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

Letter

Background

Structure of National School Lunch and Breakfast 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. 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.

Eligibility for Free and Reduced-Price Meals

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.

Federal Reimbursements for School Meals

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.

Universal Free Meal Provisions

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.

Geographic Cost-of-Living Adjustments in Federal Programs and Available Data Sources

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.

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

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.

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

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.

Implications for Food Security

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.

Challenges to Predicting the Outcome

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.

Interaction with Current Policies

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.

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

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).

Existing Data on Food, Labor, and Other Food- Establishment Prices May Not Reflect Costs Incurred by School Foodservice 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. 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.

Making Geographic Adjustments to Reimbursements Based on Existing Data Sources May Be Affected by Other Factors

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.

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

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.

USDA Has Discretionary Authority to Adjust Reimbursement Rates for Geographic Differences in Alaska, Hawaii, and U.S. Territories

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.

USDA Allows States to Vary Federal School Meal Reimbursement Rates

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.

Other State Policies Can Affect Access to Free School Meals and Reimbursement Rates

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.

Concluding Observations

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.

Agency Comments

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.

Appendix I: Scope and Methodology

Cost of Living Data Sources and Methods

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.

Composite Reimbursement Adjustment Index

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.

Appendix II: GAO Contact and Staff Acknowledgments

GAO Contact

Staff Acknowledgments

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.