SCHOOL MEAL 

PROGRAMS

Implications of 

Adjusting Income 

Eligibility Thresholds 

and Reimbursement 

Rates by Geographic 

Differences

Report to Congressional Requesters

July 2014

GAO-14-557

United States Government Accountability Office
United States Government Accountability Office

Highlights of GAO-14-557, a report to 

congressional requesters

July 2014

SCHOOL MEAL PROGRAMS

Implications of Adjusting Income Eligibility 

Thresholds and Reimbursement Rates by Geographic 

Differences

Why GAO Did This Study

In fiscal year 2013, 30.7 million 

children participated in the National 

School Lunch Program and 13.2 

million children participated in the 

School Breakfast Program, partly 

funded by $14.6 billion from the USDA. 

The majority of these children came 

from low-income families and received 

school meals free or at a reducedprice. Income eligibility and school 

reimbursement rates for school meals 

are federally set and do not consider 

geographic differences in the cost of 

living (except for Alaska and Hawaii).

GAO was asked to explore the 

potential to account for such 

differences through a variety of 

measures and cost data. 

This report, therefore, looks at a variety 

of methods by which to identify 

geographic differences in living costs 

and the potential for using them to 

adjust (1) income eligibility thresholds, 

and (2) reimbursement rates for 

schools. It also examines the extent to 

which states and localities can make 

adjustments for geographic differences 

in costs by using existing program 

rules. GAO reviewed relevant laws, 

regulations, and literature; analyzed 

available data sources and methods; 

and interviewed knowledgeable 

experts.

What GAO Recommends

GAO is not making any

recommendations.

What GAO Found

There are a number of measures by which income thresholds for the U.S. 

Department of Agriculture (USDA) school meal programs could be adjusted to 

account for geographic differences in the cost of living; doing so would likely lead 

to shifts in eligibility and program costs. For example, the Supplemental Poverty 

Measure or Regional Price Parities could be used to adjust for geographic price 

differences; each could result in fewer children qualifying for assistance in the 

South and Midwest and more children qualifying in the Northeast (see figure 

below). In general, the effects of any such cost-of-living adjustment are difficult to 

predict and would vary depending on their implementation, such as whether they 

were applied state-wide or at the sub-state level, or whether children were kept 

from losing eligibility. Overall program costs could increase if more children 

participated. 

Example of Potential Effect of a Geographic Cost Adjustment on School Meal 

Eligibility Thresholds Using 2006-2010 Data

Although the cost of delivering school meals varies by geographic region, the 

usefulness of available cost data is unclear for making such adjustments. Neither 

the Regional Price Parities for food services and goods or the U.S. Bureau of 

Labor Statistics’ wage data account for some drivers of variation in costs, such 

as economies of scale. In addition, they may not reflect the characteristics of the 

food and labor components of individual school districts. 

The national school lunch and breakfast program rules allow states to expand 

eligibility for free meals and increase reimbursements to schools using federal or 

state funds. Some schools have expanded eligibility by providing free meals to all 

students under federal program rules. At the same time, some states use state 

funds to provide additional per meal reimbursements to schools for meals 

served. However, no state has used these flexibilities to adjust for within state 

geographic differences.

View GAO-14-557. For more information, 

contact Kay Brown at (202) 512-7215 or 

brownke@gao.gov.
Page i GAO-14-557 School Meal Programs

Letter 1

Background 4

Existing Data and Methods for Adjusting for Geographic Variations 

Could Lead to Shifts in Eligibility and Might Increase Program 

Costs 10

The Cost of Delivering School Meals Varies by Geographic 

Region, but the Usefulness of Existing Data to Adjust 

Reimbursements for These Differences Is Unclear 24

Existing Federal and State Policies Offer Eligibility and 

Reimbursement Flexibility 32

Concluding Observations 36

Agency Comments 37

Appendix I Scope and Methodology 41

Appendix II GAO Contact and Staff Acknowledgments 49

Tables

Table 1: USDA Per-Meal Reimbursement Rates for the National 

School Lunch Program and School Breakfast Program in 

the Contiguous States, School Year 2013-2014 6

Table 2: Data Sources That Measure Geographic Variation in the 

Cost of Living 8

Table 3: Data Sources That Measure Geographic Variation in 

Costs of Labor 9

Table 4: Factors That Could Affect the Outcome of Geographic 

Adjustments 22

Table 5: Critical Considerations for Evaluation of Cost of Living 

Data Sources and Methods 42

Table 6: Cost of Living Methods Summarized Along Five of Six 

Critical Considerations 42

Table 7: Summary of Methodology Evaluation for Selected 

Methods 45

Table 8: Regional Price Parities (RPP) and Supplemental Poverty 

Measure (SPM) Data for Illinois 47

Contents
Page ii GAO-14-557 School Meal Programs

Figures

Figure 1: Example of Percent Change in Eligibility Threshold for 

Free and Reduced-Price Meals Resulting from StateLevel Supplemental Poverty Measure Adjustment, 2006-

2010 17

Figure 2: Example of Percent Change in Eligibility Threshold for 

Free and Reduced-Price Meals Resulting from StateLevel Regional Price Parities Adjustment, 2006-2010 18

Figure 3: Example of Changes to the 2013-2014 School Free 

Lunch $2.93 Reimbursement Rate by State Using Two 

Methods (Sorted by the Regional Price Parities Food 

Service Index) 30

Abbreviations 

ACS American Community Survey

BEA Bureau of Economic Analysis

BLS Bureau of Labor Statistics

CE Consumer Expenditure Survey

CEP Community Eligibility Provision

CPI Consumer Price Index

ERS Economic Research Service

FMR Fair Market Rents

FNS Food and Nutrition Service

HHS Department of Health and Human Services

MSA metropolitan statistical area

RPP Regional Price Parities

SNAP Supplemental Nutrition Assistance Program

SPM Supplemental Poverty Measure

TANF Temporary Assistance for Needy Families

USDA U.S. Department of Agriculture

This is a work of the U.S. government and is not subject to copyright protection in the 

United States. The published product may be reproduced and distributed in its entirety 

without further permission from GAO. However, because this work may contain 

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necessary if you wish to reproduce this material separately.
Page 1 GAO-14-557 School Meal Programs

441 G St. N.W.

Washington, DC 20548

July 8, 2014

Congressional Requesters

During fiscal year 2013, about 30.7 million children participated in the 

National School Lunch Program and more than 13.2 million participated in 

the School Breakfast Program across every state, the District of 

Columbia, Guam, Puerto Rico, and the Virgin Islands, as well as children 

of the U.S. Department of Defense armed forces personnel attending 

schools overseas. Both of these programs are administered by the U.S. 

Department of Agriculture’s (USDA) Food and Nutrition Service (FNS) 

through state agencies that oversee local entities that provide school 

meals. The federal government provides cash reimbursements for each 

meal that meets nutritional requirements that is served at schools that 

participate in the lunch and breakfast programs. In fiscal year 2013, 

USDA spent about $14.6 billion on these programs. Students who 

participate in these programs may qualify for either paid, free, or reducedprice meals depending on their household income and household size. In 

the 2013-2014 school year, for example, a child who was part of a threeperson household qualified for free meals if the child’s annual household 

income did not exceed $25,389. It does not generally matter whether this 

household resided in a high-cost metropolitan area or a lower-cost rural 

area because the income eligibility thresholds for free and reduced-price 

meals are uniform throughout the country—with the exception of Alaska 

and Hawaii, where they are higher. Similarly, except in Alaska and 

Hawaii, USDA’s reimbursement rates for meals served are the same for 

all schools, regardless of where the schools are located. The standard 

USDA reimbursement rate for the 2013-2014 school year was $2.93 for a 

school lunch served free to a qualifying child.

Because the cost of housing, transportation, food, and other necessities 

can vary by region or locality, concerns have been raised that some 

children who are in need of free or reduced-price school meals may not 

qualify for them because the current income eligibility measure does not 

recognize that households in high-cost areas may need higher incomes to 

meet their basic needs. Questions have also been raised regarding 

whether some school districts can afford the same quality of food as other 

school districts because USDA reimbursement rates do not adjust for 

geographic differences in food and labor costs. To better understand the 

issues related to making geographic adjustments for the school meal 

programs, you asked us to examine both eligibility determination and 

meal reimbursements.
Page 2 GAO-14-557 School Meal Programs

This report addresses the following questions:

1. What existing data and methods could be used to adjust USDA 

income eligibility thresholds for school meals so that they account for 

variations in the cost of living, and what are the implications of making 

such adjustments?

2. What existing data and methods could be used to adjust USDA 

reimbursement rates so that they account for variations in food, labor, 

and other costs, and what are the implications for making such 

adjustments?

3. To what extent can states and localities make adjustments to eligibility 

and reimbursement rates for school meals?

To answer these questions, we reviewed relevant federal laws and 

regulations, analyzed available data sources and methods, reviewed 

relevant literature, and interviewed knowledgeable agency officials and 

experts. Specifically, we interviewed USDA officials from FNS and the 

Economic Research Service (ERS) to learn more about the school meal 

programs, types of data available, and relevant studies. Further, we 

interviewed officials at the Bureau of Economic Analysis (BEA), the 

Bureau of Labor Statistics (BLS), and the Census Bureau regarding the 

availability and appropriateness of various types of data and methods for 

making geographic adjustments to school meals eligibility and 

reimbursement rates. We also spoke with officials at the Department of 

Health and Human Services (HHS) regarding a study they were 

mandated to conduct under the Patient Protection and Affordable Care 

Act which evaluated methods for and effects of making geographic costof-living adjustments to the health care subsidies under the act, as well as 

the Supplemental Nutrition Assistance Program (SNAP), Medicaid, and 

other programs.1

 1 The Urban Institute, Geographic Variation in the Cost of Living: Implications for Poverty 

Guidelines and Program Eligibility, a report prepared at the request of the Department of 

Health and Human Services, June 2013. This study was required by the Patient Protection 

and Affordable Care Act, Pub. L. No. 111-148, § 10105(f), 124 Stat. 119, 907 (2010).

We also spoke with School Nutrition Association 

representatives regarding state and local policy options for expanding 

eligibility and funding for school meals and the extent to which they see 

the need for such adjustments in their districts. Based on our review of 

the economic literature, we developed a list of key considerations to 

evaluate the usefulness and appropriateness of existing data sources to 
Page 3 GAO-14-557 School Meal Programs

adjust for geographic differences in cost of living. Our analysis of existing 

methods was both informed by and consistent with the findings of the 

HHS report, which included the findings of an expert panel. The HHS 

report also included calculations on how geographic cost-of-living 

adjustments would affect poverty guidelines. We used these as part of 

our analysis regarding the implications of geographic adjustments. In 

addition, we explored how geographic cost-of-living adjustments to 

eligibility guidelines might interact with existing USDA policy options. 

For school meal costs, we examined research regarding school meal 

costs, their components, and how costs vary geographically. We then 

evaluated available data sets on food costs and wages and other 

components of meal cost based on how appropriate they were for 

comparison to school food and wage costs. For the purposes of 

illustration, we calculated adjustments to reimbursements using two 

separate indexes, one of which was a composite index we constructed 

from existing data sources. Where we show potential adjustments to 

eligibility income thresholds and reimbursement rates, we do not include 

territories because the indexes we used do not currently cover them.

We evaluated the data and methodology used to create two indexes, the 

Regional Price Parities (RPP) and Supplementary Poverty Measure 

(SPM), and found them to be sufficiently reliable for the purpose of 

providing examples of how state level thresholds might change under one 

application of the RPPs and SPM to school meals. This report discusses 

several implications related to the appropriateness of applying these 

measures to geographic adjustment of school meals.

We conducted this performance audit from July 2013 to July 2014 in 

accordance with generally accepted government auditing standards. 

