Non-Discrimination Policy 

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on 

the basis of race, color, national origin, age disability, and where applicable, sex, marital status, familial 

status parental status, religion, sexual orientation, genetic information, political beliefs, reprisal or because 

all or part of an individual’s income is derived from any public assistance program. In accordance with 

Federal law and the U.S. Department of Agriculture policy, this institution is prohibited from discriminating 

on the basis of race, color, nation origin, sex, age, or disability. Persons with disabilities who require 

alternative means for communication of program information (Braille, large print, audiotape, etc.) should 

contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). 

To file a complaint of discrimination, write to USDA, Assistant Secretary for Civil Rights, Office of the 

Assistant Secretary for Civil Rights, 1400 Independence Avenue, S.W., Stop 9410, Washington, DC 20250-

9410, or call toll-free at (866) 632-9992 (English) or (800) 877-8339 (TDD) or (866) 377-8642 (English 

Federal-relay) or (800) 845-6136 (Spanish Federal-relay). USDA is an equal opportunity provider 

and employer. 

April 2012 
Non-Discrimination Policy

The U.S. Department of Agriculture (USDA) prohibits discrimination in all of its programs and 

activities on the basis of race, color, national origin, age, disability, and where applicable, sex, 

marital status, familial status, parental status, religion, sexual orientation, political beliefs, 

genetic information, reprisal, or because all or part of an individual’s income is derived from any 

public assistance program. (Not all prohibited bases apply to all programs.) 

Persons with disabilities who require alternative means for communication of program 

information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at 

(202) 720-2600 (voice and TDD). To file a complaint of discrimination, write: USDA, Director, 

Office of Adjudication, 1400 Independence Ave., SW, Washington, DC 20250-9410 or call (866) 

632-9992 (Toll-free Customer Service), (800) 877-8339 (Local or Federal relay), (866) 377-8642 

(Relay voice users). USDA is an equal opportunity provider and employer. 
National Survey of WIC Participants II 

Volume 3: Improper Payments Report (Final Report) 

Authors:

Gary Huang, Daniel M. Geller, 

Marcia Harrington, and Pedro Saavedra

Submitted by: 

ICF Macro 

11785 Beltsville Drive, Suite 300 

Calverton, Maryland 20705 

Submitted to: 

USDA, Food and Nutrition Service 

3101 Park Center Drive 

Alexandria, Virginia 22302 

Project Director: 

Daniel M. Geller 

Gary Huang 

Project Officer: 

Sheku G. Kamara (through 1/11) 

Karen Castellanos-Brown 

This study was conducted under contract number: AG-3198-D-07-0105 with the Food and Nutrition Service. 

This report is available on the Food and Nutrition Service website: http://www.fns.usda.gov/ora. 

Suggested citation: 

U.S. Department of Agriculture, Food and Nutrition Service, Office of Research and Analysis, National Survey of 

WIC Participants II: Improper Payments Report, by Gary Huang, et al. Project Officer: Sheku G. Kamara, KarenCastellanos-Brown, Alexandria, VA: 2012. 

April 2012 

Special Nutrition Programs 

Report No. CN-10-NSWP-II-R2 

 iii National Survey of WIC Participants II 

ACKNOWLEDGMENTS

ICF Macro would like to thank each of the State Special Supplemental Nutrition Program for 

Women, Infants, and Children (WIC) agencies who provided sampling frame information and 

participant data from local entities that were selected during sampling. We also would like to 

thank local clinics/agencies who supported our field interviewers by acknowledging the 

importance of their work and helping them obtain updated contact information for participants, 

when possible. In addition, we want to thank State agencies in West Virginia and the District of 

Columbia, who supported pretesting activities. Directors and IT leads and others from several 

State agencies provided extra support along the way in various capacities: Cynthia Melde 

(Arizona), Ruth Caldwell (California), Richard Bates (Colorado), Debbie Eibeck (Florida), 

Tim Mooney and Holly Esford (New York), and many others, too numerous to name. Finally, 

we would like to thank all of those at Food and Nutrition Service who helped us accomplish so 

many things necessary to the completion of the National Survey of WIC Participants II. 

 v National Survey of WIC Participants II 

CONTENTS

Acknowledgments iii

List of Exhibits ix

Executive Summary—One Page Summary xi

Executive Summary xiii

Survey Methodology xiii

Analytic Criteria xiv

Improper Payment Estimates xv

Income Eligibility Error xvi

Infant Formula Rebates xvii

Pre-rebate Annual Dollar Error xviii

Post-rebate Annual Dollar Error xviii

Three Studies: A Comparison xix

Other Certification Errors xxi

New Applicant Denials xxi

Terminations/Discontinuations xxiii

Chapter 1. Overview of the WIC Program and Program Eligibility 1

1.1 WIC Eligibility and Improper Payments 2

1.2 Goals of the Study 3

Chapter 2. Survey Methodology 5

2.1 Source of Data 6

In-Person Interview 6

State-Provided Program Eligibility and Redemption Data 8

2.2 Survey Content 10

Participant Interviews 10

Interviews with Denied Applicants and Terminated/Discontinued Participants 10

2.3 Sample Design and Weights 11

Sample Allocations 11

Selection of States 11

Selection of Agencies 13

Selection of Clinics 13

Selection of Participants 13

Weighting 14

Chapter 3. WIC Eligibility Criteria 15

3.1 Category 15

3.2 Residency and Identity 15

3.3 Economic Unit 16

3.4 Income and Adjunctive Income 17

Income 17

Adjunctive Income Eligibility 21
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II vi 

3.5 Redemption Data Processing 22

Redemption Data Request 22

Data Matching 23

Data Cleaning 23

Imputation 23

Expired Certification Error 24

Chapter 4. Improper Payment Estimates 25

4.1 Certification Error Case Counts and Rates 27

Identity, Residence, and Category Errors 28

Income Eligibility Error 29

4.2 Dollar Error Amounts and Rates 30

Summary: Case Error and Dollar Error 34

4.3 Additional Analysis 35

Characteristics of Participants with Income Eligibility Error 35

Comparing Income Eligibility Error Cases and Dollar Amounts with Prior Studies 37

Chapter 5. Denial and Termination/Discontinuation Findings 39

5.1 New Applicant Denials 39

New Applicant Reasons for Denial 41

Characteristics of Denied New Applicants 43

5.2 Terminations/Discontinuations 44

Reasons for Termination/Discontinuation 46

Characteristics of Terminated/Discontinued Participants 48

5.3 Summary 49

Chapter 6. Limitations of the Study 53

6.1 Nonresponse and Potential Bias 53

6.2 Potential Errors: Family Income Data 54

6.3 State Data Quality Concern: Expired Certification Error 55

6.4 Proxy Estimates: Infant Formula Rebate Adjustment 55

6.5 Lack of Statistical Generalization: New Applicant Denial Analysis 566

Appendices 

Appendix A: In-person Survey Instrument (Version A: Pregnant, Breastfeeding, and Postpartum) 

Appendix B: In-person Survey Instrument (Version B: Infants and Children) 

Appendix C: Denials Survey Instrument 

Appendix D: Terminations/Discontinuations Survey Instrument 

Appendix E: Communications to States for Food Issuance and Redemption Data and Data Issues 

Appendix F: Communications to States for Denials 

Appendix G: Sampling and Weighting Methodology 

Appendix H: Case Error and Dollar Error Estimation Procedures 

Appendix I: Nonresponse Analysis 

Appendix J: Contending Reasons against Denial and Termination/Discontinuation 

Appendix K: Expired Certification Error 
Volume 3: Improper Payments Report (Final Report) 

 vii National Survey of WIC Participants II 

Appendix L: Infant Formula Rebate Adjustment 

Appendix M: Pre-rebate Estimates of Full Voucher Value Annualized Dollar Amounts of Income 

Eligibility Error by WIC Category 

 ix National Survey of WIC Participants II 

LIST OF EXHIBITS

Exhibit ES-1: Process of Identifying Erroneous Certification, by Eligibility Criteria xvi

Exhibit ES-2: Number and Percent of Eligibility Errors, by WIC Category xvii

Exhibit ES-3a: Pre-rebate Annualized Dollar Amounts of Eligibility Error by WIC Category xviii

Exhibit ES-3b: Post-rebate Annualized Dollar Amounts of Eligibility Error, by WIC Category xix

Exhibit ES-4: Pre- and Post-rebate Annualized Dollar Error Amounts and Dollar Error Rates: WIC 

Eligibility Error xix

Exhibit ES-5: Estimates of Eligibility Case Error Rates: 1988, 1998, and 2009 xx

Exhibit ES-6: Estimates of Dollar Error and Dollar Error Rates from Eligibility Error: 1988, 1998, and 

2009 xx

Exhibit ES-7: Estimates of WIC Case and Dollar Error Rates: 1988, 1998, and 2009 xxi

Exhibit ES-8: Reasons for Ineligibility: Percentage of New Applicant Denials xxii

Exhibit ES-9: Percentage of Terminated/Discontinued Respondents, by Category xxiv

Exhibit ES-10: Reasons for Ineligibility in Terminated/Discontinued Respondents xxv

Exhibit 2-1: Sample Sizes and Response Rates: Data Collection Components, WIC Program Categories, 

and Nonresponse Subcategories 7

Exhibit 2-2: Income and Program Release Forms and Undocumented Employment Information Form 8

Exhibit 2-3: Estimates of Error 11

Exhibit 2-4: Count of Sampled Research Clusters in the Sampled States 12

Exhibit 3-1: Residency and Identity Proofs Collected 16

Exhibit 3-2: WIC Income Eligibility Guidelines (Effective from July 1, 2008 to June 30, 2009) 18

Exhibit 3-3: Sources of Income Used to Determine Eligibility 19

Exhibit 3-4: List of Adjunctively Eligible Programs Accepted by Each State 21

Exhibit 4-1: Program Categories, Demographics, and Food Security: Percentage Distribution of WIC 

Participants in In-person Interview 25

Exhibit 4-1 (continued) 26

Exhibit 4-2: Median and Mean Monthly Income by Economic Unit for Income-Eligible WIC Participants 

(Adjunctively Eligible Excluded) 27

Exhibit 4-3: Process of Identifying Erroneous Certification, by Eligibility Criteria 28

Exhibit 4-4: Number and Percent of Income Eligibility Errors, by WIC Category 30

Exhibit 4-5a: Pre-rebate May 2009 and Annualized Estimates of Mean and Total Redeemed Dollar 

Amounts, by WIC Category 32

Exhibit 4-5b: Infant Formula Rebate Adjustment: May 2009, Annualized Rebate Values, and Post-rebate 

Food Costs for Infants 32

Exhibit 4-5c: Post-rebate May 2009 and Annualized Estimates of Mean and Total Redeemed Dollar 

Amounts, by WIC Category 33
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II x 

Exhibit 4-6a: Pre-rebate Estimates of Annualized Dollar Amounts of Income Eligibility Error by WIC 

Category 34

Exhibit 4-6b: Post-rebate Estimates of Annualized Dollar Amounts of Income Eligibility Error by WIC 

Category 34

Exhibit 4-7: Income Eligibility Case Error Count and Rate 35

Exhibit 4-8: Pre-and Post-rebate Annualized Dollar Error Amounts and Dollar Error Rates: WIC Income 

Eligibility Error 35 

Exhibit 4-9: Education and Food Security: Participants of Income Eligibility Error and Participants 

Without the Error 36

Exhibit 4-10: Estimates of Income Eligibility Case Error Rates: 1988, 1998, and 2009 38

Exhibit 4-11: Estimates of Dollar Error and Dollar Error Rates from Income Eligibility Error: 1988, 1998, 

and 2009 38

Exhibit 5-1: Profile of Denied New Applicant Respondents, by Category 40

Exhibit 5-2: How New Applicants Heard About WIC 40

Exhibit 5-3: Perceived Benefits of WIC by Denied New Applicants 41

Exhibit 5-4: Reasons for Ineligibility 42

Exhibit 5-5: Participant Perceived Main Reason for Determination of Ineligibility, by Type of Proof 42

Exhibit 5-6: Race of Denied New Applicants 43

Exhibit 5-7: Highest Level of Education Attained by Denied New Applicants 44

Exhibit 5-8: Profile of Terminated/Discontinued Respondents, by Category 45

Exhibit 5-9: How Terminated/Discontinued Respondents Heard About WIC 45

Exhibit 5-10: Terminated/Discontinued Respondents’ Perceived Benefits of Applying for WIC 46

Exhibit 5-11: Terminated/Discontinued Participants’ Reasons for Ineligibility 47

Exhibit 5-12: Reasons for Ineligibility for Terminated/Discontinued Participants, by Category 47

Exhibit 5-13a: Race of Terminated/Discontinued Participants 48

Exhibit 5-13b: Ethnicity of Terminated/Discontinued Participants 48

Exhibit 5-14: Terminated/Discontinued Participants’ Highest Level of Education Attained 49 

Exhibit 5-15 Demographics of the Denial, Termination/Discontinuation, and the WIC Participant 

Population: Percentages of Race and Education Categories 51 
 xi National Survey of WIC Participants II 

EXECUTIVE SUMMARY—ONE PAGE SUMMARY

The National Survey of WIC Participants II (NSWP-II) Report Volume 3 presents the improper 

payment analysis for FY2009 WIC Program, focusing on income eligibility error. The data 

used were from an in-person interview of WIC participants, WIC voucher redemption records, 

and WIC administrative reports. 

Overall, the case error rate was determined to be 3.05 percent of approximately 9.11 million 

WIC participants, with the highest rates of error among Infants and Breastfeeding participants 

(5.29% and 5.18%) and the lowest among Children (1.85%). 

The dollar error rate estimate for the national population of WIC participants was calculated 

before and after rebates from infant formula manufacturers to State WIC Programs were 

considered. The total post-rebate error amount was $130,499,928 out of a total post-rebate 

program cost of $4,373,776,840—a dollar error rate of 2.98 percent. 

Case Error Rates 

(%) 

Post-Rebate Dollar 

Error Amounts ($) 

Pregnant 3.31 15,722,258 

Breastfeeding 5.18 26,051,235 

Post-Partum 2.14 6,431,074 

Infant 5.29 48,714,683 

Child 1.85 33,580,679 

Total 3.05 130,499,928 

Taking a retrospective look, the case and dollar error rates in the WIC Program have come 

down over time. 

Fiscal Year 

Case Error 

Rate (%) 

Dollar Error 

Rate (%) 

FY1988 pre-rebate 5.70 5.60 

FY1998 post-rebate 4.50 4.62 

FY2009 pre-rebate 

3.05 

3.72 

FY2009 post-rebate 2.98 

Two additional telephone surveys were conducted with (1) new applicants who were denied 

WIC benefits and (2) terminated/discontinued participants—that is, individuals whose 

eligibility came to an end and who either were turned down when they reapplied for WIC 

benefits or voluntarily decided not to apply. Among both groups, there was scant evidence that 

individuals were incorrectly being turned down for WIC benefits. 

 xiii National Survey of WIC Participants II 

EXECUTIVE SUMMARY 

For 35 years, the Special Supplemental Nutrition Program for Women, Infants, and Children 

(WIC) has provided benefits to low-income, pregnant, and new mothers and their infants and 

young children who are at nutritional risk. The WIC Program, the United States Department of 

Agriculture’s (USDA) third largest nutrition assistance program, serves more than 9 million 

participants monthly and has been growing consistently. Half of the participants are children 

ages 1 to 5 years; one-quarter are infants (0–12 months of age); and the remaining quarter are, 

in descending order of prevalence, pregnant, postpartum (i.e., nonbreastfeeding), and 

breastfeeding women. 

This report, the last of three for the National Survey of WIC Participants II (NSWP-II), addresses 

the third objective of the study—the estimation of the rate and annual cost of improper payments 

in the WIC Program due to certification error. It examines cases of overpayment and presents 

statistics on case errors and related dollar error. The analysis also explores sources of possible 

underpayment in two areas—applicants who were incorrectly turned down for WIC benefits or 

current WIC participants turned away at time of recertification—in the last chapter titled “Denial 

& Termination/Discontinuation Findings.” The findings of this volume will be used to develop a 

model for estimating future improper payments in the WIC Program. 

For the first National Survey of WIC Participants (NSWP-I), data were collected in 1998, and a 

report was issued in 2001. Data for NSWP-II were collected in late 2009. The approach used in 

the two studies differed in some important ways: NSWP-II collected benefit redemption records 

for each sampled participant to generate estimates of improper payment dollar errors, whereas 

NSWP-I used WIC average food voucher 1

 costs to estimate the dollar error. Statistics that 

measure the cost to the program based on actually redeemed voucher values are more accurate 

than those based on the average food cost because redeemed values vary, as WIC participants do 

not necessarily redeem the full value of the voucher. Technical details in data collection, 

processing, and analysis are presented in appendices to respective volumes of the NSWP-II 

report. A separate technical report is available that combines the appendices from all volumes. 

Survey Methodology 

Telephone surveys were first conducted to interview a sample of WIC participants (n = 2,538) 

about their preferences and usage of the WIC Program in late 2009 and early 2010. Closely 

following the telephone interviews, in-person interviews were arranged and conducted with half 

of the respondents (n = 1,210). The purpose of the interviews was to assess erroneous payments 

due to certification error. There are two forms of certification error: provision of service to 

ineligible applicants and denial of service to eligible applicants. 

 

1

 Food voucher (also known as food instrument or food coupon) means a check, electronic benefits transfer card (EBT), voucher 

or other document used by a participant to obtain supplemental foods. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II xiv 

 Provision of service to ineligible applicants—Erroneous payments were determined from 

information provided by applicants during eligibility determination for WIC benefits. This 

was accomplished by asking respondents to show the documents or “proofs” they had 

originally used to gain eligibility in the WIC Program. The proofs had to meet the standards 

set by FNS and/or the State agency. From the interviews, the eligibility of each WIC 

participant was determined to be either correct or improper, and the number of case errors 

was determined. Further, case error was translated to dollar error, which required researchers 

to obtain the actual redemption data for respondents from State WIC programs from May to 

July 2009. 

 Denial of service to eligible applicants—Instead of in-person interviews, two very short 

telephone interviews were conducted with samples of denied applicants (new applicants who 

were denied benefits) and terminations/discontinuations (participants whose eligibility had 

ended in May 2009 and were not recertified—either by their own choice or involuntarily by 

the WIC Program). Since incorrectly determined denials and terminations/discontinuations 

constitute a form of WIC Program underpayment, their determination is germane to improper 

payment calculations. 

Analytic Criteria 

Eligibility for WIC is based on meeting five basic criteria, as established in the Federal 

regulations and as carried out by all State WIC Programs: (1) categorical eligibility, 

(2) residential eligibility, (3) proof of identity, (4) nutritional risk eligibility, and (5) income 

(or adjunctive income) eligibility. 

 Category—To receive WIC benefits, participants must be one of the following: a pregnant 

woman, a breastfeeding woman, a postpartum woman, an infant (birth to 12 months), or a 

child (1 to 5 years old). Among participants in this category, only infants and children were 

checked, and their classification was verified via records of birthdates provided by States. 

 Residence—Participants must show proof that they live in a residence within the State or the 

local agency/clinic’s jurisdiction. Special rules apply to migrant farm workers; homeless 

individuals; or victims of theft, loss, or disaster. For recertification, WIC participants’ 

folders2

 are often considered permissible proof of residence, depending on the State policy. 

Proofs were checked against a list of acceptable document types. 

 Identity—Participants must provide credible proof of identification. For recertification, WIC 

folders are often considered permissible proof of identification, depending on the State policy. 

Proofs were checked against a list of acceptable document types. 

 Nutrition—Nutritional risk eligibility was not assessed in this study, as it is out of the scope 

of this study. 

 Income/Adjunctive Income—Participants must have gross income at or below 185 percent 

of the Federal Poverty Income Guidelines or show proof of participation in SNAP, 

Temporary Assistance for Needy Families (TANF), Medicaid, or other eligible State-

 

2

 WIC folders contain the official WIC participant identification documents issued by WIC agencies which shows eligibility 

based on extant documentation in the official case file. 
Volume 3: Improper Payments Report (Final Report) 

 xv National Survey of WIC Participants II 

administered program. Two approaches were used to assess income/adjunctive income 

eligibility: 

1. Income documents or proofs of participation in adjunctive income programs provided by 

the applicant were checked against a list of acceptable document types for the month of 

May 2009. If responding participant could not locate income documents from the time of 

last certification proof of current income or adjunctive program participation was 

accepted. 

2. Since the income guidelines rise with the size of the family, the measure of the family 

economic unit was an important part of the equation. This was measured by asking about 

all members living in the household and determining if they shared income and expenses 

as a family unit. 

In reviewing the eligibility information collected, researchers identified 234 cases where 

participants had not provided credible income or adjunctive program information. Follow-ups 

were made with State agencies directly, asking for more information about the certification 

information on file. This special case review step was used to determine the proper or improper 

status of these cases. 

To measure dollar error, redemption data were requested from the States for all the participants 

originally sampled. The data received listed all food vouchers for the month requested. One State 

was unable to provide redemption data by the study deadline, so imputation (data substitution) 

procedures were employed using data from other States in the same region. 

Improper Payment Estimates 

Measurement of improper payments (IP) includes both case and dollar errors. The case error 

refers to participants who were certified to receive, but who were not in fact eligible for, WIC 

benefits. Dollar error refers to the dollar amount of WIC benefits issued to and actually redeemed 

by ineligible participants, namely, overpayments. The term “certification error” is used for a 

broader concept that includes income eligibility error (error due to household income higher than 

WIC eligibility requirement) and other errors, such as expired certification error or error from 

incorrectly denying or terminating participants. In this study, the case and dollar error estimates 

are reported for income eligibility error only. Other types of errors were explored, with tentative 

results presented in appendices, and not included in the formal improper payment statistics. 

Based on the actual redemption data available for respondents—as required by the Contract—it 

was only possible to calculate dollar amount of overpayments, not of underpayments. 

Underpayments occur when persons are inappropriately denied benefits or given food vouchers 

worth less than that to which they are entitled. With errors caused by either inappropriately 

denied benefits or incorrectly issued low voucher values, underpayment dollar error must be 

estimated based on the assumed voucher value that the erroneously denied person should have 

received, rather than the actual redemption value as the Contract requires. This study examined 

participant perceived reasons for benefit denial and termination, but did not formally assess case 

error and related dollar errors because it was not feasible to do so in this study. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II xvi 

The study collected actual redemption records from the States for each participant sampled, from 

which the actual dollar amount of the redeemed benefits was determined for any erroneous case. 

In contrast, NSWP-I relied on estimates from the WIC average food cost per participant in the 

1998 fiscal year. 

Income Eligibility Error 

Of the different program eligibility criteria examined, the most challenging was the income 

eligibility error. It is complicated by the variety of discretionary guidelines used by States in 

defining the economic unit, treating special cases, and determining which income sources to 

count. It requires applicants to report incomes and provide supporting evidence, which is often 

subject to judgment. Income eligibility error is therefore the main focus of the improper payment 

analysis. 

Exhibit ES-1 illustrates the basic process of identifying certification errors based on WIC 

eligibility criteria. Data obtained during the in-person interview as well as information collected 

via the special case study follow-up review with States both served as independent ways to 

document the WIC certification process and confirm correct or erroneous eligibility. 

Exhibit ES-1: Process of Identifying Erroneous Certification, by Eligibility Criteria 

Source: National Survey of WIC Participants II Study 

Of the WIC participant population of 9,113,617 in May 2009, this study estimated 277,952 cases 

to have income eligibility error due to income error, equivalent to 3.05 percent of the total WIC 
Volume 3: Improper Payments Report (Final Report) 

 xvii National Survey of WIC Participants II 

participant population (95% C.I. = 2.29% and 3.70%; see Exhibit ES-2)3. Unless specifically 

noted otherwise, all statistics presented in this report are weighted by final sample weights and 

adjusted for the survey design effect (see Appendix G for details). 

Eligibility error estimates were examined by WIC Program category in Exhibit ES-2 for a group 

comparison. For breastfeeding women and infants, the eligibility error rates were 5.18 percent 

and 5.29 percent, respectively. The error rates for participants in the other three program 

categories were found to be lower (3.31% for pregnant women, 2.14% for postpartum women, 

and 1.85% for children). 

Exhibit ES-2: Number and Percent of Eligibility Errors, by WIC Category 

WIC Category 

Number of 

Respondents in Error 

(Unweighted sample 

units) 

Number of All WIC 

Participants in Error 

(Weighted sample 

units) 

Percent 

(%) 

95% Confidence 

Interval 

(%) 

Pregnant 7 31,750 3.31 0.25 6.38 

Breastfeeding 9 30,174 5.18 1.23 9.12 

Postpartum 6 13,538 2.14 0.16 4.11 

Infant 11 114,091 5.29 2.07 8.50 

Child 6 88,399 1.85 -0.32 4.02 

Total 39 277,952 3.05 1.42 4.68 

Source: National Survey of WIC Participants II Study 

Using redemption records collected from State agencies, the dollar error estimates were 

determined from food voucher values actually redeemed by sampled WIC participants whose 

eligibility was determined to have been in error. 

Infant Formula Rebates 

Adjustment for WIC infant formula rebates was necessary to measure the real cost to the WIC 

Program because State WIC agencies receive substantial rebates on the price of infant formula 

from the manufacturers. These rebate values should be subtracted from the redeemed infant 

voucher value for each infant identified as having eligibility error to generate post-rebate dollar 

error estimates. The available redemption records, however, would not allow rebate adjustments, 

because the redemption data contained only an overall redeemed value for each food voucher. 

To proxy the rebate value of the infant formula out of the redeemed value and calculate the postrebate cost, researchers integrated WIC administrative data and NSWP-II statistics 

(see Appendix L for details). 

As pre-rebate and post-rebate dollar errors were sample estimates based on May 2009 

information, both needed to be aggregated to represent the nationwide improper payment for 

 

3

 The conventional 95% confidence Interval or C.I. is used in this report to indicate the variation of the statistics. For example, 

C.I. =2. 29% and 3.70% indicating that if repeated samples were surveyed, 95 percent of the samples would generate an 

eligibility error rate ranging from 2.29% to 3.70%. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II xviii 

FY2009. This was done by applying final sample weights and an annualizing factor to represent 

the WIC total improper payment in FY2009. 

Pre-rebate Annual Dollar Error 

The resulting pre-rebate overpayment dollar errors, estimated at $233,176,463 

(95% C.I. = $117,658,694 and $348,694,109), represent the WIC total improper payments in 

FY2009 (Exhibit ES-3a). Of the approximately $6.27 billion WIC total food costs, including 

infant rebate values, the pre-rebate dollar error rate amounted to about 3.72 percent 

(Exhibit ES-4). Breaking up the annualized estimates by WIC category, the pre-rebate dollar 

error was highest for infants at approximately $151 million and lowest among postpartum 

women, at approximately $6 million (Exhibit ES-3a). 

Exhibit ES-3a: Pre-rebate Annualized Dollar 

Amounts of Eligibility Error by WIC Category 

WIC Category 

Number of WIC 

Participants in Error 

Dollar Error 

($) 

95% Confidence Interval 

(%) 

Pregnant 31,750 15,722,258 753,800 30,690,715 

Breastfeeding 30,174 26,051,235 5,915,464 46,187,006 

Postpartum 13,538 6,431,074 854,399 12,007,749 

Infant 114,091 151,391,157 47,887,348 254,894,965 

Child 88,399 33,580,679 -10,435,616 77,596,975 

WIC Total 277,952 233,176,403 117,658,694 348,694,109 

Source: National Survey of WIC Participants II Study 

Post-rebate Annual Dollar Error 

Post-rebate annual dollar error was estimated to be $130,499,928 (95% C.I. = $70,478,357 and 

$190,521,499, see Exhibit ES-3b). Dividing this by the approximately $4.37 billion WIC food 

costs (excluding infant rebate values, see Infant Formula Rebates), the post-rebate dollar error 

rate was about 2.98 percent (Exhibit ES-4). This lower rate was due to the rebate adjustment, in 

which the dollar error estimate for infants was reduced to $48,714,683, substantially lower than 

the pre-rebate estimate of $151,391,157, though still higher than the estimates for other 

categories. 
Volume 3: Improper Payments Report (Final Report) 

 xix National Survey of WIC Participants II 

Exhibit ES-3b: Post-rebate Annualized Dollar 

Amounts of Eligibility Error, by WIC Category 

WIC Category 

Number of WIC 

Participants in Error 

Dollar Error 

($) 

95% Confidence Interval 

(%) 

Pregnant 31,750 15,722,258 753,800 30,690,715 

Breastfeeding 30,174 26,051,235 5,915,464 46,187,006 

Postpartum 13,538 6,431,074 854,399 12,007,749 

Infant 114,091 48,714,683 16,222,015 81,207,352 

Child 88,399 33,580,679 -10,435,616 77,596,975 

WIC Total 277,952 130,499,928 70,478,357 190,521,499 

Source: National Survey of WIC Participants II Study 

Exhibit ES-4: Pre- and Post-rebate Annualized Dollar Error 

Amounts and Dollar Error Rates: WIC Eligibility Error 

Dollar Error Amount ($) WIC Total Food Costs ($)a Dollar Error Rateb

Pre-rebate 233,176,403 6,274,051,429 3.72% 

Post-rebate 130,499,928 4,373,776,840 2.98% 

a

 The denominator for pre- and post-rebate dollar error rates was the total food costs (the sum of the food costs and infant rebates 

billed) and the food costs, excluding infant rebates value, respectively. Both numbers were for 48 contiguous States and tribal

organizations and were obtained from the FY2009 WIC Monthly Report, http://www.fns.usda.gov/pd/wicmain.htm as of 

07/29/2011. 

b

 Dollar error rates were calculated using aggregated values without variance estimation. 

Source: National Survey of WIC Participants II Study 

Three Studies: A Comparison 

Income eligibility case error and dollar error are two improper payment indicators available from 

two previous studies conducted in FY1988 and FY1998.4 Exhibits ES-5 and ES-6 summarize the 

statistics from the three studies. For income eligibility case error rate, the FY1988 estimate was 

5.7 percent (95% CI = 3.80 and 7.60); the FY1998 estimate was 4.5 percent (95% CI = 2.80 and 

6.20); and the current study (NSWP-II) estimate for FY2009 was 3.05 percent (95% CI = 1.42 

and 4.68). 

For dollar error amount and dollar error rate due to income eligibility errors, the FY1988 study 

estimated a dollar error of approximately $84 million out of the yearly total WIC food costs of 

$1.5 billion—a dollar error rate of 5.60 percent. For FY1998, the NSWP-I estimated a dollar 

error of approximately $120 million out of total WIC food costs of $2.6 billion—a dollar error 

rate of 4.62 percent. For FY2009, this study estimated a total (pre-rebate) dollar error of 

approximately $233 million out of about $6.27 billion total WIC food costs—a pre-rebate dollar 

error rate of 3.72 percent. Adjusting for infant formula rebates, the FY2009 post-rebate dollar 

error was estimated to be approximately $130 million out of about $4.37 billion WIC food costs, 

excluding infant rebates billed—a post-rebate dollar error rate of 2.98 percent. 

 

4

 The two studies are Food and Nutrition Services, USDA (1990) WIC Income Verification Study (for FY1988) and Food and 

Nutrition Services, USDA (2001). National Survey of WIC Participants (for FY1998). 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II xx 

As illustrated in Exhibit ES-7, an overall declining pattern is observable in the declining point 

estimates of income eligibility case error rate and the dollar error rate from the prior studies for 

FY1998 and FY1988 and the current study for FY2009 (with either pre- or post-rebate estimates). 

The differences across the rates of those three years are not statistically significant as the 

estimated confidence intervals for the years overlap. The comparison suggests that the WIC 

programs have continued its steady operation in appropriately certifying applicants, perhaps even 

with improvement. Relevant law and regulation changes that require income documentation for 

WIC certification may be a factor underlying the performance. The large policy changes 

occurred—but were not implemented—just 1 year or so before the 1998 study (NSWP-I) and 

have been well implemented by 2008.5 The low error estimates from NSWP-II relative to those 

from prior studies may be partly attributable to the policy shift. 

Exhibit ES-5: Estimates of Eligibility Case Error Rates: 1988, 1998, and 2009 

Fiscal Year Eligibility Case Error Rate (%) 95% CI (%) 

FY1988 5.70 3.80 7.60 

FY1998 4.50 2.80 6.20 

FY2009 3.05 1.42 4.68 

Sources: National Survey of WIC Participants II Study, National Survey of 

WIC Participants I Study, and WIC Income Verification Study 

Exhibit ES-6: Estimates of Dollar Error and Dollar Error 

Rates from Eligibility Error: 1988, 1998, and 2009 

Fiscal Year 

Dollar Error 

($ Million) 

95% CI of Dollar 

Error ($ Million) 

WIC Total 

Food Cost 

($ Million) 

Dollar Error Rate 

(%) 

FY1988 84 56 112 1,500 5.60 

FY1998 120 69 172 2,600 4.62 

FY2009 pre-rebate 233 118 349 6,274 3.72 

FY2009 post-rebate 130 70 190 4,374 2.98 

Sources: National Survey of WIC Participants II Study, National Survey of 

WIC Participants I Study, and WIC Income Verification Study 

 

5

 Swann, C. (2008). Public policy, Economic conditions, and WIC caseloads. Accessed at http://www.uncg.edu/bae/econ/ 

seminars/2008/Swann.pdf 
Volume 3: Improper Payments Report (Final Report) 

 xxi National Survey of WIC Participants II 

Exhibit ES-7: Estimates of WIC Case and Dollar Error Rates: 1988,6 1998, and 2009 

Sources: National Survey of WIC Participants II Study, National Survey of 

WIC Participants I Study, and WIC Income Verification Study 

Other Certification Errors 

No identity or residence errors were found based on the in-person interview data reviewed on 

WIC participants’ identity and residence proofs. All of the responding participants presented 

appropriate documents for establishing eligibility based on identity and residence. 

This study explored the category error due to age misclassification of infants and children (e.g., a 

child older than 5.5 years who received benefits). The information collected to assess this type of 

error was limited as will be explained, so the analysis generated only tentative results that require 

caution and further research. Category errors among women were not examined because it would 

require extensive effort such as medical record checks, which were deemed beyond the scope of 

the study. 

Another exploratory analysis was intended to identify expired certification error, which occurs 

when the participant receives and redeems vouchers after certification has expired. To determine 

expired certification error, the analysis used the available data on certification dates from State 

agencies—a source considered not adequately reliable. Local agency data on certification are 

updated more frequently and are more accurate because local agencies actually conduct 

certification, but these data were not collected in this study. Therefore, the results generated from 

the analysis of expired certification error also require caution. Technical details and preliminary 

estimates on infant age misclassification error and expired certification error are presented in 

Appendices H and K, respectively. 

New Applicant Denials 

The term “Denials” refers to new applicants who fill out and submit an application for WIC 

benefits and are denied eligibility. As the April and May 2009 data collection revealed, the 

 

6

 Rebates were first provided to States in 1988; therefore, the amount of rebates used was too small to affect findings. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II xxii 

number of Denials is small. Of the 147 clinics sampled to collect names of Denials, 14 clinics 

did not deny any new applicants at all during the reference month, and a large number of others 

provided very few denial cases. In addition, four clinics did not provide researchers their record 

of applicants denied. The end result was that only 410 denied new applicant names were 

obtained. 

The low number of denials compelled the use of all new applicant denial cases provided by the 

responding clinics in order to produce descriptive statistics. The resulting statistics are not to be 

considered nationally representative because of the lack of information on the new applicant 

denial population and the absence of data for a large subset of states/clinics. The findings on new 

applicant denials are primarily intended to provide some insight into why new WIC applicants 

were denied eligibility and to examine respondents’ perception of agency mistakes in denying 

their applications. 

A total of 410 denied new applicant records were available from sampled clinics, resulting in 194 

completed telephone interviews. The low response rate appears to result from a 7- to 9-month lag 

time between the date of denial and the telephone survey and inability of data collectors to reach 

the denied new applicants. 

Denied new applicants were asked if the reason for their ineligibility for WIC was related to 

proofs of identification, residence, income, or something else. As shown in Exhibit ES-8, income 

is by far the main reason applicants were turned down for WIC benefits (93.3%). Only small 

proportions cite eligibility problems associated with category (2.6%), residence (0.5%), and/or 

“Other” (2.1%). No respondent reported lack of identification as an eligibility problem. The 

“Other” problems typically involved miscommunication between the applicant and the clinic; for 

instance, one applicant stated that the WIC clinic never got back to her. 

Exhibit ES-8: Reasons for Ineligibility: Percentage of New Applicant Denials 

93.3

2.6 2.1 1.5 0.5

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Income Category Other Don't know/

remember

Residence

Percentage

n = 194 

Note: Multiple responses permitted. 

Source: National Survey of WIC Participants II Study 
Volume 3: Improper Payments Report (Final Report) 

 xxiii National Survey of WIC Participants II 

The reasons for a new applicant denial depend mostly on the type of proof. Income proofs 

submitted by the denied new applicants, for example, were almost always considered ineligible 

because of excessive income (174 out of 181 cases of those denied were income ineligible, or 

96.1%), as opposed to submitting an unacceptable type of proof. Category problems all occurred 

because the applicants were out of scope—that is, not within the WIC definitions for “Pregnant,” 

“Breastfeeding,” “Postpartum,” “Infant,” or “Child.” No one reported that ineligibility was based 

on unacceptable documents—for example, one with an expired date or lacking an address or 

photo. 

When respondents were asked if they felt the agency had “made a mistake” in assessing their 

eligibility, 25.3 percent of denials responded “Yes,” 68.6 percent of respondents responded 

“No,” and 6.2 percent were either unsure or chose not to answer. All who reported that the 

agency made a mistake were rejected for reasons of income. It would appear that the most 

contentious claims were made by applicants whose incomes fell recently but who only had 

proofs showing the higher income levels. Other discontent came from individuals who had been 

led to believe by friends or medical staff that they would qualify for benefits. 

More than a quarter of total denied new applicants (25.2%) said they took follow-up actions; of 

those, 71.4 percent reapplied for benefits. From their recorded verbatim remarks, it appears that 

the vast majority (94.3%) of those who reapplied obtained WIC benefits on their second attempt. 

Terminations/Discontinuations 

Terminated/discontinued participants are WIC participants who received WIC benefits for a 

period of time and whose eligibility came to an end. In this study, the term 

“terminations/discontinuations” applies to participants whose benefits ended and who voluntarily 

withdrew from the WIC Program (discontinuations) and participants who sought to reapply when 

their eligibility expired and who were involuntarily determined to be ineligible for continued 

benefits by their WIC clinic or agency (terminations). This sample of 

terminations/discontinuations was drawn from State agency lists of all participants who received 

food issuances in April 2009 but were no longer receiving them in May 2009. The purpose of 

this part of the study is to look at the reasons why participants’ benefits were ended and their 

perception about agency mistakes in terminating their benefits. 

In all, 393 out of 607 terminated/discontinued participants randomly sampled from a national 

sample of clinics participated in the study. The data were weighted to reflect the proportion of 

terminations/discontinuations in the WIC population. 

Similar to the WIC participant population as a whole, infants and children make up almost threequarters of terminated/discontinued WIC participants; pregnant, breastfeeding, and postpartum 

women constitute the rest. Infants are slightly underrepresented, while pregnant women are 

slightly overrepresented7

 (see Exhibit ES-9). 

 

7

 In the WIC participants’ survey, infants made up 24.3 percent and pregnant women made up 10.5 percent. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II xxiv 

Exhibit ES-9: Percentage of Terminated/Discontinued Respondents, by Category 

14.3%

4.8%

9.4%

17.5%

54.0%

Pregnant

Breastfeeding

Postpartum

Infant

Child

n = 393 

Source: National Survey of WIC Participants II Study 

The reasons for ineligibility are different for terminations/discontinuations than for denied new 

applicants. Whereas income ineligibility was at the root of most denials, there was no dominating 

reason for the ineligibility of terminated/discontinued participants for further benefits. Category 

changes, typically the proper stop of the benefit, such as a child who aged out of WIC or a 

mother who stopped breastfeeding, was the most important reason (17.1%). However, other 

factors also contributed significantly, such as missed appointments (13.6%), income changes 

(10.8%), residency changes (10.3%), identification issues (8.4%), and personal decisions (5.4%)8

(see Exhibit ES-10). About 13.8 percent were not aware that their benefits had ended because 

they had been recertified so quickly they were unaware of any gap in benefits. 

 

8

 Personal decisions not to continue with WIC often revolved around scheduling issues and/or perceived inconvenience of having 

to go to the clinic to get benefits. It also included decisions to stop because food and services were no longer needed. 
Volume 3: Improper Payments Report (Final Report) 

 xxv National Survey of WIC Participants II 

Exhibit ES-10: Reasons for Ineligibility in Terminated/Discontinued Respondents 

17.1

13.8 13.6

10.8 10.3

8.4

5.4

3.7

16.9

0%

5%

10%

15%

20%

Category Not Aware

of Being

Terminated

Missed

Appointment

Income Residency Identification Personal

Decision

Other Don't Know

Percentage

n = 393 

Note: Multiple responses allowed. 

Source: National Survey of WIC Participants II Study 

Although 13.8 percent of terminated/discontinued respondents were unaware their benefits had 

ended, others had their eligibility end because of changes in their situation or personal decisions. 

