.a. n.a.

Demographic Characteristics of Respondenth

Race/Ethnicity

African American (not of Hispanic origin) 32.7 16.8 11.6

Asian/Pacific Islander 1.8 1.4 2.6

Hispanic 16.1 14.9 14.1

Native American 1.3 1.3 1.5

White (not of Hispanic origin) 46.9 64.7 69.7

Other 1.1 0.9 0.5

Missing 0.1 1.5 2.4

Age

Less than 20 years 2.9 2.2 2.7

20 to 49 years 67.3 49.1 58.3

50 to 59 years 10.5 11.2 13.6

60 years or more 19.3 37.4 25.5

Female 84.8 76.6 72.5

Marital Status

Never married 35.0 15.3 13.8

Currently married (formal or consensual union) 18.6 44.8 49.5

Separated or divorced 33.1 18.0 21.9

Widowed 12.7 21.1 13.8

Missing 0.6 0.8 1.0
TABLE II.2 (continued)

Nonparticipants

Characteristic Participants Eligible Near-Eligible a b

30

Highest Grade Completed

Less than high school 43.1 36.0 28.3

High school/GED 37.7 44.1 46.2

Associate/BA 8.9 11.4 12.7

Vocational certificate 4.1 3.1 3.8

Other 6.2 5.3 9.0

Missing 0.8 1.4 2.8

Sample Size 2,454 450 405

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Households that meet the income and asset tests for eligibility for food stamps. a

Households that do not meet the income or asset tests for eligibility for food stamps and whose gross income does not exceed two times the b

poverty level for their household size do not have non-vehicle or non-house assets greater than $15,000 and do not have vehicle assets that

exceed $25,000. 

Households that contain at least one member age 60 years or older. c

Households that contain only one member. d

Households that contain at least one member age 18 or younger. e

Households that contain only one member older than age 18 and children (at least one member age 18 or younger). f

Households that contain two or more members older than age 18 and children (at least one member age 18 or younger). g

Respondent most responsible for the finances of the household. h

n.a. = not applicable.
29

TABLE II.2

CHARACTERISTICS OF FOOD STAMP PARTICIPANT AND NONPARTICIPANT HOUSEHOLD SAMPLES

(Percentage of Households, Unless Stated Otherwise)

Nonparticipants

Characteristic Participants Eligible Near-Eligible a b

Household Characteristics

Average Household Size 3.0 2.7 3.1

Household Contains:

Elderly 26.5 44.2 31.5 c

Single person 24.5 31.0 21.5 d

Children 63.5 40.4 50.4 e

Single parent with children 34.9 6.0 10.8 f

Multiple adults with children 28.6 34.4 39.6 g

Residential Location

Urban 72.1 66.2 62.4

Rural 22.0 27.4 27.7

Missing 5.9 6.4 9.9

Household Receives:

Earned income 32.5 52.7 67.0

No income 6.0 0.0 8.4

Aid to Families with Dependent Children (AFDC) 30.0 1.1 1.2

General Assistance (GA) 5.7 0.9 0.5

Supplemental Security Income (SSI) 22.3 6.8 3.9

Social Security Income 28.3 37.2 27.4

Average Annual Gross Income $8,468 $9,953 $14,906

Average Monthly Food Stamp Benefit $166 n.a. n.a.

Demographic Characteristics of Respondenth

Race/Ethnicity

African American (not of Hispanic origin) 32.7 16.8 11.6

Asian/Pacific Islander 1.8 1.4 2.6

Hispanic 16.1 14.9 14.1

Native American 1.3 1.3 1.5

White (not of Hispanic origin) 46.9 64.7 69.7

Other 1.1 0.9 0.5

Missing 0.1 1.5 2.4

Age

Less than 20 years 2.9 2.2 2.7

20 to 49 years 67.3 49.1 58.3

50 to 59 years 10.5 11.2 13.6

60 years or more 19.3 37.4 25.5

Female 84.8 76.6 72.5

Marital Status

Never married 35.0 15.3 13.8

Currently married (formal or consensual union) 18.6 44.8 49.5

Separated or divorced 33.1 18.0 21.9

Widowed 12.7 21.1 13.8

Missing 0.6 0.8 1.0
TABLE II.2 (continued)

Nonparticipants

Characteristic Participants Eligible Near-Eligible a b

30

Highest Grade Completed

Less than high school 43.1 36.0 28.3

High school/GED 37.7 44.1 46.2

Associate/BA 8.9 11.4 12.7

Vocational certificate 4.1 3.1 3.8

Other 6.2 5.3 9.0

Missing 0.8 1.4 2.8

Sample Size 2,454 450 405

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Households that meet the income and asset tests for eligibility for food stamps. a

Households that do not meet the income or asset tests for eligibility for food stamps and whose gross income does not exceed two times the b

poverty level for their household size do not have non-vehicle or non-house assets greater than $15,000 and do not have vehicle assets that

exceed $25,000. 

Households that contain at least one member age 60 years or older. c

Households that contain only one member. d

Households that contain at least one member age 18 or younger. e

Households that contain only one member older than age 18 and children (at least one member age 18 or younger). f

Households that contain two or more members older than age 18 and children (at least one member age 18 or younger). g

Respondent most responsible for the finances of the household. h

n.a. = not applicable.
31

eligible and near-eligible nonparticipants to be African American, between 20 and 49 years of age,

unmarried/separated/divorced, and less educated (Table II.2).

E. COMPARISONS OF THE FOOD STAMP SAMPLE WITH OTHER DATA ON FOOD

STAMP RECIPIENTS

As noted above, there is at least one significant reason for believing that the sample of food

stamp participants is not fully representative--the lags in the sampling and interviewing processes,

which resulted in some of the sample having left food stamps before being contacted. Other reasons

for differences could include (1) statistical sampling variance in either stage of the sampling process

(PSUs and participants); and (2) nonresponse bias, which could be present if some categories of FSP

participants are less likely than others to be located and to agree to an interview. 

To assess the representativeness of the sample, tabulations were generated of two other national

data sources that have characteristics of samples of food stamp participants. One of these sources,

the Food Stamp Quality Control Sample (FSQC), is a data set compiled from FSP administrative

records. The second source, the Survey of Income and Program Participation (SIPP), is an ongoing

survey of American households conducted by the Bureau of the Census, with a special emphasis on

examining households’ participation in programs for low-income families. Both FSQC and the data

sets used in these tabulations can be interpreted as essentially representing cross-sections of the FSP

populations, thus making them comparable to the tabulations from the NFSPS.

Comparisons with these other nationally representative samples of FSP participants reveal that

the current NFSPS contains more participating households with elderly people and fewer receiving

welfare payments than do the other sources (Table II.3). Twenty-seven percent of NFSPS participant

households contain elderly people, compared with 16 percent of NFSPS participants in the FSQC

and 18 percent of FSP participants in the SIPP. Thirty percent of NFSPS participants receive AFDC,
32

TABLE II.3

CHARACTERISTICS OF HOUSEHOLDS RECEIVING FOOD STAMPS

Percentage of Average Number of Persons per

Households Household

Selected Characteristics of Food Stamp Households SIPP FSQC NFSPS SIPP FSQC NFSPS

Demographic Characteristics

Households That Contain:

Elderly 18.1 16.0 26.5 1.3 1.4 2.3 a

Single person 29.3 35.9 24.5 1.0 1.0 1.0 b

Children 65.5 59.7 60.8 3.6 3.4 4.0 c,f

Single parent with children 48.2 41.6 31.9 3.2 3.1 3.3 d,f

Multiple adults with children 16.2 14.9 28.8 4.7 4.5 4.7 e,f

Economic Characteristics

Households That Receive:

Aid to Families with Dependent Children (AFDC) 44.8 38.3 30.0 3.4 3.3 3.9

Supplemental Security Income (SSI) 22.1 22.6 22.3 1.9 1.9 2.3

General Assistance (GA) 5.1 7.2 5.7 2.1 1.4 3.0

Social Security 21.2 18.6 28.3 1.7 1.7 2.3

Earned income 22.0 21.4 32.5 3.6 3.3 3.9

Unearned income 83.5 86.8 82.0 2.7 2.6 2.9

No income 5.7 9.7 6.0 2.2 1.6 2.8

SOURCE: 1994 Survey of Income and Program Participation (SIPP): Eligible Reporter Units--households that reported receiving food stamps

and that are simulated as eligible based on reported income, assets, and other information; Summer 1995 Food Stamp Quality

Control sample (FSQC); 1996 National Food Stamp Program Survey (NFSPS).

NOTE: All data are weighted.

Households that contain at least one member age 60 years or older. a

Households that contain only one member. b

Households that contain at least one member under age 18. c

Households that contain only one member age 18 or older and children (at least one member under age 18). d

Households that contain two or more members age 18 or older and children (at least one member under age 18). e

NFSPS tabulations based on CAPI Food Stamp Program participant sample only (n = 1,109). The telephone data were excluded from these f

comparisons in order to ensure comparability with the food stamp quality control data. In the telephone interviews, in order to minimize

interview time, detailed age data on each household member were not obtained, and it was not possible to fully replicate the definition of

children used in the food stamp quality control data.
33

compared with 38 percent of FSQC participant households and 45 percent in the SIPP. Nearly onethird of NFSPS households participating in the FSP reported having earnings, compared with 21

percent and 22 percent, respectively, for NFSPS participants in the FSQC and SIPP data sets. In

general, FSP participants in the NFSPS reported higher income but lower food stamp benefits than

participants in the FSQC and SIPP (Table II.4). The reason for this latter finding is not clear.
34

TABLE II.4

AVERAGE MONTHLY INCOME AND FOOD STAMP BENEFITS FOR FOOD STAMP HOUSEHOLDS, 

BY SELECTED CHARACTERISTICS

(In Dollars)

Income per Household FSP Benefits per Household

Selected Characteristics of FSP Household SIPP FSQC NFSPS SIPP FSQC NFSPS

All FSP Households 590 529 706 193 177 166

Demographic Characteristics

Households That Contain:

Elderly 569 561 677 67 94 94 a

Single person 433 359 471 67 66 66 b

Children 650 618 758 254 240 219 c,f

Single parent with children 571 547 631 246 233 231 d,f

Multiple adults with children 904 877 894 287 275 206 e,f

Economic Characteristics

Households That Receive:

Aid to Families with Dependent

Children (AFDC) 549 542 752 260 246 235

Supplemental Security Income (SSI) 642 630 730 104 97 105

General Assistance (GA) 541 360 629 143 127 189

Social Security 644 630 796 87 83 95

Earned income 880 867 1121 214 191 182

Unearned income 595 580 721 186 176 162

No income 0 0 0 230 172 176

SOURCE: 1994 Survey of Income and Program Participation (SIPP): Eligible Reporter Units--households that reported receiving food stamps

and that are simulated as eligible based on reported income, assets, and other information; Summer 1995 Food Stamp Quality

Control sample (FSQC); 1996 National Food Stamp Program Survey (NFSPS).

NOTE: Amounts expressed in 1996 dollars. All data are weighted.

Households that contain at least one member age 60 years or older. a

Households that contain only one member. b

Households that contain at least one member under age 18. c

Households that contain only one member age 18 or older and children (at least one member under age 18). d

Households that contain two or more members age 18 or older and children (at least one member under age 18). e

NFSPS tabulations based on CAPI Food Stamp Program participant sample only (n = 1,109). The telephone data were excluded from these f

comparisons in order to ensure comparability with the food stamp quality control data. In the telephone interviews, in order to minimize

interview time, detailed age data on each household member were not obtained, and it was not possible to fully replicate the definition of

children used in the food stamp quality control data.
35

III. FOOD SECURITY LEVELS AND THEIR DETERMINANTS

The food security scale was developed by FNS to measure the existence and severity of

household food security through the presence of a set of indicators. Exhibit III-1 presents the

questions that were included in the food security scale as developed by Hamilton et al. and

reproduced on the NFSPS. In addition to these questions, other items asked in the survey include

indicators of broader food insufficiency, coping mechanisms, and perceived reasons for food

insecurity. 

This analysis of food security begins with a presentation of the prevalence of several indicators

of food insufficiency among the three groups of respondents in the study: FSP participants, eligible

nonparticipants, and near-eligible nonparticipants. These indicators are components of the set of

questions that were used to determine the food security status of respondents. Next, the levels of

food security in the survey population are presented, and corresponding household characteristics

are examined. Determinants of food security and coping mechanisms are also considered.

A. LEVELS OF FOOD INSUFFICIENCY AND FOOD SECURITY

The food security scale used in the current analysis is based on a series of 18 questions that ask

about various aspects of access to and adequacy of food supplies for a household. National survey

data on these 18 items (and others that were later dropped because they were not as useful

analytically) were first analyzed by Hamilton et al. (1997). The work was based on Rasch modeling

techniques, which essentially assume that an underlying phenomenon (in the current context, food

security) is reflected in responses to individual questions. The Rasch model provides a technique

for estimating parameters of the individual items in terms of the degree of food security that they
36

EXHIBIT III-1

FOOD SECURITY QUESTIONS

C In the last 12 months, did you (or other adults in your household) ever cut the size of

your meals or skip meals because there wasn’t enough money for food? 

C How often did this happen--almost every month, some months but not every month, or

in only 1 or 2 months?

C In the last 12 months, did you or other adults in your household ever not eat for a whole

day because there wasn’t enough money for food? 

C How often did this happen--almost every month, some months but not every month, or

in only 1 or 2 months?

C In the last 12 months, did you ever eat less than you felt you should because there wasn’t

enough money to buy food?

C In the last 12 months, were you ever hungry but didn’t eat because you couldn’t afford

enough food?

C Sometimes people lose weight because they don’t have enough to eat. In the last 12

months, did you lose weight because there wasn’t enough food?

C In the last 12 months, did you ever cut the size of any of the children’s meals because

there wasn’t enough money for food?

C In the last 12 months, did any of the children ever skip a meal because there wasn’t

enough money for food? 

C How often did this happen--almost every month, some months but not every month, or

in only 1 or 2 months?

C In the last 12 months were the children ever hungry but you just couldn’t afford more

food?

C In the last 12 months, did any of the children ever not eat for a whole day because there

wasn’t enough money for food?

C “I worried whether our food would run out before we got money to buy more.” Was that

often, sometimes, or never true for you in the last 12 months?
EXHIBIT III-1 (Continued)

37

C “The food that we bought just didn’t last and we didn’t have money to get more.” Was

that often, sometimes, or never true for you in the last 12 months?

C “We couldn’t afford to eat balanced meals.” Was that often, sometimes, or never true

for you in the last 12 months?

C “We couldn’t feed the children a balanced meal because we couldn’t afford that.” Was

that often, sometimes, or never true for you in the last 12 months?

C “The children were not eating enough because we just couldn’t afford enough food.”

Was that often, sometimes, or never true for you in the last 12 months?

C “We relied on only a few kinds of low-cost food to feed the children because we were

running out of money to buy food.” Was that often, sometimes, or never true for you

in the last 12 months?
Appendix I contains complete response frequencies for all the items on the food security scale. 1

38

reflect, by identifying the set of parameters that best fit the observed data. These parameters are then

used to develop a “food security scale,” measuring the degree of food security experienced by each

respondent. In the current context of measuring food security, standard statistical tests suggested a

relatively good “fit” of the data to the underlying model. Below, the food security concept is

introduced empirically by first presenting descriptive data on individual aspects of food security.

Then, tabulations based on the overall food security index are presented.

1. Aspects of Food Security

Obtaining adequate food, as measured by concern about and adequacy of the household’s food

supply or resources for food, is a greater problem for FSP participants than for either eligible or neareligible nonparticipants (Table III.1). More than 60 percent of the FSP participants reported

worrying about their food supply running out, and more than half reported actually running out of

food. (The corresponding percentages for the nonparticipant groups ranged from 32 to 43.) Almost

half of participants (46 percent) reported not always being able to afford to eat properly as compared

to 31 and 23 percent for the nonparticipant groups.1

The measurement of food security for households with children under the age of 18 included

a specific set of questions regarding the ability of the household to feed the children adequately.

These included survey items about not being able to provide (1) balanced meals, (2) enough food,

or (3) a varied diet. Participants were more likely than nonparticipants to report difficulty providing

balanced meals or enough food for the children (Table III.2). However, the eligible nonparticipants

were as likely as participants to report relying on a few kinds of food to feed the children because

of a lack of money.
39

TABLE III.1

SELECTED INDICATORS OF FOOD INSECURITY AMONG PARTICIPANTS

AND NONPARTICIPANTS

Study Group

Nonparticipants

Characteristic Participants Eligible Near-Eligible

Percentage of Respondents Who in the Past 12 Months

Were Sometimes or Often Worried Whether Their

Household’s Food Would Run Out Before They Got Money

to Buy More

Sometimes Worried 40.0 30.7 26.8

Often Worried 22.3 12.7 5.4

Sometimes or Often Worried 62.3 43.4 32.2

Percentage of Respondents Who in the Past 12 Months

Sometimes or Often Ran Out of Food and Didn’t Have

Money to Get More

Sometimes Ran Out 37.5 26.8 17.8

Often Ran Out 15.6 8.7 5.1

Sometimes or Often Ran Out 53.1 35.4 22.8

Percentage of Respondents Who in the Past 12 Months

Sometimes or Often Couldn’t Afford to Eat Properly

Sometimes Couldn’t Afford to Eat Properly 32.5 20.5 17.3

Often Couldn’t Afford to Eat Properly 13.2 10.4 5.8

Sometimes Couldn’t Afford to Eat Properly 45.6 30.9 23.0

Sample Size 2,454 450 405

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

NOTE: Sample sizes may be slightly lower in some cells because of item nonresponse. However, in no cell

is item nonresponse greater than one percent of sample size shown.
40

TABLE III.2

SELECTED INDICATORS OF FOOD INSECURITY AMONG PARTICIPANTS AND

NONPARTICIPANTS WITH CHILDREN UNDER 18 YEARS OF AGE 

Study Group

Nonparticipants

Characteristic Participants Eligible Near-Eligible

Percentage of Respondents Who in the Past 12 Months

Sometimes or Often Couldn’t Afford to Feed the Children

a Balanced Meal

Sometimes Worried 25.1 19.9 16.6

Often Worried 5.2 6.1 2.4

Sometimes or Often Worried 30.3 26.0 19.1

Percentage of Respondents Who in the Past 12 Months

Sometimes or Often Couldn’t Afford to Provide Enough

Food to Their Children

Sometimes Ran Out 17.6 12.3 10.8

Often Ran Out 3.4 3.4 1.9

Sometimes or Often Ran Out 21.0 15.6 12.7

Percentage of Respondents Who in the Past 12 Months

Sometimes or Often Relied on a Few Kinds of Food to

Feed Their Children Because They Were Running Out of

Money to Buy Food

Sometimes Couldn’t Afford to Eat Properly 31.1 33.5 21.0

Often Couldn’t Afford to Eat Properly 7.9 6.0 5.3

Sometimes or Often Couldn’t Afford to Eat Properly 39.0 39.6 26.3

Sample Size 1,522 182 204

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

NOTE: Sample sizes may be slightly lower in some cells because of item nonresponse. However, in no cell

is item nonresponse greater than one percent of sample size shown.
41

Two additional measures used to determine the severity of food insecurity were (1) the

frequency of cutting or skipping meals, and (2) the frequency of going without food for a whole day

because of a lack of food or money (Table III.3). The prevalence of having to serve smaller meals

or skip them entirely was greater among participants than among either group of nonparticipants,

with slightly less than one-third of participants reporting this experience (31 percent) as compared

to 24 percent and 19 percent for the two nonparticipant groups. More than one-third of the

participants (34 percent) and eligible nonparticipants (36 percent) who reported cutting or skipping

meals said they did so almost every month. A slightly smaller proportion of near-eligible

nonparticipants (29 percent) who cut or skipped meals did so with this frequency. Fewer than 10

percent of the survey respondents went without food for a whole day because of lack of food or

money. This included eight percent of participants, seven percent of eligible nonparticipants and

four percent of near-eligible nonparticipants.

2. Overall Levels of Food Security

Using the 18 questions presented in Exhibit III-1, the food security scale was estimated for each

sample member, with the procedures developed by Hamilton et al. (1997). Each household was

assigned to one of four discrete food security levels: (1) food secure, (2) food insecure without

hunger, (3) food insecure with moderate hunger, and (4) food insecure with severe hunger. A

household answering all 18 questions is classified as food secure if it reports two or fewer

experiences with food insecurity (that is, answers affirmatively to no more than two of the

questions). Those classified as food insecure without hunger were those answering affirmatively to

between three and seven of the component questions. A classification of food insecurity with

moderate hunger reflected affirmative answers to 8 to 12 questions. Households were classified as
42

TABLE III.3

FREQUENCY OF CUTTING/SKIPPING MEALS OR GOING WITHOUT FOOD 

FOR A WHOLE DAY BECAUSE OF LACK OF FOOD OR MONEY

Study Group

Nonparticipants

Characteristic Participants Eligible Near-Eligible

Percentage of Households Who in the Past 12 Months Ever

Cut the Size of Their Meals or Skipped Meals Because

There Wasn’t Enough Money for Food 30.9 23.7 18.5

Percentage Distribution of How Often This Happened

Among Those Cutting or Skipping Mealsa

Almost every month 34.2 36.3 29.1

Some months but not every month 39.9 35.7 37.9

Only one or two months 25.6 27.1 32.9

Percentage of Households Who in the Past 12 Months Ever

Went Without Eating for a Whole Day Because There

Wasn’t Enough Money for Food 8.2 7.3 3.9

Percentage Distribution of How Often This Happened

Among Those Not Eating for a Whole Dayb

Almost every month 24.8 33.1 37.8

Some months but not every month 42.4 36.4 24.4

Only one or two months 32.3 30.6 37.8

Sample Size 2,454 450 405

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

NOTE: Sample sizes may be slightly lower in some cells because of item nonresponse. However, in no cell

is item nonresponse greater than one percent of sample size shown.

Sample sizes for the three columns are 789, 107, and 75, respectively. a

Sample sizes for the three columns are 221, 33, and 16, respectively. b
43

experiencing food insecurity with severe hunger if they answered more than 12 of the 18 questions

affirmatively.

Slightly more than half the FSP participants in the study sample were classified as food secure,

according to the scale. Approximately 28 percent were classified as food insecure but not

experiencing hunger. Seventeen percent were food secure and experienced some hunger, while the

remainder, five percent, experience food insecurity and severe hunger.

As indicated in Table III.4, FSP participants in the survey experience one of the three levels of

food insecurity more frequently than eligible or near-eligible nonparticipants (50 percent as

compared to 34 percent and 25 percent, respectively). A comparison of participants to these

nonparticipant groups also indicates that for all groups, most of those who are food insecure are in

the least-severe category and are not experiencing direct hunger. However, 21 percent of participants

do experience food insecurity with at least some hunger compared to 14 percent of eligible

nonparticipants and 10 percent of near-eligible nonparticipants. Only three to five percent of the

various groups were classified into the most-severe hunger category.

When the sample is compared to the overall U.S. population (Table III.5), food insecurity is seen

to be much more prevalent and severe among all study groups. For instance, the overall population

estimates place the percentage of households that are food secure at 88 percent, as compared to 50

percent for FSP participants. Also, less than one percent of the population nationally is classified

as food insecure with severe hunger, compared to about five percent of the NFSPS participant

sample. These differences reflect the much higher average income levels in the overall population.

A comparison to the U.S. population with income below 130 percent of the poverty level indicates

more similarities, particularly between the food security levels of the eligible nonparticipants and

the overall population living in poverty. For instance, 32 percent of the national
44

TABLE III.4

FOOD SECURITY AMONG PARTICIPANTS AND NONPARTICIPANTS

Study Group

Nonparticipants

Constructed Food Security Scale Participants Eligible Near-Eligible

All Households

Food Secure 50.4 65.9 75.3

Food Insecure Without Hunger 28.1 20.4 15.2

Food Insecure with Moderate Hunger 16.6 9.4 6.3

Food Insecure with Severe Hunger 4.9 4.4 3.3

Sample Size 2,396 436 396

SOURCE: 1996 National Food Stamp Program Survey, weighted data.
45

TABLE III.5

COMPARISON WITH FOOD INSECURITY LEVEL FOR OTHER POPULATIONS

Food Secure Hunger Hunger Hunger

Food Insecure Food Insecure Food Insecure

Without with Moderate with Severe

Participants 50.4 28.1 16.6 4.9

Eligible Nonparticipants 65.8 20.4 9.4 4.4

Near-Eligible 75.3 15.2 6.3 3.3

Overall U.S. Population 88.1 7.8 3.3 0.8

Overall U.S. Population

Below 130 Percent of

Poverty Level 68.1 20.0 9.3 2.6

SOURCE: First three rows: 1996 National Food Stamp Program Survey, weighted data. Last two

rows are from Hamilton et al. (1997).
46

population below 100 percent of the poverty line is estimated to be insecure, as compared to 50

percent of FSP participants, 34 percent of eligible nonparticipants, and 25 percent of near-eligible

nonparticipants. The fact that the participant sample has lower rates of food security than the overall

population of households below 130 percent of poverty is consistent with the fact that the likelihood

of participating in the FSP is highest among the lowest income groups (see Ponza 1998).

B. HOUSEHOLD CHARACTERISTICS ASSOCIATED WITH FOOD INSECURITY

To understand the dynamics of food security better, selected household characteristics of FSP

participants and of eligible and near-eligible nonparticipants experiencing food insecurity were

analyzed. A descriptive analysis of household characteristics (size, income, presence of elderly

member, age of children in household, employment status, and food stamp benefit per capita) among

the FSP participants (Table III.6) indicates that household characteristics do not appear to influence

the food security level substantially. The only substantial apparent influencing factor is the presence

of an elderly person in the household: those with elderly members were somewhat more likely to be

food secure than all household types (58 percent versus 50 percent).

An in-depth analysis of the relationships between household characteristics and food security

was developed using logit regression modeling to estimate the marginal effects of household

characteristics on whether households experience food security or food insecurity without hunger

versus food insecurity with any type of hunger. For each row, the entry in Table III.7 can be

interpreted as showing the estimated effect of the corresponding independent variable on the

probability of being in one of the “hunger” categories. For instance, the entry under “one-person

household” of 7.22 indicates that being in a one-person household increases the estimated probability

of being in one of the “hunger” categories by about seven percentage points. The logit model

developed for participants confirms the finding of the descriptive analysis, showing that having an
47

TABLE III.6

FOOD SECURITY LEVELS OF FOOD STAMP PROGRAM PARTICIPANTS, 

BY HOUSEHOLD CHARACTERISTICS

Food Food Insecure, Moderate Severe Sample

Secure No Hunger Hunger Hunger Size

Food Insecure, Food Insecure,

All Households 50.4 28.1 16.6 4.9 2,396

Number in Household

1 51.7 22.6 19.7 6.0 596

2 to 3 51.1 28.4 15.7 4.8 940

4 to 5 48.4 32.0 16.0 3.6 653

6 or More 50.0 29.7 14.3 6.0 207

Number of Children

Under 18 in Household

0 50.7 25.8 18.3 5.3 901

1 54.6 28.3 12.9 4.2 446

2 50.0 27.8 17.0 5.2 494

3 or More 47.0 31.7 16.8 4.6 555

Presence of Children

Under 5 55.2 25.3 15.5 4.1 458

5 to 12 52.5 27.5 15.9 4.1 518

13 to 18 52.5 28.1 14.5 4.9 311

Income as Percentage of

Poverty

Below 50 Percent 50.8 29.0 14.3 5.9 869

50 to 74 48.3 30.3 17.9 3.5 546

75 to 99 50.2 25.0 19.2 5.5 523

100 or More 51.1 26.3 18.3 4.4 365

Household with Elderly

Member 58.0 25.8 12.9 3.2 644

Household with Wage

Income 50.6 28.5 17.2 3.7 733
TABLE III.6 (continued)

Food Food Insecure, Moderate Severe Sample

Secure No Hunger Hunger Hunger Size

Food Insecure, Food Insecure,

48

Food Stamp Benefits per

Household Member

Less than $20 48.2 24.7 23.4 3.8 347

$20 to $39 52.2 31.9 11.6 4.3 442

$40 to $59 50.8 29.2 15.7 4.4 493

$60 or More 50.3 27.6 16.9 5.2 1,076

SOURCE: 1996 National Food Stamp Program Survey, weighted data.
49

TABLE III.7

MULTIVARIATE LOGIT ANALYSIS OF THE DETERMINANTS

OF FOOD INSECURITY AMONG PARTICIPANTS

Variable Marginal Effect on Food Insecuritya

Income Less Than 100 Percent of Poverty Level -4.05

Household Has Earned Income (Has an Employed

Household Member) -2.51

One-Person Household 7.22**

Household Includes an Elderly Person -9.47**

Urban Location 3.95

Mixed Urban and Rural Location 5.28

Presence of Child Under 5 -2.77

Presence of Child 5 to 12 -0.91

Presence of Child 13 to 18 -2.30

SOURCE: 1996 National Food Stamp Program Survey. Sample size = 2,396.

NOTE: Missing value flags were used in the regressions to account for missing data on certain of the

variables. For clarity of exposition, these are not included in this table.

Entries in the column show the estimated marginal effect of the variables on the probability of the household a

being classified in one of the two food security categories involving hunger.

**Significantly different from zero at the .05 level, two-tailed test.
50

elderly person in a household decreases by about nine percentage points the chances of a household

experiencing hunger (Table III.7). (Two other variables that were included in an earlier version of

the analysis, receipt of food stamps through electronic benefit transfer (EBT) and whether the

household had been on food stamps for at least three years, were not statistically significant and were

dropped.)

The descriptive analysis for eligible nonparticipants indicated some household characteristics

that may be related to food security levels; however, most are not statistically significant in the logit

analysis (Table III.8 and III.9). Table III.8 indicates that households with more than one person,

lower income levels, or earned wages were more likely to experience food insecurity and that

households with an elderly member were less likely to do so. However, the logit analysis finds the

presence of an elderly person in a household to be the only substantiative variable that is statistically

significant. Having an elderly person in an eligible nonparticipant household decreased the chances

of experiencing hunger by 13 percentage points.

The data analyses for near-eligible nonparticipants follow a similar pattern. The descriptive

analysis suggests some variance in food security levels of households with different characteristics

(Table III.10). Once again, households with an elderly member were more likely than all households

to be food secure (91 percent versus 75 percent). Also, households with income between 75 and 99

percent of the poverty level were less likely to be food secure (65 percent) than households with

either higher or lower income levels. The logit analysis found only the presence of an elderly person

in the household to be statistically significant, decreasing the chance of experiencing hunger by 14

percentage points (Table III.11).
51

TABLE III.8

FOOD SECURITY LEVELS FOR ELIGIBLE NONPARTICIPANTS,

BY HOUSEHOLD CHARACTERISTICS

(Entries Are Row Percentages)

Food Secure No Hunger Moderate Hunger Severe Hunger Size

Food Insecure, Food Insecure, Food Insecure, Sample

All Households 65.9 20.4 9.4 4.4 436

Number in Household

1 70.7 14.4 10.4 4.5 134

2 to 3 75.0 16.8 5.3 2.9 169

4 to 5 50.2 28.6 15.5 5.7 105

6 or More 46.6 39.1 7.0 7.3 28

Number of Children

Under 18 in Household

0 76.0 12.5 7.3 4.2 257

1 59.1 28.3 9.4 3.2 63

2 49.6 27.0 15.5 7.8 52

3 or More 45.1 38.8 12.8 3.2 64

Presence of Children

Under 5 52.1 33.2 9.9 4.8 82

5 to 12 47.5 32.5 15.4 4.6 113

13 to 18 54.1 30.3 9.4 4.4 65

Income as Percent of

Poverty

Below 50 Percent 56.6 24.8 11.1 7.5 65

50 to 74 58.9 28.2 10.6 2.2 92

75 to 99 65.7 16.5 11.5 6.3 114

100 or More 65.2 22.6 7 .9 4.3 115

Household with Elderly

Member 79.9 12.3 5.7 2.0 192

Household with Wage

Income 57.4 27.4 11.0 4.3 230

SOURCE: 1996 National Food Stamp Program Survey, weighted data.
52

TABLE III.9

MULTIVARIATE LOGIT ANALYSIS OF THE DETERMINANTS OF FOOD 

INSECURITY AMONG ELIGIBLE NONPARTICIPANTS

Base Assumption Marginal Effect on Food Insecuritya

Income Less than 100 Percent of Poverty Level 3.54

Household Has Earned Income (Has an Employed

Household Member) -3.12

One-Person Household 9.43

Household Includes an Elderly Person -12.60**

Urban Location -6.53

Mixed Urban and Rural Location -4.43

Presence of Child Under 5 -3.26

Presence of Child 5 to 12 6.70

Presence of Child 13 to 18 0.85

SOURCE: 1996 National Food Stamp Program Survey. Sample size = 436.

NOTE: Missing value flags were used in the regressions to account for missing data on certain of the

variables. For clarity of exposition, these are not included in this table.

Entries in the column show the estimated marginal effect of the difference in the assumptions shown on the a

probability of the household being classified in one of the two food security categories involving hunger.

**Significantly different from zero at the .05 level, two-tailed test.
53

TABLE III.10

FOOD SECURITY LEVELS FOR NEAR-ELIGIBLE NONPARTICIPANTS,

BY HOUSEHOLD CHARACTERISTICS

(Entries Are Row Percentages)

Food Food Insecure, Moderate Food Insecure, Sample

Secure No Hunger Hunger Severe Hunger Size

Food Insecure,

All Households 75.3 15.2 6.3 3.3 396

Members in

Household

1 80.5 9.8 7.2 2.5 83

2 to 3 75.9 14.1 6.9 3.1 160

4 to 5 71.2 19.1 6.2 3.5 112

6 or More 73.2 19.5 2.3 5.0 41

Number of Children

Under 18 in

Household

0 81.4 9.4 7.7 1.5 195

1 70.4 21.4 4.9 3.3 59

2 64.8 25.2 5.0 5.0 59

3 or More 71.9 17.2 4.9 6.1 83

Presence of Children

Under 5 70.5 22.0 3.7 3.9 78

5 to 12 70.4 19.3 4.7 5.6 125

13 to 18 67.9 19.9 3.2 9.1 99

Income as

Percentage of

Poverty

Below 50 Percent 76.8 16.3 3.5 3.4 56

50 to 74 77.9 18.5 0.0 3.6 27

75 to 99 65.1 24.2 8.0 2.7 37

100 or More 74.4 13.8 8.3 3.5 231

Household with

Elderly Member 90.9 7.5 1.6 0.0 123

Household with

Wage Income 69.7 18.6 8.3 3.5 231

SOURCE: 1996 National Food Stamp Program Survey, weighted data.
54

TABLE III.11

MULTIVARIATE LOGIT ANALYSIS OF THE DETERMINANTS OF 

FOOD INSECURITY AMONG NEAR-ELIGIBLE PARTICIPANTS

Variable Marginal Effect on Food Insecuritya

Income Less Than 100 Percent of Poverty Level -6.31

Household Has Earned Income (Has an Employed

Household Member) -4.08

One-Person Household 2.49

Household Includes an Elderly Person -13.68**

Urban Location 0.64

Mixed Urban and Rural Location 5.47

Presence of Child Under 5 -3.16

Presence of Child 5 to 12 -1.25

Presence of Child 13 to 18 0.89

SOURCE: 1996 National Food Stamp Program Survey. Sample size = 396.

NOTE: Missing value flags were used in the regressions to account for missing data on certain of

the variables. For clarity of exposition, these are not included in this table.

Entries in the column show the estimated marginal effect of the variable on the probability of the

a

household being classified in one of the two food security categories involving hunger.

**Significantly different from zero at the .05 level, two-tailed test.
One surprising pattern in the data is that for eligible nonparticipants, reported use of the various 2

coping mechanisms is lower for households classified as having severe hunger than it is for

households classified as experiencing moderate hunger. It is likely that this is due to sampling

variation, since the sample sizes in the severe hunger category are extremely small.

55

C. PERCEIVED REASONS FOR NOT HAVING ENOUGH FOOD 

To understand better why households did not always have enough food, respondents reporting

that they did not always have enough food in the household were asked about their perceived reasons

for this experience. Almost all respondents mentioned not having enough money (Table III.12), and

this was also considered to be the most important reason by most respondents (not shown). 

After lack of money, difficulty in getting to the store was the reason participants mentioned

second most frequently (31 percent) for not having enough food, followed by not being able to cook

or eat because of health problems (15 percent). Health problems were also frequently cited by the

eligible nonparticipant households (22 percent). (About a third of respondents mentioning health

problems were elderly.) The other problem cited frequently was having difficulty getting to the store

(24 percent). Among the near-eligible nonparticipants, about 18 percent mentioned having difficulty

getting to the store and 8 percent mentioned not being able to cook or eat because of health

problems. 

D. COPING MECHANISMS USED BY HOUSEHOLDS 

People concerned about the adequacy of their food supply can take several actions to stretch or

supplement the food they have. These “coping mechanisms” may prevent food insecurity in some

households or lessen its severity or frequency in others. Table III.13 presents data regarding the use

of selected coping mechanisms among participants, eligible nonparticipants, and near-eligible

nonparticipants. For each of the most insecure categories, substantial majorities reported such

coping patterns as putting off bills and relying on help from friends and relatives. Drawing on

2

emergency food sources, such as food pantries, was also common, particularly for FSP participants.
56

TABLE III.12

AMONG THOSE WHO DO NOT HAVE ENOUGH FOOD TO EAT IN THEIR 

HOUSEHOLD, REASONS FOR NOT HAVING ENOUGH TO EAT

(Percentage of Individuals by Type of Reason)

Nonparticipants

Participants Eligible Eligible

NearNot Enough Money for Food 94.0 95.6 85.5

Too Hard to Get to the Store 30.6 24.0 18.2

No Working Stove 3.2 8.8 3.9

No Working Refrigerator 3.7 4.4 5.9

Not Able to Cook or Eat Because of Health

Problems 14.6 21.6 8.1

Sample Size 501 67 48

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

NOTE: Percentages add to more than 100, because multiple responses were allowed.
57

TABLE III.13

COPING MECHANISMS AND FOOD SECURITY AMONG PARTICIPANTS

(Percentages of Respondents Reporting Using Various Coping Mechanisms)

Food Security Level

Coping Mechanism Secure No Hunger Moderate Hunger Severe Hunger

Food Food Insecure, Food Insecure, Food Insecure,

Participants

Participants Borrowed Money or Food 18.1 42.7 64.7 78.8

Put Off Bills 22.0 58.1 77.7 85.1

Emergency Food Use 8.7 19.2 30.4 50.3

Soup Kitchen Use 1.2 3.7 4.8 13.4

Sample Size 1,161 709 401 121

Eligible Nonparticipants

Borrowed Money or Food 10.7 39.3 77.8 74.1

Put Off Bills 16.7 63.9 83.4 79.3

Emergency Food Use 2.8 19.1 31.5 20.7

Soup Kitchen Use 0.0 1.1 12.1 5.1

Sample Size 287 89 41 19

Near-Eligible Nonparticipants

Borrowed Money or Food 9.1 34.8 55.5 76.6

Put Off Bills 24.4 59.5 72.3 84.3

Emergency Food Use 1.3 5.3 7.8 31.1

Soup Kitchen Use 0.0 0.0 0.0 0.0

Sample Size 297 60 25 13

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

NOTE: Percentages add to more than 100, because multiple responses were allowed.
58

The participant households and both sets of nonparticipant households followed similar patterns,

reporting the use of the various coping mechanisms in the following order of frequency: putting off

bills, borrowing money, using food pantries, and, finally, eating at a soup kitchen. (None of the neareligible nonparticipants reported eating at a soup kitchen.) As is evident from the data, the

proportion of households reporting the use of each coping mechanism increased as the severity of

food insecurity increased, except for eligible nonparticipants.

