Contract No.: 53-3198-9-008 MPR Reference No.: 8659-316

Technical Working Paper Creation of the November 1999 MATH SIPP Microsimulation Model and Database October 2003 Author: Mike Bloom

Submitted to:

U.S. Department of Agriculture Food and Consumer Service 3101 Park Center Drive Room 1014 Alexandria, VA 22302

Project Officer:

Jenny Genser

Submitted by:

Mathematica Policy Research, Inc. 600 Maryland Ave., SW, Suite 550 Washington, DC 20024-2512 Telephone: (202) 484-9220 Facsimile: (202) 863-1763

Project Director:

Carole Trippe

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iii

ACRONYMS

AREERA Agricultural, Research, Extension, and Education Reform Act

CPS Current Population Survey

CSP Child Support Program

FMV Fair Market Value

FNS Food and Nutrition Service

FSP Food Stamp Program

FSPQC Food Stamp Program Quality Control

FSU Food Stamp Unit

IRA Individual Retirement Account

MATH Micro Analysis of Transfers to Households

MPR Mathematica Policy Research, Inc.

PA Public Assistance

PRWORA Personal Responsibility and Work Opportunities Reconciliation Act

PSID Panel Survey of Income Dynamics

SIPP Survey of Income and Program Participation

SSI Supplemental Security Income

TANF Temporary Assistance to Needy Families

TFP Thrifty Food Plan

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v

CONTENTS

Chapter Page

I INTRODUCTION ........................................................................................................... 1 A. OVERVIEW OF PROCESSING STEPS............................................................. 2 B. MAJOR CHANGES............................................................................................. 3

1. Treatment of Noncitizens in Reforms ......................................................... 3 2. Household Composition Algorithm Improved............................................ 3 3. Disability Definition More Restrictive........................................................ 3 4. Updated FSP Parameters............................................................................. 4

C. OTHER REFERENCE MATERIAL ................................................................... 5

1. 1999 MATH SIPP Programmer’s Guide .................................................... 5 2. 1996 MATH SIPP User’s Guide................................................................. 5

II THE SURVEY OF INCOME AND PROGRAM PARTICIPATION........................... 7 A. THE SIPP ............................................................................................................. 7 B. WEAKNESSES OF THE SIPP............................................................................ 8 III CREATING THE MODEL DATABASE..................................................................... 15

A. EXTRACT DATA FOR NOVEMBER 1999 .................................................... 15 B. CONVERT SIPP DATA INTO MATH DATABASE....................................... 15 C. EXTRACT AND MERGE DISABILITY DATA.............................................. 16 D. EXTRACT AND MERGE CITIZENSHIP DATA............................................ 17 E. EXTRACT AND MERGE FOURTH-REFERENCE-MONTH DATA............ 17 F. EXTRACT AND MERGE ASSETS AND EXPENSES ................................... 17 G. IMPUTE MISSING EXPENSES AND VEHICLES ......................................... 20

CONTENTS (continued) Chapter Page

vi

IV SIMULATING THE FSP.............................................................................................. 27

A. CLASSIFY PEOPLE INTO FOOD STAMP UNITS ........................................ 27 B. SIMULATE FSP ELIGIBILITY AND BENEFITS .......................................... 31

1. Determine Income Eligibility.................................................................... 32 2. Determine Asset Eligibility....................................................................... 33 3. Determine Food Stamp Benefit................................................................. 35

C. SELECT PROGRAM PARTICIPANTS............................................................ 35 D. FSP SIMULATION RESULTS ......................................................................... 36

REFERENCES 57

vii

TABLES AND FIGURE

Table Page II.1 TOPICAL MODULES OF THE 1996 PANEL ............................................................ 10 II.2 INTERVIEW AND REFERENCE MONTHS FOR WAVE 12................................... 11 II.3 SIPP SAMPLE SIZES FOR NOVEMBER 1999.......................................................... 12 II.4 COMPARISON OF ADMINISTRATIVE DATA AND REPORTED

PARTICIPATION IN SIPP........................................................................................... 13 III.1 ASSIGNMENT OF SHELTER EXPENSES AND DEPENDENT CARE

EXPENSES.................................................................................................................... 23 III.2 SHELTER EXPENSE IMPUTATION SELECTION CRITERIA ............................... 24 III.3 DEPENDENT CARE EXPENSE IMPUTATION SELECTION CRITERIA ............. 25 III.4 VEHICULAR ASSETS IMPUTATION SELECTION CRITERIA............................. 26 IV.1 FSP SPLITTING RULES AND RATES ...................................................................... 38 IV.2 PERCENT OF ABLE-BODIED ADULTS EXEMPT FROM TIME LIMITS,

FOR THOSE REPORTING FOOD STAMP RECEIPT.............................................. 39 IV.3 LIST OF REFUGEE COUNTRIES ............................................................................. 40 IV.4 FSP ELIGIBILITY PARAMETERS FOR NOVEMBER 1999 ................................... 41 IV.5 STANDARD UTILITY ALLOWANCE IN NOVEMBER 1999 FOR STATES

THAT DO NOT VARY THE ALLOWANCE BY HOUSEHOLD SIZE.................. 43 IV.6 STANDARD UTILITY ALLOWANCE IN NOVEMBER 1999 FOR STATES

THAT VARY THE ALLOWANCE BY HOUSEHOLD SIZE .................................. 44 IV.7 REGRESSION EQUATIONS FOR IMPUTING WHOLESALE FMV FOR

VEHICLES.................................................................................................................... 45 IV.8 2000 FSPQC TARGETS FOR THE NOVEMBER 1999 MATH SIPP .................... 46

TABLES (continued) Table Page

viii

IV.9 COMPARISON OF FSPQC DATA AND MATH SIPP MODEL FOR NOVEMBER 1999..................................................................................................... 47

IV.10 SUMMARY OF FOODS STAMP PROGRAM ........................................................ 48 IV.11 DISTRIBUTION OF ELIGIBLE AND PARTICIPATING FOOD STAMP

UNITS BY GROSS INCOME RELATIVE TO POVERTY AND UNIT SIZE--BASELAW .................................................................................................................. 49

IV.12 CHARACTERISTICS OF ELIGIBLE FOOD STAMP UNITS................................ 50 IV.13 CHARACTERISTICS OF PARTICIPATING FOOD STAMP UNITS .................... 51 IV.14 WELFARE STATUS OF ELIGIBLE FOOD STAMP UNITS ................................. 52 IV.15 WELFARE STATUS OF PARTICIPATING FOOD STAMP UNITS ..................... 53 IV.16 DEDUCTIONS OF ELIGIBLE FOOD STAMP UNITS ........................................... 54 IV.17 DEDUCTIONS OF PARTICIPATING FOOD STAMP UNITS ............................... 55 Figure

III.1 FLOWCHART OF PROGRAMS ................................................................................ 21

1

I. INTRODUCTION

The Food Stamp Program (FSP) is the largest domestic food and nutrition assistance

program administered by the U.S. Department of Agriculture’s Food and Nutrition Service.

Policymakers want to know how well the Food Stamp Program (FSP) is reaching the intended

population. For example, they want answers to the following: Are all of the eligible households

being served by this program? What percent are not being served? How do the nonparticipants

differ from the participants? Could outreach efforts be tailored so that more nonparticipants

would participate?

Policymakers also want to understand the impact of changes in the eligibility rules on FSP

caseload and costs. One way to learn this is by using a simulation model that evaluates the effect

of the change on a representative sample of households. In this way, policymakers can estimate

whether a change will be relatively small or significantly large. Using such a model,

policymakers would know whether an increase of $1,000 in the FSP asset limit would increase

program costs by $1,000 or $1 billion.

To meet these needs, Mathematica Policy Research, Inc. (MPR) developed two

microsimulation models for the Food and Nutrition Service (FNS). These models use different

surveys as their underlying databases. The first model, called the MATH� CPS model, uses the

Current Population Survey (CPS) as its underlying database. The second model, called the

MATH SIPP model, uses data from the Survey of Income and Program Participation (SIPP).

Both models simulate FSP eligibility by applying the eligibility guidelines to each household.

Both provide policymakers with an estimate of the number of eligible individuals and a

description of which eligible individuals tend to participate. Both models also simulate FSP

2

participation because the CPS and SIPP surveys tend to underreport the true number of

participants.

Every few years these models and underlying databases are updated. This report documents

the process of creating the 1999 MATH SIPP database and model. In this introductory chapter,

we briefly explain the processing steps and identify the major changes since the 1997 MATH

SIPP model (Sykes and Castner, 2002). Chapter II describes the SIPP. Chapter III describes the

creation of the model database. Chapter IV describes the simulation of the FSP. Tables fall at

the end of each chapter.

A. OVERVIEW OF PROCESSING STEPS

Since the required SIPP data are contained in several data products, the data processing

involves more than simply executing the model on more recent data. It involves:

• Extracting data about the households, families, and persons in the SIPP universe in November 1999, contained in the 1996 SIPP Panel Wave 12 Core file.1

• Converting the data into MATH format, which is a hierarchical database of households, families, and persons

• Extracting data about disability (most of which are contained in the 1996 SIPP Panel Wave 1 Core file) and merging this information onto the MATH database

• Extracting data about citizenship (contained in the 1996 SIPP Panel Wave 2 Topical Module file) and merging this information onto the MATH database

• Extracting data about the household composition and tenure in the fourth reference month (contained in the 1996 SIPP Panel Wave 12 Core file) and merging this information onto the MATH database

• Extracting data about living expenses and asset holdings (contained in the 1996 SIPP Panel Wave 12 Topical Module file) and merging this information onto the MATH database

1 We used the re-released SIPP Wave 12 Core file dated 8/4/2002.

3

• Imputing expenses and asset holdings for those households and persons who are present in the SIPP universe in November but are not in the universe when these data were collected (December through March)

• Simulating FSP eligibility and participation

B. MAJOR CHANGES

We made a number of important changes to the processing procedures and FSP simulation,

which make this version of the model different from the 1997 MATH SIPP model.