As such, very few terminated/discontinued participants (4.3%) disagreed with the decision. For 

example, those whose category status changed acknowledged the WIC Program rules; those who 

missed appointments admitted they would have to go back to get recertified; those who moved 

said they would reapply at their new clinic location; and those who decided to stop WIC for their 

own reasons had no issue with being terminated/discontinued. 

After the end of their benefits, 14.9 percent of terminated/discontinued respondents said they 

took follow-up action, which largely involved reapplying for benefits. However, it is noted that 

4.3 percent said they asked to speak to a supervisor, and 0.6 percent wrote a follow-up letter of 

protest to WIC. 

 1 National Survey of WIC Participants II 

CHAPTER 1. OVERVIEW OF THE WIC PROGRAM AND 

PROGRAM ELIGIBILITY

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is the 

United States Department of Agriculture’s (USDA) third largest food and nutrition assistance 

program. In existence for more than 35 years, the WIC Program provides benefits to low-income, 

pregnant, and new mothers and their young children who are at nutritional risk. The benefits 

consist of nutritious supplemental foods, nutrition education, and referrals to health care and 

social services. The WIC Program is funded by USDA’s Food and Nutrition Service (FNS), 

which administers the program in partnership with State and local agencies. Although research 

on the effectiveness of WIC has often been narrow in scope (thus reducing its generalizability),9

and some of it is dated, the WIC Program is still widely credited for increasing access to prenatal 

care,10 improving birth outcomes,11,12 reducing the incidence of anemia,13 and enhancing the 

nutritional quality of participants’ diets.14

Expansion of the program has been dramatic, with the number of participants increasing from 

88,000 per month in 1974 to more than 9 million per month in 2010. Today more than half of all 

infants and about one-quarter of children under 5 years old participate in the program. Current 

program emphases include promoting food security, healthy eating and weight, and 

breastfeeding. Recently, the WIC Program rolled out new food packages, which were fully 

implemented in all State agencies by October 2009. Reflecting the first significant revisions 

since 1980, the packages are designed to promote greater health by offering fruits and vegetables, 

as well as foods that have more fiber and less fat and cholesterol. In addition, foods allocated for 

breastfeeding mothers and infants have been increased to promote breastfeeding more strongly as 

the healthiest nourishment for infants. 

 

9

 U.S. Department of Agriculture, Economic Research Service. (2009). The WIC Program: Background, trends, and economic 

issues. Accessed at http://www.ers.usda.gov/publications/err73/ 10 Devaney, B., Bilheimer, L., et al. (1992). Medicaid costs and birth outcomes: The effects of prenatal WIC participation and the 

use of prenatal care. Journal of Policy Analysis and Management, 11(4): 573–592, Autumn. 11 Moss, N., and Carver, K. (1998). The effect of WIC and Medicaid on infant mortality in the United States. American Journal 

of Public Health, 88, 1354–1361. 12 Buescher P., Larson L., et al. (1993). Prenatal WIC participation can reduce low birth weight and newborn medical costs: A 

cost benefits analysis of WIC participation in North Carolina. J Am Diet Assoc., 93, 163–166. 13 Miller, V., Swaney, S. et al. (1985). Impact of the WIC Program on the iron status of infants. Pediatrics, 75(1), 100–105, 

January. 

14 Siega-Riz, A., Kranz, S., et al. (2004). The effect of participation in the WIC Program on preschoolers’ diets. The Journal of 

Pediatrics, 144(2): 229–234, February. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 2 

1.1 WIC Eligibility and Improper Payments 

To qualify for WIC benefits, applicants 15 must meet categorical, residential, identification, 

income, and nutritional requirements. These requirements are verified by the local WIC agency 

or clinic at the time of certification or subsequent recertification. The specific requirements 

involve demonstrating proof of the following: 

 Categorical Eligibility—The applicant must be either a pregnant woman (during pregnancy 

and up to 6 weeks after the birth of an infant or the end of pregnancy), a breastfeeding 

woman (defined as a postpartum woman who is breastfeeding up to 1 year after birth of 

infant), a postpartum woman who is not breastfeeding (up to 6 months after the birth of an 

infant or end of pregnancy), an infant (birth to 12 months) or a child (over 12 months and up 

to 5 years old). 

 Residential Eligibility—The applicant must live in the State in which the application 

is made. 

 Identity—The applicant must show a valid form of identification. 

 Income—The applicant’s household income must be at or below 185 percent of the Federal 

poverty line—or even lower, if so required by the State. Applicants are considered 

adjunctively income eligible if they participate in the Supplemental Nutrition Assistance 

Program (SNAP), the Temporary Assistance for Needy Families (TANF) program, or 

Medicaid. In addition, some States allow other State-administered programs to provide 

adjunctive eligibility; FNS requires that such programs document income eligibility. 

 Nutritional Risk—The applicant must be assessed by a health professional to be considered 

at nutritional risk. Conditions meeting this requirement include inadequate diet, a history of 

high-risk pregnancy, over- and under-weight, child growth problems, and anemia. 

Once the determinations of categorical, residential, identity, income, and nutritional risk 

eligibility have been accepted, the applicant is certified and enrolled in WIC. Once she/he starts 

receiving food vouchers under the Program she/he is considered to be a participant. Most WIC 

participants are eligible to receive benefits for a 6- to 12-month period, depending on their 

category. Pregnant women, for example, are certified for the duration of their pregnancy and up 

to 6 weeks postpartum; infants are certified up to their first birthday; all other categories are 

certified for 6 months. When the eligibility period expires, participants must be recertified in 

order to continue getting benefits, although WIC regulations do allow shortening or extending 

the period “not to exceed 30 days.”16

Section 17 of the Child Nutrition Act of 1966, as amended, sets the basic legislative 

requirements with which State and local agencies must comply. In addition, FNS publishes 

regulations at 7 CFR §246.7, which spell out in more detail specific requirements and options 

States follow to implement the program. In some matters—for example, nutritional risk 

priorities—the WIC regulations are very explicit and give little leeway. However, in many 

 

15 It is noted that applicants include both new applicants to the WIC Program as well as individuals who are currently 

participating in the program but are reapplying because their certification period is about to expire. 16 Code of Federal Regulations, Title 29 Part 1210, Section 354 (7 CFR §246.7 (g) (3)). 
Volume 3: Improper Payments Report (Final Report) 

 3 National Survey of WIC Participants II 

areas—for example, the list of low-income programs that will allow applicants to adjunctively 

qualify—the regulations give a fair amount of discretion to the States. 

1.2 Goals of the Study 

The objectives of the National Survey of WIC Participants II (NSWP-II) were to: 

1. Explore the characteristics and experiences of WIC participants, 

2. Provide information on the policies, procedures, operations, and staff at State and local 

WIC agencies, 

3. Estimate the annual cost of erroneous payments in the program, and 

4. Develop a model for updating estimates for the next 10 years. 

The research in this study follows up on the first National Survey of WIC Participants and their 

Local Agencies (NSWP-I), for which data were collected in 1998 and a report was issued in 

2001. Both NSWP-I and NSWP-II gathered data about WIC participants and developed national 

estimates of the case error rate and dollar error within the WIC Program. However, certain 

aspects of their methodologies differ. NSWP-I did not attempt to develop a model for future 

estimates of error. 

NSWP-II aims to add to the current body of WIC knowledge by providing updated information 

on participants’ satisfaction and use of WIC services and collecting data not covered by the other 

studies—especially in the area of State and local WIC agencies’ operations. Most importantly, 

the Program also fulfills the requirements of the 2002 Improper Payments Information Act. This 

law and subsequent Office of Management and Budget and executive directives stipulate that 

agencies must review all programs and activities and identify significant erroneous payments, 

defined as annual payment errors exceeding both 2.5 percent of program payments and $10 

million. 

This volume addresses the third goal of the Study—estimating the annual cost of erroneous 

payments in the program, focusing on overpayments due to income eligibility errors. It starts 

with an Executive Summary, and then gives an overview of the WIC Program (Chapter 1), the 

survey methodology (Chapter 2), the eligibility criteria (Chapter 3), improper payment estimates 

(Chapter 4), denial and termination/discontinuation findings (Chapter 5), and research limitations 

(Chapter 6). The report includes appendices comprising data collection instruments, 

communications to States, calculation of weights, and error estimation procedures. 

 5 National Survey of WIC Participants II 

CHAPTER 2. SURVEY METHODOLOGY

A two-part survey was administered from September 2009 to February 2010 to WIC participants 

who received benefits in May 2009, to gauge satisfaction with the WIC Program and to 

determine erroneous payments based on the assessments of eligibility for certification for WIC 

benefits. The survey consisted of two interviews: (1) a telephone interview of 2,538 WIC 

participants17 and (2) an in-person interview with 1,210 randomly selected respondents from the 

first interview. The telephone interview focused on clients’ satisfaction with the WIC Program 

and its benefits; and the findings were reported in the Participant Characteristics Report.18

The in-person interview focused on improper payments, the subject of this report. 

The in-person interviews were conducted in order to solicit and review documents or “proofs” 

that participants had originally submitted to gain eligibility in the WIC Program. The proofs 

shown during these interviews were used to determine if the standards set by FNS and/or the 

State agency had been met or not. Thus, the eligibility of each WIC participant was determined 

to be correct or improper and the number of case errors was determined. Translating case error 

into dollar error required obtaining the actual redemption data for respondents from State WIC 

programs for the period from May through July 2009. 

The last chapter of this report also presents findings from brief telephone surveys of two 

distinct groups: 

 Denied new applicants or new applicant denials—defined in this study as new WIC 

applicants who were denied benefits.19

 Terminations/Discontinuations—defined in this study as WIC participants whose eligibility 

had ended in May 2009 (who were not recertified for either voluntary or involuntary reasons). 

Incorrectly determined new applicant denials and terminations/discontinuations constitute a form 

of WIC Program underpayment, which contributes to improper payments. However, ascertaining 

the incorrect denials and terminations/discontinuations and dollar amounts associated with them 

was not feasible; therefore, the scope of the study was limited to descriptive analysis of the 

reasons for denial and termination/discontinuation and respondents’ perception about agencies’ 

mistakes in denying their applications or terminating their benefits. 

 

17 A total of 2,538 were actually interviewed to ensure sufficient completes by program category within sampled local agencies 

and clinics. 

18 U.S. Department of Agriculture, Food and Nutrition Service, Office of Research and Analysis, National Survey of WIC 

Participants II: Participant Characteristics Report, by Daniel M. Geller, et al., Project Officer: Sheku G. Kamara, Karen 

Castellanos-Brown, Alexandria, VA: 2012.

19 States that maintained new applicant denial information provided records from May 2009; States that did not keep such records 

at the State level submitted the names of denials for a 30-day period largely in the month of August 2009, depending on how 

quickly they were able to start gathering data. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 6 

2.1 Source of Data 

In-Person Interview 

In-person interviews were conducted with 1,210 randomly sampled WIC participants nationwide 

from September 2009 through early February 2010. Forty-four field interviewers administered 

the in-person interviews using laptops equipped with computer-assisted personal interviewing 

(CAPI) software. Field interviewers were each assigned a geographic cluster or area in which 

they were expected to complete approximately 30 in-person interviews each. The interviews 

lasted approximately 30 minutes or less and usually took place at the WIC participant’s residence. 

However, on occasion, at the respondent or interviewer’s request, interviews were conducted at 

another, more convenient, location such as a library or fast food chain restaurant. Respondents 

received a cash incentive of $20 for their time.20

To meet the language needs of the WIC population, 27 of the field interviewers were bilingual, 

in English and Spanish. In addition, the services of third-party telephone translators in the 

languages of Mandarin/Cantonese, Vietnamese, and Spanish were retained. 

Field interviewers each received 31⁄2 days of intensive training immediately prior to the start of 

data collection. They were instructed on recruitment and interviewing techniques, the WIC 

Program, data entry using CAPI on laptops, privacy and security measures, recordkeeping, and 

the various survey instruments. Respondents for the in-person interview were randomly chosen 

from respondents who completed the telephone survey and asked if they would participate at the 

conclusion of the telephone survey. This method worked effectively, and resulted in an overall 

response rate of 81.3 percent and a cooperation rate of 86.6 percent21 for the in-person interview. 

Exhibit 2-1 details the sample sizes and nonresponse counts and rates by WIC category, 

nonresponse subcategory, and data collection components. 

 

20 The incentive was paid even if a respondent discontinued the interview or opted not to answer all the questions. All persons 

receiving the $20 remittance were required to sign a receipt for the incentive in order to satisfy basic accounting requirements. 21 The completion rate is the total number of completes (1,210) divided by the total number of selected (1,489); the cooperation 

rate treated partial interviews as respondents (e.g., see American Association of Public Opinion Research, http://www.aapor.org/ 

AM/Template.cfm?Section=Standard_Definitions2&Template=/CM/ContentDisplay.cfm&ContentID=3156), or one minus the 

total refusal rate for this study. The difference between the two rates is attributable to those who agreed to the in-person interview 

but could not be located when interviewers appeared for or attempted to confirm the appointment. 
Volume 3: Improper Payments Report (Final Report) 

 7 National Survey of WIC Participants II 

Exhibit 2-1: Sample Sizes and Response Rates: Data Collection 

Components, WIC Program Categories, and Nonresponse Subcategories 

4,946 WIC Participants 

Sampled for the 

Telephone Survey 

(completed n=2,538)

78 Local

Agencies

148 Local

Clinics

40 Sampling Clusters

(in 23 State)

Pregnant

Sampled =974(100%)

Unreachable =30.7%

Refused =3.6%

Partial =12.0%

Complete =53.7%

Breastfeeding

Sampled =943(100%)

Unreachable =31.6%

Refused =3.3%

Partial =8.9%

Complete =56.2%

Postpartum

Sampled=1,015(100%)

Unreachable =39.1%

Refused =3.0%

Partial =9.7%

Complete =48.3%

Infants

Sampled=1,048(100%)

Unreachable =37.5%

Refused =4.0%

Partial =11.5%

Complete =46.9%

Children

Sampled =966(100%)

Unreachable =31.8%

Refused =4.0%

Partial =8.6%

Complete =55.6%

1,489 WIC Participants 

Sampled for the 

In-person Interview 

(completed n=1,210)

Pregnant

Sampled =312(100%)

Refused =12.8%

Partial =6.7%

Complete =80.4%

Breastfeeding

Sampled =304(100%)

Refused =10.2%

Partial =4.9%

Complete =84.9%

Postpartum

Sampled =274(100%)

Refused =14.6%

Partial =3.6%

Complete =81.8%

Infants

Sampled =285(100%)

Refused =15.1%

Partial =5.6%

Complete =79.3%

Children

Sampled =314(100%)

Refused =14.3%

Partial =5.7%

Complete =79.9%

Source: National Survey of WIC Participants II Study 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 8 

Questions for the in-person survey (see Appendices A and B), centered on establishing the size 

of the economic unit and validating proofs of residency, identification, and income (or adjunctive 

income).22 Field interviewers recorded the type of proof, document number, and the starting and 

expiration dates, if evident. For income documents, this process was quite involved. It entailed 

asking participants about 22 potential sources of income for each member of the economic unit, 

including the nature of the source (wages, alimony, unemployment compensation, etc.), the 

dollar amount, and frequency to be able to calculate a yearly total. 

If respondents were unable to produce clear-cut documentation of their eligibility, they were 

asked to sign a release form allowing researchers to retrieve third-party verification of their 

declared income sources and/or participation in other adjunctive eligibility programs. The release 

form could be an income release form, an undocumented employment information form, and/or a 

program release form, depending on the participant’s eligibility information from the State files. 

These are described in Exhibit 2-2. 

Exhibit 2-2: Income and Program Release Forms 

and Undocumented Employment Information Form 

Type of Form Description 

Income Release Form Release form authorizing ICF Macro to retrieve third-party verification of 

employment and gross pay of WIC participants for Spring 2009. 

Undocumented 

Employment 

Information Form 

WIC participants who were unable to provide employment verification (usually 

because of self-employment) filled out this form, which enumerated their work 

responsibilities, title (i.e., painter, babysitter), pay, and hours worked for 

Spring 2009. 

Program Release Form Release form authorizing ICF Macro to receive third-party verification of program 

participation in an accepted adjunctive program in Spring 2009. 

Source: National Survey of WIC Participants II Study 

The release forms were collected by the field interviewers and returned for verification by 

researchers through third parties. 

State-Provided Program Eligibility and Redemption Data 

The basic design for NSWP-II was a departure from NSWP-I, which sampled new 

applicants/certificants/recertificants at local agencies using intercept interviews. Intercept 

interviews, while convenient, raise several issues and potential sampling and non-sampling 

biases, including various external factors that affect the ability to contact participants. 23

Most importantly, it could affect the behaviors of respondents, staff, and interviewers, such as 

staff becoming more attentive to regulations and requirements than usual or otherwise changing 

 

22 Applicants who currently receive SNAP, TANF, or Medicaid are considered adjunctively income eligible and their actual 

income need not be further documented, as those programs have already done so. In addition, States may declare applicants 

automatically income eligible if they participate in State-administered programs that regularly document income and have 

income eligibility guidelines at or below WIC income guidelines. In this report, adjunctive eligibility refers to either adjunctive or 

automatic eligibility. 

23 For example, the selection of a single date or a few adjacent dates can introduce bias resulting from agency workload variation, 

weather, or various other external factors (e.g., near holiday, school closing). Also, sampling may not be as random as it appears, 

because of inadvertent bias associated with the appointment schedule, length of time applicants spend there, or the selection of 

participants with children in tow. 
Volume 3: Improper Payments Report (Final Report) 

 9 National Survey of WIC Participants II 

their behavior to cause a Hawthorne Effect.24 Thus, for NSWP-II, researchers sampled food 

vouchers for a single month. This had the benefit of including participants who were certified in 

many of the previous months, as well as those newly certified or recertified. It also helped avoid 

follow-up calls that would have been necessary to capture changes in status. 

In June 2009, shortly after OMB approved the data collection, States were asked to provide all 

active vouchers to participants that were redeemable in April and May 2009—that is, all food 

instruments issued to current, temporary, expired, and presumed eligible certificants—from the 

sampled clinics and local agencies. The samples of participants, terminations/discontinuations, 

and—in some States—denied applicants were selected from these data. Participants from 

vouchers redeemable in May and terminated/discontinued participants who received food 

issuances in April but none in May were sampled (Appendix E contains the data request letter 

sent to the States). 

Only five States maintained denial information in their databases: Alabama, Arizona, Louisiana, 

New York, and Virginia. Their denied applications were sampled for May 2009. Local agencies 

and clinics were asked to help identify denied applicants in the remaining States.25 Logs were 

provided for use in recording a list of denied applicants for 30 days, mostly in August 2009, but 

sometimes extending into September 200926 (see Appendix F). 

Clinics recorded the name of the applicants, all contact information, and the reason for denial. 

The applicants were intended to be sampled and later interviewed by telephone. In the majority 

of the States, there were so few names that sampling was irrelevant. In some clinics, there were 

no denied applicants for the month. It is not clear whether this is a general pattern of selfselection or whether the timing of the study—during a strong economic recession—contributed 

to the low numbers. Another related issue concerns the purported presence of applicants who are 

denied without submitting an application. Some applicants inquire about eligibility criteria over 

the telephone, and based on the communication decide not to submit an application. Thus, they 

are not applicants and cannot be considered denied; but there was no way of ascertaining 

whether they understood the information provided and acted according to their best interests. 

To collect the redemption information, States were asked to pull redemption records for all 

participants with food vouchers in May 2009. Redemption data were obtained on the entire 

sample frame, rather than being limited to the sample of those interviewed. Since WIC clients 

have up to 30 days to cash their food vouchers, and vendors and banks have up to 60 days to turn 

them in, it was not practical to ask States to extract these data until January 2010. 

Redemption data were requested from the States primarily during January to February 2010. 

States were asked to provide those data approximately 1 month later. Several States were late in 

doing so and did not comply until April or May 2010. North Carolina was unable to provide 

 

24 First described by Henry A. Landsberger, it is the process in which human subjects of an experiment change their behavior 

because they are being observed, thus creating an inherent bias. See http://www.experiment-resources.com/hawthorne-effect.html. 25 Tennessee has a system whereby applications are entered in real time and the applicant is immediately informed of the decision. 

The centralized regional offices, which maintain the processing systems, do not retain information on denied applicants. 

The State objected to keeping lists at the clinic or agency level, since offices that enter applications into the systems handle 

multiple State and Federal assistance programs and are not staffed by the Department of Health and, therefore, are not directly

accountable to the WIC Program. Thus, Tennessee was excluded from the denied new applicant sample. 26 North Carolina could not meet the timetable for providing data for the study and did not do so until nearly November 2009. 

Denied applicant lists from North Carolina were similarly delayed by 30 days in October to November 2009. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 10 

redemption records because of major system difficulties.27 Redemption information obtained 

included voucher number and type, maximum redemption value, actual redeemed dollar amount, 

issuance and redemption dates, along with client information previously provided to enable 

records to be matched with the initial sample (see Appendix E). 

2.2 Survey Content 

Participant Interviews 

The purpose of the in-person interviews with participants was to review program eligibility 

information and obtain supporting documentation to verify whether eligibility was correctly 

determined, and if not, determine whether there was an erroneous payment. To qualify for WIC 

benefits, applicants must meet WIC’s identification, residential, categorical, nutritional, and 

income requirements. This study substantiated identification, residential, income (including 

adjunctive income), and some categorical eligibility information. 

In-person interviews did not assess nutritional eligibility or categorical eligibility beyond that of 

establishing the ages of participating infants and children. Assessing category error among 

women (i.e., determining whether each woman was correctly classified by WIC category—

pregnant, breastfeeding, and postpartum) was not attempted since it would have required a more 

intrusive effort (e.g., medical document check). Data collectors observed gender and age of 

respondents during interviews and found no evidence of error. 

To assess eligibility, field interviewers were asked to request and record selected information 

from four basic sources of documentation from the WIC participants: (1) identification, (2) proof 

of residence, (3) household size or family economic unit, and (4) proof of income or adjunctive 

eligibility. The field interviewer was directed by the computer-assisted software on what types of 

evidence were acceptable for each requirement, something that varied by State. To determine 

income or adjunctive income eligibility, WIC participants were asked to show either 

(1) documentation of household gross income at or below 185 percent of the Federal poverty 

level (FPL) or other criteria set by the State, or (2) evidence—such as an award letter, voucher or 

participation card from certain Federal or State means-tested programs—that meets the WIC 

income requirements.28

Interviews with Denied Applicants and Terminated/Discontinued Participants 

Interviews with denied applicants and terminated/discontinued participants sought to establish, 

via a short telephone survey, whether respondents perceived they had been correctly declared 

ineligible for WIC. Therefore, the surveys asked targeted questions about whether respondents 

had been denied eligibility for reasons of identification, residency, income, or anything else. If so, 

respondents were asked if they agreed with the decision; if not, they were asked what specific 

document they had shown and if the document showed their name, address, photo, and other 

 

27 North Carolina was unable to provide the data within a conceivably acceptable timeframe. Therefore, ICF Macro used the 

multiple imputation procedure to impute redemption values for North Carolina cases (see Section 3.5: Redemption Data 

Processing). 28 In some interviews, where the evidence presented was not clear cut, interviewers asked for both income and adjunctive income 

proofs as an added precaution. 
Volume 3: Improper Payments Report (Final Report) 

 11 National Survey of WIC Participants II 

identifying information. Respondents were also asked whether they could “see the point” for the 

end of their WIC benefits, a question that drove to the issue of fairness of the denial decision. 

They were also asked if they had appealed the decision and, if so, the outcome of that appeal. A 

few demographic questions were asked as well (copies of surveys for denials and 

terminations/discontinuations are in Appendices B and C). 

2.3 Sample Design and Weights 

The sample design includes the sample allocation, selection of the sample of States, sample 

creation and selection of clusters, selection of WIC agencies, clinics, and participants, and 

weighting. 

Sample Allocations 

The sampling design and sample sizes required for this study were driven by the required 

estimates of case error and improper payments. Exhibit 2-3 presents the assumed estimates of 

error rate and other estimates used to calculate the sample size. In each case, the percentage of 

ineligibles among participants was estimated, with an estimate of 10 percent as a conservative 

figure. For the entire population of participants, the revised Contract required a 90 percent 

confidence interval with an error margin of ±2 percent around the estimate of the rate of 

erroneous certifications, and the same estimates around the rates of erroneous payments. For 

each of the categories of participants (pregnant women, breastfeeding mothers, postpartum 

women, infants, and children) the design called for a 90 percent confidence interval ±5 percent.29 

Exhibit 2-3: Estimates of Error 

Parameter Population 

Assumed 

Estimate (%) 

Probability 

(%) 

95% Confidence Interval 

(%) 

Error rates Combined categories 10 90 ±2 

Error rates Separate categories 10 90 ±5 

Source: National Survey of WIC Participants II Study 

The in-person survey sample of 1,210 completed interviews was a subset of the original 

telephone survey of 2,538 WIC participants. For details on sample size calculation and design 

effect adjustments, see Appendix G. 

Selection of States 

The primary sampling units (PSUs) were States, selected with probability proportionate to size 

(PPS) and probability minimum replacement (PMR).30 The use of PMR meant that multiple 

selections of the larger States were likely. This was done so that the probability of selection of 

 

29 The resulting estimated total case error rate is 3.03% with 95% C.I. = ±1.63%; breaking up by program categories, case error 

rates range from 1.85% for children to 5.29% for infants, with 95% C.I. ranging from ±1.98 to ±3.95, meeting the expected 

precision levels. See Exhibit 4-4 for details. 

30 Probability Minimum Replacement was implemented using the method proposed by Goodman and Kish in 1950, but with the 

added feature that multiple selections may occur for large States. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 12 

any participants in a given category would be approximately equal. States selected multiple times 

had more local agencies selected (two agencies, each time). 

PPS sampling uses a measure of size (MOS) so that the probability of selecting a State is 

proportional to the MOS. The MOS for the States was the average of the proportion of 

participants in each of the five categories found in the State. Note that the sampling units are 

States, not State agencies, which means that participants from Indian Tribal Organizations (ITOs) 

are counted in the context of the States in which they belong. A sample of 40 state clusters, not 

necessarily unique and different States, was selected from the 48 contiguous United States, 

including the District of Columbia, so that the total number of actual geographic States in the 

sample was 23. Exhibit 2-3 presents the sampled States by FNS region and the number of 

clusters sampled given the size of the State. The number of times a State was sampled 

determined the number of clusters sampled within that State. Seven States were sampled multiple 

times for a total of 23 separate sample States. For extended discussion of state sample selection 

method, see Appendix G. 

Exhibit 2-4: Count of Sampled Research Clusters in the Sampled States 

Sampled State FNS Region Clusters Sampled 

Massachusetts 1 1 

New York 1 3 

Maryland 2 1 

New Jersey 2 1 

Pennsylvania 2 1 

Virginia 2 1 

Alabama 3 1 

Florida 3 3 

Georgia 3 2 

North Carolina 3 1 

Tennessee 3 1 

Illinois 4 2 

Indiana 4 1 

Michigan 4 1 

Ohio 4 2 

Louisiana 5 1 

Texas 5 5 

Colorado 6 1 

Kansas 6 1 

Missouri 6 1 

Arizona 7 1 

California 7 7 

Washington 7 1 

Total = 23 Total = 40 

Source: National Survey of WIC Participants II Study 
Volume 3: Improper Payments Report (Final Report) 

 13 National Survey of WIC Participants II 

Selection of Agencies 

Two local agencies were sampled with probability proportional to size (PPS) from each cluster. 

Calculations of the local agency measure of size (MOS) were similar to the State and cluster size 

calculation described above. Instead of averaging the proportions of each category of WIC 

participants within a cluster relative to the State totals, the calculations for proportions used the 

cluster totals. The MOS was multiplied by two to ensure the sampling of two local agencies from 

each research cluster sampled. For example, with three clusters in New York, six local agencies 

were selected. 

Selection of Clinics 

Prior to the selection of the clinics, researchers allocated a total of 2,400 WIC participants across 

all local agencies sampled, with each category of WIC participants receiving 480 participants. 

For all but the one local agency sampled multiple times, 30 participants were allocated across the 

5 categories within each local agency. (The large California local agency received 90 WIC 

participants, or 3 times the normal allocation.) Allocation of the 30 participants per local agency 

occurred according to the distribution of participants across categories in the local agency 

relative to the distribution of participants among all sampled local agencies. Appendix G presents 

details of the clinic sample selection procedure. 

Selection of Participants 

After the selection of WIC clinics, the lists of respondents were obtained from all the clinics 

sampled in two consecutive months (April and May 2009)—several months before data 

collection. The later month was the target month. 

Participants were classified into the five categories: Pregnant, Breastfeeding, Postpartum women, 

Children, and Infants. If a participant changed categories within the target month, the most recent 

category was used. Thus, if an infant became a child during the month, receiving some vouchers 

(food instruments) as an infant and some as a child, that infant was considered a child for 

sampling purposes. 

Each participant in a clinic was assigned a random number. Sorted by that number, participants 

underwent a selection process by the first n from each category (where n is the allocation of 

participants at the clinic level). The remaining participants in a category were retained in their 

order of selection and acted as replacements; so if a participant (or the participant’s mother) 

refused to respond or could not be located, the next one in line was selected. Once a participant 

completed a telephone survey, they became eligible for the in-person audit. The first n / 2 

respondents were selected to participate in the audit. In other words, if six participants were to be 

sampled from a given category, each participant in turn was asked to participate in the audit until 

three agreed. If any refused to participate, the next telephone respondent was selected. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 14 

Weighting 

States were sampled with PPS using a size measure that was the average of the proportion of 

participants in each of the five participant categories. This average was then multiplied by the 

number of States (clusters) allocated to the sample (40), and the inverse was used as the primary 

sampling unit (PSU) weight. Similarly, the clusters were also sampled with PPS using a similar 

MOS. The MOS was then multiplied by the number of research clusters sampled. Local agency 

probabilities of selection were two times that of the local agency MOS divided by the sum of the 

MOS for the agencies in the cluster. California and Texas samples were treated in a modified 

procedure (see Appendix G). 

For the participant telephone interview sample, the probability of selection was calculated with 

the number of participants sampled from the category divided by, the total number of eligible 

participants from the category receiving WIC benefits from the clinic. 

For the in-person interview sample, the probability of selection was calculated with the number 

of participants sampled from the category divided by the total number of completed telephone 

participants by WIC category in each selected clinic. The initial weight for an in-person 

participant was the inverse of their probability of selection (i.e., the product of the probabilities 

of selecting States, clusters, agencies, and clinics). 

The initial weight was adjusted via a nonresponse bias analysis conducted on the in-person 

participant’s sample. It was found that in-person interview participants were significantly more 

likely to participate in SNAP and that SNAP participation was related to some variables used in 

improper payment analysis. Adjustment of the in-person sample weights and replicate weight 

was made via sample raking, applying the WIC population’s SNAP participation rates. 

Appendix G presents the concepts and key issues in nonresponse bias analysis and Appendix I 

contains tabulations of nonresponse analysis. 
 15 National Survey of WIC Participants II 

CHAPTER 3. WIC ELIGIBILITY CRITERIA

Participant eligibility for WIC benefits is assessed using five basic criteria established in the 

Federal regulations and implemented by State WIC programs. These are (1) categorical 

eligibility—the category of the applicant; (2) residential eligibility—place where the applicant 

lives; (3) proof of identity; (4) nutritional risk eligibility; and (5) income (or adjunctive income) 

status. Nutritional risk eligibility was not assessed in this study, per the instructions of FNS. This 

chapter describes the assessment and analysis of the other four eligibility requirements. 

3.1 Category 

To receive WIC benefits, an applicant must be either a pregnant woman (any time during 

pregnancy and up to 6 weeks after the birth of an infant or the end of pregnancy); a breastfeeding 

woman (up to 1 year after delivery of baby); a postpartum woman who is not breastfeeding 

(up to 6 months after the birth of an infant or end of pregnancy); an infant (birth to 12 months); 

or a child (1 to 5 years old). Many States allow up to 30 days of leeway in the cut-off dates of 

these definitions, as permitted by Federal regulations. States that do not allow a leeway operate 

by the set periods defined by the legislation. 

Birth dates of respondents were collected and the ages of infants and children were examined as 

of May 1, 2009, to ascertain if they were eligible for participation by age (i.e., under 5 years old). 

If so, further checks were made to see if participants were correctly classified as an infant or 

child, taking into account the possible 2 to 4 weeks of leeway granted by the State. 

The categorical eligibility of women participants was not assessed by researchers because it 

would require extensive effort to check respondents’ medical records or other evidence for 

pregnancy and postnatal experience. Data collectors did, however, conduct onsite observation 

and found no evidence of category errors among women. 

3.2 Residency and Identity 

Federal regulations require that agencies check the residency and identity of participants or, in 

the case of infants or children, the identity of the parent or guardian. If the applicant is a migrant 

farm worker, homeless individual, or victim of theft, loss, or disaster, agencies are permitted to 

fulfill the requirement by having the applicant confirm in writing his/her residency or identity 

(7 CFR §246.7 (c) (2) (i)). Beyond these instructions, State agencies are allowed to decide what 

they consider to be acceptable proofs of residency and identity. 

For the survey, a list of the residency and identity proofs most commonly accepted by States was 

developed. Then, proofs provided by the respondents were reviewed during the in-person 

interview and later classified as shown in Exhibit 3-1. It is noteworthy that, in many States, WIC 

folders 31 are considered permissible proofs of residency and identification for subsequent 

recertification of WIC participants. As such, WIC folders were placed as an item on the list of 

acceptable proofs. 

 

31 WIC folders contain the official WIC participant identification documents issued by WIC agencies, which show eligibility 

based on extant documentation in the official case file. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 16 

Exhibit 3-1: Residency and Identity Proofs Collected 

Residency Proofs Identification Proofs 

 State-issued license or ID w/ address  State-issued license or ID 

 State/Federal correspondence w/address  U.S. passport w/photo 

 WIC folder  Foreign passport w/photo 

 Checkbook w/address  WIC folder 

 Rent or mortgage receipt, lease w/address  W-2 form or Tax bill w/name 

 Utility or tax bill w/address  Birth certificate 

 Documents from public school w/address  Social Services letter w/ name 

 Written statement from reliable third party 

(e.g., non-profit aid organization) 

 Social Security or Green Card 

 Hospital or immunization record 

 Other: SPECIFY  Other: SPECIFY 

Source: National Survey of WIC Participants II Study 

3.3 Economic Unit 

Ascertaining the exact size of a participant’s family economic unit (or household)32 is especially 

important to the determination of income, since the larger the economic unit, the more income is 

allowed. In the survey of State agencies, the States reported that they use the national WIC 

Program definition without much variation. According to that definition, a family economic unit 

is “a group of related or nonrelated individuals who are living together as one economic unit.” 

To ascertain economic unit, all members living in the same household were asked about their age 

and their relationship to the sampled individual (spouse, child, cousin, partner, etc). In an effort 

to make the national definition more user-friendly for respondents, during the interview, the 

concept was restated in one of two ways depending on the age of the household member being 

referred to: 

 If the household member was 15 years old or older, interviewers asked— 

Do you consider [NAME] to be part of your family group—that is, you are sharing 

income and expenses as if you were a family—OR do you feel that you each keep your 

income and expenses and food separately? 

 If the household member was 14 years old or younger, interviewers asked— 

Do you consider [NAME] to be part of your family group—that is, you are responsible 

for taking care of them as if you were all in the same family? 

If the answer was “Yes” in either case, the person was considered part of the economic unit; 

if “No,” then the person was not. 

 

32 FNS Instruction 803-3, Rev. 1, defines and addresses the concept of family/economic unit. Family is specifically defined in 

Section 246.2 of the Federal WIC regulations—803-3/Rev 1 allows use of the terms family and economic unit interchangeably. 
Volume 3: Improper Payments Report (Final Report) 

 17 National Survey of WIC Participants II 

The only exception to this was a child in the temporary care of friends or relatives. Since the 

individual State policy varied in treatment of such children, and the information had been 

gathered in the State WIC Agency Survey, researchers applied the rules of the individual States 

when calculating the size of the economic unit. It was found that 11 of the sampled States 

consider such children part of the economic unit,33 8 consider them as separate,34 and 4 leave it 

to the local WIC agency to decide.35 The latter four were included under the rule used by a 

majority of the States, which was to consider those children part of the economic unit. 

3.4 Income and Adjunctive Income 

Federal regulations provide the basis for establishing income eligibility for WIC applicants and 

require all State agencies to establish guidelines and definitions of income sources to help local 

agencies and clinics determine an applicant’s eligibility (7 CFR §246.7(d) (1)). As an alternative 

to direct income documentation, regulations mandate adjunctive income eligibility for 

individuals who participate in SNAP, TANF, Medicaid, and—at the State’s discretion, any Stateadministered program that routinely requires income documentation—“provided that those 

programs have income eligibility guidelines at or below the State agency’s program income 

guidelines” (7 CFR §246.7(d) (2) (vi)). 

Income 

To be eligible on the basis of income, applicant gross income for the family economic unit had to 

fall at or below 185 percent of the U.S. Poverty Income Guidelines, which are shown in 

Exhibit 3-2 for the 2008–2009 program year.36 Since the income guidelines increase with the 

size of the family, the definition of family economic unit becomes an important part of 

the equation. 

 

33 Arizona, Florida, Indiana, Louisiana, Massachusetts, Missouri, New Jersey, New York, Pennsylvania, Texas, and Washington. 34 Alabama, California, Georgia, Illinois, Kansas, Maryland, Michigan, and Virginia. This includes States that count the children

as part of their absent parent’s household and States that count the children as an economic unit unto themselves, with 

separate income. 

35 Colorado, North Carolina, Ohio, and Tennessee. 

36 Accessed at http://www.fns.usda.gov/wic/howtoapply/incomeguidelines08-09.htm. It is appropriate to use the Federal Poverty 

Income Guideline to check income eligibility even for cases certified prior to July 1 2008 (e.g., 30 infants were found certified by 

that date), because virtually all EUs’ incomes were reported for recent times before the interview (conducted in June through the 

end of 2009) as few were able to produce income documents months earlier. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 18 

Exhibit 3-2: WIC Income Eligibility Guidelines 

(Effective from July 1, 2008 to June 30, 2009) 

The 48 Contiguous States, DC, and the Territories 

Persons in Family or 

Household Size Annual Monthly 

Twice 

Monthly Biweekly Weekly 

1 19,240 1,604 802 740 370 

2 25,900 2,159 1,080 997 499 

3 32,560 2,714 1,357 1,253 627 

4 39,220 3,269 1,635 1,509 755 

5 45,880 3,824 1,912 1,765 883 

6 52,540 4,379 2,190 2,021 1,011 

7 59,200 4,934 2,467 2,277 1,139 

8 65,860 5,489 2,745 2,534 1,267 

Each additional member add +6,660 +555 +278 +257 +129 

Since Alaska and Hawaii were not sampled areas, their special income guidelines are not shown. 

Source: Food and Nutrition Service, U.S. Department of Agriculture 

Federal regulations (CFR) give States options for counting sources of income (depending on the 

guidelines used to calculate income), but emphasize the importance of clarity and consistency in 

determining eligibility throughout the State. As a result, each State has a unique set of income 

sources that determine eligibility, which are largely similar across States. The details 

of the sources of income, which were derived from the State WIC Agency Survey 

(7 CFR §246.7(d) (2) (iii)), are shown on Exhibit 3-3. 

During the in-person surveys, to analyze income, respondents were asked about specific sources 

of income for every member in the family economic unit. For each family member, participants 

were asked about 22 possible sources of income, regardless of whether the State considered the 

source countable in their income guidelines or not. Then, during analysis of income data, the 

income sources that were not countable toward income based on the States’ individual criteria 

were removed (See Exhibit 3-3). The countable income sources were then summed to create a 

measure of total income for the household. 

With respect to military pay and allowances, researchers consulted surveys filled out by each 

State WIC agency to determine the treatment of these items as an income source for individual 

participants, since not all States treat these items the same way. 

 For example, a Leave and Earnings Statement (LES) for military pay is not considered 

countable as household income in 20 of the 23 sampled states, with the exceptions being 

Arizona, Michigan, and Texas (see Exhibit 3-3). State Agencies’ treatment of military 

housing allowances was also consulted since 76.8 percent of the sampled States did not 

count Basic Allowance for Housing for off-base housing and privatization housing in the 

U.S.; 52.4 percent did not count Family Separation Housing provided to military 

personnel for overseas housing; 54.9 percent did not count Overseas Housing Allowance 
Volume 3: Improper Payments Report (Final Report) 

 19 National Survey of WIC Participants II 

provided to military personnel living overseas; and 63.4 percent did not count Overseas 

Continental U.S. cost of living allowance (COLA). 