E. RELATIONSHIP BETWEEN FOOD SECURITY AND FOOD SUFFICIENCY LEVELS

Additional perspectives on the food security measure can be gained by comparing it to another

measure that has been used in the past to assess households’ food status: “food sufficiency.” This

measure, which has been used on both the NFCS and the CSFII, asks households to characterize the

adequacy of the food available to them by choosing one of the following categories:

C Enough of the right kinds of food 

C Enough but sometimes not the right kinds of food

C Sometimes not enough food

C Often not enough food

Typically, this is done through instrumentation consisting of a single question or a short sequence

of questions. The NFSPS also included a food sufficiency question in addition to the food security

items. Since much of the research that immediately preceded the development of the food security

concept focused on food insufficiency issues, it is of interest to examine the degree to which the two

concepts are related empirically. This is of methodological interest in itself and helps establish

linkages between the current study and earlier work.
59

As shown in Table III.14, there is a substantial degree of overlap between households’ responses

on the food sufficiency questions and their food security status classifications. For instance, among

households classified as “having enough of the right kinds of food,” 85 percent are classified at the

highest level of food security, with most of the rest (10 percent) at the second-highest food security

category. By contrast, when households classified as “often” not having enough food are examined,

only 15 percent and 18 percent, respectively, are classified in one of the two highest food security

categories, and 29 percent are classified in the most insecure food security status category. To be

sure, the correlations are far from perfect. Nevertheless, the substantial association between the two

measurement constructs is clear. 

F. CONCLUSIONS

About half of FSP participants experienced at least some level of food insecurity during the 12-

month period covered by the relevant questions. Most of those with food insecurity were classified

as food insecure without hunger. However, approximately 21 percent of participants were classified

as experiencing some hunger. The analysis also indicates that substantial numbers of eligible and

near-eligible nonparticipants also experience some degree of food insecurity, but the percentages for

nonparticipants are lower than for the participant group. 

In general, the likelihood of being food insecure does not appear to be highly correlated with

the household characteristics examined in the chapter. An exception to this, however, is that having

an elderly member increases the probability of a household being classified as food secure.

Among the coping mechanisms examined for dealing with food insecurity, the one mentioned

most often was putting off paying bills. Borrowing money or food was also a relatively common

method of coping. Among the participants classified into the most severe food insecurity category,

approximately 50 percent had obtained emergency food from some source such as a food bank, while

about 14 percent indicated that they had eaten meals at a soup kitchen or similar place.
60

TABLE III.14

FOOD SUFFICIENCY AND FOOD SECURITY

(Entries Are Row Percentages)

Food Security Level

Food Sufficiency Secure Hunger Hunger Hunger Size Distribution

Food No Moderate Severe Sample Sufficiency

Food Food Food

Insecure, Insecure, Insecure, Overall Food

Enough of the Right 

Kinds of Foods 85.2 10.3 3.6 0.9 1,378 49.8

Enough; Not Always the

Right Kinds of Foods 50.0 34.3 12.7 3.0 1,235 34.0

Sometimes Not Enough

Foods 14.8 36.4 34.3 14.6 467 13.0

Often Not Enough Foods 15.3 18.1 37.6 28.9 126 3.3

Overall Food Security

Distribution 61.8 22.1 11.8 4.3

SOURCE: 1996 National Food Stamp Program Survey, weighted data. 
61

IV. STORE ACCESS AND FOOD SECURITY

This chapter examines the potential relationship between food security and access to stores by

FSP participants and other low-income people. Some observers have suggested that lack of access

to retail food stores may be a significant barrier to good nutrition for many low-income households,

as a result of poor transportation opportunities, limited numbers of good food stores in low-income

areas, and other factors. Here, the issues using the NFSPS data are examined.

A. BACKGROUND

It is often suggested that low-income households, especially those in poor urban areas and

sparsely populated rural areas, have only limited access to food retailers and that this poses a

significant obstacle for FSP participants in using program benefits efficiently and effectively. Lowincome households may not have access to a car and may be limited to using stores that they can

reach on foot or by public transportation. This in itself is a significant limitation on access in

comparison with shopping patterns for middle-income households. The situation may be

compounded by limitations in shopping opportunities in areas with high concentrations of lowincome households. Many observers believe that major retailers shun low-income areas because of

concerns about security, limited consumer purchasing power, and other factors. Some analysts also

believe that stores in low-income areas charge higher prices and provide lower-quality merchandise

(see Kaufman et al. 1997, Appendix A for a useful literature review).

These issues are of concern in the context of the FSP, because they relate directly to the

effectiveness with which its policies can be carried out through the “normal means of trade,” as

specified in the program’s authorizing legislation. They also interact with food stamp policy issues,

since FSP participant access to stores is dependent upon what stores are authorized to accept food
62

stamps. More generally, analyzing the degree to which low-income households have access to

stores, and the shopping decisions they make in the context of those choices, sheds light on the

constraints they face in securing nutritious diets.

From a conceptual perspective, ensuring access to food retailers by low-income households

involves factors related both to the existence of food stores at reasonable distances from the

households and to the ability of low-income households to get to those stores. Further, assessing the

availability of stores in a meaningful way depends both on examining where the stores are located

and on assessing the quality of the shopping opportunities they offer, in terms of prices, quality of

merchandise, variety of merchandise, and other factors. Similarly, the ability of households to reach

stores readily depends not only on the stores’ locations but also on whether a household has access

to a car and on what other means of transportation are available.

The completed FNS Authorized Food Retailer Characteristics Study (Mantovani et al. 1997)

provided extensive insight into the store side of this “access equation.” That study examined the

availability of various types of food stores in both urban and rural areas throughout the country. For

a sample of the stores, it also obtained data on the prices charged for a standard set of food items,

as well as on other characteristics.

The key findings of that study suggest greater degrees of access to stores by low-income

households than many observers had expected. The study found that most Americans, both low- and

high-income, rely on supermarkets or large grocery stores for the bulk of their shopping and that,

nationally, “90 percent of the total population and 90 percent of the population in poverty live in

areas with at least one supermarket or large grocery present.” Proximity to stores was less common

in rural areas but did not vary by the poverty level of the population. Apparently, scarcity of food

stores in rural areas is mostly a result of retailers’ efforts to gain economies of scale.
63

The study also concluded that there did not appear to be major cost differences in areas with

different poverty concentrations: “There appears to be little effect on the cost, selection, or quality

of food [in higher-poverty areas].” If anything, supermarkets in low-income areas appeared to have

slightly lower prices.

While the Mantovani et al. study greatly increases understanding of store access by low-income

households, it focused on the issue only from the point of view of the stores. The current survey has

obtained complementary information by asking respondents about their food shopping experiences,

their transportation to food stores, their food shopping patterns, and their perceptions of the adequacy

of their food shopping opportunities.

To the extent that limitations to shopping opportunities really are an important barrier to

nutrition for low-income households, one might assume a positive association between access to

stores and measured food security. A companion report (Ohls et al. 1999) discussed several overall

issues related to access to stores by the study population. This report focuses principally on the issue

of whether food security and store access are associated.

B. STORE ACCESS IN THE NFSPS SAMPLE

As background for presenting the relevant food security data, Table IV.1 presents selected

measures of store access. As shown in the top panel of the table, the majority of FSP households live

quite near a supermarket. Overall, approximately 54 percent reported that there was a supermarket

within a mile of their home. Another 35 percent of households live one to four miles from a

supermarket.
64

TABLE IV.1

ACCESS TO STORES 

(Entries Are Column Percentages)

Nonparticipants

Characteristic Participants Eligible Eligible

NearDistance to Nearest Supermarket (Miles)

Less than 1 54.0 NA NA

1 to 4 35.0 NA NA

More than 4 11.0 NA NA

Sample Size 589 NA NA

Distance to Store Usually Used

Less than 1 30.9 27.2 24.2

1 to 4 34.8 36.5 34.8

More than 4 34.5 36.3 41.1

Sample Size 2,243 408 379

Satisfaction with Shopping Situation in Neighborhood

Satisfied or Very Satisfied 71.2 78.9 83.1

Dissatisfied or Very Dissatisfied 28.8 21.1 16.9

Sample Size 2,370 426 392

Rank Usual Store as Good/Excellent on 8 or More of

11 Criteria?

Yes 77.6 77.8 76.8

No 22.4 22.2 23.2

Sample Size 2,454 450 405

Have Access to Car?

Yes 73.9 86.4 94.7

No 26.1 13.6 5.3
TABLE IV.1 (continued)

Nonparticipants

Characteristic Participants Eligible Eligible

Near65

Sample Size 2,454 450 405

Have Access to Car or Get Rides with Friends,

Relatives

Yes 83.1 90.4 96.4

No 16.9 9.6 3.6

Sample Size 2,454 450 405

Round-Trip Travel Time

Less than 30 min. 66.3 65.1 69.0

30 Minutes to 1 Hour. 24.3 23.4 22.8

1 to 2 Hours 7.2 9.4 7.2

Over 2 Hours 2.3 1.6 1.0

Sample Size 2,343 426 391

Transportation Usually Used

Car 44.5 71.0 86.4

Get Ride with Friend or Relative 31.3 13.7 6.5

Walk 13.8 5.1 3.5

Take a Bus 3.8 2.7 0.7

Other 6.6 7.6 3.0

Sample Size 2,454 450 405

Out-of-Pocket Costs

Nothing 79.5 91.1 97.8

$.01 to $2.00 2.8 1.8 0.5

Over $2.00 17.7 7.1 1.7

Sample Size 2,454 450 405

SOURCE: 1996 National Food Stamp Program Survey, weighted tabulations.

NA = not available.
The “store where usually shop” variable includes all types of stores, not just supermarkets.

1

However, it is known from other work (see Ohls et al. 1999) that more than 85 percent of food stamp

households use a supermarket as their main store.

66

Interestingly, the data suggest that considerable numbers of food stamp households travel to a

store which is farther away than the closest supermarket. Only 31 percent regularly use a food store

that is less than a mile from their home. 

1

Approximately 71 percent of households rated themselves as either “satisfied” or “very

satisfied” with the shopping situations in their neighborhood. Further, when asked to rank the stores

where they usually shopped (which, as shown above, were not necessarily in their neighborhoods),

78 percent of the respondents ranked the stores as excellent or good on most dimensions.

Approximately 74 percent of program participants have access to a car. Another nine percent

report usually being able to get to their regular store by obtaining a ride with friends or relatives.

Participants (44.5 percent) were less likely to use a car for grocery shopping than were either eligible

(71 percent) or near-eligible (86.4 percent) nonparticipants. Conversely, they were more likely to

get a ride with a friend or relative or to walk to the store. However, participants had more out-ofpocket costs associated with grocery shopping than did either group of nonparticipants. Overall,

these findings suggest that most FSP recipients and other low-income households perceive

themselves to have quite satisfactory access to retail food shopping opportunities. Nonetheless, there

is a minority of low-income households that may not have satisfactory shopping opportunities. The

analysis now turns to an examination of whether there are any clear associations between measures

of store access and food security.
Note that the tabulations based on distance to the nearest supermarket are based on a much

2

smaller sample size than others, because the “nearest supermarket” tabulations include only inperson interviews for which it was possible to geocode the necessary location data. This is the

reason that the estimated distribution of these households across the four food security classes differs

somewhat from the distribution in the other panels of the table.

Households for which the nearest supermarket was less than one mile were mostly (72 percent) 3

urban. Households reporting that the nearest supermarket was one to four miles were 59 percent

urban, with most of the remainder living in mixed uran and rural places.

67

C. ASSOCIATIONS OF STORE ACCESS AND FOOD SECURITY

Households with access to a car are somewhat more likely to be food secure than those without

access to a car; however, the differences are small: 52 percent versus 46 percent (Table IV.2). It is

important to note that this association does not necessarily indicate causality. While it is possible

that having access to a car tends to increase a household’s ability to achieve food security, it is also

likely that having access to a car is correlated with income or other factors that are themselves

associated with food security.

When store access is gauged in terms of distance to the nearest supermarket, the lowest degree

of food security is estimated for the households living less than a mile away, with 43 percent of them

estimated to be food secure. This percentage rises to 46 percent for households that are one to four 2

miles from the nearest supermarket but falls back to 42 percent for households more than four miles

away. A similar pattern is observed for the tabulations based on distances that households travel

3

to shop. The group in the middle-distance range, one to four miles, has slightly higher levels of food

security than the lower- or higher-distance groups. A variable indicating whether households with

the double problem of not having a car and the nearest supermarket being more than a mile away

also did not seem to be correlated with participants’ levels of food security.
68

TABLE IV.2

FOOD SECURITY AND ACCESS TO STORES

(Entries Are Row Percentages)

Sample Food Insecure, Moderate Severe

Size Secure No Hunger Hunger Hunger

Food Insecure, Insecure,

Food Food

Participants

Households with Access to Car

Yes 1,770 52.0 27.8 16.0 4.2

No 626 45.9 28.8 18.3 6.9

Distance to Nearest Supermarket (Miles)

Less than 1 304 42.8 34.3 17.4 5.5

1 to 4 215 46.2 30.0 20.2 3.6

More than 4 66 41.8 26.6 21.7 9.9

Distance to Store Usually Used

Less than 1 629 46.9 30.6 16.3 6.1

1 to 4 743 52.3 26.9 16.9 3.9

More than 4 825 50.4 28.0 16.0 5.6

No Car and >1 Mile from Nearest Store?a

Yes 1 228 49.2 28.5 16.4 5.9

No 0 2,168 50.5 28.1 17.7 4.8

People “Satisfied” or “Very Satisfied”

with Neighborhood Shopping Situation

Yes 1 1,618 53.7 27.7 14.3 4.3

No 2 701 42.4 29.5 21.4 6.6

Round-Trip Travel Time

Less than 30 Minutes 1,520 50.7 28.5 16.6 4.3

30 Minutes to 1 Hour 578 49.5 29.6 16.2 4.8

1 to 2 Hours 164 46.3 28.4 13.1 12.3

Over 2 Hours 34 44.9 26.4 25.8 3.0

Transportation Usually Used

Car 1,086 52.4 27.0 16.4 4.3

Get Ride with Friend or Relative 791 52.4 29.3 14.6 3.7

Walk 294 40.6 32.7 19.7 7.0

Take a Bus 85 45.7 26.0 18.9 9.4

Other 140 50.6 2.8 20.3 7.4

Out-of-Pocket Costs

Nothing 1,901 51.7 27.8 16.4 4.1

$.01 - $2.00 73 40.5 26.4 22.8 10.3

Over $2.00 422 46.0 29.7 16.6 7.7
TABLE IV.2 (continued)

Sample Food Insecure, Moderate Severe

Size Secure No Hunger Hunger Hunger

Food Insecure, Insecure,

Food Food

69

Eligible Nonparticipants

Households with Access to Car

Yes 378 66.7 20.3 8 8 4.2

No 58 60.5 20.6 13.8 5.2

Distance to Store Usually Used

Less than 1 109 68.6 21.0 8.6 1.8

1 to 4 146 61.7 22.6 9.5 6.2

More than 4 142 64.2 19.6 11.9 4.2

No Car and >1 Mile from Nearest Store?

Yes 18 39.2 22.3 32.9 5.6

No 418 67.0 20.3 8.4 4.3

People “Satisfied” or “Very Satisfied”

with Neighborhood Shopping Situation

Yes 1 327 69.4 18.9 7.7 4.0

No 2 86 49.0 30.1 16.3 4.7

Round-Trip Travel Time

Less than 30 Minutes 272 69.2 20.2 8.1 2.6

30 Minutes to 1 Hour 95 63.0 19.0 9.5 8.5

1 to 2 Hours 40 40.3 29.3 25.2 5.1

Over 2 Hours 6 65.3 34.7 0.0 0.0

Transportation Usually Used

Car 309 67.4 20.0 8.7 3.9

Get Ride with Friend or Relative 61 62.2 21.4 11.5 4.9

Walk 23 47.8 34.4 9.2 8.6

Take a Bus 11 55.4 17.9 17.9 8.8

Other 32 74.8 12.5 9.8 3.0

Out-of-Pocket Costs

Nothing 398 67.4 20.1 8.3 4.3

$.01 to $2.00 7 56.6 13.9 16.0 13.5

Over $2.00 31 48.4 25.8 22.5 3.3

Near-Eligible Nonparticipants

Households with Access to Car

Yes 376 75.0 15.2 6.6 3.2

No 20 79.7 15.4 0.0 4.9

Distance to Store Usually Used

Less than 1 90 79.4 14.0 4.4 2.2

1 to 4 128 75.5 12.7 6.3 5.5
TABLE IV.2 (continued)

Sample Food Insecure, Moderate Severe

Size Secure No Hunger Hunger Hunger

Food Insecure, Insecure,

Food Food

70

More than 4 153 73.3 16.9 7.2 2.6

No Car and >1 Mile from Nearest Store?a

Yes 7 84.4 15.6 0.0 0.0

No 389 75.1 15.2 6.4 3.3

People“Satisfied”or“Very Satisfied” with

Neighborhood Shopping Situation

Yes 1 319 77.2 13.7 5.7 3.4

No 2 64 67.2 22.0 7.7 3.1

Round-Trip Travel Time

Less than 30 Minutes 265 76.3 14.2 6.1 3.4

30 Minutes to 1 Hour 86 75.7 15.1 4.6 4.6

1 to 2 Hours 27 62.2 26.9 11.0 0.0

Over 2 Hours 4 52.0 24.1 23.9 0.0

Transportation Usually Used

Car 344 74.8 16.0 5.8 3.5

Get Ride with Friend or Relative 23 81.9 9.6 8.6 0.0

Walk 14 72.1 13.5 7.7 6.3

Take a Bus 3 100.0 0.0 0.0 0.0

Other 12 74.1 9.4 16.5 0.0

Out-of-Pocket Costs

Nothing 388 75.8 14.9 5.9 3.3

$.01 to $2.00 2 49.2 0.0 50.8 0.0

Over $2.00 6 47.2 36.5 16.3 0.0

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Includes all kinds of food stores. a
71

Finally, FSP participants who report being satisfied or very satisfied with the shopping situations

in their neighborhoods have a higher likelihood than dissatisfied participants of being food secure,

54 percent to 43 percent.

D. CONCLUSIONS

Overall, the data tend to show that households with good access to stores have somewhat higher

levels of food security. The positive associations between food access and food security are greatest

for the variables indicating access to a car and satisfaction with the neighborhood shopping situation.

They are weakest for the distance variables.

The four-day interviews were done as part of a methodological study, and comparisons

1

between the four-day data and the seven-day data are presented and discussed in Appendix H.

73

V. NUTRIENT AVAILABILITY

In this chapter, the analysis is broadened by examining the nutrient availability levels of

households in the NFSPS sample. Section A briefly describes the data used in the analysis. Section

B provides an overview of selected characteristics of the sample as they relate to nutrient availability.

Section C then describes the nutrient availability levels of the households in the sample. The

determinants of nutrient availability are examined in a regression context in Section D.

A. OVERVIEW OF THE DATA

As discussed earlier, a key component of the data collection for the in-person sample of FSP

participants was a seven-day food use interview, which obtained detailed data on all the foods used

by the household out of home food supplies during a seven-day period (see Chapter II and Appendix

A for details). The USDA nutrient data bank files used to process data from the CSFII (adapted as

necessary for the food use concept) were then used to convert the data on food use to data on nutrient

availability. Data on the numbers of meals eaten by household members from home food supplies

and from other sources during the seven-day observation period were also collected.

The sample used in the analysis below consists of 957 seven-day food use interviews collected

from FSP participants between May 1996 and December 1996. An additional 92 food use interviews

covered a four-day period rather than a seven-day period and are not used in the present analysis.1

Another 21 cases with food data are excluded from the current analysis because of missing

information on data items other than the food use questions.
74

It is important to realize that the food use concept used in the analysis in this chapter, which is

the same as that used in previous USDA Nationwide Food Consumption Surveys, differs

significantly from a second commonly used approach to measuring food use: 24-hour intake data

collection. The food use data include all foods prepared or used at home but exclude food obtained

and eaten outside the home, such as restaurant meals or meals at friends’ homes. Also, foods taken

from home food supplies but not actually eaten, such as waste in cooking and plate waste, are

included in the food use concept but excluded from 24-hour intake data collection.

The food use data are of direct interest in themselves, in that they support an overall analysis

of the nutrient availability of FSP participant households. In addition, they permit a first opportunity

to examine relationships between nutrient availability and the recently developed food security

concept.

B. NUTRITION-RELATED CHARACTERISTICS OF THE ANALYSIS SAMPLE

As background for the analysis of nutrient availability levels, it is useful to examine a number

of characteristics of the sample, particularly several household size variables, which relate directly

to nutrient availability. The average household in the sample contains approximately 3.0 persons

(Table V.1). As might be expected, this number is considerably lower for households with elderly

members (2.2 persons) than for those not containing an elderly person (3.3 persons).

It is common in nutrition analysis to normalize the household size variable for the nutritional

requirements of the household members, using the concept of “adult male equivalents” (AMEs).

Essentially, this measure scales the food energy requirements (based on RDAs) of each household

member in relation to the food energy requirements of a 30-year-old adult male. For instance, a 30-

year-old adult male has a food energy RDA of 2,900 kilocalories per day, while a 30-
75

TABLE V.1

SELECTED CHARACTERISTICS OF THE SAMPLE AVAILABLE FOR THE

NUTRIENT AVAILABILITY ANALYSIS

Household Without Household with an All

an Elderly Member Elderly Member Households

Percentage with Elderly 0 100 27

Percentage with AFDC Income 44 10 35

Percentage with Children (Percent

of Households) 77 19 61

Presence of Children

Less than 1 yr 12 1 9

1 to 4 37 6 29

5 to 11 45 10 35

12 to 15 20 6 16

16 to 18 12 5 10

Percentage with Wage Income 34 12 28

Percentage Less than 75% Poverty 70 44 63

Persons in Household 3.30 2.15 2.99

Adult Male Equivalents (AMEs) 2.37 1.58 2.16

Average Total Number of Meals

Eaten per Day per Person 2.6 2.6 2.6

Average Number Meals Eaten at

Home per Day, per Person 2.2 2.4 2.2

Equivalent Nutrition

Units (ENUs) 2.03 1.41 1.86 a

Average Value of All Food Used

from Home Food Supplies per

Week $64.08 $44.69 $58.88

Average Value of Purchased Food

Eaten from Home Food Supplies

per Week $57.84 $41.19 $53.37

Sample Size 694 262 957

SOURCE: 1996 National Food Stamp Program Source data, weighted tabulation.

Using food energy as the nutrient. a
The man counts as 1 and the woman counts as 2,200 divided by 2,900, or 0.76. Therefore, 2

the AME for the household is 1 plus 0.76, or 1.76.

76

year-old woman has an RDA of 2,200 kilocalories. Therefore, a two-person household consisting

of a couple in their thirties would have an AME household size of 1.76. The average household in 2

the sample has an AME of 2.2. Again the number is considerably lower for households with elderly

members, 1.6 as compared to 2.4 for non-elderly households.

The typical household in the sample reported having eaten 54.6 meals in total during the sevenday observation period (not shown). Adjustment for household size produces an estimate of 2.6

meals per person per day. Most of these meals were eaten from home food supplies. The average

net household meals from home supplies per household member, after subtracting meals from other

sources, was 2.2, implying that approximately 85 percent of meals (2.2 divided by 2.6) were eaten

from home food supplies.

Because some meals are not eaten from home food supplies, it is useful to define an additional

measure of effective household size that takes eating meals away from home into account. This is

done through computing “equivalent nutritional units” (ENUs) for each household. As with the

AME measure, ENUs normalize the household size for the nutritional requirements of household

members. In addition, however, ENUs take into account the proportion of meals eaten by each

member at home. For instance, if half the meals of a household member are eaten at home, then in

counting ENUs, that member’s contribution would count only half as much to ENU household size

as it would if all the meals had been eaten at home. Another difference between the ENU measure

and the AME measure is that the ENUs are defined separately for each nutrient, thus taking account

of the fact that the relative nutrient requirements for the various members of a household may be

different for different nutrients. (For instance, an adult woman’s requirement for calcium is higher
77

than that of an adult male relative to her requirement for food energy.) The ENU also takes into

account meals and snacks eaten by guests.

ENUs, therefore, can be interpreted as showing the number of people who actually ate meals

and snacks from household food supplies, as expressed in terms of adult male nutritional

requirements and taking into account meals not eaten from home food supplies. In a previous

example, a two-person household was considered, consisting of a man and a woman, which had an

AME of 1.76. If both people ate all their meals from home food supplies and if no guests ate meals

or snacks, then that household would have an ENU, as defined by food energy, of 1.76. If, however,

the two people ate one-third of their meals outside the home, then the household’s ENU variable

would be approximately 66 percent of 1.76, or 1.17.

As illustrated above, the ENU approach adjusts assumed nutrient requirements proportionately

downward for the fraction of meals eaten from sources other than the household food supplies.

Implicit in this is an assumption that the meals eaten away from home supply approximate the same

level of nutrient availability as do the meals from home food supplies. The degree to which this

implicit assumption--which is standard in most analyses of nutrient availability--affects the results

reported below is not clear.

In the NFSPS sample, the average ENU for food energy in the sample is about 1.9 persons. The

fact that it is lower than the AME reflects the downward scaling to adjust for meals not eaten from

home food supplies.

The average value of all food used from home food supplies during the week in the sample was

$58.88. This includes both the value of purchased food and the imputed value of foods obtained

without payment, such as gifts, foods obtained from WIC, and home-grown foods. When these non-
The nutrients studied, in addition to food energy, are ones that have been cited as being

3

either current or potential public health issues (Life Sciences Research Office 1989).

78

purchased foods are subtracted, the average value of the week’s foods that were purchased with

money or with food stamps was $53.37.

C. NUTRIENT AVAILABILITY FROM HOME FOOD SUPPLIES

1. Average Nutrient Availability

On average, the nutrient availabilities per ENU of the households in the sample exceed the RDA

levels for each of the eight nutrients examined (Table V.2). Mean food energy is estimated to exceed 3

the food energy RDA by 28 percent. For the other nutrients, the amount by which the average

nutrient availabilities exceed RDAs ranges widely, from 14 percent for calcium and 17 percent for

zinc to 156 percent for vitamin C.

These nutrient availability levels estimated from the NFSPS data are broadly consistent with

those found in earlier studies. Comparisons between the earlier data and the current findings are

presented in Appendix G.

2. Percentages of Households with Food Use Exceeding RDA-Based Threshold Levels

While the mean nutrient availability variables summarized above are of considerable interest,

it has long been recognized that examining average nutrient availability can be misleading, because

the averages may hide a significant number of households with inadequate diets at the low end of

the nutrient availability distribution. A common approach to this problem is to examine the

percentages of households who fall above and below certain thresholds on the “nutrient availability

as a percent of RDA” scale. Parts of the analysis use the obvious comparison standard of 100

percent of the RDAs. However, it has been argued that use of a 100 percent standard is too stringent
79

TABLE V.2

NUTRIENT AVAILABILITY MEASURES FOR SELECTED NUTRIENTS

Food Vitamin Vitamin Vitamin

Energy A C B Folate Calcium Iron Zinc 6

Average Nutrient

Availability as Percentage

of RDA 128 178 256 147 213 114 161 117 a

Percentage of Households

with Nutrient Availability

> 100 Percent of RDA 59 65 79 65 79 47 69 49

Percentage of Households

with Nutrient Availability

> 75 Percent of RDA 77 77 87 80 88 67 85 69

Sample Size 950

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Calculated as total amount of the nutrient available in the week divided by [ENU * daily RDA for adult adult male * 7]. a
80

a test, because of the way the RDAs are defined. In particular, to be conservative, the RDAs are set

at levels of nutrient intake believed to be adequate for virtually the entire healthy population. Since

nutrient requirements vary from person to person, it is generally agreed that most people do not need

the full 100 percent of the RDAs. Therefore, it is useful to conduct some of the analysis at a lower

RDA percentage level, and a standard of 75 percent of the RDA has been used in parts of the

analysis below.

In interpreting the data on percentages of household exceeding threshold criteria (whether 100

percent or 75 percent), it should be noted that the observed food use for a specific week may not be

the same as usual food use. By focusing on a single week, there may be some tendency to classify

households as being below a threshold (and hence at nutritional risk) when, in fact, their

consumption is adequate when averaged over multiple weeks.

As shown in the second row of Table V.2, substantial numbers of households in the sample

failed to have nutrient availabilities in the reference week equal to 100 percent of their RDAs. For

calcium and zinc, which were relatively low in the analysis of average intake discussed above, the

percentage of households estimated to meet 100 percent of the RDAs is less than half: 47 percent

for calcium and 49 percent for zinc. And even for vitamin C, for which average nutrient availability

greatly exceeds RDAs, only about 79 percent of households are estimated to be at or above the

RDAs.

When the threshold for the analysis is lowered to 75 percent of the RDA, considerably higher

percentages of households are found to meet the standards. The percentage of households at or

exceeding 75 percent of RDA range from 67 percent for calcium to 88 percent for folate.
81

3. Subgroup Analysis

It is also of interest to examine nutrient availability for selected subgroups of the overall sample.

This is done here, both for the average nutrient availability measure and for percentages of

households below the 100 percent and 75 percent RDA cutoff thresholds.

Average nutrient availability as a percentage of RDA. The overall patterns observed in the

full data set hold in general for most subgroups as well, though the absolute levels of nutrient

availability vary somewhat (Table V.3). The nutrient availability for the elderly, even after

correcting for their lower nutrition requirements as reflected in the RDAs, tends to be slightly lower

than for the sample as a whole. For instance, for households with elderly members, the availability

of food energy exceeds their RDAs by only 23 percent, as compared to 28 percent for the overall

sample. Elderly households have higher nutrient availabilities than the overall sample for only two

of the nutrients, vitamin A and iron. Households with children present a mixed picture, being

slightly lower than the overall sample for some nutrients and slightly higher for others. Singleperson households are consistently somewhat higher, except for folate.

Interestingly, households at the lowest income level in relation to the poverty standards tended

to have somewhat higher than average nutrient availability, exceeding the sample average for all but

two nutrients (iron and vitamin C). Households in the other two income categories shown, 51 to 75

percent of poverty and 76 percent of poverty or higher, tended to have nutrient availability levels

similar to each other and somewhat lower than the averages. Households receiving AFDC income

tended to be very near the sample averages for most nutrients.

When the NFSPS study was designed, there was interest in assessing nutrient adequacy for

subgroups of the population defined according to levels of variables associated with determining
82

TABLE V.3

NUTRIENT AVAILABILITY AS PROPORTION OF RDA, BY HOUSEHOLD CHARACTERISTICS

Sample Food Vitamin Vitamin Vitamin

Size Energy A C B Folate Calcium Iron Zinc 6

All Households 956 1.28 1.78 2.56 1.47 2.13 1.14 1.61 1.17

With Elderly 262 1.23 1.86 2.34 1.32 1.83 1.12 1.71 1.03

With Children 579 1.28 1.71 2.63 1.53 2.35 1.08 1.49 1.18

Single Person 249 1.38 2.11 2.71 1.48 2.01 1.34 1.96 1.25

Presence of Children

Less than 1 yr 84 1.34 1.75 3.27 1.61 2.49 1.11 1.67 1.27

1 to 4 278 1.32 1.74 2.58 1.56 2.49 1.10 1.48 1.18

5 to 11 336 1.23 1.70 2.61 1.53 2.44 1.06 1.48 1.14

12 to 15 158 1.20 1.51 2.48 1.45 2.12 0.99 1.44 1.11

16 to 18 100 1.14 1.66 2.24 1.31 1.82 0.93 1.30 1.08

Income Level as Percent of

Poverty

0 to 50% 356 1.33 1.84 2.60 1.51 2.25 1.12 1.57 1.23

51 to 75% 224 1.25 1.79 2.55 1.47 2.17 1.12 1.63 1.12

76% or higher 339 1.26 1.73 2.53 1.42 2.00 1.16 1.64 1.14

Received AFDC 320 1.32 1.78 2.65 1.55 2.40 1.09 1.53 1.21

Food Stamp Benefits Level (in

Dollars)a

0 to 10 40 1.11 1.48 1.90 1.10 1.51 1.07 1.42 0.93

11 to 99 289 1.26 1.82 2.59 1.40 1.94 1.17 1.72 1.14

100 to 199 304 1.29 1.79 2.62 1.45 2.08 1.14 1.62 1.16

200 to 299 180 1.28 1.78 2.53 1.52 2.23 1.10 1.48 1.21

300 or more 137 1.38 1.77 2.62 1.68 2.68 1.11 1.56 1.26

Household Size

1 249 1.38 2.11 2.71 1.48 2.01 1.34 1.96 1.25

2 174 1.34 1.72 2.51 1.47 1.98 1.12 1.58 1.19

3 201 1.38 1.85 2.84 1.59 2.37 1.20 1.59 1.26

4 164 1.17 1.59 2.26 1.45 2.24 0.97 1.43 1.07

5 or more 168 1.09 1.49 2.38 1.32 2.07 0.92 1.33 1.01

Purchasing Powerb

$0 to $399 158 1.35 1.89 2.79 1.50 2.13 1.16 1.72 1.26

$400 to 599 216 1.26 1.85 2.42 1.39 2.02 1.14 1.64 1.14

$600 to 798 193 1.44 1.88 2.76 1.60 2.27 1.28 1.70 1.27

$800 or more 389 1.19 1.66 2.45 1.43 2.12 1.05 1.50 1.10

Housing Costs as Percent of

Income

No Costs 22 0.97 1.53 1.66 1.03 1.68 0.87 1.12 0.83

1% to 30% 180 1.20 1.52 2.13 1.30 1.88 1.11 1.52 1.07

30% to 45% 159 1.30 2.00 2.42 1.49 2.24 1.10 1.64 1.15

45% to 55% 93 1.23 1.73 2.64 1.42 2.16 1.06 1.54 1.09

Over 55% 419 1.34 1.85 2.80 1.56 2.25 1.18 1.68 1.24

SOURCE: 1996 National Food Stamp Program Survey, weighted tabulations.

Benefit levels are per household. a

Monthly food stamp benefits plus gross monthly income. b
83

program benefits. The last three panels of Table V.3 provide selected tabulations in this regard.

Overall, households with the lowest benefit levels (typically small households or households with

relatively high incomes) tend to have low levels of nutrient availability compared to the sample as

a whole, although the sample size for this group is quite small. Correspondingly, households at the

two highest benefit levels tend to exceed the averages for the sample as a whole.

As noted above, one-person households tend to have higher-than-average levels of nutrient

availability. The largest household category tends to have lower availability levels than the overall

sample, and, for several of the nutrients, these differences are substantial. This is the case, for

instance, for food energy, for which the overall sample exceeds the RDAs on average by 28 percent,

while households with five or more members exceed the standards by only 9 percent.

When the data are examined for different categories of shelter cost measured as a percent of

income, two patterns emerge. Households in the lowest shelter cost category tend to have slightly

lower average nutrient availability in relation to the RDA. In fact, those with no shelter costs have

slightly less than the RDA available for food energy, calcium, and zinc. However, as shelter costs

as a percent of income increase to 45 to 55 percent, there is a slight drop in nutrient availability

before increasing for those with costs above 55 percent of income.

Analysis of Meeting the RDAs. Similar patterns are found when the subgroup analysis is

extended to examine percentages of households that meet either 100 percent or 75 percent standards

in their nutrient availability (Tables V.4 and V.5). As with the earlier analysis, for instance, nutrient

availability levels of households with elderly members tend to be slightly below the overall sample

levels, while those of households with lower incomes tend to be somewhat higher. However, these

and similar differences are neither large nor consistent.
84

TABLE V.4

PROPORTION OF HOUSEHOLDS WITH NUTRIENT AVAILABILITY = 100 PERCENT OF RDA,

BY HOUSEHOLD CHARACTERISTIC

Sample Food Vitamin Vitamin Vitamin

Size Energy A C B Folate Calcium Iron Zinc 6

All Households 956 0.59 0.65 0.79 0.65 0.79 0.47 0.69 0.49

With Elderly 262 0.56 0.64 0.75 0.57 0.69 0.44 0.72 0.41

With Children 579 0.60 0.68 0.84 0.71 0.86 0.46 0.66 0.50

Single Person 249 0.62 0.64 0.75 0.61 0.74 0.56 0.78 0.52

Presence of Children

Less than 1 yr 84 0.56 0.71 0.86 0.64 0.85 0.45 0.72 0.44

1 to 4 278 0.64 0.71 0.86 0.72 0.91 0.49 0.68 0.52

5 to 11 336 0.58 0.67 0.83 0.71 0.89 0.42 0.68 0.49

12 to 15 158 0.59 0.68 0.83 0.68 0.83 0.41 0.67 0.47

16 to 18 100 0.58 0.66 0.75 0.67 0.14 0.39 0.63 0.43

Income Level

0 to 50% 356 0.61 0.65 0.80 0.66 0.80 0.48 0.67 0.50

51 to 75% 224 0.58 0.64 0.80 0.67 0.79 0.43 0.69 0.48

76% or higher 339 0.58 0.64 0.77 0.63 0.77 0.50 0.73 0.49

Received AFDC 320 0.64 0.68 0.83 0.74 0.87 0.48 0.67 0.52

Benefits Level (in Dollars)

0 to 10 40 0.45 0.44 0.60 0.42 0.61 0.44 0.70 0.33

11 to 99 289 0.57 0.65 0.76 0.60 0.72 0.49 0.70 0.48

100 to 199 304 0.58 0.63 0.78 0.65 0.77 0.49 0.67 0.47

200 to 299 180 0.61 0.66 0.83 0.73 0.87 0.46 0.67 0.53

300 or more 137 0.67 0.72 0.87 0.72 0.91 0.43 0.76 0.52

Household Size

1 249 0.62 0.64 0.75 0.61 0.74 0.56 0.78 0.52

2 174 0.66 0.65 0.82 0.69 0.76 0.52 0.68 0.55

3 201 0.63 0.71 0.84 0.71 0.81 0.54 0.66 0.54

4 164 0.55 0.66 0.77 0.67 0.81 0.37 0.65 0.44

5 or more 168 0.47 0.57 0.78 0.58 0.84 0.32 0.65 0.35

SOURCE: 1996 National Food Stamp Program Survey, weighted tabulations.
85

TABLE V.5

PROPORTION OF HOUSEHOLDS WITH NUTRIENT AVAILABILITY $75 PERCENT OF RDA,

BY HOUSEHOLD CHARACTERISTIC

Sample Food Vitamin Vitamin Vitamin

Size Energy A C B Folate Calcium Iron Zinc 6

All Households 956 0.78 0.77 0.87 0.80 0.88 0.67 0.85 0.69

With Elderly 262 0.76 0.73 0.82 0.75 0.83 0.66 0.86 0.63

With Children 579 0.78 0.82 0.92 0.84 0.92 0.66 0.85 0.71

Single Person 249 0.80 0.76 0.82 0.77 0.85 0.75 0.88 0.72

Presence of Children

Less than 1 yr 84 0.78 0.86 0.93 0.81 0.90 0.65 0.84 0.73

1 to 4 278 0.81 0.85 0.94 0.88 0.95 0.68 0.88 0.74

5 to 11 236 0.77 0.82 0.91 0.84 0.94 0.63 0.86 0.69

12 to 15 158 0.75 0.81 0.89 0.80 0.90 0.60 0.82 0.67

16 to 18 100 0.79 0.77 0.86 0.77 0.87 0.60 0.79 0.69

Income Level

0 to 50% 356 0.76 0.78 0.89 0.80 0.88 0.64 0.83 0.69

51 to 75% 224 0.76 0.77 0.87 0.82 0.87 0.65 0.88 0.70

76% or higher 339 0.80 0.76 0.87 0.79 0.88 0.71 0.86 0.71

Received AFDC 320 0.83 0.83 0.91 0.86 0.94 0.67 0.86 0.76

Benefits Level (in Dollars)

0 to 10 40 0.77 0.66 0.76 0.62 0.75 0.61 0.86 0.66

11 to 99 289 0.76 0.75 0.83 0.77 0.85 0.68 0.85 0.66

100 to 199 304 0.74 0.74 0.86 0.79 0.85 0.66 0.83 0.69

200 to 299 180 0.78 0.84 0.93 0.85 0.94 0.69 0.86 0.74

300 or more 137 0.85 0.82 0.94 0.87 0.96 0.66 0.89 0.73

Household Size

2 174 0.82 0.76 0.89 0.83 0.88 0.72 0.85 0.76

3 201 0.79 0.82 0.92 0.81 0.89 0.70 0.84 0.72

4 164 0.76 0.78 0.86 0.82 0.87 0.60 0.81 0.68

5 or more 168 0.69 0.73 0.88 0.78 0.90 0.53 0.86 0.58

SOURCE: 1996 National Food Stamp Program Survey, weighted tabulations.
86

D. FOOD USE BY FOOD GROUP

An additional perspective on the foods used by sample members can be obtained by examining

the percentage distributions of foods by food groups (Table V.6). In terms of food energy supplied,

the most important food groups were fats and oils, which account for 9.2 percent of food energy

availability; lower cost red meats, which account for 8.0 percent of food energy; and milk, which

accounts for 7.2 percent.