Those standards require that we plan and perform the audit to obtain 

sufficient and appropriate evidence to provide a reasonable basis for our 

findings and conclusions based on our audit objectives. We believe that 

the evidence obtained provides a reasonable basis for our findings and 

conclusions based on our audit objectives.]
Page 4 GAO-14-557 School Meal Programs

The National School Lunch Program was established in 1946 and the 

School Breakfast Program was established in 1975 to provide nutritionally 

balanced, low-cost or free lunches and breakfasts to children in 

participating schools. At the federal level, the programs are administered 

by USDA’s FNS. At the state level, the National School Lunch Program 

and School Breakfast Program are typically administered by state 

educational agencies, which operate the programs through agreements 

with school food authorities, which are the governing bodies that have the 

legal authority to operate the school meal programs in one or more 

schools.2 School food authorities provide the meals and claim 

reimbursements based on counts of meals served.3 Local educational 

agencies are responsible for providing applications, certifying eligibility, 

and verifying eligibility of a sample of children certified as eligible for free 

or reduced-price meals.4

In fiscal year 2013, approximately 70 percent of school lunches and 85 

percent of school breakfasts served went to children who qualified for 

federally-subsidized free or reduced-price meals. School districts 

determine whether children are eligible to receive free or reduced-price 

school meals based on the HHS federal poverty guidelines, which set the 

maximum eligible income by household size.5

 2 Puerto Rico, the U.S. Virgin Islands, Guam, and U.S. Department of Defense overseas 

military bases also participate under the Richard B. Russell National School Lunch Act. 

Entities located in the Northern Mariana Islands and American Samoa receive a USDA 

Nutrition Assistance Program block grant. The block grants provide flexibility to meet 

nutritional needs within broad parameters.

The HHS poverty 

guidelines are a simplified version of the Census Bureau’s federal poverty 

3 To be reimbursable, such meals must meet statutory meal pattern and nutritional 

requirements.

4 For ease of reference, in this report, we use the term “school district” to refer to school 

food authorities and local educational agencies, recognizing that the activities described 

may be the responsibility of one or the other of those entities.

5 We have raised questions about the eligibility determination process and have made 

recommendations on how the process can be improved. See GAO, School-Meals 

Programs: USDA Has Enhanced Controls, but Additional Verification Could Help Ensure 

Legitimate Program Access, GAO-14-262 (Washington, D.C.: May 15, 2014).

Background

Structure of National 

School Lunch and 

Breakfast Programs

Eligibility for Free and 

Reduced-Price Meals
Page 5 GAO-14-557 School Meal Programs

thresholds.6 Children are eligible for free meals if their household’s 

income is less than or equal to 130 percent of the federal poverty 

guideline. A child may also qualify for free meals as categorically eligible 

if the child is homeless, a runaway, foster child, or a migrant as defined in 

law, or if a child is from a household that participates in certain other 

government assistance programs, such as the Supplemental Nutrition 

Assistance Program (SNAP) or Temporary Assistance for Needy Families 

(TANF). Children may be certified as categorically eligible if their 

application indicates they meet such conditions or have someone in the 

household that participates in one of these programs. Children may also 

be directly certified as categorically eligible without the need for an 

application, such as through administrative records matching with other 

government programs. Children are eligible for reduced-price meals if 

their household income is greater than 130 percent but less than or equal 

to 185 percent of the federal poverty guidelines. The HHS 2013 federal 

poverty guideline for a three-person household, for example, was 

$19,530; so for the 2013-2014 school year, a child from a three-person 

household would qualify for free meals if the household income was at or 

below $25,389. That same child would qualify for reduced price meals if 

the household income was between $25,390 and $36,131. Income 

eligibility limits for free and reduced meals are higher in Alaska and 

Hawaii because HHS’s poverty guidelines, upon which USDA bases its 

income eligibility guidelines, are higher in those states.

School districts receive cash reimbursements from USDA through the 

state agency with the amount of the per-meal reimbursement based on 

the type of meal served (lunch or breakfast) and the meal category (free, 

reduced-price, or paid).7

 6 The current Census Bureau poverty thresholds were developed in the 1960s and have 

been adjusted annually for inflation. The poverty thresholds have been subject to 

criticisms such as they do not reflect current family incomes or the current composition of 

family expenditures; they do not account for public assistance benefits; they do not 

account for tax liabilities and some other costs, such as health care; and they do not 

adjust for geographic cost variations. There have been recommendations to address 

concerns with the measure, although the official Census Bureau poverty thresholds 

remain unchanged, except for the annual inflation adjustment.

(See table 1.) The reimbursement schedule is 

7 Schools can charge children no more than $0.40 for a reduced-price meal. Children who 

do not qualify for free or reduced-price meals are categorized as “paid,” since they pay the 

regular price that the school charges for a meal, although schools receive a small subsidy 

even for paid meals.

Federal Reimbursements 

for School Meals
Page 6 GAO-14-557 School Meal Programs

the same for all states, except Alaska and Hawaii, which receive higher 

reimbursement rates.8

Table 1: USDA Per-Meal Reimbursement Rates for the National School Lunch 

Program and School Breakfast Program in the Contiguous States, School Year 

2013-2014

Meal category Lunch Breakfast

Free $2.93 $1.58

Reduced price $2.53 $1.28

Paid $0.28 $0.28

Source: USDA | GAO-14-557.

Note: These rates are the “lower payment level” rates which apply for lunches where less than 60 

percent of lunches served in the school lunch program during the second preceding year were served 

free or at a reduced price and for breakfasts for non-severe need students.

An additional per-lunch reimbursement rate of $0.02 is required to be 

provided if 60 percent or more of a school’s lunches are free or reducedprice.9 In addition, if the school district has been certified as complying 

with federal nutritional requirements, there is a $0.06 reimbursement 

supplement per lunch served.10

State agencies establish the per-meal rates of reimbursement for school 

districts in their states. According to USDA officials, all states currently 

USDA provides additional per-breakfast 

reimbursements if a school is determined to be in severe need, which is 

defined as a school that served 40 percent of school lunches free or at a 

reduced price in the second preceding year. USDA also provides federal 

support for school meal programs through USDA food commodities. Such 

commodities provided the equivalent of an additional $0.2325 per lunch 

served nationwide in school year 2013-2014. According to USDA, the 

federal subsidy for a free meal is intended to cover, across all school 

districts, the average school’s cost of producing a reimbursable meal—

not to cover all costs for all school districts.

 8 The same reimbursement rates that apply to the contiguous states also apply to the 

District of Columbia, Virgin Islands, Puerto Rico, and Guam. In school year 2013-2014, 

the standard reimbursement rate for a free lunch was $2.93 for the contiguous states,

$3.42 in Hawaii, and $4.74 in Alaska.

9 42 U.S.C. § 1753(b)(2).

10 Pub. L. No. 111-296, § 201, 124 Stat. 3183, 3214 (codified at 42 U.S.C. § 

1753(b)(3)(C)).
Page 7 GAO-14-557 School Meal Programs

use the same USDA reimbursement schedule to provide per-meal 

reimbursements to school districts.

Some school districts have chosen to provide universal free meals under 

special provisions, designated as Provision 2, Provision 3, and 

Community Eligibility Provision (CEP).11

USDA reimbursement for schools participating under the special 

provisions is different than for schools under the standard program. Any 

school or school district can elect to participate in Provision 2 or 3. 

However, to elect CEP, schools or school districts must have at least 40 

percent of students certified for free meals without the use of an 

application (for example, those directly certified through SNAP) in the 

prior school year. USDA calculates reimbursements for meals served 

differently under each policy, so in deciding whether to participate, 

schools must consider this, as well as the amount they would have to 

cover for meals not reimbursed by USDA.

Participating schools provide free 

meals to all children, regardless of their household income. Under these 

provisions, schools cover any meal costs not reimbursed by USDA in 

exchange for a reduced administrative burden on school districts for 

processing school meal applications and determining eligibility for free 

and reduced-price meals.

Income eligibility guidelines for most means-tested federal programs have 

historically not varied across the country based on differences in the cost 

of living. Some exceptions include

• federal housing assistance eligibility, which is based on median 

income for a given location; 

• federal training program eligibility, which is based on variations in the 

cost of living across the country; and

• the federal Child Care and Development Fund, which allows states to 

provide child care subsidies to qualifying families with incomes below 

85 percent of state median income.

 11 The Community Eligibility Provision was previously named the Community Eligibility 

Option. 

Universal Free Meal 

Provisions

Geographic Cost-of-Living 

Adjustments in Federal 

Programs and Available 

Data Sources
Page 8 GAO-14-557 School Meal Programs

Despite the common use of standard federal poverty guidelines to 

determine eligibility, several data sources have been developed that show 

geographic variation in the cost of various good and services. (See table 

2.)

Table 2: Data Sources That Measure Geographic Variation in the Cost of Living

Data source Produced by

Cost of Living Index Council for Community and Economic Research

CEO (Carrillo, Early and Olsen) Index Academic researchers Carrillo, Early, and Olsen

Lower Living Standard Income Level Employment and Training Administration, U.S. Department of Labor

Regional Price Parities Bureau of Economic Analysis, U.S. Department of Commerce

Supplemental Poverty Measure U.S. Census Bureau, U.S. Department of Commerce

Self-Sufficiency Standard Center for Women’s Welfare, University of Washington 

Geographic Practice Cost Index Centers for Medicare and Medicaid Services, U.S. Department of Health 

and Human Services

EPI Family Budgets Economic Policy Institute 

Fair Market Rents (FMR) U.S. Department of Housing and Urban Development

Housing and Transportation Affordability Index Brookings Institution

Medicare Hospital Wage Index Centers for Medicare and Medicaid Services, U.S. Department of Health 

and Human Services

Milliman Medical Index Milliman Consulting 

Occupational Pay Relatives Bureau of Labor Statistics, U.S. Department of Labor

Source: GAO | GAO-14-557.

While some of these data sources measure prices of a particular basket 

of goods, services, and, in some cases, wages—some of which may vary 

by state, region, or locality—none includes quality of life measures for 

such things as pollution, crime rates, health system quality, traffic, and 

public amenities. According to the economic literature, conceptually, a 

cost-of-living index should include these quality of life measures. 

Therefore, the data sources listed in table 2 can be thought of as proxy 

measures for the cost of living.

USDA provides the same school meal reimbursement rates for all the 

contiguous states,12

 12 USDA has discretionary authority to adjust reimbursement rates for Alaska, Hawaii, 

Guam, American Samoa, Puerto Rico, the U.S. Virgin Islands, and the Commonwealth of 

the Northern Mariana Islands and has used this authority to adjust reimbursement rates 

for Alaska and Hawaii. 42 U.S.C. § 1760(f). 

similar to other federal programs, such as SNAP, 
Page 9 GAO-14-557 School Meal Programs

which provides the same benefit payment rates for participants in the 

contiguous states.13 However, USDA has collected data on school 

foodservice costs and has reported that there are variations in these 

costs among schools and these costs vary across the country.14

Table 3: Data Sources That Measure Geographic Variation in Costs of Labor 

In 

addition, there are other data sources that show geographic variation in 

labor costs, a major component of school foodservice costs. See table 3 

for a list of data sources that show geographic variation in costs of labor.

Data source Produced by

County Business Patterns Census Bureau

Occupational Employment Statistics Survey Bureau of Labor Statistics (BLS)

National Compensation Survey BLS

American Community Survey Census Bureau

Current Population Survey Census Bureau and BLS

Quarterly Census of Employment and Wages BLS

Current Employment Statistics BLS

Source: GAO | GAO-14-557.

While such sources show evidence of geographic differences in labor 

costs, it has not been established that these differences would apply to 

school foodservice operations. Food is also a major component of school 

foodservice costs, but regional variation in the cost of food faced by 

schools is not represented by any existing price data or index.

 13 Other federal programs, such as Medicare, have payment rates based on area 

differences. Medicare is the federal health insurance program for people who are 65 or 

older, certain younger people with disabilities, and people with End-Stage Renal Disease 

(permanent kidney failure requiring dialysis or a transplant, sometimes called ESRD).

14 See, USDA, FNS, Office of Research, Nutrition and Analysis, School Lunch and 

Breakfast Cost Study-II, Final Report (April 2008) and USDA, Economic Research 

Service, School Foodservice Costs: Location Matters (May 2011).
Page 10 GAO-14-557 School Meal Programs

We identified two indexes that satisfied key considerations during our 

process of reviewing data sources to identify those that could be used to 

adjust eligibility guidelines for federal school meal programs for 

geographic differences in the cost of living. In assessing the overall 

suitability of various measures for making cost-of-living adjustments to the 

school meal programs, we used the following key considerations to guide 

our analysis:15

Goods covered: Which goods are taken into account in constructing the 

cost-of-living index? Do the goods included contribute substantially to the 

cost of living?