Exhibit 3-3: Exhibit 3-3: Sources of Income Used to Determine Eligibility 

Income Source N of States in sample 

Not counted Counted 

Wages, Salary, Fees 1 22 

Military Pay* 21 2 

Tips and Bonuses 1 22 

Self-Employment 1 22 

Unemployment Comp. 1 22 

Workers Comp. 4 19 

Child Support 1 22 

Commissions 2 21 

Alimony 1 22 

Social Security 1 22 

Private Pension 1 22 

Medical Assistance 17 6 

SSI—Federal Govt. 3 20 

SSI—State-issued 4 19 

Income from Estates 2 21 

Net Royalties 1 22 

Other Cash Income 2 21 

Energy assistance 18 5 

Net Rental Income 4 19 

Dividends or Interest 2 21 

Income from Trusts 2 21 

Other 17 6 

* Defined as Leave and Earnings Statement (LES) for military pay 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 20 

Income Timeframe 

The time period used to calculate income—whether the current or the past 12 months—is left up 

to States who, in turn, often leave the decision up to the local WIC agency or clinic. 

Field interviewers asked for proofs of income for the month when the participant had been most 

recently certified. (This most recent certification date had been provided by State WIC agencies 

as part of the pre-coded information, and it populated automatically on field interviewers’ 

computer screen). Owing to necessary time lags due to sampling, requesting and receiving 

participant data from States, and carrying out data collection, the dates of (re)certification ranged 

between 5 and 19 months earlier.37 If respondents were unable to provide income documents for 

that period, current eligibility documents were accepted. The timeframe used for analysis 

depended on the document shown as income source. If the source indicated a full-time amount, 

it was multiplied by 2,080 (i.e., 40 hours × 52 weeks); if part-time, by 1,040 

(i.e., 20 hours × 52 weeks); if weekly, by 52; if biweekly, by 26; if bimonthly, by 24; if monthly, 

by 12; and if yearly, by 1. These income conversion procedures were based on workplace 

conventions across States, taking into consideration vacations and other leave. 

Determination of Eligibility 

The determination of income eligibility or ineligibility was done by obtaining proof of claimed 

income and assessing the total income of the family economic unit relative to its size. Income 

was summed for all members of the family economic unit for a specified period, and then 

multiplied by the expected frequency that the income would be received during the year, as 

described above. 

Follow-up Procedures When Evidence Was Lacking 

When evidence of income was lacking, a follow-up was conducted. When it was difficult to 

resolve uncertain cases for a significant number of cases, researchers went to the State WIC 

agency directly to ask for documentation of the proof that the agency had on file. This was quite 

successful in resolving about 80 cases. The documentation that the State provided was reviewed 

and its eligibility evidence was considered correct if it met any of the following criteria: 

1. The State provided evidence of an eligible income type38 and dollar amount at or below 

the amount permitted for the family economic unit of the participant, and the evidence 

consisted of one of the following: 

a. Scanned copy of the income item(s); 

b. Scanned copy of the original application form showing the exact income amount, 

source and frequency of income, along with a notation by the local agency that at 

least one of the above acceptable supporting forms of documentation was shown; or 

c. Screen print taken from the State’s WIC computer system of application data showing 

the exact income amount and the frequency of income, with similar notation that at 

least one of the above acceptable supporting forms of documentation was shown. 

 

37 The May 2009 participant sample theoretically included infants certified as far back as June 2008. Data collection took place 

from September 2009 through January 2010. This yields the upper limit of 19 months. 38 Such as paystub, tax return, W-2 form, letter from employer or other as specified by State and deemed acceptable. 
Volume 3: Improper Payments Report (Final Report) 

 21 National Survey of WIC Participants II 

2. The State noted exact income information from the local agency, which—while not 

specifying the type of income proof shown—gave a detailed recording of the income 

amount and frequency (e.g., $291.13 per week) that led researchers to believe that a 

specific income document was seen at certification. 

3. The State provided a scanned copy of a signed Self-Declaration of Income letter in which 

WIC participants attested to migrant or homeless status (< 5 cases). 

4. The State records showed that temporary eligibility was awarded and did not continue 

beyond 30 days, as applicant did not return with proper documents. 

Adjunctive Income Eligibility 

To qualify based on adjunctive (or automatic) income eligibility, an applicant—or someone in 

the economic unit—must be a current participant in SNAP, TANF, Medicaid, or another 

State-administered program approved by the State as meeting certain guidelines—e.g., State 

Children’s Health Insurance Program (SCHIP) and free or reduced-price breakfast and lunch 

program. In the survey of State WIC agencies, the list of eligible programs for all States was 

collected (see Exhibit 3-4).

Exhibit 3-4: List of Adjunctively Eligible Programs Accepted by States 

Adjunctive 

Program 

N of States in Sample 

Eligibility 

not 

established 

Eligibility 

established

Medicaid 1 22

SNAP 1 22

TANF 1 22

CHIP 18 5

Free & Reduced 

Breakfast/Lunch

18 5

 * Other adjunctive eligibility programs were rarely 

 established include: FDPIR, Section 8 Housing, SSI, 

 Head Start, and Refugee Resettlement Program. 

Determination of Eligibility 

Proofs of adjunctive income eligibility submitted by each participant were compared to the list of 

acceptable programs for each State, using the actual program names specific to each State 

(e.g., Medicaid in California is Medi-Cal). If the ID number provided seemed similar to other 

Medicaid numbers, the proof was considered valid. As noted earlier, many program participation 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 22 

cards did not visually show dates; therefore, a missing date was not a reason to consider a 

proof invalid. 

Eligibility Timeframe 

Similar to the time period used for income, the participant was first asked for proofs of income 

for the month when they had been most recently certified—a date that had been programmed 

into interviewers’ CAPI laptops. If a respondent was unable to provide income documents for the 

last certification period, they were asked to provide current proofs of program participation 

instead. In many cases, especially in the case of electronic benefit transfer (EBT) cards, no date 

could be ascertained from the proof. However, this was not determined to be evidence of 

improper payments. 

Follow-up Procedures When Lacking Evidence 

During follow-up when eligibility evidence was lacking, the original intent was to use the 

Program (information) release forms that were signed by respondents to verify their participation 

in Medicaid, SNAP, TANF, and other assistance programs. This proved not to be feasible since 

Social Security numbers are used by these assistance programs to identify participants and the 

research contractor had been directed not to collect Social Security numbers. 

As an alternative, State WIC agencies were contacted for the eligibility verification on record. 

This helped resolve about 160 cases. State-provided documentation was treated as evidence of 

proper adjunctive eligibility if the State could provide the individual’s adjunctive program and 

ID number for Medicaid, SNAP, TANF, or other qualified adjunctive programs for the 

May 2009 period, or for the current (April/May 2010) period if the State’s system did not allow 

checks of past eligibility (e.g., CA, NJ, and PA). Individuals who were not determined to be in 

error were subject to computerized income eligibility check (see Appendix H for listing of 

special review cases by States). 

3.5 Redemption Data Processing 

Redemption Data Request 

Redemption data were requested from the States for the months of May through September 

200939 in the clinics sampled. The data contained the WIC participant ID, the date of voucher 

issuance, the date the voucher was redeemed, the date of voucher expiration, the value of the 

redeemed voucher, and the maximum value that could be redeemed. The data received were at 

the voucher issuance level, not the WIC participant level. Each WIC participant had multiple 

voucher issuances per month requested. It is important to note that New York State had 

implemented the new food packages in early 2009, and the redemption data for that State 

contained fruit and vegetable cash value vouchers (CVV).40

 

39 Although redemption data was collected through September, only data through July were used; at that point, all May issuances 

had been redeemed. 

40 Per telephone communication (July 6, 2011) with Debbie McIntosh, New York State WIC, who confirmed that the NY 

redemption data contain the CVV components for both the max voucher value field as well as for the actual redeemed value field.
Volume 3: Improper Payments Report (Final Report) 

 23 National Survey of WIC Participants II 

Data Matching 

The size of the State redemption datasets ranged from several thousand records to more than a 

million. The size of the datasets depended on three things: (1) the number of clinics sampled 

within the State, (2) the number of participants served by the sampled clinic, and (3) the number 

of vouchers received by that participant in the months for which data were requested. Due to the 

quantity of data, it was decided to match the State redemption dataset to the final completed 

telephone interview list by WIC participant identification number. Eight of the 23 States sampled 

did not have a 100 percent match. A confirmation was made with each State that the WIC 

participants who completed the telephone interview did not redeem vouchers during the months 

of May through September. Six States confirmed that the few missing cases did not redeem 

voucher issuances. One State found the redemption data for the missing cases and one State 

required imputation of the entire redemption records (see section below on “Imputation”). 

Data Cleaning 

After resolving the missing data issues for all States but one (North Carolina), the individual 

State files were combined into one dataset. The five key variables (redemption values for the 

5 months) were examined for correct formatting, and only records with vouchers issued between 

May and September were kept. If the amount redeemed was missing, the amount redeemed was 

set to zero. If the maximum redeemable value was missing, it was also set to zero. The same 

rationale applied when the redeemed value was also missing.41 The amount redeemed and the 

maximum redeemable value to the WIC participant by month issued were aggregated, creating a 

total value redeemed and total maximum redeemable within a month. 

Imputation 

One sampled state, North Carolina, could not provide redemption data for the analysis. 

Its redemption data were imputed based on redemption data from neighboring States (Alabama, 

Florida, Georgia, and Tennessee) that were deemed to some extent similar to North Carolina, 

namely. The final imputation was done via SAS multiple imputation (MI) procedure 

(see Appendix E for SAS code). The MI procedure generated a set of five plausible values for 

filling in the missing value, rather than assigning a single value, thus retaining uncertainties due 

to missing values.42 The average of the plausible values was used in the tabulation, producing 

estimates of confidence intervals with the correct probability of coverage. MI is generally 

considered a superior procedure than the single-value procedures, assuming missing data are 

largely random. The procedure generated reasonable redemption values for missing cases. 

Note that, since no eligibility error was found in NC, the imputation of the redemption missing 

data would not impact the erroneous payment estimation. If there is any impact at all, it would be 

relevant only to the redemption estimates. 

 

41 ICF has contacted States to learn about redemption data processing. It was consistent with States data processing procedure to

assume a missing value implies no redemption. 

42 Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York: John Wiley & Sons, Inc. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 24 

Expired Certification Error 

Food vouchers issued to and redeemed by participants whose WIC eligibility has expired is a 

potential source of error, with consequential overpayments. Obtaining the most current 

certification dates for respondents is critical to determining expired certification error. 

Researchers collected certification dates from State agencies and processed the data for analysis. 

State agencies, however, often proved unable to provide the most reliable information on 

certification dates. Local agencies may be a better source as they actually conduct certification 

and handle constantly shifting participant status. In a preliminary effort, assuming that the 

available certification dates from State agencies were current, analysts examined expired 

certification error. Because of the data quality concerns, the procedure and results are not 

presented in the main report, but rather in Appendix K for technical readers. 
 25 National Survey of WIC Participants II 

CHAPTER 4. IMPROPER PAYMENT ESTIMATES

Estimation of improper payments was accomplished by determining two types of errors: case 

error and dollar error. Case error refers to the number of WIC participants certified to receive but 

who were not eligible for WIC benefits. Dollar error refers to the dollar amount of WIC benefits 

issued to and actually redeemed by participants. A major difference between this study and the 

2001 NSWP-I study is in the methods used for measuring and estimating dollar error. The 

current study collected respondent redemption records from the States and used the actual dollar 

amounts of redeemed benefits to calculate dollar error. In contrast, the 2001 study relied on 

general estimates from WIC average food cost per participant to proxy dollar errors. 

In-person interview data were analyzed to identify case errors linked to actual redemption data. 

Special reviews were conducted on cases where supporting information on income or adjunctive 

eligibility was incomplete (see Chapter 2 for methodological details). 

With sample weights, estimates of case errors were based on findings obtained from a national 

sample of WIC participants representing the 48 contiguous United States in May 2009. Note that, 

unless specifically noted otherwise, all statistics presented in this report were weighted by final 

sample weights and adjusted for the survey design effect (see Appendix G for details). 

Exhibit 4-1 shows characteristics of family economic units sampled for the in-person interview 

by category and other demographic variables analyzed. 

Exhibit 4-1: Program Categories, Demographics, and Food Security: 

Percentage Distribution of WIC Participants in In-person Interview 

Participant Category Percent Standard Error 

Category 

Pregnant 10.5 0 

Breastfeeding 6.4 0 

Postpartum 7.0 0 

Infant 23.7 0.26 

Child 52.5 0.26 

Race 

American Indian/Alaskan Native 1.1 0.49 

Asian/Pacific Islander 2.7 0.72 

African American 21.5 3.86 

White 44.6 4.63 

Other, Multiracial 30.1 4.55 

Education 

Less than High School 28.8 2.95 

High School 32.9 2.67 

More than High School 38.3 3.08 

Metropolitan Statistical Area 

Non-metro 23.6 4.98 

Metro 76.4 4.98 

Participation in WICa

New to WIC 48.3 2.94 

Participated before 51.7 2.94 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 26 

Exhibit 4-1 (continued) 

Participant Category Percent Standard Error 

Food Security Status 

High food security 81.9 1.70 

Marginal food security 1.1 0.42 

Low food security 9.3 1.22 

Very low food security 7.7 1.26 

Ethnicity

Hispanic/Latino 45.7 4.74 

Not Hispanic/Latino 54.3 4.74 

Language

English 64.4 4.03 

Spanish 31.5 3.84 

Other 4.2 1.22 

Size of Economic Unit

1 person(s) 1.1 0.44 

2 15.4 2.24 

3 24.3 1.81 

4 24.2 1.76 

5 16.0 1.59 

6 9.2 1.28 

7 4.7 0.96 

8 2.9 0.77 

9 0.6 0.29 

10 0.1 0.07 

11 0.2 0.12 

12 0.1 0.08 

13 0.0 0.03 

14 0.0 0.01 

15 0.1 0.07 

Children in Household

Yes 72.8 0.30 

No 27.2 0.30 

Other Food Program Participation

No Public Food Assistance 31.4 3.21 

Yes, but not SNAP 12.6 1.57 

Yes, includes SNAP 56.0 3.38 

TOTALS 

Unweighted sample size n 1,210 

Estimated WIC total population 9,113,617 

a

 For the sampled women, “New to WIC” refers to the first time a woman has received WIC 

benefits for herself and “Participated before” refers to having participated previously with another 

pregnancy/child. For the sampled children, the two terms refer to, respectively, the first time the 

adult respondent received WIC benefits for the sampled child and previous participation of WIC by 

this child (see Appendices A and B). 

Source: National Survey of WIC Participants II Study 

Exhibit 4-2 presents average monthly income by number of people in the family economic unit 

(EU) for participants identified as adjunctively ineligible. The mean ranges from zero dollars for 

one-person households to $3,309 for households of three, with wide ranges of 95 percent 

confidence limits. The median income was zero for household groups of size 1 through 4, 

indicating that one half (or more) of households in these groups had no income at all. The other 

two groups, of size 5 and 6, had one half of the members with income below $266.60 and 
Volume 3: Improper Payments Report (Final Report) 

 27 National Survey of WIC Participants II 

$860.00 respectively. All EU-size groups averaged below the 185 percent of the Federal Poverty 

Level (FPL), except the subgroup of the three-person household, which averaged higher than the 

185 percent poverty level. The apparent anomaly was caused by two participants whose EU 

incomes were found very high; excluding such high-income cases would lead to substantially 

lower estimates that closely relate to the EU size.43

Exhibit 4-2: Median and Mean Monthly Income by Economic Unit for 

Income-Eligible WIC Participants (Adjunctively Eligible Excluded) 

EU Size 

Sample n

(Unweighted) 

Number of 

All WIC 

Participants 

Median 

EU 

Monthly 

Incomea 

($) 

Mean EU 

Monthly 

Income 

($) 

Income 

allowed 

for 185% 

of FPLb 

($) 

95% CI for Mean 

($) 

1 43 213,069 0 0 1,604 0 0 

2 152 1,142,342 0 1168.78 2,159 -412.05 2749.61 

3 207 1,425,988 0 3309.09 2,714 -1684.13 8302.3 

4 162 1,332,434 0 1429.18 3,269 243.41 2614.94 

5 100 767,886 266.6 1038.11 3,824 628.63 1447.59 

6 or more 84 737,609 860.0 2685.58 4,379+ -603.69 5974.85 

Total (not 

adjunctively 

eligible) 

748 5,619,329 0 1910.58 -23.23 3844.40 

a

 Median values were calculated without design effect adjustments b

 This column shows the maximum income level allowed, by household size, to be within the $185% FPL limit. Effective from 

July 1, 2008 to June 30, 2009 for WIC income eligibility; for EU size greater than 6, add $555 for each additional person (see:

http://www.fns.usda.gov/wic/howtoapply/incomeguidelines08-09.htm). 

Source: National Survey of WIC Participants II Study 

4.1 Certification Error Case Counts and Rates 

Five types of certification errors may cause failure to meet WIC Program eligibility requirements. 

These include identity, category, residence, income/adjunctive income, and nutritional risk 

eligibility errors. (As mentioned previously, nutritional risk eligibility was not examined in this 

study.) Of the five types of errors mentioned, the most challenging is the determination of 

income eligibility error. Income eligibility is complicated by States’ discretionary guidelines on 

defining the family unit (or WIC economic unit), treatment of special cases, and the range of 

income sources that can be counted. Furthermore, income eligibility error reflects the difficulty 

of applicants to report incomes and to provide supporting documents to fulfill income eligibility 

requirements; thus, income eligibility error is the main focus of improper payment analysis. 

Exhibit 4-3 illustrates the basic process of identifying certification errors based on WIC 

eligibility criteria. Data obtained during the in-person interview as well as from the special case 

study follow-up review with States both serve as independent ways to document the WIC 

certification process and confirm the accuracy of eligibility. 

 

43 The two cases EU monthly incomes were $182,530 and $153,635, respectively. See Appendix H for a discussion of such highincome cases, with comparative estimates for the subsample excluding cases of very high incomes. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 28 

Exhibit 4-3: Process of Identifying Erroneous Certification, by Eligibility Criteria 

Source: National Survey of WIC Participants II Study 

Identity, Residence, and Category Errors 

Errors of identity and residence can potentially occur if proofs are not shown or if proofs 

belonging to other people are submitted by the WIC applicant. However, analysts found no 

identity or residence errors based on in-person interview data reviewed on WIC participants’ 

identity proofs and residence proofs. All of the respondents demonstrated appropriate documents 

for establishing identity and residential eligibility. 

Category errors may occur when agencies make a mistake in the age of an infant/child or the 

dates of a woman’s pregnancy, breastfeeding, and delivery as required by WIC category 

eligibility. The in-person interview, however, did not collect information on dates of women’s 

pregnancy, breastfeeding, and baby delivery; hence, category errors for women were not 

produced. Determining whether each WIC woman was in the correct WIC category (pregnant, 

breastfeeding, and postpartum) would have required more extensive and intrusive effort 

(e.g., medical document check), which was not required in the Contract. Data collectors observed 

gender and age of WIC women but found no evidence of category error for them. 

Category error among infants and children was explored by counting the number of days 

between the birth date and the certification date. Six respondents (weighted to 49,832 WIC 

participants) were found in category error. They aged over 1 year at certification time (i.e., the 

difference between the certification date and the birth date was greater than 366 days with one 

extra day as cushion for trivial errors by the hour)—older than the age the WIC Program 

requires—yet they received infant benefits rather than child benefits. As the child benefit 
Volume 3: Improper Payments Report (Final Report) 

 29 National Survey of WIC Participants II 

redemption values for these participants did not exist, category errors were not included in the 

total improper payment case and dollar error estimates. Appendix H has procedures and tentative 

results for these category errors, estimated by calculating the difference between the redeemed 

infant voucher value and the average child benefit redemption value at their clinic. No over-age 

children (older than 5 years or 1,828 days at the time of certification) were identified. 

In the preliminary analysis of expired certification error, 30 respondents (weighted to 105,023 

WIC participants) were found to be in this type of error. With certification dates collected from 

State agencies, the statistics may be questionable because State agencies were not likely to have 

the most reliable information on certification dates. Local agencies actually conduct certification 

and update the certification dates to handle constantly shifting participant status such as WIC 

benefit issuance, termination, renewal, and category change. Given potential problems with State 

agencies’ certification dates, the expired certification error estimates generated from this study 

are tentative and thus are not presented in the main body of the report (see Appendix K for a full 

discussion and the estimates). 

Income Eligibility Error 

In addition to a computerized examination of income data using SAS, special case reviews were 

completed for selected difficult cases to establish income eligibility and among all cases to verify 

adjunct eligibility. The results were integrated with computerized analysis to produce the 

estimates of income eligibility error (see Chapter 3 and Appendix H). 

A SAS program was applied to calculate income by pay period and amount based on the national 

WIC or approved State-discretionary guidelines. The algorithm determined whether a household 

member was a member of the economic unit, counted each economic unit member’s income 

sources according to State discretionary policies, summed up the countable income dollar 

amount by frequency of receipt, checked the amount against the WIC guideline for income 

eligibility, and generated a tentative decision on eligibility. The algorithm also flagged cases 

with uncertainty with respect to income source, income evidence, and adjunctive eligibility for 

special case review. 

Special case reviews sought to reclassify information collected by interviewers that was 

unclear—such as text information with coding difficulties, for example, reclassifying Peachtree 

State Health Plan as “Medicaid” (Georgia’s program for Medicaid). These reviews also included 

the results of direct communications with State agencies to provide information about the 

adjunctive or income proofs originally shown to agencies for selected problem cases 

(see Appendix H for details on special case reviews). 

Of the WIC participant population of 9,113,617 in May 2009, 277,952 cases were estimated to 

be in error due to income eligibility error, or 3.05 percent of the total WIC participant population 

(95% CI = 1.42% and 4.68%, see Exhibit 4-4). 

Income error estimates are separately presented by WIC Program category in Exhibit 4-4 for a 

group comparison.44 No statistically significant difference was found across WIC categories as 

 

44 Income eligibility error estimates were also categorized by research methods used (i.e., computerized analysis and special case 

study as shown in Appendix H). 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 30 

the 95 percent confidence limits overlapped. The substantive differences as shown by point 

estimates, however, are noteworthy. For breastfeeding women and for infants, the income 

eligibility error rates were relatively high, respectively at 5.18 percent and 5.29 percent. The 

infant error case count was also particularly high—more than 114,000—because of the group’s 

high error rate. The error rates for participants in the other three program categories were lower, 

ranging from 3.31 percent for pregnant women to 1.85 percent for children. 

Exhibit 4-4: Number and Percent of Income Eligibility Errors, by WIC Category 

WIC Category 

Number of Respondents in 

Error (Unweighted) 

Number of All 

WIC 

Participants in 

Error 

Percent 

(%) 

95% Confidence 

Interval (%) 

Pregnant 7 31,750 3.31% 0.25 6.38 

Breastfeeding 9 30,174 5.18% 1.23 9.12 

Postpartum 6 13,538 2.14% 0.16 4.11 

Infant 11 114,091 5.29% 2.07 8.50 

Child 6 88,399 1.85% -0.32 4.02 

Total 39 277,952 3.05% 1.42 4.68 

Source: National Survey of WIC Participants II Study 

4.2 Dollar Error Amounts and Rates 

Using redemption records collected from State agencies, the dollar error estimates were derived 

from food voucher values actually redeemed by sampled WIC participants who were identified 

as income eligibility error cases. The redeemed values of vouchers issued in May 2009 and 

redeemed in May, June, and July 2009 were extracted.45 Adjustments were made to cope with 

data problems such as inconsistent inclusion of family members’ redemptions and missing 

redemption data. 

Redemption records for May 2009 were used to estimate annual redemption values. The national 

cross-sectional representative sample of active participants (“the issuances”) in May 2009 was 

used to generalize the monthly estimates of redemption values to the WIC participant universe. 

Examining the records on monthly redemption (May through July), researchers found the month 

of May covered more redemption values relative to June and July—a pattern to be expected 

since the cross-sectional sample of the issuances in May included cases that would drop out from 

the redemption data system due to certification expiration or other reasons in subsequent months. 

Using the redemption data from all three months would require adjustments for such “drop off” 

factors that involve uncertainties. Using redemption data in May to generate national annualized 

dollar error was thus both sensible from a sampling perspective and efficient from a data 

processing perspective. 

 

45 New York State included the new food package, known as Fruit and Vegetable Cash Value Voucher, in its redemption records 

submitted to ICF. 
Volume 3: Improper Payments Report (Final Report) 

 31 National Survey of WIC Participants II 

Analysts annualized the estimates based on May redemption data with a multiplier of 12.285 

derived from the WIC Monthly Report on total food costs46 in order to take monthly variation of 

redemption values into account. Presumably, the error rate was constant across months and the 

monthly redemption value was closely correlated to WIC monthly food cost reported by FNS. 

Analysts obtained WIC monthly food costs data for FY2009 (October 2008 through September 

2009) and divided the FY2009 total cost ($4,640,847,313) by the May food cost ($377,758,893) 

to generate the annualized multiplier 12.285 (see Appendix H for WIC monthly food cost data 

used for this purpose). Exhibit 4-5a presents pre-rebate redemption values by WIC category. 

Adjustment for the WIC infant formula rebate to States was necessary to measure the real cost to 

the WIC Program because State WIC agencies negotiated with infant formula manufacturers a 

rebate amount that constitutes a substantial portion of the infant formula costs. Conceptually, 

the adjustment would entail subtracting the dollar rebate value from the redeemed infant voucher 

value for each infant identified as eligibility error. The available redemption records, however, 

did not allow rebate adjustments because the redemption data contain only a single redeemed 

value for each voucher. 

To proxy the infant formula rebate value, data were used from the May 2009 WIC Monthly 

Report.47 The Rebates Billed spreadsheet of this report contains the rebates billed by each State 

WIC Program to infant formula manufacturers while the Infants Participating spreadsheet shows 

the total number of infant participants in each State. The source allowed the calculation of the 

average rebate value per infant for each State. Dividing the State average rebate value by the 

national average redemption value (estimated from the NSWP-II sample) produced the rate of 

redemption due to formula rebates for each sampled State. Multiplying this rate by the actual 

redeemed voucher value for each infant in the sample generated a proxy measure of rebate value 

for each infant. For infants identified as ineligible, subtracting this estimated rebate value from 

their redeemed voucher value yielded rebate-adjusted dollar error, which was weighted and 

annualized to produce WIC total dollar error estimates (see Appendix L for details on the infant 

formula rebate adjustments). 

Both pre- and post-rebate estimates of dollar error were weighted with the final sample weights 

and then annualized (see Exhibits 4-5a and 4-5c). The annualizing multiplier was derived from 

WIC administrative data in order to take monthly variation of redemption values into account. 

For the post-rebate estimate, the multiplier used was the ratio of the FY2009 rebates billed value 

($1,900,274,589) over the May rebates billed value ($156,877,423), a multiplier of 12.113. 

 

46 Downloaded from: http://www.fns.usda.gov/pd/wicmain.htm as of 07/29/2011 47 FNS. (2009). FNS Program Data website: Monthly Data—Agency Level, Participation and Food Cost by Category per person: 

2009. Accessed 10/5/2011 at http://www.fns.usda.gov/pd/wicmain.htm . 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 32 

Exhibit 4-5a: Pre-rebate May 2009 and Annualized Estimates of 

Mean and Total Redeemed Dollar Amounts, by WIC Category 

WIC 

Category 

Monthly/ 

Annualized 

Redemptions 

Mean 

Redeemed 

($) 

95% CI for Mean 

($) 

Total 

Redeemed 

($) 

95% CI for 

Total Redeemed 

($) 

Pregnant 

n = 958,092 

May issuance 43.52 37.76 49.28 41,693,372 34,091,806 49,294,938 

Annualized 534.61 463.84 605.37 512,203,073 418,817,838 605,588,308 

Breastfeeding 

n = 582,986 

May issuance 44.17 38.84 49.51 25,753,131 21,358,212 30,148,051 

Annualized 542.68 477.20 608.17 316,377,217 262,385,629 370,368,806 

Postpartum 

n = 634,014 

May issuance 35.89 28.58 43.20 22,754,884 16,339,046 29,170,722 

Annualized 440.91 351.13 530.69 279,543,748 200,725,175 358,362,321 

Infant 

n = 2,157,909 

May issuance 110.10 91.62 128.58 237,585,105 188,174,517 286,995,692 

Annualized 1352.57 1125.60 1579.55 2,918,733,009 2,311,723,941 3,525,742,078

Child 

n = 4,780,616 

May issuance 38.89 33.79 44.00 185,938,305 159,995,332 211,881,278 

Annualized 477.82 415.14 540.49 2,284,252,074 1,965,542,651 2,602,961,496

All WIC 

participants 

n = 9,113,617 

May issuance 56.37 49.94 62.80 513,724,796 444,403,603 583,045,989 

Annualized 692.49 613.45 771.53 6,311,109,122 5,459,498,266 7,162,719,977

Source: National Survey of WIC Participants II Study 

Exhibit 4-5b: Infant Formula Rebate Adjustment: May 2009, 

Annualized Rebate Values, and Post-rebate Food Costs for Infants 

Measure 

Monthly/ 

Annualized 

Mean 

($) 

95% CI 

for Mean ($) 

Total 

($) 

95% CI for Total 

($) 

Rebate value 

for infants 

n= 2,157,909 

May 72.70 60.84 84.56 156,877,423 124,580,461 189,174,385 

Annualized 880.60 736.93 1024.27 1,900,256,225 1,509,043,120 2,291,469,329

Post-rebate 

food cost for 

infants 

n= 2,157,909 

May 37.40 30.36 44.44 80,707,682 62,802,703 98,612,660 

Annualized 453.04 367.76 538.31 977,612,146 760,729,137 1,194,495,156

Total food 

cost for 

infantsa 

n= 2,157,909 

May 110.10 237,585,105 

Annualized 1333.64 2,877,868,371 

a

 Calculated with aggregated numbers without variance estimation 

Source: National Survey of WIC Participants II Study
Volume 3: Improper Payments Report (Final Report) 

 33 National Survey of WIC Participants II 

Exhibit 4-5c: Post-rebate May 2009 and Annualized Estimates of 

Mean and Total Redeemed Dollar Amounts, by WIC Category 

WIC 

Category 

Monthly/ 

Annualized 

Redemptions 

Mean 

Redeemed

($) 

95% CI 

for Mean 

($) 

Total 

Redeemed 

($) 

95% CI for Total Redeemed 

($) 

Pregnant 

n = 958,092 

May issuance 43.52 37.76 49.28 41,693,372 34,091,806 49,294,938 

Annualized 534.61 463.84 605.37 512,203,073 418,817,838 605,588,308 

Breastfeeding 

n = 582,986 

May issuance 44.17 38.84 49.51 25,753,131 21,358,212 30,148,051 

Annualized 542.68 477.20 608.17 316,377,217 262,385,629 370,368,806 

Postpartum 

n = 634,014 

May issuance 35.89 28.58 43.20 22,754,884 16,339,046 29,170,722 

Annualized 440.91 351.13 530.69 279,543,748 200,725,175 358,362,321 

Infant 

n = 2,157,909 

May issuance 37.40 30.36 44.44 80,707,682 62,802,703 98,612,660 

Annualized 453.04 367.76 538.31 977,612,146 760729137 1194495156 

Child 

n = 4,780,616 

May issuance 38.89 33.79 44.00 185,938,305 159,995,332 211,881,278 

Annualized 477.82 415.14 540.49 2,284,252,074 1,965,542,651 2,602,961,496

All WIC 

participants 

n = 9,113,617 

May issuance 39.16 35.24 43.07 356,847,373 314,054,690 399,640,057 

Annualized 479.50 431.54 527.46 4,369,988,259 3,846,290,733 4,893,685,785

Source: National Survey of WIC Participants II Study 

Income eligibility error dollar amounts for WIC as a whole and by program category were 

calculated as the sums of the products of error cases’ actual redemption values, the annualizing 

multiplier 12.285, and the sample weights, respectively, for the entire WIC and each program 

category. The estimates were overpayment dollar errors since all participants in this error group 

should not have been awarded WIC benefits (see Appendix H for details of the calculation). 

The pre-rebate annualized WIC total improper payment dollar value for FY2009 was estimated 

at $233,176,403 (95% CI = $117,658,694 and $348,694,109; see Exhibit 4-6a). Of the five 

categories, infants had the highest estimates—a total of $151,391,157 (95% CI = $47,887,348 

and $254,894,965). The dollar error estimate was lowest among postpartum women, a total of 

$6,431,074 (95% CI = $854,399 and $12,007,749). 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 34 

Exhibit 4-6a: Pre-rebate Estimates of Annualized Dollar 

Amounts of Income Eligibility Error by WIC Category 

WIC Category 

Number of WIC 

Participants in Error 

Dollar Error 

($) 

95% Confidence Interval 

($) 

Pregnant 31,750 15,722,258 753,800 30,690,715 

Breastfeeding 30,174 26,051,235 5,915,464 46,187,006 

Postpartum 13,538 6,431,074 854,399 12,007,749 

Infant 114,091 151,391,157 47,887,348 254,894,965 

Child 88,399 33,580,679 -10,435,616 77,596,975 

WIC Total 277,952 233,176,403 117,658,694 348,694,109 

Source: National Survey of WIC Participants II Study 

Post-rebate estimates of dollar error amounts are presented in Exhibit 4-6b. Only the estimate for 

infants changed: the post-rebate dollar error amount was less than $49 million, less than 

one-third of the pre-rebate estimate of more than $151 million. Other categorical estimates 

remained the same. 

Exhibit 4-6b: Post-rebate Estimates of Annualized Dollar 

Amounts of Income Eligibility Error by WIC Category 

WIC Category 

Number of WIC 

Participants in Error 

Dollar Error 

($) 

95% Confidence Interval 

($) 

Pregnant 31,750 15,722,258 753,800 30,690,715 

Breastfeeding 30,174 26,051,235 5,915,464 46,187,006 

Postpartum 13,538 6,431,074 854,399 12,007,749 

Infant 114,091 48,714,683 16,222,015 81,207,352 

Child 88,399 33,580,679 -10,435,616 77,596,975 

WIC Total 277,952 130,499,928 70,478,357 190,521,499 

Source: National Survey of WIC Participants II Study 

Summary: Case Error and Dollar Error 

Case Error 

The 2009 NSWP-II study estimated that 277,952 out of 9,113,617 WIC participants had income 

eligibility problems, representing a case error rate of 3.05 percent (95% C.I. = 1.42% and 4.68%; 

see Exhibit 4-7); the study did not find identity eligibility error and residential eligibility error. 

Exploratory analyses were conducted to assess category error among infants and children and 

expired certification errors among all respondents (see Appendices H and K for tentative results). 
Volume 3: Improper Payments Report (Final Report) 

 35 National Survey of WIC Participants II 

Exhibit 4-7: Income Eligibility Case Error Count and Rate 

WIC Participants in Error WIC Total Participants Case Error Rate 

277,952 9,113,617 3.05% 

Source: National Survey of WIC Participants II Study 

Dollar Error 

Annual dollar errors associated with income eligibility error were derived based on dollar values 

of actual monthly voucher values redeemed by WIC participants. Dollar error rates were also 

estimated as the dollar error amount as a percentage of the yearly total WIC food costs—with 

and without infant formula rebate adjustment. For pre-rebate error rate, yearly total WIC food 

costs, including infant rebates billed, was the denominator; for the post-rebate rate, total WIC 

food costs, excluding rebate-billed values, was the denominator. Dollar error values and dollar 

error rates were estimated for income eligibility error as the total improper payments for FY2009 

(Exhibit 4-8). The pre-rebate dollar error estimate was $233,176,403 (95% C.I. = $117,658,694 

and $348,694,109), with a dollar error rate approximately 3.72 percent of the total WIC food 

costs of $6,274,051,429. The post-rebate dollar error estimate was $130,499,928 (95% 

C.I. = $70,478,357 and $190,521,499), with a dollar error rate of approximately 2.98 percent. 

Exhibit 4-8: Pre-and Post-rebate Annualized Dollar Error 

Amounts and Dollar Error Rates: WIC Income Eligibility Error 

Dollar Error Amount ($) WIC Total Food Costsa ($) 

Dollar Error Rateb 

(%) 

Pre-rebate $233,176,403 6,274,051,429 3.72 

Post-rebate 130,499,928 4,373,776,840 2.98 

a

 The denominator for pre- and post-rebate dollar error rates were, respectively, the total food costs (the sum of the food costs and 

infant rebates billed) and the food costs, excluding infant rebates value. Both numbers were for 48 contiguous States and tribal 

organizations, available from the FY2009 WIC Monthly Report: http://www.fns.usda.gov/pd/wicmain.htm as of 07/29/2011. b

 Dollar error rates were calculated using aggregated values without variance estimation. 

Source: National Survey of WIC Participants II Study 

4.3 Additional Analysis 

The background characteristics of the error cases and a comparison of the current WIC improper 

payment results with previous statistics are presented below. 

Characteristics of Participants with Income Eligibility Error 

To examine the differences between the income eligible error group and other WIC participants 

(“no error” group), two-way crosstabs were run with participant background characteristics, 

including WIC Program category, race-ethnicity, education, home language, EU size, household 

with child(ren), metro vs. non-metro locale, participation in other assistance programs, and food 

security (see Appendix H). The group differences in most variables were not found to be 

statistically significant. The only two variables in which the two groups were found statistically 

significantly different were education and food security (Exhibit 4-9), specifically: 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 36 

 Participants with less than a high school education were less likely to have eligibility in error. 

Of the “no error” group, 29.66 percent had not completed high school (95% CI = 23.46% and 

35.85%), higher than the error group’s 3.13 percent (95% CI = 0.00% and 8.17%). The error 

group seemed less likely to have an education lower than high school, relative to the rest of 

the WIC population. No significant difference, however, was found in the other two 

education categories (“high school” and “more than high school”). 

 Participants with high food security were also more likely to be improperly eligible. Among 

the participants found with no error, 81.43 percent (95% CI = 77.87% and 84.98%) were in 

the “high food security” category; in the error group, the rate was 97.72 percent 

(95% CI = 94.66% and 100.00%). 

While evidence was limited, the two findings implied that participants not eligible for but 

erroneously awarded WIC benefits tended to be better off in education and food security than 

those who were eligible and appropriately awarded the benefits. 

Exhibit 4-9: Education and Food Security: Participants of 

Income Eligibility Error and Participants Without the Error 

Income 

Eligibility 

Error 

Demographic/ 

Program 

Characteristics 

Sample n

(Unweighted) 

Number of All 

WIC 

Participants 

Percent 

Within 

Group 

95% CI for 

Percent 

Educationa

No Error 

Less than HS 340 2,615,890 29.66 23.46 35.85 

HS 403 2,850,244 32.31 26.82 37.80 

More than HS 424 3,354,695 38.03 31.67 44.39 

Error 

Less than HS 2 8,702 3.13 0.00 8.17 

HS 19 140,074 50.39 28.93 71.86 

More than HS 18 129,177 46.47 24.15 68.80 

Food Security 

No Error 

High food security 932 7,194,811 81.43 77.87 84.98 

Marginal food security 21 98,138 1.11 0.25 1.98 

Low food security 115 839,461 9.50 6.92 12.08 

Very low food security 103 703,255 7.96 5.35 10.57 

Error 

High food security 36 271,610 97.72 94.66 100.00 

Marginal food security - - - - - 

Low food security 3 6,342 2.28 0.00 5.34 

Very low food security - - - - - 

a Education data were missing in four cases. 

Source: National Survey of WIC Participants II Study 
Volume 3: Improper Payments Report (Final Report) 

 37 National Survey of WIC Participants II 

Comparing Income Eligibility Error Cases and Dollar Amounts with Prior Studies 

A comparison with earlier studies was possible in terms of income eligibility error cases and 

dollar amounts because these indicators were available from two studies conducted in past 

decades.48 Exhibits 4-10 and 4-11 summarize the statistics from the three studies. 

For income eligibility error, the FY1988 study estimated a case error rate of 5.7 percent 

(95% CI = 3.80 and 7.60). For FY1998, the NSWP-I study estimated a case error rate of 

4.5 percent (95% CI = 2.80 and 6.20). The current study, NSWP-II, estimated an income case 

error rate of 3.05 percent (95% CI = 1.42 and 4.68) for FY2009. 

For dollar error amount and dollar error rate due to income eligibility errors, the amounts may be 

calculated in two ways: Pre-rebate and post-rebate. Pre-rebate figures are the dollars paid out to 

WIC vendors. Post-rebate figures are the total dollars paid out to WIC vendors less the WIC 

infant formula rebates that all State WIC agencies receive from the manufacturers. The 

difference between pre-rebate and post-rebate amounts is substantial since States have negotiated 

sizable rebates (almost $160 million in May 2009 alone). In the FY1988 study, only the prerebate dollar error was calculated since it was the first year rebates were provided and the 

amount of rebates used was deemed too small to affect findings. In the FY1998 study, the postrebate amount was calculated for the first time. This 2009 study looks at both. 

 The FY1988 study estimated a dollar error of approximately $84 million out of an annual 

total of $1.5 billion in WIC food costs, a dollar error rate of 5.60 percent. 

 For FY1998, the NSWP-I estimated a dollar error of approximately $120 million out of 

an annual total of $2.6 billion in WIC food costs, a dollar error rate of 4.62 percent. 

 The current NSWP-II study estimated a total (pre-rebate)49 dollar error of approximately 

$233 million for FY2009; this amount was out of an annual total of nearly $6.27 billion 

total WIC food costs, a pre-rebate dollar error rate of 3.72 percent. Adjusting for infant 

formula rebates, the FY2009 post-rebate dollar error was estimated to be approximately 

$130 million out of about $4.37 billion in annual WIC food costs, excluding infant 

rebates billed in the fiscal year- a post rebate dollar error rate of 2.98 percent. 

