United States

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Agriculture

Economic

Research

Service

Economic

Research

Report

Number 117

May 2011 

Michael Ollinger

Katherine Ralston

Joanne Guthrie

School Foodservice Costs 

Location Matters
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Recommended citation format for this publication:

Ollinger, Michael, Katherine Ralston, and Joanne Guthrie. 

School Foodservice Costs: Location Matters, ERR-117, 

U.S. Dept. of Agriculture, Econ. Res. Serv. May 2011.

Photo credits: Girl with lunch tray, Comstock. All others, USDA
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United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Economic

Research 

Report

Number 117

May 2011

School Foodservice Costs 

Location Matters

Abstract

Over 42 million meals—31.2 million lunches and 11 million breakfasts—were served 

on a typical school day in fiscal year 2009 to children through USDA’s National 

School Lunch and School Breakfast Programs. School food authorities (SFAs) operate 

local school feeding programs and deliver the meals to the schools. SFAs must serve 

appealing, healthful meals while covering food, labor, and other operating costs, a challenge that may be more difficult for some SFAs than for others due to differences in costs 

per meal across locations. Analysis of data on school costs per meal from a large, nationally representative sample reveals that geographic variation is important. In the 2002-03 

school year, SFAs in the Southwestern United States had, on average, consistently lower 

foodservice costs per meal than did SFAs in other regions. Urban locations had lower 

costs per meal than did their rural and suburban counterparts. Wage and benefit rates, 

food expenditures per meal, and SFA characteristics such as the mix of breakfasts and 

lunches served each contributed to the differences in foodservice costs per meal across 

locations. The importance of these factors varied by location. 

Keywords: National School Lunch Program, School Breakfast Program, school meals, 

school foodservice costs per meal.

Michael Ollinger, ollinger@ers.usda.gov

Katherine Ralston, kralston@ers.usda.gov

Joanne Guthrie, jguthrie@ers.usda.gov
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Acknowledgments

The authors thank Jay Variyam, Elise Golan, Mark Prell, David Smallwood, 

Fred Kuchler, Laurian Unevehr, and two anonymous reviewers for their 

comments on earlier drafts of this report. We also thank Linda Hatcher for 

editing and Sue DeGeorge for design and layout.
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Introduction: Two Issues for School Meal Costs . . . . . . . . . . . . . . . . . . . . . 1

Previous Research Provides Limited Information 

on the Effects of Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Using a Statistical Model To Measure and Explain Geographic Cost 

Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Developing the Cost Function Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Isolating Geographic Cost Variation Due to Input Prices 

and Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Relative Contribution of Cost-Influencing Factors . . . . . . . . . . . . . . . . 19

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Appendix A: The Model and Model Diagnostics . . . . . . . . . . . . . . . . . . . . 24

Econometric Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Model Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Input Price Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Characteristics of School Food Authorities . . . . . . . . . . . . . . . . . . . . . . 28

Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Survey Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Tests for Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Model Diagnostics and Discussion of Model Estimates . . . . . . . . . . . . 34

Elasticities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Appendix B: School Breakfast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Examination of Costs by Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Breakfast Costs and Why They May Be the Same as Lunch Costs . . . . 43
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Summary

The National School Lunch Program and School Breakfast Program reimburse school food authorities (SFAs) for providing school meals that meet 

USDA nutritional standards. Reimbursement rates depend on whether the 

meal is lunch or breakfast and whether the student is certified to receive the 

meal for free or at reduced or full price. Reimbursement rates are the same 

for all SFAs, except those in Alaska or Hawaii and for schools in which a 

certain percentage of children receive free or reduced-price lunches even 

though cost-of-living indexes for all 50 States show considerable variation in 

food and labor costs. No previous research has rigorously examined whether 

school foodservice costs vary geographically, or identified the factors that 

help explain those differences. 

What Are the Major Findings?

In this study, we measured geographic variation in school foodservice costs, 

after accounting for nongeographic factors, to better clarify economic and 

operational factors that help explain why per meal costs vary by school location. We examined the impact of (1) location; (2) total USDA reimbursable 

breakfasts and lunches served; (3) measures of input prices for labor, food, 

and supplies; and (4) several SFA characteristics affecting total school foodservice cost: the mix of breakfasts and lunches served, a measure of meal 

value that was based on prices charged to students paying the full price for 

lunch, a la carte revenue per meal, and other aspects of foodservice operation.