Geographic coverage: What geographic areas are the underlying data 

drawn from? Does the index capture variation in costs of living at the 

regional, state, or sub-state level?

Time frame: Are the estimates regularly updated and if so, how often? 

Could the cost-of-living estimates be used to update annual eligibility 

thresholds and reimbursement levels?

 15 Similar factors were also identified in the HHS study on cost of living adjustments. They 

are drawn from the economic literature on cost of living adjustment methods. The final two 

considerations, source and methodology, are consistent with OMB’s Guidelines for 

Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information 

Disseminated by Federal Agencies, 

http://www.whitehouse.gov/omb/fedreg_final_information_quality_guidelines, as well as 

GAO’s Assessing the Reliability of Computer-Processed Data (GAO-09-680G).

Existing Data and 

Methods for Adjusting 

for Geographic 

Variations Could Lead 

to Shifts in Eligibility 

and Might Increase 

Program Costs

Existing Federal Data 

Sources Are Available to 

Adjust School Meal 

Eligibility Based on 

Geographic Differences,

but They Have 

Weaknesses
Page 11 GAO-14-557 School Meal Programs

Income group sampled: To what extent does the cost-of-living index 

reflect or include costs for the lower-income quintiles (i.e., the target 

population of free and reduced-price lunches)?

Source: Is the source of the cost-of-living index authoritative, appropriate, 

and reliable?

Methodology:

• Methodological transparency: Do we know and understand the 

process?

• Bias: Could weaknesses in the underlying data or in the methodology 

cause the cost of living to be systematically over- or underestimated in 

a given area or areas? What was done to reduce bias and improve 

accuracy in the estimates?

• Underlying data: Are the underlying data complete, accurate, and 

reliable?
Page 12 GAO-14-557 School Meal Programs

We evaluated all of the potential cost-of-living measures against these 

key considerations. Specifically, we eliminated from consideration data 

sources that (1) do not cover goods that make a significant contribution to 

overall costs of living, (2) do not cover all states, (3) are not updated 

regularly, and (4) do not include low-income individuals in the sampling 

framework. Table 6 in appendix I summarizes our analysis. We reviewed 

documentation for each data source to determine whether it had 

methodological characteristics that would limit its use for the purpose of 

adjustments for school meal eligibility. We identified Regional Price 

Parities (RPP) and the Supplemental Poverty Measure (SPM) as the two 

data sources that could be used to adjust eligibility guidelines for school 

meals programs based on these considerations. Our analysis is 

consistent with the findings of an expert panel16 cited in a report to 

Congress recently released by HHS.17

As a tool to adjust school meal eligibility, RPPs have strengths and 

limitations. RPPs cover a comprehensive market basket of goods, are 

based on reliable federal data sources, and are estimated using methods 

that are transparent and well documented.18 Another advantage of the 

RPPs is that they are available for states, as well as metropolitan 

statistical areas (MSA), and non-metropolitan areas, which would allow 

for cost-of-living adjustments to be made at either the state or sub-state 

level.19

 16 Members of the expert panel included: James Ziliak, University of Kentucky, Project 

Consultant, Poverty Institute; Steven Zuckerman, the Urban Institute, Project Consultant, 

Fellow in Health Policy; Bettina Aten, Bureau of Economic Analysis; John Greenlees, 

Bureau of Labor Statistics, Senior Statistician; Bruce Steinwald, Institute of Medicine, 

Consultant; and Trudi Renwick, Census Bureau. 

However, some estimates may be less accurate because the 

17 This report, required by the Patient Protection and Affordable Care Act, evaluated 

methods to make geographic cost-of-living adjustments to several federal programs (not 

including the National School Lunch Program).The report, Geographic Variation in the 

Cost of Living: Implications for the Poverty Guidelines and Program Eligibility, prepared for 

HHS by the Urban Institute, concludes that, of the available methods, only RPPs and the 

SPM “[...] meet the criteria of being publicly available, produced by the federal government 

on a regular basis, and not restricted to one segment of the income distribution. The RPPs 

are comprehensive—reflecting all family spending. The SPM geographic adjustment 

reflects a narrower concept—geographic variation in median rents—but is simpler to 

explain.”

18 RPP estimates were designated as official statistics on April 24, 2014. 

19 For areas within a state outside of an officially designated MSA, a separate, nonmetropolitan RPP is calculated. 

Regional Price Parities (RPP) are a type of 

price index that allows price levels of multiple 

goods and services to be compared from 

place to place. RPPs include prices from a 

wide range of consumer goods and services 

collected for use in the Consumer Price Index 

and supplemented with housing data from the 

American Community Survey. RPPs compare 

the price level of goods and services in one 

location to the average price level of goods 

and services in all locations. The United 

States national average is set at 100, and 

different locations are above 100 if price 

levels are higher than average in that area 

and below 100 if price levels are lower than 

average in that area. 

Source: Bureau of Economic Analysis. | GAO-14-577
Page 13 GAO-14-557 School Meal Programs

Bureau of Economic Analysis (BEA) calculates RPPs using a complex 

statistical aggregation procedure that uses some estimated (i.e., imputed) 

values and some survey values for county level prices; in particular, 

housing data are available at the county level, but county-level prices for 

most goods and services have to be imputed from Consumer Price Index 

(CPI) data. As a result, the RPPs may show differences in cost of living 

between two areas when in fact the costs of living are similar.20 This could 

be important if RPPs are used to adjust eligibility thresholds from state to 

state, which would also affect the flow of federal funding to cover 

reimbursement to schools for meals consumed by eligible students.

 20 For instance, if a state has an RPP of 101, and another state has an RPP of 99, BEA 

cannot state that these numbers are significantly statistically different. More detailed 

underlying data would remove the need for imputation, improve the precision of RPP 

estimates, and allow BEA to publish confidence intervals for their estimates.
Page 14 GAO-14-557 School Meal Programs

The SPM,21 a joint product of the Census Bureau and BLS, presents a 

different set of strengths and limitations. The SPM poverty thresholds 

represent a minimum dollar amount necessary for a household to 

purchase basic food, clothing, shelter, and utilities and an additional small 

amount for other needs. At present, the only portion of the SPM that 

varies by location is the housing component, which comes from American 

Community Survey (ACS) data.22 SPM thresholds are available at the 

MSA level; the recent HHS report also includes state-level adjustments 

produced by the Urban Institute using Census Bureau methodology and 

data. While housing costs represent a substantial portion of household 

budgets, reliance on housing as the only source of geographic variation 

introduces a potential source of bias because it may not account for other 

significant living costs that vary geographically.23 For instance, in rural 

areas, the cost of housing may be low and the cost of living as measured 

by the SPM may appear low as a result, even though transportation 

costs, which are not reflected in the SPM adjustment, may be even higher 

than in urban or suburban areas.

 21 Whereas RPPs are an inter-area price index, the SPM is a poverty threshold, a portion 

of which is adjusted for geographic cost differences. 

22 The SPM is a joint project of the Census Bureau and BLS. BLS produces the SPM 

thresholds using Consumer Expenditure Survey data; Census Bureau produces the SPM 

threshold estimates with the geographic housing adjustment. For the purposes of this 

report, we will focus on the geographic adjustment to the SPM thresholds, which are 

available from Census Bureau at the MSA level, and from the HHS report at the state 

level. 

23 The Census Bureau and BEA are currently evaluating how the SPM thresholds would 

change if the RPP index was applied to the SPM, either using the overall RPP index or for 

item-specific RPP indexes.

The Supplemental Poverty Measure (SPM)

is designed to take into account many of the 

government programs that assist low-income 

families and individuals and are not included 

in the current official poverty measure. While 

the official poverty measure includes only 

pretax money income, the supplemental 

measure adds the value of in-kind benefits, 

such as the Supplemental Nutrition 

Assistance Program, school lunches, housing 

assistance and refundable tax credits. 

Additionally, the supplemental poverty 

measure deducts necessary expenses for 

critical goods and services from income. 

Expenses that are deducted include taxes, 

child care and commuting expenses, out-ofpocket medical expenses, and child support 

paid to another household. The SPM makes 

use of multiple data sources, including the 

Current Population Survey, Consumer 

Expenditure Survey, and the American 

Community Survey.

Source: Census Bureau. | GAO-14-577
Page 15 GAO-14-557 School Meal Programs

Adjusting eligibility thresholds based on geographic differences could 

result in increased or decreased eligibility, participation, and costs, 

depending on the cost of living in a given area and how adjustments are 

applied. For example, figure 1 shows how the income eligibility threshold 

for free and reduced-price meals could change using the state-level SPM 

adjustments. Figure 2 shows the change using state-level RPPs. The 

RPP map assumes that in states where the cost of living is the same as 

the national average, the eligibility threshold would remain unchanged; 

the SPM map shows adjustments wherever median rents differ from the 

national average. States with a relatively high cost of living, according to 

the indexes, such as states in the Northeast and West would see their 

eligibility thresholds increase, resulting in children at higher incomes 

qualifying for free or reduced-price meals. At the same time, in states 

where the cost of living is lower than average, according to the indexes, 

such as the South and Midwest, eligibility thresholds would be lower and 

fewer children would qualify for the program.24

If adjustments were made at the sub-state level, in general, more children 

would become eligible in metropolitan and other densely populated areas, 

as these areas tend to have a higher cost of living. Meanwhile, fewer 

children would be eligible in areas with a lower cost of living, such as nonmetropolitan areas and small cities. In some cases, the cost-of-living 

differences within states are greater than the cost-of-living differences 

 24 The changes depicted in these maps are not the result of detailed simulations taking 

into account various implementation scenarios; rather they are a simple application of 

geographic cost-of-living adjustments to school meal eligibility thresholds. The affect on 

the total number of students would depend heavily on which method is selected and how it 

was implemented.

Effects from Any 

Adjustments to Income 

Eligibility Are Hard to 

Predict, but They Could 

Increase Overall Costs 

and Have Negative 

Implications for Some 

Participants

Changes in Eligibility, 

Participation, and Costs
Page 16 GAO-14-557 School Meal Programs

between states. For example, RPP data indicate that the difference in 

price levels between metropolitan and non-metropolitan areas in New 

York is greater than the overall difference between New York and 

Connecticut.25

 25 The overall state RPPs, 2006-2010, for New York State is 114.1 and Connecticut is 

110.5 (relative to an overall U.S. average of 100). These numbers indicate that both New 

York and Connecticut have higher than average prices and that New York is slightly more 

expensive. However, the RPP for metropolitan areas in New York State is 116.4 and the 

RPP for non-metropolitan areas in New York State is 95.6. 
Page 17 GAO-14-557 School Meal Programs

Figure 1: Example of Percent Change in Eligibility Threshold for Free and Reduced-Price Meals Resulting from State-Level 

Supplemental Poverty Measure Adjustment, 2006-2010

Notes: Percent changes for Alaska and Hawaii are relative to the national average and do not 

consider current Alaska and Hawaii eligibility thresholds. SPM adjustments are based on median 

gross rent (rent and utilities) for two-bedroom rental units with complete kitchen and bathroom 

facilities.
Page 18 GAO-14-557 School Meal Programs

Figure 2: Example of Percent Change in Eligibility Threshold for Free and Reduced-Price Meals Resulting from State-Level 

Regional Price Parities Adjustment, 2006-2010

Note: Percent changes for Alaska and Hawaii are relative to the national average and do not consider 

current Alaska and Hawaii eligibility thresholds.
Page 19 GAO-14-557 School Meal Programs

Alternatively, adjustments could be implemented under a “hold-harmless”

scenario that only allows for increased eligibility in areas with a high cost 

of living, but no decreases in areas with lower costs. For example, under 

a hold harmless scenario using state-level adjustments, any state in 

figure 1 or 2 with a negative change in eligibility would instead have zero 

change. This would cause the total number of eligible children to 

increase, and likely lead to increased participation and costs.

If cost-of-living adjustments were made based on geographic differences, 

which method is used would have an important effect on the total number 

of children eligible for free and reduced-price meals. According to the 

HHS study regarding geographic cost-of-living adjustments, using either 

the state- or MSA-level SPM would lead to a slight increase in the 

national level average poverty guidelines, while using the state- or MSAlevel RPP would likely yield a slight decrease in the national level average 

poverty guidelines. The study also presents results from a simulation of 

geographic cost-of-living adjustments to SNAP eligibility.26

Other details of the implementation could also affect who is eligible, the 

number eligible, and the total cost of adjusting the eligibility guidelines. 

