School Meal Programs: Implications of Adjusting Income Eligibility Thresholds and Reimbursement Rates by Geographic Differences
Published: 2014-07-08T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: 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 reduced-price. 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.
section_title: What GAO Found
paragraphs: 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.
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.
section_title: What GAO Recommends
paragraphs: GAO is not making any recommendations.

section_title: Letter
paragraphs: 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 reduced- price meals depending on their household income and household size. In  the 2013-2014 school year, for example, a child who was part of a three- person 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.
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 cost- of-living adjustments to the health care subsidies under the act, as well as  the Supplemental Nutrition Assistance Program (SNAP), Medicaid, and  other programs.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   We also spoke with School Nutrition Association  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.
subsections: section_title: Background
subsections: section_title: Structure of National School Lunch and Breakfast Programs
paragraphs: 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. School food authorities provide the meals and claim  reimbursements based on counts of meals served. 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.
section_title: Eligibility for Free and Reduced-Price Meals
paragraphs: 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. The HHS poverty guidelines are a simplified version of the Census Bureau’s federal poverty  thresholds.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.
section_title: Federal Reimbursements for School Meals
paragraphs: 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). (See table 1.) The reimbursement schedule is  the same for all states, except Alaska and Hawaii, which receive higher  reimbursement rates.
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 reduced- price. In addition, if the school district has been certified as complying  with federal nutritional requirements, there is a $0.06 reimbursement   USDA provides additional per-breakfast  supplement per lunch served.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.
State agencies establish the per-meal rates of reimbursement for school  districts in their states. According to USDA officials, all states currently  use the same USDA reimbursement schedule to provide per-meal  reimbursements to school districts.
section_title: Universal Free Meal Provisions
paragraphs: Some school districts have chosen to provide universal free meals under  special provisions, designated as Provision 2, Provision 3, and  Community Eligibility Provision (CEP). 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.
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.
section_title: Geographic Cost-of-Living Adjustments in Federal Programs and Available Data Sources
paragraphs: 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.
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.)
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   similar to other federal programs, such as SNAP,  contiguous states,which provides the same benefit payment rates for participants in the  contiguous states. 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.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.
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.
section_title: Existing Data and Methods for Adjusting for Geographic Variations Could Lead to Shifts in Eligibility and Might Increase Program Costs
subsections: section_title: Existing Federal Data Sources Are Available to Adjust School Meal Eligibility Based on Geographic Differences, but They Have Weaknesses
paragraphs: 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:  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?
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)?
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?
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.
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 panel cited in a report to  Congress recently released by HHS.
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. 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.
However, some estimates may be less accurate because the  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. 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.
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-of- pocket 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.
The SPM, 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. 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. 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.
section_title: Effects from Any Adjustments to Income Eligibility Are Hard to Predict, but They Could Increase Overall Costs and Have Negative Implications for Some Participants
subsections: section_title: Changes in Eligibility, Participation, and Costs
paragraphs: 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.
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 non- metropolitan areas and small cities. In some cases, the cost-of-living  differences within states are greater than the cost-of-living differences  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.
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 MSA- level 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. 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.
Other details of the implementation could also affect who is eligible, the  number eligible, and the total cost of adjusting the eligibility guidelines.
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.
section_title: Implications for Food Security
paragraphs: 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. 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). 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.
section_title: Challenges to Predicting the Outcome
paragraphs: 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 Study- III: Volume II: Student Participation and Dietary Intakes, November 2007. possible implementation scenarios, we concluded that conducting a  comprehensive simulation would be data and time intensive.
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.The study shows that the data in the ACS would be insufficient for  determining school meal eligibility.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.
section_title: Interaction with Current Policies
paragraphs: 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.
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 high- poverty 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  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. This number is likely to grow in the near term through expanded  use of direct certification and availability of the CEP.
section_title: The Cost of Delivering School Meals Varies by Geographic Region, but the Usefulness of Existing Data to Adjust Reimbursements for These Differences Is Unclear
subsections: section_title: School Foodservice Costs Can Vary Depending on School Size, School District Decisions, and Location
paragraphs: 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  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. 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.” 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.
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   In this case, reimbursing schools based on actual costs might  program. 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.
See GAO, Older Americans Act: Options to Better Target Need and Improve Equity,  GAO-13-74 (Washington D.C.: Nov. 30, 2012).
section_title: Existing Data on Food, Labor, and Other Food- Establishment Prices May Not Reflect Costs Incurred by School Foodservice Programs
paragraphs: 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. 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. 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.
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  food buying cooperatives, which can also reduce food prices. Further,  states and schools use USDA entitlement dollars to purchase foods for  which USDA has contracted with manufacturers through a competitive  bidding process. 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.
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  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. 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. 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   Other foodservice establishments, such as restaurants, may  budget.have to cover the full cost of their foodservice.
section_title: Making Geographic Adjustments to Reimbursements Based on Existing Data Sources May Be Affected by Other Factors
paragraphs: 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. 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. (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.
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  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 sub- state 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,  and rural categories.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.
section_title: Existing Federal and State Policies Offer Eligibility and Reimbursement Flexibility
subsections: section_title: Existing USDA Policies Can Be Used to Expand Access to Free School Meals for All Children
paragraphs: 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. 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  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 high- poverty 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. As  the CEP becomes available in all states, additional school districts may  participate. USDA data show that 15 percent of school districts may be  eligible for the CEP.
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. To the extent that states have the flexibility to expand eligibility for these other programs, eligibility  for school meals through categorical eligibility may increase.
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 categorically- 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.
section_title: USDA Has Discretionary Authority to Adjust Reimbursement Rates for Geographic Differences in Alaska, Hawaii, and U.S. Territories
paragraphs: 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. 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. 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.
section_title: USDA Allows States to Vary Federal School Meal Reimbursement Rates
paragraphs: 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. 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.” For the 2013-2014 school  year, the maximum reimbursement rate for a free school lunch is $3.10 in  the contiguous states. 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.
section_title: Other State Policies Can Affect Access to Free School Meals and Reimbursement Rates
paragraphs: 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. 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).
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 reduced- price 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. 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. An example of  such expenditures is funds appropriated by a state and used for  reimbursing schools on a per-meal basis for meals served. 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.
section_title: Concluding Observations
paragraphs: 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,  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.
section_title: Agency Comments
paragraphs: 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.
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.
section_title: Appendix I: Scope and Methodology
subsections: section_title: Cost of Living Data Sources and Methods
paragraphs: 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. 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. In addition, we reviewed the Boskin Commission findings  and recommendations relevant to improving cost-of-living indexes.  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.
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. 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.
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.
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.
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.
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).
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.
section_title: Composite Reimbursement Adjustment Index
paragraphs: 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.
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.  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.
The food and wage composite index was calculated for each state as  follows  [{(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:  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  To illustrate the change to the free lunch reimbursement rate for  Alabama:  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.
section_title: Appendix II: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Staff Acknowledgments
paragraphs: 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.