•	After accounting for nongeographic characteristics of SFAs, we found 

that average foodservice costs per reimbursable meal (including all breakfasts and lunches) in 21 locations (rural, urban, and suburban areas across 

7 U.S. regions) range from 21 percent below the national average for 

the rural Southwest to 19 percent above in the suburban Midwest. The 

Southwest and Southeast regions had average costs per meal below the 

national average, and urban locations had lower average costs per meal 

than their rural and suburban counterparts. 

•	The main drivers of differences in foodservice cost varied by location. 

Wage and benefit rates were the largest contributors in five locations. 

SFA characteristics—particularly the total number of reimbursable 

meals served, this study’s measure of meal value, and the presence of a 

la carte foods—were the most important factors behind cost differences 

in five locations. In the remaining 11 locations, per meal cost variation 

was largely due to differences in total food expenditure per meal, which 

include differences in food item prices and food items served. 

•	Per meal costs dropped when the number of meals served rose and when 

the SFA served more lower-value meals. Per meal costs rose when the 

SFA served more higher-value meals and had more than 10 cents per meal 

in a la carte food sales. 

This study examines the extent to which location influences school foodservice costs per meal. It does not examine the effects of cost variation on financial solvency of an SFA or the adequacy of USDA meal reimbursements. 

Higher per meal costs do not necessarily indicate that an SFA is operating at 
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a loss because higher cost SFAs may also have higher revenues. Due to data 

limitations, we can determine neither the extent to which higher per meal 

costs are associated with higher revenues per meal nor whether higher cost 

SFAs are more likely to serve meals that meet USDA nutrition standards. 

How Was the Study Conducted?

Previous cost estimates for school meals used accounting methods to estimate the cost per meal to SFAs of providing USDA-reimbursable lunches 

and breakfasts, but lacked the necessary sample size to obtain regional 

averages. We, however, used data from 1,432 SFAs participating in the 

2004 School Food Authority Characteristics survey, a nationally representative survey that was stratified to allow estimates by region and urbanicity. 

The survey was administered by USDA’s Food and Nutrition Service and 

collected data for the 2002-03 school year. To measure the effects of location on total school foodservice costs, we employed a flexible econometric 

approach and controlled for total reimbursable meals served (including 

breakfasts and lunches), measures for input prices, and SFA characteristics. 

Due to limitations of our data set, our measure of the input price for food was 

constructed as total food expenditure per reimbursable meal. This measure 

reflects both differences in prices paid by SFAs for individual food items and 

in food items served, making the separation of these two influences on per 

meal costs difficult. We developed a measure of meal “value” to adjust for 

differences in food items served; this measure may also reflect differences 

in food item prices to some extent. For the labor cost measure, we used local 

salaries and wages reported in the survey to estimate the cost to each SFA of 

a standardized set of foodservice personnel.

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Introduction: 

Two Issues for School Meal Costs

 On a typical school day in fiscal year (FY) 2009, 31.2 million lunches and 

11 million breakfasts were served to children in schools participating in the 

U.S. Department of Agriculture’s (USDA) National School Lunch Program 

(NSLP) and School Breakfast Program (SBP) (Oliveira, 2010). USDA reimbursements for these meals in 2009 added up to expenditures of $10 billion 

for NSLP and $2.6 billion for SBP (Oliveira, 2010). Under these programs, 

participating school food authorities1 (SFAs), which are the administering 

units for the operation of local school feeding programs, are expected to meet 

nutrition guidelines for the meals they serve. They are reimbursed for part 

or all of the meal costs by the Food and Nutrition Service (FNS), the agency 

that administers USDA’s food assistance programs at the Federal level.

Reimbursement rates depend on whether the meal is a lunch or a breakfast 

and whether the student is certified to receive the meal for free or at reduced 

or full price. Due to the large volume of meals involved, differences of even 

a few cents in meal costs incurred by SFAs, meal prices paid by students 

to SFAs, or meal reimbursements paid to SFAs by USDA can significantly 

affect the budgets for schools, households, and USDA. The reimbursement 

rates are the same for all SFAs except for adjustments for SFAs in Alaska 

or Hawaii and for individual schools in areas where most children receiving 

school meals live in low-income households. No other adjustments are made 

even though it has been argued that costs vary geographically. 