Although 

SNAP and the school meal programs have different eligibility 

requirements, they use the same poverty guidelines in determining 

eligibility. Given this, the SNAP simulation may indicate how school meals 

eligibility could be generally affected by geographic adjustments. The 

study shows that total persons eligible for SNAP declined using both 

RPPs and SPM, more so when adjustments were done at the level of 

metropolitan area, rather than at the state level. The study indicates that 

the range of changes at the state level would be quite large, leading to a 

potentially significant change in the geographic distribution of benefits. 

For example, the MSA SPM would reduce eligibility by 19.5 percent in 

West Virginia and increase eligibility in California by 16 percent. The state 

SPM also produces a wide range of SNAP eligibility effects and would 

reduce eligibility by 17.5 percent in West Virginia and increase eligibility

by 17.9 percent in California. Additionally for a few states in the SNAP 

simulation, RPPs and SPM produce quite different results—one method 

would produce increased eligibility while the other method would result in 

decreased eligibility.

 26 The simulation was done using the Urban Institute’s Trim3 microsimulation model, 

which reflects program rules, eligibility and means testing requirements, and likelihood of 

participation.
Page 20 GAO-14-557 School Meal Programs

For example, one consideration is whether to make adjustments at the 

state or sub-state level. There are states that include both areas that are 

below the U.S. average cost of living as well as areas that are above the 

average cost of living. A Census Bureau official pointed out to us that 

using a state-level adjustment would result in high cost-of-living areas 

(e.g., New York City) getting too little of an increase relative to the 

measure for their specific area and lower cost-of-living areas getting too 

much of an increase (e.g., Buffalo) relative to their area’s actual cost of 

living. Furthermore, USDA officials said that it may be administratively 

complex to make geographic adjustments. For example, it may be 

challenging to implement geographic differences by MSA because school 

districts may not align with MSA boundaries and may instead cross the 

boundaries of areas with different costs of living. Additionally, it may be 

administratively complex to calculate and implement multiple thresholds—

if different adjustments were made for each MSA, hundreds of eligibility 

threshold tables would have to be computed and published. Further 

implementation concerns that we identified that could affect the outcome 

include whether adjustments would be implemented in such a way as to 

decrease eligibility among some students (as opposed to a hold-harmless 

implementation) and how these adjustments would interact with existing 

policies affecting school meal eligibility.27

A geographic adjustment that allows for reductions and increases in the 

income eligibility guidelines could decrease food security for some 

children currently participating in the program and could increase food 

security for some children not currently benefitting from free school 

meals.28

 27 Changes in school meal eligibility could also affect additional federal and state benefits 

to schools and children that use eligibility for free and reduced-price meals to determine 

other benefits. For example, the distribution of U.S. Department of Education, Title I of the 

Elementary and Secondary Education Act funds to schools can be based, in part, on the 

number of children eligible for free and reduced-price meals. 

Children in lower cost-of-living areas from households who 

previously qualified for free or reduced-price meals may no longer qualify 

under lower income eligibility thresholds. This could affect both household 

food security and individual children’s food security, a measure of access 

28 However, children who no longer qualify for free or reduced-price school meals and 

who cannot pay for school meals may be provided meals or partial meals at some 

schools, but USDA does not reimburse schools for such meals. This is characterized as 

unpaid meal charges. The Healthy, Hunger-Free Kids Act of 2010 requires USDA to 

conduct a study of the policies and practices of schools and districts for serving meals to 

students who are unable to pay. Pub. L. No. 111-296, § 143, 124 Stat. 3183, 3213.

Implications for Food Security
Page 21 GAO-14-557 School Meal Programs

to adequate food for an active and healthy life. Food insecurity, which 

most often takes the form of poor diet quality and food access, is higher 

than average in southern states—an area that is typically lower cost 

according to RPP and SPM rankings and would likely be adjusted 

downward in terms of eligibility. Moreover, according to USDA, food 

insecurity in 2012 was higher in households with children (20 percent) 

than the U.S. average (15 percent).29

It is difficult to calculate how participation and costs would change 

because data are not available to determine how many students would 

qualify under adjusted guidelines and how many of those who qualify 

would actually participate. Determining the relationship between income 

and participation in school meals would require the collection of detailed

household and individual-level data regarding income, benefits, and meal 

participation. At present, USDA requires school food authorities and 

states to provide aggregate data.

At the same time, USDA data also 

show that there are households above 185 percent of poverty that 

experience food insecurity and that food insecurity is higher than average 

in large cities, which tend to be higher cost according to the RPP. For 

children living in such higher cost areas, a cost-of- living adjustment may 

benefit those not currently eligible for free or reduced-price meals. 

Moreover, participation in school meals, particularly school breakfasts, 

has been shown to improve children’s food security.

30

 29 Alisha Coleman-Jensen, et al., Household Food Security In the United States In 2012, 

USDA-Economic Research Service, Report Number 155, September 2013.

The underlying assumptions of such 

an analysis could also have a substantial effect on the results. The data 

source (e.g., RPP vs. SPM) would be important in determining potential 

outcomes of geographic adjustments, as would how that data source is 

used. The factors that could affect the outcome of geographic 

adjustments are summarized in table 4. Given that there are a number of 

30 In order to determine how much actual participation in school meals, and therefore 

actual costs, would change, it is necessary to know how a change in eligibility translates 

into a change in participation. Not all eligible children participate and this may vary by 

income level; for instance, it might be that the poorer the household, the more likely the 

child is to participate in school lunch. If higher-income children participate at lower rates 

than children from the poorest households, increasing the eligibility threshold may not 

increase participation proportionally. There is some empirical evidence that shows that 

participation varies by income level. See, Mathematica Policy Research Inc., Factors 

Associated With School Meal Participation and the Relationship Between Different 

Participation Measures, a report conducted for USDA’s Economic Research Service, June 

2009 and USDA, Food and Nutrition Service, School Nutrition Dietary Assessment StudyIII: Volume II: Student Participation and Dietary Intakes, November 2007.

Challenges to Predicting the 

Outcome
Page 22 GAO-14-557 School Meal Programs

possible implementation scenarios, we concluded that conducting a 

comprehensive simulation would be data and time intensive.

Table 4: Factors That Could Affect the Outcome of Geographic Adjustments

Factor affecting outcome Description Potential effect

Which method used Regional Price Parities and 

the Supplemental Poverty 

Measure could lead to 

different cost-of-living 

adjustments being made in 

some areas.

Total eligibility may 

increase or decrease; 

effect may be completely 

different in some states 

depending on method. 

Hold harmless 

implementation

Allows for eligibility to 

increase in areas with 

higher cost of living, but not 

decrease in lower cost-ofliving areas.

Potential increase in total 

eligibility, participation, and 

costs.

Implementation that causes 

some children to become 

ineligible

Allows for eligibility to 

increase in higher cost-ofliving areas and decrease 

in lower cost-of-living 

areas.

Could negatively affect 

food security for those in 

lower cost of living areas.

Likelihood of newly eligible 

students to participate 

unknown

How students at different 

levels along the income 

spectrum participate in 

school lunch is not entirely 

clear.

Changes in participation 

unknown.

Interaction with existing 

policy

Could mitigate decreased 

eligibility in some areas or 

amplify decreases to 

eligibility in others.

Source: GAO | GAO-14-557.

Other sources of data that could shed light on potential participation and 

cost implications of geographic adjustments to school meals have 

limitations. For instance, in a study conducted by the National Research 

Council on the feasibility of determining school meal eligibility on a 

community basis using the ACS household data, the National Research 

Council determined that ACS data lead to systematic underestimation of 

eligibility, sometimes by as much as 20 percent in high-poverty districts.31

 31 The National Research Council researchers noted that ACS data do not cover all 

migrant children nor do they account for the presence of charter or private schools that 

affect the composition of public school student bodies. 

The study shows that the data in the ACS would be insufficient for 
Page 23 GAO-14-557 School Meal Programs

determining school meal eligibility.32

Determining the effect of geographic adjustment on school meal eligibility 

and participation is further complicated by the existence of a number of 

federal, state, and local policies that could either dampen or amplify the 

effects of any changes. For instance, children from households receiving 

SNAP or TANF benefits; or who are foster children, migrants, or 

runaways; or who receive food from the Food Distribution Program on 

Indian Reservations are considered to be categorically eligible for free 

school meals. In the 2012-2013 school year 14.7 million children were 

identified as categorically eligible for free or reduced-price meals.

Depending on how geographic adjustments to school meal eligibility were 

set up, access to free meals for those children who are categorically 

eligible might not be affected at all. This would tend to dampen the effect 

of adjustments to eligibility thresholds based on geographic cost of living 

differences.

These same limitations would likely 

apply if ACS data were used in a simulation of adjusting school meal 

eligibility for geographic cost-of-living differences. Lack of individual-level 

data presents a significant limitation for any detailed assessment of the 

effect of geographic adjustment on school meals.

USDA’s Provision 2, Provision 3, and the Community Eligibility Provision 

(CEP), under which schools that meet certain conditions provide all 

children a free meal, would also affect and be affected by the outcome of 

geographic adjustments. Because schools are most likely to find it in their 

interest to participate in Provision 2 and Provision 3 if they serve highpoverty populations and typically serve a large portion of meals free of 

charge, a change to eligibility guidelines could affect the decision of 

schools to participate. In some cases, if the percentage of children in a 

school that are eligible for free or reduced-price meals increases, a 

school might choose to participate as a Provision 2 or 3 school. 

Conversely, if the income eligibility amounts were lowered and some 

children no longer qualified based on household income, this would 

decrease the percentage of children in a school eligible for free or 

reduced-price meals and it could cause a school to reconsider its

participation as a Provision 2 or 3 school. Meanwhile, participation in CEP 

depends on the number of children in the schools that are directly 

certified, meaning that they are both categorically eligible and certified 

without the need to fill out a separate school meal application. In the 

 32 The ACS does not ask any questions about school meal participation.

Interaction with Current 

Policies
Page 24 GAO-14-557 School Meal Programs

absence of a geographic adjustment to the eligibility criteria for programs 

affecting categorical eligibility, a school participating in the CEP would be 

unaffected by a geographic cost-of-living adjustment to school meal 

eligibility criteria. USDA reported that in the 2011-2012 school year 7,176 

schools participated as Provision 2 or Provision 3. Also, according to 

USDA officials 3,999 schools participated in CEP in the 2013-2014 school 

year.33 This number is likely to grow in the near term through expanded 

use of direct certification and availability of the CEP.34

USDA studies have shown that there are multiple cost drivers that affect 

schools’ foodservice costs differently. FNS reported in its school lunch 

and breakfast cost study that the mean labor cost per breakfast reflected 

lower breakfast labor costs per meal in school districts serving larger 

numbers of reimbursable breakfasts. According to the study, “As the 

number of breakfasts served increases, labor cost per breakfast 

 33 The CEP count may include some schools who previously participated as Provision 2 or 

3.

34 As required by law, states have been expanding direct certification with the goal of 

directly certifying 95 percent of SNAP categorically-eligible children in each state. 42 

U.S.C. § 1758(b)(4)(F)(i)(III). Additionally, the CEP is continuing to be rolled out and must 

be available to every state by July 1, 2014. 42 U.S.C. § 1759a(a)(1)(F)(vii) – (x). 

According to USDA data, as many as 15 percent of school districts nationwide might be 

able to participate in CEP at a district level. 