1. Treatment of Noncitizens in Reforms

The 1999 MATH SIPP Model allows us to study the impact of reforms on the noncitizen

population. In previous versions of the model, aliens living in households reporting FSP receipt

but not receiving benefits themselves were excluded in reforms. The 1999 model allows alien

non-reporters to participate under a reform by initially assigning them to a food stamp unit,

usually with their family or subfamily members, which will be used if alien restrictions are lifted.

2. Household Composition Algorithm Improved

We modified the household composition algorithm in the 1999 MATH SIPP model to make

it more consistent with the algorithm in the 2000 MATH CPS Model. This algorithm separates

out elderly households first, recognizing they have more opportunities to split than other

households do. We continue to separate households by the presence of unrelated persons, receipt

of TANF, and presence of children. Households are split based on patterns seen in the Food

Stamp Program Quality Control (FSPQC) data.

3. Disability Definition More Restrictive

We modified the current disability determinations to be more restrictive. A person needs to

not be working and have what appears to be a permanent or long-term illness to be subject to the

4

higher asset threshold and be allowed medical deductions. In general, the 1999 SIPP model only

classifies a person as disabled if they are receiving:

• SSI for self

• Veteran’s benefits for any reason (disability, survivor, pension, other) as long as they are not currently working due to a disability.

• Social Security for self, and due to a disability, and not currently working due to a disability.

• Pension from a company or union, federal, state, local, military, or other due to a disability.

• Disability, and not currently working due to a disability.

• Black Lung payments or Rail Benefits (for disability) and not currently working due to a disability.

4. Updated FSP Paramaters

We updated the FSP parameters to reflect the FSP eligibility rules as of November 1999.

These parameters were changed:

• Vehicle threshold

• Gross and net income screens

• Standard deduction

• Dependent care expense deduction

• Excess shelter expense deduction

• Standard utility allowances

• Maximum benefit amount

• Percent of nondisabled, nonelderly adults without children (reporters and non-reporters) eligible for Food Stamps.

5

C. OTHER REFERENCE MATERIAL

Two other documents further describe the MATH SIPP models:

1. 1999 MATH SIPP Programmer’s Guide

The 1999 MATH SIPP Programmer’s Guide (Bloom, et. al., 2003) provides programmers

and analysts with a tool to assist them in developing and maintaining the 1999 MATH SIPP

model. It describes all of the programs that create the baseline MATH database. It also describes

the various parts of the model, how they relate to each other, and the options available to the

user.

2. 1996 MATH SIPP User’s Guide

The 1996 MATH SIPP User’s Guide (Schechter, 2001) describes how to use the MATH

SIPP user interface to execute the model.

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7

II. THE SURVEY OF INCOME AND PROGRAM PARTICIPATION

SIPP provides monthly information on household composition, income, and participation in

various government programs, as well as periodic information on asset holdings, household

expenses, and citizenship status. Since the determination of FSP eligibility is based on this

information, SIPP becomes an ideal starting point for simulating eligibility. In this chapter, we

describe how SIPP is administered and what kind of data it provides. We also describe the

weaknesses of using SIPP and the changes to SIPP since the 1997 MATH SIPP model.

A. THE SIPP

SIPP is a nationally representative longitudinal survey providing detailed monthly

information on household composition, income, labor force activity, and participation in various

government programs, such as Medicaid, TANF, SSI, and the FSP. The interviewed population

is based on a multistage stratified sample of the noninstitutionalized resident population of the

United States. This includes persons living in households, as well as those persons living in

group quarters such as college dormitories and rooming houses. Inmates of institutions, such as

homes for the aged, and persons living abroad are not included. Persons residing in military

barracks, although part of the noninstitutionalized population, are also excluded. Other armed

forces personnel are included, as long as they are living in a housing unit on or off base (U.S.

Census Bureau, 2003).

For this version of the MATH SIPP model, we used data from the 1996 Panel of SIPP. In

that panel, people were interviewed every 4 months over a 4-year period. To ease the

administrative burden of interviewing such a large sample, the Census Bureau divided the panel

into four rotation groups. Only one rotation group was interviewed each month. In each round

8

(wave) of interviewing, persons age 15 or older were asked a set of core questions about their

demographic characteristics, income, program participation, and children for each of the

preceding four months. These core questions were supplemented with a set of questions on

topical issues, which vary from wave to wave, as shown in Table II.1.

In Wave 12, information about financial asset balances, vehicle data, shelter expenses,

medical expenses, and dependent care expenses, which are needed to assess FSP eligibility, were

collected in the topical module. Therefore, we decided to focus on people who were in the SIPP

sample in the common reference month of Wave 12, which was November 1999, as shown in

Table II.2.

In November 1999, the Census Bureau successfully interviewed 28,214 households and

73,205 people for the SIPP. Weighted, this represents 104,527,101 households and 268,351,958

people, as shown in Table II.3. The use of computer-assisted interviewing to record

respondent’s answers enabled immediate consistency checks among reported information.

B. WEAKNESSES OF THE SIPP

By focusing on November 1999, however, we encountered three shortcomings. First, the

topical module questions were asked with respect to the household composition as of the fourth

reference month, not for each of the retrospective four months. So, people who were present in

November but not in the fourth reference month will not have any information about vehicles,

assets, or expenses. We overcame this omission by imputing the information using a statistical

matching technique. Second, some questions about why someone receives government transfers,

which are needed to determine food stamp disability status, were administered when the person

initially entered the SIPP universe, not every month. We overcame this problem by augmenting

the data with disability data taken from Wave 1, which is the initial interview for most people.

9

Third, the questions about citizenship were administered in the Wave 2 Topical Module. We

overcame this problem by merging the topical module information onto the database.

SIPP, like most household surveys, underreports the number of persons participating in

government programs. As shown in Table II.4, SIPP reported 15.9 million persons receiving

food stamps in November 1999, while the FSP Statistical Summary of Operations (hereafter

referred to as FSP Program Operations Data) reported 17.6 million. This reflects an

underreporting of 10 percent. The amount of underreporting increases to 13 percent when we

compare the number of food stamp units.

10

TABLE II.1

TOPICAL MODULES OF THE 1996 PANEL

Wave Subject Areas

1 Recipiency History, Employment History

2 Work Disability History, Education and Training History, Marital History, Migration History, Fertility History, Household Relationships

3 Assets, Liabilities, and Eligibility; Medical Expenses/Utilization of Health Care–Adults; Medical Expenses/Utilization of Health Care–Children; Work-Related Expenses; Child Support Paid

4 Annual Income and Retirement Accounts, Taxes, Work Schedule, Child Care, Disability Questions

5 School Enrollment and Financing; Child Support Agreements; Support for Nonhousehold Members, Functional Limitations and Disability–Adults, Functional Limitations and Disability–Children, Employer-Provided Health Benefits

6 Children’s Well-Being, Assets, Liabilities, and Eligibility; Medical Expenses/Utilization of Health Care–Adults; Medical Expenses/Utilization of Health Care–Children; Work-Related Expenses; Child Support Paid

7 Annual Income and Retirement Accounts, Taxes, Retirement and Pension Plan Coverage; Home Health Care

8 Adult Well-Being, Welfare Reform

9 Assets, Liabilities, and Eligibility; Medical Expenses/Utilization of Health Care–Adults; Medical Expenses/Utilization of Health Care–Children; Work-Related Expenses; Child Support Paid

10 Annual Income and Retirement Accounts, Taxes, Work Schedule, Child Care

11 Child Support Agreements, Support for Nonhousehold Members, Functional Limitations and Disability—Adults, Functional Limitations and Disability—Children

12 Assets, Liabilities, and Eligibility; Medical Expenses/Utilization of Health Care–Adults; Medical Expenses/Utilization of Health Care–Children; Work-Related Expenses; Child Support Paid; Children’s Well-Being

SOURCE: Table 5-3, U.S. Census Bureau, 2001.

11

TABLE II.2

INTERVIEW AND REFERENCE MONTHS FOR WAVE 12

Reference Months

Interview Month

Rotation Group

Aug 1999

Sep 1999

Oct 1999

Nov 1999

Dec 1999

Jan 2000

Feb 2000

Dec 1999 1 X X X X

Jan 2000 2 X X X X

Feb 2000 3 X X X X

Mar 2000 4 X X X X

SOURCE: Table 2-2, U.S. Census Bureau, 2001.

12

TABLE II.3

SIPP SAMPLE SIZES FOR NOVEMBER 1999

Unweighted

Weighted (Using Household

and Person Weights)

Weighted (Using Household

Weight)

Households 28,214 104,527,101 104,527,101

Persons 73,205 273,465,182 268,351,958

SOURCE: Tabulations of 1996 SIPP Panel Wave 12 Core File. NOTE: When tabulating the number of households and persons read and written, the MATH SIPP

Model uses the household weight.

13

TABLE II.4

COMPARISON OF ADMINISTRATIVE DATA AND REPORTED PARTICIPATION IN SIPP

Persons (1,000)

Units (1,000)

Administrative Data

FSP 17,568 7,449 SSI 6,275 6,275 TANF 6,848 2,558 SIPP Data FSP 15,901 6,497 SSI 6,798 6,538 TANF 4,377 1,793 Underreporting FSP 9.5% 12.8% SSI -8.3% -4.2% TANF 36.1% 29.9%

SOURCES: November 1999 FSP Program Operations Data, December 1999 Federal SSI Benefits

Awarded from the 2002 SSI Annual Report, Average monthly TANF caseload from the Green Book (FY 2003), and 1996 SIPP Panel Wave 12 Core File.