E. DETERMINANTS OF NUTRIENT AVAILABILITY

In order to understand factors that influence nutrient availability among food stamp participants,

multivariate regression techniques can be employed to determine how nutrient availability is

associated with specific variables after controlling for the effects of other factors. In particular, there

is interest in determining the extent to which food stamp benefits are associated with increased levels

of nutrient availability. Past research on nutrient impacts of food stamp benefits has shown mixed

results regarding the extent to which participation in the Food Stamp Program and the level of

benefits received influence nutrition measures. While the current sample is limited to food stamp

participants, it is unique in providing the first reliable national measures of nutrient availability for

food stamp participants based on household food use data in at least a decade. The remaining

sections of this chapter describe the models estimated and discuss the findings, with a particular

emphasis on the role played by food stamp benefits.

1. Variables Used in the Regressions

Separate regressions have been estimated for each of the nutrients being studied. The dependent

variables used in the regression are nutrient availabilities over the previous seven days for the entire

household. Food consumption theory suggests the major factors influencing levels of food intake
87

TABLE V.6

SHARE OF VARIOUS TYPES OF FOODS IN HOUSEHOLD FOOD USE

Percentage Based On:

Food Group Value of Food Food Energy

Vegetables, Fruit

Potatoes 2.4 3.2

High-Nutrient Vegetables 3.8 0.9

Other Vegetables 4.8 1.7

Mixtures, Mostly Vegetables; Condiments 0.6 1.3

Vitamin C-Rich Fruit 4.0 2.3

Other Fruit 3.9 2.2

Subgroup Total 19.6 10.6

Grain Products

Whole-Grain/High-Fiber Breakfast Cereals 1.7 1.6

Other Breakfast Cereals 3.3 3.3

Whole-Grain/High-Fiber Flour, Meal, Rice, Pasta 0.4 0.7

Other Flour, Meal, Rice, Pasta 2.2 6.9

Whole-Grain/High-Fiber Bread 0.9 1.3

Other Bread 3.9 6.4

Bakery Products, Not Bread 3.9 4.9

Grain Mixtures 3.4 2.7

Subgroup Total 19.7 27.6

Milk, Cheese, Cream

Milk, Yogurt 7.6 7.2

Cheese 3.0 2.4

Cream; Mixtures, Mostly Milk 1.9 1.9

Subgroup Total 12.5 11.5

Meat and Alternatives

Lower-Cost Red Meats, Variety Meats 7.8 8.0

Higher-Cost Red Meats, Variety Meats 6.7 4.2

Poultry 5.3 4.3

Fish, Shellfish 3.1 0.9
TABLE V.6 (continued)

Percentage Based On:

Food Group Value of Food Food Energy

88

Bacon, Sausage, Luncheon Meats 5.9 7.2

Eggs 1.3 1.5

Dry Beans, Peas, Lentils 0.9 1.4

Mixtures, Mostly Meat, Poultry, Fish, Egg, Legume 3.3 1.4

Nuts, Peanut Butter 0.8 1.3

Subgroup Total 34.9 30.2

Other Foods

Fats, Oils 2.5 9.2

Sugar, Sweets 2.8 6.1

Seasonings 0.1 0.1

Soft Drinks, Punches, Ades 5.6 4.2

Coffee, Tea 1.9 0.4

Alcohol 0.5 0.1

Subgroup Total 13.3 20.1

Sample Size: 957

SOURCE: 1996 National Food Stamp Program Survey, unweighted tabulations.

Less than 0.05. a
89

include available purchasing power, household size and composition, and food prices. Other

potentially important factors, such as tastes and ease of preparation, cannot easily be observed in the

data set and are not included in the regression specifications.

The independent variables included in the model have been chosen to reflect consumption

determinants, together with other household characteristics that could affect food use. Many of the

variables have been included in the model in their logarithmic form to facilitate interpretation of the

estimated relationships.

Household purchasing power depends on the level of cash income from a variety of sources. The

income variable used in the model combines income from wage earnings, public assistance and other

forms of unearned income. For food stamp participants, the level of food stamp benefits is also

important. Income and benefits were normalized to a one week period and included in logarithmic

form. There were 37 cases with missing income and 2 cases with missing food stamp benefits where

imputations were made based on predictions from a regression model estimated on the remaining

sample relating gross income to household size and the level of food stamp benefits.

Household size and composition are captured with two variables. The “equivalent nutrition unit”

(ENU) adjusts households size for nutrient requirements of household members and guests and for

the proportion of meals eaten from the home food supply. The total number of the people in the

household is also included. The ENU measures provides a good indication of the level of need for

food from household supplies, while the total number of people in the household controls for potential

economies of scale that can be achieved in providing food in larger households.

Binary indicators for urbanization and for season are included to capture regional and seasonal

affects in availabilities and prices of food. The urban variable indicates household location in an area

with more than 90 percent of the population classified as urban; the rural variable indicates an area
Earlier specifications of the regressions included a more detailed specification of the

4

“children” variable, with binary indicators of the presence of children in each of five age categories--

less than 1 year old, 1 to 4, 5 to 10, 11 to 14, and 15 to 18. The more detailed specification yielded

essentially the same results as those reported here, and the more parsimonious specification was

retained for simplicity. Similarly, binary variables indicating use by the family of two other child

nutrition programs--WIC and the National School Lunch and School Breakfast Program--were tested

and found not to substantially influence the results.

90

with less than 10 percent urban. The seasonal indicators identify when the food use observation

occurred in the fall and winter. The omitted variable is summer; no interviewing was done in the

spring.

Other variables included in the regression include binary variables for whether the household

contained an elderly member or a child 18 or under; variables indicating time since the most recent

food stamp application; binary variables indicating ethnicity (white but not Hispanic and black but

not Hispanic); and a variable indicating the receipt of food stamps through electronic benefit transfer.4

The log of monthly housing cost divided by household size and the log of the percentage of food used

that was not bought were also included. 

2. Regression Results

Table V.7 presents the regression results for the estimated equations for the eight nutrients. For

those variables indicated in log form, the associated coefficients are elasticities--that is, they show

the percentage by which nutrient availability will increase if there is a percentage increase in the

explanatory variable. Surprisingly, the estimated coefficients on income are quite small and not

statistically significant. However, for all nutrients the results show the expected positive association

between nutrient availability and food stamp benefits. For income, the elasticities are small in size

and generally insignificant statistically. However, the elasticities associated with food stamp benefits
91

TABLE V.7

REGRESSION ANALYSIS OF DETERMINANTS OF NUTRIENT AVAILABILITY

(Standard Errors in Parentheses)

Calories Vitamin A Vitamin C Vitamin B6 Folate Calcium Iron Zinc

ENU (log) 0.611*** 0.87*** 0.589*** 0.589*** 0.574*** 0.639*** 0.48*** 0.545***

(9.46) (4.85) (4.48) (9.02) (7.54) (6.99) (8.48) (7.13)

Income (log) -0.016 0.019 -0.018 0 0.002 0.001 -0.001 -0.002

(1.27) (0.82) (0.8) (0.02) (0.1) (0.04) (0.09) (0.16)

FSP Benefits (log) 0.112*** 0.123*** 0.122*** 0.129*** 0.136*** 0.102*** 0.119*** 0.113***

(4.89) (3.07) (3.110) (4.12) (4.29) (3.66) (4.43) (4.45)

HH Size (log) 0.051 -0.289 0.041 0.07 0.05 -0.023 0.226*** 0.087

(0.67) (1.57) (0.30) (0.84) (0.47) (0.23) (2.97) (0.95)

Urban (1,0) -0.107 -0.001 0.023 -0.119 -0.124 -0.082 -0.107 -0.107

(1.45) (0.01) (0.26) (1.78) (1.82) (1.06) (1.55) (1.74)

Rural (1,0) 0.005 -0.039 0.033 -0.055 -0.015 -0.067 -0.007 -0.003

(0.11) (0.38) (0.38) (0.85) (0.19) (0.85) (0.11) (0.08)

Winter Interview (1,0) -0.304** -0.743*** -0.469** -0.379*** -0.545*** -0.459*** -0.337*** -0.224

(2.38) (3.48) (2.11) (4.15) (3.96) (3.35) (3.26) (1.22)

Fall Interview (1,0) -0.027 -0.106 -0.18*** -0.058 -0.027 -0.03 -0.006 -0.015

(0.62) (1.86) (3.33) (1.16) (0.5) (0.59) (0.14) (0.3)

HH Has Elderly Member (1,0) 0.005 0.187*** 0.106 0.018 0.063 0.006 0.041 -0.076

(0.09) (2.95) (1.39) (0.29) (1.07) (0.11) (0.7) (1.35)

HH Has Child # 18 (1,0) 0.174** 0.461*** 0.402*** 0.271*** 0.397*** 0.221** 0.059 0.193**

(2.34) (3.04) (3.02) (2.72) (3.24) (2.34) (0.63) (2.36)

African-Am; not Hispanic (1,0) 0.046 -0.128 -0.19** -0.016 -0.142 -0.179** -0.053 0.101

(0.68) (1.36) (2.09) (0.25) (1.69) (2.34) (0.76) (1.34)

White, not Hispanic (1,0) -0.013 -0.023 -0.215** -0.082 -0.115 0.074 -0.035 0.044

(0.24) (0.23) (2.18) (1.35) (1.44) (1.13) (0.6) (0.73)

Applied 1 to 4 Years Ago -0.045 -0.038 -0.117 -0.072 -0.101 -0.008 -0.034 -0.058

(0.83) (0.62) (1.5) (1.4) (1.81) (0.17) (0.67) (1.21)

Applied 5 or More Years Ago -0.061 -0.103 -0.243*** -0.088 -0.14 -0.054 -0.094 -0.076
TABLE V.7 (continued)

Calories Vitamin A Vitamin C Vitamin B6 Folate Calcium Iron Zinc

92

(0.98) (1.16) (2.85) (1.29) (1.83) (0.67) (1.42) (1.15)

Electronic Benefit (1,0) 0.068 -0.017 -0.009 0.017 0.023 0.059 0.06 -0.027

(0.96) (0.17) (0.09) (0.24) (0.26) (0.73) (0.86) (0.29)

Shelter Cost Per Person (log) 0.056*** 0.068** 0.09*** 0.073*** 0.063*** 0.038 0.061*** 0.051***

(3.84) (2.03) (3.75) (4.49) (2.65) (1.84) (3.53) (3.3)

Percent Food Not Brought (log) -0.034*** -0.052*** -0.043*** -0.038*** -0.041*** -0.042*** -0.024** -0.03***

(3.4) (3.240 (2.94) (3.47) (3.380 (3.91) (2.07) (2.67)

Intercept 7.544*** 8.346*** 4.177*** 2.156*** 7.098*** 6.304*** 2.229*** 4.092***

(47.7) (32.13) (16.5) (11.07) (32.77) (33.12) (12.73) (26.64)

SOURCE: National Food Stamp Program Survey weighted data. Sample size = 957.

NOTE: Statistical significance tests take the survey design into account, using a Taylor’s series expansion algorithm as implemented in the STATA statistical

package.

**Significantly different from zero at the .05 level, two-tailed .01 level, two-tailed test.

***Significantly different from zero at the .01 level, two-tailed test.

NA = Not applicable.
93

show highly significant positive impacts. For example, the results imply that a 10 percent increase

in food stamp benefits would result in a 1.1 percent increase in availability of calories and a 1.4

percent increase in the availability of folate. 

Regarding the household size and composition variables, as expected, the level of ENUs is shown

to be a highly significant factor determining the level of nutrients available with the household with

coefficient size ranging from .48 to .87. The size variable measuring the number of people in the

household is generally positive, suggesting that, even after controlling for food purchasing power and

the nutrient needs of household members and guests, larger households tend to have more nutrients

available, presumably due to scale efficiencies. However, the variable is usually not statistically

significant. The urbanization and seasonal indicator variables generally show negative associations

with levels of nutrient availability.

3. Summary

The eight regressions summarized in Table V.7 suggest that levels of nutrient availability follow

expected patterns in relation to household size, household composition and food purchasing power.

Some locational and seasonal effects are also evident. A key finding of the analysis is the strong

positive association between the level of food stamp benefits and nutrient availability for the nutrients

analyzed. The next chapter explores how these results differ when we take into account the

household’s food insecurity status.

95

VI. CORRELATIONS BETWEEN FOOD SECURITY

AND NUTRIENT AVAILABILITY

Both the food security and the nutrient availability measures attempt to provide indicators of the

adequacy of households’ access to food. This chapter examines the degree to which they are

correlated in order to gain insight as to whether the two measures are tapping essentially the same

phenomenon or different aspects of people’s well-being. Section A discusses basic correlations

between the two measures. Section B then examines the correlations in a multivariate regression

context, which makes it possible to control at least partially for the influence of confounding

variables. Section C briefly summarizes a number of other lines of analysis that have been pursued

for additional insight into the statistical results. Possible interpretations of the findings are discussed

in Section D.

A. NUTRIENT AVAILABILITY FOR DIFFERENT LEVELS OF FOOD SECURITY

As can be seen in Table VI.1, higher levels of nutrient availability appear to be associated with

higher levels of food insecurity. The basic pattern of results, which is common to each of the

nutrients studied, can be illustrated by focusing on food energy availability (Table VI.1). Households

classified as food secure have an average food energy availability of 122 percent of the RDAs of

their members (and guests). The number rises to 128 percent and 127 percent for the two middle

groups on the food security scale and to 153 percent for the portion of the sample classified as having

severe hunger. For three of the nutrients analyzed, the positive association between
96

TABLE VI.1

HOUSEHOLD NUTRIENT AVAILABILITY, BY FOOD SECURITY INDEX LEVELS

(Food Stamp Participants)

(Entries Are Nutrient Availability as a Proportion of RDA)a

Level of Food Security

All Food Food Insecure/ Food Insecure/ Food Insecure/ Related to Nutrient

Households Secure No Hunger Some Hunger Severe Hunger Availability

Significance Level

for Analysis of

Variance Test of

Whether Food

Security Level

b

Food Energy 1.28 1.22 1.28 1.37 1.53 -

Vitamin A 1.78 1.64 1.78 1.97 2.32 -

Vitamin C 2.56 2.48 2.50 2.70 3.16 -

Vitamin B6 1.47 1.38 1.49 1.56 1.75 **

Folate 2.13 2.00 2.18 2.22 2.63 **

Calcium 1.13 1.06 1.12 1.25 1.36 **

Iron 1.61 1.55 1.60 1.68 1.89 -

Zinc 1.17 1.11 1.18 1.21 1.51 -

Sample Size 956 415 313 173 54

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Calculated as total amount of nutrient available in week divided by [ENU * daily RDA for adult male * 7]. ENU = Equivalent

a

Nutrition Unit.

Entries are calculated based on an F-test for a regression of nutrient availability on a constant term plus three binary variables,

b

indicating three of the four food security levels. (The omitted category was the food secure group.) Significance tests account for

sample design, based on a Taylor’s series expansion method as implemented by the STATA statistical package.

The sample size for the “all households” column is greater than the sum of the other column sample sizes, because some households c

had missing food security data and were not classified into the separate columns.

**Significant at 95 percent level.
To analyze sensitivity, the tests were rerun, omitting the three observations in the severe hunger 1

category with the highest levels of nutrient availability. With this reduced sample, none of the eight

relationships was statistically significant at the 95 percent level. Also, while three of the eight

relationships are significant with the regular sample, the relationships are weak in terms of variance

explained. In all but one case, the classification by food security group explains less than one

percent of the variance in nutrient availability.

97

nutrient intake and the severity of food insecurity is statistically significant at the 95 percent level

as can be seen in the righthand column of the table.1

The above findings were unexpected. One would expect that within the general population,

households reporting more severe levels of food insecurity would be observed to have fewer

nutrients available. Indeed, recent work by Rose and Oliveira (1997a) on a general population

sample has shown significantly lower nutrient intakes for food insufficient households. However,

the food stamp participant population is different from the general population in that it is receiving

food assistance specifically targeted toward alleviating food insecurity and hunger. The next section

extends the multivariate analysis of the previous chapter to investigate how the results are affected

when key other variables are controlled for and to examine the interactions between nutrient

availability, food stamp benefits, and food security.

B. REGRESSION ANALYSIS

Regression analysis can be useful in helping us to gain a better understanding of the different

factors which might be leading to the unexpected results reported above. In particular, regression

analysis can control for the effects of potentially confounding factors which may be associated with

both nutrient availability and food security. In addition, regression analysis can make it possible to

explore important interactions between various influences on nutrient availability through the use

of interaction terms in the equations.
98

In specifying the regressions, the analysis has drawn on past research which has found various

household characteristics, such as household size, income level, and composition, to be important

determinants of nutrient availability. It has also drawn on recent discussions in the literature on food

security, which emphasize that “hunger is a managed process,” in which a household’s response to

its circumstances may vary, depending on what level of food security it is currently experiencing.

As summarized in Carlson, Andrews, and Bickel (1999), this approach recognizes the experience

of 

food insecurity and hunger as a sequence of stages reflecting increasingly severe

deprivation of basic food need and characterized by a managed process of decision

making and behavior in response to increasingly constrained household resources...This

is the “economic” perspective, in which the experience of resource inadequacy to fully

meet basic needs and the pattern of chosen behavioral responses revealed by the

household in seeking to cope with this constraint on diets exemplify individual and

household economizing decisions and behavior generally...This insight into measurement

of the economic-behavioral aspect of the phenomenon is nicely captured in the

metaphorical phrase “hunger is a managed process”....

To facilitate understanding of the regression results, the analysis is presented below in two

stages. First, the relationship between food nutrient availability and food security, holding selected

other variables constant, is descriptively examined. Next, after examining these regression results,

additional structure is placed on the regression specifications to take into account the potential

interactions between food security and the determinants of food security as highlighted in the

Carlson, Andrews, and Bickel reference cited above.

1. Descriptive Regressions

Table VI.2 presents the results of simply adding food security indicators to the nutrient

availability equations that were estimated in Chapter V. In particular, in addition to the explanatory

variables discussed in Chapter V, two additional variables are added. One is a binary indicator of
99

TABLE VI.2

REGRESSION ANALYSIS OF DETERMINANTS OF NUTRIENT AVAILABILITY

(Standard Errors in Parentheses)

Calories Vitamin A Vitamin C Vitamin B6 Folate Calcium Iron Zinc

ENU (log) 0.611*** 0.871*** 0.595*** 0.589*** 0.575*** 0.642*** 0.48*** 0.545***

(9.78) (4.81) (4.5) (9.15) (7.65) (7.07) (8.73) (7.3)

Income (log) -0.017 0.019 -0.018 -0.001 0.001 0 -0.002 -0.003

(1.35) (0.78) (0.79) (0.06) (0.08) (0.03) (0.13) (0.21)

FSP Benefits (log) 0.112*** 0.123*** 0.123*** 0.129*** 0.136*** 0.1*** 0.118*** 0.111***

(4.93) (3.1) 3.18 4.16 4.26 3.6 4.37 4.35

HH Size (log) 0.049 -0.291 0.037 0.068 0.048 -0.028 0.225*** 0.084

(0.64) (1.58) (0.26) (0.81) (0.45) (0.280 (2.94) (0.93)

Urban (1,0) -0.106 0 0.024 -0.118 -0.124 -0.081 -0.107 -0.107

(1.49) (0) (0.27) (1.81) (1.84) (1.09) (1.57) 1.79

Rural (1,0) 0.007 -0.037 0.039 -0.053 -0.013 -0.066 -0.007 -0.004

(0.15) (0.35) (0.45) (0.85) (0.160 (0.840 (0.11) 0.09

Winter Interview (1,0) -0.285** -0.726*** -0.455** -0.364*** -0.534*** -0.439*** -0.327*** -0.213

(2.21) (3.34) (2.09) (3.9) (3.83) (3.11) (3.14) 1.18

Fall Interview (1,0) -0.023 -0.103 -0.179*** -0.055 -0.025 -0.025 -0.003 -0.011

(0.54) (1.78) (3.32) (1.1) (0.47) (0.48) (0.08) 0.23

HH Has Elderly Member (1,0) 0.02 0.201*** 0.116 0.031 0.072 0.024 0.051 -0.065

(0.39) (3.11) (1.54) (0.49) (1.21) (0.45) (0.85) (1.12)

HH Has Child # 18 (1,0) 0.182** 0.468*** 0.407*** 0.278*** 0.402*** 0.23** 0.064 0.199**

(2.45) (3.13) (3.06) (2.8) (3.3) (2.45) (0.68) (2.43)

African-Am; not Hispanic (1,0) 0.052 -0.124 -0.191** -0.012 -0.14 -0.172** -0.049 0.107

(0.74) (1.31) (2.1) (0.18) (1.64) (2.25) (0.69) 1.39

White, not Hispanic (1,0) -0.012 -0.024 -0.219** -0.082 -0.116 0.076 -0.034 0.046

(0.23) (0.24)` (2.23) (1.36) (1.46) (1.14) (0.58) 0.75

Applied 1 to 4 Years Ago -0.027 -0.022 -0.108 -0.058 -0.091 0.013 -0.022 -0.045

(0.54) (0.39) (1.46) (1.21) (1.72) (0.29) (0.46) (0.98)
TABLE VI.2 (continued)

Calories Vitamin A Vitamin C Vitamin B6 Folate Calcium Iron Zinc

100

Applied 5 or More Years Ago -0.056 -0.099 -0.24*** -0.084 -0.137 -0.048 -0.09 -0.072

(0.92) (1.12) (2.86) (1.26) (1.81) (0.61) (1.39) (1.13)

Electronic Benefit (1,0) 0.06 -0.025 -0.018 0.011 0.018 0.051 0.056 -0.031

(0.92) (0.25) (0.18) (0.16) (0.21) (0.68) (0.84) (0.36)

Shelter Cost Per Person (log) 0.054*** 0.066 0.089*** 0.071*** 0.062** 0.035 0.059*** 0.049***

(3.54) (1.92) (3.69) (4.2) (2.53) (1.61) (3.34) 3.08

Percent Food Not Brought (log) -0.033*** -0.051*** -0.042*** -0.037*** -0.04*** -0.041*** -0.024** -0.029***

(3.28) (3.17) (2.9) (3.36) (3.3) (3.81) (2.01) (2.6)

Food Insecure (1,0) 0.051 0.031 -0.025 0.041 0.018 0.077 0.047 0.059

(1.05) (0.38) (0.4) (0.78) (0.31) (1.27) (0.97) (1.19)

Hunger (1,0) 0.124*** 0.108 0.07 0.099** 0.072 0.149** 0.08 0.09**

(2.62) (1.4) (0.88) (2.3) (1.35) (2.43) (1.94) (2.01)

Intercept 7.501*** 8.311*** 4.163*** 2.121*** 7.075*** 6.25*** 2.199*** 4.056***

(50.4) (34.18) (18.06) (11.74) (35) (35.53) (13.57) (27.83)

SOURCE: National Food Stamp Program Survey weighted data. Sample size = 957.

NOTE: Statistical significance tests take the survey design into account, using a Taylor’s series expansion algorithm as implemented in the STATA statistical

package.

**Significantly different from zero at the .05 level, two-tailed test.

***Significantly different from zero at the .01 level, two-tailed test.

NA = Not applicable.
The findings are quite robust to the exact regression specification employed. Appendix K

2

summarizes the range of specifications that was explored.

101

whether a household is experiencing food insecurity but no evident hunger (as compared to the

omitted category of food secure), while the other is a binary variable indicating food insecure with

hunger. 

As shown in the table, the introduction of control variables does not alter the basic relationship

between nutrient availability and food insecurity that was observed in tabular form in Table VI.1.

The coefficient on the variable indicating food insecurity with hunger is positive in all eight

regressions, indicating that relatively high levels of food insecurity are associated in the same

household with relatively higher levels of nutrient availability. For four of the eight nutrients, this

estimated relationship is statistically significant. 

These findings suggest that the unexpected associations found in Table VI.1 are not simply due

to spurious correlation with other variables. While the possibility of spurious correlations cannot

be fully ruled out, many of the most obvious potential control variables have been included, and the

unexpected correlations remain.2

2. Regressions with an Interactive Specification

Additional insight can be gained by exploring a somewhat more elaborate specification of the

regression model. In particular, the conceptual model underlying the measures of food security used

in this study suggests differing behavioral responses for households, depending on their food

insecurity level. At more severe levels of food insecurity, households may manage their resources

more intensively to try and maintain the quality and quantity of food availability. This may be done,

for instance, by buying cheaper, more nutrient dense foods with available food purchasing resources

or by utilizing social and community networks to obtain emergency food or food resources. Using
There are three food security level variables and two purchasing power variables (income and 3

food stamp benefits). This yields six (that is, 3 times 2) possible interaction terms, all of which are

included. We also include two of the three food security level binary indicators with the third, food

secure, being the omitted reference category. This yields eight food security variables in all in this

specification. Note that it would not be appropriate to also include the income variable on the food

stamp variable separately, because they are the sums of the three associated interaction terms and

are thus not linearly independent of those terms.

102

data from the 1977/78 NFCS, Basiotis (1992) showed differing behavioral responses of food

insufficient households. While the NFSPS data provide the opportunity for a quite broad

investigation of this assumptions of the conceptual framework, the analysis in this section is

specifically oriented toward examining the higher levels of nutrient availability observed among the

food insecure.

In implementing this more detailed approach, the multivariate model used earlier is modified

to relax the assumption that each additional dollar of income and/or food stamp benefits will have

the same impact on nutrient availability, irrespective of the food security status of the household.

A more flexible model specification is used allowing for the income and food stamp benefit

elasticities to be different depending on which food insecurity status category a household has been

assigned to. This is done by interacting three binary variables indicating:

1. Food secure household

2. Food insecure household without hunger 

3. Food insecure household with hunger 

with the income and food stamp benefit variables. The binary variables themselves (except for one

omitted reference category for food secure) are also included. The estimated coefficients for the

3

interactions should indicate whether behavioral differences by food insecurity status are evident.

Likewise, the estimated coefficients for the included binary variables should describe any difference
It should be noted, however, that, less in conformance with the a priori theory, the coefficient 4

on the interaction involving less severe insecurity tends to be higher than the coefficients on the

interaction terms involving more severe insecurity.

103

means between the included groups and the food secure (omitted group) that is not explained by the

other variables in the model.

Results of the estimated equations for the eight nutrients are shown in Table VI.3. Regarding

the food stamp benefit elasticities, the general pattern is for these elasticities to be larger for food

insecure households. For example, in the folate equation, the interaction coefficients indicate that

for a food secure household, an increase in food stamp benefits of 10 percent would increase folate

availability by .95 percent, as compared to 2.01 percent in a food insecure household without hunger

and 1.40 percent in a food insecure household with hunger. Although the differences between

elasticities are not statistically significant, the extent to which the pattern of difference conforms to

a priori expectations supports use of the flexible specification. Overall, in 5 of the 8 equations, the

coefficients on both of the interaction terms between food insecure status and food stamp benefits

are higher than the coefficient on the interaction terms involving food secure households.4

It should be noted that the regression results reviewed above do not alter the basic descriptive

findings, noted at the beginning of the chapter. It remains true in the regression context that food

insecure households tend, other things equal, to have higher levels of nutrient availability than

households that are food secure. When the regressions are evaluated at the values of the independent
104

TABLE VI.3

REGRESSION ANALYSIS OF ANALYSIS OF INTERACTIONS BETWEEN FOOD SECURITY AND NUTRIENT AVAILABILITY

(Standard Errors in Parentheses)

Calories Vitamin A Vitamin C Vitamin B6 Folate Calcium Iron Zinc

ENU (log) 0.608*** 0.849*** 0.58*** 0.589*** 0.568*** 0.636*** 0.476*** 0.548***

(9.66) (4.86) (4.47) (9.22) (7.71) (7.16) (8.87) (7.3)

Income (log) NA NA NA NA NA NA NA NA

FSP Benefits (log) NA NA NA NA NA NA NA NA

HH Size (log) 0.061 -0.252 0.045 0.08 0.07 -0.013 0.236*** 0.092

(0.79) (1.35) (0.32) (0.95) (0.65) (0.13) (3.14) (1.0)

Urban (1,0) -0.109 0.001 0.026 -0.119 -0.124 -0.084 -0.105 -0.109

(1.53) (0.01) (0.28) (1.8) (1.85) (1.13) (1.54) (1.84)

Rural (1,0) 0.004 -0.035 0.043 -0.056 -0.015 -0.066 -0.008 -0.008

(0.09) (0.35) (0.5) (0.89) (0.2) (0.84) (0.13) (0.18)

Winter Interview (1,0) -0.281*** -0.719*** -0.463** -0.355*** -0.526*** -0.434*** -0.325*** -0.205

(2.45) (3.57) (2.14) (4.24) (4.17) (3.45) (3.44) (1.23)

Fall Interview (1,0) -0.023 -0.099 -0.177*** -0.055 -0.025 -0.022 -0.005 -0.013

(0.53) (1.69) (3.19) (1.08) (0.46) (0.42) (0.1) (0.26)

Income × Food Security -0.032** -0.055*** -0.036 -0.022 -0.035*** -0.028 -0.024** -0.01

(2.13) (3.23) (1.87) (1.85) (2.78) (1.51) (2.15) (0.67)

Income × Food Insecurity 0.009 0.082 -0.026 0.035 0.045 0.033 0.014 0.022

(0.32) (1.53) (0.59) (1.08) (1.21) (1.24) (0.42) (0.84)

Income × Hunger -0.021 0.086 0.033 -0.007 0.017 0.011 0.022 -0.025

(0.95) (1.06) (0.72) (0.25) (0.49) (0.3) (0.71) (0.96)

FS Benefit × Food Security 0.086*** 0.084 0.125*** 0.106*** 0.095** 0.085** 0.089** 0.085***

(2.85) (1.680 (3.48) (2.8) (2.55) (2.53) (2.5) (2.68)

FS Benefit × Food Insecurity 0.156*** 0.22*** 0.161*** 0.152*** 0.201*** 0.153*** 0.151*** 0.137***

(4.84) (4.19) (2.76) (3.85) (4.14) (3.76) (4.25) (3.69)

FS Benefit × Hunger 0.111*** 0.084 0.08 0.142*** 0.14** 0.072 0.135*** 0.132***
TABLE VI.3 (continued)

Calories Vitamin A Vitamin C Vitamin B6 Folate Calcium Iron Zinc

105

(3.1) (1.2) (0.98) (2.9) (2.560 (1.32) (4.08) (3.4)

Food Insecurity -0.37** 1.057*** -0.19 -0.376 -0.707*** -0.439** -0.337 -0.267

(2.09) (2.77) (0.63) (1.75) (2.84) (2.01) (1.76) (1.49)

Hunger -0.002 -0.532 -0.1 -0.086 -0.313 0.011 -0.282 0.002

0.01 1.02 0.26 0.36 1.13 0.03 1.26 0.01

HH Has Elderly Member (1,0) 0.019 0.211*** 0.128 0.026 0.071 0.03 0.047 -0.071

0.37 3.33 1.84 0.43 1.19 0.57 0.78 1.23

HH Has Child # 18 (1,0) 0.173** 0.446** 0.411** 0.264** 0.384*** 0.224** 0.055 0.188**

2.31 2.89 3.01 2.67 3.1 2.35 0.59 2.32

African-Am; not Hispanic (1,0) 0.054 -0.125 -0.197** -0.008 -0.138 -0.17** -0.049 0.111

0.79 1.38 2.2 0.12 1.67 2.19 0.7 1.43

White, not Hispanic (1,0) -0.013 -0.031 -0.225** -0.081 -0.119 0.074 -0.038 0.048

0.24 0.33 2.34 1.34 1.52 1.09 0.64 0.76

Applied 1 to 4 Years Ago -0.02 -0.006 -0.102 -0.054 -0.081 0.022 -0.017 -0.041

0.42 0.1 1.42 1.14 1.59 0.49 0.36 0.91

Applied 5 or More Years Ago -0.056 -0.104 -0.242*** -0.085 -0.138 -0.049 -0.092 -0.071

0.93 1.24 2.94 1.28 1.89 0.63 1.44 1.12

Electronic Benefit (1,0) 0.064 -0.027 -0.023 0.014 0.022 0.051 0.058 -0.025

0.98 0.28 0.23 0.21 0.26 0.68 0.88 0.29

Shelter Cost Per Person (log) 0.054*** 0.063 0.088*** 0.071*** 0.061*** 0.034 0.058*** 0.049***

3.6 1.95 3.73 4.34 2.6 1.65 3.35 3.15

Percent Food Not Brought (log) -0.033*** -0.052*** -0.043*** -0.037*** -0.04*** -0.041*** -0.023** -0.028***

3.3 3.13 2.96 3.32 3.3 3.95 2 2.59

Intercept 7.648*** 8.766*** 4.236*** 2.292*** 7.375*** 6.423*** 2.4*** 4.175***

48.96 45.86 21.49 14.81 38.19 35.35 15.02 27.69

SOURCE: National Food Stamp Program Survey weighted data. Sample size = 957.

NOTE: Statistical significance tests take the survey design into account, using a Taylor’s series expansion algorithm as implemented in the STATA statistical

package.

**Significantly different from zero at the .05 level, two-tailed .01 level, two-tailed test.

***Significantly different from zero at the .01 level, two-tailed test.

NA = Not applicable.
Preliminary investigations of the data do indicate higher variances in nutrient availability for

5

the more seriously food insecure as would be expected by this hypothesis.

106

variables applicable to a typical person in the data set, the predicted availability of nutrients remains,

in the regression context, greater for food insecure household than for a food secure household,

holding all variables other than the direct and interaction variables involving of food security

constant. What the equations suggest, however, is that one important reason for this result is that

food insecure households may obtain more nutrients from using their food stamp benefits than do

food secure households. Other possible factors that could help explain this unexpected positive

association of food insecurity and nutrient availability are summarized in the next section.

C. DISCUSSION OF FINDINGS

While the previously reported analysis has explained to some extent why more severely food

insecure food stamp households might be observed to have more nutrients available, a major lesson

to be learned is that the statistical and conceptual relationships between nutrient intake and food

insecurity are more complicated than might have been expected. Data from this survey are available

to support investigation of a number of more detailed hypotheses about the possible behavioral

linkages between food insecurity and food use. Specifically, further work could be done on the use

of non-purchased foods and the utilization of free meals outside the home as related to food

insecurity. The data also might be used to determine whether more severely food insecure

households are more likely to “binge” (that is, consume large quantities of food at the beginning of

a month when they received food assistance benefits) and subsequently experience hunger due to

shortages of food at the end of a month. The role of transportation costs and food store access might 5

also be studied. This section lays a groundwork for additional research by examining a number of

possible reasons for the patterns observed in the data. 
107

The positive association of nutrient availability and food insecurity was unanticipated. While

a number of factors were identified that should be considered in assessing these results, there is as

yet no full interpretation of the findings. Thus the following discussion is intended more as an

identification of “factors to consider” rather than as a strict interpretation of the data. 

Section 1 below highlights from the literature some key evidence relevant to examining the

current findings. Section 2 then considers whether the timing of various aspects of the data

collection could, in part, be leading to the findings being observed. Sections 3 and 4 discuss a

number of possible patterns in low-income households’ food consumption that could be consistent

with the findings.

1. Relevant Past Research

One important step in assessing the puzzling findings on the association of food security and

nutrient availability is to examine the previous research for any indications that the relationship

between these variables is more complicated than had been expected. As noted earlier, the NFSPS

is the first data set to have both full food security information and nutrient-denominated data on food

use. Therefore, there is no direct past literature that can be applied to the current analysis. However,

indirect evidence can be gained by examining previous studies of food sufficiency in relation to

nutrient availability or intake. This is done here.

The focus in this section is on past data about food sufficiency (see Section III.C). This concept

uses either one survey question or a short sequence of survey questions to classify households into

four categories: (1) having enough of the right kinds of foods to eat, (2) having enough but not

always the right kinds of foods to eat, (3) sometimes not having enough food to eat, and (4) usually

not having enough food to eat. This concept thus appears to tap conditions that are very similar to

those examined with the food security measure. Further, the food security and food sufficiency
Basiotis, whose paper uses the data for other purposes, does not directly report variances.

6

However, based on the variance of similar variables in the NFSPS data set, it is virtually certain that

none of the differences in the table even approach being statistically significant.

Unpublished tabulations of data from the 1987-1988 Nationwide Food Consumption Survey

7

by Biing-Hwan Lin of the USDA Economic Research Service lead to conclusions that are broadly

consistent with those implied by the Basiotis tabulations of 1977-1978 data. In the tabulations of

the more recent data, both nutrient availability and 24-hour intake information were cross-tabulated

by food sufficiency level. In neither case did the observed amount of nutrients decline consistently

as food sufficiency decreased.

108

measures are strongly and positively correlated with one another both in the CPS data (Hamilton et

al. 1997) and in the NFSPS data. Here, three past studies based on the food sufficiency measure are

examined.

a. Relationship Between Food Energy Availability and Food Sufficiency in Nationwide Food

Consumption Data

As part of a broader analysis of food energy demand relationships in the 1977-78 NFCS data,

Basiotis (1992) displays the relationship summarized in Table VI.4 between food energy availability

in kilocalories and household responses to a food sufficiency scale. (The two most severe categories

of insufficiency are aggregated in the Basiotis analysis.)

As shown, the differences in food energy availability between the different categories of food

sufficiency are quite small and do not completely follow the pattern that might be anticipated. While

the food energy availability is lowest in the most severe food sufficiency category, as might be

expected, food energy availability is highest in the middle category, rather than in the category with

no food insufficiency. Also, none of the differences between the three numbers is statistically

significant. Overall, these data do not provide significant support for the hypothesis that food

6

energy availability increase with food sufficiency.7
109

TABLE VI.4

AVAILABILITY OF FOOD ENERGY PER WEEK PER ENU,

BY LEVELS OF FOOD SUFFICIENCY

Food Sufficiency Level (Kilocalories per ENU) Sample Size

Weekly Food Energy

Enough and Kinds of Food Wanted 21,092 1,306

Enough but Not Always the Right Kinds 21,327 1,144

Not Enough 20,679 297

SOURCE: 1977-78 Nationwide Food Consumption Survey Data, as tabulated in Basiotis (1992),

weighted data.
The Alabama cashout sample is considerably poorer than the San Diego sample. This leads to 8

an interesting conjecture that would be consistent with the data in Table VI.3. It is possible that in

poor populations, where a substantial fraction of households do not have adequate nutrient

availability, the relationship between food sufficiency and nutrient availability follows the expected

pattern, whereas in more affluent populations, the food sufficiency questions have different meanings

to respondents and yield different patterns of results.