The Cost of 

Delivering School 

Meals Varies by 

Geographic Region, 

but the Usefulness of 

Existing Data to 

Adjust 

Reimbursements for 

These Differences Is 

Unclear

School Foodservice Costs 

Can Vary Depending on 

School Size, School 

District Decisions, and 

Location
Page 25 GAO-14-557 School Meal Programs

decreases.” A prior FNS cost study report also stated that larger school 

districts may purchase greater volumes of food and get better food prices 

than smaller districts.35 ERS reported that the use of foodservice 

management companies and school district size with associated 

economies of scale could influence meal costs, stating that, “Knowing the 

numbers of school districts that use foodservice management companies 

or the different mixes of larger and smaller school districts in different 

parts of the country could help explain per-meal cost differences across 

locations.”36

Because differences in actual school foodservice costs may be affected 

by school district decisions and practices as reported by USDA, such 

costs may not be appropriate for making adjustments to reimbursement 

rates. School district decisions—including how to staff their school meal 

programs, the quality of food purchased, where they purchase food, and 

the extent to which they purchase already prepared foods versus 

ingredients that then must be prepared by school foodservice staff, 

among others—can affect costs. As we have previously reported, data 

that can be influenced or controlled by program beneficiaries should not 

be used in funding formulas, because it can introduce disincentives into a 

program.

In fact, ERS found that, the sizes of the school districts are 

associated with economies of scale, which leads to differences between 

urban school districts, which are the largest school districts, and rural 

school districts, which are the smallest. Thus, costs per meal are lower in 

the far larger school districts and higher in the smaller school districts. 

The ERS analysis also shows that the mix of breakfasts and lunches 

served contributed to differences in foodservice costs per meal across 

locations.

37

 35 See, USDA, FNS, Office of Analysis and Evaluation, School Lunch and Breakfast Cost 

Study, Final Report (October 1994).

In this case, reimbursing schools based on actual costs might 

give schools an incentive to make higher-cost decisions since their costs 

would be covered by increased reimbursements. However, to the extent 

that school districts face different food prices and wage costs depending 

on their geographic location, data on how these costs vary by location 

could be used to adjust reimbursements for geographic differences.

36 See, USDA, Economic Research Service, School Foodservice Costs: Location Matters

(May 2011).

37 See GAO, Older Americans Act: Options to Better Target Need and Improve Equity, 

GAO-13-74 (Washington D.C.: Nov. 30, 2012).
Page 26 GAO-14-557 School Meal Programs

Only one federal, nationally-representative source—the RPP index for 

food goods—adjusts for geographic variation in the price of food, but it 

may not reflect school food items and the mix of items used.38 The RPP 

index uses food items from the CPI “food at home” data series for food 

expenditures at grocery stores, which includes food items that would not 

typically be used for children’s school meals or may not meet the 

competitive food nutrition standards, such as carbonated drinks, baby 

food, candy, chewing gum, and coffee.39

At the same time, because the ways in which schools are able to buy 

food differs from the ways households do, the prices schools and 

individual households pay are not comparable. First, because schools 

buy greater volumes than an average household, the prices they pay are 

likely lower. Second, schools may purchase food in a variety of ways, 

including (1) directly from manufacturers, (2) through brokers, (3) from 

distributors, or (4) through a foodservice management company that 

procures food for them. Some schools even participate in multi-school 

In addition, USDA requires 

schools to choose items from specific food categories that may not be 

included in the RPP index. The relative quantities of different food items 

included in the index may also differ from the mix of foods used by a 

school foodservice. USDA prescribes daily and/or weekly requirements 

for serving sizes and number of servings of fruits and vegetables 

(including vegetable subgroups), grains, milk, meat, and meat 

alternatives. USDA also prescribes calorie ranges and limits on saturated 

fats, sodium, and trans-fats. Further, RPPs use weights for food items 

based on U.S. Consumer Expenditure Survey data, which may not 

correspond to the food bought by schools to satisfy USDA’s 

requirements.

 38 USDA has established the Price Index for Foods Used in Schools and Institutions, 

which is used to annually adjust the national average value of donated food assistance 

given to states for each lunch served. The Price Index is computed using five major food 

components in the BLS Producer Price Index (cereal and bakery goods; meats, poultry 

and fish; dairy; processed fruits and vegetables; and fats and oils). However, the data 

from the Producer Price Index are only available at the national level.

39 As of July 1, 2014, any food sold on a school campus during the school day, outside of 

the school meal programs, must meet the competitive food nutrition standards. National 

School Lunch Program and School Breakfast Program: Nutrition Standards for All Foods 

Sold In School as Required by the Healthy, Hunger-Free Kids Act of 2010, 78 Fed. Reg. 

39,068 (June 28, 2013) (codified at 7 C.F.R. pts. 210 and 220). Under regulations in effect 

until June 30, 2014, foods of minimal nutritional value could not be sold in food service 

areas during breakfast or lunch periods. 

Existing Data on Food, 

Labor, and Other FoodEstablishment Prices May

Not Reflect Costs Incurred 

by School Foodservice 

Programs
Page 27 GAO-14-557 School Meal Programs

food buying cooperatives, which can also reduce food prices.40 Further, 

states and schools use USDA entitlement dollars to purchase foods for 

which USDA has contracted with manufacturers through a competitive 

bidding process.41

Similarly, available geographic foodservice wage data may not reflect 

costs for school foodservice labor. For example, BLS data from its 

Occupational Employment Statistics survey includes average hourly wage 

data for Food Preparation and Serving Related Occupations at state and 

sub-state geographic areas. These data represent a broad labor pool 

from which a school could draw for foodservice workers and therefore 

include some types of foodservice establishments that differ from schools. 

According to BLS, school foodservice workers may have more regular 

hours than workers at other foodservice establishments and work only 

during the school year, which is usually 9 to 10 months. Other foodservice 

establishment workers may have work shifts that include late evenings, 

weekends, and holidays. The BLS-defined labor pool also includes a 

much larger proportion of workers aged 16 to 19 years—persons who 

might not be available to work during school hours—compared with other 

occupations. There may also be differences in part-time and full-time 

employment opportunities for school foodservices and other foodservice 

establishments, which could affect wages. For example, BLS reports that 

about half of all foodservice workers were employed part-time and half 

Such USDA foods account for 15 to 20 percent of food 

served in school meals. Schools may also use their USDA entitlement 

dollars and other funds to purchase fresh produce through a program with 

the Department of Defense. Despite differences in the composition of the 

index and the composition of school lunch purchasing patterns, if the 

prices of the foods in the index vary by geographic region in a way that is 

similar to variations in the prices of foods purchased by school meals, the 

index could be a reasonable proxy for school foodservice cost 

differences. However, given our review of the various factors that affect 

school meal costs and the differences in foodservice purchasing patterns, 

it is unclear how well the RPP would reflect school foodservice costs.

 40 For various procurement methods, see National Food Service Management Institute, 

Orientation to School Nutrition Management, Procurement and Inventory Management

(2013).

41 Each state is assigned its entitlement amount annually, based on the total number of 

lunches served the previous year. This amount has a dollar value and functions as an 

account against which states can draw from a list of available offerings. Many states also 

allow local school districts to select among the available offerings.
Page 28 GAO-14-557 School Meal Programs

were employed full-time. On a national basis, BLS indicates that the 

hourly mean wage for foodservice workers at elementary and secondary 

schools is higher than for restaurants and other eating places, although it 

points out that the best job opportunities are at upscale restaurants.

In addition to the separate indexes for food and labor costs, geographic 

food establishment price data can be used to measure the price of a 

meal, but these data may not reflect meals provided in schools. The RPP 

food service data are derived from prices from cafeterias (including 

schools), restaurants, and vending. However, to the extent that 

institutions other than schools provide food items, portions, and levels of 

service that differ significantly from those in schools, the index may not 

accurately reflect school foodservice costs.42 For example, unlike 

restaurants, participating schools must adhere to federal school meal 

nutritional standards and calorie limits and serve food items from specific 

food categories.43 The RPP food service data may also be influenced by 

the mix of types of foodservice establishments in a geographic area (e.g., 

number of fast food versus full-service restaurants), whereas the school 

meal requirements for categories of foods and portions are the same 

throughout the country. Another difference between schools and some 

foodservice establishments may be how costs for rent, utilities, and 

custodial services are covered. USDA reported in its school lunch and 

breakfast cost study that most school districts incur some costs in support 

of their foodservice operations that are not charged to the foodservice 

budget.44 Other foodservice establishments, such as restaurants, may 

have to cover the full cost of their foodservice.

 42 Nonetheless, the national average USDA school reimbursement rates are adjusted 

annually for changes in the CPI, Food Away From Home series, from which the RPPs for 

food service are based. Although the CPI may provide a national measure of overall 

changes in meals prices among foodservice establishments, the CPI does not measure 

differences between cities. 

43 For more information on implementing nutrition standards, see GAO, School Lunch: 

Implementing Nutrition Changes Was Challenging and Clarification of Oversight 

Requirements Is Needed, GAO-14-104 (Washington, D.C.: Jan. 28, 2014).

44 USDA reported that in school year 2005-2006, these unreported costs accounted for an 

average of 19 percent of school districts’ full cost of foodservice. USDA, School Lunch and 

Breakfast Cost Study-II.
Page 29 GAO-14-557 School Meal Programs

The effect of adjusting reimbursements for geographic differences in 

prices and costs would differ depending on the index used. While we 

have determined that available data sources may not necessarily reflect 

particular aspects of school foodservice programs, we believe it is 

instructive to illustrate the potential effect of adjusting school meals for 

geographic price differences, using available data. We compared two 

different methodologies and computed the changes that would have 

occurred to the $2.93 free lunch reimbursement rate in each state for the 

2013-2014 school year. (See fig. 3.) Our analysis assumes that states 

with costs equal to the national average would see no change in their 

reimbursements, but those with above-average costs would experience 

an increase in reimbursements and those with below-average costs 

would experience a decrease. First, we used the RPP food service index, 

which shows the geographic variation in the price of meals, such as those 

served at restaurants. Applying the RPP food services index, the 

adjustments would range from a $0.42 decrease in Kansas to a $0.38 

increase in Maryland. Second, we created a state-level composite index

using RPP food goods data and BLS hourly wage data for Food 

Preparation and Serving Related Occupations.45 The food and labor 

components were weighted based on the average percentage of food and 

labor costs of total foodservice costs that schools reported in USDA’s 

school lunch and breakfast cost study, which uses data from a national 

sample of schools.46

 45 USDA used a similar methodology to establish the reimbursement adjustments for 

Alaska and Hawaii in 1979 and 1981, respectively. However, USDA used the Thrifty Food 

Plan for food values and the County Business Patterns data for labor costs. We did not 

use either the Thrifty Food Plan, since data are not available for all states, or the County 

Business Patterns data, since it does not provide an hourly wage for occupational 

categories.

(See app. I for additional detail on our methodology.) 

Applying the composite food and wage index, states would see a change 

in reimbursements ranging from a $0.22 decrease in South Dakota to a 

$0.53 increase in the District of Columbia. Using either method, 

reimbursement rates would go down in most states, particularly in the 

South and Midwest, while reimbursement rates would go up for most 

states in the Northeast and for California.

46 These proportions may differ among schools by geographic area, which are not 

reflected in our computations. An alternative to using a composite food and labor index 

would be to use just one component to make partial adjustments. 

Making Geographic 

Adjustments to 

Reimbursements Based 

on Existing Data Sources 

May Be Affected by Other 

Factors
Page 30 GAO-14-557 School Meal Programs

Figure 3: Example of Changes to the 2013-2014 School Free Lunch $2.93 Reimbursement Rate by State Using Two Methods 

(Sorted by the Regional Price Parities Food Service Index)

Notes: Changes for Alaska and Hawaii are relative to the national average and do not consider 

current Alaska and Hawaii reimbursement rates. These changes in reimbursement rates are 

presented for illustrative purposes only and should not be considered the actual changes that would 

occur if reimbursement rates were adjusted for geographic differences.

For most states, the two indexes track in the same direction; that is, both 

indexes go up for a state or both indexes go down for a state. However, in 

eight states, one index showed an increase, while the other index showed 

a decrease in the reimbursement rate. For example, in Washington State, 

the composite food and wage index would increase the free lunch 
Page 31 GAO-14-557 School Meal Programs

reimbursement rate by $0.30, while the RPP food services index would 

reduce the reimbursement rate by $0.05. Further analysis would be 

needed to determine why the two indexes provided different results for 

such states.

Although the indexes show increases and decreases in reimbursement 

rates compared to a national average, the effect on overall program costs 

would depend on how the adjustments were implemented. For example, 

even if reimbursements were adjusted up or down relative to a national 

average of $2.93 for a free school lunch in our example, the overall 

reimbursement costs would depend on how many meals are served by 

each school in each local area and the change in reimbursement rates 

that apply to that local area. If, on the other hand, the adjustments were 

made under a hold-harmless scenario, where there would be no 

adjustments for local areas where costs were below the national average 

and only those local areas with higher than average costs would have 

their rates adjusted, the overall cost of the program would increase.