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15

III. CREATING THE MODEL DATABASE

The SIPP Wave 12 Core questionnaire provides most of the information needed to simulate

the FSP. The Wave 1 Core questionnaire and the Waves 2 and 12 Topical Module questionnaires

provide the rest. Since the Census Bureau distributes the information collected by each

questionnaire as a separate data file, we must combine the data files before simulating the FSP.

This process involves a series of over 20 programs, as shown in Figure III.1. In this chapter, we

describe in general terms how the information needed to simulate the FSP was compiled.

A. EXTRACT DATA FOR NOVEMBER 1999

Since each wave contains four months of data, we began our process by selecting all persons

who were present in November 1999 from the 1996 SIPP Panel Wave 12 Core file. We

extracted all of the SIPP variables, including household composition, family composition, earned

and unearned income, asset income, and participation in the various government programs.2

These data formed the bulk of the data elements.

B. CONVERT SIPP DATA INTO MATH DATABASE

We converted the SIPP data into a MATH database. A MATH database consists of two

files: the data file (MATHPC.BIN) and the header file (MATHPC.HDR). The data file is a

hierarchical database of household, family, and person records. The header file is a text file that

describes the contents, organization, and data types in the data file. The header file also includes

information that is readily needed by the model, such as the poverty guidelines, the year and

month of the data, and the version number of the model.

2 Later in the process, we deleted variables that were not relevant to the FSP.

16

C. EXTRACT AND MERGE DISABILITY DATA

The FSP considers persons under age 60 to be disabled if they receive SSI or if they receive

certain types of other unearned income due to a disability. Households containing disabled

persons are then subjected to different FSP eligibility rules. This usually makes the household

eligible for more benefits than it would have been if it did not contain a disabled person.

The Wave 12 Core questionnaire includes information about how much a person received

from SSI, Social Security, government pensions, railroad retirement, veteran's benefits, workers’

compensation, black lung payments, sickness benefits, and disability payments.

We extract people who reported receiving the income as well as the reason why they

received it from the Wave 1 Core file. We then merged the Wave 1 information onto the MATH

database.

Using the information from Wave 1 and the information from November 1999, we

determined if a person was classified as disabled by the FSP. Nonelderly persons receiving SSI

were automatically classified as disabled.3 The disability status of those not receiving SSI was

based on the reason why the person received the other types of unearned income. If the person

received the income in either Wave 1 or November 1999 due to a disability, the person was

classified as disabled. If the person started receiving the income between Wave 1 and November

1999, disability was inferred based on the presence of Medicare coverage or the presence of a

work-limiting condition that precluded the person from participating in the labor force. We

determined that 3.2 million persons in November 1999 were disabled according to the FSP

standards.

3 Nonelderly persons can receive SSI only if they are disabled.

17

D. EXTRACT AND MERGE CITIZENSHIP DATA

Due to the Personal Responsibility and Work Opportunities Reconciliation Act (PRWORA),

most types of noncitizens are now excluded from the FSP. In November 1999, noncitizens who

met the work history or veteran requirements were exempt, as were refugees.

The Wave 2 Topical Module questionnaire asked about citizenship and migration of each

adult. We extracted this information and merged it onto the MATH database.

E. EXTRACT AND MERGE FOURTH-REFERENCE-MONTH DATA

Asset holdings and expenses are needed to simulate the FSP. This information is collected

in the Wave 12 Topical Module questionnaire. But, some of this information pertains to the

household as defined in the fourth reference month, which may not be the same as the household

as defined in November 1999.4 The accuracy of the topical module information, consequently,

depends on whether the person is living in the same household, whether the household has

changed its location, and whether the household composition has changed between November

and the fourth reference month. Thus, we extracted information about each person’s household

as of the fourth reference month and merged it onto the MATH database.

F. EXTRACT AND MERGE ASSETS AND EXPENSES

As the previous section mentioned, asset holdings and expenses are needed to simulate the

FSP. Assets holdings, such as financial assets and vehicular assets, are subjected to the FSP

asset test. Expenses incurred for medical care, dependent care, shelter, and child support

payments are deducted from FSP gross income.

4 Prior to the 1996 SIPP Panel, this information pertained to the household as defined in the

interview month.

18

The Wave 12 Topical Module questionnaire asked about asset holdings and those expenses.

Some questions were presented to every adult. Some were presented only to the household

reference person, who responded on behalf of all of the individuals in the household as of the

fourth reference month. The way the question was asked dictated how we processed the

information.

Questions about financial assets, medical care expenses, and child support payments were

presented to each working-age person (age 15 or older). Thus, we simply extracted the

information and merged it onto the MATH database.

Questions about shelter and dependent care expenses were presented only to the household

reference person. We extracted this information, but merging it onto the MATH database is not

as straightforward as it appears. The data represent the household composition as of the fourth

reference month, not as of November 1999, which is the date of the MATH database. Thus, the

household reference person in the fourth reference month may not be the same as the household

reference person in the MATH database. Consider the following scenarios:

• What if the reference person in the fourth reference month was not in the sample in the MATH database? Should that information be assigned, even though other persons in that household are in the sample in the MATH database?

• What if the household reference person in the fourth reference month lives in a different dwelling unit or has a different tenure status (owner versus renter) compared to the status in the MATH database? Do the shelter and utility expenses still apply?

• What if the reference person in the MATH database is not present in the fourth reference month? How should expenses be assigned to that household?

We designed our approach to meet these challenges. If the reference person in the MATH

database was also a reference person in a fourth reference month, we assumed the information

reported in the fourth reference month was valid for the MATH database. If the reference person

in the MATH database was not a reference person in the fourth reference month or if the

19

reference person in the fourth reference month was a non-interview (and therefore the Census

Bureau imputed all of the information), we imputed the shelter and dependent care expenses

(described later in this chapter). We also assumed, for shelter expenses, that if the reference

person in the MATH database lived at the same address in the fourth reference month, the

household expenses as reported by someone else in that household would apply to the household

in the MATH database even though a different person acted as the reference person.5

As shown in Table III.1, 98 percent of the households in the MATH database contain a

household reference person who lives in the same household in the fourth reference month and

acts as the household reference person in both periods. Only 2 percent of the households in the

MATH database had their data completely imputed by the Census Bureau or had a change in

their household circumstances, such as a different household reference person, a different

address, or a different number of persons living at that address. Less than 100 households (0.3

percent) needed expenses imputed.

Questions about vehicle ownership were also presented only to the household reference

person, but in this case the reference person identified who owned each vehicle, whether the

vehicle was used for work or to transport disabled persons, the FMV of the vehicle, and how

much was owed on the vehicle. Thus, unlike the household expenses, the vehicle data

corresponded to individuals. So, merging the vehicle data was not as restrictive as merging the

expense data. One scenario, however, needs special consideration:

• What if a son lived with his parents during the fourth reference month but did not live with them in November? Should the son’s vehicles, which were reported by his parents, be assigned to the son, even though he doesn’t live with his parents anymore?

5 An example is when a son lives with his parents in January (the father is the reference

person) and the father sells the house to his son before the interview month (now the son is the reference person).

20

We assumed the vehicle information was valid even though the son had moved. If,

however, a person was not present in the fourth reference month (and therefore no one had

information about that person’s vehicle(s)) or if the reference person in the fourth reference

month was a non-interview (and therefore the Census Bureau imputed all of the information), we

imputed his/her vehicle data (see next section). 6

G. IMPUTE MISSING EXPENSES AND VEHICLES

We used a statistical matching technique known as the hot-deck imputation method to

impute missing expenses and vehicles. The hot-deck method matches households for which data

are missing with households that report data based on characteristics that are highly correlated

with the missing data.

The characteristics that are highly correlated with the missing data differ according to the

data that are being imputed. For shelter expenses, we used the following characteristics:

household poverty status, geographic region, age and education of the reference person, and

tenure status (Table III.2). For dependent care expenses, we used the following criteria:

household poverty status, presence of children under age 4, presence of children between the

ages of 12 and 18, employment and education level of all of the parents in the household, and

earnings of the highest paid parent (Table III.3). For vehicle ownership, we used the following:

household poverty status; the individual's relationship to the household head; and the individual's

employment status, earnings, gender, marital status, veteran's status, and age (Table III.4).

6 People who are non-interviews have EPPINTVW= 3 or 4.

21

FIGURE III.1

FLOWCHART OF PROGRAMS

EXTRACTNOV 1999

EXTRPER.F90

XTPERW12.PER

SORT BYHHLD & FAM ID

SRTPERW12.BAT

XTPERW12.SRT

CONVERT TOBINARY FORMAT

CONV2BIN.F90

XTPERW12.BIN

MAKE MATHSIPPDATA FILE

MAKESIPP.F90

MATHPC.BIN

MAKE MATHSIPPHEADER FILE

MAKEHEAD.F90

SIP96W12D.TXT

MATHPC.HDR

RECODEVARIABLES

RECODE.F90

MATHPC.BINMATHPC.HDR

MATHPC.PRM

MERGEDISABILITY

TALLY.F90

MATHPC.BINMATHPC.HDR

EXTRACTDISABILITY

1996 SIPPWAVE 1 CORE

W1CORE96.ASC

EXTRWAV1.PER

1999 SIPPWAVE 12 CORE

W12CORE96.ASC

MATHPC.PRM

MERGECITIZENSHIP

MATHPC.BINMATHPC.HDR

EXTRACTCITIZENSHIP

1996 SIPPWAVE 2 TOPMOD

W2TOP96.ASC

EXTRW2TM.PERMATHPC.PRM

EXTRWAV2.F90

1

MERGEMONTH 4 INFO

MATHPC.BINMATHPC.HDR

EXTRACTMONTH 4 INFO

1996 SIPPWAVE 12 CORE

W12CORE96.ASC

XTMON4.PERMATHPC.PRM

EXTRPER4.F90

TALLY.F90

MERGEPERSON-LEVEL

ASSETS & EXPENSES

MATHPC.BINMATHPC.HDR

EXTRACTPERSON-LEVEL

ASSETS & EXPENSES

1996 SIPPWAVE 12 TOPMODP96PUTM12.DAT

XTASTW12.PERMATHPC.PRM

EXTRW3AS.F90

TALLY.F90

MERGEHOUSEHOLD-LEVEL

ASSETS & EXPENSES

MATHPC.BINMATHPC.HDR

EXTRACTHOUSEHOLD-LEVEL

ASSETS & EXPENSES

1996 SIPPWAVE 12 TOPMODP96PUTM12.DAT

XTREFW12T.PER

MATHPC.PRM

XTRW12REF.F90

TALLY.F90

SORT BY PERSON ID

XTREFW12T.SRT

SRTREFW3.BAT

1

2

EXTRWAV1.F90

EXTRWAV2.F90

TPLA.BIN

TABULATE JOINT ASSETS

TAB1.REQ

DISTRIBUTION OFJOINT ASSETS

22

FIGURE III.1 (continued)