Another factor relevant to dollar error estimation involves the different data sources and methods 

used to calculate estimates. The first study in 1988 used annual WIC food costs, multiplying the 

case error rate to estimate the total dollar error amounts. Ten years later, NSWP-I used the 

national average food cost per participant to sum the dollar error values for identified error 

cases.50 While both studies were based on actual food costs (i.e., redeemed voucher values), the 

data and calculation were at an aggregated level, rather than individual error cases. For example, 

applying total or average food costs would ignore the lower food costs as redeemed by 

improperly certified people who, due to their higher incomes, were less likely to use the full 

benefits. The current study, as required by the Contract, used each individual participant’s actual 

redemption values as the basis to calculate dollar error amounts. This resulted in more realistic 

 

48 The two studies are: Food and Nutrition Services, USDA (1990) WIC Income Verification Study (for FY1988) and Food and 

Nutrition Services, USDA (2001) National Survey of WIC Participants (for FY1998). 49 Pre-rebate means Program costs before rebates from infant formula manufacturers are factored in. 50 Food and Nutrition Services, USDA (2001). National Survey of WIC Participants, p.194. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 38 

estimates, reflecting the fact that some vouchers were not fully redeemed or never redeemed by 

participants. 

Furthermore, the dollar value of infant formula was adjusted to produce a post-rebate estimate of 

dollar error. For reference purposes, dollar error estimates based on the full voucher values (and 

without the infant formula rebate adjustment) were calculated, using the maximum values of 

issued vouchers available in the redemption dataset (see Appendix 4-11). This annualized dollar 

error was $329,932,975 (95% CI = $168,451,359 and $491,414,592). 

Exhibit 4-10: Estimates of Income Eligibility Case Error Rates: 1988, 1998, and 2009 

Fiscal Year Eligibility Case Error Rate 95% CI 

FY2009 3.05% 1.42% 4.68% 

FY1998 4.50% 2.80% 6.20% 

FY1988 5.70% 3.80% 7.60% 

Sources: National Survey of WIC Participants II Study, National Survey 

of WIC Participants I Study, and WIC Income Verification Study 

Exhibit 4-11: Estimates of Dollar Error and Dollar Error 

Rates from Income Eligibility Error: 1988, 1998, and 2009 

Fiscal Year 

Dollar Error 

($ Million) 

95% CI of Dollar Error 

($ Million) 

WIC Total Food 

Cost ($ Million) 

Dollar Error 

Rate (%) 

FY2009 

pre-rebate 233 118 349 6,274 3.72% 

FY2009 

post-rebate 130 70 190 4,374 2.98% 

FY1998 120 69 172 2,600 4.62% 

FY1988 84 56 112 1,500 5.60% 

Sources: National Survey of WIC Participants II Study, National Survey 

of WIC Participants I Study, and WIC Income Verification Study 
 39 National Survey of WIC Participants II 

CHAPTER 5. DENIAL AND TERMINATION/DISCONTINUATION 

FINDINGS

5.1 New Applicant Denials 

The findings on new applicant denials are primarily intended to provide insight into the reasons 

why WIC applicants are denied eligibility as perceived by the applicants and to describe 

characteristics of denied new applicants. The study did not directly identify erroneous denials, 

but collected information on respondents’ perception of agency mistakes in denying their 

applications, which would imply potential erroneous denials. The task was difficult still, as WIC 

agencies conduct the certification process and handle denial records in different ways. In 

communicating with sampled agencies and clinics, it became clear that many did not have a 

formal definition of the denial and few had a rigorous data system in place to keep records for 

applications that are turned down. With a potential applicant, a clinic staff usually talks over the 

phone and either tells her about her ineligibility or schedules an appointment for certification. 

Such personal and often amiable “pre-screening” procedures do not result in denial records of the 

ineligible cases. Most applicants who go through the appointment are issued benefits. Only very 

few applicants who go through the appointment receive a “notice of ineligibility,” which may be 

documented in some scant form, if at all. The numbers of denial records are very small because 

potential participants are given the qualifications over the phone most of the time. 

The original research plan envisioned 480 interviews with denied applicants, or about 3 per 

clinic sampled; the April and May 2009 data collection produced a smaller pool of denied 

applicants: 410 denials. The smaller than expected pool of denied applicants may have been due 

in part to 14 out of the 147 clinics asked to submit denial names reporting having no denials and 

too many other clinics providing five or fewer names. 

All the new applicant denial cases provided by the responding States/clinics were used in the 

denial analysis. Analysts did not weight or adjust the denial sample because of the lack of 

information on the denial population and the absence of data for a large subset of states/clinics. 

Neither was a nonresponse bias analysis conducted because clinics provided little information in 

the denial records. Other than the applicants’ contact information, the requested “WIC category” 

and “reasons for denial” were provided by very few clinics. Without any external information, it 

is impossible to generate a nationally representative denials sample. In anticipating such 

problems, ICF revised the data analysis plan to implement a supplemental study to the denial 

study that collected and analyzed termination/discontinuation data. The 

termination/discontinuation data analysis recognized that denials often occur at the time of 

benefit renewal. Termination/discontinuation data were weighted to generate the national 

estimates (see Section 5.2: Terminations/Discontinuations). Together, the denial and 

termination/discontinuation analyses generated knowledge on specific difficulties facing denial 

research, as well as descriptive estimates of the denial patterns. 

As recorded by the clinics and shown in Exhibit 5-1, the largest category of denied new 

applicants was pregnant women (37.6%) followed by children (27.8%). Not infrequently, more 

than one family member applied for benefits at the same time; in such instances, a random 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 40 

member—an adult—was selected for the survey. It is noted that clinics did not record the 

category in approximately 25 percent of the cases. 

Exhibit 5-1: Profile of Denied New Applicant Respondents, by Category 

37.6%

7.2%

5.2% 10.8%

27.8%

11.3%

Pregnant

Breastfeeding

Postpartum

Infant

Child

Unknown

 n = 194 

Source: National Survey of WIC Participants II Study 

Denied new applicants reported that they are most likely to learn of the WIC Program through 

friends/coworkers (33.0%) and family members (29.4%). Doctors and other health professionals 

also constituted an important source (14.9%). A smaller group of denials (5.7%) did not 

remember where they first heard of WIC, mostly stating that they have “always known about 

WIC” or “It’s always been there.” Other ways applicants learned about WIC include previous 

participation in WIC, word of mouth, the military, schools, and the WIC clinic itself 

(see Exhibit 5-2). 

Exhibit 5-2: How New Applicants Heard About WIC 

Source Percent Unweighted n

Friends/coworkers 33.0 64 

Family members 29.4 57 

Doctor/health professional 14.9 29 

Don’t remember; it’s always been there 5.7 11 

Had WIC benefits before 4.6 9 

Word of mouth 4.1 8 

Military or school 3.6 7 

WIC clinic 3.1 6 

Work 1.5 3 

Other 6.7 13 

n = 194 Note: Multiple responses are permitted. 

Source: National Survey of WIC Participants II Study 
Volume 3: Improper Payments Report (Final Report) 

 41 National Survey of WIC Participants II 

When denied new applicants were asked what benefits they were interested in getting out of the 

WIC Program, virtually all mentioned WIC food vouchers (97.9%). Other reasons included 

information about other community programs (67.2%), support for breastfeeding, nutritional 

counseling, referrals for immunizations, and referrals for other health care. More than half of 

applicants mentioned all these benefits (see Exhibit 5-3). 

Exhibit 5-3: Perceived Benefits of WIC by Denied New Applicants 

n = 194 Note: Multiple responses permitted. 

Source: National Survey of WIC Participants II Study 

New Applicant Reasons for Denial 

Denied new applicants were asked if the reason for their denial was related to proofs of 

identification, residence, income, or something else. Although multiple responses were 

acceptable, all denied applicants cited just one reason each. The responses (Exhibit 5-4) reveal 

that income was by far the main reason applicants were turned down for WIC benefits (93.3%). 

Additional problems listed were participant category (2.6%), residence (0.5%), and other 

problems (2.1%). No one mentioned lack of identification. Other problems cited with very low 

frequencies related to failed communications between the applicant and the clinic; one applicant 

noted, “They never got back to me.” 

51.0

53.6

59.1

61.1

67.2

97.9

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Referrals for Other Health Care

Referrals for Immunizations

Nutritional Counseling

Breastfeeding Support

Info About Other Community Programs

Food Vouchers

Percentage
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 42 

Exhibit 5-4: Reasons for Ineligibility 

93.3

2.6 2.1 1.5 0.5

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Income Category Other Don't know/

remember

Residence

Percentage

n = 194 Note: Multiple responses permitted but each respondent only cited one reason. 

Source: National Survey of WIC Participants II Study 

The reasons why proofs were rejected depended mostly on the type of proof presented. 

With regard to income proofs, for example, applicants were always ineligible because of 

excessive income. A total of 174 out of 181 cases of income ineligibility (96.1%) were due to 

excessive income. Category problems all occurred because applicants were of the wrong 

participant category. No one reported that their ineligibility ruling was based on unacceptable 

documents, such as one with an expired date or without an address or a photograph. The various 

reasons are shown in Exhibit 5-5. Since the base numbers were very small for all groups but the 

one for the income reason, the actual mentions are shown in lieu of percentages. 

Exhibit 5-5: Participant Perceived Main Reason for 

Determination of Ineligibility, by Type of Proof 

Reason 

Number of Mentions, by Type of Proofa

Income 

(n = 181) 

Category 

(n = 5) 

Other 

(n = 4) 

Residence 

(n = 1) 

Income was too high 174 – – – 

Did not bring proof (e.g. income, residency, 

identity) to the WIC agency 4 – – 1 

Applicant was out of scope (e.g., too old for 

child, not pregnant) – 5 – – 

Expired date on any required proof – – – – 

Proof was missing address or photo – – – – 

Unacceptable type of proof 1 – – – 

Other 2 – 4 – 

n = 191. 

a

 The three respondents who did not know the reason for their ineligibility were excluded. 

Source: National Survey of WIC Participants II Study 
Volume 3: Improper Payments Report (Final Report) 

 43 National Survey of WIC Participants II 

With respect to the determination of ineligibility, respondents were asked whether the 

determination was correct or whether it was a mistake. While 68.6 percent agreed with the WIC 

agency’s rationale, 25.3 percent felt a mistake was made, and 6.2 percent were unsure or chose 

not to answer. Those contending the agency made a mistake were all rejected for income reasons. 

Some of the verbatim responses are listed in Appendix J. It would appear that the most 

contentious situations occurred among applicants whose incomes had changed but whose 

documents reflected a situation that no longer existed. 

Of those determined ineligible, 64.6 percent confirmed that the clinic told them what would be 

needed to change the decision, or encouraged them to come back if their situation changes. 

About one-fourth of all denied applicants (25.2%) said they took follow-up action to get the 

decision changed. Of those, 71.4 percent reapplied, 4.1 percent asked to speak with a WIC 

supervisor, and less than 1 percent wrote a letter of appeal to WIC. The three-fourths of those 

who took no follow-up action said they did not either because they felt there was nothing else 

they could do, their incomes had risen, or they were too busy. 

Characteristics of Denied New Applicants 

In terms of race, denied applicants were most likely to identify themselves as White (38.7%), 

followed by Other (33%). Further exploration of the participants that identified themselves as 

Other revealed that 93.8 percent of them also classified themselves as Hispanic/Latino. A smaller 

number of respondents identified themselves as African American (13.4%), Asian/Pacific 

Islander (11.9%), or multiracial (2.1%) (see Exhibit 5-6). Ethnically, about half say they were 

Hispanic/Latino (51.1%). 

Exhibit 5-6: Race of Denied New Applicants 

Race Percent Unweighted n

White 38.7 75 

Other 33.0 64 

African American 13.4 26 

Asian/Pacific Islander 11.9 23 

Multiracial 2.1 4 

American Indian/Alaska Native 0.0 0 

Refused 0.9 2 

n = 194 

Source: National Survey of WIC Participants II Study 

More than half of the denied applicants (58.8%) received education beyond high school, such as 

attending some college, or completing a degree (see Exhibit 5-7). This contrasts with only 

33.2 percent of all applicants in the Participant Characteristics report. Most of the remaining 

denied applicants (33.5%) had a high school diploma, and about 1 in 20 had less education than a 

high school diploma. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 44 

Exhibit 5-7: Highest Level of Education Attained by Denied New Applicants 

Level of Education Percent Unweighted n

Advanced degree 4.1 8 

Bachelor’s degree 11.9 23 

Associate degree 10.3 20 

Some college 32.5 63 

High school/GED 33.5 65 

Some high school 6.2 12 

Elementary school 0.5 1 

Refused 1.0 2 

n = 194 

Source: National Survey of WIC Participants II Study 

English is reported to be the primary language of about two-thirds (66.0%) of denied new 

applicants, while Spanish is spoken by nearly one-fourth (23.3%). The remaining 10.8 percent of 

denied applicants said they speak other languages or refused to identify their primary language. 

5.2 Terminations/Discontinuations 

The termination/discontinuation data collection and analysis was designed to supplement the 

new applicant denial analysis. As the new applicant denial study encountered difficulties in 

obtaining sufficient data and generating nationally representative statistics, the 

termination/discontinuation study provided supplemental information on relevant issues, 

specifically the proper stop of WIC benefits and other forms of benefit cessation. Most benefits 

ended, as expected and evidenced by data, by voluntary withdrawal or due to other situation 

changes (e.g., moved away). The analysis examined reasons why the benefits of this group had 

ceased as perceived by participants, and provided a description of terminated/discontinued 

participants’ basic demographics. As in the new application denial analysis, this study did not 

formally determine incorrect termination/discontinuation; instead it only collected data on 

respondents’ perception of agency’s mistake in termination/discontinuation. 

In this study, terminated/discontinued participants are defined as WIC participants whose 

eligibility for WIC benefits ended in May 2009 and they were not recertified. The cross-sectional 

sample that was interviewed was drawn from State agency lists of participants who had received 

food vouchers in April 2009 but were no longer on the list of participants in May 2009. A total of 

607 terminated/discontinued participants were randomly selected from selected clinics 

(combining the five categories of WIC participants) in the larger NSWP-II sample. Of these 

selected, 393 responded to the survey, a response rate of 64.7 percent. The probability of 

selecting the clinic and the termination/discontinuation cases from the clinic were combined to 

obtain an initial termination/discontinuation sample weight, which was subsequently adjusted for 

nonresponse using region as the adjustment category. The resulting statistics from the 

termination/discontinuation analysis were generalizable to the national population of WIC 

participants who received food vouchers in April 2009 but did not in May 2009 (weighted 

sample n = 1,066,567). 
Volume 3: Improper Payments Report (Final Report) 

 45 National Survey of WIC Participants II 

Terminated/discontinued respondents were similar to the population of all WIC participants in 

that infants and children comprised almost three-quarters of terminated/discontinued respondents 

(although infants were somewhat underrepresented compared to the total WIC population), while 

pregnant, breastfeeding, and postpartum women made up the rest (with pregnant women slightly 

overrepresented compared to all WIC participants)51 (see Exhibit 5-8). 

Exhibit 5-8: Profile of Terminated/Discontinued Respondents, by Category 

14.3%

4.8%

9.4%

17.5%

54.0%

Pregnant

Breastfeeding

Postpartum

Infant

Child

 n = 393 

Source: National Survey of WIC Participants II Study 

When asked about how they first learned about the WIC Program, the majority of 

terminated/discontinued respondents reported hearing about it through their doctor or health 

professional (39.4%), friends (25.0%), and family members (24.9%). Other sources included 

through word of mouth, the military, a WIC clinic, or a school (see Exhibit 5-9). 

Exhibit 5-9: How Terminated/Discontinued Respondents Heard About WIC 

Source Percent Unweighted n

Doctor/Health professional 39.4 155 

Friends/Coworkers 25.0 99 

Family members 24.9 98 

Other 13.6 54 

n = 393 Note: Multiple responses accepted. 

Source: National Survey of WIC Participants II Study 

When asked what benefits they were interested in getting out of the WIC Program, the vast 

majority of terminated/discontinued participants mentioned WIC food vouchers (91.9%), 

followed by more than half indicating each of the following: nutritional counseling, referrals for 

immunizations, information about other community programs, referrals for other health care, and 

breastfeeding support programs (see Exhibit 5-10). 

 

51 In the WIC Participants survey, infants made up 24.3 percent and pregnant women 10.5 percent. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 46 

Exhibit 5-10: Terminated/Discontinued Respondents’ Perceived Benefits of Applying for WIC 

 n = 393 

Source: National Survey of WIC Participants II Study 

Reasons for Termination/Discontinuation 

The reasons for ineligibility were quite different for terminated/discontinued participants than for 

denied new applicants. Denied new applicants were largely denied based on income eligibility. 

On the other hand, with regard to terminated/discontinued participants, there was no predominant 

reason (see Exhibit 5-11). Category changes such as a child aging out of WIC or a mother who 

has stopped breastfeeding were at the top of the list of reasons (17.1%). Other factors also 

contributed, including missed appointments (13.6%), income changes (10.8%), residency 

changes (10.3%), identification issues (8.4%), personal decisions (5.4%),52 and others (3.7%). 

Interestingly, about 13.8 percent were not aware of any ending of or gap in benefits, because 

they had recertified so quickly. 

 

52 Personal decisions not to continue with WIC often revolved around scheduling issues and/or perceived inconvenience of 

having to go to the clinic to get benefits. It also included decisions to stop because food and services were no longer needed.

52.3

56.4

58.1

58.3

64.4

91.9

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Breastfeeding Support

Referrals for Other Health Care

Info About Other Community Programs

Referrals for Immunizations

Nutritional Counseling

Food Vouchers

Percentage
Volume 3: Improper Payments Report (Final Report) 

 47 National Survey of WIC Participants II 

Exhibit 5-11: Terminated/Discontinued Participants’ Reasons for Ineligibility 

17.1

13.8 13.6

10.8 10.3

8.4

5.4

3.7

16.9

0%

5%

10%

15%

20%

Category Not Aware

of Being

Terminated

Missed

Appointment

Income Residency Identification Personal

Decision

Other Don't Know

Percentage

n = 393 Note: Multiple responses allowed. 

Source: National Survey of WIC Participants II Study 

The various reasons for ineligibility are broken out by category in Exhibit 5-12. With relatively 

small base sample sizes that made conclusions difficult, the reported major reasons for benefit 

end were: missed appointments for infants, income change for breastfeeding women, and 

respondents’ personal decisions (not to continue with WIC) for postpartum women. 

Exhibit 5-12: Reasons for Ineligibility for Terminated/Discontinued Participants, by 

Category 

Pregnant 

(%) 

Breastfeeding 

(%) 

Postpartum 

(%) 

Infant 

(%) 

Child 

(%) 

Identification 4.5 – 6.0 2.9 11.1 

Residency 4.3 – 1.6 1.5 14.7 

Income change 6.0 24.5 8.1 6.0 12.6 

Missed appointment 18.3 11.1 1.7 27.0 11.2 

Category change 14.1 17.7 7.4 4.6 19.3 

Personal decision 1.8 2.7 31.4 3.7 4.3 

Not aware of being 

terminated 26.0 39.2 25.3 23.9 8.0 

Other reason 2.8 24.5 4.5 .7 3.6 

Don’t know/ remember 11.4 4.8 12.2 28.8 18.6 

Refused to answer the 

question 17.1 – 3.4 2.4 5.9 

n=380 Note: For categories other than the “Child,” base sizes were less than 60 respondents. 

Source: National Survey of WIC Participants II Study 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 48 

Very few terminated/discontinued participants (4.3%) disagreed with the decision. Over half 

(61.4%) of the terminated/discontinued WIC applicants did not take any follow-up action to 

overturn the decision. When asked why, virtually all reported that they did not believe it would 

do any good, stating that they no longer met the residency, income, or category requirements. 

While 30.0 percent of terminated/discontinued WIC participants who did not take action 

specifically said it would not do any good, another 59.9 percent gave reasons such as their child 

aging out of the WIC Program, moving outside of the residency boundaries, and earning too 

much income. 

Characteristics of Terminated/Discontinued Participants 

In racial and ethnic background, terminated/discontinued participants were not drastically 

different from the general WIC population.53 The largest proportion of them reported their race 

as White (43.1%), followed by Multiracial/Other (23.1%), then African American (12.6%). 

Asian/Pacific Islander and American Indian/Alaskan Native fall under 3 percent. Note that 17.3 

percent of terminated/discontinued participants failed to identify their race (see Exhibit 5-13a). 

As to ethnicity, 34.4 percent of the terminated/discontinued participants identified themselves as 

Hispanic/Latino, and 11.3 percent did not report their ethnicity (Exhibit 5-13b). Note that the 

bulk of Hispanic/Latino terminated/discontinued participants identified themselves as 

Multiracial/Other (58.2%) or White (26.8%, estimates not presented in exhibits). 

Exhibit 5-13a: Race of Terminated/Discontinued Participants 

Race Percent Unweighted n

American Indian/ Alaskan Native 1.5 3 

Asian/Pacific Islander 2.4 11 

African American 12.6 59 

White 43.1 176 

Multiracial/ Other 23.1 77 

Nonresponse 17.3 67 

n = 393 

Source: National Survey of WIC Participants II Study 

Exhibit 5-13b: Ethnicity of Terminated/Discontinued Participants 

Ethnicity Percent Unweighted n

Non-Hispanic/Latino 54.3 195 

Hispanic/Latino 34.4 159 

Nonresponse 11.3 39 

n = 393 

Source: National Survey of WIC Participants II Study 

 

53 In the Participant Characteristics report, there were 42.3 percent Whites, 19.5 percent African American, 33.4 percent 

Other/Multiracial, 3.1 percent Asian/Pacific Islander, and 1.8 percent American Indian/Alaskan Native. Hispanics comprised 

45.1 percent of the population. 
Volume 3: Improper Payments Report (Final Report) 

 49 National Survey of WIC Participants II 

More than one-third of the terminated/discontinued WIC participants (39.3%) reported that they 

had received education beyond high school–some college, an associate’s, bachelor’s, or 

advanced degree (see Exhibit 5-14). Another 37.4 percent completed a high school diploma or 

GED, 15.2 percent attended some high school, and 7.8 percent attended just elementary school. 

Exhibit 5-14: Terminated/Discontinued Participants’ Highest Level of Education Attained 

Level of Education Percent Unweighted n

Advanced degree 1.4 6 

Bachelor’s degree 7.1 28 

Associate degree 5.6 22 

Some college 25.2 99 

High school/GED 37.4 147 

Some high school 15.2 60 

Elementary school 7.8 31 

Refused 0.3 1 

n = 393 

Source: National Survey of WIC Participants II Study 

English is the primary language of 64.9 percent of terminated/discontinued WIC participants, 

while Spanish is spoken by over one-fourth (25.2%). Other languages accounted for the 

remaining 9.9 percent. 

5.3 Summary 

Denied new applicants reported that income was by far the main reason they were turned down 

for WIC benefits (93.3%). The reasons for a new applicant denial depend mostly on the type of 

proof. Income proofs submitted by the denied new applicants were often considered ineligible 

because of excessive income (96.1%). Only small proportions of the denied new applicants cited 

eligibility problems associated with other criteria such as category or residence. Category 

problems all occurred because the applicants were out of scope—that is, not within the WIC 

definitions for “Pregnant,” “Breastfeeding,” “Postpartum,” “Infant,” or “Child.” No respondent 

reported lack of identification as an eligibility problem. 

Approximately a quarter (25.3%) of the denied new applicants thought that the agency had made 

a mistake in determining their eligibility. Importantly, however, as all who reported that the 

agency made a mistake were rejected for reasons of income, it is possible that some errors were 

perceived by applicants whose incomes fell recently but who only had proofs showing the higher 

income levels. 

There was no dominating reason for ending benefits across all categories. Category changes, 

typically the proper stop of the benefit, such as a child who aged out of WIC or a mother who 

stopped breastfeeding, appeared to be an important reason (17.1%). Other reasons included 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 50 

missed appointments (13.6%), income changes (10.8%), residency changes (10.3%), 

identification issues (8.4%), and personal decisions (5.4%). 

Few terminated/discontinued participants (4.3%) disagreed with the decision. Many respondents 

admitted valid reasons for their termination/discontinuation, such as category status changes; 

missing appointments for recertification; moving to a new clinic location; and self-made 

decisions to stop WIC for their own reasons. Also, a substantial proportion (13.8 %) of 

terminated/discontinued respondents were unaware of their benefit ending; others had their 

eligibility end because of changes in their situation or personal decisions. 

The denial and termination/discontinuation data analysis revealed general demographic 

backgrounds of the denied applicants and terminated/discontinued participants. Comparing two 

key demographic characteristics—race and education level (available in both denial and 

termination/discontinuation data)—against the WIC participant population estimated with 

NSWP-II in-person survey data showed large patterns of denial and termination/discontinuation. 

Note that such patterns may not reflect complex relationships between denial/discontinuation and 

demographics because the descriptive statistics did not account for many factors that lead to 

denial or termination/discontinuation. The following large differences can be observed 

(see Exhibit 5-15): 

 The denial rate of Whites was lower (38.6%) relative to their share in the WIC 

population (44.6%); 

 African Americans’ denial and termination/discontinuation rates (13.4% and 12.8%, 

respectively) were lower than their share in the WIC population (21.5%); 

 Asian/Pacific Islanders received a higher denial rate (11.9%) than their share in the WIC 

population (2.7%); and 

 Education level was inversely associated with denial or termination/discontinuation rates. For 

example, the group with advanced degrees had higher rates of denial and 

termination/discontinuation (4.1% and 1.4%, respectively) compared with their share in the 

population (0.9%), whereas the group that had some high school had two rates (6.2% and 

15.2%, respectively) lower than the group rate in the WIC population. 
Volume 3: Improper Payments Report (Final Report) 

 51 National Survey of WIC Participants II 

Exhibit 5-15 Demographics of the Denial, Termination/Discontinuation, and the WIC 

Participant Population: Percentages of Race and Education Categories 

Denials (n=194)

(%) 

Termination/Discontinued (n=394) 

(%) 

WIC Population 

(NSWP-II n=1,210)a

(%) 

Race 

White 38.7 43.6 44.6 

Other 33.0 36.0 30.1b

African American 13.4 12.8 21.5 

Asian/Pacific Islander 11.9 2.4 2.7 

Multiracial 2.1 1.5 N/A 

American Indian/Alaska 

Native 0 1.3 1.1 

Refused or not reported 0.9 2.4 N/A 

Total 100.0 100.0 100.0 

Level of Education 

Advanced degree 4.1 1.4 0.9 

Bachelor’s degree 11.9 7.1 4.2 

Associate degree 10.3 5.6 5.0 

Some college 32.5 25.2 26.4 

High school/GED 33.5 37.4 34.9 

Some high school 6.2 15.2 21.2 

Elementary school 0.5 7.8 6.5 

Refused 1.0 0.3 0.5 

Total 100.0 100.0 99.7c

a

 Race categories may be different from those presented in the report that entailed consolidation due to low cell frequencies in

two-way crosstabs. 

b

 Includes category of “two and more races.” 

c

 Does not total 100 percent due to rounding. 

Source: National Survey of WIC Participants II Study 

CHAPTER 6. LIMITATIONS OF THE STUDY

Data collection for NSWP-II was inherently challenging because of frequent changes in WIC 

participants’ contact information, income and employment situation, and program participation 

status. An inability to speak English and skepticism about government efforts to gather sensitive 

information also made many WIC participants reluctant to cooperate with data collection. The 

end result was that it was challenging even to make a first contact with respondents. 

Data collection was also hampered by a lack of the most current administrative data from WIC 

State and local agencies. This was due in part because of the lag time between data retrieval and 

actual data collection; in part because agencies usually only update participant records at 

certification or re-certification (i.e., every 6 to 12 months); and in part because updated 

information is stored in different places, depending on the State WIC Program. Despite extensive 

efforts in fieldwork and data processing, the NSWP-II data and analysis are subject to a number 

of limitations. The problems are documented here for the sensible use of the information 

generated from the study and the improvement of future research. 

6.1 Nonresponse and Potential Bias 

Complete data were obtained for approximately 81 percent of participants in the in-person 

interview (WIC rates by category ranged from 79.3% for infants to 84.9% for breastfeeding 

women). Statistical bias, however, may exist because of the low response rate for the telephone 

survey (an overall 51.3%) that provided the pool of respondents for in-person interview sampling. 

The low completion rate was primarily due to the high rate of “unreachables” across WIC 

categories (ranging from 30.5% for pregnant women to 39.1% for postpartum women). Data 

collectors relied on State agency–provided contact information to locate sampled participants 

and were therefore, unable to locate some sampled participants due to WIC recipients’ address 

and phone number changes. 

As required by OMB, analysts conducted nonresponse bias analysis, using the available survey 

data, State agency data, and WIC administrative data for the participation population. The 

analysis examined participant race/ethnicity, gender, family size, months since the recent 

certification, proof of adjunctive income eligibility, and residence locale (metropolitan statistical 

area [MSA] or a non-MSA). Response rates were compared for each of the categories in these 

variables, and each variable was cross-tabulated with program outcome variables 

(e.g., satisfaction with WIC staff and services, attendance at any group nutrition education 

session, nutrition counseling, various food programs received, and food security). Race and 

ethnicity were found to be significantly related to both nonresponse and the outcome measures. 

The original plan was to use States-provided race/ethnicity data for weight adjustment to 

compensate for the greater response rates of some groups over others. However, race and 

ethnicity provided by the States did not match the telephone survey respondents’ self-reported 

race and ethnicity (or were missing) in 31.3 percent54 of cases. Self-reported race and ethnicity 

could not be used for weight adjustments based on response rates (since the variables were not 

available for nonrespondents). Thus, the weights were adjusted to fit the WIC participant 

 

54 Refer to Section 2.3, Non-Response Bias Analysis, of Volume 1 of NSWP-II (Participant Characteristics Report) 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 54 

population totals based on the WIC administrative data (see Appendix I for details). With the 

2008 WIC Participant and Program Characteristics Survey estimates on race and ethnicity as the 

framework, analysts conducted a form of post-stratification known as raking55 to compensate for 

race/ethnicity-related bias. The raking-generated weights were then applied to the tabulations of 

the key outcome variables. The resulting statistics were not substantively different from statistics 

using the original weights for all the outcome variables: The largest difference was 1.5 percent, 

and the bulk of the differences were under 1 percent. 

Nevertheless, failing to include a large proportion of targeted sample units in the telephone 

survey introduced uncertainties to the statistical analysis. It is questionable as to what 

characteristics other than those examined in the bias analysis are different between survey 

respondents and nonrespondents, particularly those who were “unreachable.” It is also unclear as 

to what extent such potential bias in the telephone sample may affect the in-person interview 

sample since the latter was an adjusted subsample from the former. Such unknown potential bias 

calls for caution in interpreting the NSWP-II statistics. 

6.2 Potential Errors: Family Income Data 

Similar to NSWP-I,56 the in-person interview in NSWP-II encountered difficulties collecting 

income data which may have introduced potential errors relevant to the determination of income 

eligibility error. First, income measurement errors may exist. Income data were collected for 

family members on each of 20 or more income sources (depending on States’ discretionary 

specification of countable incomes), each including dollar amount, pay period, and income 

documentation availability. The approach was purposely designed to collect the components of 

income (e.g., amount of payment, frequency) that would later be calculated into an annual dollar 

amount by researchers. However, it was out of the study scope to re-interview respondents when 

problems were discovered during data cleaning. Thus, derivation of the measure of family 

monthly income involved systematic, informed judgments. For example, in 12 cases where a 

nonstandard pay period was recorded but not detailed, researchers checked similar income 

sources and dollar amounts to estimate the pay period so the amount could be converted to 

monthly income. 

The problem stems from a dilemma facing income data collection: On the one hand, onsite 

clarification to maximize income data validity needs more communication between the data 

collector and the respondent; on the other, respondents’ cooperation is facilitated by data 

collectors’ role as a neutral agent gathering information with few judgments. 

Second, differing methodologies in NSWP-I and NSWP-II resulted in a difference in timing 

between income data collection and certification in this study that may have affected the 

determination of eligibility. Data were largely collected on incomes of the month immediately 

before the in-person interview, rather than for the month when certification was given. This was 

the case because respondents, in general, were unable to trace income information long enough 

 

55 Raking is a form of iterative post-stratification, frequently used when totals come from different tables. In this case, different 

tables presented race and Hispanic status. 

56 In NSWP-I, 38.8 percent of the in-home interview respondents did not provide income documents and of these, 74.3 percent 

changed their income information from what was previously reported in the onsite clinic interview, leading to two sets of income 

eligibility error estimates (NSWP-I, p. 178 and p. 180). 
Volume 3: Improper Payments Report (Final Report) 

 55 National Survey of WIC Participants II 

to cover the time gap between certification and the income data collection. The time gap may 

have been more than 12 months in some cases: The sample consisted of participants who 

received a voucher in May 2009 (hence, certified earlier than or in May) and the data collection 

occurred between June 2009 and early 2010. This time gap may cause discrepancies in 

determining income eligibility by NSWP-II and the agency’s certification. 

6.3 State Data Quality Concern: Expired Certification Error 

NSWP-II explored analysis of expired certification error but did not formally present the results 

(procedures and tentative results are available in Appendix K) because of data quality concerns. 

Expired certification errors occur when benefits are awarded and redeemed after the expiration 

date of the benefit. Accurate certification dates must be made available to identify expired 

certification error. NSWP-II generated tentative statistics of this error using State agencies’ data 

on certification dates. State agencies typically do not have the most updated and reliable 

information on certification dates. Local agencies actually conduct certification and update 

certification dates to handle constantly shifting participant status such as WIC benefit issuance, 

termination, renewal, and category change. Efforts to obtain data from local agencies were made. 

However, it was difficult to obtain the cooperation of local agencies because of resource 

restraints and local agency daily work overburden, among other reasons (see Volume 2: State 

and Local Agencies Report for information on staff shortages). The issue points to the 

importance of collaboration with local agencies and clinics, perhaps by providing incentives 

(e.g., compensation for data handling, technical assistance, and legal or regulation requirement). 

6.4 Proxy Estimates: Infant Formula Rebate Adjustment 

Estimating improper payment (dollar error) with adjustment for infant formula rebate generated 

more realistic improper payment estimates because participants often do not redeem all the 

voucher values. The procedure was, however, subject to data restrictions: (1) redemption data do 

not separate the infant formula amount from other food redemption values; (2) States’ negotiated 

rebate rates differ by formula products, requiring data consolidation that may introduce error; 

and (3) rebate rates are applied to wholesale prices only, but the survey collected redemption 

data are based on retail prices, hence requiring complicated procedures of adjustment. In short, 

post-rebate estimates of dollar error cannot be calculated from the survey data alone. 

WIC administrative data and the NSWP-II statistics were combined to generate a proxy measure 

of rebate values for infants (see Appendix L). Subtracting the rebate value from each infant’s 

actual redeemed voucher value, analysts obtained post-rebate food costs, which is the improper 

payment or dollar error for an infant with identified income ineligibility error. The weighted sum 

of this measure was the post-rebate improper payment due to income ineligibility error for WIC 

infants. The proxy estimates based on State average rebate rates and the national average 

redemption value are likely to be less accurate than estimates based on the actual rebate values 

for individual error cases, had the data been available. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II 56 

6.5 Lack of Statistical Generalization: New Applicant Denial 

Analysis 

Only a descriptive new applicant denial analysis was conducted in NSWP-II with limited data 

collected from WIC agencies because few agencies maintained a data system to keep records for 

applications that are turned down. Typically, the numbers of denial records are very small in 

agencies that keep the records, since many potential participants learn about their likely 

eligibility over the telephone; and do not complete a formal application. The planned 480 

interviews (roughly three per clinic) resulted in only 410 denied new applicant interviews. 

The new applicant denials data was not weighted and, therefore, should not be considered 

nationally representative. 
APPENDICES
Volume 3: Improper Payments Report (Final Report) 

APPENDIX A: 

IN-PERSON SURVEY INSTRUMENT

(VERSION A: PREGNANT, BREASTFEEDING, AND 

POSTPARTUM) 
 A-1 National Survey of WIC Participants II 

Appendix A 

IN-PERSON INTERVIEW (Version B: Pregnant, Breastfeeding, and 

Postpartum) 

1. Hi. Thanks for agreeing to do the second part of our survey. Your answers are completely 

confidential and, as I mentioned when we set this up, nothing you say will have any bearing 

on your benefits. The WIC program is just trying to get a better idea of who participates in 

the program and their circumstances. I will be giving you $20 in appreciation of your time. 

READ INFORMED CONSENT STATEMENT AND GET CONSENT BEFORE 

PROCEEDING. 

IDENTITY AND RESIDENCY 

2. The first thing we need is some identification—silly as it may seem—and proof that you live 

here. [IF R. HAS TROUBLE WITH THIS REQUEST, READ OFF SOME OF THE 

ACCEPTABLE TYPES OF ID AND RESIDENCY PROOF FROM LIST.] 

Identification proofs [CHECK AT LEAST 

ONE]

Residency proofs [CHECK AT LEAST 

ONE]

 State-issued license or ID  State-issued license or ID w/address 

 U.S. passport w/photo  State/federal correspondence 

w/address 

 Foreign passport w/photo  WIC folder 

 WIC folder  Checkbook w/address 

 W-2 form or Tax bill w/name  Rent or mortgage receipt, lease 

w/address 

 Birth certificate  Utility or tax bill w/address 

 Social Services letter w/ name  Documents from public school 

w/address 

 Social Security or Green card  Written statement from reliable third 

party (e.g. non-profit aid organization)  Hospital or immunization record 

 Other: SPECIFY ________________  Other: SPECIFY ________________ 

PRIMARY FAMILY/ECONOMIC UNIT 

3. Let’s begin by having you tell me the names of all the persons who live or stay with you 

whether they are related to you or not. I will type in the names so I can follow up with some 

questions. [PROBE: ANYONE ELSE?] 

RECORD ALL NAMES IN LIST FORM. 

1) _____________________________ 

2) _____________________________ 

3) _____________________________ 

4) ETC. 
National Survey of WIC Participants II A-2 

AFTER ALL PERSONS ARE LISTED, ASK FOLLOWING QUESTIONS FOR EACH PERSON:

4.What is their relationship to you-? 1. Spouse 

2. Partner 

3. Child 

4. Step-child 

5. Foster child 

6. Parent/ Guardian 

7. Step-parent 

8. Foster parent 

9. Brother/Sister 

10. Grandparent 

11. Uncle/aunt 

12. Cousin 

13. Nephew/niece 

14. Parent-in-law 

15. Brother-in-law/sister-inlaw 

16. Other relative 

17. Other non-relative 

18. Child in Temporary Care 

of Friends/Relatives 

5. Is this individual male or female?  1-Male 

 2-Female 

6. How old is this person? _____ YEARS 

7. FOR ANY CHILD LESS THAN 5 YEARS OR ANY WOMAN 

GREATER THAN 14 YEARS ASK: Is this person receiving WIC now?

 1-Yes 

 2-No 

8. OTHERWISE, IF Q6≥15, ASK: Do you consider [READ NAME] to be 

part of your family group -- that is, you are sharing income and expenses 

as if you were a family -- OR do you feel that you each keep your income 

and expenses and food separately? 

IF Q6<15, ASK: Do you consider [READ NAME] to be part of your 

family group -- that is, you are responsible for taking care of them as if 

you were all in the same family?

 1-Share like family 

 2-Separate finances 

9. PROGRAM WILL CALCULATE NUMBER OF PEOPLE IN 

PRIMARY ECONOMIC UNIT PLUS ADD ONE FOR WIC 

PARTICIPANT

[Q4=1,2,3, 4 OR 5] or [Q8=1] and [Q6≥15]

NUMBER OF 

PEOPLE IN 

PRIMARY 

ECONOMIC UNIT 

10. COMPUTER WILL COMPARE THE NUMBER OF PEOPLE IN PRIMARY ECONOMIC UNIT (Q9) 

WITH # OF HOUSEHOLD MEMBERS IN WIC RECORDS (P5-TOTAL IN PRIMARY ECONOMIC 

UNIT). 

IF Q9=P5, SKIP TO Q11 

IF Q9<P5, SAY: The WIC records show that back in [INSERT MONTH/DATE OF 

CERTIFICATION]you had [INSERT #] adults and [INSERT #] children in this household, which is 

more than we listed today. Have we left someone off the list? Or perhaps there is someone on our 

list today who should be counted as part of your main family unit but was not? [PROBE & ADD 

NEW NAMES OR INFORMATION IN Q2-9 AS APPROPRIATE] 

IF Q9>P5, SAY: The WIC records show that back in [INSERT MONTH/DATE OF CERTIFICATION] 

you had [INSERT #] adults and [INSERT #] children in this household, which is fewer than we have 

listed here. Can I verify that everyone on our list here IS part of your main family unit? [PROBE & 

DELETE NAMES OR INFORMATION IN Q2-9 AS APPROPRIATE] 

NOTE: IF RESPONDENT HAS DIFFICULTY RECALLING TIME PERIOD USE NARRATIVE 

APPROACH IN WHICH A TIME IS LINKED TO A SALIENT EVENT] 

FROM PRIMARY ECONOMIC UNIT LIST, COMPUTER WILL GENERATE A LIST OF 

“POTENTIAL WAGE EARNERS” – DEFINED AS ALL THE ADULTS AND ALL CHILDREN > 15 

YEARS
Volume 3: Improper Payments Report (Final Report) 

 A-3 National Survey of WIC Participants II 

ADJUNCTIVE OR AUTOMATIC ELIGIBILITY 

11. [IF ADJ ELIGIILITY IS SHOWN IN RECORDS, ASK:] The WIC records show that you 

qualified for WIC because you, or a member of your family, participate in the [FILL IN 

FROM P4] program. Can you show me the document that you showed to WIC to 

demonstrate participation in that program such as the certification card, the award letter you 

got, or an active program voucher? [IF NO, PROBE: Do you have anything at all?] 