State-level adjustments may mask differences within states. BLS wage 

data are not available for all areas within states. However, where BLS 

wage data for the Food Preparation and Serving Related Occupations are 

available for some sub-state areas, the data show variation within states. 

For example, in California, the May 2010, hourly mean wage for the Food 

Preparation and Serving Related Occupations was $9.69 in the Chico 

area, but was $12.40 in the San Francisco-San Mateo- Redwood City 

Metropolitan Division. Similarly, in Illinois, the May 2010, BLS hourly 

mean wage for the Food Preparation and Serving Related Occupations in 

the Cape Girardeau-Jackson, MO-IL area was $8.99 while the hourly 

mean wage in the Bloomington-Normal area was $10.33. The RPP data 

on food expenditures for goods and services are not reported for substate areas. However, the overall RPP cost of living index that includes 

food, housing, and clothing, among other items, shows that there is 

variation in prices within states, which suggests that the RPP food 

expenditures within states could also vary. Further, both the USDA’s ERS 

simulation of school per-meal costs and a BEA analysis of RPP food 

goods and services also show price differences among urban, suburban, 
Page 32 GAO-14-557 School Meal Programs

and rural categories.47 Without price data that cover all areas within 

states, the state-level average adjustments would be higher than prices 

faced in some parts of a state and lower than prices faced in other parts 

of a state.

Schools participating as Provision 2, Provision 3, or under the Community 

Eligibility Provision (CEP) provide free school meals for all children, which 

can increase access for children who would not otherwise qualify for free 

meals based on their household income. These provisions are available 

for both breakfast and lunch. USDA has encouraged the use of such 

provisions to expand program participation as a way to eliminate the 

possible financial burden for children, so that any child can participate 

regardless of income.48

 47 ERS simulated meal costs in the 2002-2003 school year and found that average per 

meal costs ranged from $2.22 in the FNS Southwest Region to $2.93 in the Mid Atlantic 

Region. The report made some effort to include a quality measure in its analysis, although 

the report states that its findings did not answer the question of whether USDA 

reimbursement is sufficient to produce a nutritious meal, and the report did not consider 

school district revenues in its analysis. U.S. Department of Agriculture, Economic 

Research Service, School Foodservice Costs: Location Matters ERR-117 (May 2011). 

According to USDA, many schools find that using 

Provision 2 or 3 at breakfast increases participation so drastically that 

they do not actually realize a loss from otherwise paying students. In 

addition, schools can save on administrative costs due to reduced 

application burdens, as well as simplified meal counting and claiming 

procedures. Because school districts are responsible for covering the cost 

of providing free meals to all students not covered by federal 

reimbursement, they must determine if it is in their financial interest to use 

48 For example, see USDA, Strategies for Expanding School Breakfast Program Access

(undated), http://www.fns.usda.gov/sites/default/files/toolkit_expandingaccess.pdf. 

Provisions 2 and 3 can be implemented for the School Breakfast programs only, whereas 

Community Eligibility requires the participating school to also include the National School 

Lunch Program.

Existing Federal and 

State Policies Offer 

Eligibility and 

Reimbursement 

Flexibility

Existing USDA Policies 

Can Be Used to Expand 

Access to Free School 

Meals for All Children
Page 33 GAO-14-557 School Meal Programs

such provisions. USDA has found that schools most likely to find it in their 

financial interest to use Provision 2 or Provision 3 are those schools that 

serve high-poverty populations and typically serve a large proportion of 

their meals free of charge. The CEP is intended for schools in highpoverty areas and requires a minimum percentage of students certified 

for free meals in order for a school to participate. A recent evaluation of 

the implementation of the CEP in its first 2 years found that participating 

school districts had increased breakfast and lunch program participation 

and federal reimbursements, as well as administrative cost savings.49 As 

the CEP becomes available in all states, additional school districts may 

participate.50 USDA data show that 15 percent of school districts may be 

eligible for the CEP.51

Eligibility for free school meals may also be expanded through increased 

participation in other federal programs. Children who are from households 

receiving SNAP or TANF benefits, or who receive food from the Food 

Distribution Program on Indian Reservations are considered to be 

“categorically eligible” for free school meals.52

State and school district efforts to directly certify children who are 

categorically eligible may also increase access/enrollment for free school 

meals. In an effort to ensure that these children have access to free 

school meals and to reduce the paperwork burden on eligible families, 

USDA requires states to set up systems that directly certify categoricallyTo the extent that states 

have the flexibility to expand eligibility for these other programs, eligibility 

for school meals through categorical eligibility may increase.

 49 Abt Associates, Community Eligibility Provision, USDA Nutrition Assistance Program 

Report, Food and Nutrition Service, Office of Policy Support, a report prepared for USDA, 

February 2014.

50 The Healthy, Hunger-Free Kids Act of 2010 requires CEP to be available to schools in 

all states by July 1, 2014. Pub. L. No. 111-296, § 104, 124 Stat. 3183, 3193 (codified at 42 

U.S.C. § 1759a(a)(1)(F)(x)).

51 Individual schools may also be eligible for CEP.

52 While the federal gross income-eligibility for SNAP aligns with that for free school 

meals, we previously found that 27 states had provisions that increased SNAP gross 

income eligibility limits between 160 and 200 percent of the federal poverty guidelines. As 

a result, some children from households receiving SNAP would qualify for free school 

meals even though their household income was greater than 130 percent poverty 

guidelines limit for free school meals. See GAO, Supplemental Nutrition Assistance 

Program: Improved Oversight of State Eligibility Expansions Needed, GAO-12-670 

(Washington, D.C.: July 26, 2012). 
Page 34 GAO-14-557 School Meal Programs

eligible children by obtaining information from SNAP records. This system 

requires no action by a child’s parents or guardians to have the school 

district recognize the child as eligible for free meals. According to USDA, 

many states and school districts directly certify eligible children through 

computer matching of SNAP, TANF, and the Food Distribution Program 

on Indian Reservations records against student enrollment lists. This 

reduces the practice of states sending letters to SNAP, TANF, and the 

Food Distribution Program on Indian Reservations households with 

school-age children, which the households then needed to forward to 

their children’s schools. The Healthy, Hunger-Free Kids Act of 2010 

mandated an expansion of direct certification with the goal of directly 

certifying 95 percent of all SNAP categorically-eligible children in each 

state.53

USDA has discretionary authority to adjust reimbursement rates for 

Alaska, Hawaii, Guam, American Samoa, Puerto Rico, the U.S. Virgin 

Islands, and the Commonwealth of the Northern Mariana Islands to reflect 

differences between the costs of providing meals in those locations and 

the costs of providing meals in all other states.54 USDA used this authority 

to set higher reimbursement rates for Alaska in 1979 and Hawaii in 1981 

and has adjusted these rates annually by the increase in the national 

average rates.55

 53 Pub.L. No. 111-296, § 101(b), 124 Stat. 3183, 3186 (codified at 42 U.S.C. § 

1758(b)(4)(F)(i)(III)).

In 2007, Puerto Rico requested higher reimbursement 

rates; however, USDA declined the request because, although Puerto 

Rico had somewhat higher than average food and labor costs when 

compared to the continental states, its costs were within the range of 

those of the continental states.

54 42 U.S.C. § 1760(f).

55 At those times, USDA found that costs in Alaska and Hawaii for providing breakfasts 

and lunches to be substantially higher than the national average. According to USDA at 

the time there was ample evidence the reimbursement presented financial hardships to

schools in Alaska. The number of participating schools and meals served had declined in 

Alaska and schools had indicated that high costs and financial losses were a factor in their 

decision to leave the program and additional schools indicated that the same financial 

problems threatened their ability to continue to offer the program. National School Lunch, 

School Breakfast and Child Care Food Programs; Payment Rates for the State of Alaska 

for the Period July 1 – December 31, 1979, 44 Fed. Reg. 60,344 (Oct. 19, 1979). National 

School Lunch and School Breakfast Programs; Payment Rates for the State of Hawaii for 

the Period January 1- June 30, 1981, 46 Fed. Reg. 7414 (Jan. 23, 1981). 

USDA Has Discretionary

Authority to Adjust 

Reimbursement Rates for 

Geographic Differences in 

Alaska, Hawaii, and U.S. 

Territories
Page 35 GAO-14-557 School Meal Programs

Although state agencies are paid based on a USDA schedule for meals 

served by school districts, state agencies are required to annually 

establish their own per-meal reimbursement rates for school districts in 

their states. According to USDA, all states currently use the same USDA 

reimbursement schedule to provide reimbursements to school districts. 

According to USDA regulations, rates of reimbursement established by 

state agencies may be assigned at levels based on financial need, except 

that rates are not to exceed the maximum rates set by USDA.56 In its

Federal Register notice setting school meal reimbursement rates, USDA 

says that, “These maximum rates are to ensure equitable disbursement of 

Federal funds to school food authorities.”57 For the 2013-2014 school 

year, the maximum reimbursement rate for a free school lunch is $3.10 in 

the contiguous states.58 According to USDA officials, if a state provides 

federal reimbursement rates up to the maximum rate for some school 

districts, the state would have to offset this by decreasing rates for some 

other school districts so that the total federal funding in the state remains 

the same. Using this flexibility, states could adjust school meal 

reimbursements to help address cost variations in different parts of the 

states. According to USDA officials, no states have taken advantage of 

this ability to adjust federal reimbursement rates within their states.

Some states and localities have expanded access to free school meals. 

For example, as we reported in July 2009, some school districts had 

elected to implement programs that eliminated reduced-price meals.59

 56 7 C.F.R. § 210.7(b).

In 

these programs, students qualifying for reduced-price meals were given 

free meals and state or local governments paid the reduced-price fee (not 

more than 40 cents for each lunch and 30 cents for each breakfast). 

57 National School Lunch, Special Milk, and School Breakfast Programs, National Average 

Payments/Maximum Reimbursement Rates, 78 Fed. Reg. 45178 (July 26, 2013).

58 For the 2013-2014 school year, the maximum rate plus the 6 cent certification 

supplement is $3.16 for a free lunch. This rate is $0.15 - $0.23 higher than the free lunch 

reimbursement rates of $3.01 for schools that serve 60 percent or more free or reducedprice lunches and receive the 6 cent supplement and the reimbursement rate of $2.93 for 

schools that serve less than 60 percent free and reduced-price lunches and do not receive 

the 6 cent supplement.

59 See GAO, School Meal Programs: Experiences of the States and Districts That 

Eliminated Reduced-price Fees, GAO-09-584 (Washington, D.C.: July 17, 2009).

USDA Allows States to 

Vary Federal School Meal 

Reimbursement Rates

Other State Policies Can 

Affect Access to Free 

School Meals and 

Reimbursement Rates
Page 36 GAO-14-557 School Meal Programs

Schools reported increased participation and decreased administrative 

costs with such programs.

Some states provide additional school meal reimbursement using state

funds. For example, California provided $0.22 for every free or reducedprice meal in the 2013-2014 school year. New York provides additional 

reimbursement of $0.06 for each free or paid lunch and $0.20 for each 

reduced-price lunch. Neither New York nor California vary the additional 

state reimbursements for geographic cost differences within their states.

Sources of state funds to support school district meal programs include 

required state matching funds and other state funds. States are required 

to provide matching funds to their school meal programs of 30 percent of 

the funds received by each state under Section 4 of the Richard B. 

Russell National School Lunch Act during the school year beginning July 

1, 1980.60 For the 2011-2012 school year, the required match from all 

states totaled $206 million. However, states reported spending $548 

million to support school meal programs in that year.61 An example of 

such expenditures is funds appropriated by a state and used for 

reimbursing schools on a per-meal basis for meals served.62

Making geographic adjustments to school meal eligibility thresholds and 

reimbursement rates is complex and outcomes are difficult to predict. 