To understand school meal operations and reimbursement policies, one needs 

to distinguish between two issues associated with school meal costs:

•	Issue 1: How large are differences in foodservice costs per meal across 

locations after accounting for nongeographic factors? 

•	Issue 2: What factors help explain which locations have higher per meal 

costs and which have lower per meal costs? 

Previous research has not rigorously examined the geographic variations in 

foodservice costs per reimbursable meal or the economic and operational 

factors that can help explain the differences. This report provides information 

and analysis on both issues.

Differences between reimbursement rates and per meal costs can create not 

only budgetary issues but also nutrition issues and issues of access to and 

participation in school meals by their target population. USDA reimburses 

school food authorities for meals at rates that vary based on student economic 

need (see box, “USDA School Meal Reimbursements,” for a more detailed 

explanation of USDA reimbursement rates). SFAs that participate in the 

USDA school meals programs are required to operate on a nonprofit basis, 

but even covering their costs to avoid a loss may be a challenge for some 

SFAs. Such SFAs may attempt to obtain more revenue from sources other 

than USDA reimbursements. Sources include subsidies from the State or 

the school district, revenues from students who pay full price, and revenues 

from meals and snacks sold by SFAs but not subsidized by USDA, which 

1An SFA is the management unit that 

provides the meal to the local school. 

In terms of geographic size or student 

population, an SFA often has the same 

boundaries as a school district but can 

be smaller than the district or made up 

of more than one district.
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USDA School Meal Reimbursements

The table shows reimbursement rates for the 2005-06 school year for which meal cost data were collected for a 

Food and Nutrition Service study (the following section highlights selected results from the study). USDA reimburses school food authorities (SFAs) for meals served as part of the National School Lunch Program (NSLP) 

and the School Breakfast Program (SBP) at levels that are set nationally but differ depending on a student’s 

household income. Students may be certified to receive the meals for free if household income is below 130 

percent of the poverty level, or at a reduced price of no more than 40 cents for lunch and 30 cents for breakfast 

for households with income between 130 and 185 percent of poverty. For example, a student in a family of four 

with a household annual income below $28,665, which is 130 percent of $22,050 (the 2010 poverty level for a 

family of four), could be eligible to receive free meals. 

Free and reduced-price meals are reimbursed at higher rates than those of higher income students who pay a 

“full” price (or “paid”) established by the SFA. In addition, schools receive an extra 2 cents per lunch if at least 

60 percent of lunches served in the second preceding school year were reimbursed at the free or reduced-price 

rates. In the SBP, the bar is set lower and additional reimbursement is higher: schools are designated as “severe 

need” and receive an additional 24 cents for free and reduced-price breakfasts if 40 percent of lunches served in 

the second preceding school year were free or reduced price. Each year, reimbursement rates are updated based 

on the national average Consumer Price Index for all Urban Consumers for Food Away From Home.

USDA cash reimbursement rates to school food authorities, 2005-06 school year1,2

School classification

Program and benefits

Less than 60 percent of lunches 

are free or reduced price 

60 percent or more of lunches are 

free or reduced price 

---------------Dollars per meal per student---------------

National School Lunch Program:

Paid 0.22 0.24

Reduced price 1.92 1.94

Free 2.32 2.34

Nonsevere need

(up to 40 percent of students receive 

free or reduced-price lunches)

Severe need

(more than 40 percent of students receive 

free or reduced-price lunches)

---------------Dollars per meal per student---------------

School Breakfast Program:

Paid 0.23 0.23

Reduced price 0.97 1.21

Free 1.27 1.51

1Reimbursement rates for the 2010-11 school year and earlier are available at http://www.fns.usda.gov/cnd/

Governance/notices/naps/NAPs.htm

2In addition to cash reimbursements, school food authorities (SFAs) receive USDA foods that can be used to augment purchased foods. Some USDA foods are “entitlement” foods that are assured to SFAs by law, whereas some 

USDA foods are “bonus” foods that depend on availability from surplus stocks. The per meal value of entitlement 

foods was 17.5 cents in the 2005-06 school year (more recently, in the 2010-11 school year, the figure is 20.25 

cents). In 2005-06, USDA cash reimbursements and support with USDA foods made up, on average, 51 percent of 

SFA revenues (Bartlett et al., 2008).