M A T H P C .P R MR E C O D E

V A R IA B L E S

M A T H P C .B INM A T H P C .H D R

R E C O D E 2 .F 9 0

2

M A T H P C .P R MS IM U L A T E

F O O D S T A M PP R O G R A M

M A T H P C .B INM A T H P C .H D R

F S T A M P .F 9 0

M A T H P C .P R ME X T R A C T

E L IG IB L E U N IT S

F S E L IG 1 .D A T

T A L L Y .F 9 0

D E T E R M IN EP A R T IC IP A T IO N

P R T 1 _ N E W .R A W

M S C A L N E W .D O

S O R T B YH H L D & P E R S O N ID

P R T 1 _ N E W .S R T

M S 1 .B A T

M E R G EP A R T IC IP A T IO N

M A T H P C .B INM A T H P C .H D R

T A L L Y .F 9 0F S T A M P .F 9 0

M A T H P C .P R M

T P L 1 .D A T

T A B U L A T EF O O D S T A M P U N IT S

T A B 1 .R E Q

D IS T R IB U T IO N O FF O O D S T A M P U N IT S

D IS T R IB U T IO N O FF O O D S T A M P U N IT S

IM P U T E M IS S IN GV E H IC L E S

V E H D E C K .D A TM A T H P C .P R M

IM P V E H .F 9 0

M A T H P C .B INM A T H P C .H D R

IM P U T E M IS S IN GE X P E N S E S

S H L D E C K .D A TD E P D E C K .D A T

M A T H P C .P R M

IM P E X P .F 9 0

M A T H P C .B INM A T H P C .H D R

23

TABLE III.1

ASSIGNMENT OF SHELTER EXPENSES AND DEPENDENT CARE EXPENSES

Unweighted

Number Percent Total Households in MATH Database

28,214 100.0

Assignment Accuracy of Shelter Expenses

Reference person in MATH database is the reference person in the fourth reference month and lives at the same address

27,790 98.5

Reference person in MATH database is the reference person in the fourth reference month but lives at a different address and yet the tenure status (own versus rent) is the same as the previous address

210 0.7

Reference person in MATH database is the reference person in the fourth reference month but lives at a different address and the tenure status (own versus rent) is different

119 0.4

Reference person in MATH database lives at the same address but is not a reference person in the fourth reference month

7 0.0

Reference person in MATH database lives at a different address and is not a reference person in the fourth reference month

81 0.3

Assignment Accuracy of Dependent Care Expenses

Reference person in MATH database is the reference person in the fourth reference month and lives with the same number of persons

27,587 97.7

Reference person in MATH database is the reference person in the fourth reference month but lives with a different number of persons (and reports no dependent care expenses)

505 1.8

Reference person in MATH database is the reference person in the fourth reference month but lives with a different number of persons (and reports dependent care expenses)

27 0.1

Reference person in MATH database but is not a reference person in the fourth reference month

88 0.3

SOURCE: 1999 MATH SIPP database

24

TABLE III.2

SHELTER EXPENSE IMPUTATION SELECTION CRITERIA

Gross Income Relative to the Poverty Threshold Less than 1.85 Equal or Greater than 1.85 Geographic Region South Northeast North Central West Age Less than 35 35 to 54 55 to 64 More than 64 Education At Least College Less than College Tenure Own a Home Rent No Cash Rent

25

TABLE III.3

DEPENDENT CARE EXPENSE IMPUTATION SELECTION CRITERIA

Gross Income Relative to the Poverty Threshold Less than 1.85 Equal or Greater than 1.85 Number of Children Less than Age 4 Zero One More than One Number of Children between Age 12 and 18 Zero More than One Labor Force Participation of All Family Heads and Spouses All Employed Full-Time None Employed Full- or Part-Time Other Earnings per Hour No Earnings for All Family Heads and Spouses Minimum Wage for Highest Paid Family Head or Spouse Other Education Attainment At Least High School for at Least One of the Family Heads or Spouses Other

26

TABLE III.4

VEHICULAR ASSETS IMPUTATION SELECTION CRITERIA

Gross Income Relative to the Poverty Threshold Less than 1.85 Equal or Greater than 1.85 Family Relationship Family Head Family Spouse Other Labor Force Participation Employed Full-Time Employed Part-Time Other Earnings per Hour More than Minimum Wage Minimum Wage or Less No Earnings Gender Male Female Marital Status Single Other Veteran Status Served in the Armed Forces Never Served in the Armed Forces Other Age Less than 60 60 or More

27

IV. SIMULATING THE FSP

Once the information needed to simulate the FSP was compiled, we simulated the FSP

eligibility rules and selected participants. This chapter describes the eligibility simulation, which

classifies people into food stamp units and identifies if the unit is eligible for a benefit.7 It also

describes the way in which participants were selected and the simulation results.

A. CLASSIFY PEOPLE INTO FOOD STAMP UNITS

Persons who customarily purchase and prepare food together form a food stamp unit. In

most cases, the food stamp unit includes all members of the household. In some cases, however,

people may form a separate food stamp unit from other members of the household, as long as

they purchase and prepare food separately. Some exceptions are:

• Spouses must apply together.

• Parents and their children under age 22 must apply together, even if the children have a spouse or child of their own.

• Persons who are both elderly and disabled, along with their spouses, are allowed to form a food stamp unit separate from other members of the household regardless of food purchase and preparation practices, provided that the total income of the other household members does not exceed 165 percent of poverty.

Since FSP eligibility rules apply only to persons in the food stamp unit, deciding who

belongs in the food stamp unit is of utmost importance. Unfortunately, the SIPP data do not

contain detailed food purchasing and preparation information. We can, however, infer which

7 The discussion that follows is an overview of how we modeled the regulations that govern

FSP eligibility and benefits. We omit from this discussion aspects of the FSP that were not modeled. The complete regulations appear in the Code of Federal Regulations (FCR, parts 270-273).

28

persons would probably be in the food stamp unit based on information in SIPP. Our inference

is based on the following rules:

• If the household reports receipt of food stamps, those persons reporting coverage by the FSP are in the reported food stamp unit. Everyone else is excluded.

• If the household does not receive food stamps, we approximated the unit definition and assumed that a certain percentage of the households with potentially more than one food stamp unit actually had more than one unit. We used the FSP Quality Control Database (FSPQC), which is a random sample of FSP participants extracted from FSP administrative data, to determine the rate at which multiple units were formed.

For the households that did not receive food stamps, we approximated the unit formation

rules as follows:

Type 1: Household contains elderly persons

Type 2: Household contains unrelated individuals

� Type 2A: Household receives TANF � Type 2B: Household does not receive TANF

Type 3: Household contains kids

� Type 3A: Household receives TANF � Type 3B: Household does not receive TANF

Type 4: Household contains only related adults, no elderly, and no children

Table IV.1 summarizes the unit formation rules in the model and displays what percentage

of households split into multiple food stamp units in the MATH database compared to FSPQC

data. The FSPQC split-off rates served as the starting point for the FSP non-reporting

households in the MATH database. The final split-off rates for non-reporting households, as

shown in column 4, were adjusted until the characteristics of total selected participants

(described later) were similar to the FSPQC data. The last column of the table shows the split-

off rates among all simulated FSP households.

29

There are a number of exceptions to the aforementioned unit formation rules. First, persons

living in California who receive SSI are excluded from the food stamp unit because California’s

SSI program includes a special monetary supplement in lieu of food stamps. We excluded these

people even if the SIPP reported them as being in the food stamp unit.

Second, postsecondary students are excluded from the food stamp unit. These are people

who are 17 to 50 years of age, physically and mentally fit for work, and enrolled more than 50

percent of the time in postsecondary education. They are exempt from this exclusion if they also

work 20 or more hours per week, receive TANF, or are a single parent of a child under age 12.

Third, persons living in group quarters are excluded from the FSP, even though in actuality

persons living in group arrangements can apply for food stamps if their living arrangement meets

certain criteria. Unfortunately, SIPP does not include the data needed to model these criteria, so

we cannot determine whether the persons in group quarters actually are eligible. Since this group

is small, we decided to be conservative and exclude all persons in group quarters from the FSP.

Fourth, unmarried partners of household reference persons are put in the same food stamp

unit as the household reference person.8 Family members, such as children, of the unmarried

partner are also put in the household reference person’s food stamp unit.