Decisions regarding (1) whether indexes are appropriate to use in making 

the adjustments; (2) whether adjustments are made at the level of the 

school district, the state, or other geographic region; and (3) how often 

adjustments are reassessed to account for changes in the cost of living, 

Changes to 

other government programs, such as SNAP, may also affect eligibility for 

school meals, since children from SNAP households are automatically 

eligible for free school meals. For example, if states expand eligibility for 

SNAP, such as by increasing qualifying income levels, children from 

households that qualify under the expanded SNAP eligibility would be 

eligible for free school meals.

 60 42 U.S.C. § 1756(a). If the per capita income of any state is less than the per capita 

income of the United States, the matching requirements may be decreased. Section 4 of 

the Richard B. Russell National School Lunch Act is codified at 42 U.S.C. § 1753. 

61 States report on Form FNS-13, Annual Report of State Revenue Matching. 

62 States are not allowed to count revenues used for state salaries or other state level 

administrative costs.

Concluding 

Observations
Page 37 GAO-14-557 School Meal Programs

food prices, and wages that may affect geographic areas differently could 

all significantly affect the outcome. While adjusting eligibility could help 

increase access to free and reduced-price meals in areas where housing 

and other costs are significantly higher than average, to the extent that 

cost-of-living differences do not align with measures of need, such as 

food insecurity, adjusting income eligibility may not achieve greater 

equity. In addition, if adjustments are made in a way that reallocates 

limited resources among geographic areas rather than adding to the cost 

of the program, such changes would likely lead to some children living in 

lower-cost areas no longer receiving free or reduced-price meals. 

Likewise, making adjustments to meal reimbursement rates using 

available data and methods for food price and wage differences may not 

account for other considerations, such as size of schools, that affect 

school costs. Other considerations include how to adjust eligibility and 

reimbursements in geographic areas for which there are limited data on 

cost of living, food costs, and wages, including some less populated rural 

areas, as well as U.S. territories and outlying areas.

States are allowed to adjust reimbursements to different areas within the 

state, which could be used to adjust for differences in foodservice prices. 

Given that no state presently takes advantage of this and given that there 

is more room for states to increase their participation in existing federal 

options designed to expand access to school meals, it seems there are a 

number of opportunities for states to address both eligibility and 

reimbursement issues at a state level.

We provided a draft of this report to the Department of Commerce, the 

Department of Health and Human Services, the Department of Labor, and 

USDA for review and comment. The agencies did not provide formal 

comments; however, the Department of Labor and USDA provided 

technical comments, which we incorporated as appropriate. As part of its 

technical comments, the Department of Labor expressed concern that our 

use of the term “cost of living” is not consistent with the very specific 

meaning of the term as used in economic literature. As we acknowledge 

in the report, the indexes we discuss do not account for differences in 

quality of life, and therefore cannot be considered true measures of cost 

of living. As a result, we consider these indexes—geographic indexes 

that account for differences in prices of what consumers buy for the same 

or similar goods and services across areas—to be proxies for the cost of 

living.

Agency Comments
Page 38 GAO-14-557 School Meal Programs

As agreed with your offices, unless you publicly announce its contents 

earlier, we plan no further distribution of this report until 30 days from its 

issue date. At that time we will send copies of this report to relevant 

congressional committees and other interested parties. In addition, this 

report will be available at no charge on GAO’s website at 

http://www.gao.gov. If you or your staff have any questions concerning 

this report, please contact me at (202) 512-7215 or brownke@gao.gov. 

Contact points for our Offices of Congressional Relations and Public 

Affairs may be found on the last page of this report. GAO staff who made 

key contributions to this report are listed in appendix II.

Kay E. Brown

Director, Education, Workforce, and Income Security Issues
Page 39 GAO-14-557 School Meal Programs

List of Requesters

The Honorable Sam Farr

Ranking Member, Subcommittee on Agriculture, Rural Development Food 

and Drug Administration, and Related Agencies

Committee on Appropriations

House of Representatives

The Honorable Xavier Becerra

House of Representatives

The Honorable Julia Brownley

House of Representatives

The Honorable Lois Capps

House of Representatives

The Honorable Tony Cardenas

House of Representatives

The Honorable Anna G. Eshoo

House of Representatives

The Honorable Michael M. Honda

House of Representatives

The Honorable Jared William Huffman

House of Representatives

The Honorable Zoe Lofgren

House of Representatives

The Honorable Grace Flores Napolitano

House of Representatives

The Honorable Lucille Roybal-Allard

House of Representatives

The Honorable Linda T. Sanchez

House of Representatives

The Honorable Adam Schiff

House of Representatives
Page 40 GAO-14-557 School Meal Programs

The Honorable Jackie Speier

House of Representatives

The Honorable Mark Takano

House of Representatives
Appendix I: Scope and Methodology

Page 41 GAO-14-557 School Meal Programs

We reviewed government studies and selected academic literature 

relating to geographic cost-of-living adjustments and interviewed experts 

from federal agencies, academia, and other organizations. Such studies 

include a prior GAO report that lists 12 types of methods or data sources 

that may be used to make cost-of-living adjustments to the federal 

poverty line.1 We also reviewed the National Research Council Panel on 

Poverty and Family Assistance recommendations regarding the 

measurement of poverty, including making geographic cost-of-living 

adjustments.2 In addition, we reviewed the Boskin Commission findings 

and recommendations relevant to improving cost-of-living indexes.3

As part of the Patient Protection and Affordable Care Act, the Department 

of Health and Human Services (HHS) was required to conduct a study to 

examine the feasibility and implication of adjusting the application of the 

federal poverty thresholds for different geographic areas so as to reflect 

the variations in the cost of living among different areas within the United 

States. HHS contracted with the Urban Institute to do the study and as 

part of the study the Urban Institute published a literature review on 

geographic variation in the cost of living.

Further, we reviewed peer reviewed journal articles regarding the 

development and improvement of new methodologies to measure costs 

of living and geographic differences in those costs.

4

 1 See GAO, Poverty Measurement: Adjusting for Geographic Cost-of-Living Difference, 

The Urban Institute identified 12 

available approaches for capturing geographic differentials in cost of 

living and focused its review on the quality of each approach and how it 

might be used to adjust poverty guidelines for program eligibility. Based 

on our interviews with the agencies’ officials and document reviews we 

also identified the Lower Living Standard Income Level as another 

method that is currently used to adjust for geographic cost-of-living 

differences in a federally-funded program.

GAO/GGD-95-64 (Washington, D.C.: Mar. 9, 1995).

2 The National Research Council Panel recommended that poverty thresholds be indexed 

to reflect variation in housing costs across the country, as a “first and partial step” to 

addressing geographic cost of living differences. See Measuring Poverty: A New 

Approach, C. Citro and R. Michaels (Eds.) (National Academies Press, 1995).

3 Michael J. Boskin, et al., “The CPI commission: Findings and recommendations,” The 

American Economic Review, 87.2 (1997): 78-83.

4 The Urban Institute, Geographic Variation in the Cost of Living: Implications for Poverty 

Guidelines and Program Eligibility Appendix A-C (May 2013) and Geographic Variation in 

the Cost of Living: Literature Review (submitted to HHS on May 22, 2012).

Appendix I: Scope and Methodology

Cost of Living Data 

Sources and Methods
Appendix I: Scope and Methodology

Page 42 GAO-14-557 School Meal Programs

We evaluated each of the available cost-of-living data sources along six 

considerations, summarized in the table below to determine which data 

sources and methods we would further discuss in this report. These 

considerations emerged from our literature review and are consistent with 

the findings of an expert panel convened by the Urban Institute in the 

course of its study for HHS. The considerations are summarized in table 

5.

Table 5: Critical Considerations for Evaluation of Cost of Living Data Sources and Methods

Consideration Description

Geographic coverage Are cost of living measures provided for the whole country? How detailed is the geographic 

coverage?

Goods included Are goods relevant to household costs of living included?

Income group sampled Does the cost-of-living measure reflect or take into account costs for lower income Americans?

Time frames What time frame do the data cover? How frequently is the measure updated?

Source Is the source of the data authoritative?

Methodology Is the methodology transparent? How are potential sources of bias addressed? Are the underlying 

data reliable?

Source: GAO | GAO-14-557.

In applying these factors, we eliminated from consideration methods that 

do not (1) cover goods that make a significant contribution to overall costs 

of living; (2) cover all states and do not include both urban and rural data; 

(3) are not updated regularly; and (4) do not include lower income 

individuals in the sampling framework. Our analysis is summarized in 

tables 6 and 7.

Table 6: Cost of Living Methods Summarized Along Five of Six Critical Considerations

Method name Source Goods covered

Geographic 

coverage Time frame

Income 

group 

sampled

Included 

for 

further 

review? Reason

Cost-of-Living 

Indexa Council for 

Community and 

Economic 

Research; used 

by researchers 

and businesses

Broad range Urban only Quarterly Top Income 

Quintile

No Only urban, 

upper income 

coverage

CEO (Carrillo, 

Early and 

Olsen) Indexb

Academic 

researchers 

Carrillo, Early 

and Olsen

Broad range Urban plus 

imputed rural

One time Top Income 

Quintile

No Upper income 

only; not updated
Appendix I: Scope and Methodology

Page 43 GAO-14-557 School Meal Programs

Method name Source Goods covered

Geographic 

coverage Time frame

Income 

group 

sampled

Included 

for 

further 

review? Reason

Lower Living 

Standard 

Income Levelc

Employment and 

Training 

Administration

Broad range Metro and 

non-metro

Not updated; 

adjusted for 

inflation 

annually

All (uses 2/3 

of median for 

4 person 

family)

No Outdated; 

primary series 

discontinued

Regional Price 

Parities (RPP)d Bureau of Economic 

Analysis

Broad range Metro and 

non-metro 

regions in all 

states

Annual All (weighted 

average 

across all 

groups)

Yes Basic standards 

for 

considerations 

met

Supplemental 

Poverty 

Measure 

(SPM)e

Census Bureau Housing Metropolitan 

Statistical 

Area (MSA), 

states

Annual based 

on 5 year 

American 

Community 

Survey (ACS)

All (overall 

median) 

used)

Yes Basic standards 

for 

considerations 

met

Self-Sufficiency 

Standardf Center for Women’s 

Welfare, 

University of 

Washington 

Broad range County level 

for majority of 

states

Varies Varies; 

generally all

Yes Basic standards 

for 

considerations 

met

Geographic 

Practice Cost 

Indexg

Centers for 

Medicare and 

Medicaid 

Services

Medical practice 

costs

County data 

aggregated to 

89 areas

Every 3 years Samples 

(work, 

practice, and 

malpractice 

costs)

No Only covers 

medical practice 

costs

Economic Policy 

Institute Family 

Budgetsh

Economic Policy 

Institute 

Rent, food, 

transportation, 

childcare, taxes, 

healthcare, other

521 MSAs, 45 

MSAs that 

cross states; 

one rural area 

in each state 

(except RI,NJ)

Some, but not 

all, data 

annually 

updated

Bottom 40% 

from CE 

surveys, 

sampled 

costs for 

some goods

Yes Basic standards 

for 

considerations 

met

Fair Market 

Rentsi Housing and 

Urban 

Development

Housing Metro and 

non-metro 

counties

Annual 40th 

Percentile of 

gross rents

Yes Basic standards 

for 

considerations 

met

H+T 

Affordability 

Indexj

Brookings 

Institution

Housing and 

transportation

Urban areas 

only

Varies “Typical”

households

No Urban only, not 

updated regularly

Medicare 

Hospital Wage 

Indexk

Centers for 

Medicare and 

Medicaid 

Services

Hospital wages County, 

aggregated to 

444 areas

Annual Hospitals No Covers medical/ 

hospitals only

Milliman 

Medical Indexl Milliman Consulting 

Medical costs 14 cities Annual “Typical”

employer

No Covers medical 

costs only

Occupational 

Pay Relativesm Bureau of Labor Statistics

Wages 77 Metro 

areas

Discontinued Subset of 

employers

No Discontinued

Source: GAO | GAO-14-557.
Appendix I: Scope and Methodology

Page 44 GAO-14-557 School Meal Programs

a

This index was formerly known as the ACCRA Cost of Living Index and is now known simply as the 

Cost of Living Index (COLI) produced by the Council for Community and Economic Research (C2ER), 

http://www.coli.org/AboutIndex.asp.

b

Paul Carrillo, et al., A Panel of Price Indices for Housing Services, Other Goods, and All Goods for 

All Areas in the United States 1982-2010 (2012). 

c

Workforce Investment Act of 1998 (WIA); Lower Living Standard Income Level (LLSIL), 79 Fed. Reg. 