Source: Food and Nutrition Service, USDA, http://www.fns.usda.gov/cnd/Governance/notices/naps/NAPs02-03.pdf
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are often called “competitive” or “a la carte” foods. Cash-strapped SFAs 

have an incentive to control costs by reducing the offerings of reimbursable 

meals, perhaps by eliminating (or not adding) a school breakfast service or 

by reducing the quality of meals. 

We know from previous findings, which are reviewed in detail in the 

following section, that costs per meal vary across SFAs. However, analysis 

of the factors that can help account for cost differences has been limited. 

Some information does suggest that differences in food and labor costs associated with location are a source of cost variation. 

Cost-of-living indexes are directly related to food and labor costs, and 

indexes for all 50 States show considerable variation (Missouri Economic 

and Research Information Center, 2009).2 For example, the cost of living 

in Alabama (ranked ninth lowest) was 0.91 in 2008, meaning that the cost 

of living there is 91 percent of the national average, or 9 percent less than 

the national average. The cost of living in New Hampshire (ranked ninth 

highest) was 1.19, or 19 percent above the national average. The cost of 

living in Hawaii, the most expensive State, is nearly twice as high as the cost 

of living in the least expensive State, Tennessee (1.63 versus 0.88). These 

cost-of-living data and anecdotal comments from school foodservice professionals suggest that their foodservice operation costs may vary, leading to the 

hypothesis that SFA location affects costs per meal.

Understanding the sources of cost variation across location can provide 

useful insights for program and policy officials. In addition to food, labor, 

and supply cost differences, SFA characteristics may also influence costs per 

meal. For example, both operational decisions, such as the use of foodservice management companies, and SFA size, with its associated economies 

of scale, could influence per meal costs. Knowing the numbers of SFAs that 

use foodservice management companies or the different mixes of larger and 

smaller SFAs in different parts of the country could help explain per meal 

cost differences across locations. 

This study provides new information and analysis that examines the extent 

to which location and other factors influence school foodservice costs. We 

used data from 1,432 SFAs participating in the School Food Authority 

Characteristics Survey (see box, “Study Data,” for a more detailed description of the survey). We addressed the first issue—how large are locationbased differences in school meal costs?—by comparing foodservice costs 

per meal across locations after adjusting for characteristics. Location is 

defined as one of 21 combinations of 7 FNS regions and 3 levels of urbanicity. FNS organizes States into seven geographic regions for administrative 

purposes (fig. 1). Urbanicity is defined as whether the SFA is located in an 

urban, rural, or suburban area. Per meal cost is defined as total foodservice 

costs divided by total USDA reimbursable breakfasts and lunches. Using 

these definitions of output makes accommodating SFAs with different levels 

of breakfast service— including no breakfast service—possible, which is 

critical because although lunch service is ubiquitous, breakfast service varies 

widely. Therefore, results of this study are not directly comparable with 

results of other studies discussing lunch and breakfast costs separately.

2The Bureau of Economic Analysis 

provides several types of price indexes 

for different periods for the country as 

a whole, but it does not provide similar 

indexes by which to compare cost of 

living across regions of the country.
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We used a single output translog cost function to address both issues. The 

model combines USDA reimbursable breakfasts and lunches into one output 

and controls for differences in the proportion of meals that are breakfasts. 

Previous research shows that the cost of serving breakfast drops as the 

number of breakfasts served rises (Sackin, 2008; Hilleren, 2007). To account 

for differences in the types of food served in different SFAs and other characteristics, the model also includes a measure of meal value that is based 

on the quality/appeal of meals and the resultant prices charged to students 

paying the full price.

Due to limitations in our data set, the study’s measure of the input price for 

food is defined as total food expenditure per meal. This measure reflects 

both differences in prices paid by SFAs for individual food items and in 

food items served, making these two influences on costs difficult to separate. 