110

b. Relationship Between Nutrient Availability and Food Sufficiency in the Food Stamp

Cashout Demonstration Data 

In an unpublished memorandum, Fraker and Schirm (1993) use San Diego and Alabama Food

Stamp Cashout Demonstration data to tabulate the relationship between (1) whether households

report having enough food (based on a food sufficiency question); and (2) whether the households

meet or exceed the RDAs in all of nine selected nutrients (Table VI.5). For Alabama, the data follow

the expected pattern: about 39 percent of the households that characterized themselves as having

enough food met the RDA for the nine nutrients, as compared to only 31 percent of households who

said they did not have enough food. Furthermore, the difference between the 39 percent and the 31

percent estimates is statistically significant.

However, in the San Diego data the pattern reverses itself. The probability of meeting the RDA

is greater for the households who report not having enough food, although the difference is not

statistically significant. Thus, taking the two sites together, the evidence as to the relationship

between food security and nutrient availability is quite mixed.8

c. Relationship Between Nutrient Intake and Food Sufficiency in CSFII Data

A third salient piece of evidence is provided by an article recently published by Rose and

Oliveira (1997a), which examines the relationship between nutrient intake (as opposed to nutrient

availability in the two previous subsections) and food sufficiency. Table VI.6 summarizes selected
111

TABLE VI.5

NUTRIENT AVAILABILITY BY LEVELS OF FOOD SUFFICIENCY

Does Availability of Each of Nine Nutrients

Exceed 100 Percent of RDA?

Food Sufficiency Yes No Total Sample Size

Alabama

Enough food 39.2 60.8 100.0 1,901

Not enough food 30.9 69.1 100.0 385

San Diego

Enough food 69.5 30.5 100.0 763

Not enough food 79.5 20.5 100.0 308

SOURCE: Food Stamp Cashout Demonstration, as tabulated in Fraker and Schirm (1993), weighted

data.

NOTE: The differences between rows in percentage for Alabama are statistically significant; those

for San Diego are not.
112

TABLE VI.6

MEAN INTAKES FOR WOMEN 19 TO 50, EXPRESSED AS PERCENTAGE OF RDAs

Answers to Food Sufficiency Question

Food Sufficient Not Food Sufficient Difference

Food Energy 73.1 61.3 11.8*

Calcium 75.0 56.1 18.9*

Iron 78.5 66.6 11.9

Vitamin A 104.1 82.2 21.9*

Vitamin C 137.6 95.4 42.2*

Vitamin B6 85.8 73.3 12.5

Folate 115.5 102.2 13.3

Zinc 74.7 66.2 8.5

SOURCE: 1989-91 Continuing Survey of Food Intake by Individuals, weighted data as tabulated by

Rose and Oliveiri (1997a).

*Statistically significant at the five percent level.
They obtain similar results for two other groups, small children and the elderly. 9

The Rose and Oliveira results are broadly consistent with an earlier study of nutrient intakes 10

by Cristofar and Basiotis (1992), as well as with other recent work by Rose and Oliveira (1997b),

which extends the work summarized in Table IV.5 from the individual to the household level of

analysis (but is still based on food intakes).

113

illustrative results from their analysis for the nutrients that overlap with those being examined in the

current study. All the estimated differences in intakes between food sufficient and food insufficient

households are in the expected direction, and four out of eight are statistically significant. Thus,

9

of the three studies, this seems to be the one that most strongly supports prior expectations.

However, it uses intake data rather than nutrient availability information. Also, the Rose-Oliveira 10

study covers a complete range of income groups, instead of focusing on food stamp participants.

Having a broader range of income levels may help sharpen their estimates of the relationship

between food sufficiency and nutrient intake. In particular, since income is likely to be correlated

with both food sufficiency and nutrient intake, having a relatively broad range of income in the data

is likely to increase the range of observations on these other two variables as well. This in turn may

accentuate any relationship that exists and thus make it easier to detect.

d. Summary

While none of these studies provides direct evidence about the association of food security with

nutrient availability, their findings suggest that the statistical relationships involved may be more

complicated than had been realized. Two of the studies parallel the current study in that they focus

on nutrient availability. In one of these two works (that by Basiotis), the relationship between food

energy availability and food sufficiency is neither in the expected pattern nor statistically significant.

In the other study (based on cashout data), one of two sites displays the expected pattern in the data,

but at the other site the relevant observed relationship is not statistically significant and has the
114

“wrong” sign. Overall, these past studies suggest that the findings of the current study, while still

puzzling, are not quite as surprising as they may at first seem.

2. The Time Periods Covered by the Alternative Measures

In assessing the results reported above, it is also useful to consider the time periods covered by

the food security and nutrient availability measures. As described in Chapter II, and Appendix A,

the module of food security questions was administered during the first of the series of two

interviews that were conducted with the in-person FSP sample. Therefore, the 12-month questions

on which the main food security measure being analyzed is based cover the 12-month period

preceding this interview, and the 30-day food security measure covers the 30-day period preceding

the interview. On the other hand, the seven-day food use data collection was done in the second of

the interviews, which occurred about seven days later. Thus, there is no overlap in the periods

covered between the food security measures and the seven-day food use questions. Furthermore,

even if there were an overlap of a full week, the longest period possible, most of the period covered

by the food security scales would still not have been in the period covered by the food use analysis.

To the degree that episodes of food insecurity last over a considerable period of time, this nonoverlap between the periods covered by the various measures being examined would be of little

consequence to the analysis, since it could reasonably be expected that most of the food insecurity

episodes reflected in the food security scale would still be ongoing during the food use data

collection. However, to the degree that hunger is episodic and comes and goes quickly, perhaps to

be followed by another episode later, then the non-overlap of data collection periods could seriously

weaken any observed statistical relationship between food use and food security.

This timing issue may be of considerable importance in explaining the lack of the expected

relationship between food security and nutrient availability in the data. However, the timing issue
115

would appear to explain, at most, the lack of the expected positive relationship between food security

and nutrient availability; it does not explain the observation of an association in the unexpected

direction. 

Furthermore, if timing of data collection is an issue, one might expect that the use in the current

analysis of the 30-day food security measure instead of the 12-month measure would lead to results

different from those of the 12-month measure, since the periods covered are, if not overlapping, more

proximate on average with the 30-day data. However, as noted in Appendix K, use of the 30-day

measure does not substantially alter the findings. (On the other hand, this could be due in part to the

lower values of statistical reliability associated with the 30-day index.) 

3. Use of Coping Mechanisms

As discussed in Section III.E, many of the households classified on the food security scale as

experiencing hunger appear to make substantial use of various coping mechanisms, ranging from

borrowing money to using emergency food sources such as food pantries and soup kitchens. One

reason for failure to find the expected negative relationship between food insecurity and nutrient

availability may be that households use these coping mechanisms to mitigate significantly the worst

effects of their food insecurity on the availability of food. To be sure, as with the previous

explanation, which focused on data collection timing, this one does not explain the positive

relationship between food insecurity and nutrient availability. However, it may be a partial reason

that the expected negative relationship is not observed.

4. Possible Behavior-Based Explanations of the Patterns in the Data

It is also useful to consider whether there are possible behavioral explanations of the patterns

seen in the data. Two hypotheses are suggested below.
116

a. Do Low-Income Households That Consume Large Amounts of Food Also Tend to Have

Episodes of Food Insecurity?

One conjecture consistent with the patterns seen in the data is that (1) those households with

particularly strong preferences for food may tend to “binge” and consume large quantities of food

when they have the resources, but (2) by so doing, such households may use up their food budget

resources too quickly and thereby place themselves at risk of having an episode of hunger when their

resources run out. Recognizing that most households find a way to obtain at least some food during

periods of food insecurity, the result of the set of behaviors could be that households observed to

consume the most food would also be the most prone to food insecurity.

This explanation is consistent not only with the current findings but also with past attempts to

explain a number of other phenomena related to the FSP, such as (1) FSP households tending to have

higher nutrient intakes in the early part of the month, shortly after receiving food stamps; (2) FSP

households saying that they both buy and sell food stamps at different part of the month; and (3)

households having higher propensities to consume food out of food coupons than out of income,

even though most have not been constrained to do so since the elimination of the purchase

requirement.

b. Do Households That Are Frequently Food Insecure Tend to Compensate by Consuming

More Food than Other Households After Their Episodes of Insecurity Are Over?

A second conjecture is different from the first in that it considers the case where food insecurity

is exogenous and not related to anything the household does or perceives. Arguably, households that

for some external reason are more prone to food insecurity may tend to compensate by consuming

more food when they are not experiencing food insecurity. This might happen either as planned

behavior or, more likely, as an unconscious adaptation mechanism. 
117

Both this and the previous explanation imply higher variances in the nutrient availability

variables for food insecure households, as compared to the corresponding variances for food secure

households. The data are consistent with this expectation: most of the estimated variances of the

nutrient availability variables are lower for food secure households than for those in the most severe

hunger category (Table VI.7). (The pattern is reversed, however, for the two middle groups.) 

5. Conclusion

Although further research on these issues is needed before the policy implications are clear,

overall the findings suggest that it may not be reasonable to expect a simple association between

food insecurity and nutrient availability. One potential implication is that perhaps nutrition

education messages should focus somewhat less on increasing the efficiency of food purchasing

patterns and more on helping participants develop strategies for stabilizing the consumption of high

quality food throughout the month. 
118

TABLE VI.7

STANDARD DEVIATIONS OF NUTRIENT AVAILABILITY

VARIABLES, BY FOOD SECURITY LEVEL

Estimated Standard Deviation for the Study Populationa

Nutrient Mean Food Secure No Hunger Some Hunger Severe Hunger

Food Insecure/ Food Insecure/ Food Insecure/

Food Energy 1.28 0.70 0.75 0.75 1.01

Vitamin A 1.78 1.35 1.64 1.97 3.32

Vitamin C 2.56 2.15 2.21 2.17 2.65

Vitamin B6 1.47 0.86 0.96 0.86 1.14

Folate 2.13 1.35 1.56 1.43 1.99

Calcium 1.13 0.75 0.70 0.79 1.16

Iron 1.61 1.07 1.10 1.07 1.55

Zinc 1.17 0.74 0.77 0.71 1.30

Sample Size 956 415 313 173 54

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Entries are standard deviations of nutrient availability as a proportion of RDA, as scaled by ENU. a
119

REFERENCES

Anderson, S. (ed.). “Core Indicators of Nutritional State for Difficult-to-Sample Populations.” A

report prepared by the Life Sciences Research Office, Federation of American Societies for

Experimental Biology, for the American Institute of Nutrition under Cooperative Agreement

No. HPU 880004-01-0, Nutritional Status Indicators of Low-Income Populations, with the

Office of Disease Prevention and Health Promotion, Department of Health and Human Services.

Journal of Nutrition, vol. 120, suppl. 11, pp. 1557-1600, 1990.

Basiotis, P. Peter. “Validity of Self-Reported Food Sufficiency Status Item in the U.S. Department

of Agriculture’s Food Consumption Surveys.” Proceedings for the 38 Annual Conference of th

the American Council on Consumer Interest, March 25-28, Toronto, Ontario, edited by Virginia

A. Haldeman. Columbia, Missouri: American Council on Consumer Interests, 1992.

Bickel, Gary, Margaret Andrews, and Bruce Klein. “Measuring Food Security in the United States:

A Supplement to the CPS.” Nutrition and Food Security in the Food Stamp Program. Food

and Consumer Service, U.S. Department of Agriculture, January 1996, pp. 91-111.

Carlson, Steven J., Margaret S. Andrews, and Gary W. Bickel. “Measuring Food Insecurity and

Hunger in the United States: Development of a National Benchmark Measure and Prevalence

Estimates.” Journal of Nutrition 129:510S-516S, February 1999. 

Cristofar, Sharron P., and P. Peter Basiotis. “Dietary Intakes and Selected Characteristics of Women

Ages 19-50 Years and Their Children Ages 1-5 Years by Reported Perception of Food

Sufficiency.” Journal of Nutrition Education, vol. 24, no. 2, 1992.

Fraker, Tom, and Allen Schirm. “Relationship Between Perception of Food Sufficiency and

Availability of Nine Nutrients: San Diego.” In An Analysis of the Food Security Measures in

the Alabama and San Diego Cashout Data Sets, Table A-4.S. Princeton, NJ: Mathematica

Policy Research, Inc., 1993.

Frongillo, Edward A., Jr., Christine M. Olson, Barbara S. Rauschenbach, and Anne Kendall.

“Nutritional Consequences of Food Insecurity in a Rural New York State County.” Paper

presented at the Poverty Research Seminar, May 3, 1996. Food and Consumer Service, U.S.

Department of Agriculture, 1996.

Hamilton, William L., John T. Cook, William W. Thompson, Lawrence F. Buron, Edward A.

Frongillo, Jr., Christine M. Olson, and Cheryl A. Wehler. “Household Food Security in the

United States.” Technical Report on the Food Security Measurement Project. Washington, DC:

Food and Consumer Service, U.S. Department of Agriculture, September 1997.

Kaufman, Philip R., James M. MacDonald, Steven M. Lutz, and David M. Smallwood. “Do the

Poor Pay More for Food?” Agricultural Economic Report Number 759. Washington, DC:

Economic Research Service, U.S. Department of Agriculture, 1997.
120

Life Sciences Research Office, Federation of American Societies for Experimental Biology.

Nutrition Monitoring in the United States--An Update Report on Nutrition Monitoring.

Prepared for the U.S. Department of Agriculture and the U.S. Department of Health and Human

Services. DHHS Publication No. (PHS) 89-1255. Public Health Service. Washington, DC:

U.S. Government Printing Office, September 1989.

Lutz, Steven M., David M. Smallwood, James R. Blaylock, and Mary Y. Hama. “Changes in Food

Expenditures in American Households During the 1980’s.” Statistical Bulletin Number 849.

Economic Research Service, U.S. Department of Agriculture, 1992.

Mantovani, Richard E., Lynn Daft, et al. “Food Retailers in the Food Stamp Program:

Characteristics and Service to Program Participants.” Prepared by Macro International, Inc. for

the Office of Analysis and Evaluation, Food and Consumer Service, U.S. Department of

Agriculture, February 1997.

Ohls, James C., and Harold Beebout. The Food Stamp Program--Design Tradeoffs, Policy, and

Impacts. Washington, DC: The Urban Institute Press, 1993.

Ohls, James C., Michael Ponza, Lorenzo Moreno, Amy Zambrowski, and Rhoda Cohen. “Food

Stamp Participants’ Access to Food Retailers.” Report submitted to the U.S. Department of

Agriculture. Princeton, NJ: Mathematica Policy Research, Inc., February 1998.

Olson, Christine, E. Frongillo, Jr., and A. Kendall. “Validation of Measures for Estimating the

Prevalence of Hunger and Food Insecurity in the Current Population Survey Module: A

Combination of Cornell and CCHIP Items.” In Food Security Measurement and Research

Conference: Papers and Proceedings. Appendix A. Alexandria, VA: U.S. Department of

Agriculture, June 1995.

Peterkin, BB., R.L. Rizek, and K.S. Tippett. “Nationwide Food Consumption Survey, 1987.”

Nutrition Today, vol. 23, no. 1, 1988, pp. 18-24.

Ponza, Michael, James C. Ohls, Lorenzo Moreno, Amy Zambrowski, and Rhoda Cohen. “Customer

Service in the Food Stamp Program.” Report submitted to the U.S. Department of Agriculture.

Princeton, NJ: Mathematica Policy Research, Inc., January 1998.

Radimer, Katherine, C.M. Olson, J.C. Greene, C.C. Campbell, and J.P. Habicht. “Understanding

Hunger and Developing Indicators to Assess It in Women and Children.” Journal of Nutrition

Education, vol. 24, January/February 1992, Supplement, pp. 36S-45S.

Rose, Donald, and Victor Oliveira. “Nutrient Intakes of Individuals from Food-Insufficient

Households in the United States.” American Journal of Public Health, vol. 87, no. 12,

December 1997a, pp. 1956-1961.

Rose, Donald, and Victor Oliveira. “Validation of a Self-Reported Measure of Household Food

Insufficiency with Nutrient Intake Data.” Technical Bulletin No. 1863. Washington, DC:

Economic Research Service, U.S. Department of Agriculture, 1997b.
121

U.S. Department of Agriculture. Food Consumption and Dietary Levels of Households in the United

States, 1987-88. Nationwide Food Consumption Survey Report No. 87-H-1. Washington, DC:

Agricultural Research Service, USDA, 1997.

U.S. Department of Agriculture. Food Consumption and Dietary Levels of Low-Income Households,

1979-1980. Nationwide Food Consumption Survey Report No. 10. Washington, DC: Human

Nutrition Information Service, USDA, July 1982.

Wehler, C., Richard Ira Scott, and Jennifer J. Anderson. “Development and Testing Process of the

Community Childhood Hunger Identification Project Scaled Hunger Measure and Its

Application for a General Population Survey.” In Food Security Measurement and Research

Conference: Papers and Proceedings. Appendix A. Alexandria, VA: Food and Consumer

Service, U.S. Department of Agriculture, June 1995.

Wehler, C., Richard Ira Scott, and Jennifer J. Anderson. Community Childhood Hunger

Identification Project: A Survey of Childhood Hunger in the United States. Washington, DC:

Food Research and Action Center, March 1991.

APPENDIX A

DATA COLLECTION METHODS

These numbers were from spring 1995. 1

Frame size before removing certainty selections was 10,858,961, and the sampling interval for 2

selecting 35 PSUs was 310,256. The frame size after removing the certainty selections was

9,462,582.

A.3

The survey of Food Stamp Program (FSP) participants and nonparticipants was conducted from

June 1996 to January 1997. This appendix describes the methods used to select the sample, conduct

the Food Stamp Survey, and process the data. It also includes response rates and reasons for

ineligibility.

A. METHODS FOR SELECTING AND LOCATING RESPONDENTS

MPR used a dual frame approach to select the samples of FSP households and households

containing eligibles who do not receive food stamps.

1. List Frame

List frame samples in this survey were selected from administrative lists of FSP participants.

Before identifying the sample, an MPR sampling statistician randomly selected 35 primary sampling

units (PSUs) systematically with probability proportional to size. The PSU was usually a county,

but sometimes it was a state (in cases where county-level information was unavailable) or a city (the

five boroughs of New York). Before selection, the PSUs were first sorted by region, then by state

within a region, and finally by size (number of food stamp recipients) within state. Because the

1

three largest PSUs were the same size as or larger than the sampling interval, they were selected with

certainty and removed from the systematic sampling process. New York City had a size equivalent 2

to two sampling intervals, so it counted as two PSUs. Thirty-one PSUs were then selected out of the

remaining 2,862. Two of these were at the state level and so required subsampling. For the three

certainty selections, the decision was made to subsample areas within counties. Three areas were
A trial run was conducted with most of the selected sites a couple of months earlier, where they 3

supplied their most current data file at the time. It was at this trial stage that the two selected states

were subsampled and the three certainty selections were made, using more-current estimates the

states provided of food stamp recipients.

A.4

sampled from Cook County, three areas from Los Angeles County, and two boroughs and three areas

within each borough for New York City.

In spring 1996, FNS provided the names of contacts in the seven regional offices to assist with

obtaining list samples for the survey. These regional contacts, in turn, provided the names of

contacts in the state offices for the 34 areas selected for the survey. (In California, the state contact

provided referrals to county offices.) These offices provided data files containing lists of all active

food stamp cases as of the beginning of April 1996.3

As these data files were received from the field, the sampling statistician read them in from their

various formats and standardized them into SAS data files. For most of the PSUs, 180 cases were

selected systematically. For Cook County, 60 cases were selected systematically from each of the

three subsampled areas. In Los Angeles County, 81 cases were selected from each of the three

subsampled areas. In each of New York City’s six selected areas (three from each of the two

selected boroughs), 60 cases were selected. The selected cases were then sorted into a random order.

The first two-thirds were then assigned to the field sample, and the last third was assigned to the

telephone sample. 

a. Field List Frame Sample

For the field sample, the objective for most of the survey sites was to obtain 29 completions at

each site. However, for the three survey sites that had been selected with certainty and which were

self-representing in the sample, the target numbers of completion were set higher, reflecting their

relative sizes in the overall population. These targets were as follows: 30 for Cook County, 42 for
A.5

Los Angeles County, and 60 for New York City. The total number of targeted completes for the

field sample was 1,031. Cases were released as needed in a random order by site from among the

4,242 cases selected for the field component. A total of 2,200 cases were ultimately released.

b. Telephone List Frame Sample

For the telephone sample, the targeted number of completes from each of the non-certainty sites

was 14. For the selected areas in the three certainty selections, the targeted number of completes was

15 for Cook County (combined), 21 for Los Angeles County (combined), and 30 for New York City

(combined). The total number of targeted completes for the telephone list sample was 500. Cases

were released as needed in a random order by site from among the 2,121 cases selected for the

telephone component. Ultimately, all 2,121 cases were released.

2. Random-Digit-Dialing Sample

For the random-digit-dialing (RDD) sample, software from Genesys, Inc. was used to obtain a

stratified sample of 20,003 telephone numbers in working telephone banks in the United States. A

telephone bank is defined as the first 8 digits of a 10-digit telephone number (area code plus

exchange plus next two digits). The possible combinations of its last two digits create 100 telephone

numbers for a bank to contain, and it is considered a working bank if at least one is a published

residential telephone number. Each telephone number was defined as being in one of five strata

based on the area code plus exchange (first 6 digits of the 10-digit telephone number). There was

no oversampling by stratum. The five strata were defined by the estimated percentage of households

with income less than $15,000:

C Low Income. Exchanges where estimated percentage $ 35 percent

C Mid-Low Income. Remaining exchanges where estimated percentage $ 25 percent
A.6

C Middle Income. Remaining exchanges where estimated percentage $ 15 percent

C Mid-High Income. Remaining exchanges where estimated percentage $ 10 percent

C High Income. Remaining exchanges (where estimated percentage < 10 percent)

After removing known nonworking and nonresidential telephone numbers, cases were released

in a random order as needed to obtain the targeted number of completes: 495 participants and 990

eligible and near-eligible nonparticipants. A total of 14,514 telephone numbers were released.

3. Obtaining Contact Information

Contact information for the FSP study sample was obtained with the original sample from state

or county FSP offices. This information, current as of March 1996, included sample member name,

address, telephone number (if available), date of birth, and, in some cases, a caseworker identifier.

The information received varied widely by site in terms of completeness and accuracy.

a. Contacting Local FSP Offices

Local FSP offices were first contacted, with permission of the state offices, in May 1996. This

contact served to inform the local offices about the survey so they could encourage participation and

confirm the validity of the survey, should any of the recipients contact them. 

MPR survey staff contacted the local offices in July to obtain updated contact information for

recipients who could not be located. In addition, offices were asked to confirm if each sampled

person was still receiving food stamps. Project staff provided the birth date of the recipient, and the

client ID#, case ID#, or caseworker ID#, if this information was available to assist the local offices

in identifying the cases. All offices contacted were responsive to requests. Some offices consulted

with the caseworkers, while others used their computer files or hard copy files to obtain the

information. 
Often, the telephone number data in the estimated files from which the sample were drawn is 4

incomplete.

A.7

The local offices were recontacted in August and September of 1996 to obtain information on

additional recipients who could not be located. In September, selected field interviewers went to the

local offices and worked with the local contacts to update contact information. Overall, these efforts

yielded some addresses and telephone numbers, but the most helpful information provided was

whether the recipients were still receiving food stamps and hence eligible for the survey.

b. MPR Locating Department

Telephone numbers were available on the samples provided for approximately 54.5 percent of

the list frame telephone sample. However, many of these numbers were either nonworking or

4

incorrect. As a first strategy, telephone interviewers called local directory assistance to obtain

telephone numbers for cases with nonworking or incorrect numbers. When these efforts failed, FSP

offices were contacted as discussed in the previous section. If the FSP offices could not update the

information, MPR’s Locating Department searched for sample members. 

MPR’s Locating Department made extensive use of a service bureau that searches using a

crisscross or reverse directory, surnames, and the existing telephone number. The on-line system

was accessed from a terminal in the Locating Department. MPR’s Locating Department also utilized

directory assistance, involving locations neighboring the sample member’s city or town. In total, 642

cases were referred to the Locating Department. Reliable contact information was obtained for 16

percent (105) of these cases. 
A.8

B. SELECTION AND TRAINING OF DATA COLLECTION STAFF

1. Hiring and Training of Field Staff

Field interviewers were hired in each of 35 PSUs. It was determined that some PSUs would

require two interviewers, while one experienced interviewer would be sufficient for other areas. A

single interviewer was hired in each of 17 PSUs, while two interviewers were hired in each of 18

PSUs. Approximately one month after the start of the field period, six additional interviewers were

hired because of attrition among original interviewers and a reevaluation of field needs. Field

interviewers were recruited from three sources: an MPR database, local community contacts, and

state job services. Preference was given to people with Computer-Assisted Personal Interviewing

(CAPI) experience or experience in food management or nutrition. Twenty-eight of the 53

interviewers had experience conducting CAPI interviews. Three additional interviewers had some

experience in field interviewing or field locating. Seventeen interviewers had no direct survey

experience. Four PSUs were targeted as requiring bilingual interviewers. In addition, three of the

interviewers were trained nutritionists. Three field supervisors were hired to manage the field effort.

All field supervisors had experience conducting food use surveys. Two of the supervisors had

experience working for MPR.

The main field interviewer training was held May 4-10, 1996. A two-day trainers’ training was

conducted for field supervisors, trainers, and assistant trainers at the MPR offices immediately before

the general training session. This training included a question-by-question review of the survey

instrument, and testing and practice on the CAPI questionnaire.

One week before the general training session, interviewers were sent an advance study manual

that contained an introduction to the survey and a review of basic interviewing techniques.

Interviewers were required to complete an assignment related to food use data collection before
A.9

leaving their homes. They were also instructed to schedule a practice interview to be completed at

the conclusion of training.

The six-day intensive training was held off site, at a conference and training center in Princeton,

New Jersey. Two training formats were used: (1) large-group lecture format, and (2) small-group

practice sessions. During the training, interviewers moved from large format to small-group sessions

as dictated by the agenda. Interviewers were divided into five small groups based on interviewing

and computer experience. Each small group was led by one senior trainer and one assistant trainer.

One-on-one CAPI enrichment sessions were also provided each evening. The first two-and-a-half

days of training included a general introduction and background to the study, instruction and practice

with the hard-copy screener and hands-on practice with the CAPI interview. In addition, an MPR

training tape about the role of the interviewer was shown during an evening session, with discussion

afterward. Training on the hard-copy food use instrument was conducted for three days by MPR

staff, including MPR’s nutritionist; Margaret Andrews, the Contracting Officer’s technical

representative; and Pat McKinney, an FNS nutritionist. In large-group sessions, trainers presented

an overview to the food use module as well as specific rules for completing the food use instrument.

In small-group sessions, interviewers were paired for one-on-one practice and question-by-question

review. Key definitions of food categories and instruction in reporting food use quantities were

reviewed in the smaller sessions. Trainers administered CAPI proficiency exercises and food use

recording exercises to evaluate interviewer performance before the conclusion of training. A small

number of interviewers were able to be identified who required one-on-one supplementary training

during evening sessions. Interviewers spent the final half day of training integrating data collection

components, reviewing administrative issues, and meeting with field supervisors. 
A.10

2. Hiring and Training of Telephone Interviewers 

By early June 1996, 74 telephone interviewers were hired and trained to administer screening

and survey instruments. The group contained experienced and inexperienced interviewers.

Inexperienced interviewers received eight hours of general interviewer training prior to participating

in project-specific training. Both experienced and inexperienced interviewers participated in projectspecific training, which included overviews of the program and study, sample member screening,

item-by-item review of the questionnaire, role plays, questions and answers, and Computer-Assisted

Telephone Interviewing (CATI) practice. Project-specific training lasted for close to eight hours.

About seven percent of the interviewing staff was bilingual. 

C. METHODS FOR COLLECTING THE DATA 

1. Field Data Collection

Data collection for the in-person component included a telephone or in-person screener and a

two-interview series. Part I of the main interview was administered by CAPI and collected

information about the household, program access, food security, and shopping patterns. Part II

involved both CAPI and hard-copy administrations and included either a four- or a seven-day

recording of foods used from the home food supply. Part II was conducted either four or seven days

following Part I. 

a. Survey Materials

In addition to Dell 486 Latitude laptop computers with English and Spanish versions of the

CAPI instrument, materials for the survey included:

C Advance Letter. Mailed to the respondent three to five days before telephone contact

was made
A.11

C Record of Contacts Form. For documenting attempts made to locate and interview

sample persons

C Eligibility Screener. Brief hard-copy interview to determine respondent eligibility

C Reminder Postcard. To remind respondents of their appointment for the second part

of the interview 

C Food Use Instrument. Hard-copy instrument administered during Part II of the

interview to obtain detailed information about household food use

C Food Use Checklist. To help respondents keep track of food use during the survey

period

All hard-copy materials were available in both English and Spanish.

b. Components of the Interview

Advance Letter. All persons selected to participate in the National Food Stamp Survey were

notified of their selection by a letter in advance of any other form of contact. The advance letter

explained the study, encouraged participation, and informed the sample member that the interviewer

would be contacting him or her. Letters were mailed to respondents three to five days before the

screening contact was made.

Screener. Next, the interviewers screened the respondents by telephone. They called their

assigned sample members to introduce themselves, administer a brief eligibility screener, answer any

questions the respondent might have, and, if the household passed the screen, schedule the two parts

of the interview with the food manager for the household. (If telephone contact was not possible,

this screening was done in person.)

Part I of Main Interview. Part I of the main interview was conducted by CAPI. At the

conclusion of the interview, respondents were instructed to keep track of foods used and shopping

trips made during the seven-day period before Part II of the interview. The interviewer provided
A.12

materials to aid the respondent in keeping detailed records of all the food purchased and used by the

household. These materials included a plastic bag for saving food receipts and a large envelope for

the collection of food labels. Two days after completion of the Part I interview, interviewers mailed

the respondent a reminder postcard that included the date of the appointment for the Part II interview.

Part II of Main Interview. The first section of the Part II interview was conducted by CAPI.

This section collected information about shopping trips and identified household members and

guests who used food from the household food supply. The second section of the interview used

hard-copy administration. It identified what foods were used, with a level of detail sufficient to

determine actual nutritional availability, such as calories, fat, and vitamins. This section also

captured the cost of each of the foods. Upon the completion of the Part II interview, respondents

were given a $20 incentive for their time and cooperation. (Respondents were told of $20 payment

when they were first contacted as an inducement to participate and maintain the food use records.)

c. Field Management

Field interviewers reported progress to their field supervisor weekly by telephone at prearranged

times. They reported hours worked, expenses, and field progress. During the reporting session, the

supervisor reviewed each case being worked by the interviewer and suggested modifications to

searching and interviewing techniques where appropriate. Supervisors also handled administrative

needs (such as supply orders) and answered non-urgent questions. In turn, the supervisors reported

summaries of field progress and expenses to an MPR survey specialist weekly. Interviewers were

encouraged to contact the MPR help line immediately for urgent matters.
A.13

d. The MPR Help Line

Interviewers and field supervisors had 24-hour access to the survey director and to technical

support staff by means of a toll-free number that reverted to a paging system during non-business

hours.

e. Bonuses

To encourage interview productivity at the end of the project, MPR offered field interviewers

a bonus of $10 for every interview completed after November 21, 1996. This kept enthusiasm high

when sample was sparse. It also kept interviewers motivated to finish their final assignment rather

than move to new projects.

f. Special Procedures Used for the Seven-Day Food Use Data Collection

A week before the food use food use data collection interview, MPR field personnel discussed

the data collection in person with the respondent, establishing the boundaries of the seven-day period

covered, explaining the food use concept, and requesting that the respondent keep grocery receipts,

food labels, and other material that would help her or him provide information on the foods used

during the relevant period. For storing receipts and labels, the respondent was given an envelope,

which also contained a grid for recording the foods used each day, along with a plastic bag for

holding messy labels.

The interview itself was conducted as soon as possible after the conclusion of the observation

period--usually within 24 hours. It proceeded as a detailed assisted-recall process, based on a set of

categories of foods listed in the data collection instrument. In particular, within the instrument, all

possible foods were divided into major categories, such as meats, fish, fruits, sweets, baby food, and

so forth. A separate page on the instrument corresponded to each category. For each page, the
A.14

interviewer first asked if any of the category (for example, meats) was used during the seven-day

period. If the respondents said no, the interviewer recorded that answer and went on to the next

page. When the respondent replied that the household had eaten something from a food category,

the interviewer then read a detailed list of possible items to identify what item or items the person

had used (for example, pork chops, ground beef, veal cutlets). This information was then recorded,

along with auxiliary information about prices, costs, and so forth, and this process continued until

all the foods used in a category had been recorded. After one category was finished, the next was

asked about, until all the categories had been covered. 

The data collection was usually done in the respondents’ kitchen. This allowed the respondent

to refer to packages and containers when supplying information about the foods recorded (for

example, the size of the oatmeal box the household typically uses).

2. Telephone Data Collection

For the telephone sample, CATI techniques were used to facilitate the screening and

interviewing. Sample points were electronically assigned to individual interviewers, and the CATI

system stored the results of interview attempts. An automated system reassigned unsuccessful

attempts and scheduled callbacks. Interviewers who conducted the screening interviews also

conducted the telephone interviews of both participants and nonparticipants. A senior staff member

at the survey operations center supervised the interviewers, and assistant supervisors assessed

interviewer performance by monitoring randomly selected segments of the interviewing. 

a. Bonuses

A bonus system was instituted in the survey operations center on September 13, 1996, as an

incentive to maintain interviewer interest and commitment when it became increasingly difficult to
A.15

obtain completed interviews. One dollar was offered for each completed RDD or list frame

interview and one dollar and fifty cents for each refusal that was converted to a complete interview.

b. In-Person Locating of Telephone List Frame Sample Members

In mid-November, field locators with cellular telephones were deployed in 24 areas to locate

telephone list frame sample members who could not be contacted by telephone. Locators received

written training materials and participated in telephone training on implementing locating strategies

and operating the telephone equipment. 

Field locators searched for sample members by starting with the last known address and then

contacting neighbors and community sources. After locating a sample member, if a telephone was

available in the household, the locator was responsible for facilitating a phone call to MPR’s survey

operations center. Staff were available throughout the day and evening hours to conduct the

interview. If the sample member could not participate in the interview at that time, a telephone

number was obtained and communicated to the operations center. Appointments were made when

possible. If a telephone was not available in the household, the locator saw that the interview was

conducted by cell phone and remained with the sample member until it was completed. Within a

six-week period, the locators were able to facilitate 122 interviews from the 625 sample members

that were previously unlocatable by telephone. They also determined that an additional 44 sample

members were ineligible for the study.

3. Problems Faced During the Survey Period

The data collection began at a time when the government was contemplating major changes in

the welfare program. This news created nervousness among respondents. Uncertain about their
A.16

eligibility for food stamps and other entitlement programs, they were reluctant to participate in the

study and had to be reassured that their responses would not affect their future eligibility.

Immigrant ethnic communities would have been severely affected by the policies considered.

In contrast with previous successful interviewing in the Vietnamese community in California for the

cashout evaluations, a Vietnamese interpreter and community worker was unsuccessful in facilitating

interviews in that community. A Russian interpreter had a similar experience with the immigrant

Russian community in New York City.

D. QUALITY ASSURANCE AND DATA PROCESSING

1. Transmittal and Tracking of Field Data

On a weekly basis, field interviewers submitted completed work to MPR by Federal Express.

Weekly field shipments included the transmittal forms used to report cases submitted, hard-copy

food use instruments, supporting food use materials, and data diskettes.

The packages were received by the MPR data clerk, who checked the contents against the

transmittal form to verify that all materials had been included. An ACCESS database was developed

to track the field cases. Interim status codes were entered on a weekly basis following receipt of

supervisor reports. The database also included fields for entering dates when the MPR office

received completed cases.

The database identified cases reported as complete but not received within 10 days after the

supervisor’s report. Using weekly reports, the data clerk made reminder calls to field interviewers

who had outstanding cases. 

After logging in completed cases, the data clerk delivered MPR diskettes to MPR’s systems

analyst for downloading into a SAS data file. Food use instruments and contact records were
The discussion in this section focuses principally on the operational aspects of the coding work. 5

See Appendix D for a discussion of the conceptual basis of the coding operations. 

A.17

delivered to a coding center set up to implement coding using the Food Intake Analysis System

(FIAS) developed by the University of Texas (see Appendix C).

Verification and Callbacks. FIAS coding center staff conducted verification of completed

cases. Coders were required to telephone at least 10 percent of the respondents interviewed by each

interviewer. Using a verification form designed by MPR, coders asked about the date and length of

their interview, the mode of the interview (telephone or in person), and the names and locations of

the stores the respondent used. Coders also asked about foods and recorded the answers on the food

instrument. Food use instruments that were not completed according to specifications were

reviewed. As a result of the verification process, two interviewers were terminated and their cases

assigned to other field staff. For each of these interviewers, the MPR survey director personally

contacted each one of the households who had previously been submitted by the interviewers as

completions to test their validity. In most instances, the interview could be validated and was

retained. In a small number of instances, the interviews were assigned to a different interviewer or

a supervisor to be redone.

2. Food Coding5

Analysis of home food use required coding all the foods from hard-copy food instruments, as

well as data entry of all foods purchased and the prices paid by respondents. To facilitate these

goals, a coding room was set up at MPR. Coders were hired, trained, and then provided with their

own coding stations and reference materials in the coding room. A supervisor directed the flow of

activity in the coding room and consulted with the MPR nutritionist or the co-principal investigators
The six-digit coding system was developed by MPR and its subcontractor, ROW Sciences, Inc., 6

to convert the food assumptions used in previous USDA food use studies to codes that were

compatible with the FIAS coding system.

A.18

for the project to resolve problems arising from unavailable codes, missing data on the hard copy,

or any other causes.

Hard-copy food instruments delivered to the coding room were logged into an ACCESS

database by the coding supervisor and then filed according to interviewer. All coders were required

to code instruments by all interviewers, and instruments were coded in chronological order so that

those instruments received first were usually coded first. Coding entailed reading the nine-digit

survey code on the food instrument, assigning a corresponding six-digit FIAS code, and then entering

this six-digit code and the amount of the food that was used into the FIAS file.6

a. Staffing and Training of Food Coders

Following the recommendations of the FIAS staff at the University of Texas, coders were

required to have completed high school (though some college education was preferred), to be the

food manager at home, and to be familiar with simple mathematical computations. In addition, MPR

required coders to have some basic computer experience.

Including practice experience, coders were required to participate in 2.5 days of training at MPR.

After being given an overview of the project, coders were shown how to start a new file in FIAS,

how to move around while in FIAS, and how to close a file. Coders were then shown how to extract

the nine-digit survey code from the food instrument and how to relate this code to its corresponding

six-digit FIAS code. They were also taught how to input the six-digit FIAS code for each food into

a FIAS file along with the amount of that food used during the seven-day period. For each food line,

coders were also required to compute, if applicable, the total amount of food bought
A.19

and the amount of money paid. The mathematical operations that facilitated these steps were

reviewed, and coders were provided with a training manual, written by the project director and the

MPR nutritionist, which contained all the topics covered during training. (At a later time during the

project, coders were taught how to “clean” and data-enter the completed price-related data on the

food instruments.)

Ongoing Procedures. The coders were responsible mainly for coding the hard-copy food

instruments as outlined above. They also called the respondent when more-detailed information was

required for a reported food. For example, if the amount of food used or purchased was missing or

unclear or if the form of the food was not indicated (dehydrated, ready-to-eat, condensed, etc.), the

coder called the respondent for clarification. Many food instruments generated questions about

package size and price paid for a food item. Since some respondents were not able to remember

these details, a list was constructed of all the foods that required information on package size or price

paid. Two of the coders then went shopping at regular intervals to obtain this information.