[IF ADJ ELIGIBILITY IS NOT SHOWN IN RECORDS, ASK:] Did you qualify for WIC 

by showing that you participated in another benefits program such as Medicaid, SNAP or 

TANF? [OPTIONAL:] If yes, can you show me the document that you showed to WIC to 

demonstrate participation in that program such as the certification card, the award letter you 

got, or an active program voucher? 

 YES, PROOF SHOWN – ENTER : 

a) NAME OF PROGRAM RECIPIENT ON 

PROOF SHOWN. 

[MAKE SURE IT MATCHES SOMEONE IN 

THE PRIMARY ECONOMIC UNIT; 

OTHERWISE ASK FOR ANOTHER 

DOCUMENT/CARD AS PROOF] 

___________________________ 

b) DATE OF DOCUMENT/CARD ISSUANCE

(MM/DD/YYYY) [TYPE IN]______________________ 

99 NO DATE [PROBE: Do you have anything that shows the 

dates for your participation in the program? IF NO, ASK FOR 

DATE OF FIRST ELIGIBILITY.] 

c) DATE OF DOCUMENT/CARD OR 

ELIGIBILITY EXPIRATION 

(MM/DD/YYYY) 

[TYPE IN]______________________ 

99 NO DATE [PROBE: Do you have anything that shows the 

expiration date?] 

d) NAME OF ISSUING AGENCY [TYPE IN]______________________ 

99 NOT EVIDENT [PROBE: Do you have anything that 

shows the agency name?] 

e) NUMBER ON DOCUMENT/CARD [TYPE IN]______________________ 

99 NO NUMBER 

f) DOCUMENT/CARD SHOWN  Certification card [SKIP TO Q13 IF ADJ ELIGIBLE IN 

RECORDS; CONTINUE TO Q12 IF NOT ADJ 

ELIGIBLE IN RECORDS.] 

 Award letter [SKIP TO Q13 IF ADJ ELIGIBLE IN 

RECORDS; CONTINUE TO Q12 IF NOT ADJ 

ELIGIBLE IN RECORDS.] 

 Active program voucher [SKIP TO Q13 IF ADJ 

ELIGIBLE IN RECORDS; CONTINUE TO Q12 IF NOT 

ADJ ELIGIBLE IN RECORDS.] 

 Food Stamp EBT card [SKIP TO Q13 IF ADJ ELIGIBLE 

IN RECORDS; CONTINUE TO Q12 IF NOT ADJ 

ELIGIBLE IN RECORDS.] 

 Other [IF ANY DOUBTS ABOUT VALIDITY, HAND 

R. PROGRAM PARTICIPATION RELEASE FORM. 

THEN CONTINUE TO Q12] 
National Survey of WIC Participants II A-4 

 NO, PROOF NOT SHOWN OR WRONG PROOF SHOWN. 

[IF PERSON IS ADJUNCTIVELY ELIGIBLE IN RECORDS: HAND R. PROGRAM 

PARTICIPATION RELEASE FORM & CONTINUE TO Q12 

IF PERSON IS NOT ADJUNCTIVELY ELIGIBLE IN RECORDS, CONTINUE TO 

Q12] 

INCOME ELIGIBILITY 

12. Now I am going to ask you about the income earned by you and other primary members of 

this household. WIC is interested in the accuracy of their data records in this area. The 

information you share with me will be confidential and will be combined with that from 

other people, so WIC won’t know your or anybody else’s personal information. 

So let’s start with [READ NAME OFF LIST OF PRIMARY ECONOMIC UNIT. 

RESPONDENT SHOULD BE FIRST ON LIST.] 

[NOTE: IF RESPONDENT HAS DIFFICULTY RECALLING TIME PERIOD USE 

NARRATIVE APPROACH IN WHICH A TIME IS LINKED TO A SALIENT EVENT] 

a) Thinking back to [INSERT 

MOST RECENT 

CERTIFICATION 

MONTH/YEAR], did 

[INSERT you/NAME] receive 

any income from... [READ 

FROM BELOW]? 

CHECK ONLY IF YES 

b) FOR EACH ITEM 

CHECKED IN a), ASK: 

How much did [INSERT 

you/NAME] earn? 

c) Can you show me 

some evidence of 

that income such 

as [READ FROM 

LIST 

ACCOMPANYIN

G EACH ITEM] 

 Wages, salary, fees 

(excluding military pay) 

YES 

NO

$_______

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Pay stub/earnings 

statement 

 W-2 form 

 2007 IRS tax return 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM AND 

UNDOCUMENTED 

EMPLOYMENT 

INFO FORM 

 Military pay 

YES 

NO $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: 

 Leave and earnings 

statement 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 
Volume 3: Improper Payments Report (Final Report) 

 A-5 National Survey of WIC Participants II 

 Tips and bonuses 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 W-2 form 

 Other: _____ 

 None 

 Net income from self 

employment (from farm and 

non-farm business) 

YES 

NO

 

$_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE 

UNDOCUMENTED 

EMPLOYMENT FORM

 Unemployment compensation 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other:_____ 

 2007 IRS tax return 

 Copy of check, check 

stub 

 Letter of 

determination 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 

 Workers compensation 

YES 

NO

 

$_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Copy of check, check 

stub 

 Award statement 

 Statement from 

insurance company 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 

 Child Support 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Copy of check 

 Support agreement 

 Divorce/separation 

decree 

 Court order 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 
National Survey of WIC Participants II A-6 

 Alimony 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Copy of check 

 Support agreement 

 Divorce/separation 

decree 

 Court order 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 

 Social Security 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 SSA Award letter 

 Statement of benefits 

 2007 IRS tax return 

(line 14a on 1040A) 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 

 SSI – Fed government 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 

 SSI—State issued 

YES 

NO 

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 

 Any private or public 

pension, annuity or survivor’s 

benefits 

YES 

NO 

 

$_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _______

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 
Volume 3: Improper Payments Report (Final Report) 

 A-7 National Survey of WIC Participants II 

 Medical assistance (any) 

YES 

NO

 

$_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 

 Veteran’s payments 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 Other cash income 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: 

 2007 IRS tax return 

 Benefits statement 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 Energy assistance 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Notice of benefits 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 

 Net rental income 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM

 Income from trusts 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM
National Survey of WIC Participants II A-8 

 Commissions 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM

 Income from estates 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM

 Net royalties 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM

 Interest or dividends 

YES 

NO

 

$_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Earnings statement 

 Copy of check, check 

stub 

 Other: ______ 

 None: GIVE 

INCOME RELEASE 

FORM 

REPEAT INCOME QUESTIONS (Q12) FOR EVERY ADULT MEMBER OF PRIMARY 

ECONOMIC UNIT. 

CLOSING 

13. READ: This completes our survey. It was great talking to you. Thank you so much for 

helping us out. Here is $20 in appreciation for your time. [FILL OUT RECEIPT FOR 

INCENTIVE AND GET SIGNATURE.] 

Do you have any questions before I leave? 

Have a great day/evening. 
APPENDIX B: 

IN-PERSON SURVEY INSTRUMENT

(VERSION B: INFANTS AND CHILDREN) 

 B-1 National Survey of WIC Participants II 

Appendix B 

IN-PERSON INTERVIEW (Version B: Infants and Children) 

1. Hi. Thanks for agreeing to do the second part of our survey. Your answers are completely 

confidential and, as I mentioned when we set this up, nothing you say will have any bearing 

on your benefits. The WIC program is just trying to get a better idea of who participates in 

the program and their circumstances. I will be giving you $20 in appreciation of your time. 

READ INFORMED CONSENT STATEMENT AND GET CONSENT BEFORE 

PROCEEDING. 

IDENTITY AND RESIDENCY 

2. The first thing we need is some identification for the child—silly as it may seem—and proof 

that you live here. [IF R. HAS TROUBLE WITH THIS REQUEST, READ OFF SOME OF 

THE ACCEPTABLE TYPES OF ID AND RESIDENCY PROOF FROM LIST.] 

Identification proofs [CHECK AT LEAST 

ONE]

Residency proofs [CHECK AT LEAST 

ONE]

 State-issued license or ID  State-issued license or ID w/address 

 U.S. passport w/photo  State/federal correspondence w/address 

 Foreign passport w/photo  WIC folder 

 WIC folder  Checkbook w/address 

 W-2 form or Tax bill w/name  Rent or mortgage receipt, lease w/address 

 Birth certificate  Utility or tax bill w/address 

 Social Services letter w/ name  Documents from public school w/address 

 Social Security or Green card  Written statement from reliable third party 

(e.g. non-profit aid organization)  Hospital or immunization record 

 Other: SPECIFY ________________  Other: SPECIFY ________________ 

PRIMARY FAMILY/ECONOMIC UNIT 

3. Let’s begin by having you tell me the names of all the persons who live or stay with [NAME 

OF SAMPLED INFANT/CHILD RESPONDENT] whether they are related or not. I will 

type in the names so I can follow up with some questions. Be sure to include yourself. 

[PROBE: ANYONE ELSE?] 

RECORD ALL NAMES IN LIST FORM. 

1. _____________________________ 

2. _____________________________ 

3. _____________________________ 

4. _____________________________ 

5. _____________________________ 

6. _____________________________ 

7. _____________________________ 

8. ETC. 
National Survey of WIC Participants II B-2 

AFTER ALL PERSONS ARE LISTED, ASK FOLLOWING QUESTIONS FOR EACH PERSON:

4. What is their relationship to [NAME 

OF SAMPLED INFANT/CHILD 

PARTICIPANT]?

6. Parent/ Guardian 

7. Step-parent 

8. Foster parent 

 9. Brother/Sister 

10.Grandparent 

11. Uncle/aunt 

7. Cousin 

8. Nephew/niece 

9. Parent-in-law 

10. Brother-in-law/ sisterin-law 

11. Other relative 

12. Other non-relative 

18. Child in Temporary 

Care of 

Friends/Relatives 

5. Is this individual male or female? [IF TALKING TO PERSON, 

DON’T ASK . JUST MAKE NOTE OF GENDER.] 

 1-Male 

 2-Female 

6. How old is this person? [IF LESS THAN ONE YEAR, RECORD 

“0”] _____ YEARS 

7. FOR ANY CHILD LESS THAN 5 YEARS OR ANY WOMAN 

GREATER THAN 14 YEARS ASK: Is this person receiving WIC 

now?

 1-Yes 

 2-No 

8. OTHERWISE, IF Q6≥15, ASK: Do you consider [READ 

NAME] to be part of your family group -- that is, you are sharing 

income and expenses as if you were a family -- OR do you feel that 

you each keep your income and expenses and food separately? 

IF Q6<15, ASK: Do you consider [READ NAME] to be part of 

your family group -- that is, you are responsible for taking care of 

them as if you were all in the same family?

 1-Share like family

 2-Separate finances

9. PROGRAM WILL CALCULATE NUMBER OF PEOPLE IN 

PRIMARY ECONOMIC UNIT PLUS ADD ONE FOR WIC 

PARTICIPANT

 Q8=1 and Q6 ≥15

 NUMBER OF 

PEOPLE IN 

PRIMARY 

ECONOMIC UNIT

10. COMPUTER WILL COMPARE THE NUMBER OF PEOPLE IN PRIMARY ECONOMIC 

UNIT (Q10) WITH # OF HOUSEHOLD MEMBERS IN WIC RECORDS (P5-TOTAL IN 

PRIMARY ECONOMIC UNIT). 

IF Q10=P5, SKIP TO Q11 

IF Q10<P5, SAY: The WIC records show that you had [INSERT #] adults and [INSERT #] 

children in this household, which is more than we listed today. Have we left someone off the 

list? Or perhaps there is someone on our list today who should be counted as part of your 

main family unit but was not? [PROBE & ADD NEW NAMES OR INFORMATION IN 

Q2-10 AS APPROPRIATE] 

IF Q10>P5, SAY: The WIC records show that you had [INSERT #] adults and [INSERT #] 

children in this household, which is fewer than we have listed here. Can I verify that 

everyone on our list here IS part of your main family unit? [PROBE & DELETE NAMES 

OR INFORMATION IN Q2-10 AS APPROPRIATE] 

FROM PRIMARY ECONOMIC UNIT LIST, COMPUTER WILL GENERATE A LIST OF 

“POTENTIAL WAGE EARNERS” – DEFINED AS ALL THE ADULTS AND ALL 

CHILDREN > 15 YEARS
Volume 3: Improper Payments Report (Final Report) 

 B-3 National Survey of WIC Participants II 

ADJUNCTIVE OR AUTOMATIC ELIGIBILITY 

11. IF ADJ ELIGIILITY IS SHOWN IN RECORDS, ASK: The WIC records show that you 

qualified for WIC because your child, or a member of your family, participate in the [FILL 

IN FROM P4] program. Can you show me the document that you showed to WIC to 

demonstrate participation in that program such as the certification card, the award letter you 

got, or an active program voucher? [IF NO, PROBE: Do you have anything at all?] 

IF ADJ ELIGIBILITY IS NOT SHOWN IN RECORDS, ASK: Did your child qualify for 

WIC by showing that he/she, or another family member, participated in another benefits 

program such as Medicaid, SNAP or TANF? [OPTIONAL:] If yes, can you show me the 

document that you showed to WIC to demonstrate participation in that program such as the 

certification card, the award letter you got, or an active program voucher? 

 YES, PROOF SHOWN – WRITE DOWN : 

a) NAME OF PROGRAM RECIPIENT ON PROOF 

SHOWN. [MAKE SURE IT MATCHES SOMEONE IN 

THE PRIMARY ECONOMIC UNIT; OTHERWISE 

ASK FOR ANOTHER DOCUMENT/CARD AS 

PROOF]

___________________________ 

b) DATE OF DOCUMENT/CARD ISSUANCE 

(MM/DD/YYYY) [TYPE IN]______________________ 

99 NO DATE [PROBE: Do you have anything that 

shows the dates for your participation in the program?] 

c) DATE OF DOCUMENT/CARD OR ELIGIBILITY 

EXPIRATION (MM/DD/YYYY) [TYPE IN]______________________ 

99 NO DATE [PROBE: Do you have anything that 

shows the expiration date?] 

d) NAME OF ISSUING AGENCY [TYPE IN]______________________ 

99 NOT EVIDENT [PROBE: Do you have anything that 

shows the agency name?] 

e) NUMBER ON DOCUMENT/CARD [TYPE IN]______________________ 

99 NO NUMBER 

f) DOCUMENT/CARD SHOWN  Certification card [SKIP TO Q13 IF ADJ 

ELIGIBLE IN RECORDS; CONTINUE TO Q12 IF 

NOT ADJ ELIGIBLE IN RECORDS.] 

 Award letter [SKIP TO Q13 IF ADJ ELIGIBLE IN 

RECORDS; CONTINUE TO Q12 IF NOT ADJ 

ELIGIBLE IN RECORDS.] 

 Active program voucher [SKIP TO Q13 IF ADJ 

ELIGIBLE IN RECORDS; CONTINUE TO Q12 IF 

NOT ADJ ELIGIBLE IN RECORDS.] 

 Food Stamp EBT card [SKIP TO Q13 IF ADJ 

ELIGIBLE IN RECORDS; CONTINUE TO Q12 IF 

NOT ADJ ELIGIBLE IN RECORDS.] 

 Other [IF ANY DOUBTS ABOUT VALIDITY, 

HAND R. PROGRAM PARTICIPATION 

RELEASE FORM TO SIGN. THEN CONTINUE 

TO Q12] 
National Survey of WIC Participants II B-4 

 NO, PROOF NOT SHOWN OR WRONG PROOF SHOWN. 

[IF PERSON IS ADJUNCTIVELY ELIGIBLE IN RECORDS: HAND R. PROGRAM 

PARTICIPATION RELEASE FORM & CONTINUE TO Q12. 

IF PERSON IS NOT ADJUNCTIVELY ELIGIBLE IN RECORDS, CONTINUE TO 

Q12] 

INCOME ELIGIBILITY 

12. Now I am going to ask you about the income earned by you and other primary members of 

this household. WIC is interested in the accuracy of their data records in this area. The 

information you share with me will be confidential and will be combined with that from 

other people, so WIC won’t know your or anybody else’s personal information. 

So let’s start with [READ NAME OFF LIST OF PRIMARY ECONOMIC UNIT] 

[NOTE: IF RESPONDENT HAS DIFFICULTY RECALLING TIME PERIOD USE 

NARRATIVE APPROACH IN WHICH A TIME IS LINKED TO A SALIENT EVENT] 

d) Thinking back to [INSERT 

MOST RECENT 

CERTIFICATION 

MONTH/YEAR], did [INSERT 

you/NAME] receive any income 

from... [READ FROM 

BELOW]? 

CHECK ONLY IF YES 

e) FOR EACH ITEM 

CHECKED IN a), ASK: 

How much did [INSERT 

you/NAME] earn? 

f) Can you show me 

some evidence of that 

income such as 

[READ FROM LIST 

ACCOMPANYING 

EACH ITEM] 

 Wages, salary, fees 

(excluding military pay) 

YES 

NO

$_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Pay stub/earnings 

statement 

 W-2 form 

 2007 IRS tax return 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

AND 

UNDOCUMENTED 

EMPLOYMENT INFO 

FORM 

 Military pay 

YES 

NO $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: 

 Leave and earnings 

statement 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 
Volume 3: Improper Payments Report (Final Report) 

 B-5 National Survey of WIC Participants II 

 Tips and bonuses 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 W-2 form 

 Other: _____ 

 None 

 Net income from self 

employment (from farm and 

non-farm business) 

YES 

NO

 

$_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE 

UNDOCUMENTED 

EMPLOYMENT FORM 

 Unemployment compensation 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other:_____ 

 2007 IRS tax return 

 Copy of check, check 

stub 

 Letter of determination 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 Workers compensation 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Copy of check, check 

stub 

 Award statement 

 Statement from 

insurance company 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 Child Support 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Copy of check 

 Support agreement 

 Divorce/separation 

decree 

 Court order 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 Alimony 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Copy of check 

 Support agreement 

 Divorce/separation 

decree 

 Court order 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 
National Survey of WIC Participants II B-6 

 Social Security 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 SSA Award letter 

 Statement of benefits 

 2007 IRS tax return 

(line 14a on 1040A) 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 SSI – Fed government 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 SSI—State issued 

YES 

NO 

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 Any private or public pension, 

annuity or survivor’s benefits 

YES 

NO 

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _______ 

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 Medical assistance (any) 

YES 

NO

 

$_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 Veteran’s payments 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Notice of benefits 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 
Volume 3: Improper Payments Report (Final Report) 

 B-7 National Survey of WIC Participants II 

 Other cash income 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: 

 2007 IRS tax return 

 Benefits statement 

 Copy of check, check 

stub 

 Other: _____ 

 None: GIVE INCOME 

RELEASE FORM 

 Energy assistance 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 Notice of benefits 

 Other: _____ 

 None: GIVE 

INCOME RELEASE 

FORM 

 Net rental income 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM

 Income from trusts 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM

 Commissions 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM

 Income from estates 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM
National Survey of WIC Participants II B-8 

 Net royalties 

YES 

NO

 $_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Business records 

 Expense receipts 

 Other: ______ 

 None: GIVE INCOME 

RELEASE FORM

 Interest or dividends 

YES 

NO

 

$_______ 

 Per week 

 Per 2 weeks 

 2 Times a month

 Month 

 Quarter 

 Year 

 Other: _____ 

 2007 IRS tax return 

 Earnings statement 

 Copy of check, check 

stub 

 Other: ______ 

 None: GIVE 

INCOME RELEASE 

FORM 

REPEAT INCOME QUESTIONS (Q12) FOR EVERY ADULT MEMBER OF PRIMARY 

ECONOMIC UNIT. 

CLOSING 

13. READ: This completes our survey. It was great talking to you. Thank you so much for 

helping us out. Here is $20 in appreciation for your time. [FILL OUT RECEIPT FOR 

INCENTIVE AND GET SIGNATURE.] 

Do you have any questions before I leave? 

Have a great day/evening. 
APPENDIX C: 

DENIALS SURVEY INSTRUMENT

 C-1 National Survey of WIC Participants II 

APPENDIX C 

WIC Denied Applicants Survey—English 

PART 1: PRE-CODED FROM AGENCY DATA 

P0. Interviewer Name: ______________________________ 

P1. Sampled Recertificant 

a. Name: _______________________________ 

b. Address: ______________________________ 

 ______________________________ 

c. Phone number(s): ______________________ 

d. WIC client ID: _________________________ 

 

P2. Information on Sampled Recertificant 

a. Category 

 Pregnant 

 Breastfeeding 

 Postpartum 

 Infant (<12 months) 

 Child (1 - < 5 years) 

b. Parent/Guardian (IF APPLICABLE) 

 Name: __________________ 

 Address: ________________ 

 Phone: _________________ 

P3. State where local agency is located: _________ 

1. Alabama 

2. Arizona 

3. California 

4. Colorado 

5. Florida 

6. Georgia 

7. Illinois 

8. Indiana 

9. Kansas 

10. Louisiana 

11. Maryland 

12. Massachusetts 

13. Michigan 

14. Missouri 

15. New Jersey 

16. New York 

17. North Carolina 

18. Ohio 

19. Pennsylvania 

20. Tennessee 

21. Texas 

22. Virginia 

23. Washington 

P4. Date of Interview (MM/DD/YYYY): _________ 

P5. Time of Interview: ________:__________ AM PM 

PART 2: SCREENER 

SA. NAME OF RECERTIFICANT SAMPLED... _______________________ 

 THIS IS THE “RECERTIFICANT” 

 SB. NAME OF ADULT PERSON WHO WILL BE 

DOING INTERVIEW [WILL BE THE SAME NAME 

FOR PRG, BF, & PTP] ________________________ 

THIS IS THE “INTERVIEWEE” 
National Survey of WIC Participants II C-2 

Hello, may I speak to [INTERVIEWEE]______________? 

Yes [CONTINUE] 

 No [GET TIME AND DATE WHEN R. CAN BE REACHED. TERMINATE.] 

This is ____________ of ICF Macro calling on behalf of the USDA’s WIC Women, Infants and 

Children food program. According to the agency’s records, [INSERT “you” OR APPLICANT’S 

NAME] recently applied for WIC food benefits and, apparently, were/was turned down. We are 

conducting a confidential survey among people who got turned down so that we can see if the 

agency is following correct procedures. Your responses are confidential and will not be shared 

with the local WIC agency. 

You are under no obligation to answer any question, and you can end the interview at any time. 

The survey takes approximately 5 minutes. Because it is confidential, it won’t change the 

decision. However, if in our conversation it appears that the local WIC agency may have made

a mistake, you may want to apply for the WIC benefits again. And, of course, we will be 

recommending changes if problems are found. 

[IF R. ASKS HOW HE/SHE WAS CHOSEN FOR SAMPLE, SAY: “Your name [OR CHILD’S 

NAME] was chosen by chance from a list of all WIC applicants. Your answers are confidential; 

that is, they will not be identified with you but rather will be grouped with answers from 

hundreds of other WIC applicants to give us a better idea of how WIC is used and ways to make 

it better.] 

P1. May we continue? 

___ ACCEPT [SKIP TO Q1] 

___ REFUSE [SKIP TO P3] 

___LANGUAGE ISSUES [CONTINUE TO P2] 

 P2. IF POSSIBLE LANGUAGE DIFFICULTIES, ASK: May we continue in English? 

___YES [CONTINUE TO Q1] 

___NO [ASK “What language do you speak?” AND RECORD ANSWER. 

o TELL R. YOU WILL CALL BACK LATER IF NECESSARY.] 

P3. IF REFUSAL, SAY: This research is really important to the WIC program – to make sure 

mistakes don’t get made in changing or ending the benefits people get. We’re actually 

interviewing lots of people like you all over the country. So your answers and identity will 

just be grouped with others in your situation. Like I said, it’s confidential and it only takes 

about 5 minutes. 

 YES, NOW [GO TO Q1] 

 YES, LATER [RECORD TIME/DATE. THANK & TERMINATE.] 

 NO. [THANK & TERMINATE.] 
 C-3 National Survey of WIC Participants II 

TELEPHONE SURVEY - English 

1. Let me start by asking how you heard about the WIC – Women, Infants and Children – 

program? [CIRCLE ALL THAT APPLY] 

A) FRIEND/COLLEAGUE/CO-WORKER

B) FAMILY MEMBER

C) DOCTOR/HEALTH PROFESSIONAL

D) TELEVISION ADVERTISEMENT

E) RADIO ADVERTISEMENT

F) NEWSPAPER 

G) BILLBOARD

W) OTHER: SPECIFY ______________________ 

2. Which of the following benefits were you interested in getting out of the WIC program? 

You can answer yes or no. Were you interested in...: [CIRCLE 1 FOR EACH] 

a. The food package or vouchers to get healthy food? YES=1 NO=0 

b. Health and nutrition classes and individual counseling? YES=1 NO=0 

c. Support for breastfeeding your baby? YES=1 NO=0 

d. Information about immunizations for your child(ren)? YES=1 NO=0 

e. Information on how to get other health care services for 

your family? 

YES=1 NO=0 

f. Information about what other community programs are 

available to help your family? 

YES=1 NO=0 

3. THIS QUESTION WAS INTENTIONALLY SKIPPED 

4. THIS QUESTION WAS INTENTIONALLY SKIPPED 

5. When [INSERT “you” OR applicant’s name] were/was turned down for WIC benefits, did 

the WIC clinic give you any reason for their action that was related to proof of identity, that 

is showing identification? 

1) YES 

0) NO/DON’T KNOW [SKIP TO Q6] 

5A.What reason did they give? [IF MORE THAN ONE IS MENTIONED, ASK “WHICH WAS 

THE MAIN REASON GIVEN?”] [CIRCLE ONE ONLY.] 

1) DID NOT HAVE PROOF WITH THEM AT WIC AGENCY 

2) EXPIRED DATE ON PROOF 

3) UNACCEPTABLE TYPE OF PROOF 

4) PROOF WAS MISSING NAME

5) PROOF WAS MISSING PHOTO 

 8) OTHER: SPECIFY ____________ 

5B. Did you see their point or do you feel they made a mistake? 

1) SAW THEIR POINT 

2) MADE A MISTAKE
National Survey of WIC Participants II C-4 

5C. Did the WIC representative say anything about what you could do to change the 

decision, such as what items you could bring back to help yourself qualify for 

benefits? 

1) YES 

0) NO 

ASK Q5D – Q5F ONLY IF Q5A=3 (UNACCEPTABLE TYPE OF PROOF)

5D. What did you show them to identify 

yourself? [IF MORE THAN ONE ITEM 

LISTED, ASK FOR THE MAIN ONE.] 

5a) DRIVER’S LICENSE 

5b) PASSPORT 

5c) IDENTIFICATION CARD 

5d) OTHER ITEM: PLEASE SPECIFY

____________________________ 

5e) NOTHING [SKIP TO Q6] 

5f) DON’T KNOW/ REMEMBER [SKIP TO Q6] 

5E. Who issued this item? 

[CIRCLE ONE] 

1) FEDERAL GOVT/ 

AGENCY

2) STATE GOVT/AGENCY

3) LOCAL GOVT/AGENCY

4) PRIVATE COMPANY

5) COURT SYSTEM

6) FOREIGN GOVERNMENT

7) OTHER

8) DON’T KNOW/ 

REMEMBER

5F. Did it 

have your 

name and

your photo 

on it? 

1) YES 

0) NO/ 

DON’T

KNOW 

6. Was a reason given related to showing proof of residency, that is where you live? 

1) YES 

0) NO/DON’T KNOW [SKIP TO Q7] 

6A. What reason did they give? [IF MORE THAN ONE IS MENTIONED, ASK “WHICH WAS 

THE MAIN REASON GIVEN?”] [CIRCLE ONE ONLY.] 

1) DID NOT HAVE PROOF WITH THEM AT WIC AGENCY 

2) EXPIRED DATE ON PROOF 

3) UNACCEPTABLE TYPE OF PROOF 

4) PROOF WAS MISSING NAME 

5) PROOF WAS MISSING PHOTO 

6) PROOF WAS MISSING ADDRESS 

7) ADDRESS WAS NOT IN LOCAL AGENCY’S COVERAGE AREA 

8) OTHER: SPECIFY ____________ 

6B. ASK: Did you see their point or do you feel they made a mistake? 

1) SAW THEIR POINT 

2) MADE A MISTAKE 

6C. Did the WIC representative say anything about what you could do to change the 

decision, such as what items you could bring back to help yourself qualify for 

benefits? 

1) YES 

0) NO 
 C-5 National Survey of WIC Participants II 

ASK Q6D – Q6F ONLY IF Q6A=3 (UNACCEPTABLE TYPE OF PROOF)

6D. What item did you show them to prove your 

address? [IF MORE THAN ONE ITEM 

LISTED, ASK FOR THE MAIN ONE. ] 

6a) DRIVER’S LICENSE 

6b) IDENTIFICATION CARD 

6c) CURRENT UTILITY/TAX BILL 

6d) CHECKBOOK 

6e) RENT RECEIPT, MORTGAGE RECEIPT OR 

LEASE 

6f) WRITTEN STATEMENT BY 3RD PARTY

6g) OTHER ITEM: PLEASE SPECIFY

____________________________ 

6h) NOTHING [SKIP TO Q7] 

6i) DON’T KNOW/ REMEMBER [SKIP TO Q7] 

6E.Who issued this item? 

(CIRCLE ONE) 

1) FEDERAL GOVT/AGENCY

2) STATE GOVT/AGENCY

3) LOCAL GOVT/AGENCY

4) PRIVATE COMPANY

5) COURT SYSTEM

6) FOREIGN GOVERNMENT

7) NON-PROFIT 

ORGANIZATION

8) RELIGIOUS 

ORGANIZATION

9) OTHER: SPECIFY 

___________________ 

10) DON’T 

KNOW/REMEMBER

6F. Did it 

have your 

name and

address on it? 

1) YES 

0) 

NO/DON’

T KNOW 

7. Was a reason given related to household income? 

1) YES 

0) NO/DON’T KNOW [SKIP TO Q8] 

7A. What reason did they give? [IF MORE THAN ONE IS MENTIONED, ASK “WHICH WAS 

THE MAIN REASON GIVEN?”] [CIRCLE ONE ONLY.] 

1) DID NOT HAVE INCOME PROOF WITH THEM AT THE WIC AGENCY 

2) EXPIRED DATE ON PROOF 

3) UNACCEPTABLE TYPE OF PROOF 

4) PROOF WAS MISSING NAME 

6) INCOME WAS TOO HIGH 

 8) OTHER: SPECIFY ____________ 

7B. Did you see their point or do you feel they made a mistake? 

1) SAW THEIR POINT 

2) MADE A MISTAKE 

7C. Did the WIC representative say anything about what you could do to change the 

decision, such as what items you could bring back to help yourself qualify for 

benefits? 

1) YES 

0) NO 
National Survey of WIC Participants II C-6 

7D. [IF Q7A=5 INCOME WAS TOO HIGH , SKIP TO Q8. ASK ALL OTHERS: ] When it 

turned out that you didn’t have the right document with you at the WIC office... 

a. Were you given a new appointment with WIC after 

they refused to certify you? 

YES=1 NO=0 

b. Did they ask you to describe your income? YES=1 NO=0 

c. Did they give you a month of temporary WIC food 

benefits? 

YES=1 NO=0 

d. Did they instruct you to come back with the proof in 30 

days or so? 

YES=1 NO=0 

ASK Q7D – Q7E ONLY IF Q7A=3 (UNACCEPTABLE TYPE OF PROOF)

 7E. What proof or document did you show them to demonstrate 

income? [IF MORE THAN ONE ITEM LISTED, ASK FOR THE 

MAIN ONE.] 

7F. Who issued this item? 

[CIRCLE ONE] 

1) FEDERAL GOVT/AGENCY

2) STATE GOVT/AGENCY

3) LOCAL GOVT/AGENCY

4) PRIVATE COMPANY

5) COURT SYSTEM

6) FOREIGN GOVERNMENT

7) OTHER. SPECIFY ____

___________________ 

8) DON’T 

KNOW/REMEMBER

7a) MOST RECENT TAX RETURN

7b) W-2 FORM

7c) STATEMENT FROM BANK OR OTHER 

FINANCIAL INSTITUTION

7d) CHECK OR PAY STUB 

7e) SIGNED STATEMENT BY EMPLOYER 

7f) ELIGIBILITY LETTER SIGNED BY 

OFFICIAL STATE/LOCAL AGENCY

7g) STATEMENT OF BENEFITS (BY PUBLIC 

AGENCY OR COURT) 

7h) WRITTEN STATEMENT FROM 

RELIABLE THIRD PARTY

7i) FOOD STAMPS 

7q) OTHER ITEM: PLEASE

SPECIFY_________

7r) DON’T KNOW/ REMEMBER 

7s) NOTHING 

7j) MEDICAID

7k) TANF 

7l) SUPPLEMENTAL 

SECURITY INCOME 

(SSI) 

7m) FOOD 

DISTRIBUTION 

PROGRAM ON INDIAN 

RESERVATIONS 

(FDPIR) 

7n) CHILDREN’S 

MEDICAID

7o) FREE/REDUCEDMEAL SCHOOL 

LUNCH/ BREAKFAST 

PROGRAM

7p) LOW-INCOME 

ENERGY ASSISTANCE

 _____________

8. Did you take any follow-up actions to see if you could get the decision changed? 

1) YES 

0) NO [SKIP TO Q10] 

9. What action did you take? [DON’T READ. CIRCLE ALL THAT APPLY. THEN SKIP TO Q11.] 

A) WROTE LETTER OF COMPLAINT, PROTEST TO WIC 

B) ASKED TO SPEAK TO, OR DID SPEAK WITH, WIC SUPERVISOR 

C) COMPLAINED TO AN ELECTED PUBLIC OFFICIAL 

D) MADE A PHONE CALL AFTERWARDS, CHALLENGING THE DECISION 

W) OTHER: SPECIFY: _________________ 
 C-7 National Survey of WIC Participants II 

10. Why not? 

1) TOO BUSY 

2) DON’T SPEAK ENGLISH WELL 

3) IT WOULDN’T DO ANY GOOD 

9) OTHER: SPECIFY: ______________

I just have a couple more questions for categorization purposes only. 

11. Are you Hispanic or Latino? 

1) YES 

0) NO 

R) REFUSED 

12. How would you characterize yourself in terms of race? [READ ALL. CIRCLE AS MANY 

AS APPLY] 

A) American Indian or Alaska Native 

B) Asian American 

C) Black or African American 

D) Native Hawaiian or Other Pacific Islander 

E) White 

R) REFUSED 

13. What is the highest level of education you have attained? [READ UNTIL R. INDICATES 

ANSWER] 

1) Elementary school (6 years or less of education) 

2) Some high school (7 – 11 years of education) 

3) High school diploma or GED 

4) Some college 

5) Associate’s degree 

6) Bachelor’s degree 

7) Advanced degree 

8) REFUSED 

14. What is your first language, that is, the language you speak at home? 

1) English 

2) Arabic 

3) Cambodian 

4) Cantonese/ Mandarin 

5) Farsi 

6) French/Creole 

7) Fulani 

8) Hindi 

9) Hmong 

10) Khmer 

11) Korean 

12) Laotian 

13) Punjabi 

14) Russian 

15) Somali 

16) Spanish 

17) Swahili 

18) Tamil 

19) Tagalog 

20) Urdu 

21) Vietnamese 

22) Other: SPECIFY 

_________ 

Thank you so much for your help in answering this survey. Your feedback, combined with other 

confidential responses, will help improve the WIC program. Thanks again. Have a great 

day/evening. 

APPENDIX D: 

TERMINATIONS SURVEY INSTRUMENT

 D-1 National Survey of WIC Participants II 

APPENDIX D 

WIC Terminations Survey - English 

PART 1: PRE-CODED FROM AGENCY DATA 

P0. Interviewer Name: ______________________________ 

P1. Sampled Recertificant 

a. Name: _______________________________ 

b. Address: ______________________________ 

 ______________________________ 

c. Phone number(s): ______________________ 

d. WIC client ID: _________________________ 

 

P2. Information on Sampled Recertificant 

a. Category 

 Pregnant 

 Breastfeeding 

 Postpartum 

 Infant (<12 months) 

 Child (1 - < 5 years) 

b. Parent/Guardian (IF APPLICABLE) 

 Name: __________________ 

 Address: ________________ 

 Phone: _________________ 

P3. State where local agency is located: _________ 

1. Alabama 

2. Arizona 

3. California 

4. Colorado 

5. Florida 

6. Georgia 

7. Illinois 

8. Indiana 

9. Kansas 

10. Louisiana 

11. Maryland 

12. Massachusetts 

13. Michigan 

14. Missouri 

15. New Jersey 

16. New York 

17. North Carolina 

18. Ohio 

19. Pennsylvania 

20. Tennessee 

21. Texas 

22. Virginia 

23. Washington 

P4. Date of Interview (MM/DD/YYYY): _________ 

P5. Time of Interview: ________:__________ AM PM 

PART 2: SCREENER 

SA. NAME OF RECERTIFICANT SAMPLED... _______________________ 

 THIS IS THE “RECERTIFICANT” 

 SB. NAME OF ADULT PERSON WHO WILL BE 

DOING INTERVIEW [WILL BE THE SAME NAME 

FOR PRG, BF, & PTP] ________________________ 

THIS IS THE “INTERVIEWEE” 
National Survey of WIC Participants II D-2 

Hello, may I speak to [INTERVIEWEE]______________? 

Yes [CONTINUE] 

 No [GET TIME AND DATE WHEN R. CAN BE REACHED. TERMINATE.] 

This is ____________ of ICF Macro calling on behalf of the USDA’s WIC Women, Infants and 

Children food program. According to the agency’s records, there may have been a change in 

[YOUR or RECERTIFICANT’S NAME] WIC benefits. Or they may have ended, perhaps 

temporarily. We are conducting a very short, confidential survey among people who got turned 

down so that we can see if the agency is following correct procedures. Your responses are 

confidential and will not be shared with the local WIC agency. 

You are under no obligation to answer any question, and you can end the interview at any time. 

The survey takes approximately 5 minutes. Because it is confidential, it won’t change the 

decision. However, if in our conversation it appears that the local WIC agency may have made

a mistake, you may want to apply for the WIC benefits again. And, of course, we will be 

recommending changes if problems are found. 

[IF R. ASKS HOW HE/SHE WAS CHOSEN FOR SAMPLE, SAY: “Your [OR CHILD’S] name 

was chosen by chance from a list of all WIC participants. Your answers are confidential; that is, 

they will not be identified with you but rather will be grouped with answers from hundreds of 

other WIC participants to give us a better idea of how WIC is used and ways to make it better.] 

P1. May we continue? 

___ ACCEPT [SKIP TO Q1] 

___ REFUSE [SKIP TO P3] 

___LANGUAGE ISSUES [CONTINUE TO P2] 

 P2. IF POSSIBLE LANGUAGE DIFFICULTIES, ASK: May we continue in English? 

___YES [CONTINUE TO Q1] 

___NO [ASK “What language do you speak?” AND RECORD ANSWER. 

o TELL R. YOU WILL CALL BACK LATER IF NECESSARY.] 

P3. IF REFUSAL, SAY: This research is really important to the WIC program – to make sure 

mistakes don’t get made in changing or ending the benefits people get. We’re actually 

interviewing lots of people like you all over the country. So your answers and identity will 

just be grouped with others in your situation. Like I said, it’s confidential and it only takes 

about 5 minutes. 

 YES, NOW [GO TO Q1] 

 YES, LATER [RECORD TIME/DATE. THANK & TERMINATE.] 

 NO. [THANK & TERMINATE.] 
 D-3 National Survey of WIC Participants II 

TELEPHONE SURVEY - English 

1. Let me start by asking how you heard about the WIC – Women, Infants and Children – 

program? [CIRCLE ALL THAT APPLY] 

A) FRIEND/COLLEAGUE/CO-WORKER

B) FAMILY MEMBER

C) DOCTOR/HEALTH PROFESSIONAL

D) TELEVISION ADVERTISEMENT

E) RADIO ADVERTISEMENT

F) NEWSPAPER 

G) BILLBOARD

W) OTHER: SPECIFY ______________________ 

2. Which of the following benefits were you interested in getting out of the WIC program? 

You can answer yes or no. Were you interested in...: [CIRCLE 1 FOR EACH] 

a. The food package or vouchers to get healthy food? YES=1 NO=0 

b. Health and nutrition classes and individual counseling? YES=1 NO=0 

c. Support for breastfeeding your baby? YES=1 NO=0 

d. Information about immunizations for your child(ren)? YES=1 NO=0 

e. Information on how to get other health care services for 

your family? 