Fifth, the Personal Responsibility and Work Opportunities Reconciliation Act (PRWORA)

set time limits for many able-bodied adults without children. Individuals age 18 to 49, who are

mentally and physically able to work, not living with children, and not exempt from the FSP

work registration requirements, must meet certain work requirements. If they do not meet the

work requirements, they are limited to 3 months of benefits in any 36-month period. To meet the

8 With the 1996 Panel, the identification of unmarried partners is now possible.

9 This percentage is based on the percentage measured in the Panel Survey of Income Dynamics (PSID) data.

30

work requirements, they must be working at least 20 hours per week (or be paid an equivalent of

20 hours per week at minimum wage) or participate in an employment and training program. Of

those who do not meet the work requirements, some remain eligible because of waivers and

others are in their first three months of FSP receipt. In the model, we randomly chose some able-

bodied 18- to 49-year-olds, with no children in the family, who were also not students or

receiving unemployment compensation to be eligible.10 We chose some to be eligible because

they are in their first 3 months, and some because of the waiver areas. The percentages used for

each of these categories are shown in Table IV.2, by state.11

Sixth, in 1996, PRWORA disqualified many noncitizens from the FSP allowing only those

with sufficient work history, those who were current or former members of the U.S. Armed

Forces, and refugees who had been in the United States for less than five years to be eligible,

along with their families. In 1998, the Agricultural Research Extension and Education Reform

Act (AREERA) restored eligibility to noncitizen children, elderly, and disabled noncitizens who

had been in the United States since 1996. AREERA also extended the eligibility period for

refugees from five years to seven years.

In the SIPP, we only know citizenship from Wave 2, or in early 1996. Thus, each person on

the file whose year of entry was known, arrived in 1996 or earlier. Instead of assuming that all

noncitizen children, elderly, and disabled were eligible, we required them to have been in the

United States for at least three years. We determine the child’s year of arrival in the United

10 The students and individuals receiving unemployment compensation are exempt from

work registration.

11 We use different percentages for individuals in their first 3 months of participation, based on current participation in the FSP, accounting for the fact that current participants are more likely to have 3 months of participation than non-participants.

31

States based on the mother’s year of arrival. If there is no mother, we use the father’s year of

arrival. If there is no parent, the year of arrival of the closest relative is used.

Refugees are not identifiable in the SIPP data. To model the exception for refugees who

have been living in the United States less than 7 years, we assumed that noncitizens emigrating

within the past 7 years from countries with a large refugee population (see Table IV.3) met this

exception. PRWORA also made an exception for noncitizens who accumulated forty quarters of

work (in combination with their spouse and parents) or who were U.S. veterans. To simplify

matters, we assumed that a certain percentage (17.8%) of noncitizens who did not have their

eligibility restored by the AREERA provisions described above would be categorically eligible

for the FSP due to work history or veteran requirements.12 The remaining noncitizens were

excluded from the FSP but were required to deem a pro-rata share of their income and all of their

assets to the people in the household who could receive food stamps.

B. SIMULATE FSP ELIGIBILITY AND BENEFITS

The MATH SIPP model replicates the FSP eligibility criteria in effect in November 1999. In

a sense, the model acts as an FSP caseworker. On a case-by-case basis, it determines whether

the food stamp unit is eligible for food stamps, a function of both available cash income and

assets. If the unit is income eligible and asset eligible, the model then determines the amount of

food stamp benefit for which the unit is entitled. Table IV.4 summarizes the FSP eligibility

parameters in effect in November 1999.

12 This percentage is based on the percentage measured in the Panel Survey of Income

Dynamics (PSID) data.

32

1. Determine Income Eligibility

To be income eligible, the unit’s gross income must not exceed 130 percent of the Federal

poverty guideline and the unit’s net income (gross income less certain deductions) must not

exceed 100 percent of the Federal poverty guideline.13 There are two exceptions to these rules.

First, if the unit contains an elderly or disabled person, it is exempt from the gross income

screen. Second, if the unit contains only persons on public assistance (PA) (e.g. only persons

with TANF, SSI, GA, other welfare, or foster care), the unit is automatically income eligible

regardless of the amount of its income. These PA programs have more restrictive eligibility

guidelines than the FSP, so presumably the unit would already be income eligible.

Gross income is all cash income, including all earned cash income (salary, self-employment,

moonlighting, and, depending on the amount, miscellaneous income and income from family and

friends) and most sources of unearned income, such as TANF, SSI, GA, and Social Security.

Since PA units do not necessarily match the food stamp unit, we distribute the PA income

equally over all members of the respective PA unit. Then, for each food stamp unit, we add each

food stamp member�s pro-rata share of PA income to the food stamp unit’s gross income.

Earned income tax credits, energy assistance, education assistance, and the earnings of high

school students are examples of the kinds of income not included.

PRWORA requires that the income of aliens who have been disqualified from participation

in the FSP be allocated, in part, to the remaining members of the food stamp unit. We pro-rate

both the earned and unearned income of the disqualified alien and return it to the unit. The unit’s

deductions are then determined off the total unit income.

13 The poverty guidelines are based on the official monthly poverty guidelines published by

the U.S. Department of Health and Human Services, which are adjusted each year to account for inflation. These guidelines and other FSP parameters are generally the same for the 48 contiguous states and the District of Columbia and vary slightly for Alaska and Hawaii.

33

After some discussions with FNS and a review of the FSP regulations, we modified

unearned income to include or exclude certain types of income, depending on the amount of the

income. Miscellaneous income and income from friends and family are counted as earned

income when the amount is greater than $10 per month ($30 per quarter) and unearned income

otherwise. Charity income is unearned income when it is more than $100 per month (or $300

per quarter) and a financial asset otherwise. Lump sum retirement is now counted as assets

instead of unearned income.

Net income is gross income less the following five deductions:

• Standard deduction of $134 (continental U.S.), $229 (Alaska), or $189 (Hawaii).

• Earnings deduction equaling 20 percent of earnings, in recognition of taxes and work-related expenses

• Dependent care expense deduction of no more than $200 per dependent under age 2 and $175 per dependent age 2 or older.

• Medical expense deduction equaling sum of the unit’s total medical expenses in excess of $35, as long as these expenses were incurred by elderly (age 60 or older) or disabled persons.

• Child support payment expense deduction equaling the amount of the expense.

• Excess shelter deduction equaling the unit’s shelter expense in excess of 50 percent of the unit’s net income after the previous five deductions are taken. For those units without an elderly or disabled person, this deduction is subject to a cap of $275 (continental U.S.), $478 (Alaska), or $393 (Hawaii). The shelter expense includes the larger of the unit’s reported utility expenses and the standard utility allowance. These utility allowances vary by state and are listed in Tables IV.5 and IV.6.

2. Determine Asset Eligibility

The food stamp unit can have no more than $2,000 in countable assets. If the food stamp

unit contains an elderly person, the limit increases to $3,000. If the food stamp unit contains

only persons on public assistance (SSI, TANF, or GA), the unit is automatically identified as

asset-eligible regardless of the amount of its countable assets. Presumably, these units that

34

contain only persons on public assistance would already be asset-eligible for the FSP, since those

programs have more restrictive asset guidelines than the FSP.

Countable assets include financial and vehicular assets. Most financial and nonfinancial

assets are considered countable. For example, countable financial assets include money in

savings accounts, money markets, certificates of deposit, interest-earning checking accounts,

stock and mutual funds, and money in IRAs and KEOGH accounts (less an early withdrawal

penalty fee). As described earlier, a few income sources can now be counted as assets. Charity

income is considered an asset if it is less than $100 per month. Lump sum retirement and

severance pay are now counted as assets. The assets of noncitizens are counted, too. In contrast,

selected pieces of property such as the principal home, adjacent land, and most household goods

are excluded. The assets of TANF and/or SSI persons are also excluded.

In most instances, assets are counted at their equity value (i.e., value minus debt). One

principal exception is the treatment of vehicular assets. Vehicles used for producing income

(such as a taxi or ice cream truck) or for transporting disabled individuals are not counted.

Vehicles required for work-related travel are valued at the FMV of the vehicle in excess of

$4,650. One additional car per household is valued at the FMV of the vehicle in excess of

$4,650. All other vehicles owned by members of the food stamp unit are valued at the larger of

(1) the FMV of the vehicle in excess of $4,650 or (2) the equity value. SIPP data do not identify

which vehicles are used for work-related travel. Therefore, we had to infer which vehicles were

required for work-related travel by determining how many commuters were present in the

household. We defined commuters as persons age 16 and over who have wages. For each

commuter found in the household, we select a vehicle (in order of highest equity to lowest

equity) and value it at the FMV of the vehicle in excess of $4,650.

35

The FMV of a vehicle refers to the wholesale FMV. Wholesale refers to the average trade-

in value for a vehicle. SIPP data, however, include the retail FMV. Retail refers not to the

trade-in value but to the price the owner could garner selling the vehicle him/herself on the open

market. To estimate the wholesale FMV, we used two sets of two regression equations (Table

IV.7). The first set includes the age of the vehicle, when it is present on the file, while the

second set does not. Both sets have two equations, one is used when the retail value is greater

than or equal to $1,200, and the other is used when the retail value is under $1,200. We assumed

the wholesale equity value is simply the wholesale FMV less the amount owed on the vehicle.

3. Determine Food Stamp Benefit

If the unit is income and asset eligible, it must also be eligible for a food stamp benefit to be

considered officially eligible for the FSP. The food stamp benefit equals the maximum food

stamp benefit less 30 percent of the unit’s net income. The maximum benefit is 100 percent of

the Thrifty Food Plan (TFP), which represents the United States Department of Agriculture’s

lowest-cost food plan. Since eligibility is based on federal poverty guidelines and not the TFP,

some units may be eligible for zero benefits. However, units containing one or two persons are

guaranteed a minimum $10 benefit. Larger units are not guaranteed a minimum benefit, so these

units may qualify for zero benefits. When this happens, we consider these units to be technically

ineligible for food stamps, since they cannot possibly participate in the FSP.

C. SELECT PROGRAM PARTICIPANTS

The final step in the food stamp simulation is the selection of FSP participants from the pool

of simulated eligibles. We used an algorithm that selects participants in such a way that the

overall simulated FSP caseload as well as the characteristics of the simulated FSP population

would compare well with FSP administrative data.