Once most of the hard-copy food instruments had been coded and entered into FIAS, the coders

were trained to data-enter the information on the food purchased and the price paid into a Lotus

spreadsheet.

b. Problems Encountered in Coding 

Five main problems delayed the food-coding process: (1) missing information about the food

or the price paid for the food, (2) new foods that had no assigned nine-digit survey code or six-digit

FIAS code, (3) nonfunctional six-digit FIAS codes, (4) foods that were miscoded in the instrument,

and (5) ethnic foods (Russian, Vietnamese, Mexican, among others) that were unfamiliar to the

coders.
A.20

Several approaches were used to resolve these problems. Information about the unit weight of

a food, package size, or unit price was obtained from advertisements from food stores across the

country and from food lists solicited from large supermarket chains. In addition, published reference

material from the USDA, cookbooks, and food preparation books was used. Uncertainty about the

type or amount of food recorded in the instrument was clarified by telephoning the respondent. In

other instances, the coders kept a list of unknown package sizes or cost, and at regular intervals one

or two of the coders themselves visited a large supermarket to ascertain this information. When none

of the above measures supplied the resolution, the problem was referred to the MPR nutritionist, who

in turn consulted with a nutritionist at MPR’s subcontractor, ROW Sciences, Inc.

c. Data Cleaning

When all food items of a case were completely entered into FIAS and there were no outstanding

problems, the case was “cleaned”--that is, all the foods were analyzed for specific nutrients, and

cases with outlier values on key nutrients were examined by the project director and/or the project

nutritionist. If any problems were uncovered in these outliers checks, the FIAS files were changed

accordingly, and the cases were recleaned.

d. Data Entry and Edit Checks

After cleaning, price-related data on each case were also data entered into a Lotus file. The

information required for data entry was the six-digit code, the amount of food purchased, and the

total price paid for the food. 

For each case, the FIAS analysis file and the Lotus file were used to generate a FIAS edit file

and a Lotus edit file. For a given case, the FIAS edit resulted in a list of those foods that exceeded

a preset standard for the normal consumption of specific nutrients in those foods, and the Lotus edit
Thisis derived as follows: 14,514 cases released minus 7,026 ineligible or undetermined cases 7

(5,219 % 1,807) yields 7,488 working numbers.

A.21

resulted in a list of foods that seemed to exceed the usual unit price, had different FIAS and Lotus

codes, or showed a higher amount used than bought. The MPR nutritionist reviewed the FIAS edits

and made appropriate adjustments, while the coders reviewed and corrected the Lotus edits, under

the supervision of the coding supervisor.

While the coders were encouraged to use reference materials to resolve questions about package

size or price, the MPR nutritionist resolved all questions about portion sizes, usual weekly amounts

of consumption, and classification of unusual foods or foods not included in the food instrument.

She also developed new codes for foods as appropriate and periodically reviewed completed files

for quality control purposes.

E. COMPLETION AND OTHER FINAL STATUSES

Eligibility for Surveys. Among the 14,514 cases that were released for the RDD sample, 7,488

were determined to be working residential telephone numbers, making those numbers eligible to

complete the income-screening questions (see Table A.1). Among the remaining cases, 5,219 were 7

determined to be either nonworking telephone numbers or nonresidences. It was not possible to

make this determination for the remaining 1,807 cases. Among the 7,488 eligible to complete the

income screener, 6,429 completed the screener. Among these cases, 4,973 were determined to be

ineligible for the interview because the household income was too high, leaving 1,456 cases eligible

for the interview.

For the telephone list sample, among the 2,121 released cases, 546 were determined not to be

receiving food stamps at that time, 7 were deceased, and 33 had moved out of state. This left 1,535
A.22

TABLE A.1

ELIGIBILITY RATES AND REASONS FOR INELIGIBILITY

Eligibility Status Reason Sample Sample Sample

RDD Phone List Field List

a

Total Released 14,514 2,121 2,200

Undetermined Did not determine if

working residential

telephone number 1,807

Ineligible for Survey Nonworking telephone

number or non-residence 5,219

Income too high 4,973

Not receiving food

stamps 546 508

Deceased 7 7

Institutionalized 25

Moved 33 56

Eligible for Survey Working residential

telephone number

meeting income criteria 1,456

Receiving food stamps

in sampled area 1,535 1,604

SOURCE: Administrative files for the 1996 National Food Stamp Program Survey, Mathematica

Policy Research, Inc.

For the RDD sample, eligibility refers to the interview itself, not eligibility for the screener. Of

a

course, if a household is ineligible for the screener, it is also ineligible for the interview. Similarly,

if it is not determined that the telephone number is a working residential number, then eligibility

for the interview is not determined either.
A.23

eligible cases for the telephone list sample. For the in-person sample, among the 2,200 cases

released, 508 were no longer receiving food stamps, 7 were deceased, 25 were institutionalized, and

56 had moved out of the sampled area. This left 1,604 eligible cases for the in-person sample.

Completion Status. Among the 1,456 known eligible cases in the RDD sample, 1,159

completed the interview (see Table A.2). Most of the remaining cases were refusals and broken

appointments (n=144) or cases that could not be contacted by the end of the field period (n=134).

Among the 1,535 known eligible cases in the phone list sample, 1,041 completed the interview.

One hundred five cases were nonrespondents due to refusal or broken appointment; 39 were cases

of a language, cognitive, or physical barrier; 17 were cases where the person was hospitalized or too

ill to complete the interview; and in 333 cases, the person could not be contacted or located.

The field sample had two parts to the interview. Among the 1,604 cases determined to be

eligible for the interview, 1,109 completed at least Part I. There were 196 refusals or broken

appointments, 41 with an illness or hospitalization, 123 cases unable to be contacted or located, 93

other cases that could not be resolved by the end of the field period, and 42 “other.” Among the

1,109 cases that completed Part I, all but 39 completed Part II.
A.24

TABLE A.2

COMPLETION TOTALS AND REASONS FOR NONRESPONSE

(Among Known Eligibles)

Response Status Reason Sample Sample Part I Part II

RDD List Sample Sample

Phone List List

Field Field

a

Completed Interview 1,159 1,041 1,109 1,070

Did Not Complete Interview Refusal/broken

appointment 144 105 196 39

Language/cognitive/

physical barrier 10 39

Too ill or hospitalized 17 41

Unable to locate or

contact 333 123

Exhausted attempts 134 93

Other 9 42

Total Known Eligibles 1,456 1,535 1,604 1,109

SOURCE: Administrative files for the 1996 National Food Stamp Program Survey, Mathematica Policy Research, Inc.

Among those who completed Part I. a
APPENDIX B

WEIGHTING

Note that in what follows the “slash” character, i.e., “\”, is used to denote “given.” 1

B.3

This appendix describes the steps taken to calculate analysis weights for the 1996 Food Stamp

Survey (FSS). Each of the following four groups is discussed separately. Then ways are reviewed

for combining results across the various groups. The four groups are (1) the in-person list frame

sample, (2) the telephone list frame sample, (3) the telephone random-digit-dialing (RDD) sample

of Food Stamp Program (FSP) participants, and (4) the telephone RDD sample of FSP-eligible and

near-eligible nonparticipants.

A. IN-PERSON LIST FRAME SAMPLE

To estimate the in-person list frame sample weights, first the probabilities of selection for each

sample member were calculated. The inverses of these probabilities were then used to calculate an

initial set of weights. Next, these initial weights were adjusted to reflect survey nonresponse.

Section 1 below describes how the selection probabilities were calculated. Section 2 then describes

the nonresponse adjustments.

1. Sampling Weight 

The first step in calculating weights for the in-person list frame sample was to determine the

probability of selection. Both the in-person and the telephone list frame samples originated from the

same sample frames. For the in-person list frame cases, probabilities of selection were computed

as the product of five terms:1

(1) overall prob selection = prob [PSU] * prob [sub-PSU\PSU] * prob [local area\PSU and

sub-PSU]

* prob [case selected for either the in-person or field samples\earlier stages]

* prob [case selected for the in-person sample\previous step]
P(PSUi

) '

31 @ MOSi

'

2862

j'1

MOSj

MOSi

The measures of size used were figures reported to the FCS in spring 1995. Note that they refer 2

to cases, not persons.

B.4

a. First Stage 

The first step in the process was to select with probability proportional to size (PPS) the 35

primary sampling units (PSUs), which were counties (or sometimes states, if county-level size

measures not available) in the contiguous United States. Four PSUs were set aside as certainty

2

selections because their measures of size were larger than the sampling interval: New York City

(which counted for two selections), Cook County, and Los Angeles County. Once these four PSUs

were removed, 31 other counties were selected PPS. Thus, the first term in the equation for the

probability of selection (for the noncertainty selections) was:

where was the measure of size of PSU i. Note that 2,862 non-certainty PSUs were eligible for

selection, with a combined measure of size of 9,462,582. For the certainty selections, the first term

in the equation was simply 1. The three certainty PSUs had a combined measure of size of

1,396,379.

b. Second and Third Stages

For the three certainty selections and for two PSUs that were at the state level, there were one

or two more stages of selection prior to the selection of FSP participants. Each of these will be

discussed in turn:
P(countyk

\PSUMaine)'

1 @ CMOSk

'

16

j'1

CMOSj

CMOSk

P(officek\PSUCook ) '

3 @ OMOSk

'

25

j'1

OMOSj

OMOSk

P(districtk\PSULA ) '

3 @ DMOSk

'

29

j'1

DMOSj

DMOSk

B.5

Maine. One county within Maine was selected PPS, based on November 1995 counts provided

by the state. The second term of the equation for the probability of selection was then:

where was the measure of size for county k in Maine.

Cook County. Three offices were selected PPS, based on counts provided by Cook County in

January 1996. The second term of the equation for the probability of selection was then:

where was the measure of size for office k in Cook County.

Los Angeles County. Three districts were selected PPS, based on December 1995 counts

provided by Los Angeles County. The second term of the equation for the probability of selection

was then:

where was the measure of size for district k in Los Angeles County.

Oregon. One district within Oregon was selected PPS, based on October 1995 counts provided

by the state. The second term of the equation for the probability of selection was then:
P(districtl

\PSUOregon ) '

1 @ DMOSl

'

15

j'1

DMOSj

DMOSl

P(countyk\districtl) '

1 @ CMOSk

'

5

j'1

CMOSj

CMOSk

P(boroughl\PSUNYC ) '

2 @ BMOSl

'

5

j'1

BMOSj

BMOSl

P(zipcodek\boroughl) '

3 @ ZMOSk

'

J

j'1

ZMOSj

B.6

where was the measure of size for district l in Oregon. Because each district contained

multiple counties, one county was selected PPS within the selected district. The third term of the

equation was then:

where was the measure of size for county k in selected district l in Oregon.

New York City. Two boroughs were selected PPS, based on December 31, 1995, counts

provided by the state. The second term of the equation for the probability of selection was then:

where was the measure of size for borough l in New York City. Then three zip codes were

selected PPS within each selected borough. The third term of the equation was then:
ZMOSk

P(casem\laststagek) '

nk

Nk

nk Nk

P(casem\selected for in&person sample [independent]) '

2

3

@

f

k

nk@ 2/3

'

f

k

nk

f

k

B.7

where was the measure of size for zip code k in selected borough l in New York City, and

J is the total number of zip code areas within the borough.

All Other PSUs. For the other 29 PSUs, the second and third terms of the equation for the

probability of selection were equal to 1. For Maine, Cook County, and Los Angeles County, the

third term of the equation was equal to 1.

c. Fourth and Fifth Stages

The last terms in the equation for the probability of selection pertain to the selection of cases

within the last stage selected (county, office, district, zip code). Cases were selected with equal

probability at the last stages. The fourth term of the equation was:

where was the number of cases selected from, and was the frame size for, last-stage unit k.

From these selected cases, two-thirds were randomly selected for the in-person sample. From these

two-thirds, a certain number of cases were actually released. For estimates being made only from

the in-person list sample, this sample was treated as though it were independent from the telephone

list sample, in which case, the fifth and last term of the equation would be:

where was the number of cases released for the in-person (or “field”) list sample from last-stage

unit k. However, as discussed below, estimates were made that combined the two list samples, in
P(casem

\selected from Nk) '

f

k

nk

% 1&

f

k

nk

t

k

nk&f

k

'

f

k

% t

k

nk

B.8

which case this sample must not be treated as independent from the telephone list sample. The fifth

and last term of the equation is then quantified as:

when the two list samples were being used to produce an estimate, and where t was the number of k

cases released for the telephone list sample from last-stage unit k. The second term in this formula

accounts for the fact that the case could have been selected into either the in-person sample or the

telephone sample (but not both). 

d. Summary

The probability of selection for each selected case was the product of these five terms. The

sampling weight was the reciprocal of the probability of selection. All released cases (including

nonrespondents and those later found to be ineligible) have a sampling weight greater than zero.

2. Weighting Adjustments 

The sampling weight was then adjusted to account for nonresponse. To do this, all released

cases were classified as one of the following: eligible respondent, eligible nonrespondent, ineligible,

or eligibility status undetermined. Movers were classified as undetermined for weighting purposes.

To carry out this nonresponse adjustment, weighting classes were formed that met both of the

following criteria: (1) information used to form these classes must be available for all released cases

(that is, it must be information provided on the sample file), and (2) the cases within each class

should be relatively homogeneous with respect to characteristics expected to be related to study

(dependent) variables and the propensity to respond. In addition, each class should have at least 20
s

c

'

'

i0c

SWTi

'

i0cdet

SWTi

SWTi

EWTi' SWTi@ s

c

r

c

'

'

i0celig

EWTi

'

i0cresp

EWTi

B.9

respondents and the adjustment factor (described below) for each class should be less than or equal

to 2. Classes were collapsed with similar classes when they failed to meet these criteria. Classes

defined by the site (generally, the PSU) usually met these criteria.

The first step adjusted for the determination of eligibility. Only movers fell into the

undetermined eligibility category. The first adjustment factor was:

where was the sampling weight for case I, c was the weighting class indicator for the in-person

listsample (site), and c was the subgroup within class c for which eligibility status was determined. det

Those with undetermined eligibility have s set equal to 0. Then the eligibility-adjusted weight was c

calculated as:

The next step adjusted for interview nonresponse among those known to be eligible. This

adjustment factor was calculated as:
WTi

' EWTi@ r

c

nk

P(casem\selected for telephone sample [independent]) '

1

3

@

t

k

nk@ 1/3

'

t

k

nk

B.10

where celig was the subgroup within class c determined to be eligible, and cresp was the subgroup

within class c for which the interview was completed. Those with undetermined eligibility and those

known to be ineligible have rc set equal to 1, and those who were eligible but did not respond have

r set equal to 0. Then the nonresponse-adjusted weight was calculated as: c

Finally, outlier weights (both too small and too large) were examined, and it was determined

whether to truncate smooth the weights. In this sample, no truncation was indicated.

B. TELEPHONE LIST FRAME SAMPLE

1. Sampling Weight 

The first four terms of the equation for the probability of selection were the same as for the inperson list frame sample. From the cases selected from last-stage unit k, one-third were randomly

selected for the telephone sample. From this one-third, a certain number of cases were actually

released. For estimates being made from only the telephone list sample, this sample as though it

were independent from the in-person list sample, in which case the fifth and last term of the equation

would be:

However, as discussed below, estimates were made that combined the two list samples, in which

case this sample must not be treated as independent from the in-person list sample. The fifth and

last term of the equation would then be quantified as:
P(casem

\selected from Nk) '

t

k

nk

% 1&

t

k

nk

f

k

nk&t

k

'

t

k

% f

k

nk

B.11

when the two list samples were being used to produce an estimate. The second term in this formula

accounts for the fact that the case could have been selected into either the telephone sample or the

in-person sample (but not both). The probability of selection for each selected case was the product

of these five terms. The sampling weight was the reciprocal of the probability of selection. Again,

all released cases (including nonrespondents and those later found to be ineligible) have a sampling

weight greater than 0.

2. Weighting Adjustments 

The weighting adjustments for the telephone list frame sample were carried as outlined above

for the in-person list frame sample, again using site as the weighting class. No weight truncation was

indicated.

C. TELEPHONE RDD SAMPLE OF PARTICIPANTS, ELIGIBLE NONPARTICIPANTS,

AND NEAR-ELIGIBLE NONPARTICIPANTS

1. Sampling Weight

The RDD sample was selected in multiple steps, and the procedures employed in each of these

steps determine the probabilities of selection. In the first step, a stratified random sample of

telephone numbers was selected. The second and third steps consisted of using the Genesys ID

procedure to identify presumptively nonworking telephone numbers and then releasing other

numbers for calling by interviewers. In the fourth step, numbers were screened to identify whether

they reached households and, if so, whether the household was eligible for the survey (that is,

whether it contained food stamp participants or eligible or near-eligible nonparticipants). Although

sampling these subgroups differentially was considered, this was not done. Thus, in the RDD
Ws

RDDjh '

1

P(RDD)

jh

'

1

P(init)

h

P(rel)

j

(numphoneih)

P(init)

h

'

n(ph.num)

h

N(ph.num)

h

P(rel)

j

'

n(rel)

j

j

H

h'1

n(ph.num.)

jh

Numbers were identified as “bad” using Genesys Sampling Systems’ proprietary ID software. 3

B.12

sample, probabilities of selection among survey-eligible households may vary somewhat by stratum,

but not by characteristics.

The sample weight was the inverse of a case’s overall probability of selection, which in turn

was the product of the probabilities of selection for those steps where sampling took place:

where: 

P(RDD) was the cumulative probability of selection for a case sampled in stratum h;

jh

P(init)h was the initial probability of selection for a telephone number sampled in stratum

h;

P(rel)j was the probability of releasing a telephone number for calling in group j; there were

two groups: (1) “bads” were those listed as business numbers or those that, when dialed

with an automatic dialer, returned a signal indicating a disconnected or nonworking

number; and (2) “goods,” which included all other sampled numbers.3

numphoneih was the number of unique telephone numbers that can be called to reach the

ith household in stratum h; numphone was assumed to be 1, since the data on number of

telephones were not collected;
s

c

'

'

i0c

SWTi

'

i0cdet

SWTi

SWTi

N(ph. num.) was the number of phone numbers available for sampling in stratum h; the list1

h

assisted method used to select the RDD sample restricts selection to consecutive banks of 100 (a

bank would include XXXYYYZZ00 through XXXYYYZZ99) 10-digit telephone numbers in which

at least one number was published in a telephone company residential directory.

B.13

n(ph. num.) was the number of phone numbers initially selected in stratum h; h

N(ph. num.) was the population of phone numbers in stratum h; h

1

n(rel)j was the total number of telephone numbers released for calling in group j; strata

were pooled for release of sample; 150 “bads” were released, chiefly to see if any bias was

introduced by the method used to identify them.

n(ph. num.)jh was the number of phone numbers selected in stratum h and assigned to group

j.

2. Weighting Adjustments

Nonresponse adjustments employed procedures similar to those specified above for the list

frame samples. For the RDD sample, the cells were defined by sampling strata, and no collapsing

of cells was necessary. However, the RDD survey had different types of eligibility criteria from

those of the two list samples.

The first step adjusted for the determination of telephone eligibility; that is, whether it was

determined if the selected telephone number was a working number associated with a residence. The

first adjustment factor was:

where was the sampling weight for case I, c was the weighting class indicator for the RDD

sample (stratum), and c was the subgroup within class c for which telephone eligibility status was det
B.14
EWTi

' SWTi@ s

c

i

c

'

'

i0cres

EWTi

'

i0cinc

EWTi

IWTi' EWTi@ i

c

B.14

determined. Those with undetermined telephone eligibility had sc set equal to 0. Then the telephone

eligibility-adjusted weight was calculated as:

The next step adjusted for the determination of income eligibility among known residences, that

is, whether the income questions were answered. This adjustment factor was:

where cres was the subgroup within class c determined to be residences and cinc was the subgroup

within class c for which income was determined. Those with undetermined telephone eligibility and

those known to be telephone-ineligible had i

c

 set equal to 1. Those with undetermined income

eligibility had i set equal to 0. Then the income eligibility-adjusted weight was calculated as: c

The next step adjusted for interview nonresponse among those known to be income-eligible.

This adjustment factor was calculated as:
r

c

'

'

i0celig

IWTi

'

i0cresp

IWTi

WTi' IWTi@ r

c

That the four cases initially had very high weights was largely an artifact of the RDD sampling 5

process. Following standard procedures, an early step in the RDD sampling was to use specialized

software to screen out nonresidential telephone numbers. As a check on this work, a few numbers

that had been screened out were introduced into the sample. For the most part, the screening was

found to be accurate; however, four of the telephone numbers that were checked in this way were

found to be residential and ultimately received a screening interview. Because the probability of

entering the sample in this route was extremely low, an application of the standard probability-based

weighting algorithms led to these households initially receiving very high weights. However, it was

decided to trim the weights as described in the text, because there was no reason to believe that these

household were dramatically different from others in the sample, and the very high weights would

lead to very high variances in the estimation work.

B.15

where celig was the subgroup within class c determined to be income-eligible, and cresp was the

subgroup within class c for which the interview was completed. Those with undetermined telephone

eligibility, those known to be telephone-ineligible, those with undetermined income, and those with

ineligible income had r set equal to 1; those who were income-eligible but did not respond had r c c

set equal to 0. Then the nonresponse-adjusted weight was calculated as:

Four RDD weights were determined to be outliers. The range of the weights after the above

adjustments was 17,692.46 to 21,064.07, except for four outlier weights having values equal to

approximately 400,000. These four weights were trimmed to the value 21,064.07, and their excess

values were not redistributed to the rest of the sample.5
Whereas FSP participant households without phones were included in the in-person list sample 6

frame, such households were not included in either the CATI participant list frame or the RDD

frame. Thus, the issue regarding coverage of households without phones is also relevant for the

participant sample. However, the number of FSP participants identified from the RDD frame is

small (304 cases, or 12 percent of the unweighted FSP sample). In addition, some of the phone list

sample cases without phones were followed up in person by field staff using cellular phones to

complete the interview. Therefore, it was decided that the statistical gain from adjusting the

participant sample for telephone coverage did not warrant the costs.

B.16

3. Post-Stratification Adjustments

Because the nonparticipants were the only group targeted in the survey whose non-telephoneaccessible members were not covered by any of the samples, a ratio adjustment was done for this

group so that they better reflected the targeted population. An iterative raking procedure was used 6

to adjust their weighted proportions so that certain distributions matched those found on the March

1996 and March 1997 Current Population Survey (CPS) estimates for households with gross income

under 150 percent of the poverty guideline and not receiving food stamps.

First, the weights of the nonparticipants were adjusted so that the proportion in various poverty

level ranges matched the 1997 CPS. The next adjustment was for household size, followed by an

adjustment for race of the householder (using the 1996 CPS). Then the weights were adjusted once

more by poverty level. The last step was to do an overall post-stratification adjustment so that

weights for this group summed to the same total they had prior to the raking procedure.

4. Combining List Frame and RDD Participants

When the combined list frame sample (including both in-person and telephone together) was

pooled with the RDD participant sample, a weighting system was used that was designed to

maximize the statistical efficiency--that is, minimize the variances--of the resulting estimates. This

was done by making the relative weights for the two samples proportional to the effective sample

sizes for the two samples. This gives more weight to the sample with the larger effective sample size
yˆ

T

' f

1

yˆ

LF % f2

yˆ

RDD

f

1

'

n

eff

LF

n

eff

LF % n

eff

RDD

'

nLF / deff(yˆ

LF)

nLF / deff(yˆ

LF) % nRDD / deff(yˆRDD)

f

2

' 1 & f

1

B.17

while still giving some weight to the information contained in the sample with the smaller effective

sample size. In implementing this approach, the focus was on effective sample sizes, rather than

actual sample sizes, to take into account the impacts on the relevant variances of the design effects

associated with the two samples. Following is a more formal treatment.

As an initial step, the weights were normalized by scaling both the combined list frame weights

and the RDD weights so that the weighted sums were the same. (The number each is scaled to does

not matter for the tabulations included in the report; in fact, it was decided to scale both sets of

weights to an estimate of the approximate size of the food stamp household population, 10,060,000.)

This involved multiplying the list frame weights by 1.40 and the RDD weights by 1.81.

Now, to derive the relative weights, assume it is desired to estimate the combined estimate y

^

T

as follows:

where y and y are the estimates for the statistic y from the LF and RDD samples. ^ ^

LF RDD

The weights f and f are defined as follows: 1 2

where deff( y ) and deff( y ) are the design effects of the estimates y and y , and n and n

^ ^ ^ ^

LF RDD LF RDD LF RDD

are the actual sample sizes for the LF and RDD samples. 
B.18

In implementing these algorithms, it was assumed, based on tabulations of selected illustrative

variables, that the list frame design effect was 3.78 and the RDD design effect was 1.13 (see

Appendix C). The effective sample sizes were then calculated as (2150/3.78 = 569) and (304/1.13

= 269), respectively. The final weights were then calculated as .68 and .32.
APPENDIX C

VARIANCES

C.3

This appendix describes the estimation of variances for representative variable estimates

reported in the text. First, the overall approach is discussed. Then selected variance estimates are

presented.

A. APPROACH

The “Design Effect” Concept. A common way of characterizing the changes (usually

increases) in variances in estimated variables due to survey design features is to focus on the “design

effect (deff).” The deff is defined as the proportional change in variance caused by the survey design

as compared to the variance that could be achieved by a simple random sample of the same size. In

most contexts, design effects are greater than 1, meaning that variances are increased as a result of

the survey design features.

Approach Being Followed. A very large number of estimates of variable means and other

parameters are being made in the current study, and, while procedures exist for making individual

estimates of the true variances, their application to all the estimates included in the study would be

unwieldy. Hence, the approach is to estimate the true variances for the means of a number of

representative variables and to compute average design effects based on these variables. These

design effects can then be used by readers of the report to approximate variances associated with the

means of other variables. This analysis is based on estimated means. It is likely that the design

effects associated with regression coefficients may be lower, since the regressions can control for

factors that differ across primary sampling units.

The STATA computer program was used to estimate the true variances of selected variables.

This package is based on a Taylor Series approximation of the true variances. It directly computes

the estimated variances and design effects using standard formulas that relate the size of the design

effect to the relative sizes of two variables: (1) the component of the variances of those variables
C.4

due to variation within individual clusters in the survey design, and (2) the component of the

variances due to differences between clusters in the relevant underlying population characteristics.

B. FINDINGS

The following tables present illustrative design effects for selected variables from the analysis.

Tables C.1 to C.5 report typical design effects for the in-person sample of participants, the combined

in-person and telephone survey of participants, the RDD sample of participants, the sample of

eligible nonparticipants, and the sample of “near-eligible” nonparticipants. It is likely that these

design effects are typical of those which would be found more generally. 

Implications for the Width of Confidence Intervals. In general, 95 percent confidence

intervals extend ± 1.96 times the true standard error of an estimate, which is equal to the square root

of the variance of the estimate. Design effects are defined as a multiplier on the variance, while

confidence intervals are based on the standard error, which is the square root of the variance.

Therefore, observed design effects in the range of 2 and 4 imply that the size of confidence intervals

are increased by a factor of between 1.7 and 2, relative to what they would be with a simple random

sample. For instance, if, for a given sample size, a confidence interval around an estimated

percentage--say 55 percent--was plus-or-minus 4 percentage points in a simple random sample, the

confidence interval would have a width of 6.9 percentage points with a design effect of 3.

Illustrative Confidence Intervals. Given information about the size of the design effects, it

is relatively straightforward to compute estimated confidence intervals for estimates of proportions,

such as the proportion of food stamp households whose heads of households are female, or the

proportion receiving AFDC. Table C.6 presents representative confidence intervals for different

sample sizes and different assumed design effects.
C.5

TABLE C.1

ILLUSTRATIVE DESIGN EFFECTS FOR THE COMBINED IN-PERSON AND TELEPHONE SURVEY

SAMPLE OF PARTICIPANTS

Variable Size Mean Effect Estimated Mean

Sample Estimated Design Standard Error of

a

“Corrected”

Household Size 2,150 3.0 4.2 .079

Annual Earnings 2,074 $3,043 2.3 186

Whether Household Has an

Elderly Member 2,150 .274 3.0 .017

Whether Single-Person

Household 2,150 .257 2.2 .014

Whether Household Has

AFDC Income 2,123 .311 4.1 .020

Whether Household Has

General Assistance Income 2,134 .061 6.8 .014 

Average Design Effect 3.8

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Means may differ slightly from those reported in text because of slight differences in samples. a
C.6

TABLE C.2

ILLUSTRATIVE DESIGN EFFECTS FOR THE IN-PERSON INTERVIEW

SAMPLE OF PARTICIPANTS

Variable Size Mean Effect Estimated Mean

Sample Estimated Design Standard Error of

a

“Corrected”

Household Size 1,109 3.0 2.0 .074

Annual Earnings 1,071 $2,858 1.5 204

Whether Household Has an

Elderly Member 1,109 .266 1.8 .018

Whether Single-Person

Household 1,109 .255 1.6 .018

Whether Household Has

AFDC Income 1,089 .351 3.2 .026

Whether Household Has

General Assistance Income 1,099 .061 7.9 .020 

Average Design Effect 3.0

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Means may differ slightly from those reported in text because of slight differences in samples. a
C.7

TABLE C.3

ILLUSTRATIVE DESIGN EFFECTS FOR THE RDD SURVEY

SAMPLE OF PARTICIPANTS

Variable Size Mean Effect Estimated Mean

Sample Estimated Design Standard Error of

a

“Corrected”

Household Size 304 3.1 1.0 .107

Annual Earnings 296 $3,811 1.0 369

Whether Household Has an

Elderly Member 304 .245 1.0 .025

Whether Single-Person

Household 304 .220 1.0 .023

Whether Household Has

AFDC Income 301 .278 1.0 .026

Whether Household Has

General Assistance Income 299 .047 1.0 .012

Average Design Effect 1.0

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Means may differ slightly from those reported in text because of slight differences in samples. a
C.8

TABLE C.4

ILLUSTRATIVE DESIGN EFFECTS FOR THE RDD SURVEY

SAMPLE OF ELIGIBLE NONPARTICIPANTS

Variable Size Mean Effect Estimated Mean

Sample Estimated Design Standard Error of

a

“Corrected”

Household Size 450 2.1 .9 .066

Annual Earnings 450 $4,180 1.1 279

Whether Household Has an

Elderly Member 450 .514 1.4 .027

Whether Single-Person

Household 450 .493 1.4 .027

Whether Household Has

AFDC Income 449 .012 1.3 .006

Whether Household Has

General Assistance Income 449 .008 1.0 .004

Average Design Effect 1.2

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Means may differ slightly from those reported in text because of slight differences in samples. a
C.9

TABLE C.5

ILLUSTRATIVE DESIGN EFFECTS FOR THE RDD SURVEY

SAMPLE OF “NEAR ELIGIBLE” NONPARTICIPANTS

Variable Size Mean Effect Estimated Mean

Sample Estimated Design Standard Error of

a

“Corrected”

Household Size 405 2.5 1.0 .090

Annual Earnings 347 $8,118 1.0 $509

Whether Household Has an

Elderly Member 405 .407 1.3 .029

Whether Single-Person

Household 405 .379 1.4 .030

Whether Household Has

AFDC Income 405 .008 .004

b

Whether Household Has

General Assistance Income 405 .004 .003

b

Average Design Effect 1.2

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

Means may differ slightly from those reported in text because of slight differences in samples. a

Design effects could not be estimated satisfactorily because of the very low probability being computed. b
C.10

TABLE C.6

WIDTH OF 95 PERCENT CONFIDENCE INTERVALS

WHEN ESTIMATING A PROPORTION

Proportion Being Estimated

Variable .1 .2 .5

If Design Effect =1 and:

N=200 ±.04 ±.06 ±.07

N=400 ±.03 ±.04 ±.05

N=600 ±.02 ±.03 ±.04

N=800 ±.02 ±.03 ±.03

N=1,200 ±.02 ±.02 ±.03

If Design Effect =2 and:

N=200 ±.06 ±.08 ±.10

N=400 ±.04 ±.06 ±.07

N=600 ±.03 ±.05 ±.06

N=800 ±.03 ±.04 ±.05

N=1,200 ±.02 ±.03 ±.04

If Design Effect =3 and:

N=200 ±.07 ±.10 ±.12

N=400 ±.05 ±.07 ±.08

N=600 ±.04 ±.06 ±.07

N=800 ±.04 ±.05 ±.06

N=1,200 ±.03 ±.04 ±.05

If Design Effect =4 and:

N=200 ±.08 ±.11 ±.14

N=400 ±.06 ±.08 ±.10

N=600 ±.05 ±.06 ±.08

N=800 ±.04 ±.06 ±.07

N=1,200 ±.03 ±.05 ±.06
APPENDIX D

CONVERSION OF FOOD USE DATA INTO

NUTRIENT AVAILABILITY ESTIMATES

D.3

During the in-person survey, data were collected on the foods used by the household over a

seven-day period. (See Section II.E and Appendix A.) This appendix describes how those data were

converted into estimates of the nutrient contents of those foods, through use of a modified version

of the Food Intake Analysis System (FIAS), developed by the University of Texas at Houston. 

First, a summary of the steps involved in the nutrient coding/conversion process is provided.

Subsequent sections then provide details of how each step was performed.

SUMMARY

The following steps were followed in the nutrient conversion work:

C Development of FIAS recipe files and recipe codes. It was necessary to create a coding

structure that linked each food code used in the current survey data collection instrument

to a “recipe” that was expressed in constituent food codes and quantities and that could

be used to access the nutrient data base used in FIAS. 

C Setting up a coding center and hiring staff.

C Manual entry of food recipe codes and the weights of the foods used into the FIAS

system.

C Manual entry of the survey data on amounts bought and prices paid into a separate

LOTUS spreadsheet format, to determine unit prices, which were subsequently merged

back into the food quantity data.

C Calculation of nutrient values.

C Quality control checks of the FIAS entry process, together with extensive edits of the

FIAS data at the individual food level, using “high” value checks. 

C Aggregation of the individual food-level data to the household level by summing over

food lines.

C Additional household-level edits, based on “high” and “low” value checks.

C Imputation of prices for foods that had not been bought or whose purchase price was

unknown.
D.4

These steps are described below.

1. Preliminary Development of Recipe Files

To support the entry of food data into FIAS, a preliminary set of FIAS recipe codes was

developed. For every food item covered by the survey, a recipe into FIAS was entered, using the

FIAS recipe feature. In general, these recipes were taken from similar ones that were used in coding

the 1987-88 Nationwide Food Consumption Survey (NFCS). In some instances, the recipes

consisted of a single ingredient. For instance, orange juice was orange juice. In such situations, the

use of the FIAS recipe codes simply translated the coding structure of the survey into a coding

structure for which FIAS could supply nutrient information. In other instances, recipes had more

than one ingredient and also embodied cooking assumptions, as discussed below.

The recipes served several purposes:

C As noted above, the recipes allowed conversion of the coding structure of the instrument

to that of FIAS. A “link file” was used from the Washington State Food Stamp Cashout

Demonstration Evaluation to convert the codes used on the survey for that study to 11-

digit USDA codes that were then linked to FIAS codes. (The Washington State survey

had used the same codes as in the current study.)

C The use of recipes provided a convenient way of incorporating the assumptions from the

1987-1988 NFCS coding into the current coding procedures.

C The recipes provided a context for dealing with “mixtures,” where assumptions had to

be made as to what is included in foods with multiple ingredients. For instance, a “Big

Mac” sandwich, which might have been brought into respondents’ homes as a take-out

food item, consists of bread, ground beef, vegetables, and other ingredients. To account

for this, recipe files were read into FIAS to link individual food codes from the survey

(in this case, the code for a Big Mac) into their individual constituent ingredient codes.

C The FIAS recipes also allowed incorporation of assumptions about cooking methods

used for the foods reported. In the current food use survey, as in previous food use

surveys, it was not known how the foods brought into the home were ultimately cooked,

and thus what their ultimate nutrient availability was (since cooking can affect nutrient

availability). For example, the nutrient availability of raw carrots differs from that of

cooked carrots, so “retention codes” were used that account for nutrient loss (or gain)
The USDA recipe files that were used were ones that had been used in the 1987-88 NFCS

1

coding. Each food was identified by an 11-digit USDA code.

Two types of recipes were created, depending on the nature of a new food. If a new food could 2

be characterized in terms of a combination of foods already in the FIAS database, then a “regular”

FIAS recipe was created. If a food was so different that it couldn’t be characterized in terms of

existing foods, then FIAS’s “user data set” feature was used, making it possible to enter nutritional

information directly into the database.

D.5

from cooking. Previous USDA surveys had dealt with this matter by creating recipes

even for some single-ingredient foods. For instance, a recipe for a food that can be eaten

raw or cooked might consist of a certain proportion (for example, 30 percent) of the food

being eaten raw and the remainder (for example, 70 percent) being cooked, with, for the

latter, an appropriate retention code indicating how the cooking changed nutrient

availability. This convention was followed in the current survey coding.

C Recipes allowed for situations where a single survey code may track into several

possible, slightly different food codes. For instance, if a respondent reported using

frankfurters but didn’t know what kind, an assumption had to be made about whether

they were made from beef or pork. This was done using a recipe that assumed part pork

and part beef, based on how common the two kinds of frankfurters are estimated to be.

In developing FIAS recipes for use in the coding work, it was necessary to take into account that

some foods encountered in the survey were not in the previous USDA files that formed the basis of

most of the recipe-coding work. An example is that “no-fat cream cheese” had not been developed 1

when the previous files were created. Ethnic foods for recent immigrant groups were also frequently

not represented in the earlier files. Therefore, project nutritionists created new FIAS recipes, using

a variety of information sources, including information from food labels, information from food

manufacturers, a later version of FIAS (FIAS-3, which became available midway through the

survey), and recipe books. A total of 6,090 recipes were used. Of these, 5,724 were developed from 2

previous USDA recipes, 213 were new recipes composed using nutrient data on the FIAS files, and

153 were recipes for which new nutrient data had to be entered into the FIAS system.
Two types of recipes were created, depending on the nature of a new food. If a new food could 3

be characterized in terms of a combination of foods already in the FIAS database, then a “regular”

FIAS recipe was created. If a food was so different that it couldn’t be characterized in terms of

existing foods, then FIAS’s “user data set” feature was used, making it possible to enter nutritional

information directly into the database.

D.6

The FIAS recipe database that was created can be interpreted as showing the food ingredients

and their retention factors (expressed in terms of the seven-digit USDA food codes and the USDA

survey), and recipe books. A total of 6,090 recipes were used. Of these, 5,724 were developed

3

“primary data set” codes) assumed to have been associated with a unit amount--such as 100 grams--

of each of the foods reported in the survey. Staff of MPR’s subcontractor, ROW, Inc., under the

supervision of one of the principal investigators, used the recipe creation feature of FIAS to enter

the recipes into FIAS as FIAS recipe files and assigned them six-digit codes. Both principal

investigators undertook extensive spot-checking to ensure the accuracy of this entry.

Besides recipe files, the coding required a set of “refuse” factors, reflecting the fact that not all

of certain foods are available for eating. For instance, a whole cauliflower gets trimmed before

cooking, and thus some of the original weight is thrown away as refuse. Similarly, a significant

amount of a whole fish is discarded in preparation. The 1987-88 NFCS recipe files, in addition to

listing ingredients and their codes, noted these refuse factors where appropriate, and these codes

were carried over into the files for the current survey.