YES=1 NO=0 

f. Information about what other community programs are 

available to help your family? 

YES=1 NO=0 

3. According to our records, there was a change in [YOUR or RECERTIFICANT’S] 

WIC benefits back in May [INSERT “July”, if North Carolina], First, what do you recall 

was the change? Was it that...? [READ ALL AND CIRCLE BEST ANSWER.] 

1) The WIC benefits ended [SKIP TO Q4] 

2) The WIC benefits changed [SKIP TO Q4] 

3) There was a gap or pause in benefits and you had to prove eligibility again [SKIP TO Q4] 

4) DON’T KNOW/DON’T REMEMBER 

5) DO NOT READ: THERE WAS NO CHANGE OR INTERRUPTION IN 

BENEFITS [SKIP TO Q11] 

6) DO NOT READ: OTHER. [ASK THEM TO EXPLAIN, THEN SKIP TO Q4] 

___________________________________________________________________ 

___________________________________________________________________ 

3A. You say you don’t remember and that’s understandable. It was 6 months ago. Still, 

the WIC records indicate there was some kind of a change—perhaps a move from one 

type of food package to another, a need to recertify with WIC, or a pause or ending of 

benefits. Can you remember anything? 

1) YES 

0) NO [SKIP TO Q11] 
National Survey of WIC Participants II D-4 

4. Do you remember, did the WIC clinic give you any reason for the change that was related 

to a change in status such as ... 

 FOR PRG, BF AND PTP: the delivery of a baby, breastfeeding changes, or 

eligibility ending? 

 FOR INF AND CHD: a change in the child’s age? For example, your baby turning 

1 year old or your older child turning 5 years old? 

1) YES 

0) NO/DON’T KNOW [SKIP TO Q5] 

4A. Did you see their point or do you feel they made a mistake? 

1) SAW THEIR POINT [SKIP TO Q5] 

0) MADE A MISTAKE 

4B. What did you see as their mistake? [WRITE ANSWER CONCISELY AS POSSIBLE] 

__________________________________________________________________ 

5. Did the WIC clinic give you any reason for their action that was related to proof of identity, 

that is showing identification? 

1) YES 

0) NO/DON’T KNOW [SKIP TO Q6] 

5A.What reason did they give? [IF MORE THAN ONE IS MENTIONED, ASK “WHICH WAS 

THE MAIN REASON GIVEN?”] [CIRCLE ONE ONLY.] 

1) DID NOT HAVE PROOF WITH THEM AT WIC AGENCY 

2) EXPIRED DATE ON PROOF 

3) UNACCEPTABLE TYPE OF PROOF 

4) PROOF WAS MISSING NAME

5) PROOF WAS MISSING PHOTO 

 9) OTHER: SPECIFY ____________ 

5B. Did you see their point or do you feel they made a mistake? 

1) SAW THEIR POINT 

2) MADE A MISTAKE

5C. Did the WIC representative say anything about what you could do to change the 

decision, such as what items you could bring back to help yourself qualify for 

benefits? 

1) YES 

0) NO 
 D-5 National Survey of WIC Participants II 

ASK Q5D – Q5F ONLY IF Q5A=3 (UNACCEPTABLE TYPE OF PROOF)

5D. What did you show them to identify 

yourself? [IF MORE THAN ONE ITEM 

LISTED, ASK FOR THE MAIN ONE.] 

5a) DRIVER’S LICENSE 

5b) PASSPORT 

5c) IDENTIFICATION CARD 

5d) OTHER ITEM: PLEASE SPECIFY

____________________________ 

5e) NOTHING [SKIP TO Q6] 

5f) DON’T KNOW/ REMEMBER [SKIP TO Q6] 

5E. Who issued this item? 

[CIRCLE ONE] 

1) FEDERAL GOVT/ 

AGENCY

2) STATE GOVT/AGENCY

3) LOCAL GOVT/AGENCY

4) PRIVATE COMPANY

5) COURT SYSTEM

6) FOREIGN GOVERNMENT

7) OTHER

8) DON’T KNOW/ 

REMEMBER

5F. Did it 

have your 

name and

your photo 

on it? 

2) YES 

0) NO/ 

DON’T

KNOW 

6. Was a reason given related to showing proof of residency, that is where you live? 

1) YES 

0) NO/DON’T KNOW [SKIP TO Q7] 

6A. What reason did they give? [IF MORE THAN ONE IS MENTIONED, ASK “WHICH WAS 

THE MAIN REASON GIVEN?”] [CIRCLE ONE ONLY.] 

1) DID NOT HAVE PROOF WITH THEM AT WIC AGENCY 

2) EXPIRED DATE ON PROOF 

3) UNACCEPTABLE TYPE OF PROOF 

4) PROOF WAS MISSING NAME 

5) PROOF WAS MISSING PHOTO 

6) PROOF WAS MISSING ADDRESS 

7) ADDRESS WAS NOT IN LOCAL AGENCY’S COVERAGE AREA 

8) OTHER: SPECIFY ____________ 

6B. ASK: Did you see their point or do you feel they made a mistake? 

1) SAW THEIR POINT 

2) MADE A MISTAKE 

6C. Did the WIC representative say anything about what you could do to change the 

decision, such as what items you could bring back to help yourself qualify for 

benefits? 

1) YES 

0) NO 
National Survey of WIC Participants II D-6 

ASK Q6D – Q6F ONLY IF Q6A=3 (UNACCEPTABLE TYPE OF PROOF)

6D. What item did you show them to prove your 

address? [IF MORE THAN ONE ITEM 

LISTED, ASK FOR THE MAIN ONE. ] 

6a) DRIVER’S LICENSE 

6b) IDENTIFICATION CARD 

6c) CURRENT UTILITY/TAX BILL 

6c) CHECKBOOK 

6e) RENT RECEIPT, MORTGAGE RECEIPT OR 

LEASE 

6f) WRITTEN STATEMENT BY 3RD PARTY

6g) OTHER ITEM: PLEASE SPECIFY

____________________________ 

6h) NOTHING [SKIP TO Q7] 

6i) DON’T KNOW/ REMEMBER [SKIP TO Q7] 

6E.Who issued this item? 

(CIRCLE ONE) 

1) FEDERAL GOVT/AGENCY

2) STATE GOVT/AGENCY

3) LOCAL GOVT/AGENCY

4) PRIVATE COMPANY

5) COURT SYSTEM

6) FOREIGN GOVERNMENT

7) NON-PROFIT 

ORGANIZATION

8) RELIGIOUS 

ORGANIZATION

9) OTHER: SPECIFY 

___________________ 

10) DON’T 

KNOW/REMEMBER

6F. Did it 

have your 

name and

address on it? 

1) YES 

0) NO/ 

DON’T

KNOW 

7. Was a reason given related to household income? 

1) YES 

0) NO/DON’T KNOW [SKIP TO Q8] 

7A. What reason did they give? [IF MORE THAN ONE IS MENTIONED, ASK “WHICH WAS 

THE MAIN REASON GIVEN?”] [CIRCLE ONE ONLY.] 

1) DID NOT HAVE INCOME PROOF WITH THEM AT THE WIC AGENCY 

2) EXPIRED DATE ON PROOF 

3) UNACCEPTABLE TYPE OF PROOF 

4) PROOF WAS MISSING NAME 

5) INCOME WAS TOO HIGH 

 8) OTHER: SPECIFY ____________ 

7B. Did you see their point or do you feel they made a mistake? 

1) SAW THEIR POINT 

2) MADE A MISTAKE 

7C. Did the WIC representative say anything about what you could do to change the 

decision, such as what items you could bring back to help yourself qualify for 

benefits? 

1) YES 

0) NO 

7D. [IF Q7A=5 INCOME WAS TOO HIGH , SKIP TO Q8. ASK ALL OTHERS: ] When it 

turned out that you didn’t have the right document with you at the WIC office... 

a. Were you given a new appointment with WIC after 

they refused to certify you? 

YES=1 NO=0 
 D-7 National Survey of WIC Participants II 

b. Did they ask you to describe your income? YES=1 NO=0 

c. Did they give you a month of temporary WIC food 

benefits? 

YES=1 NO=0 

d. Did they instruct you to come back with the proof in 30 

days or so? 

YES=1 NO=0 

ASK Q7D – Q7E ONLY IF Q7A=3 (UNACCEPTABLE TYPE OF PROOF)

 7E. What proof or document did you show them to demonstrate 

income? [IF MORE THAN ONE ITEM LISTED, ASK FOR THE 

MAIN ONE.] 

7F. Who issued this item? 

[CIRCLE ONE] 

1) FEDERAL GOVT/AGENCY

2) STATE GOVT/AGENCY

3) LOCAL GOVT/AGENCY

4) PRIVATE COMPANY

5) COURT SYSTEM

6) FOREIGN GOVERNMENT

7) OTHER. SPECIFY ____

___________________ 

8) DON’T KNOW/ 

REMEMBER

7a) MOST RECENT TAX RETURN

7b) W-2 FORM

7c) STATEMENT FROM BANK OR OTHER 

FINANCIAL INSTITUTION

7d) CHECK OR PAY STUB 

7e) SIGNED STATEMENT BY EMPLOYER 

7f) ELIGIBILITY LETTER SIGNED BY 

OFFICIAL STATE/LOCAL AGENCY

7g) STATEMENT OF BENEFITS (BY PUBLIC 

AGENCY OR COURT) 

7h) WRITTEN STATEMENT FROM 

RELIABLE THIRD PARTY

 7i) FOOD STAMPS 

7q) OTHER ITEM: PLEASE

SPECIFY_________

7r) DON’T KNOW/ REMEMBER 

7s) NOTHING 

7j) MEDICAID

7k) TANF 

7l) SUPPLEMENTAL 

SECURITY INCOME 

(SSI) 

7m) FOOD 

DISTRIBUTION 

PROGRAM ON INDIAN 

RESERVATIONS 

(FDPIR) 

7n) CHILDREN’S 

MEDICAID

7o) FREE/REDUCEDMEAL SCHOOL 

LUNCH/BREAKFAST 

PROGRAM

7p) LOW-INCOME 

ENERGY ASSISTANCE

_________________

8. Did you take any follow-up actions to see if you could get the decision changed? 

1) YES 

0) NO [SKIP TO Q10] 

9. What action did you take? [DON’T READ. CIRCLE ALL THAT APPLY. THEN SKIP TO 

Q11.] 

A) WROTE LETTER OF COMPLAINT, PROTEST TO WIC 

B) ASKED TO SPEAK TO, OR DID SPEAK WITH, WIC SUPERVISOR 

C) COMPLAINED TO AN ELECTED PUBLIC OFFICIAL 

D) MADE A PHONE CALL AFTERWARDS, CHALLENGING THE DECISION 

W) OTHER: SPECIFY: _________________ 
National Survey of WIC Participants II D-8 

10. Why not? 

1) TOO BUSY 

2) DON’T SPEAK ENGLISH WELL 

3) IT WOULDN’T DO ANY GOOD 

6) OTHER: SPECIFY: _________________ 

I just have a couple more questions for categorization purposes only. 

11. Are you Hispanic or Latino? 

1) YES 

0) NO 

R) REFUSED 

12. How would you characterize yourself in terms of race? [READ ALL. CIRCLE AS MANY 

AS APPLY] 

A) American Indian or Alaska Native 

B) Asian American 

C) Black or African American 

D) Native Hawaiian or Other Pacific Islander 

E) White 

R) REFUSED 

13. What is the highest level of education you have attained? [READ UNTIL R. INDICATES 

ANSWER] 

1) Elementary school (6 years or less of education) 

2) Some high school (7 – 11 years of education) 

3) High school diploma or GED 

4) Some college 

5) Associate’s degree 

6) Bachelor’s degree 

7) Advanced degree 

8) REFUSED 

14. What is your first language, that is, the language you speak at home? 

1) English 

2) Arabic 

3) Cambodian 

4) Cantonese/ Mandarin 

5) Farsi 

6) French/Creole 

7) Fulani 

8) Hindi 

9) Hmong 

10) Khmer 

11) Korean 

12) Laotian 

13) Punjabi 

14) Russian 

15) Somali 

16) Spanish 

17) Swahili 

18) Tamil 

19) Tagalog 

20) Urdu 

21) Vietnamese 

22) Other: SPECIFY 

_________ 

Thank you so much for your help in answering this survey. Your feedback, combined with other 

confidential responses, will help improve the WIC program. Thanks again. 

Have a great day/evening. 
APPENDIX E: 

COMMUNICATIONS TO STATES FOR

FOOD ISSUANCE AND REDEMPTION DATA AND 

DATA ISSUES

 E-1 National Survey of WIC Participants II 

APPENDIX E

Communication to States for Food Issuance and Redemption Data and 

Data Issues 

June 15, 2009 

[STATE WIC DIRECTOR NAME] 

[TITLE] 

[ADDRESS1] 

[ADDRESS2] 

[CITY,STATE,ZIP] 

Dear [MR/MS] [LASTNAME]: 

As you may recall from previous letters, Macro International (now known as ICF Macro) is 

under contract with the U.S. Department of Agriculture/Food and Nutrition Services (FNS) to 

conduct the second National Survey of WIC Participants. [YOURSTATE] was one of the states 

selected to take part in the WIC participant survey phase of the project. A brief history is 

attached, reviewing the study as it was first introduced to you in an April 2008 letter from the 

director of the Supplemental Food Programs Division. 

The success of this national effort depends on your cooperation and we thank you in advance. In 

order to make this process as clean and efficient as possible, we need your help with the 

following tasks: 

Tasks: Deadline 

1. Fill out the enclosed paper survey on your State’s policies and 

procedures and return in stamped envelope provided. (Attachment A. 

Estimated completion time: 66 minutes). 

July 15, 2009 

2. Submit participant-level data for all participants in the specified WIC 

clinics* who received food issuances at any point during April and 

May of 2009. (Attachment B has details of this request.) 

July 15, 2009 

3. Submit data for all applicants who were denied WIC benefits at the 

specified clinics during May 2009. 

 If maintain this information at the State level, please fill out 

yellow postcard immediately and follow the instructions in 

Attachment C. 

 If you do not keep this information at the State level, please let 

us know by returning the enclosed yellow postcard 

immediately so that we may request the individual clinics to 

collect this information in July. 

(Postcard immediate) 

July 15, 2009 

Postcard immediate 

OMB# 0584-0484 

Exp. 06/30/2012 
National Survey of WIC Participants II E-2 

Tasks: Deadline 

4. Inform the local agencies and clinics chosen for the study* of the 

importance of their participation and cooperation with the study. 

Specifically, let them know they will be contacted by ICF Macro in the 

near future with details. 

June 26, 2009 

If you, or any of the staff working on this request, have any difficulties, questions or concerns 

regarding these requests, feel free to contact Walter Rives via e-mail at 

WICSurvey@mmail.macrointernational.com or by calling our toll-free WIC Survey hotline at 1-

888-285-7976. 

Thank you for your continued support in making the Second National Survey of WIC 

Participants a success. 

Sincerely, 

Daniel Geller, Ph.D. 

Project Director 

ICF Macro 

11785 Beltsville Dr. 

Calverton, MD 20705 

* The specified clinics are as follows: 

1. [CLINIC1] - [LOCALAGENCY1] 

2. [CLINIC2] - [LOCALAGENCY2] 

3. [CLINIC3] - [LOCALAGENCY3] 

4. [CLINIC4] - [LOCALAGENCY4] 

5. [CLINIC5] - [LOCALAGENCY5] 

6. [CLINIC6] - [LOCALAGENCY6] 

7. [CLINIC7] - [LOCALAGENCY7] 

8. [CLINIC8] - [LOCALAGENCY8] 

9. [CLINIC9] - [LOCALAGENCY9] 

10. [CLINIC10] - [LOCALAGENCY10] 

11. [CLINIC11] - [LOCALAGENCY11] 

12. [CLINIC12] - [LOCALAGENCY12] 

13. [CLINIC13] - [LOCALAGENCY13] 

14. [CLINIC14] - [LOCALAGENCY14] 
 E-3 National Survey of WIC Participants II 

ATTACHMENT B (Task 2) – Submit data for WIC participants who 

received food issuances at any point during April and May of 2009

Please submit data by July 15, 2009.

To select the individuals for the WIC Participants survey, ICF Macro needs you to draw the 

following fields of information, listed on next page, for all food instruments issued (not 

redeemed) for April and May 2009 by the clinics specified as follows: 

1. [CLINIC1] - [LOCALAGENCY1] 

2. [CLINIC2] - [LOCALAGENCY2] 

3. [CLINIC3] - [LOCALAGENCY3] 

4. [CLINIC4] - [LOCALAGENCY4] 

5. [CLINIC5] - [LOCALAGENCY5] 

6. [CLINIC6] - [LOCALAGENCY6] 

7. [CLINIC7] - [LOCALAGENCY7] 

8. [CLINIC8] - [LOCALAGENCY8] 

9. [CLINIC9] - [LOCALAGENCY9] 

10. [CLINIC10] - [LOCALAGENCY10] 

11. [CLINIC11] - [LOCALAGENCY11] 

12. [CLINIC12] - [LOCALAGENCY12] 

13. [CLINIC13] - [LOCALAGENCY13] 

14. [CLINIC14] - [LOCALAGENCY14] 

Note that food instruments may have been distributed at an earlier date, for example, in March; 

what is important is that they were issued for and were valid for April and/or for May 2009. 

 By sampling food instruments issued, we realize that WIC clients will appear more 

than once on the lists submitted. 

 We would like separate lists for April and May, understanding of course that there 

will be substantial overlap of individuals. 

 We would prefer the data in Excel format, although we will accept data submitted in 

Access, SAS or SPSS in a CSV (comma separated values) format. 

 We will need a list of your State agency’s definitions for certain alphanumeric data 

fields submitted, so that we understand the assigned values for each record. 

 Please read the attached “Directions for FTP Transmittal of Data” document. 

 Questions? Email Walter Rives at WICSurvey@mmail.macrointernational.com or call 

the toll-free WIC Survey hotline at 1-888-285-7976. 
National Survey of WIC Participants II E-4 

Requested Data Fields - WIC Participants

FIELD 

NAME 

Description Data Type 

Preferred 

 

PANAME Name of WIC Local Agency Alphanumeric 

CLNAME Name of WIC Clinic providing services Alphanumeric 

CLID WIC Clinic ID (If Assigned) Alphanumeric 

P02A Client’s WIC category (i.e., Pregnant woman, Postpartum woman, 

Breastfeeding woman, Infant, or Child) 

Alphanumeric* 

P0601 Type of food package issued Alphanumeric* 

P0602 Month food package was issued Numeric (1-12) 

P0803M First date voucher can be used (Month). Numeric 

P0803D First date voucher can be used (Day). Numeric 

P0803Y First date voucher can be used (Year). Numeric 

P0804 Food package maximum dollar amount value as of date requested Numeric 

P0805 Food package number assigned Numeric 

P0806 Dollar amount of Food package redeemed as of date requested Numeric 

P01A1 WIC client first name Alphanumeric 

P01A2 WIC client last name Alphanumeric 

P01ID Identification number assigned to individual Alphanumeric 

P02FM WIC client date of birth-Month Numeric 

P02FD WIC client date of birth-Day Numeric 

P02FY WIC client date of birth-Year Numeric 

P02G WIC client gender (if child or infant) 1=Male 2=Female 

P02H Is client Hispanic or Latino? 1=Yes 0=No 

P02R1 WIC client race? Alphanumeric 

P01ID Identification number assigned to participant Alphanumeric 

P02ID Family Economic Unit / Household ID number assigned to participant Alphanumeric 

P01B WIC client street address – line 1 Alphanumeric 

P01C WIC client street address – line 2 Alphanumeric 

P01CA WIC client address – city Alphanumeric 

P01CB WIC client address – State Alphanumeric 

P01CC WIC client address – zip code Numeric 

P01D WIC client primary phone: xxx-xxx-xxxx Alphanumeric 

P01E WIC client second phone: xxx-xxx-xxxx Alphanumeric 

P01F WIC client third phone: xxx-xxx-xxxx Alphanumeric 

P02B11 First name of Parent/Guardian of WIC client (if infant or child) Alphanumeric 
 E-5 National Survey of WIC Participants II 

FIELD 

NAME 

Description Data Type 

Preferred 

P02B12 Last name of Parent/Guardian of WIC client (if infant or child) Alphanumeric 

P02B2 Parent/Guardian street address – line 1 Alphanumeric 

P02B3 Parent/Guardian street address – line 2 Alphanumeric 

P02B3A Parent/Guardian city Alphanumeric 

P02B3B Parent/Guardian state Alphanumeric 

P02B3C Parent/Guardian zip Numeric 

P02B4 Parent/Guardian – phone 1 xxx-xxx-xxxx Alphanumeric 

P02B5 Parent/Guardian – phone 2 xxx-xxx-xxxx Alphanumeric 

P02B6 Parent/Guardian – phone 3 xxx-xxx-xxxx Alphanumeric 

P02AA What proof of Identification was provided? Alphanumeric* 

P04C9 What proof of residency was provided? Alphanumeric* 

P04AJ What proof of adjunctive eligibility was provided? Alphanumeric* 

P04BJ What proof of income was provided? Alphanumeric* 

P0608M Date of first (original) certification related to this child or pregnancy 

(Month) 

Numeric 

P0608D Date of first (original) certification related to this child or pregnancy (Day) Numeric 

P0608Y Date of first (original) certification related to this child or pregnancy 

(Year) 

Numeric 

P0609M Date of most recent certification (Month). Should be < 6 months ago 

except for infants where it could be < 1 year ago 

Numeric 

P0609D Date of most recent certification (Day). Should be < 6 months ago except 

for infants where it could be < 1 year ago 

Numeric 

P0609Y Date of most recent certification (Year). Should be < 6 months ago except 

for infants where it could be < 1 year ago 

Numeric 

P02J OPTIONAL: Language spoken by WIC client, if known Alphanumeric* 

P0610 Number of persons in family “economic unit” -- Total number of adult 

and child household members who are part of economic unit, including 

the WIC Participant. 

Numeric 

P0 Current status (participant, denial, termination) Alphanumeric* 

PCH Is there a change in status in or after the month of food issuance? 1=Yes 0=No 

PCHR1 Reason for status change, if applicable? Alphanumeric 

PCHD1 Date of status change (Month) Numeric 

PCHD2 Date of status change (Day) Numeric 

PCHD3 Date of status change (Year) Numeric 

* PLEASE PROVIDE EXPLANATORY LABELS SO WE UNDERSTAND THE CODES. 
National Survey of WIC Participants II E-6 

ATTACHMENT C (Task 3) – Submit data for WIC applicants who 

Were denied eligibility during May 2009

Please submit data by July 15, 2009.

To select the individuals for the Denied Applicants survey, ICF Macro needs you to draw the 

following fields of information, listed on the next page, for all applicants who were denied WIC 

benefits during May 2009 OR June 2009, the latest month for which you have this information. 

We need the data for only the clinics specified as follows: 

1. [CLINIC1] - [LOCALAGENCY1] 

2. [CLINIC2] - [LOCALAGENCY2] 

3. [CLINIC3] - [LOCALAGENCY3] 

4. [CLINIC4] - [LOCALAGENCY4] 

5. [CLINIC5] - [LOCALAGENCY5] 

6. [CLINIC6] - [LOCALAGENCY6] 

7. [CLINIC7] - [LOCALAGENCY7] 

8. [CLINIC8] - [LOCALAGENCY8] 

9. [CLINIC9] - [LOCALAGENCY9] 

10. [CLINIC10] - [LOCALAGENCY10] 

11. [CLINIC11] - [LOCALAGENCY11] 

12. [CLINIC12] - [LOCALAGENCY12] 

13. [CLINIC13] - [LOCALAGENCY13] 

14. [CLINIC14] - [LOCALAGENCY14] 

Note that: 

 We would prefer the data in Excel format, although we will accept data submitted in 

Access, SAS or SPSS in a CSV (comma separated values) format. 

 We will need a list of your State agency’s definitions for certain alphanumeric data 

fields submitted, so that we understand the assigned values for each record. 

 Please read the attached “Directions for FTP Transmittal of Data” document. 

 Questions? Email Walter Rives at WICSurvey@mmail.macrointernational.com or call 

the toll-free WIC Survey hotline at 1-888-285-7976. 
 E-7 National Survey of WIC Participants II 

Requested Data Fields - Denied WIC Applicants

* PLEASE PROVIDE EXPLANATORY LABELS SO WE UNDERSTAND THE CODES. 

FIELD 

NAME 

Description Data Type 

Preferred 

PANAME Name of WIC Local Agency Alphanumeric 

CLNAME Name of WIC Clinic providing services Alphanumeric 

CLID WIC Clinic ID (If assigned) Alphanumeric 

P01A1 WIC client first name Alphanumeric 

P01A2 WIC client last name Alphanumeric 

P02ID Family Economic Unit / Household ID number assigned to 

participant (if applicable) 

Alphanumeric 

P01B WIC client street address – line 1 Alphanumeric 

P01C WIC client street address – line 2 Alphanumeric 

P01CA WIC client address – city Alphanumeric 

P01CB WIC client address – State Alphanumeric 

P01CC WIC client address – zip code Numeric 

P01D WIC client primary phone: xxx-xxx-xxxx Alphanumeric 

P01E WIC client second phone: xxx-xxx-xxxx Alphanumeric 

P01F WIC client third phone: xxx-xxx-xxxx Alphanumeric 

P02A Client’s requested WIC category (if determined) 

i.e., Pregnant woman, Postpartum woman, Breastfeeding 

woman, Infant, or Child 

Alphanumeric* 

D01L Was a letter of denied benefits sent out? 1=Yes 0=No 

D01R Reason for denied eligibility Alphanumeric* 

D01D Date of denied eligibility Numeric 
National Survey of WIC Participants II E-8 

Directions for FTP Data Transfer 

We are requesting that the data be returned to ICF Macro in the form of an Excel spreadsheet57. 

ICF Macro understands that not every WIC administration collects and compiles data similarly, 

so we ask that you comment on any information you cannot access, or that may not be supplied 

to us in the form requested. 

Since these data will contain sensitive information (names, identifiers, etc.), the data needs to be 

sent in a secure manner. Please follow these instructions to ensure the secure transmission of 

data to ICF Macro:

The data files must be sent by secure FTP (file transport protocol) using a small 

software package that we have included on the enclosed CD. This software 

enables you to send your data file to ICF Macro’s secure server. The software 

does not require any installation and is very easy to use. You will be provided a 

username and password once you contact Walter Rives and notify ICF Macro of a 

probable date of data transfer. 

If you should encounter any difficulties with the FTP application:

The application included on this CD, to transmit the data to ICF Macro, requires the .Net 

Framework version 2.0. This is normally shipped as part of Windows Vista, and may not be 

installed on systems using Windows XP or earlier. If an error message is displayed when you 

first insert this CD, it may be because you do not have a recent enough version of this application 

installed on your computer. 

If such an error message is displayed, the correct version of the .Net Framework should be 

installed from the CD provided by ICF Macro. The name of this file is DOTNETFX.EXE. To 

install it, navigate to this file on the CD and double-click it. After the installation is complete, 

try re-inserting the CD. If the application still does not work, call me at 1-888-285-7976. 

Thank you for your continued support in making The Second national Survey of WIC 

Participants a success. 

*IMPORTANT NOTE: FOR ALPHANUMERIC FIELDS WITH ASTERISK, PLEASE PROVIDE 

EXPLANATORY LABELS SO WE UNDERSTAND THE CODES. FOR EXAMPLE: 

Field Name Field Description Preferred Data Type 

*Codes Used: 1=Employed full time, 2= Employed part time, 3=Full time student, 4=Not working for pay, 5=On 

temporary leave, 6=Other 

 

57 We prefer the Excel format but will accept Access, SAS or SPSS if provided in a CSV (comma separated values) format. 

P0607 WIC women’s (or parent of the child/infant) employment status Alphanumeric*
 E-9 National Survey of WIC Participants II 

Texas Redemption Issues 

Texas was the only state in the sample that had moved WIC redemption to an electronic benefit 

transfer (EBT) system.58 Initial tabulation found that for most cases in Texas, the redemption 

values were greater than maximum values permitted for the food package. The State agency 

explained that Texas redemption data were for the household while the maximum voucher value 

was for the sampled individual. The agency had no way of separating the individual redemption 

values from the household value because transactions were made on a common EBT card. 

Adjustments were made to estimate the sampled individual’s redemption values by removing the 

portion of redeemed values for non-sampled WIC participants in each household from the total 

household redeemed value. 

Using information from the In-person Interview, the SAS program identified household members 

other than the sampled participant and counted the numbers of household members who were 

WIC women (no specific program subgrouping), WIC infants, and WIC children, as well as the 

total number of WIC participants in the household (see Table F-1). 

Table F-1: Average Redemption Values by WIC Category 

WIC Category Redemption 

Value ($) 

Pregnant Woman 44.52 

 

Breastfeeding Woman 43.83 

Postpartum Woman 32.47 

Infant 116.93 

Child 39.87 

As indicated, the bulk of difference occurred between infants (average redemption value of 

$116.93) and the rest of the categories (average redemption values ranged from $32.47 to $44.52). 

The adjustment applied the ratio of infant average redemption value over the rest of the 

participants to calculate the portion of redeemed values for the household WIC members other than 

the respondent. This portion was then removed from the total redemption value of the household. 

The calculation was done differently depending on the WIC program status of the respondent and 

the rest of the WIC household members, namely, whether the respondent was an infant, whether 

one or more other household members were infants, and whether there was no WIC infant in the 

household. 

 

58 Other than New York, State agencies included no new food package cost in their redemption data. See also Section 3.5 Redemption Data 

Processing on ICF’s communication with New York State regarding its new food package inclusion.
National Survey of WIC Participants II E-10 

SAS code for the procedure is cited below (original: ip.sas):

/*Count household members of WIC participant by category*/ 

data ip2; set ip; 

HHwicwoman =0; if p02a < 4 then hhwicwoman=1 ; *count R by 3 categories; 

 HHwicinfant=0; if p02a = 4 then hhwicinfant=1 ; 

 HHwicchild=0; if p02a = 5 then hhwicchild=1 ; 

 HH_n_kid=0; 

array hhage(20) P0906N_01--P0906N_20; 

array HHwic(20) P0907_01--P0907_20; *HH member is in WIC; 

array HHsex(20) p0905_01-- p0905_20; 

do w =1 to 20; *count total HH members by 3 categories; 

 if 0 <= hhage(w) <=1 and hhwic(w) =1 then hhwicinfant=hhwicinfant+1; 

 if 1 < hhage(w) < 6 and hhwic(w) =1 then hhwicchild=hhwicchild+1; 

 if hhsex(w) =2 and hhage(w) > 6 and hhwic(w) =1 then hhwicwoman=hhwicwoman+1; 

 if hhage(w) <18 and hhage(w) ne . then HH_n_kid =HH_n_kid +1; 

end; 

if hhwicchild =. then hhwicchild =0; 

HHwicN= hhwicwoman+ hhwicchild+ hhwicinfant; 

/*Fix Texas data: redemption for family but maxval for sampled person*/ 

*Weight infant benefit by 2.9 (ratio of average benefit infant over others); 

 if state=21 then do; 

 if p02a=4 

 then rredeem=redeem-redeem*(((hhwicinfant-1)*2.9+(hhwicn-hhwicinfant))/ 

 ((hhwicinfant)*2.9+(hhwicn-hhwicinfant))); 

 if p02a ne 4 and hhwicinfant>0 

 then rredeem=redeem-redeem*((hhwicinfant*2.9+(hhwicn-hhwicinfant-1))/ 

 (hhwicinfant*2.9+(hhwicn-hhwicinfant))); 

 if p02a ne 4 and hhwicinfant=0 

 then rredeem=redeem-redeem*((hhwicn-1)/hhwicn); 

 redeem= rredeem; 

 drop rredeem; 

 end; 

 if redeem> maxval then redeem=redeem/hhwicn; 

 if redeem> maxval then redeem=maxval; 

 diff =redeem-maxval; 

proc sql; select state, p02a from tx where cat_err =1; 

 select state, agency, clinic,partic,redeem 

 from tx where p02a="5" and state=22; 

 select state, p02a, redeem from tx where state<3; 

quit; 

Caution is called for regarding the adjusted Texas redemption data. A comparison of the adjusted 

data against other states' average values was performed to provide an external check on the 

adjustment and the result suggested that the adjusted values appeared in the reasonable range. 

Note that by the study design, State average estimates are not necessarily representative of the 

population. 
 E-11 National Survey of WIC Participants II 

SAS Code for North Carolina Redemption Data Imputation 

options nocenter nofmterr ls=max ps=max; 

libname red '/home/lharding/WIC/RED'; 

libname inh "/home/lharding/WIC/inhome"; 

libname tel "/home/lharding/WIC/paper"; 

data red; 

 set red.idmatch; 

 if r04m in(. 0) then r04m_a = p0802m; 

 else r04m_a = r04m; 

run; 

data red1; 

 set red; 

 /* Some variable Recodes */ 

 

 if p0802m = 5 and r04m_a in(5 6 7 8 9); * subsetting to the months we requested; 

 if r05fpr in(. 999.99 9999.99 99999.99 9999 999 99999 999999.99 999999) then redeemflg = 1; 

 if redeemflg = 1 then redeemed = 0; 

 else redeemed = r05fpr; * Redeemed Value; 

 if r05fmv in(. 999.99 9999.99 99999.99 9999 999 99999 999999.99 999999) then maxflg = 1; 

 if maxflg = 1 then maxvalue = 0; 

 else maxvalue = r05fmv; * Max value redeemable; 

 if r04y = 9 then r04y = 2009; 

 if r04y in(0 .) then r04y = 2009; 

 if p0802y = 9 then p0802y = 2009; 

 if p0803y = 9 then p0803y = 2009; 

 

 inred = 1; * In redemption file flag; 

run; 

data paper ; *(keep = status state agency clinic partic); 

 set tel.pwgt; 

 inpap = 1; * In telephone weight flag; 

run; 

proc sort data = paper; 

 by state agency clinic partic; 

run; 

proc freq data = paper; 

 tables state/list missing; 

run; 

proc sort data = red1; 

 by state agency clinic partic; 

run; 

data redall; 

 merge paper (in=a) red1 (in=b); 

 by state agency clinic partic; 

 if a; 

 

 if r04m_a = . then r04m_a = 5; 

 if p0802m = . then mnthflg = 1; 

 if p0802m = . then p0802m = 5; 

 if inred = . then imputeflg = 1; 

 else imputeflg = 0; 

 

 if redeemed = 0 then zero = 1; 

 else if redeemed = . then zero = 2; 

 else zero = 0; 
National Survey of WIC Participants II E-12 

 /* State = 17 is North Carolina, they did not provided redemption data */ 

 /* Setting values for NC to . because they will be imputed */ 

 

 if state = 17 then redeemed = .; 

 else if zero = 2 then redeemed = 0; 

 if state = 17 then maxvalue = .; 

 else if maxvalue = . then maxvalue = 0; 

 else maxvalue = maxvalue; 

 

 /* Creating a Infant category indicator. Infants redemption values where */ 

 /* different from all other wic categories. */ 

 

 if wiccat = 4 then wcat2 = 1; 

 else wcat2 = 0; 

run; 

data redx; 

 set red; 

 

 if r05fpr in(. 999.99 9999.99 99999.99 9999 999 99999 999999.99 999999) then redeemflg = 1; 

 if redeemflg = 1 then redeemed = 0; 

 else redeemed = r05fpr; 

 if r05fmv in(. 999.99 9999.99 99999.99 9999 999 99999 999999.99 999999) then maxflg = 1; 

 if maxflg = 1 then maxvalue = 0; 

 else maxvalue = r05fmv; 

 if r04y = 9 then r04y = 2009; 

 if r04y in(0 .) then r04y = 2009; 

 if p0802y = 9 then p0802y = 2009; 

 if p0803y = 9 then p0803y = 2009; 

 

 inred = 1; 

run; 

proc sort data = redx; 

 by state agency clinic partic; 

run; 

data redallx; 

 merge paper (in=a) redx (in=b); 

 by state agency clinic partic; 

 if a; 

 

 if r04m_a = . then r04m_a = 5; 

 if p0802m = . then mnthflg = 1; 

 if p0802m = . then p0802m = 5; 

 if inred = . then imputeflg = 1; 

 else imputeflg = 0; 

 

 if redeemed = 0 then zero = 1; 

 else if redeemed = . then zero = 2; 

 else zero = 0; 

 if state = 17 then redeemed = .; 

 else if zero = 2 then redeemed = 0; 

 if state = 17 then maxvalue = .; 

 else if maxvalue = . then maxvalue = 0; 

 else maxvalue = maxvalue; 

 

 if wiccat = 4 then wcat2 = 1; 

 else wcat2 = 0; 

run; 
 E-13 National Survey of WIC Participants II 

proc freq data = redall; 

 tables inpap*inred/list missing; 

run; 

data lastredeem; 

 set redallx; 

 if redeemed > 0; 

 id = state||agency||clinic||partic; 

run; 

proc sort data = lastredeem; 

 by id r04m_a r04d r04y; 

run; 

data lastredeem; 

 set lastredeem; 

 by id r04m_a r04d r04y; 

 if last.id then last = 1; 

run; 

data lastredeem; 

 set lastredeem; 

 where last = 1; 

 lredeem_m = r04m_a; 

 lredeem_d = r04d; 

 lredeem_y = r04y; 

 drop r04m r04m_a r04d r04y; 

run; 

proc print data = lastredeem (obs = 100); 

 var id lredeem_m lredeem_d lredeem_y redeemed last; 

run; 

proc freq data = lastredeem; 

 tables state; 

run; 

proc sort data = redall; 

 by state agency clinic partic wiccat imputeflg p0802m; 

run; 

/* Create the sum of all redeemed amounts and maximum amount for each participant */ 

proc means data = redall sum noprint; 

 by state agency clinic partic wiccat p0802m; 

 id imputeflg r04y r04m_a p0802y p0803m p0803y; 

 var redeemed maxvalue; 

 output out=redall2 sum=redeem maxval; 

run; 

proc sort data = redall2; 

 by state agency clinic partic p0802m; 

run; 

data test1; 

 set redall2; 

 id = state||agency||clinic||partic; 

run; 

proc sort data = test1; 

 by id ; 

run; 

data test; 

 set test1; 

 by id r04m_a ; 

 if first.id then flg1 = 1; 

run; 
National Survey of WIC Participants II E-14 

proc freq data = test; 

 tables flg1 flg1*r04m_a/list missing; 

run; 

proc print data = test; 

 where r04m_a = .; 

 var state agency clinic partic flg1 redeem maxval wiccat; 

run; 

 * endsas; 

/* North Carolina Imputation */ 

/* Subset to NC and neighboring States */ 

data redall3; 

 set redall2; 

 where state in(1 5 6 17 20); 

run; 

proc sort data = redall3; 

 by wiccat; 

run; 

proc mi data = redall3 

 out = in_imp 

 nimpute = 5 

 seed = 145 

 minimum = 0 ;* noprint; 

 

 by wiccat; 

 mcmc nbiter=50 niter=10; 

 var state agency clinic redeem maxval; 

run; 

proc sort data = in_imp; 

 by state agency clinic partic redeem; 

run; 

proc print data = in_imp (obs=20); 

 where imputeflg = 1; 

 var _imputation_ imputeflg state agency clinic partic wiccat r04m_a redeem maxval; 

run; 

data redemption; 

 set redall2; 

 where p0802m = 5 and state ne 17; 

 inredfile = 1; 

run; 

proc sort data = redemption; 

 by state agency clinic partic; 

run; 

proc sort data = lastredeem; 

 by state agency clinic partic; 

run; 

data redemption; 

 merge redemption (in=a) lastredeem (in=b); 

 by state agency clinic partic; 

 if a; 

run; 

 proc contents data = redemption; 

 run; 

 proc freq data = redemption; 

 tables last state*last 

 /list missing; 

 run; 
 E-15 National Survey of WIC Participants II 

 

 proc print data = redemption; 

 where last = .; 

 var state agency clinic partic last redeem maxval; 

 run; 

proc freq data = redemption; 

 tables lredeem_m*state 

 /list missing; 

run; 

proc freq data = redemption; 

 tables state p0802m p0802m*r04m_a r04m_a 

 /list missing; 

run; 

/* CHECKS */ 

 data check; 

 merge redemption (in=a) paper (in=b); 

 by state agency clinic partic; 

 run; 

 proc freq data = check; 

 tables inredfile inredfile*state/list missing; 

 run; 

 data check2; 

 merge check (in=a) redall2 (in=b); 

 by state agency clinic partic; 

 if a and inredfile = .; 

 run; 

 proc freq data = check2; 

 tables r04m_a; 

 run; 

data ncredemption; 

 set in_imp; 

 where state = 17; 

run; 

proc sort data = ncredemption; 

 by state agency clinic partic wiccat; 

run; 

proc sort data = ncredemption; 

 by wiccat; 

run; 

proc univariate data = ncredemption; 

 by wiccat; 

 var redeem maxval; 

run; 

proc print data = ncredemption; 

run; 

data red.redemption (keep = imputeflg state agency clinic partic r04m_a r04y p0802m p0802y p0803m p0803y lredeem_m lredeem_d lredeem_y 

redeem maxval wiccat); 

 set redemption; 

run; 

data red.ncredemption (keep = _imputation_ imputeflg state agency clinic partic r04m_a r04y p0802m p0802y p0803m p0803y lredeem_m 

lredeem_d lredeem_y redeem wiccat maxval); 

 set ncredemption; 

run; 

ENDSAS; 

APPENDIX F: 

COMMUNICATIONS TO STATES FOR DENIALS

 F-1 National Survey of WIC Participants II 

APPENDIX F 

Communication to States for Denials 

July 22, 2009 

[NAME OF CLINIC DIRECTOR] 

[TITLE] 

[AGENCY] 

[ADDRESS] 

[CITY], [STATE], [ZIPCODE] 

You may have recently heard from your Local WIC Agency or State WIC Director that your 

clinic was randomly selected to participate in the Second National Survey of WIC Participants

along with many others from throughout the country. We will not require much from your clinic; 

however, what this means is that between the months of September and December of 2009, our 

organization, ICF Macro, will be conducting a telephone survey with approximately 15 of your 

WIC participants and a smaller number of Denied Applicants and Denied Recertificants. About 

half of the WIC participants will also be asked to take part in an In-person survey for which they 

will be offered $20. The information collected is confidential and will be used to improve 

administration of and services offered by WIC. It will not impact participants’ WIC benefit 

levels. 