The meal value variable adjusts for differences in food items served, but 

this measure too reflects differences in food item prices to some extent. The 

measure of labor costs uses local salaries and wages to estimate the cost to 

each SFA of a standardized set of foodservice personnel. 

Results of this statistical model were used to examine the separate contributions of input price measures (wage and benefit rates, food expenditures 

per meal, and supply costs per meal) and SFA characteristics to geographic 

differences in per meal costs.

Study Data

We used data from 1,432 SFAs participating in the School Food Authority 

Characteristics Survey. The sample was administered by Mathematica Policy 

Research, Inc. (MPR), in spring 2004 to collect data for the 2002-03 school 

year (Mathematica Policy Research, Inc., 2004) to support the School Lunch 

and Breakfast Cost Study II (SLBCS-II). Two strengths of the sample are that it 

is nationally representative and it was stratified by Food and Nutrition Service 

region. The stratification at the regional level is advantageous for developing 

cost estimates at the subnational level. The survey data were collected with 

three instruments: a one-page fax-back form, a brief telephone interview, and 

a four-page self-administered survey on costs and revenues and related characteristics. The fax-back form requested general SFA characteristics, such as 

student enrollment; the telephone survey obtained information on the use of 

foodservice management companies and other nonnumeric information; the 

self-administered cost and revenue file contains detailed information on 1,665 

SFAs and contains detailed information on food, labor, and material costs. MPR 

also constructed a link file containing information on school district enrollment 

and demographic and wealth characteristics that was drawn from the National 

Center for Education Statistics Common Core of Data CCD (National Center 

for Education Statistics, 2004) and from U.S. Census Bureau data.
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NM

TX

OK AR

LA SWRO

MS AL GA

TN

KY

NC

SC

FL

SERO

MT

WY

UT CO

KS MO

IA NE

ND

SD

MPRO

MN

WI

IL IN

MI

OH

MWRO

NY

VT ME

NH

RI

MA

CT

NERO

AZ

NV

OR

WA

ID

HI

AK

CA

American Samoa

CNMI

WRO

Guam

PR

WV VA

PA

MD

DE

NJ

DC

VI

MARO

Figure 1

U.S. regions established by the Food and Nutrition Service

Source: Food and Nutrition Service, USDA. 

MARO = Mid-Atlantic Regional Office

MPRO = Mountain Plains Regional Office

MWRO = Midwest Regional Office

NERO = Northeast Regional Office

SERO = Southeast Regional Office

SWRO = Southwest Regional Office

WRO = Western Regional Office

Key:
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Previous Research Provides Limited 

Information on the Effects of Location

FNS has supported several nationally representative school lunch and 

breakfast cost studies to determine national reimbursement rates for school 

meals. The most recent of these, the School Lunch and Breakfast Cost Study 

(SLBCS-II), was completed by Abt Associates for the 2005-06 school year 

(Bartlett et al., 2008). Previous studies sponsored by FNS include the Child 

Nutrition Programs Operations Study (St. Pierre et al., 1991, 1992), collected 

in 1987-88 and 1988-89, and the first School Lunch and Breakfast Cost Study 

(SLBCS-I), collected during the 1992-93 school year (Glantz et al., 1994).

SLBCS-II used direct accounting methods and actual meal production 

records to estimate the reported cost and the full cost of producing a lunch or 

a breakfast. To understand the findings of both the SLBCS-II and this study, 

one must distinguish between “reported” costs, “unreported” costs, and “full” 

costs, which are the sum of reported and unreported costs. “Reported costs” 

include only costs that are charged to SFA budgets. From the SFA’s perspective, reported costs are the costs that the SFA is expected to cover in running 

the NSLP and SBP. Examples of reported costs are the costs of labor, food, 

and supplies. Food and labor costs account, on average, for about 90 percent 

of reported costs, with each accounting for approximately half of the total 

(Bartlett et al., 2008). Nonfood supplies and miscellaneous other costs make 

up the remaining reported expenses. Unreported costs, in contrast, are paid 

by the school district but not charged to the SFA. SFAs, for example, use 

facilities that require capital expenditures, yet these costs are not charged to 

the SFA.