Once FIAS recipe files were assembled for this project, they were tested with completed data

collection instruments that had been used in the San Diego Food Stamp Cashout Evaluation. (These

data collection instruments had been coded by National Analysts, Inc., the same firm that coded the

most recent several Nationwide Food Consumption Surveys.) A sample of the San Diego cases was

coded using the FIAS-based procedure, and the nutrient values computed with FIAS were compared

line by line to the values of the nutrients on the San Diego database. These tests proved satisfactory
D.7

in that most of the food lines yielded the same nutrients in both coding structures, and the

discrepancies were, in general, explicable in terms of either coding errors or likely changes in the

underlying nutrient databases.

2. Setting Up the Coding Center and Hiring Staff

To facilitate the work, MPR set up a separate coding room. Coders were hired and trained and

then provided with their own coding stations and reference materials in the coding room. A

supervisor was also selected from MPR’s ongoing coding staff to direct the flow of activity in the

coding room.

Following the recommendations of the FIAS staff at the University of Texas, coders were

required to have completed high school with, preferably, some college education; to be the food

manager at home; and to be familiar with simple mathematical computations. In addition, MPR

required coders to have some basic computer experience.

Including practice experience, coders were required to participate in 2.5 days of training at MPR.

After being given an overview of the project, coders were shown how to start a new file in FIAS,

how to move around while in FIAS, and how to close a file. Coders were then shown how to extract

the nine-character survey code from the food instrument and how to relate this code to its

corresponding six-digit FIAS recipe code. They were also taught how to input the six-digit FIAS

code for each food into a FIAS file, along with the amount of that food that was used during the

seven-day period. The mathematical operations that facilitated these steps were reviewed. Coders

were provided with a training manual, written by the project director and the MPR nutritionist,

containing all the topics covered during training. (At a later time during the project, coders were

taught how to “clean” and data-enter the completed price-related data on the food instruments.
D.8

3. Manual Entry of Foods into FIAS

Once the FIAS recipe files were set up, coding work could be started. This section describes

how the food coding was done.

As data collection instruments were received in Princeton, they were logged into an ACCESS

database and then taken to the coding room at MPR’s Princeton facility. Upon arrival in the coding

room, cases were given a quick line-by-line review to determine whether all the necessary

information was available. Frequently, additional information was needed about a quantity or a type

of food. When possible, the problem was resolved through a call-back to the respondent, either by

telephone directly from Princeton or by the original interviewer. 

a. Entering Food Data

After the necessary data were available, the coder determined the survey code of the food being

used, for each coded line on the food use instrument. Then, using either a hard-copy look-up table

or an automated look-up program, the coder accessed a database to determine the six-digit FIAS

recipe code (see the previous section) that had been assigned to that food and also noted whether or

not there was a refuse factor associated with it. The appropriate FIAS recipe code was then entered

into FIAS.

If the quantity of a food was expressed in weight, the coder then entered the weight directly into

FIAS, after subtracting the “refuse factor” amount, if appropriate. If the quantity was expressed in

some other way, such as “units” or a volume measure, then the coder attempted to identify a factor

for converting that quantity to a weight, often using food label information that had been obtained

from the respondents during the interviews. In other situations, the weight equivalent codes built
No information on portion sizes or weight equivalents was directly available for the recipes 4

read into FIAS. However, the coders could access unit weight information in FIAS by independently

entering the name of the food and viewing the relevant screen. Having observed that information,

the coder had to exit from the FIAS portion screen and enter the relevant weight directly into the

original screen where the food code had been entered.

The project nutritionist had a Master’s Degree in nutritional science and extensive experience 5

in food preparation.

D.9

into FIAS were used to determine the weights of various measures, such as a medium apple. Other 4

sources, such as supermarket flyers, recipe books, the household weight file used in the 1987-88

NFCS, and visits to stores, were also sometimes used. (The visits to the stores were done to weigh

unit quantities of various produce and to examine food labels.) After weights were determined,

refuse factors were subtracted where appropriate, and the weights were then entered into FIAS.

Any problems (such as lack of a recipe for a food or uncertainty about how to translate an

amount into a weight) were referred to the project nutritionist. If the project nutritionist was not 5

able to resolve a problem, the nutritionist who acted as the co-principal investigator for the project

made final resolution. 

In addition to entering food items into FIAS, coders also entered from the hard copy the

approximate number of meals eaten during the observation period. This information was entered

into an unused field in one of the preliminary FIAS data entry screens for each case. This numberof-meals variable was not used in the final analysis, since a more accurate meal count was available

in the CAPI portion of the interview. But the appropriate meal count was useful in conducting edit

checks, before the food data and the CAPI data had been merged.

The project nutritionist and the project director reviewed the first two or three cases coded by

each coder. After that, the project nutritionist reviewed random cases for quality control. In
D.10

addition, the extensive edit-checking the project nutritionist (see below) conducted provided

additional quality control. Any problems were brought to the attention of the coder for resolution.

The coders were responsible mainly for coding the hard-copy food instruments as outlined

above. They also called the respondent when more-detailed information was required for a reported

food. If the amount of food used or purchased was missing or unclear, or if the form of the food was

not indicated (for example, dehydrated/ready-to-eat/condensed), the respondent was called for

clarification. Many food instruments generated questions about package size and price paid for a

food item. Since some respondents were not able to remember these details, a list was constructed

of all the foods that required information on package size or price paid. Two of the coders then went

shopping locally to obtain this information. 

Once most of the hard-copy food instruments had been coded and entered into FIAS, the coders

were trained to data-enter the information on the food purchased and the price paid into a LOTUS

spreadsheet. (See Section 4.)

4. Entry of Data on Amounts Bought and Prices

The data collection instrument also obtained information on the amounts of foods bought (as

opposed to the amounts used, as discussed above) and on the prices paid for the foods. Because

there was no obvious way of incorporating these data into the FIAS software, they were data-entered

separately and then merged with the FIAS information through use of SAS. 

The data on amounts bought and on prices paid were keyed into a LOTUS spreadsheet. Each

case had a separate spreadsheet, and each line in the spreadsheet corresponded to a food line in FIAS.

The data were entered twice, by different coders, on two different spreadsheets, and then
FIAS 3 became available partway through the survey. Although it was not practical at that

6

point to convert the coding operation to the new version, the new version was often useful in

providing information to help the coding, particularly with regard to new foods.

D.11

reconciled against each other to detect and correct data entry errors. Missing price data were left

blank in the file and were then imputed at a later step (see below).

5. Assigning Nutrient Values to Foods

The standard FIAS software and its corresponding nutrient database were used to assign nutrient

values to the foods consumed. This procedure drew on the fact that the FIAS recipes were expressed

in terms of the foods in the database. In a small number of cases, the project nutritionist had to use

the “user dataset” of FIAS to add foods to the database to reflect new foods encountered in the

survey. Nutrient values were assigned on the basis of food labels, manufacturer information, a later

version of FIAS, and recipe information.6

6. Edit Checks

After each case was entered and nutrient values were assigned to the foods, edit checks were run

line by line on each food to identify foods that exceeded threshold quantities of key nutrients. In

particular, the nutrients and their cutoff limits for the edits for the first round of checks were:

Nutrient Edit Threshold

Food Energy 7700 kc* (household size)

Calcium 3200 mg* (household size)

Vitamin A (re) 2700 μg* (household size)

Vitamin C 160 mg* (household size)

Riboflavin 4.8 mg* (household size)
D.12

These cutoffs are considerably higher than the standard edit thresholds built into the FIAS

system. They were set higher because the current study focused on food used for the entire

household during the week, rather than 24-hour intake for an individual. Thus, quantities tended to

the much larger than with individual intakes. For instance, a food line on the present survey might

typically include 5 or 10 pounds of potatoes, rather than an individual serving of potatoes, as would

be the case as with an intake record. The threshold cutoffs were chosen so as to be low enough to

identify potentially erroneous entries but high enough to discriminate between likely problems and

likely correct entries.

Typically, on the first round of edits, about four to six foods for each case were highlighted by

the edit runs. Each of these flagged food items was manually checked by the project nutritionist,

who consulted the hard-copy data collection instrument if an item appeared questionable based on

the printout information. Changes were made as appropriate.

On a subsequent round of edits, essentially the same computer checks were performed, but the

cutoff thresholds were set approximately three times higher. Typically, this caused about half the

cases to be flagged, usually with just one to three items highlighted. On this round, the project

director for the study reviewed the output and manually identified food entries that appeared

problematic. These were then reviewed against the hard copy by coding personnel, who made any

changes needed to correct clear errors. The results of this coder review were then examined by the

project director, who made final edit determinations.

An additional type of automated checking was a comparison, for all foods, of the amounts

reported used during the week and the amounts reported bought. All items where the amount

consumed exceeded the amount bought were flagged for manual review against the hard copy. In

most situations, the food item was found to be coded correctly, since it was sometimes the case that
D.13

the amount used was based on more than one shopping trip, but only the latest one was reported.

However, this set of edits was also found to be useful in identifying miscoded cases.

All the checks described so far were based on the individual food items. In addition, the foods

for a household were aggregated, and editing was performed at the household level. In particular,

for food energy, vitamin A, vitamin B6, vitamin B12, calcium, and vitamin C, the households with

the highest levels of each nutrient per meal were reviewed manually, food line by food line, and any

apparently problematic entries were examined against the hard copy.

Editing on the food prices computed from the data was done for each food code. Whenever one

of the reported prices for a food code was more than twice or less than half the median price, the

relevant data were printed out and reviewed manually. In addition, the 50 lowest prices and the 50

highest prices in the data set were printed out and reviewed manually to identify any apparent errors.

7. Price Imputations

In some instances, respondents were unable to remember the prices they had paid for the foods

they had used. In other instances, there was no actual price, because the food was home produced,

received as a gift, or otherwise obtained without a direct payment. For estimation of the value of all

food used by households, prices had to be imputed in these instances. For each food code where a

price imputation was needed, the following algorithm was used:

1. If there were at least five valid reported prices for a food code (that is, at least five

respondents had reported price information for that item), then the median of the

reported prices was automatically imputed.

2. If there were between one and four valid prices in a food code, the project director

reviewed the range of prices and considered the food at issue to determine whether or

not the median represented a reasonable estimate of the price. If it was judged to be

reasonable, the median was imputed; if not, Step 3 below was used.
D.14

3. If there were no reported prices for the food code or if it was determined that the median

was not appropriate, then a price was imputed, usually either from the price of a similar

food or from store prices. This was done using the rules summarized in Exhibit D.1.
D.15

EXHIBIT D.1

IMPUTATION PROCEDURES WHEN INSUFFICIENT DATA WERE AVAILABLE

FOR IMPUTING BASED ON OTHER PRICES OF THE SAME FOOD

1. If the project nutritionist determined that there was in the dataset a very similar food that

did have a valid price, then the median price of that similar food was imputed. For

instance, the price of low-sodium canned corn might be imputed from the price of

regular canned corn.

2. If the project nutritionist determined that two foods were essentially the same except that

their “form” led to different refuse factors, the median price of the food for which a price

was available was used to impute the other, adjusting for the refuse factor. For instance,

suppose that for a certain type of fish a price was available for the fillet, but not for the

whole fish, including head and bones. And assume that, on the basis of the refuse

factor, the fillet weight was known to be approximately 60 percent of the whole weight.

Then the per-pound price of the whole fish was imputed as the median per-pound price

of the fillets times .60.

3. If none of the above methods applied, the price was estimated by examining the prices

in a supermarket in a low-income area in central New Jersey. (This was necessary in

only about half of one percent of the foods.)

4. For a very small number of foods, mostly game, where no reasonable direct market

price could be found, the price was imputed based on the price of similar foods. For

instance, the price of venison could conceivably have been imputed based on the price

of beef. To be sure, a price for venison could have been found in a specialty shop. But

all instances of venison in the data were of venison obtained through hunting, and it was

judged that the price of beef provided a better representation of the value of the meat to

the households. The number of foods for which this type of imputation was done was

less than 40 out of a total of more than 40,000 food lines in the data set.

APPENDIX E

GEOCODING

E.3

This appendix describes the development of the database on geographic locations of households

and stores, the database used to compute certain of the distance measures cited in the report.

Potential biases in the data are also assessed.

Basic Procedures

During the in-person survey operations, information was obtained on the locations of (1) the

respondents’ homes, (2) the stores where they shopped, and (3) the supermarkets nearest their homes.

Both the address and the name of the nearest cross street were obtained, when possible. In addition,

for the store data, an attempt was made to identify the stores on hard-copy lists the interviewers

carried of authorized food stamp retailers. When possible, the data were linked through the store

program identification codes used in administering the program.

All the address information was then transmitted to a geocoding vendor, Geographic Data

Technology (GDT) of Lebanon, New Hampshire, which, when they could locate the address,

returned precise longitude and latitude of the location. Interview information on the stores and

household locations GDT could not code on the first attempt was printed out at MPR, manually

edited, and then sent to GDT a second time, leading to the identification of additional locations.

Altogether, these procedures produced geocodes for about 80 percent of the households and 70

percent of the stores. Reflecting these “hit” rates, geocoded distance to the nearest store was

available for approximately 58 percent of the in-person sample, while geocoded distance to the store

most often used was available in about 55 percent of the cases.
E.4

Potential Biases in the Data

Because about 40 percent of the store/home pairs of locations could not be fully geocoded, it

is important to examine whether there may be biases implicit in the resulting data. For examination

of this issue, Table E.1 displays two sets of data on distance to the store usually shopped: one set

based on the geocoding and the other on a direct question asked during the interview. Comparison

of the two columns shows that the geocoded data clearly imply shorter distances, on average, than

the direct interview data. This suggests the possibility that the stores that could not be geocoded may

be disproportionately the ones at greater distances from respondents. Based on the interviewing and

coding experience, this type of bias is indeed likely to have occurred, since it tended to be harder for

respondents to supply detailed address information for stores that were outside their own

neighborhoods. 

This potential bias needs to be taken into account in interpreting data based on the geocoding.

It is believed, however, that it does not reverse any conclusions made in the report. This issue is

examined further in the next section.

Reassessment of Whether Respondents Frequently Travel Farther than the Nearest

Supermarket to Shop, in Light of the Possible Biases in the Geocoded Data

An analysis in an earlier NFSPS Report (Ohls 1998) concludes that FSP participants frequently

travel farther than the nearest supermarket for their food shopping. However, as noted in that

discussion, the conclusion may be influenced by the fact that direct survey responses about distances

to the store most often used are being compared with geocoded information about the nearest

supermarket. (Use of the two different types of data maximized available sample sizes. No direct

interview data are available on distance to the nearest supermarket.) 
E.5

TABLE E.1

DISTANCE TO STORE USUALLY USED, WITH 

ALTERNATIVE DATA SOURCES

(Percentage of FSP Participants)

Direct Response to 

Survey Question Geocoding

Less than .5 miles 8.3 16.2

.5 to .99 miles 22.6 19.4

1 to 1.99 miles 22.0 24.3

2 to 3.99 miles 11.2 20.0

Over 4 miles 35.9 20.2

Sample Size 1,091 635

SOURCE: Unweighted data from the 1996 National Food Stamp Program Survey.

NOTE: Percentages may not add to 100, due to rounding.
E.6

However, while comparable direct interview data are not available, comparable geocoded data

are available for both the most-used stores and the nearest stores for the subset of the sample for

which full geocoding was possible. It is therefore possible to make comparisons of the relevant

distances with consistent data. These comparisons, as shown in Table E.2, suggest that, even when

the analysis is confined to the same type of data, the analysis still supports the conclusion that

substantial numbers of households do their primary shopping at stores more distant than the closest

stores. For instance, 53 percent of households with full geocode data have a supermarket within a

mile of their residence, but only 36 shop within a mile. Further, only 11 percent of the nearest

supermarkets are more than four miles away from the households, but 20 percent say they usually

go more than four miles to shop. 

The assessment, therefore, is that, even though there may be some bias in comparing the direct

interview estimates of distance with geocode-based distance estimates, the conclusions reached in

the report are not caused by this bias. Even with comparable data, the analysis suggests that many

households in the sample travel beyond their nearest supermarket to shop.
E.7

TABLE E.2

DISTANCES TO STORE USUALLY USED AND TO NEAREST SUPERMARKET, 

BASED ON COMPARABLE DATA SOURCES

(Percentage of FSP Participants)

Distance to Store Distance to Nearest

 Usually Used Supermarket

Less than .5 miles 16.2 25.9

.5 to .99 miles 19.4 26.8

1 to 1.99 miles 24.3 22.2

2 to 3.99 miles 20.2 13.9

Over 4 miles 20.2 11.2

Sample Size 635 598 a b

SOURCE: Unweighted data from the 1996 National Food Stamp Program Survey.

NOTE: Percentages may not add to 100, due to rounding.

Sample consists of all households for which full geocode data were available on the household

a

location and the location of the store usually used.

Sample consists of all households for which full geocode data were available on the household

b

location and the location of the nearest supermarket.

APPENDIX F

COMPARISON OF NFSPS SEVEN-DAY FOOD USE DATA

WITH OTHER SOURCES

Lutz et al. (1992), in comparing the results of the 1977-78 and the 1987-88 NFCSs, estimate 1

a drop in average low-income household consumption per week from $65 to $51, a decrease of more

than 20 percent. Similarly, using 1977-78 NFCS data and data from the 1979-80 NFCS Low Income

Supplement Survey, the U.S. Human Nutrition Information Service, in “Preliminary Report No. 10,”

estimated that between 1977-78 and 1979-80, the weekly real value of food used at home per

household declined about six percent over that two-year period (see HNIS Table 13 and p. 31).

F.3

As a partial check on the validity of the seven-day food use data collected during the NFSPS,

it is useful to compare summary variables from this data collection with a number of benchmarks

from other data sources. This is done below for two key sets of variables: data on the value of food

used at home, and data on nutrient availability, scaled by equivalent nutrition unit (ENU).

DATA ON THE VALUE OF FOOD USED AT HOME

The discussion of the value of food used at home as measured by alternative sources draws

heavily on the examination of these data in Lutz et al. (1992). The focus is on two sets of

comparisons, as summarized in Table F.1. One comparison is with data on households in the lowest

income quintile in the 1987-88 NFCS. That study estimated the value of all food used at home per

low- income household per week to be $50.65 (in 1987-88 dollars). As shown in the table,

adjustment for inflation produces a value in 1996 dollars of $66.64. The corresponding estimate

in the current survey is $59.10. The number here may be lower because the value of food used at

home by low-income households appears to be declining over time, the result of greater use of meals

bought away from home and possibly other factors.1

The second comparison shown in Table F.1 is with data from the Bureau of Labor Statistics

Consumer Expenditure Survey (CES), which provides an estimate of the value of purchased food

used at home. How foods to be included in CES data are defined is not exactly the same as in the

NFSPS (see Lutz et al.). Nevertheless, the definitions are sufficiently similar to make the

comparisons of interest, and as explained in the notes to Table F.1, some of the noncomparable

factors may at least partially offset one another.
F.4

TABLE F.1

FOOD USE AT HOME BY LOW-INCOME HOUSEHOLDS

(Per Week per Household)

1987-88 1994 1996

Nationwide Food Consumer National Food

Consumption Expenditure Survey Stamp Participant

Survey Survey

Purchased Food Used at

Home (Current Dollars) NA $52.15 $53.59

All Food Used at Home

(Current Dollars) $50.65 NA $59.10

Consumer Price Index

for Food at Home .76 .93 1.00

Purchased Food Used at

Home (1996 Dollars) NA $56.08 $53.59

All Food Used at Home

(1996 Dollars) $66.64 NA $59.10

NOTE: NA means not available.

Line 1, Column 2: From U.S. Statistical Abstract 1996, Table 707. Calculated as

$2,712/52. Note that this is for all households, not just low-income households. A

possible adjustment that has not been made is to multiply by a factor of .82, based on

Table 4 of Lutz et al., which estimates that for food at home, the ratio of low-income

average household expenditures to overall average expenditures is 1102/1348, or .82. On

the other hand, the Consumer Expenditure data included in the table do not include the

value of take-out food purchased away from home and then brought into the home,

whereas the NFCS and current survey numbers in the table do include this. Any

adjustments for the low-income household factor and for this exclusion of take-out food

would be at least partly offsetting.

Line 2, Column 1: Data for lowest income quintile, based on Table 4 of Lutz et al.

Calculated as $1,102 times the average equivalent household size of 2.39 divided by 52

weeks.

Line 3: Based on 1982-1984 = 100, the price index values used are as follows: 1988 =

116.6; 1994 = 144.1; and 1996 = 154.3.
F.5

As summarized in the table, the CES estimate of the value of purchased food as of 1994, the

latest year for which the data are available, is $52.15. After adjusting for inflation, this estimate

becomes $56.08, which is similar to the estimate here of $53.59.

From this analysis, it is believed that these benchmark comparisons support the assertion that

the NFSPS data appear reasonable in light of earlier studies.

NUTRIENT AVAILABILITY DATA

As summarized in Table F.2, the nutrient availability data from the current survey are also quite

similar to those observed in the 1987-88 NFCS. The first two columns compare mean nutrient

availability as a percentage of RDA from the two sources. The NFSPS data are slightly higher than

the NFCS data for six of the nutrients, slightly lower for one nutrient, and exactly the same for the

remaining nutrient. In no case is the difference large.

The picture is somewhat different when percentages of households meeting 100 percent of the

RDAs are examined, as shown in the second set of columns in the table. Here the NFSPS is

consistently lower than the NFCS on all nutrients, with all the differences generally being in the

range of two to eight percentage points. The reasons for these differences are not clear, particularly

in light of the comparability of the averages, as discussed in the previous paragraph. It is possible

that the differences reflect true changes over time. They might also reflect differences in the

populations for which data are available in the two survey (FSP participants in the case of the NFSPS

versus all households below $12,500 in the case of the NFCS). Another possible explanation is that

the NFSPS underestimated food used at the low-to-moderate end of the range. In any event, the

discrepancies are not large, and there is no evidence that they have had any material influence on the

analysis presented in the current report.
F.6

TABLE F.2

COMPARISON OF NFSPS NUTRIENT AVAILABILITY WITH

1987-88 NFCS DATA ON HOUSEHOLDS WITH

INCOME BELOW $12,500

Mean Nutrient Availability as Percentage of Households

Percentage of RDA Meeting RDA

1987-88 NFCS 1996 NFSPS 1987-88 NFCS 1996 NFSPS

Food Energy (Kcal) 127 130 63 59

Vitamin A (re.) 189 181 73 65

Vitamin C 246 262 84 79

Vitamin B6 135 149 68 65

Folate 191 216 82 79

Calcium 114 114 55 47

Iron 161 163 78 69

Zinc 112 118 52 49

SOURCE: NFCS data from NFCS Report Number 87-H-1, USDA, Agriculture Research Service,

1994.
APPENDIX G

COMPARISON OF SEVEN-DAY AND FOUR-DAY FOOD USE

INTERVIEW RESULTS

G.3

The food use data collection method has traditionally been implemented over a seven-day

period, which has the advantage of minimizing distortions due to cycles of food use over the course

of the week. In addition, as opposed to shorter possible observation periods, it allows enough time

for day-to-day variation in food use to be smoothed out.

However, the seven-day food use technique imposes considerable burden on respondents, and

in planning the current study, there was interest within the government in assessing whether it would

be reasonable for similar future data collections to use a shorter observation period. Accordingly,

it was decided to conduct approximately 10 percent of the interviews as four-day observations and

to compare the results. This appendix makes these comparisons.

COMPARISONS OF RESULTS

To allow meaningful comparisons, the data on total amounts of nutrients used have been

converted to a daily basis, by dividing by seven or four, as appropriate (Table G.1). This

normalization is not necessary when considering variables that have been scaled by equivalent

nutrition units (ENUs), since these estimates do not have a direct time dimension.

As shown in the table, when the expenditure data and the nutrient data are compared between

the seven-day and the four-day data collection, the estimates of expenditures and nutrients per day

are consistently higher for the four-day data collection. Most of the differences in the per-day

variables are in the range of 10 to 20 percent. An exception to this is that when micrograms of

vitamin C per day are compared, the difference is more than 30 percent. Interestingly, however,

when vitamin C availability is normalized by ENU, the difference is reduced to about 15 percent.

Only one of the differences between the four-day and the seven-day estimates is statistically

significant--that for total vitamin C. Since the differences are substantial, this largely reflects the

small sample size for the four-day interviews: 92 observations. 
G.4

TABLE G.1

COMPARISON OF SELECTED EXPENDITURE AND NUTRIENT DATA PER HOUSEHOLD

BETWEEN SEVEN-DAY AND FOUR-DAY FOOD USE DATA COLLECTION

Seven-Day Food Use Four-Day Food Use Difference of Daily Average

Variable Total Average Daily Average Day Total Average Daily Average Diff. Diff. Signif.

Seven-Day Daily Std. Error of Seven- Daily Std. Error of Absolute Percent Statistical

Household Size 2.99 2.99 3.08 3.08 .09 3.0 --

Household Size in Adult Male Equivalent 2.16 2.11 2.23 2.23 .07 3.2 --

Value of Food Used $59.10 $8.44 .38 $37.50 $9.38 .85 $0.94 11.1 --

Value of Food Used per Person $11.77 $3.24 .15 $12.18 $3.62 .31 $0.41 3.5 --

Calories (kcal) 44,386 6,340 308 28,606 7,151 675 811 12.8 --

a

Calories per ENU 1.30 1.30 .05 1.45 1.45 .11 .15 11.5 --

Iron (μg) 313.4 44.8 2.19 199.0 49.7 6.12 4.9 10.9 --

b

Iron per ENU 1.63 1.63 .06 1.84 1.84 .22 .21 12.9 --

Calcium (mg) 15,699 2,242 103 10,893 2,723 310 181 21.4 --

Calcium per ENU 1.14 1.14 .04 1.36 1.36 .13 .22 19.3 --

Vitamin C (μg) 2,291 327 14.8 1,717 429 43.5 102 33.6 **

Vitamin C per ENU (μg) 2.62 2.62 .11 3.00 3.00 .27 .38 14.5 --

Sample Size 957 92

SOURCE: 1996 National Food Stamp Program Survey, weighted data.

kcal = kilocalories

ENU = equivalent nutritional units

**Statistically significantly with 95 percent two-tailed test.
G.5

DISCUSSION

The reasons for the apparent tendency for the four-day interviews to record more food use are

unclear. One possibility is obviously statistical sampling variance; however, this is probably not the

entire explanation. Another possibility is that respondents may have better recall over a shorter

period of time and are thus able to supply more complete information. A third potential explanation

is that with the shorter period of time there is more likely to be what is known in the survey literature

as “telescoping” error, whereby respondents confuse time boundaries. With this type of error,

respondents tend to report more events (in this case, food use) in a limited amount of time than

actually occurred, because they include events that happened just before or just afterwards.

In summary, the experiment that has been conducted suggests that the four-day and the

seven-day data collection approaches tend to get somewhat different results. However, the

differences are not enormous, and it is not clear which approach yields the more accurate data.

APPENDIX H

DETAILED REGRESSION RESULTS

H.3

Chapter V summarized results of regression analysis where the dependent variable was nutrient

availability and the independent variables included various demographic and economic

characteristics of the households. Chapter VI summarized similar regressions that also included

indicators of food security. This appendix presents the detailed regression results on which the

summaries in Chapters V and VI are based.

REGRESSIONS FOR TABLE V.7

. set mem 50000;

(50000k)

. set matsize 150;

. set more off;

. use "D:\FSS\codedata.dta", clear;

. svyset strata stratlf;

. svyset psu psuid;

. svyset pweight hhwt1;

. svyreg ltotvb6 lenuvb6 linc lfsb lhsze nurban nrural mdurbloc wintint fallint

> elderly child18

> afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 76.37

 Prob > F = 0.0000

 R-squared = 0.4919

------------------------------------------------------------------------------

 ltotvb6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 lenuvb6 | .5885555 .0652659 9.018 0.000 .4560587 .7210523

 linc | -.0002973 .0152018 -0.020 0.985 -.0311587 .030564

 lfsb | .1292071 .031369 4.119 0.000 .0655247 .1928895

 lhsze | .0697502 .0831695 0.839 0.407 -.099093 .2385933

 nurban | -.1189287 .0669149 -1.777 0.084 -.2547732 .0169158

 nrural | -.0548666 .0643698 -0.852 0.400 -.1855441 .075811

mdurbloc | .024792 .1247115 0.199 0.844 -.2283858 .2779698

 wintint | -.3785371 .0912301 -4.149 0.000 -.5637441 -.1933302

 fallint | -.0575672 .0498248 -1.155 0.256 -.158717 .0435826

 elderly | .0180564 .0625704 0.289 0.775 -.1089684 .1450811

 child18 | .271119 .0995944 2.722 0.010 .0689316 .4733064

afnothis | -.0156967 .0640676 -0.245 0.808 -.1457609 .1143674

whnothis | -.0818947 .0604756 -1.354 0.184 -.2046666 .0408773

 ap1to4 | -.0721032 .0516488 -1.396 0.171 -.1769559 .0327495

 ap5up | -.0884301 .0685509 -1.290 0.206 -.2275959 .0507357

ltotshlt | .0734033 .0163465 4.490 0.000 .0402182 .1065883

lpctnotb | -.0381943 .0110214 -3.465 0.001 -.060569 -.0158196

 elecben | .0167451 .0712609 0.235 0.816 -.1279223 .1614125

 _cons | 2.155821 .194796 11.067 0.000 1.760364 2.551278

------------------------------------------------------------------------------

. svyreg ltotcalc lenucalc linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt elderly child18

> afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39
 Population size = 6305419.8

 F( 18, 18) = 49.34

 Prob > F = 0.0000

 R-squared = 0.4554

------------------------------------------------------------------------------

ltotcalc | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenucalc | .6387202 .0913324 6.993 0.000 .4533055 .8241349

 linc | .0005002 .0143092 0.035 0.972 -.0285491 .0295495

 lfsb | .1016367 .0277372 3.664 0.001 .0453271 .1579463

 lhsze | -.0225823 .1005548 -0.225 0.824 -.2267194 .1815549

 nurban | -.0818499 .077193 -1.060 0.296 -.2385601 .0748603

 nrural | -.066512 .0784722 -0.848 0.402 -.225819 .0927951

mdurbloc | .04639 .1216107 0.381 0.705 -.2004929 .2932728

 wintint | -.4593057 .1369433 -3.354 0.002 -.7373154 -.1812961

 fallint | -.029745 .0502729 -0.592 0.558 -.1318044 .0723143

 elderly | .0056466 .0518576 0.109 0.914 -.0996299 .1109231

 child18 | .2206702 .0943634 2.339 0.025 .0291024 .412238

afnothis | -.1788691 .0764264 -2.340 0.025 -.3340228 -.0237153

whnothis | .0740961 .0657839 1.126 0.268 -.0594523 .2076445

 ap1to4 | -.0084952 .0503609 -0.169 0.867 -.1107333 .0937428

 ap5up | -.0544709 .0819601 -0.665 0.511 -.2208587 .1119169

ltotshlt | .0382658 .0208464 1.836 0.075 -.0040547 .0805862

lpctnotb | -.041705 .0106613 -3.912 0.000 -.0633485 -.0200614

 elecben | .059271 .0815849 0.726 0.472 -.1063551 .2248971

 _cons | 6.30387 .1903596 33.116 0.000 5.91742 6.690321

------------------------------------------------------------------------------

. svyreg ltotcals lenucalo linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt elderly child18

> afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 135.90

 Prob > F = 0.0000

 R-squared = 0.5274

------------------------------------------------------------------------------

ltotcals | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenucalo | .6107473 .0645522 9.461 0.000 .4796994 .7417952

 linc | -.0158892 .0125054 -1.271 0.212 -.0412765 .0094981

 lfsb | .1123953 .022976 4.892 0.000 .0657515 .1590392

 lhsze | .0513456 .0771705 0.665 0.510 -.1053189 .2080102

 nurban | -.1069802 .0738728 -1.448 0.156 -.25695 .0429896

 nrural | .0050854 .0448563 0.113 0.910 -.0859778 .0961486

mdurbloc | .0091889 .0964419 0.095 0.925 -.1865987 .2049764

 wintint | -.303561 .1277344 -2.377 0.023 -.5628755 -.0442464

 fallint | -.026807 .0434828 -0.616 0.542 -.1150818 .0614679

 elderly | .0048002 .0518704 0.093 0.927 -.1005024 .1101028

 child18 | .1742575 .0744352 2.341 0.025 .0231459 .325369

afnothis | .0464296 .0687877 0.675 0.504 -.0932168 .1860759

whnothis | -.01271 .053622 -0.237 0.814 -.1215685 .0961485

 ap1to4 | -.0447955 .0537956 -0.833 0.411 -.1540064 .0644155

 ap5up | -.0613863 .062852 -0.977 0.335 -.1889826 .06621

ltotshlt | .0563819 .014701 3.835 0.001 .0265373 .0862265

lpctnotb | -.034273 .010095 -3.395 0.002 -.054767 -.0137789
 elecben | .0676638 .0708445 0.955 0.346 -.0761582 .2114859

 _cons | 7.544277 .1581786 47.695 0.000 7.223158 7.865397

------------------------------------------------------------------------------

. svyreg ltotfola lenufola linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt elderly child18

> afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 69.34

 Prob > F = 0.0000

 R-squared = 0.4259

------------------------------------------------------------------------------

ltotfola | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenufola | .5737019 .076122 7.537 0.000 .4191661 .7282378

 linc | .0017963 .0174279 0.103 0.918 -.0335842 .0371768

 lfsb | .1361592 .0317792 4.285 0.000 .0716441 .2006743

 lhsze | .0502938 .1076124 0.467 0.643 -.1681709 .2687585

 nurban | -.1240207 .0682725 -1.817 0.078 -.2626212 .0145798

 nrural | -.0145779 .0782146 -0.186 0.853 -.173362 .1442062

mdurbloc | .0212312 .1259851 0.169 0.867 -.234532 .2769945

 wintint | -.545172 .1376553 -3.960 0.000 -.8246272 -.2657168

 fallint | -.0269596 .0542202 -0.497 0.622 -.1370326 .0831133

 elderly | .0628973 .0590007 1.066 0.294 -.0568804 .182675

 child18 | .3969308 .1225411 3.239 0.003 .1481592 .6457024

afnothis | -.1423381 .0844927 -1.685 0.101 -.3138673 .0291912

whnothis | -.1149936 .079781 -1.441 0.158 -.2769577 .0469705

 ap1to4 | -.1008508 .0558228 -1.807 0.079 -.2141772 .0124755

 ap5up | -.1398612 .0764765 -1.829 0.076 -.2951168 .0153945

ltotshlt | .0634218 .0239309 2.650 0.012 .0148395 .112004

lpctnotb | -.0405411 .0120123 -3.375 0.002 -.0649273 -.016155

 elecben | .0232476 .0889428 0.261 0.795 -.1573158 .203811

 _cons | 7.098049 .2166 32.770 0.000 6.658328 7.537771

------------------------------------------------------------------------------

. svyreg ltotiron lenuiron linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt elderly child18

> afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 45.21

 Prob > F = 0.0000

 R-squared = 0.4737

------------------------------------------------------------------------------

ltotiron | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuiron | .4803313 .0566459 8.480 0.000 .365334 .5953286

 linc | -.0013507 .0146136 -0.092 0.927 -.0310178 .0283164

 lfsb | .1190072 .0268657 4.430 0.000 .0644669 .1735475

 lhsze | .2263399 .076233 2.969 0.005 .0715787 .3811012
 nurban | -.1069355 .0691494 -1.546 0.131 -.2473163 .0334453

 nrural | -.0071536 .0641616 -0.111 0.912 -.1374086 .1231014

mdurbloc | .0116422 .0949665 0.123 0.903 -.1811499 .2044344

 wintint | -.3371268 .1034749 -3.258 0.002 -.547192 -.1270616

 fallint | -.0062791 .0455509 -0.138 0.891 -.0987523 .0861941

 elderly | .0414398 .0592119 0.700 0.489 -.0787667 .1616463

 child18 | .0589194 .0941489 0.626 0.535 -.1322131 .250052

afnothis | -.053237 .0704467 -0.756 0.455 -.1962514 .0897774

whnothis | -.0351076 .0589523 -0.596 0.555 -.1547872 .084572

 ap1to4 | -.0338644 .0502723 -0.674 0.505 -.1359225 .0681937

 ap5up | -.093552 .0658596 -1.420 0.164 -.227254 .04015

ltotshlt | .0610337 .017302 3.528 0.001 .0259088 .0961585

lpctnotb | -.0240913 .0116357 -2.070 0.046 -.047713 -.0004696

 elecben | .0597157 .069394 0.861 0.395 -.0811616 .200593

 _cons | 2.229435 .1750962 12.733 0.000 1.873971 2.584899

------------------------------------------------------------------------------

. svyreg ltotvita lenuvita linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt elderly child18

> afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 48.51

 Prob > F = 0.0000

 R-squared = 0.3267

------------------------------------------------------------------------------

ltotvita | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuvita | .8700862 .179352 4.851 0.000 .5059823 1.23419

 linc | .0194249 .0237063 0.819 0.418 -.0287016 .0675513

 lfsb | .1228736 .0400401 3.069 0.004 .0415878 .2041593

 lhsze | -.2893354 .1841125 -1.572 0.125 -.6631036 .0844328

 nurban | -.0005113 .0935129 -0.005 0.996 -.1903526 .1893301

 nrural | -.0392268 .1042428 -0.376 0.709 -.2508508 .1723973

mdurbloc | .0079103 .0898014 0.088 0.930 -.1743963 .1902169

 wintint | -.7430068 .2136827 -3.477 0.001 -1.176806 -.3092078

 fallint | -.1057239 .0569592 -1.856 0.072 -.2213573 .0099094

 elderly | .1870756 .0633903 2.951 0.006 .0583864 .3157648

 child18 | .4610126 .1517996 3.037 0.004 .1528429 .7691822

afnothis | -.1277191 .0940553 -1.358 0.183 -.3186615 .0632232

whnothis | -.0231242 .1024922 -0.226 0.823 -.2311945 .1849461

 ap1to4 | -.0377488 .0612501 -0.616 0.542 -.1620931 .0865955

 ap5up | -.103348 .0889913 -1.161 0.253 -.2840099 .0773139

ltotshlt | .0678602 .0335124 2.025 0.051 -.0001736 .135894

lpctnotb | -.0522463 .0161492 -3.235 0.003 -.0850309 -.0194616

 elecben | -.0173564 .103651 -0.167 0.868 -.2277792 .1930664

 _cons | 8.346023 .2597744 32.128 0.000 7.818653 8.873393

------------------------------------------------------------------------------

. svyreg ltotvitc lenuvitc linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt elderly child18

> afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4
PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 19.55

 Prob > F = 0.0000

 R-squared = 0.3465

------------------------------------------------------------------------------

ltotvitc | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuvitc | .5888066 .1314626 4.479 0.000 .3219233 .85569

 linc | -.0182158 .0229199 -0.795 0.432 -.0647456 .028314

 lfsb | .1215784 .039084 3.111 0.004 .0422336 .2009232

 lhsze | .041372 .1398931 0.296 0.769 -.2426261 .3253702

 nurban | .0232222 .090469 0.257 0.799 -.1604397 .2068842

 nrural | .0332283 .0887212 0.375 0.710 -.1468853 .2133419

mdurbloc | .0000696 .1513918 0.000 1.000 -.3072721 .3074113

 wintint | -.4691128 .2227736 -2.106 0.042 -.9213674 -.0168583

 fallint | -.1795714 .0539237 -3.330 0.002 -.2890423 -.0701005

 elderly | .1062464 .0765943 1.387 0.174 -.0492483 .2617411

 child18 | .4016696 .1329514 3.021 0.005 .131764 .6715752

afnothis | -.1901612 .0912082 -2.085 0.044 -.3753236 -.0049988

whnothis | -.2150043 .0987724 -2.177 0.036 -.4155229 -.0144856

 ap1to4 | -.1168631 .0778747 -1.501 0.142 -.2749571 .0412309

 ap5up | -.2434029 .0854381 -2.849 0.007 -.4168515 -.0699542

ltotshlt | .0901928 .0240332 3.753 0.001 .0414029 .1389828

lpctnotb | -.04286 .0145706 -2.942 0.006 -.0724399 -.0132801

 elecben | -.0093756 .106428 -0.088 0.930 -.225436 .2066848

 _cons | 4.176995 .2530899 16.504 0.000 3.663195 4.690795

------------------------------------------------------------------------------

. svyreg ltotzinc lenuzinc linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt elderly child18

> afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 117.96

 Prob > F = 0.0000

 R-squared = 0.4750

------------------------------------------------------------------------------

ltotzinc | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuzinc | .5450828 .0764561 7.129 0.000 .3898687 .7002968

 linc | -.0023577 .0145193 -0.162 0.872 -.0318334 .0271181

 lfsb | .1125191 .0252976 4.448 0.000 .0611623 .1638759

 lhsze | .0865714 .0909877 0.951 0.348 -.0981436 .2712863

 nurban | -.1072776 .0616249 -1.741 0.091 -.2323828 .0178276

 nrural | -.0030182 .0403956 -0.075 0.941 -.0850257 .0789893

mdurbloc | -.0036691 .0873666 -0.042 0.967 -.1810328 .1736945

 wintint | -.2243769 .1844707 -1.216 0.232 -.5988723 .1501184

 fallint | -.0148927 .049055 -0.304 0.763 -.1144796 .0846942

 elderly | -.075896 .0564361 -1.345 0.187 -.1904674 .0386755

 child18 | .1926 .0817047 2.357 0.024 .0267307 .3584694

afnothis | .1013682 .0755409 1.342 0.188 -.051988 .2547245

whnothis | .0444783 .0608527 0.731 0.470 -.0790592 .1680158

 ap1to4 | -.0581068 .0478485 -1.214 0.233 -.1552444 .0390309

 ap5up | -.0755493 .0657614 -1.149 0.258 -.209052 .0579533

ltotshlt | .0513147 .0155557 3.299 0.002 .019735 .0828945
lpctnotb | -.0295563 .0110922 -2.665 0.012 -.0520746 -.0070381

 elecben | -.0267415 .0908313 -0.294 0.770 -.2111389 .1576558

 _cons | 4.091698 .1536005 26.639 0.000 3.779872 4.403524

------------------------------------------------------------------------------

. svyreg ltotprot lenuprot linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt elderly child18

> afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 130.50

 Prob > F = 0.0000

 R-squared = 0.4834

------------------------------------------------------------------------------

ltotprot | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuprot | .5218497 .0535437 9.746 0.000 .4131502 .6305493

 linc | -.0090619 .013345 -0.679 0.502 -.0361538 .0180299

 lfsb | .1365039 .0240918 5.666 0.000 .0875949 .1854128

 lhsze | .0965578 .0659733 1.464 0.152 -.0373751 .2304908

 nurban | -.1183569 .060677 -1.951 0.059 -.2415378 .0048241

 nrural | -.0067241 .0396328 -0.170 0.866 -.0871829 .0737348

mdurbloc | .0094617 .0833513 0.114 0.910 -.1597505 .1786739

 wintint | -.2766024 .1497619 -1.847 0.073 -.5806352 .0274305

 fallint | -.0296055 .0404649 -0.732 0.469 -.1117536 .0525426

 elderly | -.0646355 .0540083 -1.197 0.239 -.1742782 .0450072

 child18 | .1899525 .0779683 2.436 0.020 .0316684 .3482367

afnothis | .0571131 .063173 0.904 0.372 -.0711348 .185361

whnothis | -.0715402 .0541126 -1.322 0.195 -.1813947 .0383143

 ap1to4 | -.0422665 .0454575 -0.930 0.359 -.1345501 .0500172

 ap5up | -.0605228 .0652738 -0.927 0.360 -.1930357 .0719901

ltotshlt | .0585167 .0151652 3.859 0.000 .0277296 .0893038

lpctnotb | -.032619 .0107416 -3.037 0.004 -.0544256 -.0108124

 elecben | -.0017644 .0786521 -0.022 0.982 -.1614366 .1579079

 _cons | 6.132764 .1583485 38.730 0.000 5.811299 6.454229

------------------------------------------------------------------------------
REGRESSIONS FOR TABLE VI.2

------------------------------------------------------------------------------

ltotcals | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenucalo | .6108217 .0624716 9.778 0.000 .4839976 .7376458

 linc | -.0165813 .0122714 -1.351 0.185 -.0414936 .0083311

 lfsb | .1117146 .0226426 4.934 0.000 .0657477 .1576815

 lhsze | .0493144 .077001 0.640 0.526 -.1070059 .2056346

 nurban | -.106383 .0716234 -1.485 0.146 -.2517862 .0390202

 nrural | .0068346 .0444009 0.154 0.879 -.083304 .0969731

mdurbloc | -.0037865 .0913471 -0.041 0.967 -.189231 .1816581

 wintint | -.2852959 .1292988 -2.206 0.034 -.5477865 -.0228054

 fallint | -.0231703 .0433027 -0.535 0.596 -.1110794 .0647389

 elderly | .0204205 .0528055 0.387 0.701 -.0867803 .1276213

 child18 | .182261 .0744471 2.448 0.020 .0311254 .3333966

afnothis | .0515307 .069349 0.743 0.462 -.0892553 .1923166

whnothis | -.0123557 .053983 -0.229 0.820 -.121947 .0972356

 ap1to4 | -.0268848 .0497931 -0.540 0.593 -.1279702 .0742005

 ap5up | -.0560905 .0606779 -0.924 0.362 -.1792731 .0670921

ltotshlt | .0537943 .0152009 3.539 0.001 .0229349 .0846538

lpctnotb | -.0332751 .0101498 -3.278 0.002 -.0538803 -.0126699

 elecben | .0601754 .0657609 0.915 0.366 -.0733263 .1936772

 fdsec1 | .0513234 .0489836 1.048 0.302 -.0481186 .1507653

 fdsh | .1238529 .047343 2.616 0.013 .0277415 .2199643

 _cons | 7.501269 .1488281 50.402 0.000 7.199131 7.803406

------------------------------------------------------------------------------

------------------------------------------------------------------------------

ltotvita | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuvita | .8708972 .1812583 4.805 0.000 .5029233 1.238871

 linc | .0189255 .024243 0.781 0.440 -.0302904 .0681414

 lfsb | .1227049 .0396393 3.096 0.004 .0422328 .203177

 lhsze | -.291295 .1846752 -1.577 0.124 -.6662057 .0836156

 nurban | .0001127 .0916705 0.001 0.999 -.1859884 .1862137

 nrural | -.0365609 .1035453 -0.353 0.726 -.2467691 .1736472

mdurbloc | -.0042643 .0883388 -0.048 0.962 -.1836016 .175073

 wintint | -.7260427 .2174954 -3.338 0.002 -1.167582 -.2845035

 fallint | -.1030932 .057854 -1.782 0.083 -.2205431 .0143568

 elderly | .2009611 .0646158 3.110 0.004 .069784 .3321383

 child18 | .4680719 .1496186 3.128 0.004 .1643299 .7718139

afnothis | -.1243026 .0951399 -1.307 0.200 -.317447 .0688417

whnothis | -.023799 .1009657 -0.236 0.815 -.2287704 .1811724

 ap1to4 | -.0224908 .0581925 -0.386 0.701 -.1406279 .0956464

 ap5up | -.0986223 .0879307 -1.122 0.270 -.2771312 .0798866

ltotshlt | .0657685 .0343352 1.915 0.064 -.0039357 .1354727

lpctnotb | -.0513215 .0161997 -3.168 0.003 -.0842087 -.0184343

 elecben | -.0248869 .0994354 -0.250 0.804 -.2267515 .1769776

 fdsec1 | .0312137 .0826188 0.378 0.708 -.1365114 .1989389

 fdsh | .1078139 .0768822 1.402 0.170 -.0482654 .2638932

 _cons | 8.311178 .2431357 34.183 0.000 7.817586 8.804769

------------------------------------------------------------------------------

------------------------------------------------------------------------------

ltotvitc | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuvitc | .594967 .1320977 4.504 0.000 .3267945 .8631396

 linc | -.0182329 .0231125 -0.789 0.435 -.0651538 .028688

 lfsb | .1228562 .0386469 3.179 0.003 .0443989 .2013135

 lhsze | .0367299 .1404792 0.261 0.795 -.248458 .3219177

 nurban | .0240565 .0895861 0.269 0.790 -.1578129 .2059259

 nrural | .0389046 .0860987 0.452 0.654 -.1358851 .2136942

mdurbloc | -.0105389 .1481469 -0.071 0.944 -.3112931 .2902153

 wintint | -.4545401 .2172185 -2.093 0.044 -.895517 -.0135631

 fallint | -.1794964 .0540748 -3.319 0.002 -.2892741 -.0697187

 elderly | .1160505 .0753409 1.540 0.132 -.0368997 .2690008

 child18 | .4068288 .1331405 3.056 0.004 .1365392 .6771184
afnothis | -.1912859 .0912338 -2.097 0.043 -.3765004 -.0060713

whnothis | -.2185872 .0980694 -2.229 0.032 -.4176787 -.0194957

 ap1to4 | -.1081257 .0743085 -1.455 0.155 -.2589799 .0427285

 ap5up | -.2398571 .0839209 -2.858 0.007 -.4102256 -.0694885

 elecben | -.0178008 .1010658 -0.176 0.861 -.2229752 .1873737

ltotshlt | .0893882 .0242237 3.690 0.001 .0402115 .1385648

lpctnotb | -.0420517 .0144908 -2.902 0.006 -.0714695 -.0126339

 fdsec1 | -.025117 .0624979 -0.402 0.690 -.1519944 .1017604

 fdsh | .0701597 .0795124 0.882 0.384 -.0912591 .2315786

 _cons | 4.162947 .2305242 18.059 0.000 3.694958 4.630936

------------------------------------------------------------------------------

------------------------------------------------------------------------------

 ltotvb6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 lenuvb6 | .5892307 .0643837 9.152 0.000 .4585249 .7199366

 linc | -.0008599 .0154597 -0.056 0.956 -.0322448 .030525

 lfsb | .128653 .0309645 4.155 0.000 .0657918 .1915142

 lhsze | .067514 .0834372 0.809 0.424 -.1018725 .2369005

 nurban | -.118435 .065494 -1.808 0.079 -.2513949 .0145249

 nrural | -.0534671 .0631761 -0.846 0.403 -.1817213 .0747872

 wintint | -.3639313 .0933169 -3.900 0.000 -.5533746 -.174488

 fallint | -.0546123 .0496397 -1.100 0.279 -.1553863 .0461617

 elderly | .0305642 .0626047 0.488 0.628 -.0965301 .1576585

 child18 | .2777267 .0991216 2.802 0.008 .0764991 .4789544

afnothis | -.0115735 .0650309 -0.178 0.860 -.1435933 .1204462

whnothis | -.0815646 .0600409 -1.358 0.183 -.203454 .0403249

 ap1to4 | -.0577483 .0478423 -1.207 0.236 -.1548733 .0393767

 ap5up | -.0841733 .0670292 -1.256 0.218 -.2202499 .0519033

 elecben | .0107366 .0667416 0.161 0.873 -.124756 .1462293

ltotshlt | .0713285 .0169841 4.200 0.000 .036849 .105808

lpctnotb | -.0373942 .0111452 -3.355 0.002 -.0600203 -.0147682

 fdsec1 | .0413617 .0531194 0.779 0.441 -.0664764 .1491999

 fdsh | .0993239 .0432628 2.296 0.028 .0114958 .187152

 _cons | 2.121409 .1807696 11.735 0.000 1.754427 2.488391

------------------------------------------------------------------------------

------------------------------------------------------------------------------

ltotfola | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenufola | .5751029 .0752044 7.647 0.000 .4224298 .7277759

 linc | .0014781 .0177608 0.083 0.934 -.0345783 .0375345

 lfsb | .1361509 .0319718 4.258 0.000 .0712446 .2010571

 lhsze | .0483091 .1075856 0.449 0.656 -.1701012 .2667195

 nurban | -.1235697 .0672722 -1.837 0.075 -.2601395 .013

 nrural | -.012561 .0769093 -0.163 0.871 -.1686952 .1435731

 wintint | -.5337079 .1394761 -3.827 0.001 -.8168594 -.2505564

 fallint | -.025276 .0543738 -0.465 0.645 -.1356607 .0851087

 elderly | .072166 .0595731 1.211 0.234 -.0487739 .1931059

 child18 | .4019587 .12186 3.299 0.002 .1545697 .6493477

afnothis | -.1402678 .085375 -1.643 0.109 -.3135883 .0330527

whnothis | -.1156321 .0794556 -1.455 0.154 -.2769355 .0456714

 ap1to4 | -.0907327 .0526751 -1.722 0.094 -.1976687 .0162034

 ap5up | -.1366653 .0755516 -1.809 0.079 -.2900432 .0167126

 elecben | .0179837 .0854175 0.211 0.834 -.1554231 .1913905

ltotshlt | .0620628 .0245168 2.531 0.016 .0122911 .1118345

lpctnotb | -.0399099 .0121043 -3.297 0.002 -.0644828 -.0153369

 fdsec1 | .0180935 .0590579 0.306 0.761 -.1018003 .1379874

 fdsh | .0719311 .0531835 1.353 0.185 -.0360371 .1798994

 _cons | 7.075338 .2021455 35.001 0.000 6.66496 7.485715

------------------------------------------------------------------------------

------------------------------------------------------------------------------

ltotcalc | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------

lenucalc | .6415637 .0907408 7.070 0.000 .4573501 .8257772

 linc | -.0004777 .0144332 -0.033 0.974 -.0297786 .0288233

 lfsb | .1002994 .0278389 3.603 0.001 .0437834 .1568153

 lhsze | -.0282449 .0995468 -0.284 0.778 -.2303357 .1738458

 nurban | -.0811228 .0745686 -1.088 0.284 -.2325052 .0702595

 nrural | -.0655768 .077877 -0.842 0.405 -.2236755 .092522

 wintint | -.4385956 .1408461 -3.114 0.004 -.7245285 -.1526628

 fallint | -.0246355 .0513642 -0.480 0.634 -.1289104 .0796394

 elderly | .023994 .0533624 0.450 0.656 -.0843375 .1323255

 child18 | .2304655 .0942284 2.446 0.020 .0391718 .4217593

afnothis | -.1715325 .0763862 -2.246 0.031 -.3266047 -.0164603

whnothis | .0758043 .0665319 1.139 0.262 -.0592628 .2108713

 ap1to4 | .0132723 .0458499 0.289 0.774 -.0798079 .1063524

 ap5up | -.0481416 .078872 -0.610 0.546 -.2082603 .1119771

 elecben | .0513579 .0753337 0.682 0.500 -.1015776 .2042935

ltotshlt | .0349932 .0217101 1.612 0.116 -.0090807 .0790671

lpctnotb | -.0405522 .0106552 -3.806 0.001 -.0621834 -.0189209

 fdsec1 | .0766662 .0601886 1.274 0.211 -.0455232 .1988556

 fdsh | .1485262 .0610293 2.434 0.020 .0246301 .2724223

 _cons | 6.2497 .1759028 35.529 0.000 5.892598 6.606801

------------------------------------------------------------------------------

------------------------------------------------------------------------------

ltotiron | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuiron | .4796517 .0549483 8.729 0.000 .3681007 .5912027

 linc | -.0019039 .0148966 -0.128 0.899 -.0321456 .0283378

 lfsb | .118151 .0270342 4.370 0.000 .0632686 .1730334

 lhsze | .2251114 .0766654 2.936 0.006 .0694724 .3807505

 nurban | -.1066798 .0678483 -1.572 0.125 -.2444191 .0310595

 nrural | -.0072187 .0641521 -0.113 0.911 -.1374543 .123017

 wintint | -.3265109 .1038391 -3.144 0.003 -.5373156 -.1157062

 fallint | -.0033792 .0452507 -0.075 0.941 -.095243 .0884846

 elderly | .0511382 .0600359 0.852 0.400 -.0707411 .1730175

 child18 | .0642311 .0943406 0.681 0.500 -.1272905 .2557526

afnothis | -.0488641 .0713344 -0.685 0.498 -.1936807 .0959524

whnothis | -.0338606 .0588389 -0.575 0.569 -.1533098 .0855887

 ap1to4 | -.021988 .047663 -0.461 0.647 -.118749 .074773

 ap5up | -.090264 .0647893 -1.393 0.172 -.2217933 .0412653

 elecben | .0559275 .0663302 0.843 0.405 -.0787299 .190585

ltotshlt | .0592022 .0177201 3.341 0.002 .0232285 .0951758

lpctnotb | -.0235119 .0116913 -2.011 0.052 -.0472465 .0002227

 fdsec1 | .0471018 .0483635 0.974 0.337 -.0510812 .1452849

 fdsh | .0797718 .0410296 1.944 0.060 -.0035228 .1630663

 _cons | 2.199172 .1620259 13.573 0.000 1.870242 2.528102

------------------------------------------------------------------------------

------------------------------------------------------------------------------

ltotzinc | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuzinc | .544887 .0746891 7.295 0.000 .3932601 .6965139

 linc | -.0030347 .0144942 -0.209 0.835 -.0324596 .0263901

 lfsb | .1113585 .0256319 4.345 0.000 .059323 .163394

 lhsze | .0844935 .0908848 0.930 0.359 -.1000125 .2689995

 nurban | -.10698 .0597261 -1.791 0.082 -.2282304 .0142703

 nrural | -.0035161 .0402342 -0.087 0.931 -.085196 .0781637

 wintint | -.2127568 .1799954 -1.182 0.245 -.5781669 .1526532

 fallint | -.0113537 .0485221 -0.234 0.816 -.1098588 .0871514

 elderly | -.0649762 .0581358 -1.118 0.271 -.1829981 .0530458

 child18 | .1985928 .0816198 2.433 0.020 .0328958 .3642899

afnothis | .1067101 .0765647 1.394 0.172 -.0487244 .2621446

whnothis | .0463159 .0616491 0.751 0.458 -.0788384 .1714703

 ap1to4 | -.0445272 .0453476 -0.982 0.333 -.1365877 .0475333
 ap5up | -.0718476 .0637065 -1.128 0.267 -.2011786 .0574835

ltotshlt | .0491785 .0159767 3.078 0.004 .0167442 .0816129

lpctnotb | -.0289146 .0111326 -2.597 0.014 -.0515151 -.0063142

 elecben | -.0306527 .0863104 -0.355 0.725 -.2058721 .1445668

 fdsec1 | .058527 .0492731 1.188 0.243 -.0415027 .1585566

 fdsh | .0903749 .0449229 2.012 0.052 -.0008234 .1815732

 _cons | 4.056428 .1457813 27.825 0.000 3.760476 4.35238

------------------------------------------------------------------------------

------------------------------------------------------------------------------

ltotprot | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuprot | .5209535 .0518462 10.048 0.000 .4157 .6262069

 linc | -.0096651 .0134238 -0.720 0.476 -.0369168 .0175866

 lfsb | .1355216 .0241516 5.611 0.000 .0864914 .1845519

 lhsze | .0954314 .0654804 1.457 0.154 -.0375008 .2283636

 nurban | -.1180595 .0590294 -2.000 0.053 -.2378955 .0017765

 nrural | -.0068522 .0394067 -0.174 0.863 -.0868519 .0731476

mdurbloc | .0014827 .0803248 0.018 0.985 -.1615853 .1645506

 wintint | -.2651458 .1443621 -1.837 0.075 -.5582163 .0279248

 fallint | -.026456 .0398492 -0.664 0.511 -.1073542 .0544422

 elderly | -.0541199 .055308 -0.979 0.335 -.1664012 .0581614

 child18 | .1952966 .0772949 2.527 0.016 .0383796 .3522137

afnothis | .0618594 .0645878 0.958 0.345 -.0692608 .1929797

whnothis | -.070135 .054502 -1.287 0.207 -.18078 .0405099

 ap1to4 | -.029438 .0430442 -0.684 0.499 -.1168224 .0579465

 ap5up | -.0569831 .0634434 -0.898 0.375 -.18578 .0718139

ltotshlt | .0565288 .0154697 3.654 0.001 .0251237 .0879339

lpctnotb | -.031991 .0105886 -3.021 0.005 -.0534869 -.010495

 elecben | -.0057681 .0747066 -0.077 0.939 -.1574306 .1458943

 fdsec1 | .0514903 .0488013 1.055 0.299 -.0475816 .1505621

 fdsh | .0860399 .0406969 2.114 0.042 .0034208 .1686589

 _cons | 6.099833 .1511103 40.367 0.000 5.793063 6.406603

------------------------------------------------------------------------------
REGRESSIONS FOR TABLE VI.3

. set mem 50000;

(50000k)

. set matsize 150;

. use "D:\FSS\codedata.dta", clear;

. svyset strata stratlf;

. svyset psu psuid;

. svyset pweight hhwt1;

. svyreg ltotcals lenucalo lhsze nurban nrural mdurbloc wintint fallint ncrinc0

> ncrinc1 ncrinch ncrfsb0 ncrfsb1 ncrfsbh

> fdsec1 fdsh elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb

> elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 24, 12) = 69.18

 Prob > F = 0.0000

 R-squared = 0.5336

------------------------------------------------------------------------------

ltotcals | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenucalo | .6080435 .0629399 9.661 0.000 .4802688 .7358182

 lhsze | .0613874 .078162 0.785 0.438 -.0972899 .2200648

 nurban | -.108867 .0711599 -1.530 0.135 -.2533293 .0355953

 nrural | .0041938 .0456066 0.092 0.927 -.0883926 .0967801

mdurbloc | -.0089434 .08961 -0.100 0.921 -.1908615 .1729746

 wintint | -.2807217 .1147877 -2.446 0.020 -.5137532 -.0476902

 fallint | -.0234391 .0440707 -0.532 0.598 -.1129074 .0660293

 ncrinc0 | -.0316967 .0148998 -2.127 0.041 -.061945 -.0014485

 ncrinc1 | .0086242 .0270051 0.319 0.751 -.046199 .0634474

 ncrinch | -.0210775 .0222761 -0.946 0.351 -.0663004 .0241454

 ncrfsb0 | .0862041 .0302317 2.851 0.007 .0248305 .1475776

 ncrfsb1 | .1563472 .0322852 4.843 0.000 .0908047 .2218897

 ncrfsbh | .1107729 .0357176 3.101 0.004 .0382624 .1832835

 fdsec1 | -.370262 .1775383 -2.086 0.044 -.7306839 -.0098402

 fdsh | -.0023443 .199031 -0.012 0.991 -.4063987 .40171

 elderly | .0194054 .0530902 0.366 0.717 -.0883734 .1271842

 child18 | .1728856 .0748271 2.310 0.027 .0209786 .3247926

afnothis | .0543052 .0692179 0.785 0.438 -.0862145 .1948249

whnothis | -.0134189 .0549201 -0.244 0.808 -.1249126 .0980749

 ap1to4 | -.0203572 .048845 -0.417 0.679 -.1195178 .0788034

 ap5up | -.0558464 .0599125 -0.932 0.358 -.1774752 .0657825

ltotshlt | .0537127 .0149109 3.602 0.001 .0234419 .0839834

lpctnotb | -.0331494 .0100565 -3.296 0.002 -.0535653 -.0127336

 elecben | .0642105 .0655277 0.980 0.334 -.0688178 .1972387

 _cons | 7.648141 .1562262 48.956 0.000 7.330985 7.965298

------------------------------------------------------------------------------

. svyreg ltotiron lenuiron lhsze nurban nrural mdurbloc wintint fallint ncrinc0

> ncrinc1 ncrinch ncrfsb0 ncrfsb1 ncrfsbh

> fdsec1 fdsh elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb

> elecben;
Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 24, 12) = 30.35

 Prob > F = 0.0000

 R-squared = 0.4774

------------------------------------------------------------------------------

ltotiron | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuiron | .4758699 .0536783 8.865 0.000 .3668971 .5848427

 lhsze | .2358008 .0749985 3.144 0.003 .0835457 .3880559

 nurban | -.1046881 .0677826 -1.544 0.131 -.242294 .0329178

 nrural | -.0081769 .0646145 -0.127 0.900 -.1393513 .1229975

mdurbloc | .0096302 .0940824 0.102 0.919 -.1813672 .2006276

 wintint | -.3250886 .0944702 -3.441 0.002 -.5168732 -.1333039

 fallint | -.0046707 .0453093 -0.103 0.918 -.0966534 .087312

 ncrinc0 | -.0243724 .0113262 -2.152 0.038 -.0473658 -.0013791

 ncrinc1 | .014225 .033573 0.424 0.674 -.0539318 .0823819

 ncrinch | .0215971 .0302472 0.714 0.480 -.0398079 .0830021

 ncrfsb0 | .0890329 .0356316 2.499 0.017 .0166968 .161369

 ncrfsb1 | .1510283 .0355314 4.251 0.000 .0788958 .2231609

 ncrfsbh | .1351949 .0331549 4.078 0.000 .0678868 .202503

 fdsec1 | -.3371225 .1913402 -1.762 0.087 -.7255637 .0513188

 fdsh | -.2815931 .223129 -1.262 0.215 -.7345692 .1713829

 elderly | .0468096 .0599879 0.780 0.440 -.0749724 .1685915

 child18 | .0549286 .0930147 0.591 0.559 -.1339013 .2437585

afnothis | -.0491809 .0704601 -0.698 0.490 -.1922226 .0938607

whnothis | -.0375251 .0589269 -0.637 0.528 -.157153 .0821029

 ap1to4 | -.0169924 .0466956 -0.364 0.718 -.1117895 .0778048

 ap5up | -.0918183 .0639499 -1.436 0.160 -.2216436 .038007

ltotshlt | .0584674 .0174377 3.353 0.002 .0230671 .0938677

lpctnotb | -.0231943 .0115785 -2.003 0.053 -.0466999 .0003114

 elecben | .0580199 .0663159 0.875 0.388 -.0766086 .1926483

 _cons | 2.399871 .1597322 15.024 0.000 2.075598 2.724145

------------------------------------------------------------------------------

. svyreg ltotcalc lenucalc lhsze nurban nrural mdurbloc wintint fallint ncrinc0

> ncrinc1 ncrinch ncrfsb0 ncrfsb1 ncrfsbh

> fdsec1 fdsh elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb

> elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 24, 12) = 33.76

 Prob > F = 0.0000

 R-squared = 0.4629

------------------------------------------------------------------------------

ltotcalc | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenucalc | .6357407 .0888478 7.155 0.000 .4553701 .8161114

 lhsze | -.0134259 .1009888 -0.133 0.895 -.218444 .1915921

 nurban | -.0835556 .0738975 -1.131 0.266 -.2335755 .0664643

 nrural | -.0660087 .0783987 -0.842 0.406 -.2251666 .0931492
mdurbloc | .0338713 .1177675 0.288 0.775 -.2052094 .272952

 wintint | -.4341103 .1259473 -3.447 0.001 -.6897969 -.1784236

 fallint | -.0219152 .0524694 -0.418 0.679 -.1284338 .0846034

 ncrinc0 | -.028479 .0188788 -1.509 0.140 -.066805 .009847

 ncrinc1 | .0332915 .0269074 1.237 0.224 -.0213335 .0879164

 ncrinch | .0112367 .0376713 0.298 0.767 -.0652401 .0877136

 ncrfsb0 | .0845951 .0335089 2.525 0.016 .0165685 .1526218

 ncrfsb1 | .15289 .0406156 3.764 0.001 .070436 .2353441

 ncrfsbh | .0719909 .0544555 1.322 0.195 -.0385597 .1825415

 fdsec1 | -.4386858 .2181326 -2.011 0.052 -.8815186 .004147

 fdsh | .010589 .3320159 0.032 0.975 -.6634391 .6846172

 elderly | .0302619 .0534528 0.566 0.575 -.0782531 .1387769

 child18 | .2238131 .0954601 2.345 0.025 .0300189 .4176073

afnothis | -.1696412 .0775254 -2.188 0.035 -.3270261 -.0122563

whnothis | .0742759 .0679604 1.093 0.282 -.063691 .2122429

 ap1to4 | .0216473 .044058 0.491 0.626 -.0677951 .1110897

 ap5up | -.0492958 .0780615 -0.631 0.532 -.2077691 .1091775

ltotshlt | .0340892 .0207045 1.646 0.109 -.0079431 .0761216

lpctnotb | -.0408677 .0103518 -3.948 0.000 -.061883 -.0198524

 elecben | .0514927 .0756017 0.681 0.500 -.1019868 .2049722

 _cons | 6.422593 .1816685 35.353 0.000 6.053787 6.7914

------------------------------------------------------------------------------

. svyreg ltotvitc lenuvitc lhsze nurban nrural mdurbloc wintint fallint ncrinc0

> ncrinc1 ncrinch ncrfsb0 ncrfsb1 ncrfsbh

> fdsec1 fdsh elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb

> elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 24, 12) = 10.02

 Prob > F = 0.0001

 R-squared = 0.3497

------------------------------------------------------------------------------

ltotvitc | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuvitc | .5800775 .1297122 4.472 0.000 .3167477 .8434072

 lhsze | .0454484 .1410037 0.322 0.749 -.2408044 .3317011

 nurban | .0255132 .0902143 0.283 0.779 -.1576316 .208658

 nrural | .0431176 .0865876 0.498 0.622 -.1326647 .2188998

mdurbloc | .0065097 .1557057 0.042 0.967 -.3095897 .3226091

 wintint | -.4631703 .216476 -2.140 0.039 -.9026398 -.0237007

 fallint | -.1770158 .0554357 -3.193 0.003 -.2895563 -.0644753

 ncrinc0 | -.0362384 .0193491 -1.873 0.069 -.0755191 .0030423

 ncrinc1 | -.026295 .0442498 -0.594 0.556 -.1161269 .0635369

 ncrinch | .0327321 .0453094 0.722 0.475 -.0592509 .1247151

 ncrfsb0 | .1251951 .0360234 3.475 0.001 .0520638 .1983265

 ncrfsb1 | .1609202 .0583127 2.760 0.009 .0425391 .2793013

 ncrfsbh | .0801547 .0815776 0.983 0.333 -.0854567 .2457661

 fdsec1 | -.1901534 .3044324 -0.625 0.536 -.808184 .4278772

 fdsh | -.1000645 .3823872 -0.262 0.795 -.8763518 .6762228

 elderly | .1280345 .06971 1.837 0.075 -.0134844 .2695533

 child18 | .4114947 .1369135 3.006 0.005 .1335455 .6894439

afnothis | -.1966634 .0892553 -2.203 0.034 -.3778612 -.0154655

whnothis | -.2251096 .0964085 -2.335 0.025 -.4208293 -.02939

 ap1to4 | -.1024115 .0721427 -1.420 0.165 -.2488689 .044046

 ap5up | -.242123 .0823065 -2.942 0.006 -.4092142 -.0750319

ltotshlt | .0884396 .0236968 3.732 0.001 .0403325 .1365468
lpctnotb | -.0433485 .0146446 -2.960 0.005 -.0730785 -.0136184

 elecben | -.0230168 .0999186 -0.230 0.819 -.2258624 .1798289

 _cons | 4.235816 .1971223 21.488 0.000 3.835637 4.635996

------------------------------------------------------------------------------

. svyreg ltotvita lenuvita lhsze nurban nrural mdurbloc wintint fallint ncrinc0

> ncrinc1 ncrinch ncrfsb0 ncrfsb1 ncrfsbh

> fdsec1 fdsh elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb

> elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 24, 12) = 44.18

 Prob > F = 0.0000

 R-squared = 0.3386

------------------------------------------------------------------------------

ltotvita | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuvita | .8489246 .174681 4.860 0.000 .4943033 1.203546

 lhsze | -.2517704 .1860421 -1.353 0.185 -.6294558 .1259151

 nurban | .0013668 .0956637 0.014 0.989 -.1928409 .1955744

 nrural | -.0348463 .0988117 -0.353 0.726 -.2354447 .165752

mdurbloc | .015913 .0872336 0.182 0.856 -.1611807 .1930066

 wintint | -.7188984 .2011892 -3.573 0.001 -1.127334 -.3104627

 fallint | -.0987273 .0584486 -1.689 0.100 -.2173843 .0199297

 ncrinc0 | -.0548132 .0169509 -3.234 0.003 -.0892253 -.0204011

 ncrinc1 | .0823097 .0537676 1.531 0.135 -.0268443 .1914637

 ncrinch | .0856805 .0810375 1.057 0.298 -.0788344 .2501953

 ncrfsb0 | .084179 .0501732 1.678 0.102 -.0176781 .186036

 ncrfsb1 | .2200356 .0525293 4.189 0.000 .1133955 .3266758

 ncrfsbh | .0839574 .0700791 1.198 0.239 -.0583107 .2262254

 fdsec1 | -1.056985 .3819483 -2.767 0.009 -1.832381 -.281589

 fdsh | -.5324526 .5211167 -1.022 0.314 -1.590376 .5254705

 elderly | .2106877 .0632698 3.330 0.002 .0822433 .3391321

 child18 | .4457915 .1542485 2.890 0.007 .1326505 .7589326

afnothis | -.1249408 .0908465 -1.375 0.178 -.309369 .0594874

whnothis | -.0309591 .0935241 -0.331 0.743 -.2208231 .158905

 ap1to4 | -.0062441 .0605181 -0.103 0.918 -.1291025 .1166142

 ap5up | -.1038612 .0839543 -1.237 0.224 -.2742975 .0665752

ltotshlt | .0626677 .0321067 1.952 0.059 -.0025123 .1278477

lpctnotb | -.0516343 .0164796 -3.133 0.003 -.0850897 -.0181789

 elecben | -.0272462 .096391 -0.283 0.779 -.2229304 .168438

 _cons | 8.766471 .1911746 45.856 0.000 8.378366 9.154576

------------------------------------------------------------------------------

. svyreg ltotzinc lenuzinc lhsze nurban nrural mdurbloc wintint fallint ncrinc0

> ncrinc1 ncrinch ncrfsb0 ncrfsb1 ncrfsbh

> fdsec1 fdsh elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb

> elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 24, 12) = 97.11

 Prob > F = 0.0000
 R-squared = 0.4792

------------------------------------------------------------------------------

ltotzinc | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuzinc | .5477366 .0750246 7.301 0.000 .3954286 .7000446

 lhsze | .0923184 .0921657 1.002 0.323 -.094788 .2794248

 nurban | -.109257 .0593672 -1.840 0.074 -.2297788 .0112648

 nrural | -.0076443 .0415172 -0.184 0.855 -.0919287 .0766401

mdurbloc | -.0224822 .0831981 -0.270 0.789 -.1913833 .1464188

 wintint | -.2054139 .1666298 -1.233 0.226 -.5436904 .1328627

 fallint | -.0130395 .0494203 -0.264 0.793 -.1133681 .0872891

 ncrinc0 | -.0104245 .015549 -0.670 0.507 -.0419907 .0211417

 ncrinc1 | .0222538 .0265543 0.838 0.408 -.0316544 .0761619

 ncrinch | -.0245544 .0256722 -0.956 0.345 -.0766717 .0275629

 ncrfsb0 | .0846021 .031629 2.675 0.011 .0203918 .1488125

 ncrfsb1 | .1365865 .0370488 3.687 0.001 .0613734 .2117996

 ncrfsbh | .1323929 .0389469 3.399 0.002 .0533265 .2114593

 fdsec1 | -.2667328 .1787726 -1.492 0.145 -.6296605 .0961949

 fdsh | .0016048 .2195926 0.007 0.994 -.4441919 .4474014

 elderly | -.0713687 .0578578 -1.234 0.226 -.1888264 .0460889

 child18 | .1876942 .0808654 2.321 0.026 .0235288 .3518596

afnothis | .1111077 .077661 1.431 0.161 -.0465524 .2687679

whnothis | .0475318 .0626689 0.758 0.453 -.0796928 .1747563

 ap1to4 | -.0409364 .0448009 -0.914 0.367 -.131887 .0500142

 ap5up | -.0709154 .0634065 -1.118 0.271 -.1996373 .0578066

ltotshlt | .0494356 .0157173 3.145 0.003 .0175279 .0813434

lpctnotb | -.0282282 .0109039 -2.589 0.014 -.0503642 -.0060922

 elecben | -.0246168 .0860098 -0.286 0.776 -.199226 .1499924

 _cons | 4.175272 .1507863 27.690 0.000 3.86916 4.481385

------------------------------------------------------------------------------

. svyreg ltotfola lenufola lhsze nurban nrural mdurbloc wintint fallint ncrinc0

> ncrinc1 ncrinch ncrfsb0 ncrfsb1 ncrfsbh

> fdsec1 fdsh elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb

> elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 24, 12) = 65.49

 Prob > F = 0.0000

 R-squared = 0.4318

------------------------------------------------------------------------------

ltotfola | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenufola | .5681467 .0737013 7.709 0.000 .4185251 .7177683

 lhsze | .070272 .1081641 0.650 0.520 -.1493129 .2898568

 nurban | -.1243425 .067322 -1.847 0.073 -.2610134 .0123285

 nrural | -.015115 .076914 -0.197 0.845 -.1712587 .1410286

mdurbloc | .0139678 .1233373 0.113 0.910 -.2364202 .2643558

 wintint | -.5261878 .1262787 -4.167 0.000 -.7825471 -.2698285

 fallint | -.0253234 .0549487 -0.461 0.648 -.1368752 .0862284

 ncrinc0 | -.0352664 .012669 -2.784 0.009 -.0609858 -.009547

 ncrinc1 | .0445343 .0368535 1.208 0.235 -.0302822 .1193508

 ncrinch | .0170802 .0348844 0.490 0.627 -.053739 .0878994

 ncrfsb0 | .0946397 .0371654 2.546 0.015 .0191899 .1700896

 ncrfsb1 | .2011337 .0486222 4.137 0.000 .1024253 .2998421

 ncrfsbh | .1398898 .0546421 2.560 0.015 .0289605 .2508191
 fdsec1 | -.706778 .248959 -2.839 0.007 -1.212192 -.2013643

 fdsh | -.312903 .2772607 -1.129 0.267 -.8757721 .2499662

 elderly | .070644 .0594978 1.187 0.243 -.0501429 .191431

 child18 | .3837456 .1237041 3.102 0.004 .132613 .6348782

afnothis | -.1375966 .0825815 -1.666 0.105 -.3052458 .0300527

whnothis | -.118581 .0778179 -1.524 0.137 -.2765598 .0393977

 ap1to4 | -.0806933 .0508256 -1.588 0.121 -.1838747 .022488

 ap5up | -.1381817 .0733253 -1.885 0.068 -.2870399 .0106765

ltotshlt | .0610188 .0235034 2.596 0.014 .0133043 .1087333

lpctnotb | -.039546 .0119709 -3.304 0.002 -.0638482 -.0152438

 elecben | .0221963 .0842077 0.264 0.794 -.1487545 .1931471

 _cons | 7.374813 .1931054 38.191 0.000 6.982788 7.766837

------------------------------------------------------------------------------

. svyreg ltotvb6 lenuvb6 lhsze nurban nrural mdurbloc wintint fallint ncrinc0 n

> crinc1 ncrinch ncrfsb0 ncrfsb1 ncrfsbh

> fdsec1 fdsh elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb

> elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 24, 12) = 55.14

 Prob > F = 0.0000

 R-squared = 0.4963

------------------------------------------------------------------------------

 ltotvb6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 lenuvb6 | .5888935 .0638954 9.217 0.000 .4591789 .7186081

 lhsze | .0803511 .0850392 0.945 0.351 -.0922876 .2529898

 nurban | -.1192061 .0661666 -1.802 0.080 -.2535313 .0151192

 nrural | -.0561392 .06307 -0.890 0.379 -.1841781 .0718998

mdurbloc | .010408 .1189485 0.088 0.931 -.2310703 .2518863

 wintint | -.3546281 .0836777 -4.238 0.000 -.5245028 -.1847534

 fallint | -.0547082 .0505099 -1.083 0.286 -.1572487 .0478324

 ncrinc0 | -.0221071 .0119528 -1.850 0.073 -.0463726 .0021583

 ncrinc1 | .0348848 .0322357 1.082 0.287 -.0305572 .1003268

 ncrinch | -.0073855 .0299527 -0.247 0.807 -.0681927 .0534217

 ncrfsb0 | .1057226 .0378062 2.796 0.008 .028972 .1824733

 ncrfsb1 | .1517482 .0394404 3.848 0.000 .0716799 .2318166

 ncrfsbh | .1424913 .0492064 2.896 0.006 .042597 .2423856

 fdsec1 | -.3762919 .2156757 -1.745 0.090 -.8141369 .0615531

 fdsh | -.0860034 .240547 -0.358 0.723 -.5743398 .402333

 elderly | .0263833 .0620132 0.425 0.673 -.0995103 .1522768

 child18 | .2643857 .0991715 2.666 0.012 .0630569 .4657145

afnothis | -.0078939 .0650117 -0.121 0.904 -.1398746 .1240868

whnothis | -.0805631 .0599843 -1.343 0.188 -.2023376 .0412114

 ap1to4 | -.0536453 .0470458 -1.140 0.262 -.1491534 .0418627

 ap5up | -.0848588 .0664714 -1.277 0.210 -.219803 .0500854

ltotshlt | .0706614 .0162669 4.344 0.000 .0376379 .1036849

lpctnotb | -.036642 .0110373 -3.320 0.002 -.0590488 -.0142352

 elecben | .01414 .0685038 0.206 0.838 -.1249301 .1532101

 _cons | 2.292226 .1547579 14.812 0.000 1.978051 2.606401

------------------------------------------------------------------------------

. svyreg ltotprot lenuprot lhsze nurban nrural mdurbloc wintint fallint ncrinc0

> ncrinc1 ncrinch ncrfsb0 ncrfsb1 ncrfsbh

> fdsec1 fdsh elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb

> elecben;
Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 24, 12) = 83.24

 Prob > F = 0.0000

 R-squared = 0.4881

------------------------------------------------------------------------------

ltotprot | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuprot | .5200543 .0511185 10.174 0.000 .4162783 .6238304

 lhsze | .1081915 .0658224 1.644 0.109 -.0254352 .2418181

 nurban | -.117499 .0594612 -1.976 0.056 -.2382115 .0032136

 nrural | -.0099662 .0414674 -0.240 0.811 -.0941494 .0742171

mdurbloc | -.0021238 .0809911 -0.026 0.979 -.1665444 .1622969

 wintint | -.2568354 .1313179 -1.956 0.059 -.5234249 .009754

 fallint | -.0281273 .040038 -0.703 0.487 -.1094088 .0531541

 ncrinc0 | -.0279113 .0119689 -2.332 0.026 -.0522093 -.0036132

 ncrinc1 | .0196767 .0289742 0.679 0.502 -.0391441 .0784974

 ncrinch | -.0139975 .0263145 -0.532 0.598 -.0674189 .0394239

 ncrfsb0 | .1074183 .0311356 3.450 0.001 .0442096 .170627

 ncrfsb1 | .1519938 .0319877 4.752 0.000 .0870554 .2169323

 ncrfsbh | .1658658 .0368814 4.497 0.000 .0909926 .240739

 fdsec1 | -.3168319 .1820423 -1.740 0.091 -.6863973 .0527335

 fdsh | -.1673244 .2061235 -0.812 0.422 -.5857774 .2511286

 elderly | -.0621099 .0546625 -1.136 0.264 -.1730807 .0488609

 child18 | .1806238 .0767752 2.353 0.024 .0247619 .3364857

afnothis | .0648744 .065385 0.992 0.328 -.0678642 .197613

whnothis | -.0698317 .0550936 -1.268 0.213 -.1816776 .0420142

 ap1to4 | -.026483 .0423508 -0.625 0.536 -.1124596 .0594936

 ap5up | -.0577099 .0632102 -0.913 0.367 -.1860334 .0706135

ltotshlt | .0559894 .0151712 3.691 0.001 .0251902 .0867886

lpctnotb | -.0310125 .0103641 -2.992 0.005 -.0520527 -.0099724

 elecben | -.0013243 .0753556 -0.018 0.986 -.1543042 .1516556

 _cons | 6.276554 .1420851 44.175 0.000 5.988106 6.565002

------------------------------------------------------------------------------

. svyreg ltotcals lenucalo linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt

> elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 135.90

 Prob > F = 0.0000

 R-squared = 0.5274

------------------------------------------------------------------------------

ltotcals | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenucalo | .6107473 .0645522 9.461 0.000 .4796994 .7417952

 linc | -.0158892 .0125054 -1.271 0.212 -.0412765 .0094981

 lfsb | .1123953 .022976 4.892 0.000 .0657515 .1590392

 lhsze | .0513456 .0771705 0.665 0.510 -.1053189 .2080102

 nurban | -.1069802 .0738728 -1.448 0.156 -.25695 .0429896
 nrural | .0050854 .0448563 0.113 0.910 -.0859778 .0961486

mdurbloc | .0091889 .0964419 0.095 0.925 -.1865987 .2049764

 wintint | -.303561 .1277344 -2.377 0.023 -.5628755 -.0442464

 fallint | -.026807 .0434828 -0.616 0.542 -.1150818 .0614679

 elderly | .0048002 .0518704 0.093 0.927 -.1005024 .1101028

 child18 | .1742575 .0744352 2.341 0.025 .0231459 .325369

afnothis | .0464296 .0687877 0.675 0.504 -.0932168 .1860759

whnothis | -.01271 .053622 -0.237 0.814 -.1215685 .0961485

 ap1to4 | -.0447955 .0537956 -0.833 0.411 -.1540064 .0644155

 ap5up | -.0613863 .062852 -0.977 0.335 -.1889826 .06621

ltotshlt | .0563819 .014701 3.835 0.001 .0265373 .0862265

lpctnotb | -.034273 .010095 -3.395 0.002 -.054767 -.0137789

 elecben | .0676638 .0708445 0.955 0.346 -.0761582 .2114859

 _cons | 7.544277 .1581786 47.695 0.000 7.223158 7.865397

------------------------------------------------------------------------------

. svyreg ltotiron lenuiron linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt

> elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39

 Population size = 6305419.8

 F( 18, 18) = 45.21

 Prob > F = 0.0000

 R-squared = 0.4737

------------------------------------------------------------------------------

ltotiron | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenuiron | .4803313 .0566459 8.480 0.000 .365334 .5953286

 linc | -.0013507 .0146136 -0.092 0.927 -.0310178 .0283164

 lfsb | .1190072 .0268657 4.430 0.000 .0644669 .1735475

 lhsze | .2263399 .076233 2.969 0.005 .0715787 .3811012

 nurban | -.1069355 .0691494 -1.546 0.131 -.2473163 .0334453

 nrural | -.0071536 .0641616 -0.111 0.912 -.1374086 .1231014

mdurbloc | .0116422 .0949665 0.123 0.903 -.1811499 .2044344

 wintint | -.3371268 .1034749 -3.258 0.002 -.547192 -.1270616

 fallint | -.0062791 .0455509 -0.138 0.891 -.0987523 .0861941

 elderly | .0414398 .0592119 0.700 0.489 -.0787667 .1616463

 child18 | .0589194 .0941489 0.626 0.535 -.1322131 .250052

afnothis | -.053237 .0704467 -0.756 0.455 -.1962514 .0897774

whnothis | -.0351076 .0589523 -0.596 0.555 -.1547872 .084572

 ap1to4 | -.0338644 .0502723 -0.674 0.505 -.1359225 .0681937

 ap5up | -.093552 .0658596 -1.420 0.164 -.227254 .04015

ltotshlt | .0610337 .017302 3.528 0.001 .0259088 .0961585

lpctnotb | -.0240913 .0116357 -2.070 0.046 -.047713 -.0004696

 elecben | .0597157 .069394 0.861 0.395 -.0811616 .200593

 _cons | 2.229435 .1750962 12.733 0.000 1.873971 2.584899

------------------------------------------------------------------------------

. svyreg ltotcalc lenucalc linc lfsb lhsze nurban nrural mdurbloc wintint falli

> nt

> elderly child18 afnothis whnothis ap1to4 ap5up ltotshlt lpctnotb elecben;

Survey linear regression

pweight: hhwt1 Number of obs = 957

Strata: stratlf Number of strata = 4

PSU: psuid Number of PSUs = 39
 Population size = 6305419.8

 F( 18, 18) = 49.34

 Prob > F = 0.0000

 R-squared = 0.4554

------------------------------------------------------------------------------

ltotcalc | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lenucalc | .6387202 .0913324 6.993 0.000 .4533055 .8241349

 linc | .0005002 .0143092 0.035 0.972 -.0285491 .0295495

 lfsb | .1016367 .0277372 3.664 0.001 .0453271 .1579463

 lhsze | -.0225823 .1005548 -0.225 0.824 -.2267194 .1815549

 nurban | -.0818499 .077193 -1.060 0.296 -.2385601 .0748603

 nrural | -.066512 .0784722 -0.848 0.402 -.225819 .0927951

mdurbloc | .04639 .1216107 0.381 0.705 -.2004929 .2932728

 wintint | -.4593057 .1369433 -3.354 0.002 -.7373154 -.1812961

 fallint | -.029745 .0502729 -0.592 0.558 -.1318044 .0723143

 elderly | .0056466 .0518576 0.109 0.914 -.0996299 .1109231

 child18 | .2206702 .0943634 2.339 0.025 .0291024 .412238

afnothis | -.1788691 .0764264 -2.340 0.025 -.3340228 -.0237153

whnothis | .0740961 .0657839 1.126 0.268 -.0594523 .2076445

 ap1to4 | -.0084952 .0503609 -0.169 0.867 -.1107333 .0937428

 ap5up | -.0544709 .0819601 -0.665 0.511 -.2208587 .1119169

ltotshlt | .0382658 .0208464 1.836 0.075 -.0040547 .0805862

lpctnotb | -.041705 .0106613 -3.912 0.000 -.0633485 -.0200614

 elecben | .059271 .0815849 0.726 0.472 -.1063551 .2248971

 _cons | 6.30387 .1903596 33.116 0.000 5.91742 6.690321

------------------------------------------------------------------------------

. log close;
APPENDIX I

RESPONSES ON INDIVIDUAL FOOD SECURITY ITEMS,

BY PARTICIPATION STATUS

The SAS System 

 13:37 Thursday, August 26, 1999 13

FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=PARTICIPANTS

 WORRIED FOOD WOULD RUN OUT, LAST 12 MTHS

 Cumulative Cumulative

 E45 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 95159.59 0.5 95159.59 0.5

OFTEN TRUE 4503848 22.5 4599008 23.0

SOMETIMES TRUE 7976108 39.9 12575116 62.9

NEVER TRUE 7402021 37.1 19977137 100.0

 FOOD RAN OUT, LAST 12 MTHS

 Cumulative Cumulative

 E46 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 119039.7 0.6 119039.7 0.6

OFTEN TRUE 3140084 15.7 3259123 16.3

SOMETIMES TRUE 7502279 37.6 10761403 53.9

NEVER TRUE 9215735 46.1 19977137 100.0

 NOT AFFORD TO EAT PROPERLY, LAST 12 MTHS

 Cumulative Cumulative

 E47 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 138541.8 0.7 138541.8 0.7

OFTEN TRUE 2642825 13.2 2781366 13.9

SOMETIMES TRUE 6485799 32.5 9267165 46.4

NEVER TRUE 10709972 53.6 19977137 100.0

 ONLY AFFORD A FEW KINDS OF FOOD

 Cumulative Cumulative

 E51 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 7413271 37.1 7413271 37.1

OFTEN TRUE 1012472 5.1 8425743 42.2

SOMETIMES TRUE 3944466 19.7 12370209 61.9

NEVER TRUE 7606929 38.1 19977137 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=PARTICIPANTS

 CUT SIZE/SKIPPED MEALS B/C NOT ENOUGH MO

 Cumulative Cumulative

 E14 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 48231.38 0.2 48231.38 0.2

NO 13705316 68.6 13753547 68.8

OFTEN TRUE 6223590 31.2 19977137 100.0

 NOT AFFORD TO FEED CHLD BALANCED MEAL, L

 Cumulative Cumulative

 E49 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 7382338 37.0 7382338 37.0

OFTEN TRUE 672576.3 3.4 8054914 40.3

SOMETIMES TRUE 3194180 16.0 11249094 56.3

NEVER TRUE 8728043 43.7 19977137 100.0

 ATE LESS THAN YOU FELT YOU SHOULD, LACK

 Cumulative Cumulative

 E22 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 61817.81 0.3 61817.81 0.3

NO 13644572 68.3 13706390 68.6

OFTEN TRUE 6270747 31.4 19977137 100.0

 HOW OFTEN CUT SIZE/SKIPPED MEALS, NOT EN

 Cumulative Cumulative

 E15 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 13771301 68.9 13771301 68.9

OFTEN TRUE 2123593 10.6 15894894 79.6

SOMETIMES TRUE 2475714 12.4 18370608 92.0

NEVER TRUE 1606529 8.0 19977137 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=PARTICIPANTS

 NOT AFFORD ENOUGH FOOD FOR CHLD, LAST 12

 Cumulative Cumulative

 E50 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 7394770 37.0 7394770 37.0

OFTEN TRUE 439709.8 2.2 7834480 39.2

SOMETIMES TRUE 2229668 11.2 10064148 50.4

NEVER TRUE 9912989 49.6 19977137 100.0

 Cumulative Cumulative

 E25 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

DON T KNOW 86157.58 0.4 86157.58 0.4

REFUSED 28321.5 0.1 114479.1 0.6

NO 16578561 83.0 16693040 83.6

OFTEN TRUE 3284097 16.4 19977137 100.0

 LOST WEIGHT B/C NOT ENOUGH FOOD, LAST 12

 Cumulative Cumulative

 E28 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 309831.7 1.6 309831.7 1.6

NO 17602464 88.1 17912296 89.7

OFTEN TRUE 2064841 10.3 19977137 100.0

 Cumulative Cumulative

 E31 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

DON T KNOW 35443.33 0.2 35443.33 0.2

MISSING 7322097 36.7 7357540 36.8

REFUSED 17764.13 0.1 7375304 36.9

NO 11624491 58.2 18999796 95.1

OFTEN TRUE 977341.6 4.9 19977137 100.0

 NOT EAT FOR ENTIRE DAY DUE TO LACK OF MO

 Cumulative Cumulative

 E18 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 29114.65 0.1 29114.65 0.1

NO 18267986 91.4 18297101 91.6

OFTEN TRUE 1680036 8.4 19977137 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=PARTICIPANTS

 Cumulative Cumulative

 E38 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

DON T KNOW 37724.76 0.2 37724.76 0.2

MISSING 7322097 36.7 7359821 36.8

REFUSED 24024.4 0.1 7383846 37.0

NO 11678955 58.5 19062800 95.4

OFTEN TRUE 914336.9 4.6 19977137 100.0

 HOW OFTEN NOT EAT WHOLE DAY DUE TO LACK

 Cumulative Cumulative

 E19 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 18307173 91.6 18307173 91.6

OFTEN TRUE 429859.8 2.2 18737032 93.8

SOMETIMES TRUE 712050.3 3.6 19449083 97.4

NEVER TRUE 528054.7 2.6 19977137 100.0

 Cumulative Cumulative

 E34 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

DON T KNOW 19882.03 0.1 19882.03 0.1

MISSING 7322097 36.7 7341979 36.8

REFUSED 17764.13 0.1 7359743 36.8

NO 12040900 60.3 19400643 97.1

OFTEN TRUE 576494.7 2.9 19977137 100.0

 Cumulative Cumulative

 E35 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 19400643 97.1 19400643 97.1

OFTEN TRUE 140721.8 0.7 19541364 97.8

SOMETIMES TRUE 255385.9 1.3 19796750 99.1

NEVER TRUE 180387.1 0.9 19977137 100.0

 Cumulative Cumulative

 E41 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

DON T KNOW 19882.03 0.1 19882.03 0.1

MISSING 7322097 36.7 7341979 36.8

REFUSED 17764.13 0.1 7359743 36.8

NO 12478784 62.5 19838527 99.3

OFTEN TRUE 138610.6 0.7 19977137 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=ELIGIBLE, NONPARTICIPANTS

 WORRIED FOOD WOULD RUN OUT, LAST 12 MTHS

 Cumulative Cumulative

 E45 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 39466.2 0.5 39466.2 0.5

OFTEN TRUE 1047236 12.7 1086702 13.1

SOMETIMES TRUE 2539335 30.7 3626037 43.8

NEVER TRUE 4645299 56.2 8271336 100.0

 FOOD RAN OUT, LAST 12 MTHS

 Cumulative Cumulative

 E46 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 35384.93 0.4 35384.93 0.4

OFTEN TRUE 713962.1 8.6 749347 9.1

SOMETIMES TRUE 2204419 26.7 2953766 35.7

NEVER TRUE 5317570 64.3 8271336 100.0

 NOT AFFORD TO EAT PROPERLY, LAST 12 MTHS

 Cumulative Cumulative

 E47 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 56162.35 0.7 56162.35 0.7

OFTEN TRUE 852155.7 10.3 908318.1 11.0

SOMETIMES TRUE 1683997 20.4 2592315 31.3

NEVER TRUE 5679021 68.7 8271336 100.0

 ONLY AFFORD A FEW KINDS OF FOOD

 Cumulative Cumulative

 E51 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 4874836 58.9 4874836 58.9

OFTEN TRUE 219489.7 2.7 5094326 61.6

SOMETIMES TRUE 1120885 13.6 6215211 75.1

NEVER TRUE 2056125 24.9 8271336 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=ELIGIBLE, NONPARTICIPANTS

 CUT SIZE/SKIPPED MEALS B/C NOT ENOUGH MO

 Cumulative Cumulative

 E14 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

NO 6311209 76.3 6311209 76.3

OFTEN TRUE 1960126 23.7 8271336 100.0

 NOT AFFORD TO FEED CHLD BALANCED MEAL, L

 Cumulative Cumulative

 E49 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 4874836 58.9 4874836 58.9

OFTEN TRUE 204172.5 2.5 5079008 61.4

SOMETIMES TRUE 682644.6 8.3 5761653 69.7

NEVER TRUE 2509683 30.3 8271336 100.0

 ATE LESS THAN YOU FELT YOU SHOULD, LACK

 Cumulative Cumulative

 E22 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 18402.14 0.2 18402.14 0.2

NO 6236944 75.4 6255346 75.6

OFTEN TRUE 2015990 24.4 8271336 100.0

 HOW OFTEN CUT SIZE/SKIPPED MEALS, NOT EN

 Cumulative Cumulative

 E15 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 6329612 76.5 6329612 76.5

OFTEN TRUE 711120.8 8.6 7040732 85.1

SOMETIMES TRUE 700064.3 8.5 7740797 93.6

NEVER TRUE 530539.3 6.4 8271336 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=ELIGIBLE, NONPARTICIPANTS

 NOT AFFORD ENOUGH FOOD FOR CHLD, LAST 12

 Cumulative Cumulative

 E50 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 4913306 59.4 4913306 59.4

OFTEN TRUE 130017.2 1.6 5043323 61.0

SOMETIMES TRUE 409341.3 4.9 5452664 65.9

NEVER TRUE 2818672 34.1 8271336 100.0

 Cumulative Cumulative

 E25 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

DON T KNOW 17692.46 0.2 17692.46 0.2

NO 6972121 84.3 6989813 84.5

OFTEN TRUE 1281523 15.5 8271336 100.0

 LOST WEIGHT B/C NOT ENOUGH FOOD, LAST 12

 Cumulative Cumulative

 E28 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 36094.6 0.4 36094.6 0.4

NO 7441501 90.0 7477595 90.4

OFTEN TRUE 793740.6 9.6 8271336 100.0

 Cumulative Cumulative

 E31 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 4874836 58.9 4874836 58.9

NO 3023800 36.6 7898636 95.5

OFTEN TRUE 372699.9 4.5 8271336 100.0

 NOT EAT FOR ENTIRE DAY DUE TO LACK OF MO

 Cumulative Cumulative

 E18 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

NO 7666888 92.7 7666888 92.7

OFTEN TRUE 604448.3 7.3 8271336 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=ELIGIBLE, NONPARTICIPANTS

 Cumulative Cumulative

 E38 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 4874836 58.9 4874836 58.9

NO 3154717 38.1 8029553 97.1

OFTEN TRUE 241782.5 2.9 8271336 100.0

 HOW OFTEN NOT EAT WHOLE DAY DUE TO LACK

 Cumulative Cumulative

 E19 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 7666888 92.7 7666888 92.7

OFTEN TRUE 199831.1 2.4 7866719 95.1

SOMETIMES TRUE 219953.1 2.7 8086672 97.8

NEVER TRUE 184664.2 2.2 8271336 100.0

 Cumulative Cumulative

 E34 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 4874836 58.9 4874836 58.9

NO 3285554 39.7 8160390 98.7

OFTEN TRUE 110945.7 1.3 8271336 100.0

 Cumulative Cumulative

 E35 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 8160390 98.7 8160390 98.7

OFTEN TRUE 53787.07 0.7 8214177 99.3

SOMETIMES TRUE 57158.67 0.7 8271336 100.0

 Cumulative Cumulative

 E41 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 4874836 58.9 4874836 58.9

NO 3304953 40.0 8179789 98.9

OFTEN TRUE 91547.28 1.1 8271336 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=NEAR-ELIGIBLE, NONPARTICIPANTS

 WORRIED FOOD WOULD RUN OUT, LAST 12 MTHS

 Cumulative Cumulative

 E45 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 55602.94 0.7 55602.94 0.7

OFTEN TRUE 408985.2 5.4 464588.1 6.2

SOMETIMES TRUE 2011852 26.8 2476440 33.0

NEVER TRUE 5037122 67.0 7513562 100.0

 FOOD RAN OUT, LAST 12 MTHS

 Cumulative Cumulative

 E46 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 17692.46 0.2 17692.46 0.2

OFTEN TRUE 379170.4 5.0 396862.8 5.3

SOMETIMES TRUE 1330805 17.7 1727667 23.0

NEVER TRUE 5785895 77.0 7513562 100.0

 NOT AFFORD TO EAT PROPERLY, LAST 12 MTHS

 Cumulative Cumulative

 E47 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 57827.96 0.8 57827.96 0.8

OFTEN TRUE 429722.2 5.7 487550.2 6.5

SOMETIMES TRUE 1286465 17.1 1774015 23.6

NEVER TRUE 5739547 76.4 7513562 100.0

 ONLY AFFORD A FEW KINDS OF FOOD

 Cumulative Cumulative

 E51 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 3745320 49.8 3745320 49.8

OFTEN TRUE 199885.3 2.7 3945206 52.5

SOMETIMES TRUE 796292.6 10.6 4741498 63.1

NEVER TRUE 2772064 36.9 7513562 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=NEAR-ELIGIBLE, NONPARTICIPANTS

 CUT SIZE/SKIPPED MEALS B/C NOT ENOUGH MO

 Cumulative Cumulative

 E14 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 17692.46 0.2 17692.46 0.2

NO 6103006 81.2 6120698 81.5

OFTEN TRUE 1392864 18.5 7513562 100.0

 NOT AFFORD TO FEED CHLD BALANCED MEAL, L

 Cumulative Cumulative

 E49 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 3727478 49.6 3727478 49.6

OFTEN TRUE 91451.28 1.2 3818929 50.8

SOMETIMES TRUE 629867.7 8.4 4448797 59.2

NEVER TRUE 3064766 40.8 7513562 100.0

 ATE LESS THAN YOU FELT YOU SHOULD, LACK

 Cumulative Cumulative

 E22 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 56599.26 0.8 56599.26 0.8

NO 6084740 81.0 6141339 81.7

OFTEN TRUE 1372223 18.3 7513562 100.0

 HOW OFTEN CUT SIZE/SKIPPED MEALS, NOT EN

 Cumulative Cumulative

 E15 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 6120698 81.5 6120698 81.5

OFTEN TRUE 405654 5.4 6526352 86.9

SOMETIMES TRUE 528409.2 7.0 7054761 93.9

NEVER TRUE 458800.9 6.1 7513562 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=NEAR-ELIGIBLE, NONPARTICIPANTS

 NOT AFFORD ENOUGH FOOD FOR CHLD, LAST 12

 Cumulative Cumulative

 E50 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 3727478 49.6 3727478 49.6

OFTEN TRUE 72489.73 1.0 3799967 50.6

SOMETIMES TRUE 408425.8 5.4 4208393 56.0

NEVER TRUE 3305169 44.0 7513562 100.0

 Cumulative Cumulative

 E25 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

REFUSED 17692.46 0.2 17692.46 0.2

NO 6808994 90.6 6826686 90.9

OFTEN TRUE 686876.1 9.1 7513562 100.0

 LOST WEIGHT B/C NOT ENOUGH FOOD, LAST 12

 Cumulative Cumulative

 E28 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 17692.46 0.2 17692.46 0.2

NO 6973153 92.8 6990846 93.0

OFTEN TRUE 522716.6 7.0 7513562 100.0

 Cumulative Cumulative

 E31 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 3727478 49.6 3727478 49.6

NO 3621338 48.2 7348816 97.8

OFTEN TRUE 164746.7 2.2 7513562 100.0

 NOT EAT FOR ENTIRE DAY DUE TO LACK OF MO

 Cumulative Cumulative

 E18 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 17692.46 0.2 17692.46 0.2

NO 7201052 95.8 7218744 96.1

OFTEN TRUE 294818.1 3.9 7513562 100.0
FREQ OF THE 18 FOOD SECURITY QUESTIONS WEIGHTED WITH HHWT1

FRAME=NEAR-ELIGIBLE, NONPARTICIPANTS

 Cumulative Cumulative

 E38 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

DON T KNOW 20067.75 0.3 20067.75 0.3

MISSING 3727478 49.6 3747545 49.9

NO 3600561 47.9 7348106 97.8

OFTEN TRUE 165456.4 2.2 7513562 100.0

 HOW OFTEN NOT EAT WHOLE DAY DUE TO LACK

 Cumulative Cumulative

 E19 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 7218744 96.1 7218744 96.1

OFTEN TRUE 111519 1.5 7330263 97.6

SOMETIMES TRUE 71780.06 1.0 7402043 98.5

NEVER TRUE 111519 1.5 7513562 100.0

 Cumulative Cumulative

 E34 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 3727478 49.6 3727478 49.6

NO 3676941 48.9 7404418 98.5

OFTEN TRUE 109143.7 1.5 7513562 100.0

 Cumulative Cumulative

 E35 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 7404418 98.5 7404418 98.5

OFTEN TRUE 36244.87 0.5 7440663 99.0

SOMETIMES TRUE 55206.41 0.7 7495870 99.8

NEVER TRUE 17692.46 0.2 7513562 100.0

 Cumulative Cumulative

 E41 Frequency Percent Frequency Percent

))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

MISSING 3727478 49.6 3727478 49.6

NO 3767683 50.1 7495160 99.8

OFTEN TRUE 18402.14 0.2 7513562 100.0
APPENDIX J

FOOD USE SHARES BY FOOD GROUP FOR FOOD STAMP HOUSEHOLDS,

BY HUNGER CLASSIFICATION STATUS

J.3

To further assess possible reasons for the observed relationship between food security and

nutrient availability, differences have been tabulated in the distribution of food among food groups

between households that are measured on the food security scale as experiencing hunger and those

that are not. The food group classification used is the same as that discussed in Section V.D. Two

criteria for determining food groups shares have been used in this work. One is based on

expenditures on food, the other on food energy provided by the food.

In general, the pattern of expenditures is similar for the two groups (Table J.1). Only one of the

subgroup totals in the distribution, meat products and alternatives, differs more than a percentage

point between households classified as experiencing hunger and those not classified.

Of the differences that exist at the detailed group level, one of the largest is in the use of grain

products, where households experiencing hunger are substantially more likely to use foods in the

“other bread” category, which includes white bread from refined flour. The difference between the

groups is .9 percentage points when the criterion for assigning shares is expenditures and .8

percentage points when it is food energy. In addition, the group experiencing hunger is more likely

to use milk products, poultry, and sugars or sweets. They are less likely to use red meats and soft

drinks.
J.4

TABLE J.1

SHARE OF VARIOUS TYPES OF FOODS IN HOUSEHOLD FOOD USE

BY FOOD SECURITY STATUS

Share Based on Value of Food Used Share Based on Food Energy

Food Group with Hunger Hunger Difference with Hunger Hunger Difference

Households No Households No

Vegetables, Fruit

Potatoes 2.3 2.5 !0.2 3.1 3.2 !0.1

High-Nutrient Vegetables 3.9 3.8 0.1 0.9 0.9 0.0

Other Vegetables 4.9 4.8 0.1 1.7 1.7 0.0

Mixtures, Mostly Vegetables;

Condiments 0.7 0.6 0.1 0.3 0.3 0.0

Vitamin C-Rich Fruit 4.0 4.0 0.0 2.5 2.3 0.2

Other Fruit 4.2 3.8 0.4 2.3 2.1 0.2

Subgroup Total 20.0 19.5 0.5 10.8 10.5 0.3

Grain Products

Whole-Grain/High-Fiber

Breakfast Cereals 1.7 1.7 0.0 1.7 1.6 0.1

Other Breakfast Cereals 3.1 3.4 -0.3 2.9 3.4 -0.5

Whole-Grain/High-Fiber Flour,

Meal, Rice, Pasta 0.4 0.4 0.0 0.6 0.7 -0.1

Other Flour, Meal, Rice, Pasta 2.4 2.2 0.2 6.8 7.1 -0.3

Whole-Grain/High-Fiber Bread 0.8 0.9 -0.1 1.0 1.3 -0.3

Other Bread 4.6 3.7 0.9 7.0 6.2 0.8

Bakery Products, Not Bread 4.3 3.8 0.5 5.0 4.8 0.2

Grain Mixtures 3.1 3.6 -0.5 2.7 2.7 0.0

Subgroup Total 20.4 19.7 0.7 27.7 27.8 -0.1

Milk, Cheese, Cream

Milk, Yogurt 8.3 7.6 0.7 7.8 7.1 0.7

Cheese 2.9 3.0 -0.1 2.5 2.4 0.1

Cream; Mixtures, Mostly Milk 2.0 1.8 0.2 2.1 1.9 0.2

Subgroup Total 13.2 12.4 0.8 12.4 11.4 1.0
TABLE J.1 (continued)

Share Based on Value of Food Used Share Based on Food Energy

Food Group with Hunger Hunger Difference with Hunger Hunger Difference

Households No Households No

J.5

Meat and Alternatives

Lower-Cost Red Meats, Variety

Meats 7.1 7.9 -0.8 7.5 8.1 -0.6

Higher-Cost Red Meats, Variety

Meats 5.9 7.0 -1.1 3.6 4.4 -0.8

Poultry 5.6 5.2 0.4 4.3 4.3 0.0

Fish, Shellfish 3.1 3.0 0.1 0.9 0.9 0.0

Bacon, Sausage, Luncheon Meats 5.8 5.8 0.0 6.9 7.1 -0.2

Eggs 1.3 1.3 0.0 1.4 1.6 -0.2

Dry Beans, Peas, Lentils 0.9 0.9 0.0 1.3 1.5 -0.2

Mixtures, Mostly Meat, Poultry,

Fish, Eggs, Legumes 2.8 3.4 -0.6 1.2 1.4 -0.2

Nuts, Peanut Butter 0.8 0.8 0.0 1.6 1.2 0.4

Subgroup Total 33.3 35.3 -2.0 28.7 30.5 -1.8

Other Foods

Fats, Oils 2.3 2.5 -0.2 9.3 9.2 0.1

Sugar, Sweets 3.4 2.6 0.8 7.0 5.8 1.2

Seasonings 0.2 0.1 0.1 0.1 0.1 0.0

Soft Drinks, Punches, Ades 5.1 5.7 -0.6 3.7 4.4 -0.7

Coffee, Tea 2.0 1.9 0.1 0.5 0.3 0.2

Alcohol 0.2 0.6 -0.4 0.1 0.2 -0.1

Subgroup Total 13.2 13.4 -0.2 20.7 20.0 0.7

Sample Size 216 703 216 703

SOURCE: 1996 National Food Stamp Program Survey, unweighted tabulations.

Less than 0.05. a

Criteria for assigning share to food groups. b
J.4

TABLE J.1

SHARE OF VARIOUS TYPES OF FOODS IN HOUSEHOLD FOOD USE

BY FOOD SECURITY STATUS

Share Based on Value of Food Used Share Based on Food Energy

Food Group with Hunger Hunger Difference with Hunger Hunger Difference a

Households No Households No

Vegetables, Fruit

Potatoes 2.3 2.5 !0.2 3.1 3.2 !0.1

High-Nutrient Vegetables 3.9 3.8 0.1 0.9 0.9 0.0

Other Vegetables 4.9 4.8 0.1 1.7 1.7 0.0

Mixtures, Mostly Vegetables;

Condiments 0.7 0.6 0.1 0.3 0.3 0.0

Vitamin C-Rich Fruit 4.0 4.0 0.0 2.5 2.3 0.2

Other Fruit 4.2 3.8 0.4 2.3 2.1 0.2

Subgroup Total 20.0 19.5 0.5 10.8 10.5 0.3

Grain Products

Whole-Grain/High-Fiber

Breakfast Cereals 1.7 1.7 0.0 1.7 1.6 0.1

Other Breakfast Cereals 3.1 3.4 -0.3 2.9 3.4 -0.5

Whole-Grain/High-Fiber Flour,

Meal, Rice, Pasta 0.4 0.4 0.0 0.6 0.7 -0.1

Other Flour, Meal, Rice, Pasta 2.4 2.2 0.2 6.8 7.1 -0.3

Whole-Grain/High-Fiber Bread 0.8 0.9 -0.1 1.0 1.3 -0.3

Other Bread 4.6 3.7 0.9 7.0 6.2 0.8

Bakery Products, Not Bread 4.3 3.8 0.5 5.0 4.8 0.2

Grain Mixtures 3.1 3.6 -0.5 2.7 2.7 0.0

Subgroup Total 20.4 19.7 0.7 27.7 27.8 -0.1

Milk, Cheese, Cream

Milk, Yogurt 8.3 7.6 0.7 7.8 7.1 0.7

Cheese 2.9 3.0 -0.1 2.5 2.4 0.1

Cream; Mixtures, Mostly Milk 2.0 1.8 0.2 2.1 1.9 0.2

Subgroup Total 13.2 12.4 0.8 12.4 11.4 1.0
TABLE J.1 (continued)

Share Based on Value of Food Used Share Based on Food Energy

Food Group with Hunger Hunger Difference with Hunger Hunger Difference a

Households No Households No

J.5

Meat and Alternatives

Lower-Cost Red Meats, Variety

Meats 7.1 7.9 -0.8 7.5 8.1 -0.6

Higher-Cost Red Meats, Variety

Meats 5.9 7.0 -1.1 3.6 4.4 -0.8

Poultry 5.6 5.2 0.4 4.3 4.3 0.0

Fish, Shellfish 3.1 3.0 0.1 0.9 0.9 0.0

Bacon, Sausage, Luncheon Meats 5.8 5.8 0.0 6.9 7.1 -0.2

Eggs 1.3 1.3 0.0 1.4 1.6 -0.2

Dry Beans, Peas, Lentils 0.9 0.9 0.0 1.3 1.5 -0.2

Mixtures, Mostly Meat, Poultry,

Fish, Eggs, Legumes 2.8 3.4 -0.6 1.2 1.4 -0.2

Nuts, Peanut Butter 0.8 0.8 0.0 1.6 1.2 0.4

Subgroup Total 33.3 35.3 -2.0 28.7 30.5 -1.8

Other Foods

Fats, Oils 2.3 2.5 -0.2 9.3 9.2 0.1

Sugar, Sweets 3.4 2.6 0.8 7.0 5.8 1.2

Seasonings 0.2 0.1 0.1 0.1 0.1 0.0

Soft Drinks, Punches, Ades 5.1 5.7 -0.6 3.7 4.4 -0.7

Coffee, Tea 2.0 1.9 0.1 0.5 0.3 0.2

Alcohol 0.2 0.6 -0.4 0.1 0.2 -0.1

Subgroup Total 13.2 13.4 -0.2 20.7 20.0 0.7

Sample Size 216 703 216 703

SOURCE: 1996 National Food Stamp Program Survey, unweighted tabulations.

Criteria for assigning share to food groups, are based on the coding structure used by the USDA, Human Nutrition a

Information Service for analyzing data from the 1977-78 Nationwide Food Consumption Survey.

APPENDIX K

ANALYSIS OF THE ROBUSTNESS OF THE FINDINGS IN CHAPTER VI

These checks were in addition to the routine editing of all observations, described in Chapter 1

II and Appendix A.

K.2

Because the finding in Chapter VI that food insecure households have higher levels of nutrient

availability is unexpected, extensive analysis of factors that could help account for the result has

been conducted. None of these lines of analysis has provided an explanation, and none of them is

described in detail here. However, to provide readers with an overview of the range of factors that

have been explored, the main types of analysis that were undertaken in this work are listed here.

The findings reported in Chapter VI, that there is a positive association between measured food

insecurity and nutrient availability and that in some analysis specifications this relationship is

statistically significant, have proven robust when examined through the following research activities:

C The analysis was repeated using the 30-day rather than the 12-month food security scale.

While the 30-day scale is not viewed as statistically as strong as the 12-month scale (see

Hamilton et al. (1997), it was thought that the 30-day scale might be more closely

associated with food use over the seven-day period of food data collection.

C Outliers on the nutrient availability variables were hand-checked against the hard-copy

interview information to see if any explanations for the findings would be apparent. In 1

addition, to examine sensitivity to outliers, the regression equations were rerun, omitting

the 23 lowest and 23 highest observations in terms of nutrient availability.

C The dependent variables in the regressions, which are defined in terms of nutrient

availability, were replaced first with a set of dichotomous variables indicating whether

the household met 100 percent of the RDAs and then again with the comparable variable

for meeting 75 percent of the RDAs.

C The coding of the food security variable was spot-checked manually against printouts

of the 18 constituent items.

C The analysis was performed with both weighted and unweighted data.

C To limit sensitivity of the analysis to households that consumed very little from home

food supplies, the analysis was limited to households that consumed at least 10 meals

from home food supplies during the observation week.
K.3

C The analysis was done separately for households that had children and those without

children, so that any interaction caused by the fact that the food security index has to be

calculated using fewer data items for households without children could be discovered.

C To verify that the complex ENU calculations were being done correctly, for a sample

of cases from the CAPI interviewing data the raw information on ages and genders of

household members and on meals eaten for each household member was printed out.

C The distribution of food by food group was examined separately for households

classified as experiencing hunger and those not so classified. 

C The calculations were performed using “food sufficiency” rather than “food security”

as a variable. Food sufficiency is a simpler measure that Hamilton et al. (1997) found

correlates with food security. This was done in part to examine whether the findings

could be stemming from some error in the food security calculations. However, the

same type of unexpected findings were found for the food sufficiency variable as for the

food security measure.

C Various changes in the functional form of the regression were tried, such as (1) using

interaction terms, and (2) using a set of three dichotomous variables to fully characterize

the four-level food security measure.

C Parts of the analysis were limited to households with no missing data in the variable set

needed to compute food security.

C Different ways of scaling the nutrient availability variable were tried. Besides ENU,

adult male equivalents, household size, and no scale factor were used. 

The basic patterns in the data, which are evident in Table VI.1 and in the regression analysis of Table

VI.2, have proven to be very robust to all the above variants of the analytic methods used.