The survey is being administered on behalf of the United States Department of Agriculture, Food 

and Nutrition Service. ICF Macro is already working with your State agency to obtain data on 

participants, from which we will select a random sample of people for interview about their 

experiences with the certification, re-certification, denial, or termination processes and food 

instrument selection, preferences and usage. 

To help us in the success of this survey we request the help of WIC clinic staff in two ways: 

1. Using the enclosed “Denials” log, we need you to keep a record of applicants who apply 

for WIC but are determined ineligible -- so-called “Denied” Applicants -- during the 30-

day period from July 30, 2009 to August 28, 2009. 

2. Please help us inform participants who might be selected for interview about the study 

using the materials enclosed. And reassure WIC clients that their answers will be kept 

confidential, be grouped with others, and not affect their benefits. 

Denied Applicant Log 

IMPORTANT – PREPARE FOR THIS IMMEDIATELY 

The only method of capturing information on Denied Applicants is through documentation at the 

local level. Please use the attached Denials Log to provide the information requested for denied 

applicants. The period of documenting denials must occur every day your clinic is open from 
National Survey of WIC Participants II F-2 

July 30, 2009 through August 28, 2009. For the purposes of this study, a Denied Applicant is a 

person who has applied for WIC and been deemed ineligible but does not include Denied 

Recertificants (that is, people who were denied when the reapplied for benefits since we are 

researching them separately.) 

Return the completed log in the postage-paid envelope provided as soon as possible after August 

28. (You should retain a photocopy as a record of your compliance with the request.) 

Note: Due to data integrity and privacy concerns, the DENIED APPLICANTS information 

should be collected and recorded legibly by WIC staff persons. Individuals who apply for 

benefits should not see or record information on the log. 

Posters and Information about the Interviews 

Enclosed, you will find materials that should help warn WIC participants that they might be 

contacted and will give them some information and reassurance about the interviews. Included 

are: 

 

‐ Posters to be displayed in highly visible areas at the clinic site. 

‐ FAQ sheets to be provided to clinic staff about the purpose and importance of the study. 

This will help staff answer questions that may arise. 

‐ Half-page flyers for distribution to WIC participants. You may make copies as needed 

for distribution to individuals who request more information. 

Please contact your WIC Local Agency administration if you have any questions or concerns 

regarding The Second National Survey of WIC Participants. They have been briefed about the 

study. ICF Macro support staff are also available by email at: 

WICSurvey@mmail.macrointernational.com or at the WIC Survey toll free hotline at 

1-888-285-7976. 

Thank you for your efforts in making The Second National Survey of WIC Participants a success, 

and in turn, improving the quality of future WIC programs and services. 

Sincerely, 

Daniel M. Geller, PhD. 

Project Director 
APPENDIX G: 

SAMPLING AND WEIGHTING METHODOLOGY

 G-1 National Survey of WIC Participants II 

APPENDIX G 

Sampling and Weighting Methodology 

The sample design includes the sample allocation, selection of the sample of States, creation and 

selection of clusters, selection of WIC agencies, clinics, and participants, and weighting. 

Sample Allocations 

To calculate the needed sample sizes, n, for the required estimates, the sample size needed for a 

simple random sample of WIC participants across the nation (specifically for the 48 contiguous 

States and the District of Columbia) was first calculated. This resulted in a simple random 

sample (SRS) of 609 respondents for the estimate of erroneous payment with a 90 percent 

confidence interval, plus or minus 2 percent error margin (without a finite population correction). 

Once we obtained the sample size from an SRS, we multiplied it by the design effect to get the 

needed sample size given the design. 

For all estimates obtained for the In-person survey, a design effect of 1.97 was assumed for the 

combined WIC categories and 1.63 for the separate WIC categories. Table G-1 shows the sample 

size requirements. The assumed design effects are based on the average design effects for several 

key estimates in the NSWP-I survey. 

The third column shows the number of cases needed (for the combined WIC categories and each 

of the separate categories) if a random sample was to be selected. The fourth column shows the 

cases needed (again for the whole sample or for each category) given the assumed design effects. 

Finally, the fifth column multiplies the number needed for each category by the number of 

categories (5) for the separate WIC categories. These numbers represent the number of 

participants in each category. 

Table G-1: Sample Requirements 

Parameter Population Random Sample 

With Design 

Effect per Cell Total Needed 

Error Rates Combined WIC Categories 609 1,199 1,199 

Error Rates Separate WIC Categories 97 159 795 

Ultimately, the allocations were made for 480 participants per category for the telephone survey 

(2,400 overall) and 240 participants per category for the In-person survey (1,200 in all). The inperson survey sample had a total of 1,210 completed interviews, and it was a subset of the 

original telephone survey of 2,538 WIC participants. 

Selection of States 

The primary sampling units (PSUs) were States, selected with probability proportionate to size 

(PPS) with probability minimum replacement (i.e. the large PSUs could be selected more than 

once). A measure of size was used that would yield the same number of participants in each of 

the five categories. 
National Survey of WIC Participants II G-2 

The following is a description of the sampling approach. Let ti represent the measure of size for 

State i. This was defined as the average of the proportion of participants in each category that 

were found in the state. 

5



















    

 Nation

State

Nation

State

Nation

State

Nation

State

Nation

State

C

C

I

I

N

N

B

B

P

P

State Size

Where for the five categories of WIC participants, 

P = Pregnant, 

B = Breastfeeding, 

N = Postpartum Non-breastfeeding, 

I = Infants, and 

C = Children. 

 Let T be the sum of all the measures of size for the 48 States. Now we define— 

ei = 40ti / T 

—to be the selection expectation of State i. 

The expectation is the same as a probability if ei < 1, and if it is greater than 1, the integer part of 

e represents the minimum number of times the State can be selected, and the fractional part 

represents the probability that it will be selected an additional time. 

To order the 48 PSUs, grouped together by WIC region, given that the States are in the desired 

order (where the order guarantees proportional representation by the ordering variable), a 

random number r between 0 and 1 is selected. 

If lim(x) – the limiting value of x – means the largest integer less than or equal to x, then we can 

define— 

c0 = r and 

ci = e1 + e2 + ... + ei. 

Finally, let si = lim(ci) – lim(ci-1), where si defines the number of times PSUi is selected. 
 G-3 National Survey of WIC Participants II 

Creation and Selection of Clusters 

For efficiency in interviewing, the sampled States were divided into regions from which clusters, 

i.e. combinations of local agencies within a sampled State, were selected. Clusters were created 

in such a way that no single local agency constituted more than half of the total cluster size and, 

to the extent possible, were geographically compact. Similarly to the State MOS, the cluster 

MOS was defined as the average of the proportions of each category of WIC participants within 

a cluster relative to the State total of each category. An equation parallel to the creation of the 

measure of size for states was derived: 

5



















    

 State

cluster

State

cluster

State

cluster

State

cluster

State

cluster

C

C

I

I

N

N

B

B

P

P

Cluster Size

As previously stated, the number of times a State was sampled determined the number of clusters 

sampled in that State. Most of the 23 States sampled had only one cluster with probability 

proportional to size (PPS) based on the cluster size described earlier. Seven of the sample States 

had two or more clusters sampled. 

In preparation for sampling of clusters most States were divided into multiple regions based on 

geography, administrative areas (if applicable), and number of local agencies. The regions were 

used for sampling clusters of local agencies in which an interviewer would work. The number of 

regions per State ranged from 2 to 11. Arizona was divided into only two regions to ensure that 

no single local agency constituted more than half of the size. New York was divided into 11 

regions, with 4 regions in New York City alone; most States had 4 to 7 regions. California and 

Texas were not divided into regions for sampling of clusters due to the large number of clusters 

to be sampled in each (7 and 5, respectively) and the magnitude of several local agencies. In 

these States, we sampled local agencies directly and then grouped them to facilitate interview 

assignments. Using geographical regions would have been complicated and would not have 

yielded any gains in efficiency. 

Selection of Agencies 

Two local agencies were sampled with PPS from each cluster. Calculations of the local agency 

MOS were similar to the State and cluster size calculation described above. The MOS was 

multiplied by two in order to sample two local agencies from each research cluster sampled. 

As noted, agencies were sampled directly in California and Texas, with two agencies selected for 

each time the State was sampled with PPS. Calculations for the local agency MOS were done 

based on State totals instead of cluster totals. Sampling of agencies in these States occurred with 

probability minimum replacement, so that sampling of very large local agencies could occur 

more than once. 

The goal was to sample 80 local agencies, two for each of the 40 State clusters. One very large 

California local agency was sampled three times. Thus, across all States and clusters, 78 separate 

local agencies were sampled. 
National Survey of WIC Participants II G-4 

Selection of Clinics 

Thirty participants were allocated for each local agency, with the exception of one large 

California agency that was sampled three times, and was thus allocated 90 participants. Each 

local agency was not allocated the same number of participants in each category. The 

distribution of the 30 participants from a local agency by category depended on the distribution 

of participants across categories in the local agency relative to the distribution of participants 

among all sampled local agencies. 

The initial allocation was determined as follows: 

  







 









  5

1

30

i i

i

i

i

i

Where Ai = the allocation to category i, and i equals one of the five categories of WIC 

participants 

Ni = the number of participants in category i for the sampled local agency 

Ti = the number of participants in category i across all sampled local agencies. 

An iterative rounding algorithm was used in order to obtain exactly 480 participants per category. 

Thus, if for example, a local agency had a larger proportion of participants in the sampled 

agencies in one category compared with the remaining four categories, or more in a category 

compared with the remaining sampled local agencies, we would allocate more of the 30 

participants to that category. 

Once sampling of local agencies occurred, clinic-level data were obtained for each of the five 

categories of WIC participants. The clinic size is similar to the other size calculations above with a 

few major differences. Each clinic-level proportion was weighted by the local agency allocation 

within the category. The adjusted proportions were then divided by 15, half the number of 

participants within each local agency. 

Wherever possible, two clinics were sampled with PPS from each local agency. If a local agency 

had only one clinic, sampling of that clinic was automatic and the participant allocation at the 

local agency level remained as before. When local agencies had one extremely large clinic and 

several small clinics, they were collapsed at the local-agency level. If a local agency had only 

two clinics and the clinics were both sufficiently large (greater than 60 total participants per 

clinic), the 30 participants were allocated 15 to each clinic. However, the partial allocations (i.e., 

the number of participants in each category) could vary from clinic to clinic, with a rounding 

algorithm used to decide the final allocations. In local agencies with multiple clinics, two clinics 

were sampled with PPS and the same total number of participants was allocated to each. 

However, if one of the two sufficiently large clinics selected had 70 percent or more of the total 

participants in the local agency, the 30 participants were allocated proportionally among the 

2 clinics. This occurred once. 
 G-5 National Survey of WIC Participants II 

Selection of Participants 

After the selection of WIC clinics, the lists of respondents were obtained from all the clinics 

sampled in 2 consecutive months (April and May 2009) – several months before data collection. 

The later month was the target month. 

Participants were classified into the five categories: Pregnant, Breastfeeding, and Postpartum 

women, Children, and Infants. If a participant changed categories within the target month, the 

most recent category was used. Thus, if an infant became a child during the month, receiving 

some instruments as an infant and some as a child, that infant was considered a child for 

sampling purposes. 

Each participant in a clinic was assigned a random number. Sorted by that number, participants 

underwent a selection process by the first n from each category (where n is the allocation of 

participants at the clinic level). The remaining participants in a category were retained in their 

order of selection and acted as replacements; so if a participant (or the participant’s mother) 

refused to respond or could not be located, the next one in line was selected. Once a participant 

completed a telephone survey they became eligible for the in-person audit. The first n/2

respondents were selected to participate in the audit. In other words, if six participants were to be 

sampled from a given category, each participant in turn was asked to participate in the audit until 

three agreed. If any refused to participate the next telephone respondent was selected. 

Weighting 

States were sampled with PPS using a size measure that was the average of the proportion of 

participants in each of the five participant categories. This average was then multiplied by the 

number of States (clusters) allocated to the sample (40), and the inverse was as the PSU weight. 

Note that since it is the inverse, the number will be smaller than 1.00 if selection of the PSU 

occurred more than once. However, any PSU weight that was lower than 1 was set to 1. 

Similarly the clusters were also sampled with PPS using a similar MOS. The MOS was then 

multiplied by the number of research clusters sampled. For California and Texas, where no 

regional clustering occurred, this weight is 1. Local agency probabilities of selection were two 

times that of the local agency MOS divided by the sum of the MOS for the agencies in the 

cluster. In California and Texas the local agency’s probabilities of selection were twice the 

number of clusters selected (14 and 10, respectively) times the ratio of the local agency MOS to 

the sum of the MOS for all local agencies in the state. When clinics were selected, their 

probabilities of selection were two times the ratio of the clinic MOS to the local agency MOS. 

For the telephone participants the probability of selection was calculated by dividing the number 

of participants sampled from the category by the total number of eligible participants from the 

category receiving WIC benefits from the clinic. Calculations were done for intermediate 

probability of selection of the clinics as well. 

For the In-person audit participants, the probability of selection was calculated by dividing the 

number of participants sampled from the category by the total number of completed telephone 

participants from the category of eligible participants receiving WIC benefits from the clinic. 
National Survey of WIC Participants II G-6 

The initial weight for an in-person participant was the inverse of their probability of selection. 

The probability of selecting a participant Pijkrt, is the probability of selection of a participant in 

State i, cluster j, agency k, clinic r, participant category t, and telephone participant q. This is 

equal to the product of the individual probabilities, i.e., Pi Pj Pk Pr Pt Pq, which denote the 

probability of selection of, respectively, the State, the cluster in the selected State, the agency in 

the selected cluster, the clinic in the selected agency, the telephone participant within the selected 

agency or clinic, and the in-person participant if selected for the telephone survey. 

A nonresponse bias analysis was conducted on the In-person participants sample to detect any 

nonresponse and coverage problems with the sample, and to adjust the In-person sample weights. 

Appendix H contains resulting tabulations of nonresponse bias analysis. 

The relationship between responding to the In-person survey and key variables in telephone 

survey and state administrative files was examined. The crosstabs comparing responses to the inperson survey and key variables are presented in Appendix I. The variable “Participated in the 

food stamp program” (SNAP) was significantly associated with responding to the In-person 

survey (39.6% of non-respondents to the survey participated in SNAP, in contrast to 51.5% of 

survey respondents). 

The significant relationship between participation in the food stamp program (SNAP) and 

responding to the In-person survey may lead to a bias in the in-person data. Among in-person 

respondents, crosstabs comparing SNAP participants and key analysis variables were produced 

to test for any significant relationship which may indicate a bias. Appendix I contains a table 

which shows a significant relationship between SNAP participation and several of the key 

derived in-person variables. A nonresponse bias adjustment was applied to the respondent inperson weights to account for the bias. 

This adjustment in essence takes the weights for those telephone participants sampled for the inperson survey and who refused, and spreads their weight over the responding participants in a 

particular nonresponse adjustment cell. Nonresponse adjustment cells were defined by crossing 

WIC category and SNAP participation, for a total of 10 cells (see Table G-2). 

Table G-2: In-person Nonresponse Adjustment Cells 

WIC Category Participation in SNAP Cell Number 

Pregnant Women Yes 1 

 No 2 

Breastfeeding Yes 3 

 No 4 

Post Partum Yes 5 

 No 6 

Child Yes 7 

 No 8 

Infant Yes 9 

 No 10 
 G-7 National Survey of WIC Participants II 

For example, in the cell defined as pregnant women participating in SNAP, the weight of the 

non-responding pregnant women would be distributed over the responding pregnant women and 

the non-respondent’s weight would be set to zero. 

After the nonresponse adjustment, the in-person survey weights were checked for extreme 

weights within each WIC category. Large weights, any weights in a WIC category that were 

greater than three times the median weight, were trimmed. There were 14 cases with large 

weights and their weights were trimmed, set equal to three times the median59. After trimming, 

the in-person weights were then poststratified to national WIC population totals within the WIC 

category. The idea is to use relevant population parameters as the reference to adjust the survey 

data weight and correct the cell frequency distributions of the key variables that are biased or 

unreliable because of undercoverage and/or nonresponse. With both the nonresponse and 

poststratification adjustments, we are able to generate estimates with less bias and greater 

precision. We obtained national totals by WIC category and adjusted the final weights to sum to 

those known totals. 

 

59 This routine process with survey data is frequently needed because of the unequal probability of sample selection and 

adjustments used to reduce sampling bias, some weights are rather large. As a result, the design effect may be larger and the 

effective sample size smaller than what the original design intended to achieve. Weight trimming usually reduces the variances 

by a greater amount, so that the mean squared deviations of the estimate is likely to be reduced. 

APPENDIX H: 

CASE ERROR AND DOLLAR ERROR 

ESTIMATION PROCEDURES

 H-1 National Survey of WIC Participants II 

Appendix H 

Improper Payment Data Analysis and Estimation 

Improper payment estimation entailed identifying case error, which in turn required determining 

the eligibility of each participant for WIC benefits. Identity and residence errors were 

straightforward to determine. No such errors were found, as all participants were able to produce 

the required documentation. The challenge was determining income eligibility and category 

eligibility in addition to verifying adjunctive eligibility based on specified non-WIC program 

participation. 

We made extensive efforts to verify adjunctive eligibility, largely by checking documents and 

communicating with State agencies. For participants who were found not adjunctively eligible, 

we used in-person interview data to determine the economic unit (EU), calculate EU total 

income, and apply State-specific criteria to identify income eligibility. Based on the estimated 

case errors, we used participants’ actual redemption values to generate improper payment dollar 

amounts with seasonality and annualizing adjustments. 

Special Case Review Study 

ICF Macro undertook a series of efforts to process in-person interview data and to integrate 

various external information for improper payment (IP) analysis. The in-person interview 

collected information on adjunctive eligibility status among the sampled 1,210 cases by checking 

documents of adjunctive programs. Respondents who were referred to a recognized program also 

had to show the interviewer a recognized document as proof that they are participating in that 

program. These documents included— 

 Certification cards; 

 Award letters; 

 Active program vouchers; and 

 EBT cards. 

Any respondent who did not display one of the above types of documents was considered 

adjunctively ineligible. Programs supporting adjunctive or automatic eligibility were identified 

based on the types of documents used to prove this eligibility. Of the 1,210 respondents, 462 

were identified as adjunctively eligible based on an in-person document check. 

There were 234 respondents whose adjunctive status and income eligibility were not clear based 

on the in-person interview data. Case-by-case research ensued to clarify this group’s adjunctive 

status (see Income Adjunctive Eligibility in Chapter 3). Staff called State agencies to clarify 

issues relating to certification and income in determining each case’s WIC eligibility. Of the 234 

cases flagged for special case reviews, five were identified as ineligible for income certification. 

The special case review generated an indicator of income eligibility for the 234 cases. 

Considering the in-depth review entailed, this indicator from the special case study would, for 

the five ineligible cases, overrule adjunctive eligibility established in the in-person interview as 

well as the computerized income eligibility check, which would cover all of the 1,210 

respondents. Table H-1a shows the distribution of weighted and unweighted respondents by WIC 
National Survey of WIC Participants II H-2 

category and adjunctive status (generated from the in-person interview) and eligibility status 

(generated from the special case review study). Table H-1b lists the cases under the special case 

review study by State. 

Table H-1a: Number and percentage of respondents by WIC category: Adjunctive 

eligibility established in the in-person interview and income eligibility determined in the 

special case review 

Adjunctive Eligibility: In-person Interview 

WIC category 

Adjunctive 

eligibility 

verified by 

document 

check 

Sample n 

(unweighted) 

WIC 

participant 

N 

Percent 95% confidence 

interval 

Pregnant No 172 677,872 70.75 61.39 80.11 

Yes 79 280,220 29.25 19.89 38.61 

Breastfeeding No 169 401,095 68.80 58.80 78.80 

Yes 89 181,891 31.20 21.20 41.20 

Postpartum No 129 351,420 55.43 42.45 68.41 

Yes 95 282,594 44.57 31.59 57.55 

Infant No 135 1,316,186 59.55 47.94 71.16 

Yes 91 894,065 40.45 28.84 52.06 

Child No 143 2,803,992 59.30 46.99 71.62 

Yes 108 1,924,282 40.70 28.38 53.01 

Total No 748 5,550,565 60.90 50.11 71.70 

Yes 462 3,563,052 39.10 28.30 49.89 

Income Eligibility: Special Case Review 

WIC category 

Income 

eligibility by 

special case 

review study 

Sample n 

WIC 

population 

N 

(Weighted 

sample) 

Percent 95% confidence 

interval 

Pregnant No 47 183,210 98.42 95.16 100.00 

Yes 1 2,937 1.58 0.00 4.84 

Breastfeeding No 41 113,911 95.13 84.96 100.00 

Yes 1 5,831 4.87 0.00 15.04 

Postpartum No 41 126,278 96.74 89.97 100.00 

Yes 1 4,255 3.26 0.00 10.03 

Infant No 47 431,112 96.38 90.97 100.00 

Yes 2 16,177 3.62 0.00 9.03 

Child No 53 978,346 100.00 100.00 100.00 

Yes 0 . . . . 

Total 

No 229 1,832,857 98.43 96.89 99.97 

Yes 5 29,199 1.57 0.03 3.11 

Total 234 1,862,056 100.00 

The special case review study on income eligibility identified and flagged five erroneous 

certification cases out of 234 cases studied, with supporting adjunctive program or income 

documentation identified. The dataset then was merged with the main dataset, which covered all 

1,210 cases, of which 462 cases were verified and flagged as adjunctively eligible by the 

in-person interview. 
 H-3 National Survey of WIC Participants II 

Table H-1b: Unweighted number of respondents by State: Special case reviews 

State* For income check Ineligibility error 

determined State total 

R 0 0 0 

F 18 0 18 

G 41 1 42 

L 3 0 3 

W 6 1 7 

B 3 1 4 

E 6 0 6 

P 0 0 0 

V 0 0 0 

K 14 0 14 

U 6 0 6 

A 0 0 0 

O 13 0 13 

J 0 0 0 

Q 20 0 20 

C 20 0 20 

T 13 0 13 

D 29 1 30 

M 12 0 12 

H 7 0 7 

S 11 0 11 

N 1 0 1 

I 6 1 7 

Total 229 5 234 

*State names have been de-identified using a random process that also renders comparison between states in 

other tables impossible. 

For all the 1,210 respondents, analysts created an SAS program to measure their income 

eligibility. The resulting ineligibility identification was modified (i.e., overruled) by the 

adjunctive eligibility flag and the income eligibility error flag established by, respectively, the 

in-person interview and the special case review study. For one case that was adjunctively eligible 

by the in-person interview but not income eligible by special case review study, the conclusion 

followed the special case review study and treated the case as not eligible. Table H-2 shows a 

three-way crosstab between adjunctive eligibility (from the in-person interview) and the income 

eligibility (from computerized income checking), within categories of the special case review 

study—namely income eligible, income ineligible, and not in special case review study. 
National Survey of WIC Participants II H-4 

Table H-2: Distribution of the unweighted number of respondents by final eligibility status 

(from modified result of computerized income check) and adjunctive eligibility (from inperson interview), by special case review study category 

In-person interview: 

Adjunctive status 

(unweighted 

n=1,210) 

Modified result from computerized income 

check (unweighted sample n=1,210) 

Eligible Not eligible 

Special case 

review: 

Eligible 

(unweighted 229) 

not adjunctive (179) 170 9 

adjunctive (50) 50 0 

Special case 

review: Ineligible 

(unweighted 5) 

not adjunctive (4) 0 4 

adjunctive (1) 0 1 

Not in special 

study 

(unweighted 976) 

not adjunctive (565) 540 25 

adjunctive (411) 411 0 

Column total (final eligibility status) 1,171 39 

 

Economic Unit Determination 

Measuring income to determine WIC income eligibility required defining Economic Units

(EUs). Using in-person interview data, analysts sorted out each named household member’s 

relationship with the sampled WIC participant and counted them as an EU member if a named 

household member reportedly shared financial resources with the respondent. 

The SAS routine also attempted to identify household members who were reported to be children 

under temporary care, as such members are treated differently by the sampled State’s guidelines 

in defining EUs. 60 The process, however, did not affect the EU size measurement because 

sampled EUs had no child under temporary care. 

Calculating EU Income/Assets

States differ in counting income sources for determining WIC eligibility. In addition to a State 

agency survey that collected information on countable incomes, ICF Macro staff called State 

agencies to ensure accurate documentation of each State’s varying ways of counting income 

sources. Analysts used this information to differentially count the income dollar amounts by 

income/asset sources and to generate the total income for the economic unit (see Exhibit 3-2 for 

details of State discretionary countable income specifications). 

The SAS program defined three two-dimensional arrays with in-person interview data that 

measure, respectively, income dollar amount of each income source by each EU member who 

had incomes/assets; income evidence presented for each income source by each EU member; and 

income pay periods for each income sources by each EU member. 

 

60 The SAS algorithm counted the children under temporary care into EU size according to State guidelines, including Arizona, 

California, Colorado, Florida, Indiana, Louisiana, Massachusetts, Missouri, New Jersey, New York, North Carolina, Ohio, 

Pennsylvania, Tennessee, Texas, Virginia, and Washington. For the rest of the States, children under temporary care were 

excluded from the EU size counting. 
 H-5 National Survey of WIC Participants II 

With the three two-dimensional arrays, two SAS program loops were run to total the income 

dollar amounts from all income sources by all EU members who had incomes/assets and to 

convert varying measures into monthly total income for each EU. For income dollars reportedly 

paid by specific pay periods (weekly, biweekly, half-monthly, monthly, quarterly, and annually), 

the calculation was done as below: 

 Weekly—multiply by 4.3; 

 Biweekly—multiply by 2.15; 

 Half-monthly—multiply by 2; 

 Monthly—no multiplier; 

 Quarterly—multiply by 1/3; 

 Annually—multiply 1/12; 

Note that the income data were initially only collected from participants who were determined as 

adjunctively ineligible by the field data collectors. At the beginning of data collection, for those 

who were able to provide adjunctive eligibility documents, income data were not collected. It 

was later found that uncertainties occurred regarding onsite determination of the adjunctive 

status; thus, data collectors were instructed to collect income data from all respondents. For those 

initially interviewed without income data, data collectors contacted them again via telephone or 

in-person interview to ensure that income data were collected for all respondents. 

Each economic unit’s monthly income was compared against the WIC income eligibility 

guidelines for 2008–09.61 The monthly mean and median income estimates for the adjunctively 

ineligible subgroup are presented in Table H-3.

 

61 See http://www.fns.usda.gov/wic/howtoapply/incomeguidelines08-09.htm. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II H-6 Table H-3: Mean and median monthly income of economic unit (EU) with and without cases of very high i

Adjunctively eligible vs. adjunctively ineligible participants and EU size 

Adjunct verified EU size Case inclusion Sample n (unweighted) WIC population EU monthly income mean 95% CI for M

Not adjunctively eligible 

1 

All cases 43 213,069 0.00 0.00 

Excluding 

cases of very 

high income 

43 213,069 0.00 0.00 

2 

All cases 152 1,142,342 1,168.78 -412.05 

Excluding 

cases of very 

high income 

151 1,134,322 406.28 231.54 

3 

All cases 207 1,425,988 3,309.09 -1,684.13 

Excluding 

cases of very 

high income 

204 1,400,724 465.87 251.84 

4 

All cases 162 1,332,434 1,429.18 243.41 

Excluding 

cases of very 

high income 

161 1,310,485 882.53 584.66 

5 

All cases 100 767,886 1,038.11 628.63 

Excluding 

cases of very 

high income 

100 767,886 1,038.11 628.63 

6 or 

more 

All cases 84 737,609 2,685.58 -603.69 

Excluding 

cases of very 

high income 

82 713,424 1,027.76 542.70 

Total (Not adjunctively eligible) 

All cases 748 5,619,329 1,910.58 -23.23 

Excluding 

cases of very 

high income 

741 5,539,909 685.99 500.37 
 H-7National Survey of WIC Participants II 

Table H-3 (continued)

Adjunct verified EU size Case inclusion Sample n (unweighted) WIC population EU monthly income mean 95% CI for M

Adjunctively eligible 

1 

All cases 11 54,068 0.00 0.00 

Excluding 

cases of very 

high income 

11 54,068 0.00 0.00 

2 

All cases 89 628,224 51.23 -20.17 

Excluding 

cases of very 

high income 

89 628,224 51.23 -20.17 

3 

All cases 124 1,012,579 106.70 -17.19 

Excluding 

cases of very 

high income 

124 1,012,579 106.70 -17.19 

4 

All cases 93 764,645 77.64 -53.19 

Excluding 

cases of very 

high income 

93 764,645 77.64 -53.19 

5 

All cases 72 516,928 105.67 -35.08 

Excluding 

cases of very 

high income 

72 516,928 105.67 -35.08 

6 or 

more 

All cases 73 517,844 11.04 -12.18 

Excluding 

cases of very 

high income 

73 517,844 11.04 -12.18 

Total (Adjunctively eligible) 

All cases 462 3,494,288 74.39 -2.30 

Excluding 

cases of very 

high income 

462 3,494,288 74.39 -2.30 

Grand Total 

All cases 1,210 9,113,617 1,206.56 -0.87 

Excluding 

cases of very 

high income 

1,204 9,034,197 449.43 318.37 

a Medians were calculated without design effect adjustments. 
Volume 3: Improper Payments Report (Final Report) 

National Survey of WIC Participants II H-8 

As WIC certification requires that income be documented with pay stubs or other evidence, the 

SAS program identified available income sources that had no supporting evidence. Participants 

who reported one or more incomes and presented evidence of at least one income were treated as 

valid with income evidence. Only those who reported one or more incomes but had no evidence 

at all were treated as lacking income evidence. There were 18 such cases lacking income 

evidence. 

Staff conducted case reviews to see if these cases were erroneous certifications. Of these, three 

cases were found Medicaid adjunctively eligible; whereas the rest were not found adjunctively 

eligible; hence, the latter were subject to an income eligibility test, which resulted in no 

eligibility error. Table H-4 lists the 18 cases without income documentation.62 It is important to 

note that lack of income documentation was not used in determining eligibility error; this 

exercise was only for the sake of data processing documentation. 

Table H-4: List of the 18 cases that reported incomes* without any documentation 

State** WIC category EU monthly income ($) Income eligibility error 

A Pregnant 400 No 

E Breastfeeding 0 No 

D Breastfeeding 1,500 No 

B Breastfeeding 0 No 

A Breastfeeding 2,300 No 

E Postpartum 0 No 

A Postpartum 0 No 

A Postpartum 0 No 

C Postpartum 0 No 

E Infant 0 No 

E Infant 1,290 No 

B Infant 6 No 

A Infant 0 No 

A Infant 1,333 No 

A Infant 0 No 

C Infant 0 No 

A Child 3,440 No 

A Child 0 No 

*Income amounted to zero because no State-defined countable income reported. 

**State names have been de-identified using a random process that also renders comparison between states in other tables 

impossible. 

There were another 12 cases that reported one or more incomes that are countable and the 

associated income pay periods: “others” without specification of details. The ambiguity made it 

difficult to convert the reported income into monthly income for determining income eligibility. 

Staff again conducted case reviews to determine the pay periods. Four cases were determined as 

receiving monthly pay when identified as receiving medical assistance ($157), State SSI 

payment ($941), and wage/tip ($1,000 and $1,239, respectively). The rest (eight cases) that 

reported wage/tip valued from $1 through $12 were determined to have an hourly pay rate. 

 

62 To protect respondents’ confidentiality, detailed identification information in this and other listings of individual cases is not 

presented. 
 H-9 National Survey of WIC Participants II 

Monthly income conversion was done according to these conclusions. Table H-5 lists these cases 

with their derived monthly income and resulting income eligibility status. 

Table H-5: List of 12 cases that reported incomes with pay periods of “others” 

State* WIC category EU monthly income ($) Income eligibility error 

A Pregnant 2,816 Yes 

A Pregnant 0 No 

A Pregnant 5,280 Yes 

B Pregnant 941 No 

D Pregnant 2,000 No 

F Breastfeeding 1,239 No 

A Postpartum 5,280 No 

A Postpartum 2,816 No 

B Postpartum 1,507 No 

E Infant 800 No 

C Infant 4,224 Yes 

A Child 6,336 Yes 

*State names have been de-identified using a random process that also renders comparison between states in other tables 

impossible. 

Another issue emerged with seven cases that were found to have excessively high incomes (see 

Table H-6). Staff reviewed these cases, and they found one to be Medicaid adjunctively eligible 

and overruled the income counting result to treat this case as no income eligibility error. The rest 

(six cases) were found not adjunctively eligible and were treated as certification error in the final 

income eligibility analysis. It was necessary to document these cases in detail for the reader’s 

caution, as it was possible that data errors occurred with self-reported incomes; but it was 

infeasible to confirm and remedy such potential errors because of the limited scope of work.63 

Table H-6: List of the seven cases identified as having excessively high incomes 

State* WIC category EU size EU monthly income $ Income eligibility error 

B Pregnant 6 16,045 Yes 

D Breastfeeding 3 182,531 Yes 

C Postpartum 3 267,458 Yes 

A Infant 6 59,555 Yes 

C Child 3 153,636 No 

C Child 4 34,067 Yes 

C Child 2 109,005 Yes 

*State names have been de-identified using a random process that also renders comparison between states in other tables 

impossible. 

To examine the difference between cases with very high incomes and the rest of the sample, 

analysts compared the two groups’ categories for race-ethnicity, education, native language, 

metro vs. non-metro locale, household size, new experience with WIC, and food security. The 

only statistically significant difference between the two groups was in food security. For cases of 

high income, the high food security rate was 98.68 percent (95% CI = 95.88% and 100.00%); 

 

63 Double-checking the reported income entails efforts that are more extensive, such as third-party verification via data collection 

from the respondent’s employers or social service agencies. Such activities are, however, beyond the scope of this study. 
National Survey of WIC Participants II H-10 

whereas for the rest in the sample, the rate was 81.78 percent (95% CI = 78.311% and 85.25%). 

Other differences, substantial albeit not statistically significant, are listed in Table H-7a. 
 H-11 National Survey of WIC Participants II 

Table H-7a: Comparison of six cases with very high income vs. the rest of the sample: 

Percentage by demographics and food security categories 

Demographics Percent S. E. 95% CI 

Cases with very high income (unweighted n=6) 

Non-Hispanic or Latino 72.36 28.82 14.12 100.00 

Asian Pacific Islander 28.45 29.77 0.00 88.62 

English as native language 44.73 57.64 0.00 100.00 

High food security 98.68 1.39 95.88 100.00 

Other cases in the sample (unweighted n=1,204) 

Non-Hispanic or Latino 54.12 4.75 44.51 63.73 

Asian Pacific Islander 2.46 0.70 1.04 3.89 

English as native language 64.53 4.08 56.27 72.78 

High food security 81.78 1.716 78.31 85.25 

For reference, Table H-7b presents estimates of income eligibility case error and pre-rebate 

annualized dollar error, excluding the very-high-income cases. 

Table H-7b: Estimates excluding the six cases of very high income: Income eligibility case 

error and pre-rebate dollar error 

Income eligibility case error 

WIC category 

Sample n in 

error 

(unweighted) 

WIC 

population 

in error 

Income 

eligibility 

case error 

rate 

95% CI 

Pregnant 6 27,321 0.30 - 0.61 

Breastfeeding 8 27,906 0.31 0.05 0.57 

Postpartum 5 12,492 0.14 0.00 0.27 

Infant 10 94,334 1.04 0.36 1.73 

Child 4 58,429 0.65 - 1.45 

Total 33 220,482 2.43 1.10 3.77 

Annualized income eligibility dollar error (pre-rebate) 

WIC category 

Sample n in 

error 

(unweighted) 

WIC total 

population 

Annualized 

income 

eligibility 

dollar error 

95% CI 

Pregnant 6 953,664 12,349,855 (1,297,947) 25,997,656 

Breastfeeding 8 580,718 23,339,242 3,625,329 43,053,154 

Postpartum 5 632,967 5,973,886 422,564 11,525,207 

Infant 10 2,138,152 115,522,475 36,032,055 195,012,895 

Child 4 4,750,646 21,828,787 (9,910,186) 53,567,759 

Total 33 9,056,146 179,014,243 91,684,837 266,343,649 

Determining Income Eligibility 

The SAS algorithm compared each sampled participant’s EU monthly income against the WIC 

Income Eligibility Guideline by EU size. A total of 39 sampled cases were identified as income 

ineligible upon incorporating the indicators of income/adjunctive ineligibility from the special 

case review study and in-person interview file. The six cases with very high income were 

included in the analysis and were counted as income eligibility errors because there was some 
National Survey of WIC Participants II H-12 

evidence that these cases tended to have higher rates of high food security and better education 

than the rest in the sample (see Table H-6). 

Identifying Other Types of Error 

Other types of errors due to identity ineligibility, residence ineligibility, and WIC category 

eligibility were examined as well. In-person interviews requested the respondents to present 

evidence of their identity and residence. All of the respondents did so; no identity or residence 

error was found among study participants. 

Category Error 

Category errors occur largely when agencies mistake the age of infants/children or the dates of 

women’s pregnancy, breastfeeding, and delivery as required by WIC category eligibility. The 

available in-person interview data, however, did not contain information on dates of women’s 

pregnancy, breastfeeding, and delivery; hence, category errors for women were not produced. 

Analysts examined the ages of infants and children (in days) by calculating the length of days 

between the birth date and the certification date. Six sampled units (weighted to 49,832 WIC 

participants) were identified as category errors, as they aged over 1 year at certification time (i.e., 

the difference between the certification date and the birth date was greater than 366 days) but had 

received infant benefits. Note that none of the category error cases was identified as income 

eligibility error (i.e., they were all income eligible for child benefits) and therefore are not 

included in the case and dollar error estimates in Chapter 4. No over-age children (older than 5 

years or 1,828 days at the time of certification) were identified. 

Dollar error due to category error should be the difference between the erroneously redeemed 

infant benefit value and the child benefit value that would have been appropriate for the given 

participant. To measure dollar error due to category error based on the actual redeemed benefit 

value, analysts calculated the difference for each category error case between respondents’ actual 

redeemed infant benefit and their affiliated clinic’s average redeemed value of child benefits. In 

two cases where clinic average redeemed child benefit value was not available, analysts used 

their agencies’ average value. Five cases had their redeemed infant benefit values greater than 

the clinic/agency average child benefit redemption value, thus incurring overpayment error. Only 

one case had redeemed an infant value smaller than the clinic average of child benefit 

redemption value, incurring no dollar error (i.e., the dollar error was assigned zero). 

Table H-8 lists the six cases by State with dollar error amounts calculated and aggregated 

(annualized) dollar error estimates for category error cases only. 
 H-13 National Survey of WIC Participants II 

Table H-8: List of category error cases (infant over 12 months) by State: Unweighted dollar 

error and weighted annualized dollar error 

State* Sample n (unweighted) 

in error

Category error 

Dollar error amount (unweighted)

C 1 1,218 

C 1 166 

B 1 -780 (assigned zero) 

B 1 765 

A 1 985 

A 1 777 

Weighted annualized 

total dollar amount 95% CI 

Category error $ 19,888,965 -1,802,465 41,580,395 

 *State names have been de-identified using a random process that also renders comparison between states in other 

tables impossible. 

Estimate Income Eligibility Dollar Error 

Linking income eligibility indicator to the redemption data (see Section 3.5: Redemption Data 

Processing), the SAS algorithm calculated the erroneous payments due to income-ineligible 

cases. All of the redeemed dollar values by income-ineligible cases were considered as 

overpayment error. Staff have examined redemption data and found that the May issuances were 

redeemed in May through July in all sampled States. 