17,184 (March 27, 2014).

d

Bettina Aten, Eric Figueroa, and Troy Martin, Notes on Estimating the Multi-Year Regional Price 

Parities by 16 Expenditure Categories: 2005-2009, 

http://www.bea.gov/papers/pdf/notes_on_estimating_the_multi_year_rpps_and_appendix_tables.pdf.

e

Kathleen Short and Thesia Garner, The Supplemental Poverty Measure: A Joint Project between the 

Census Bureau and the Bureau of Labor Statistics, presented to the Federal Economic Statistics 

Advisory Committee (FESAC), June 8, 2012, http://stats.bls.gov/pir/spm/spm_pap_joint12.pdf.

f

http://www.selfsufficiencystandard.org/.

g

A brief overview and Geographic Practice Cost Index data, http://www.cms.gov/apps/physician-feeschedule/documentation.aspx.

h

Elise Gould, Nicholas Finio, Natalie Sabadish, and Hilary Wething, Economic Policy Institute 2013 

Family Budget Calculator: Technical Documentation, EPI Working Paper #297, July 3, 2013, 

http://www.epi.org/publication/wp297-2013-family-budget-calculator-technical-documentation./.

i

U.S. Department of Housing and Urban Development, Office of Policy Development & Research, Fair 

Market Rents For The Section 8 Housing Assistance Payments Program, July 2007 (rev.) , 

http://www.huduser.org/portal/datasets/fmr.html.

j

http://htaindex.cnt.org/about.php.

k

http://www.cms.gov/Medicare/Medicare-Fee-for-ServicePayment/AcuteInpatientPPS/wageindex.html.

l

Christopher S. Girod, Lorraine W. Mayne, and Scott A. Weltz, Susan K. Hart, 2014 Milliman Medical 

Index, Milliman Research Report, May 20, 2014, 

http://www.milliman.com/mmi/.mhttp://www.bls.gov/news.release/ncspay.toc.htm.

mhttp://www.bls.gov/news.release/ncspay.toc.htm

Next we conducted a review of the source data and estimation 

methodology underlying the remaining methods. To do this, we reviewed 

the documentation produced along with the cost-of-living methods and, 

where questions remained, interviewed individuals involved in creating 

the estimates. We focused on the source of the data and methodology 

used to produce the estimate. Table 7 summarizes this analysis.
Appendix I: Scope and Methodology

Page 45 GAO-14-557 School Meal Programs

Table 7: Summary of Methodology Evaluation for Selected Methods

Method name Methodology

Description

Economic model/statistical 

technique

Potential sources of 

bias/uncertainty

Underlying data 

quality

Regional Price 

Parities (RPP)

Inter-area price index 

that compares prices for 

a market basket based 

on BLS Consumer 

Expenditure Survey 

(CE) data.

Adjusts Consumer Price Index 

(CPI) prices with CE weights; uses 

hedonic methods to control for 

quality differences (such as 

packaging, size and outlet for 

goods; rooms, fixtures, etc. for 

housing); multilateral aggregation of 

estimated prices for defined areas.a

Analysis of standard 

errors, namely the 

difficulty of calculating 

standard errors for 

imputed values; cannot 

construct confidence 

intervals; increasing 

sample size for some 

areas would decrease 

uncertainty.b

Uses CPI prices; 

adjusts using weights 

from CE; American 

Community Survey 

(ACS) rent data.

Supplemental 

Poverty Measure 

(SPM)

Experimental poverty 

measure that includes 

food, shelter, clothing, 

and utilities with 

deductions for out of 

pocket medical 

expenses and taxes.

Thresholds calculated as a function 

of the sum of expenditures with 

adjustments for a family of four. 

Housing component adjusted with 

ACS median rents. Sets the 

threshold based on a level of 

spending that two-thirds of 

American families are able to 

achieve or exceed.

Does not adjust for 

geographic differences 

in items other than 

housing; no adjustment 

for quality differences in 

goods. Lacking 

documentation 

regarding standard 

errors.

Uses CE expenditure 

data on food, shelter, 

clothing, and utilities. 

Housing costs 

separated by housing 

tenure; but uses 

consumption for all 

other goods. Uses 

ACS 5-year average 

median rent for 2-

bedroom apartment to 

adjust housing 

component of 

bundle.c

Self-Sufficiency 

Standard

Defines adequate 

budget categories for a 

number of family types 

and provides average 

cost for budget items in 

different areas.

Lacks an underlying statistical or 

economic model to justify budget 

weights/amounts. No tests of 

statistical significance in comparing 

cost differences.d

Data sampling is nonrandom; no analysis of 

sampling error or 

uncertainty. e

Data sources vary 

from place to place; 

lack of comparable 

data across locations; 

small samples.

Economic Policy 

Institute Family 

Budgets

Defines adequate 

budget categories for a 

number of family types 

and provides average 

cost for budget items in 

different areas. Some 

costs extrapolated from 

older data.

Lacks an underlying statistical or 

economic model to justify budget 

weights/amounts. No tests of 

statistical significance in comparing 

cost differences.

No analysis of sampling 

error or uncertainty.f Uses Fair Market 

Rents (FMR) data, 

which are 

problematic. Not all 

data is updated 

regularly. Some data 

provided by advocacy 

organizations.
Appendix I: Scope and Methodology

Page 46 GAO-14-557 School Meal Programs

Method name Methodology

Description

Economic model/statistical 

technique

Potential sources of 

bias/uncertainty

Underlying data 

quality

Fair Market Rents Administratively 

determined as the 40th 

percentile of median 

rent. 

No economic or statistical model to 

justify use as a cost-of-living index 

or determination of 40th percentile 

as relevant statistic.

Random digit dialing

has low response rates; 

responders may differ 

systematically from 

non-responders. This 

may contribute to bias 

and non-random 

variation in the final 

determination.

Data quality and 

reliability problems. 

The Department of 

Housing and Urban 

Development 

recommends against 

using FMRs to adjust 

for cost-of-living 

differences. FMR only 

sample recent 

movers, and local 

boards can appeal 

their FMR 

determination, all of 

which lead to the data 

not being 

recommended for any 

purpose beyond the 

Section 8 program.

Source: GAO | GAO-14-557.

a

Bettina Aten, et al., Notes on Estimating the Multi-Year Regional Price Parities by 16 Expenditure 

Categories: 2005-2009, April 2011, BEA Working Paper WP2011-03.

b

Bettina Aten, et al., Standard Errors in the U.S. Regional Price Parities, BEA Working Paper 

WP2013-5 (May 10, 2013).

c

Kathleen Short and Thesia Garner, The SPM: A Joint Project Between the Census Bureau and 

Bureau of Labor Statistics, (June 8, 2012).

d

Based on documentation from the Center for Women’s Welfare and interview with developer of 

standard.

e

Based on interview with developer of standard.

f

Elise Gould, Nicholas Finio, Natalie Sabadish, and Hilary Wething, Economic Policy Institute 2013 

Family Budget Calculator: Technical Documentation, EPI Working Paper #297, July 3, 2013,

http://www.epi.org/publication/wp297-2013-family-budget-calculator-technical-documentation/.

We determined that the Regional Price Parities (RPP) and Supplemental 

Poverty Measure (SPM) best satisfied these considerations and we chose 

to further discuss in this report how these methods could be used to 

adjust for variations in the cost of living for U.S. Department of Agriculture 

(USDA) school meal income eligibility and the implications of doing so. 

Table 8 provides an example of what the data look like for one state: 

Illinois. The metropolitan statistical area-level adjustment factors show a 

range of costs of living around the state (see table 8).
Appendix I: Scope and Methodology

Page 47 GAO-14-557 School Meal Programs

Table 8: Regional Price Parities (RPP) and Supplemental Poverty Measure (SPM) 

Data for Illinois

Metropolitan area RPP SPMa

Illinois—State metropolitan areas 102.9 86.7

Illinois—State non-metropolitan areas 83.8 84.8

Bloomington-Normal, IL 94.6 93.3

Champaign-Urbana, IL 94.5 93.9

Chicago-Joliet-Naperville, IL-IN-WI 106.0 105.9

Danville, IL 81.0 —

Decatur, IL 91.1 87.7

Davenport-Moline-Rock Island, IA-IL 92.0 89.4

Kankakee-Bradley, IL 99.3 95.3

Peoria, IL 92.8 91.0

Rockford, IL 92.7 92.7

Springfield, IL 93.4 90.7

Sources: Bureau of Economic Analysis data (2006-2010) and Census Bureau data (2005-2009) | GAO-14-557.

a

Metropolitan statistical area adjustment factor for Illinois ranges from .848 to 1.06; numbers were 

multiplied by 100 for easier comparison to the RPP.

We interviewed agency officials about the reliability of these data for the 

purposes of our analysis. We evaluated the data and methodology used 

to create the RPPs and SPM and found them to be sufficiently reliable for 

our purposes. This report presents several implications that relate to the 

appropriateness of applying these measures to geographic adjustment of 

school meals.

It is important to note that many of the potential data sources we 

examined were not necessarily intended to be used for the purpose of 

making geographic cost of living adjustments. Our decision to narrow our 

focus to RPPs and the SPM should be seen neither as an endorsement 

or recommendation of these methods, nor as a criticism of the other 

methods.

To illustrate the potential effects of adjusting school meal reimbursement 

for geographic price differences we used two indexes, the RPP food 

service index and a composite index using labor and food price data. We 

assembled a food and wage composite index using 2006-2010 RPP data 

for food goods and 2010 Bureau of Labor Statistics (BLS) hourly wage 

data for Food Preparation and Serving Related Occupations. Both the 

RPP data and the BLS data were available at the state and national level. 

Composite 

Reimbursement 

Adjustment Index
Appendix I: Scope and Methodology

Page 48 GAO-14-557 School Meal Programs

We used the RPP food goods index for the food component. We then 

computed a BLS index by dividing each state BLS average wage by the 

national BLS average wage. We then weighted each index by the 

proportion of food and labor costs to total food and labor costs, as 

reported by USDA in its school lunch and breakfast cost study.5

The food and wage composite index was calculated for each state as 

follows

According to the USDA study, food and labor costs accounted for 90.1 

percent of school meal costs of which 45.6 percent was for food and 44.5 

percent was for labor.

[{(state RPP food goods index/100) x (proportion of average school 

food costs/total proportion of average labor and food costs)} + {(state 

BLS wage/national BLS wage) x (proportion of labor costs/total 

proportion of labor and food costs)}]

For example, to determine the composite index for the state of Alabama:

[{(96.1/100) x (45.6/90.1)} + {($9.16/$10.21) x (44.5/90.1)}] = .93

To show the change to the free school lunch reimbursement rate, 1.0 was 

subtracted from the composite index and then multiplied by the 90.1 

percent proportion of the $2.93 free lunch reimbursement rate, as follows

[(composite index-1) x (.901 x lunch reimbursement rate)]

To illustrate the change to the free lunch reimbursement rate for 

Alabama:

[(.93-1) x (.901 x $2.93)] = - $0.186

This illustration shows that in the 2013-2014 school year Alabama 

schools would have received 18.6 cents less in reimbursement for a lunch 

served to a child eligible for a free lunch.

 5 USDA, FNS, Office of Research, Nutrition and Analysis, School Lunch and Breakfast 

Cost Study-II, Final Report (2008).
Appendix II: GAO Contact and Staff 

Acknowledgments

Page 49 GAO-14-557 School Meal Programs

Kay E. Brown, (202) 512-7215, brownke@gao.gov

In addition to the contact named above, Kathryn A. Larin (Assistant 

Director), Daniel S. Meyer (Analyst-in-Charge), and Jessica K. Rider 

made key contributions to this report. Also contributing were Jessica A. 

Botsford, David M. Chrisinger, Heather J. Dunahoo, Kirsten B. Lauber, 

Thomas J. McCool, Brittni L. Milam, Mimi Nguyen, Susan E. Offutt, and 

David M. Reed.

Appendix II: GAO Contact and Staff 

Acknowledgments

GAO Contact

Staff 

Acknowledgments

(131265)
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