36

The selection algorithm contains the following processing steps:

1. Select a set of key characteristics for which simulated participants are supposed to resemble FSP administrative data. As shown in Table IV.8, we selected FSP caseload size, TANF participation, SSI participation, the presence of children in the unit, earnings in the unit, elderly in the unit, unit size, gross income relative to the poverty level, and the FSP benefit size.

2. Tabulate the FSPQC data to get the control totals by the key characteristics for November 1999.

3. Extract eligible food stamp units from the MATH SIPP database.

4. Use a “raking” procedure to classify the eligible food stamp units by the key characteristics and to determine what proportion of each group needs to be selected in order to reach the FSPQC targets.

5. Use a probit maximum likelihood function to estimate the probability of FSP participation for each eligible food stamp unit. This probability function is a function of the key characteristics and whether the unit reported receiving food stamps in the SIPP.

6. Give eligible food stamp unit a propensity score indicating its likelihood of FSP participation. Units reporting FSP always have a higher score than non-reporters within each group of eligible units.

7. Within each group, sort by the propensity score. Starting with the unit with the highest propensity score, select participants until the FSPQC targets have been reached. In the event that more units report FSP participation than are required to meet the target, do not select them to participate. Given the way in which the propensity scores were created, reporting units will be selected to participate before any non-reporting units are selected within a given group.

8. Generate a logit equation of participation based on the simulated participants. This equation will be used to predict participation for newly eligible units during reform simulations.

D. FSP SIMULATION RESULTS

Table IV.9 compares the simulation results to the FSPQC data. The simulated caseload in

the 1999 MATH SIPP model is about 12 percent lower than the caseload in the FSPQC data.

However, we were more interested in simulating units with the characteristics of the FSP than

with matching the absolute numbers. Overall, the characteristics of the MATH SIPP simulated

population closely match those of the FSPQC data characteristics. The difference between the

simulated characteristic and the FSPQC target is less than five percent for most characteristics.

37

Only four characteristics deviate from their target by five to ten percent; and only one

characteristic (average medical deduction) deviates by more than ten percent. The simulated

average medical deduction is $91 compared with $124 in the FSPQC data.

Table IV.10 displays the overall simulation results. The total number of simulated

participants is 6.5 million units and 15.1 million persons. The total of the benefits simulated to

be paid to these participants is approximately $1 billion.

Table IV.11 displays the distribution of eligible and participating food stamp units by gross

income relative to poverty and unit size.

Tables IV.12 and IV.13 display the characteristics of eligible and participating food stamp

units, respectively.

Tables IV.14 and IV.15 display the welfare status of eligible and participating food stamp

units, respectively.

Tables IV.16 and IV.17 display the deductions of eligible and participating food stamp units,

respectively.

38

TABLE IV.1

FSP SPLITTING RULES AND RATES

Split-off Rates

Household Type FSP Splitting Rule 2000 FSPQC

Among FSP Non-

Reporting Households in 1999 MATH

SIPP

Among FSP Participating

Households in 1999 MATH

SIPP TYPE 1: Household contains elderly persons

Split elderly persons, along with their spouse, into separate units.

54%

5%

62%

TYPE 2: Household contains unrelated individual

Split unrelated subfamilies into separate units. Split unrelated individuals into separate unit.

2a: Receives TANF 80% 30% 57% 2b: Does not receive TANF 67% 5% 58%

TYPE 3: Household contains children

Split the parents/ caretakers and their children from the other relatives. Split related subfamilies into separate units.

3a: Receives TANF 22% 5% 19% 3b: Does not receive TANF 16% 5% 12%

TYPE 4: Household contains only related adults, no elderly, and no children.

Split each person into a separate unit, but keep spouses together

40% 5% 43%

SOURCE: FY 1999 FSPQC Database, parameters for the 1999 MATH SIPP model, and tabulations of the

1999 MATH SIPP database

39

TABLE IV.2

PERCENT OF ABLE-BODIED ADULTS EXEMPT FROM TIME LIMITS

Percent Eligible by Reason For Eligibility State

Living in Waiver Area

Have Not Exceeded Time Limitsa In E & T Program

Received Exemption

Total Percent Eligible for the

FSPa Alabama 27.0 61.7 / 71.9 5.9 2.5 74.4 / 81.2 Alaska 56.5 61.7 / 71.9 0.0 1.2 83.6 / 87.9 Arizona 23.6 61.7 / 71.9 0.2 7.3 73.0 / 80.2 Arkansas 44.5 61.7 / 71.9 0.0 3.6 79.5 / 85.0 California 4.7 61.7 / 71.9 64.5 0.9 87.2 / 90.6 Colorado 2.7 61.7 / 71.9 52.7 3.1 82.9 / 87.5 Connecticut 63.5 61.7 / 71.9 0.2 2.5 86.4 / 90.0 Delaware 0.0 61.7 / 71.9 0.3 0.0 61.9 / 72.0 District of 100.0 61.7 / 71.9 0.0 0.0 100.0 Florida 46.4 61.7 / 71.9 1.0 5.4 80.8 / 85.9 Georgia 23.6 61.7 / 71.9 3.7 0.8 72.1 / 79.5 Hawaii 41.2 61.7 / 71.9 0.2 1.2 77.8 / 83.7 Idaho 0.0 61.7 / 71.9 0.0 5.7 63.9 / 73.5 Illinois 44.9 61.7 / 71.9 0.0 8.6 80.8 / 85.9 Indiana 9.1 61.7 / 71.9 5.2 3.6 68.2 / 76.7 Iowa 0.0 61.7 / 71.9 3.2 12.1 67.4 / 76.1 Kansas 0.0 61.7 / 71.9 0.0 24.7 71.2 / 78.9 Kentucky 47.2 61.7 / 71.9 1.5 4.9 81.1 / 86.1 Louisiana 56.4 61.7 / 71.9 0.3 6.9 84.5 / 88.6 Maine 35.5 61.7 / 71.9 0.6 16.0 79.4 / 84.9 Maryland 49.7 61.7 / 71.9 0.6 5.6 81.9 / 86.8 Massachusetts 0.0 61.7 / 71.9 5.7 16.6 69.9 / 77.9 Michigan 0.0 61.7 / 71.9 1.6 0.0 62.4 / 72.4 Minnesota 9.4 61.7 / 71.9 5.3 8.7 70.0 / 78.0 Mississippi 0.0 61.7 / 71.9 18.1 25.6 76.7 / 82.9 Missouri 31.7 61.7 / 71.9 0.4 0.8 74.2 / 81.1 Montana 13.6 61.7 / 71.9 26.8 0.0 75.8 / 82.2 Nebraska 0.0 61.7 / 71.9 2.2 10.4 66.5 / 75.4 Nevada 7.2 61.7 / 71.9 0.0 0.1 64.5 / 74.0 New Hampshire 6.1 61.7 / 71.9 59.4 22.9 88.7 / 91.7 New Jersey 33.4 61.7 / 71.9 59.3 1.8 89.8 / 92.5 New Mexico 52.2 61.7 / 71.9 0.1 0.0 81.7 / 86.6 New York 8.5 61.7 / 71.9 49.0 0.9 82.3 / 87.0 North Carolina 0.0 61.7 / 71.9 1.7 32.2 74.5 / 81.3 North Dakota 9.6 61.7 / 71.9 0.5 0.0 65.6 / 74.7 Ohio 0.0 61.7 / 71.9 24.2 0.0 71.0 / 78.7 Oklahoma 0.0 61.7 / 71.9 0.0 0.0 61.7 / 71.9 Oregon 0.0 61.7 / 71.9 2.9 17.3 69.3 / 77.5 Pennsylvania 67.9 61.7 / 71.9 0.6 1.3 88.0 / 91.2 Rhode Island 59.6 61.7 / 71.9 0.0 0.0 84.5 / 88.7 South Carolina 34.3 61.7 / 71.9 14.8 7.6 80.2 / 85.5 South Dakota 14.2 61.7 / 71.9 3.1 0.0 68.2 / 76.6 Tennessee 29.1 61.7 / 71.9 9.7 0.0 75.5 / 82.0 Texas 23.7 61.7 / 71.9 3.9 0.0 71.9 / 79.4 Utah 1.7 61.7 / 71.9 9.7 0.8 66.3 / 75.3 Vermont 0.0 61.7 / 71.9 5.4 7.8 66.6 / 75.5 Virginia 12.5 61.7 / 71.9 0.9 0.0 66.8 / 75.7 Washington 38.3 61.7 / 71.9 8.9 9.3 80.5 / 85.7 West Virginia 60.4 61.7 / 71.9 1.9 0.2 85.2 / 89.1 Wisconsin 0.0 61.7 / 71.9 8.6 0.0 65.0 / 74.3 Wyoming 0.0 61.7 / 71.9 0.0 0.0 61.7 / 71.9

a The lower number is for individuals in households reporting food stamp receipt in the SIPP. The higher number is for individuals in households not reporting food stamp receipt in the SIPP.

40

TABLE IV.3

LIST OF REFUGEE COUNTRIES

SOURCE: U.S. Immigration and Naturalization Service, Statistical Yearbook of the

Immigration and Naturalization Service, 1999, U.S. Government Printing Office: Washington, DC 2002.