To annualize the estimates from May redemption data, we used a multiplier (12.285) derived 

from the WIC total food costs data (Table H-9), rather than a simple month count of 12 to take 

monthly variation of redemption values into consideration. Presumably, the error rate is constant 

across months and the monthly redemption value is closely correlated to WIC monthly food cost 

reported by the Food and Nutrition Service (FNS). We obtained WIC monthly food costs data for 

FY 2009 (October 2008 through September 2009) and divided the FY 2009 total cost by the food 

cost in May to generate the annualizing multiplier 12.285 (see Table H-4 for WIC monthly food 

cost data used for this purpose). 

Table H-9: Monthly and cumulative WIC food benefit costs: FY 2009 

State Agency or Indian Tribal Organization Monthly costs $ 

Oct 2008 402,815,721 

Nov 2008 392,249,549 

Dec 2008 391,525,368 

Jan 2009 393,314,732 

Feb 2009 378,397,365 

Mar 2009 383,695,958 

Apr 2009 380,414,599 

May 2009 377,758,893 

Jun 2009 382,549,508 

Jul 2009 381,892,874 

Aug 2009 386,878,505 

Sep 2009 389,354,241 

Cumulative Cost 4,640,847,313 

Annualizing multiplier 12.28520996 
National Survey of WIC Participants II H-14 

Analysts produced the total erroneous payment estimates for income eligibility error in FY 2009. 

The procedure applied sample weighting that adjusted the estimates proportionally to represent 

the WIC participant universe in 48 States. Replicate weights were used to compensate for the 

potential error due to the complex sample design. The sample re-weighting was conducted after 

initial analysis found in-person survey nonrespondents differed from respondents on some 

variables, particularly participation in SNAP (see Appendix G: Sampling and Weighting 

Methodology and Appendix I: Nonresponse Tabulations). The final case error and improper 

payment estimates were generated using the re-weighted data. 

A comparison in demographics, program category, and food security between participants 

identified as an eligibility error and other participants are presented in Table H-10. 

All statistical procedures used SAS 9.2 on UNIX to generate estimates of WIC participant 

demographics, income, case error counts and rates, and dollar error amounts. PROC 

SURVEYMEANS and PROC SURVEYFREQ were used, applying sample weights and replicate 

weights to produce nationwide WIC participant population estimates, with complex sample 

design effects adjusted. 

Table H-10: Demographic and program background variables: Participants with income 

eligibility error and participants without the error 

Income 

eligibility 

error 

Demographic/program 

characteristics 

Sample 

(unweighted) 

n 

WIC 

Population 

Percent 

within group 95% CI for percent 

WIC program category 

No error 

Pregnant 244 926,342 10.48 10.13 10.84 

Breastfeeding 249 552,812 6.26 5.98 6.54 

Postpartum 218 620,476 7.02 6.87 7.17 

Infant 210 2,043,818 23.13 22.38 23.88 

Child 250 4,692,217 53.11 52.37 53.84 

Error 

Pregnant 7 31,750 11.42 0.00 23.62 

Breastfeeding 9 30,174 10.86 0.31 21.41 

Postpartum 6 13,538 4.87 0.16 9.58 

Infant 11 114,091 41.05 22.61 59.49 

Child 6 88,399 31.80 3.83 59.78 

Hispanic or Latino 

No error Hispanic or Latino 520 4,052,747 46.01 36.42 55.60 

Error Hispanic or Latino 15 101,822 36.63 11.14 62.13 

Frequency Missing = 5 
 H-15 National Survey of WIC Participants II 

Table H-10 (continued) 

Income 

eligibility 

error 

Demographic/program 

characteristics 

Sample 

(unweighted) 

n 

WIC 

Population 

Percent 

within group 95% CI for percent 

Race 

No error 

American Indian 11 85,976 0.98 0.00 1.99 

Asian Pacific Islander 26 220,020 2.52 1.06 3.98 

African American 234 1,892,474 21.67 13.73 29.60 

white 505 3,905,339 44.71 35.33 54.09 

other 375 2,631,236 30.12 20.98 39.26 

Error 

American Indian 1 9,220 3.38 0.00 10.54 

Asian Pacific Islander 1 21,949 8.04 0.00 24.74 

African American 9 42,475 15.56 0.80 30.31 

white 17 115,765 42.40 14.26 70.54 

Other 9 83,634 30.63 0.00 64.50 

Frequency Missing = 22 

Education*

No error 

less than HS 340 2,615,890 29.66 23.46 35.85 

HS 403 2,850,244 32.31 26.82 37.80 

more than HS 424 3,354,695 38.03 31.67 44.39 

Error 

less than HS 2 8,702 3.13 0.00 8.17 

HS 19 140,074 50.39 28.93 71.86 

more than HS 18 129,177 46.47 24.15 68.80 

Frequency Missing = 4 

Home language

No error 

English 750 5,691,951 64.42 56.28 72.56 

Spanish 371 2,793,402 31.62 23.91 39.33 

Others 50 350,312 3.96 1.44 6.49 

Error 

English 23 172,977 62.23 43.02 81.45 

Spanish 12 74,677 26.87 2.65 51.08 

Others 4 30,299 10.90 0.00 26.86 

Metro area locale 

No error metro 900 6,756,075 76.46 66.42 86.51 

Error metro 32 207,507 74.66 49.85 99.46 

New to WIC

No error New to WIC 641 4,209,209 47.64 41.58 53.70 

Error New to WIC 25 190,454 68.52 44.12 92.92 

Other assistance program participation

No error 

No Assistance 411 2,668,244 30.55 24.08 37.01 

Yes. Not SNAP 138 1,118,729 12.81 9.52 16.09 

Yes. Includes SNAP 602 4,948,072 56.65 49.86 63.43 

Error 

No Assistance 23 163,717 59.96 25.59 94.33 

Yes. Not SNAP 4 15,879 5.82 0.00 13.09 

Yes. Includes SNAP 10 93,447 34.22 0.00 68.95 

Frequency Missing = 22 
National Survey of WIC Participants II H-16 

Table H-10 (continued)

Income 

eligibility 

error 

Demographic/program 

characteristics 

Sample 

(unweighted) 

n 

WIC 

Population 

Percent 

within group 95% CI for percent 

EU size 

No error 

1 54 267,137 3.02 1.22 4.82 

2 233 1,712,464 19.38 14.67 24.09 

3 320 2,375,301 26.88 22.60 31.16 

4 242 1,983,115 22.44 18.74 26.14 

5 170 1,274,086 14.42 11.08 17.76 

6 or more members 152 1,223,562 13.85 10.55 17.14 

Error 

1 - . . . . 

2 8 58,102 20.90 2.76 39.05 

3 11 63,266 22.76 7.53 37.99 

4 13 113,964 41.00 21.41 60.60 

5 2 10,729 3.86 0.00 10.88 

6 or more members 5 31,891 11.47 0.00 27.54 

Household with child(ren) 

No error with child 913 6,441,845 72.91 67.12 78.69 

Error with child 29 188,440 67.80 53.67 81.93 

Food security* 

No error 

High food security 932 7,194,811 81.43 77.87 84.98 

Marginal food security 21 98,138 1.11 0.25 1.98 

Low food security 115 839,461 9.50 6.92 12.08 

Very low food security 103 703,255 7.96 5.35 10.57 

Error 

High food security 36 271,610 97.72 94.66 100.00 

Marginal food security - . . . . 

Low food security 3 6,342 2.28 0.00 5.34 

Very low food security - . . . . 

*The two groups’ difference was statistically significant at p < .05 level. 
APPENDIX I: 

NONRESPONSE ANALYSIS

 I-1 National Survey of WIC Participants II 

Appendix I 

Non-response Tabulations: Comparison of In-person Survey respondents with 

nonrespondents 

Table I-1 compares 1,188 In-person survey respondents64 and 275 nonrespondents on telephone 

and administrative variables, without sample weighting. Chi-square tests were used to assess the 

statistical significance of these differences. This analysis informed the sample re-weighting to 

remedy potential nonresponse bias for the In-person survey. Three variables were found to be 

significantly different between the respondents and nonrespondents: 

 

 Participation in SNAP (formerly Food Stamp) program (variable name P0532_1, Chisquare = 12.61, p<.0001)—this was the key concern that motivated the sampling 

reweighting and nonresponse analysis (See Appendix G: Sampling and Weighting 

Methodology). 

 Food Distribution Program on Indian Reservations (variable name P0532_4, Chi-square 

= 5.58, p=.019). 

 Commodity Supplemental Food Program (variable name P0532_9, Chi-square = 6.42, 

p=.012). 

Table I-2 further shows the association between SNAP participation and the key variables used 

in the improper payment analysis, without sample weighting. As expected, substantial 

differences were found between the SNAP participants and nonparticipants. Sample re-weighting 

thus was justified to remedy the bias and the final improper payment estimation was based on the 

re-weighted sample. 

Table I-3 presents a comparison of, by WIC category, participants in the in-person interview 

against participants in the telephone interview but not in the in-person interview, with sample 

weighting. The weighted frequency and percentage distribution are statistically equivalent as 

evidenced by the point estimates and standard errors. 

 

64 There were 22 cases that did not complete the telephone interview but were asked and participated in the in-person interview. 

They were included in the improper payment analysis-- the WIC categories are shown below: 

 

Pregnant 4

Breastfeeding 12

Postpartum 1

Infants 3

Children 2

Total 22
National Survey of WIC Participants II I-2 

Table I-1 In-person Survey Respondents vs. Non-respondents (Those Who Refused to 

Participate in In-person Survey): Unweighted Frequencies and Column Percentages

WAS ASKED FOR IN HOME INTERVIEW Total NO YES

IN HOME INTERVIEW

NO 1075 275 1350 

100.0% 18.8% 53.2% 

YES 0 1188 1188 

.0% 81.2% 46.8% 

Total

1075 1463 2538 

100.0% 100.0% 100.0% 

IN HOME 

INTERVIEW Total

NO YES

P0507 Thinking about the WIC clinic that you are familiar 

with, how satisfied are you with the people that work there 

and the services they provide?

Neutral, Not 

Satisfied 7.3% 7.1% 7.1% 

Satisfied 92.7% 92.9% 92.9% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .01; p = .91, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0507A Thinking about the WIC clinic's location and 

building facility, would you say you are 

Neutral, Not 

Satisfied 12.7% 10.0% 10.5% 

Satisfied 87.3% 90.0% 89.5% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 1.72; p = .19, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0508_01 How would you rate the: Customer 

friendliness of the WIC staff 

Not Good 4.4% 5.1% 5.0% 

Excellent, Very 

Good, Good 95.6% 94.9% 95.0% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .28; p = .60, non-significant. 
 I-3 National Survey of WIC Participants II 

IN HOME 

INTERVIEW Total

NO YES

P0508_02 How would you rate the: Quality of 

service you get 

Not Good 2.2% 4.5% 4.1% 

Excellent, Very Good, 

Good 97.8% 95.5% 95.9% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 3.17; p = .08, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0508_03 How would you rate the: Helpfulness 

of the staff 

Not Good 4.0% 4.7% 4.6% 

Excellent, Very Good, 

Good 96.0% 95.3% 95.4% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .26; p = .61, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0508_04 How would you rate the: Staff's ability to 

speak your language 

Not Good 5.1% 4.0% 4.2% 

Excellent, Very Good, 

Good 94.9% 96.0% 95.8% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .61; p = .44, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0508_05 How would you rate the: Safety of the 

clinic's location 

Not Good 4.0% 5.6% 5.3% 

Excellent, Very Good, 

Good 96.0% 94.4% 94.7% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 1.08; p = .30, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0508_06 How would you rate the: Convenience of 

the clinic's location for you 

Not Good 4.0% 6.9% 6.4% 

Excellent, Very 

Good, Good 96.0% 93.1% 93.6% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 3.16; p = .08, non-significant. 
National Survey of WIC Participants II I-4 

IN HOME 

INTERVIEW Total

NO YES

P0508_07 How would you rate the: Convenience of 

its operating hours 

Not Good 5.8% 5.9% 5.9% 

Excellent, Very Good, 

Good 94.2% 94.1% 94.1% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .011; p = .96, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0508_08 How would you rate the: Amount of time you 

must wait until you are seen by WIC staff

Not Good 19.3% 18.1% 18.3% 

Excellent, Very 

Good, Good 80.7% 81.9% 81.7% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .21; p = .65, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0508_09 How would you rate the: Size and space 

of the waiting area 

Not Good 16.7% 18.4% 18.0% 

Excellent, Very Good, 

Good 83.3% 81.6% 82.0% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .40; p = .53, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0508_10 How would you rate the: Activities provided 

to occupy children while you wait 

Not Good 32.4% 28.5% 29.3% 

Excellent, Very 

Good, Good 67.6% 71.5% 70.7% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 1.58; p = .21, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0508_11 How would you rate the: Way they handle 

paperwork for certification 

Not Good 2.5% 5.1% 4.6% 

Excellent, Very 

Good, Good 97.5% 94.9% 95.4% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 3.21; p = .07, non-significant. 
 I-5 National Survey of WIC Participants II 

IN HOME 

INTERVIEW Total

NO YES

P0508_12 How would you rate the: How they 

deliver your food 

Not Good 2.5% 5.1% 4.6% 

Excellent, Very Good, 

Good 97.5% 94.9% 95.4% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 3.22; p = .07, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0509_1 How would you rate the food benefits for 

Providing the right quantity of food?

Not Good 7.6% 10.7% 10.1% 

Excellent, Very 

Good, Good 92.4% 89.3% 89.9% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 2.29; p = .13, non-significant. 

IN HOME 

INTERVIEW Total

0 1

P0509_2 How would you rate the food benefits for 

Offering foods that you like to eat? 

Not Good 9.5% 11.3% 10.9% 

Excellent, Very 

Good, Good 90.5% 88.7% 89.1% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .76; p = .38, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0509_3 How would you rate the food benefits for 

Offering food choices in sizes and brands that you can 

find on the shelf? 

Not Good 13.5% 11.8% 12.1% 

Excellent, Very 

Good, Good 86.5% 88.2% 87.9% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .59; p = .44, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0518 have you attended any group education sessions that were 

recommended to you by the WIC staff? 

NO 64.0% 60.3% 61.0% 

YES 36.0% 39.7% 39.0% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 1.31; p = .25, non-significant. 
National Survey of WIC Participants II I-6 

IN HOME 

INTERVIEW Total

NO YES

P0524 How much one-on-one nutrition counseling have you 

received in person for this most recent pregnancy/baby? 

None at all 40.0% 34.3% 35.4% 

One session 

or more

60.0% 65.7% 64.6% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 3.12; p = .08, non-significant. 

IN HOME INTERVIEW

Total

NO YES

P0532_1 Food Stamp program? NO 60.4% 48.5% 50.7% 

YES 39.6% 51.5% 49.3% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 12.61, p<.0001. 

IN HOME 

INTERVIEW Total

NO YES

P0532_2 Free or reduced price School Lunch or Breakfast 

program? 

NO 74.5% 70.0% 70.9% 

YES 25.5% 30.0% 29.1% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 2.20; p = .14, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0532_3 Summer Food Service program, for kids when not in 

school? 

NO 95.6% 93.9% 94.2% 

YES 4.4% 6.1% 5.8% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 1.30; p = .26, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0532_4 Food Distribution Program on Indian Reservations 

(FDPIR)? 

NO 98.9% 99.8% 99.7% 

YES 1.1% .2% .3% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 5.58, p=.019. 

IN HOME INTERVIEW

Total

NO YES

P0532_5 Temporary Emergency Food Assistance program? NO 97.8% 98.1% 98.0% 

YES 2.2% 1.9% 2.0% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .07; p = .79, non-significant. 
 I-7 National Survey of WIC Participants II 

IN HOME 

INTERVIEW Total

NO YES

P0532_6 Child and Adult Care Food program, which provides free 

lunches for children at day care centers? 

NO 96.4% 96.9% 96.8% 

YES 3.6% 3.1% 3.2% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .20; p = .66, non-significant. 

IN HOME INTERVIEW

Total

NO YES

P0532_7 Local/community food bank or pantry? NO 94.5% 93.5% 93.7% 

YES 5.5% 6.5% 6.3% 

Total 100.0% 100.0% 100.0% 

Note: Chi-square = .40; p = .53, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0532_8 Commodity Supplemental Food Program, which provides 

food packets that are distributed through State and local agencies?

NO 98.9% 98.8% 98.8% 

YES 1.1% 1.2% 1.2% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = .02; p = .90, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

P0532_9 Have you ever participated in Commodity Supplemental 

Food Program in the past? 

NO 98.2% 99.6% 99.3% 

YES 1.8% .4% .7% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 6.42, p=.012. 

IN HOME INTERVIEW

Total

NO YES

P0533 Food consumed during last 12 months Have enough to eat 84.0% 79.9% 80.7% 

do not have enough to eat 16.0% 20.1% 19.3% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 2.43; p = .12, non-significant. 
National Survey of WIC Participants II I-8 

IN HOME INTERVIEW

Total

NO YES

Months since recent certification

.00 11.6% 15.5% 14.8% 

1.00 18.9% 18.6% 18.7% 

2.00 18.9% 15.3% 16.0% 

3.00 9.8% 12.2% 11.8% 

4.00 10.9% 10.5% 10.6% 

5.00 8.4% 8.4% 8.4% 

6.00 5.1% 3.7% 4.0% 

7.00 16.4% 15.7% 15.9% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 6.30; p = .50, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

ETHNICITY 

(not 

imputed)

White 24.0% 26.1% 25.7% 

Black of African American 15.6% 13.6% 14.0% 

Asian American, American Indian, Alaska Native, Native 

Hawaiian or Pacific Islander 8.4% 8.9% 8.8% 

Other 10.2% 5.4% 6.3% 

Hispanic 41.8% 46.0% 45.2% 

Total 100.0% 100.0% 100.0%

Note: Chi-square =10.10, p=.04. 

IN HOME INTERVIEW

Total

NO YES

ETHNICITY (imputed and collapsed)

White 34.2% 34.8% 34.7% 

Black of African American 18.9% 19.5% 19.4% 

Hispanic 46.9% 45.6% 45.9% 

Total 100.0% 100.0% 100.0%

Note: Chi-square =.15; p = .93, non-significant. 

IN HOME INTERVIEW

Total

NO YES

P02A

Pregnant 21.8% 20.8% 21.0% 

Breastfeeding 16.4% 20.7% 19.9% 

Postpartum 18.2% 18.8% 18.7% 

Infant (<12 months) 20.7% 18.8% 19.1% 

Child (1 - < 5 years) 22.9% 21.0% 21.3% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 3.12; p = .54, non-significant. 
 I-9 National Survey of WIC Participants II 

IN HOME INTERVIEW

Total

NO YES

P02G GENDER MALE 25.1% 20.5% 21.3% 

FEMALE 74.9% 79.5% 78.7% 

Total 100.0% 100.0% 100.0%

Note: Chi-square = 2.86; p = .09, non-significant. 

IN HOME 

INTERVIEW Total

NO YES

Family Size

1.00 3.6% 2.4% 2.6% 

2.00 19.6% 19.1% 19.2% 

3.00 31.6% 30.7% 30.9% 

4.00 18.9% 21.3% 20.8% 

5.00 26.2% 26.5% 26.5% 

Total 100.0% 100.0% 100.0% 

Note: Chi-square = 2.12; p = .71, non-significant. 

IN HOME INTERVIEW

Total

NO YES

MSA NO 21.7% 23.1% 22.4% 

YES 78.3% 76.9% 77.6% 

Total 100.0% 100.0% 100.0%

Note: Chi-square =4 .61; p = .64, non-significant. 
National Survey of WIC Participants II I-10 

Table I-2 Variables differentiated by SNAP Participation Status: Percentages and Chi square 

tests 

Responses to request for Proof by SNAP participation status 

P0812 (Proof shown) SNAP (receive SNAP) 

No Yes Total 

Yes, can show documents (for any qualifying 

program) 

63.6 83.79 

Yes, but no adequate documentation 4.13 2.93 

No, did not qualify based on those programs 32.27 13.28 

Total 533 580 1113 

Frequency Missing = 97 

Statistic DF Value Prob 

Chi-Square 2 61.2031 <.0001 

 

Document type shown by SNAP participation status 

P0812F (Document shown) SNAP (receive SNAP) 

0 1 Total 

Certification card 65.98 49.08 

Award letter 7.69 19.1 

Active program voucher 7.69 11.5 

SNAP/Food Stamp EBT card 3.85 12.94 

Other 14.79 7.39 

Total 338 487 825 

Frequency Missing = 385 

Statistic DF Value Prob 

Chi-Square 4 59.4553 <.0001 

WIC Category by SNAP participation status 

WIC Category (WICCAT) SNAP (receive SNAP) 

No Yes Total 

Pregnant 22.24 19.24 

Breastfeeding 25.69 17.41 

Postpartum 17.07 19.4 

Infant 19.66 17.74 

Child 15.34 26.2 

Total 580 603 1183 
 I-11 National Survey of WIC Participants II 

Frequency Missing = 27 

Statistic DF Value Prob 

Chi-Square 4 28.8726 <.0001 

 

N of HH infants in WIC SNAP (receive 

SNAP) 

 

 0 1 Total 

0 32.26 37.52 

1 64.18 56.84 

2 3.57 4.83 

3 0 0.32 

4 0 0.48 

Total 589 621 1210 

Statistic DF Value Prob 

Chi-Square 4 10.9758 <.05 

N of HH children in WIC SNAP (receive 

SNAP) 

 

0 1 Total 

0 74.19 53.3 

1 22.92 35.59 

2 2.72 9.34 

3 0.17 1.77 

Total 589 621 1210 

Statistic DF Value Prob 

Chi-Square 3 66.7771 <.0001 

 

N of HH member in WIC SNAP (receive 

SNAP) 

 

 0 1 Total 

1 34.8 27.38 

2 51.44 47.5 

3 10.19 16.43 

4 2.89 6.44 

5 0.68 1.29 

6 0 0.81 

7 0 0.16 

Total 589 621 1210 

Statistic DF Value Prob 

Chi-Square 6 30.0514 <.0001 

 
National Survey of WIC Participants II I-12 

N of income sources SNAP (receive 

SNAP) 

 

 0 1 Total 

0 60.44 71.66 

1 29.88 23.19 

2 7.64 4.19 

3 1.53 0.64 

4 0.17 0.16 

5 0.17 0.16 

37 0.17 0 

Total 589 621 1210 

Statistic DF Value Prob 

Chi-Square 6 20.2644 0.0025 

 

Adjunct verified SNAP (receive 

SNAP) 

 

0 1 Total 

Not verified 72.84 51.37 

Verified 27.16 48.63 

Total 589 621 1210 

Statistic DF Value Prob 

Chi-Square 1 59.0165 <.0001 

 

P0549 (Hispanic) SNAP (receive 

SNAP) 

 

0 1 Total 

Others 54.61 34.73 

Hispanic 45.39 65.27 

Total 586 619 1205 

Frequency Missing = 5 

Statistic DF Value Prob 

Chi-Square 1 48.1637 <.0001 

 

Table of Self-identified race by SNAP 

TRACE (Self-identified race) SNAP (receive 

SNAP) 

 

0 1 Total 

American Indian/Alaskan Native 0.52 1.47 

Asian/Pacific Islander 2.08 2.45 

African American 13.89 26.63 

White 44.1 43.79 

Multiracial 2.08 1.14 

Other 37.33 24.51 
 I-13 National Survey of WIC Participants II 

Total 576 612 1188 

Frequency Missing = 22 

Statistic DF Value Prob 

Chi-Square 5 43.8991 <.0001 

 

Table of First time received benefits by SNAP 

P0501(First time received benefits) SNAP (receive 

SNAP) 

 

 

0 1 Total 

1 65.53 45.09 

2 34.47 54.91 

Total 589 621 1210 

Statistic DF Value Prob 

Chi-Square 1 51.0677 <.0001 

 

Table of Participate in food programs by SNAP 

p0532 (Participate in food programs) SNAP (receive 

SNAP) 

 

 

0 1 Total 

0 75.35 0 

1 24.65 0 

2 0 100 

Total 576 612 1188 

Frequency Missing = 22 

Statistic DF Value Prob 

Chi-Square 2 1188 <.0001 

 

Table of Final eligible status STATE GUIDE) 

by SNAP 

 

 

eligible_ST2 (Final eligible status STATE GUIDE) SNAP (receive 

SNAP) 

 

 

 

0 1 Total 

0 4.27 1.29 

1 95.73 98.71 

Total 586 618 1204 

Frequency Missing = 6 

Statistic DF Value Prob 

Chi-Square 1 9.9645 0.0016 
National Survey of WIC Participants II I-14 

Table I-3 Weighted numbers and percentage distributions by WIC categories: Subsamples that 

participated in both telephone and in-person interview vs. participants only in telephone 

interview 

Sample 

components 

WIC 

category Unweighted n Weighted1

 n Weighted1 % 

Std Err o 

weighted1 % 

In telephone 

interview but 

not in inperson 

interview Pregnant 266 488,788 10.30 0.39 

Breastfeeding 261 299,075 6.30 0.24 

Postpartum 266 341,510 7.20 0.25 

Infant 269 1,162,180 24.49 0.66 

Child 288 2,454,406 51.72 0.93 

Total 1350 4,745,959 100.00 

In both 

interviews Pregnant 251 489,087 10.95 0.38 

Breastfeeding 258 311,365 6.97 0.27 

Postpartum 224 295,577 6.62 0.24 

Infant 226 1,072,430 24.00 0.66 

Child 251 2,299,322 51.46 0.97 

Total 1210 4,467,781 100.00 

Total Pregnant 517 977,875 10.61 0.10 

Breastfeeding 519 610,440 6.63 0.09 

Postpartum 490 637,086 6.91 0.03 

Infant 495 2,234,610 24.25 0.13 

Child 539 4,753,728 51.59 0.17 

Total 2560 9,213,739 100.00 

Pearson ChiSquare 1.06 

Pr > ChiSq 0.63 

1

 Statistics are weighted by the Telephone Survey weights. 
APPENDIX J: 

CONTENDING REASONS AGAINST DENIAL 

 J-1 National Survey of WIC Participants II 

Appendix J 

Verbatim Responses Contending Agencies’ Denial of WIC Eligibility: Denial 

Analysis 

I hadn’t worked for 12 weeks, but my income was calculated anyway. 

My income had changed; I was making less. 

Many friends told me I would qualify. 

It isn’t fair. It looks like I’m making more than I am. 

I’m still in need of assistance. 

People lie about their personal situations. Sometimes we had, and sometimes we 

didn’t. 

Someone else told me I was eligible. 

The doctor and the hospital said I was entitled because I was pregnant. 

She added more than I was making. 

They didn’t take into consideration how my finances worked. 

I didn’t understand. I’m not working and they counted my 6 weeks of short-term 

disability. 

My income has been cut by 15 percent. 

My husband was only making half of what he used to. 

Some people make more than my husband, and they’re receiving WIC. 

APPENDIX K: 

EXPIRED CERTIFICATION ERROR

 K-1 National Survey of WIC Participants II 

Appendix K 

Expired Certification Error

Expired certification errors occur when benefits are awarded and redeemed after the expiration 

date of the certification period. Obtaining the most current certification dates for participants is 

critical to determining expired certification error. ICF Macro explored expired certification error 

assessment using certification dates collected from State agencies. The resulting statistics should 

be interpreted with great caution because State agencies are not likely to have the most reliable 

information on certification dates. Local agencies actually conduct certifications and update 

certification dates to handle constantly shifting participant status such as WIC benefit issuance, 

termination/discontinuation, renewal, and category change. Unfortunately, data on certification 

dates were only collected from State agencies, not from local agencies. 

ICF Macro asked States to provide the most recent certification date before or during the targeted 

months for sampling when food packages were issued (April and May 2009), but some were not 

able to do so. The bulk of the available data were before or within the two target months, though 

some stretched to as late as the end of year. The reason for this might be in the data requirements 

of what agencies must retain and provide to the Food and Nutrition Service (FNS). 

The Minimum Data Set (MDS) requires that States provide the most recent certificate date. 

The Supplemental Data Set (SDS), which is voluntary, includes the original certification date. 

The available certification data from States are likely to be part of the MDS and thus largely 

represent recent certifications, as revealed by initial tabulation: Of the respondents, 40.28 percent 

of the respondents had their certification dates after April 1, 2009, and 59.72 percent were 

certified prior to April 1 (Table K-1). 

It seems reasonable to assume that the available certification dates from State agencies’ 

certification status were largely current and useful for examining expired certification error. 

Uncertainties remain for the following: 

 For participants who had certification before April 1. Some that are to be identified as 

expired certification error may in fact have renewed their benefits before April 1, but it 

may not have been recorded by the State agency—a scenario of false positive. 

 Among participants who were certified after April 1 and who are all to be treated as no 

expired certification error. Some might have had a period in which they received a 

voucher while certification was expired, but then later renewed before the end of 2009. 

Again, this may not have been recorded by the State agency—a scenario of false negative. 

Nevertheless, analysts may be able to examine expired certification error for those whose 

certification dates were before April and may be able to treat participants whose certification 

dates were after April 1 as apparently free of expired certification error. In such an exploratory 

study, analysts did the following for each case: 

 obtained the length in WIC program (LIP) from the available certification dates from 

State agencies (i.e., days between April 1, 2009 and the certification date). Table K-2 

presents descriptive statistics of LIP; 

 calculated the official benefit length (BL) specified for each WIC category (see SOW, p. 

4). For pregnant women, 9 months and 6 weeks—but considering it is extremely unlikely 
National Survey of WIC Participants II K-2 

for a women to determine pregnancy in the first month, an adjusted 8 months plus 6 

weeks (equivalent to 286 days =30 × 8+7 × 6+4); for postpartum, 6 months (183 days 

=30 × 6+3); for breastfeeding, infant, and child, 1 year (365 days); 

 calculated the difference between the LIP and BL and assigned initial expired 

certification error status to participants whose LIP was greater than BL, treating 

participants whose LIP was smaller than BL (including those that had a negative value of 

the difference—i.e., certified after April 1 as free of expired certification error). Table K1 documents two categories of participants by certification dates: after April 1, 2009 and 

before April 1, 2009, 

 further qualified expired certification errors by checking redemption records for the 

month of May 2009: if the redeemed value was greater than zero, then an expired 

certification error was identified (i.e., the given case not only had received but also 

redeemed the benefits beyond the official specified program length, hence incurring 

improper payments; otherwise, no expired certification error was identified). Table K-3 

summarizes expired certification error case number and rate by WIC category and Table 

K-4 is a listing of the 30 cases of expired certification error. 

The majority of expired certification errors occurred among breastfeeding women, unweighted 

equaling 27 of the 30 error cases and weighted equaling 64,992 of a total of 105,023 error cases 

(61.87% of all the expired certification error cases). 

Table K-1: Certification dates collected from State agency: Prior to or after April 1, 2009 

Available certification date 

from state agencies 

Sample n 

(unweighted) 

WIC 

population Percent 95% CI for percent 

Certified after April 1, 2009 471 3,670,590 40.28 30.90 49.66 

Certified before April 1, 2009 739 5,443,027 59.72 50.34 69.10 

Total 1210 9,113,617 100.00 

Table K-2: Length in program (days receiving WIC benefits) by WIC category 

WIC 

category 

Sample n 

(unweighted) 

WIC 

population Minimum Maximum Range Mean 95% CI for mean 

Pregnant 251 958,092 -185 293 478 4.36 -11.80 20.51 

Breastfeeding 258 582,986 -244 475 719 47.90 25.03 70.78 

Postpartum 224 634,014 -217 316 533 5.35 -17.15 27.85 

Infant 221 2,157,909 -192 405 597 127.43 92.16 162.71 

Child 256 4,780,616 -218 415 633 12.31 -4.99 29.61 
 K-3 National Survey of WIC Participants II 

Table K-3: Expired certification error case count and rate by WIC category 

WIC category Sample n in error 

(unweighted) 

WIC population 

in error 

Expired certification 

error rate 95% CI 

Pregnant 1 3,780 0.04 0.00 0.13 

Breastfeeding 27 64,992 0.71 0.18 0.34 

Postpartum 0 . . . . 

Infant 1 8,531 0.09 0.09 0.00 

Child 1 27,721 0.30 0.30 0.00 

Total 30 105,023 1.15 0.49 0.15 

Table K-4: Listing of 30 cases of expired certification error by State and WIC category 

State* WIC category Length (day) receiving WIC benefits Income eligibility error 

C Pregnant 293 No 

A Breastfeeding 244 No 

A Breastfeeding 301 No 

A Breastfeeding 254 No 

A Breastfeeding 184 No 

A Breastfeeding 259 Yes 

A Breastfeeding 197 No 

A Breastfeeding 226 No 

A Breastfeeding 224 No 

A Breastfeeding 251 No 

A Breastfeeding 285 Yes 

A Breastfeeding 210 No 

A Breastfeeding 295 Yes 

A Breastfeeding 244 No 

B Breastfeeding 203 No 

B Breastfeeding 204 No 

B Breastfeeding 243 No 

B Breastfeeding 253 No 

B Breastfeeding 239 Yes 

B Breastfeeding 240 No 

E Breastfeeding 215 No 

E Breastfeeding 216 No 

G Breastfeeding 258 No 

C Breastfeeding 355 No 

C Breastfeeding 202 No 

D Breastfeeding 230 No 

D Breastfeeding 191 No 

F Breastfeeding 226 No 

C Infant 388 No 

C Child 415 No 

*State names have been de-identified using a random process that also renders comparison between states in other tables 

impossible. 

APPENDIX L: 

INFANT FORMULA REBATE ADJUSTMENT

 L-1 National Survey of WIC Participants II 

APPENDIX L 

INFANT FORMULA REBATE ADJUSTMENT 

Estimating improper payment (dollar error) with adjustments for infant formula rebate was 

necessary to generate more realistic improper payment estimates because participants often do 

not redeem all the voucher values. The task was subject to data restrictions, including 

(1) redemption data do not separate the infant formula amount from other food redemption 

values; (2) States’ negotiated rebate rates differ by formula products, requiring data 

consolidation that may introduce error; and (3) rebate rates are applied to wholesale prices only, 

but the survey-collected redemption data are based on retail prices, an issue requiring 

complicated procedures of adjustment. In short, post-rebate estimates of dollar error cannot be 

calculated from survey data alone. 

We integrated WIC administrative data and the NSWP II statistics to generate a proxy measure 

of rebate values for infants. Subtracting the rebate value from each infant’s actual redeemed 

voucher value, we obtained post-rebate food costs, which is the improper payment or dollar error 

for an infant identified to have income ineligibility error. 

The key is to estimate the infant rebate values. WIC administrative data (WIC Monthly 

Spreadsheet)65 include rebates’ billed amounts and numbers of infant participants for each State. 

This source allows us to obtain the average rebate value per infant for each State. Dividing the 

State average rebate value by the national average redemption value (estimated from the NSWP 

sample), we have the proportion of redemption that is due to rebates for each sampled State. 

Multiplying this rate to the actual redeemed voucher value for each infant in the sample, we have 

a proxy measure of rebate value for each infant. The details follow. 

Let Rs be the rebate total value for a given State and let ns be the number of infants participating 

in the program in the same State, both taken from the WIC administrative data. Now Rs / ns is the 

average rebate value for the particular State. 

For each infant in the survey, we have a redemption value cj and a weight wj. Let C be the 

average redeemed value nationwide, C=(∑ cj wj)/ (∑ wj). Let Ps =( Rs / ns)/C be the proportion of 

redemptions covered by rebates in State s. Table L-1 lists these parameters. 

Table L-1: Constant parameters used in calculation

Value Source Note 

Ru 156,877,423 WIC administrative 

data 

48 contiguous States and tribal organizations, not all WIC 

agencies 

Nu 2,150,231 WIC administrative 

data ditto 

C 110.09 NSWP II Weighted estimate 

P=Ru/Nu/C 0.66 Derived from the above 

 

65 Downloaded from: http://www.fns.usda.gov/pd/wicmain.htm as of 07/29/2011. 
National Survey of WIC Participants II L-2 

Let Ij = 1 if infant j is determined ineligible for benefit (a case error) and 0 if not; let cj be the 

infant’s actual redeemed value. Thus, for each infant in the sample, Ij cj is the estimate of the 

total pre-rebate improper payment, Ij cj Ps is the infant’s proxy rebate value, and Ij cj (1–Ps) is 

the estimate of the post-rebate improper payment, where s denotes the state corresponding to 

infant j.

Summing the weighted estimates, we have ∑ cj Ij wj (1–Ps) as the estimate of the total postrebate improper payments for infants identified as having eligibility error. Replacing the Ps by its 

components, the formula is— 

∑ cj Ij wj (1-(Rs / ns )/ ((∑ cj wj)/ (∑ wj) ).

Tables L-2a and L-2b show the resulting estimates for May 2009. The estimates are rough 

because we have used a single estimate of average redemption value across States, C, and have 

assumed that the average redemption cost for a participant improperly certified will be the same 

as for one properly certified. 

Table L-2a: Estimated WIC infant average rebate value and post-rebate food cost (infant 

n = 2,157,909)

Variable Label* Mean $ SE of mean 95% CL for mean 

Rebate value (ij cj)*P, where P=Rs/ns/C; rebate 

value with state aggregate 73.23 5.91 61.28 85.18 

Post-rebate 

food cost 

(ij cj)*(1-P), where P=Rs/ns/C; postrebate food cost with state aggregate 36.87 3.44 29.91 43.83 

Table L-2b: Estimated WIC infant total rebate value and post-rebate food cost (infant 

n = 2,157,909)

Variable Label* National 

total $ SE of sum 95% CL for sum 

Rebate value (ij cj)*P, where P=Rs/ns/C; rebate 

value with state aggregate 158,030,146 16,084,651 125,495,868 190,564,424

Post-rebate 

food cost 

(ij cj)*(1-P), where P=Rs/ns/C; postrebate food cost with state aggregate 79,554,959* 8,742,833 61,870,911 97,239,007 

*The national total food cost for infants (i.e., pre-rebate total redeemed voucher value for infants in May 2009 is $237,585,105, 

estimated from the NSWP II sample). 

The difference in the national total rebate value between the estimate ($158,030,146) and the 

WIC administrative data ($156,877,423) for 48 contiguous states and tribal organizations in May 

2009 is $1,152,723 (or 0.73 %). Consequently, the annualized measure between the estimate and 

the WIC administrative data differed as well. 

To match the administrative data on total rebates billed, we made further adjustments. For the 

May estimate, we applied a ratio, 1.0073 (the total May rebate value from WIC administrative 

data over the initial May estimate of the rebate total from our sample), to the States’ rebate rates 

(Ps). For annualizing, we used the WIC administrative data to calculate a ratio, 12.1131 (the 

FY 2009 total rebate values over the May rebate values), and then multiplied this factor to the 

above-adjusted May estimate. The adjusted results are presented in Table L-3 and the procedures 

were used in computing the final improper payment estimates. 
 L-3 National Survey of WIC Participants II 

Table L-3: Infant formula rebate adjusted estimates: May 2009 and annualized rebate 

values and post-rebate food costs for infants 

Measure Monthly/ 

annualized Mean ($) 95% CI for Mean ($) Total ($) 95% CI for Total ($) 

Rebate value 

for infants n= 

2,157,909 

May 72.7 60.84 84.56 156,877,423* 124,580,461 189,174,385 

Annualized 880.6 736.93 1024.27 1,900,256,225* 1,509,043,120 2,291,469,329 

Post-rebate 

food cost for 

infants n= 

2,157,909 

May 37.4 30.36 44.44 80,707,682 62,802,703 98,612,660 

Annualized 453.04 367.76 538.31 977,612,146 760,729,137 1,194,495,156 

Total food 

cost for 

infants n= 

2,157,909 

May 110.1 237,585,105 

Annualized 1333.64 2,877,868,371 

*The WIC administrative data on rebate billed for May and FY 2009 are, respectively, $156,877,423 and $1,900,274,589. 

Post-rebate food costs for infants were calculated by subtracting the estimated annualized rebate 

value from the annualized redeemed value for each sampled infant. The aggregated post-rebate 

food cost for infants that were identified to have certification error was the post-rebate improper 

payment (dollar error value). The aggregation entailed multiplying the infant post-rebate dollar 

error values by the sample weights, generating a post-rebate total dollar error estimate for the 

WIC infant population in FY 2009. For the estimated 114,091 infants who were found to have 

eligibility error, the estimated total improper payment was $48,714,683 (95% C.I. = $16,222,015 

and $81,207,352). 

APPENDIX M: 

PRE-REBATE ESTIMATES OF FULL VOUCHER 

VALUE ANNUALIZED DOLLAR AMOUNTS OF 

INCOME ELIGIBILITY ERROR BY 

WIC CATEGORY

 M-1 National Survey of WIC Participants II 

Pre-rebate Estimates of Full Voucher Value Annualized 

Dollar Amounts of Income Eligibility Error by WIC Category 

WIC Category 

Number of WIC 

Participants in Error 

Dollar Error 

($) 

95% Confidence Interval 

($) 

Pregnant 31,750 19,672,471 1,563,888 37,781,054 

Breastfeeding 30,174 37,503,440 8,878,016 66,128,863 

Postpartum 13,538 9,374,944 1,102,831 17,647,058 

Infant 114,091 208,446,184 67,072,211 349,820,157 

Child 88,399 54,935,936 (17,132,701) 127,004,573 

WIC Total 277,952 329,932,975 168,451,359 491,414,592