Country

Cuba

Ethiopia

Haiti

Iran

Iraq

Laos

USSR

Vietnam

Yugoslavia

TA

BL

E I

V.4

FSP

EL

IGIB

ILIT

Y P

AR

AM

ET

ER

S FO

R N

OV

EM

BE

R 1

999

BE

NM

AX

M

axim

um F

ood

Stam

p B

enef

its

G

RSS

CR

N

Gro

ss I

ncom

e Sc

reen

NE

TSC

RN

N

et I

ncom

e Sc

reen

BE

NM

IN

Min

imum

B

onus

H

ouse

hold

Siz

e

48 +

D.C

. A

lask

a H

awai

i

48 +

D.C

. A

lask

a H

awai

i

48 +

D.C

. A

lask

a H

awai

i

1

$127

$1

58

$199

$893

$1

,118

$1

,029

$687

$8

60

$791

10

2

234

290

365

1,

199

1,50

0 1,

380

92

2 1,

154

1,06

1 10

3

335

415

523

1,

504

1,88

1 1,

731

1,

157

1,44

7 1,

331

0

4

426

528

664

1,

810

2,26

2 2,

082

1,

392

1,74

0 1,

601

0

5

506

627

789

2,

115

2,64

4 2,

433

1,

627

2,03

4 1,

871

0

6

607

752

947

2,

421

3,02

5 2,

784

1,

862

2,32

7 2,

141

0

7

671

831

1,04

7

2,72

6 3,

406

3,13

5

2,09

7 2,

620

2,41

1 0

8

767

950

1,19

6

3,03

2 3,

788

3,48

6

2,33

2 2,

914

2,68

1 0

Eac

h A

ddt’

l Pe

rson

96

119

150

30

6 38

2 35

1

235

294

270

0

41

42

TA

BL

E I

V.4

(co

ntin

ued)

48

+ D

.C.

Ala

ska

Haw

aii

ASS

ET

LIM

--

Ass

et L

imit

s

N

o E

lder

ly in

Uni

t $2

,000

$2

,000

$2

,000

E

lder

ly in

Uni

t $3

,000

$3

,000

$3

,000

BR

R -

- B

enef

it R

educ

tion

Rat

e 30

%

30%

30

%

CA

RS

-- V

ehic

le T

hres

hold

$4

,650

$4

,650

$4

,650

DE

PMA

X--

Dep

ende

nt C

are

Ded

uctio

n C

ap

Per

Chi

ld U

nder

Age

2

$200

$2

00

$200

Per

Chi

ld O

ver

Age

1

$175

$1

75

$175

EA

RN

MA

X -

- E

arni

ngs

Ded

uctio

n C

ap

$99,

999

$99,

999

$99,

999

EX

_PR

A -

- Pe

rcen

t of

Non

citiz

ens

Mee

ting

Wor

k H

isto

ry a

nd V

eter

an R

equi

rem

ents

17

.8%

17

.8%

17

.8%

MD

AG

E –

Min

imum

Age

for

Ded

uctib

le M

edic

al E

xpen

ses

60

60

60

MD

TH

RE

SH -

- M

edic

al T

hres

hold

$3

5 $3

5 $3

5

SHE

LC

AP

-- E

xces

s Sh

elte

r D

educ

tion

Cap

$2

75

$478

$3

93

SHL

TR

PCT

--

Prop

orti

on o

f ne

t inc

ome

abov

e w

hich

she

lter

cost

s ar

e de

duct

ible

50

%

50%

50

%

STU

DA

GE

--

Max

imum

Age

of

Post

seco

ndar

y St

uden

t 17

17

17

SO

UR

CE:

Unp

ublis

hed

data

fro

m U

.S. D

epar

tmen

t of

Agr

icul

ture

, Foo

d an

d N

utri

tion

Serv

ice.

43

TABLE IV.5

STANDARD UTILITY ALLOWANCE IN NOVEMBER 1997 FOR STATES THAT DO NOT VARY THE ALLOWANCE BY HOUSEHOLD SIZE

State or MSA Name Standard Utility Allowance Alabama

$222

Alaska 228 Arizona 237 Arkansas 172 California 183 Colorado 198 Connecticut 262 Delaware 255 District of Columbia 209 Florida 208 Georgia 232 Idaho 171 Illinois 249 Indiana 283 Iowa 254 Kansas 211 Louisiana 221 Maine 368 Maryland 216 Massachusetts 354 Michigan 267 Minnesota 270 Mississippi 177 Missouri 221 Montana 225 Nebraska 235 Nevada 192 New Hampshire 367 New Jersey 226 New Mexico 164 New York

New York City 465 Other, NY* 408

North Dakota 316 Ohio 267 Oklahoma 176 Oregon 216 Pennsylvania 310 Rhode Island 297 South Carolina 176 South Dakota 329 Texas 171 Utah 170 Vermont 365 Virginia 207 Washington 211 West Virginia 246 Wisconsin 228 Wyoming 270

SOURCE: Unpublished data from U.S. Department of Agriculture, Food and Nutrition Service.

44

TABLE IV.6

STANDARD UTILITY ALLOWANCE IN NOVEMBER 1997 FOR STATES THAT VARY THE ALLOWANCE BY HOUSEHOLD SIZE

Household Size

State Name 1 2 3 4 5 6 7 8 9 10+

Hawaii 153 170 187 204 221 237 251 251 251 251

Kentucky 208 208 236 236 253 253 253 253 253 253

North Carolina

152 175 203 203 240 240 240 240 240 240

Tennessee 210 219 226 226 233 240 247 254 262 270

SOURCE: Unpublished data from U.S. Department of Agriculture, Food and Nutrition Service.

45

TABLE IV.7

REGRESSION EQUATIONS FOR IMPUTING WHOLESALE FMV FOR VEHICLES

�

Variable Definitions

WHOLESALE = Imputed amount RETAIL = Retail FMV as reported in SIPP CAR_AGE = Year made as reported in SIPP minus date of interview ABOVE900 = Dummy variable, which is 1 if the retail value is more than $900, 0 otherwise

Regression Equations

If year of car is available (reported in SIPP):

(1) If retail value is equal to or greater then $1,200: WHOLESALE = -615.527466 - (18.398013*CAR_AGE) + (0.875601*RETAIL) +

(0.000001029*RETAIL*RETAIL) – (0.002177*CAR_AGE*RETAIL) (2) If retail value is less than $1,200: WHOLESALE = 225.0 + 25.0*(ABOVE900)

If year of car is not available (not reported in SIPP):

(1) If retail value is equal to or greater then $1,200: WHOLESALE = -864.252385 + (0.890871*RETAIL) + (0.000000698*RETAIL*RETAIL) (2) If retail value is less than $1,200: WHOLESALE = 225.0 + 25.0*(ABOVE900)

46

TABLE IV.8

1999 FSPQC TARGETS FOR THE NOVEMBER 1999 MATH SIPP

Characteristic FSPQC Targeta

Total FSP Caseload 7,402,220

FSP units with TANF 1,943,171

FSP units with SSI 2,345,208

FSP units with Children 3,991,490

FSP units with Earnings Deduction > 0% and < 10% of the poverty level 821,763

FSP units with Earnings Deduction > 10% and < 20% of the poverty level 962,785

FSP units with Earnings Deduction > 20% of the poverty level 232,754

FSP units with Elderly 1,570,146

FSP unit size = 2 1,481,594

FSP unit size > 3 2,780,135

FSP gross income > 50% and < 100% of the poverty level 4,161,207

FSP gross income > 100% of the poverty level 807,957

FSP Benefit�> 50% and less than 100% of the maximum benefit 2,892,826

FSP Benefit�> 100% of the maximum benefit 1,446,132

FSP units with noncitizens 469,919

FSP units with medical deduction > 0 305,796

SOURCE: 1999 FSPQC Database aThe FSPQC target numbers are based on a three-month average (October, November, December 1999), excluding Guam and the Virgin Islands.

47

TABLE IV.9

COMPARISON OF FSPQC DATA AND MATH SIPP MODEL FOR NOVEMBER 1999

Characteristic of Participating FSP Unit FY 2000

FSPQC Dataa

Simulated Participants in

1999 SIPP Model Difference Total Participants (in 1000s)

Total Units 7,335 6,472 -12% Total Persons 17,091 15,116 -12% Total Benefits

1,159,008 1,047,919 -10%

Unit Size Distribution 1 43% 45% 2 points 2 20% 19% -1 point 3-5 33% 32% -1 point 6+

4% 4% 0 points

Gross Income Relative to Poverty 0 - 50% 33% 33% 0 points >50 - 100% 56% 57% 1 point > 100-130% 10% 8% -2 points >131%

1% 3% 2 points

Income Sources With Earnings 27% 27% 0 points With TANF 26% 22% -4 points With SSI 32% 32% 0 points With GA 5% 3% -2 points With Public Assistance

57% 51% -6 points

Average Benefit/Income Amounts Avg Gross Income $620 $619 0% Avg Net Income $355 $356 0% Avg Benefit (per Unit)

$158 $162 3%

Deductions Earnings Deduction $150 $150 0% Percent with Deduction

27% 27% 0 points

Dependent Care Deduction $125 $136 9% Percent with Deduction

4% 4% 0 points

Medical Deduction $124 $91 -27% Percent with Deduction

4% 4% 0 points

Shelter Deduction $186 $190 2% Percent with Deduction

59% 64% 5 points

SOURCE: 2000 FSPQC Data and 1999 MATH SIPP Model.

aThe 2000 FSPQC results are based on a three-month average (October, November, December) and do not include Guam and the Virgin Islands.

TA

BL

E I

V.1

0

SUM

MA

RY

OF

FOO

D S

TA

MP

PRO

GR

AM

E

ligi

bles

Part

icip

ants

Ben

efit

s

Plan

No.

U

nits

%

Chg

Pers

ons

% C

hg

U

nits

%

Chg

Pers

ons

% C

hg

U

nits

%

Chg

Wei

ghte

d

Bas

elaw

14

,224

,215

N

A

30

,684

,996

N

A

6,

471,

640

NA

15,1

16,2

93

NA

1,04

7,91

8,74

5 N

A

U

nwei

ghte

d

B

asel

aw

4,04

4 N

A

8,

560

N

A

1,

851

NA

4,20

3

NA

285,

200

NA

48

TA

BL

E I

V.1

1

DIS

TR

IBU

TIO

N O

F E

LIG

IBL

E A

ND

PA

RT

ICIP

AT

ING

FO

OD

ST

AM

P U

NIT

S B

Y G

RO

SS I

NC

OM

E R

EL

AT

IVE

TO

PO

VE

RT

Y A

ND

UN

IT S

IZE

--B

ASE

LA

W

N

umbe

r of

Uni

ts b

y U

nit S

ize

1 2

3 4

5 6

Tot

al U

nits

% o

f T

otal

U

nits

T

otal

Ben

efits

(d

olla

rs)

Elig

ible

s

<=

0.0

77

9,11

3 25

9,34

0 76

,669

99

,562

39

,727

20

,914

1,

275,

325

9.00

%

261,

943,

637

1-50

%

566,

685

517,

902

403,

231

367,

764

229,

547

103,

963

2,18

9,09

1 15

.40%

61

2,64

6,43

8 51

-100

%

3,49

4,66

4 1,

247,

726

730,

243

567,

799

361,

476

237,

126

6,63

9,03

4 46

.70%

80

1,55

5,29

4 10

1-13

0%

1,50

0,46

0 71

6,43

4 38

1,80

5 32

8,62

7 21

6,20

4 12

4,99

9 3,

268,

527

23.0

0%

193,

146,

891

131+

60

8,63

4 21

4,96

0 12

,119

14

,936

0

1,58

8 85

2,23

8 6.

00%

25

,521

,541

T

otal

Uni

ts

6,94

9,55

5 2,

956,

362

1,60

4,06

7 1,

378,

688

846,

954

488,

590

14,2

24,2

15

100.

00%

1,

894,

813,

801

Tot

al P

erso

ns

6,94

9,55

5 5,

912,

724

4,81

2,20

1 5,

514,

750

4,23

4,76

8 3,

260,

998

30,6

84,9

96

% o

f T

otal

Uni

ts

48.9

20

.8

11.3

9.

7 6

3.4

100.

00%

Par

tici

pant

s

<=

0.0

39

1,81

8 10

4,57

7 38

,345

42

,786

31

,847

18

,621

62

7,99

4 9.

70%

13

3,37

7,45

6 1-

50%

24

3,59

7 31

5,35

7 34

5,69

6 31

8,25

1 17

8,13

9 91

,830

1,

492,

868

23.1

0%

453,

339,

698

51-1

00%

1,

966,

946

622,

714

418,

430

352,

516

193,

418

140,

895

3,69

4,91

8 57

.10%

43

2,86

4,37

0 10

1-13

0%

196,

836

108,

227

70,3

92

51,1

74

46,3

81

20,3

26

493,

335

7.60

%

26,2

57,2

37

131+

10

8,75

2 44

,980

3,

108

5,68

5 0

0 16

2,52

5 2.

50%

2,

079,

985

Tot

al U

nits

2,

907,

948

1,19

5,85

4 87

5,97

1 77

0,41

1 44

9,78

5 27

1,67

1 6,

471,

640

100.

00%

1,

047,

918,

745

Tot

al P

erso

ns

2,90

7,94

8 2,

391,

709

2,62

7,91

4 3,

081,

643

2,24

8,92

4 1,

858,

155

15,1

16,2

93

% o

f T

otal

Uni

ts

44.9

18

.5

13.5

11

.9

7 4.

2 10

0.00

%

49

50

TABLE IV.12

CHARACTERISTICS OF ELIGIBLE FOOD STAMP UNITS CHARACTERISTIC Baselaw

Units with:

Earners 5,392,887 % Chg from Baselaw N/A % Of Total Units 37.9

Elderly 4,802,235 % Chg from Baselaw N/A % Of Total Units 33.8

Elderly or Disabled 6,872,282 % Chg from Baselaw N/A % Of Total Units 48.3

Children 6,034,015 % Chg from Baselaw N/A % Of Total Units 42.4

Children 5 to 17 4,973,134 % Chg from Baselaw N/A % Of Total Units 35.0

Zero net income 3,152,263 % Chg from Baselaw N/A % Of Total Units 22.2

Minimum benefit 2,622,110 % Chg from Baselaw N/A % Of Total Units 18.4

Average Income Amounts ($): Avg Monthly Gross Income 729 % Chg from Baselaw N/A Avg Monthly Net Income 409 % Chg from Baselaw N/A

Total Units 14,224,215 % Chg from Baselaw N/A Total Benefits ($) 1,894,813,801 % Chg from Baselaw N/A

51

TABLE IV.13

CHARACTERISTICS OF PARTICIPATING FOOD STAMP UNITS CHARACTERISTIC Baselaw

Units with:

Earners 1,747,572 % Chg from Baselaw N/A % Of Total Units 27.0

Elderly 1,403,334 % Chg from Baselaw N/A % Of Total Units 21.7

Elderly or Disabled 2,930,331 % Chg from Baselaw N/A % Of Total Units 45.3

Children 3,364,737 % Chg from Baselaw N/A % Of Total Units 52.0

Children 5 to 17 2,770,710 % Chg from Baselaw N/A % Of Total Units 42.8

Zero net income 1,288,184 % Chg from Baselaw N/A % Of Total Units 19.9

Minimum benefit 774,230 % Chg from Baselaw N/A % Of Total Units 12.0

Average Income Amounts ($): Avg Monthly Gross Income 619 % Chg from Baselaw N/A Avg Monthly Net Income 356 % Chg from Baselaw N/A

Total Units 6,471,640 % Chg from Baselaw N/A Total Benefits ($) 1,047,918,745 % Chg from Baselaw N/A

52

TABLE IV.14

WELFARE STATUS OF ELIGIBLE FOOD STAMP UNITS WELFARE STATUS Baselaw

Units with:

No TANF, SSI, or GA 9,938,815 % Chg from Baselaw N/A % Of Total Units 69.9 Only TANF 1,092,133 % Chg from Baselaw N/A % Of Total Units 7.7 Only GA 134,503 % Chg from Baselaw N/A % Of Total Units 0.9 Only SSI 2,676,850 % Chg from Baselaw N/A % Of Total Units 18.8 Only TANF and GA 32,837 % Chg from Baselaw N/A % Of Total Units 0.2 Only TANF and SSI 286,800 % Chg from Baselaw N/A % Of Total Units 2.0 Only GA and SSI 53,943 % Chg from Baselaw N/A % Of Total Units 0.4 TANF, SSI, and GA 8,332 % Chg from Baselaw N/A % Of Total Units 0.1 Total Units 14,224,215 % Chg from Baselaw N/A % Of Total Units 100.0

Units with:

Any TANF 1,420,103 % Chg from Baselaw N/A % Of Total Units 10.0 Any GA 229,616 % Chg from Baselaw N/A % Of Total Units 1.6 Any SSI 3,025,926 % Chg from Baselaw N/A % Of Total Units 21.3 Pure PA* 3,318,074 % Chg from Baselaw N/A % Of Total Units 23.3 *Pure PA means all persons in the unit receive TANF, SSI, and/or GA (PUREPA = 3)

53

TABLE IV.15

WELFARE STATUS OF PARTICIPATING UNITS WELFARE STATUS Baselaw

Units with:

No TANF, SSI, or GA 3,164,939 % Chg from Baselaw N/A % Of Total Units 48.9 Only TANF 1,092,133 % Chg from Baselaw N/A % Of Total Units 16.9 Only GA 110,867 % Chg from Baselaw N/A % Of Total Units 1.7 Only SSI 1,735,753 % Chg from Baselaw N/A % Of Total Units 26.8 Only TANF and GA 32,837 % Chg from Baselaw N/A % Of Total Units 0.5 Only TANF and SSI 286,800 % Chg from Baselaw N/A % Of Total Units 4.4 Only GA and SSI 39,979 % Chg from Baselaw N/A % Of Total Units 0.6 TANF, SSI, and GA 8,332 % Chg from Baselaw N/A % Of Total Units 0.1 Total Units 6,471,640 % Chg from Baselaw N/A % Of Total Units 100.0

Units with:

Any TANF 1,420,103 % Chg from Baselaw N/A % Of Total Units 21.9 Any GA 192,016 % Chg from Baselaw N/A % Of Total Units 3.0 Any SSI 2,070,864 % Chg from Baselaw N/A % Of Total Units 32.0 Pure PA* 2,610,006 % Chg from Baselaw N/A % Of Total Units 40.3 *Pure PA means all persons in the unit receive TANF, SSI, and/or GA (PUREPA = 3)

54

TABLE IV.16

DEDUCTIONS OF ELIGIBLE FOOD STAMP UNITS

Baselaw

All Units Units with Deduction

Avg Standard 134.4 134.4 % Chg from Baselaw N/A N/A % Units with Deduct 100.0

Avg Earnings 66.2 174.8 % Chg from Baselaw N/A N/A % Units with Deduct 37.9

Avg Medical 26.7 165.7 % Chg from Baselaw N/A N/A % Units with Deduct 16.1

Avg Dependent Care 4.5 138.8 % Chg from Baselaw N/A N/A % Units with Deduct 3.3

Avg Shelter 141.6 220.5 % Chg from Baselaw N/A N/A % Units with Deduct 64.2

Avg Total 376.9 376.9 % Chg from Baselaw N/A N/A % Units with Deduct 100.0

55

TABLE IV.17

DEDUCTIONS OF PARTICIPATING FOOD STAMP UNITS

Baselaw

All Units Units with Deduction

Avg Standard 134.5 134.5 % Chg from Baselaw N/A N/A % Units with Deduct 100.0

Avg Earnings 40.4 150.0 % Chg from Baselaw N/A N/A % Units with Deduct 26.9

Avg Medical 3.2 91.2 % Chg from Baselaw N/A N/A % Units with Deduct 3.5

Avg Dependent Care 4.8 135.9 % Chg from Baselaw N/A N/A % Units with Deduct 3.5

Avg Shelter 122.2 190.3 % Chg from Baselaw N/A N/A % Units with Deduct 64.2

Avg Total 306.7 306.7 % Chg from Baselaw N/A N/A % Units with Deduct 100.0

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57

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