SLBCS-II provides information on variation across SFAs in both reported 

costs and full costs for reimbursable lunches and breakfasts. Because our 

findings are based on data for SFA reported costs, our summary of the 

SLBCS-II results mainly focus on reported costs rather than on full costs.3

For the remainder of this report, “cost” refers to “reported cost” unless otherwise noted, and “lunch” and “breakfast” are understood to be complete meals 

that meet the USDA standard for reimbursement. SLBCS-II estimated the 

average cost per meal for a “typical” SFA to be $2.36 for a lunch and $1.92 

for a breakfast.4 

Figure 2 illustrates differences in SFA costs per lunch, as reported in 

SLBCS-II. For example, about a quarter (26.8 percent) of SFAs had costs 

per lunch in the range of $2.20-$2.40—the range with the largest percentage 

of SFAs and the range that includes the $2.36 average for a typical SFA. 

At the extremes of the distribution, 2.9 percent of SFAs had costs per lunch 

below $1.40 and more than 1 in 10 (12.7 percent) had per meal costs of $3.00 

or more. These findings from SLBCS-II are useful in providing estimates 

of the number of SFAs for which estimated per meal costs exceed USDA 

subsidies—about a fifth of SFAs for lunches and half for breakfasts, given 

reimbursement amounts in the study year (see box, “USDA School Meal 

Reimbursements"). Although these findings indicate that cost per meal varies 

across SFAs, they do not themselves identify the roles of location and other 

factors in influencing these costs. 

3For the sake of completeness, note that 

SLBCS-II estimated the average full 

cost for a “typical” SFA to be $2.91 for 

lunch and $2.50 for breakfast.

4These averages are based on an SFAlevel analysis that weights the sample 

in order to count each SFA equally nationwide, regardless of size. From this 

perspective, estimated costs represent 

the average cost for a “typical” SFA 

(Bartlett et al., 2008, p. i). An alternative approach develops cost estimates 

for an average reimbursable meal, 

recognizing that larger SFAs produce 

relatively more meals. For the purposes 

of this study, with its focus on school 

meal costs at the SFA level but across 

locations, the SFA-level analysis provides the most appropriate perspective.
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Despite interest in the topic, none of the studies reviewed here were 

designed to produce estimates of cost differences by region or urbanicity. 

Responding to public interest in the issue, a 1993 analysis by the U.S. 

General Accounting (now Government Accountability) Office used data 

from the Child Nutrition Programs Operations Study, collected in 1987-88 

and 1988-89, to estimate average costs for each FNS region separately. 

However, no conclusive findings could be obtained because the samples from 

some regions were small, resulting in imprecise estimates with large standard 

errors around estimated costs (U.S. General Accounting Office, 1993). 

Bartlett et al. (2008) provided some additional analysis on the effects of location on costs. The purpose of their analysis was to examine how close an 

econometric method could come to calculating a nationwide average school 

meal cost, as estimated by the accounting method used in SLBCS-II. Using 

data from the School Food Authority Characteristics Survey, they conducted 

an econometric analysis in which region and urbanicity were treated as 

control variables. They found cost per meal to be negatively associated with 

urban location. They did not find cost per meal to vary by region.

Bartlett et al. (2008) invoked two assumptions in their analysis that are 

subject to scrutiny. First, they assumed that nonreimbursable costs (i.e., the 

cost of a la carte foods that do not meet the FNS nutrient or serving-size standards of “reimbursable” meals) are independent of reimbursable costs (those 

subsidized by USDA). This assumption allowed them to deduct estimated 

nonreimbursable costs from total costs to obtain reimbursable costs. Yet, it 

is unlikely that the two types of costs are independent because SFAs produce 

reimbursable and non-reimbursable meals with the same people, under the 

same conditions, and with ingredient sharing. Second, Bartlett et al. assumed 

Figure 2

Distribution of school food authorities (SFAs) by reported production cost per lunch, 

2005-06 school year1

Percent of SFA’s

1

In the School Lunch and Breakfast Cost Study-II, reported costs were costs paid by the SFA, while unreported 

costs were covered by the school district and not charged to the SFA.

Source: School Lunch and Breakfast Cost Study-II: Final Report (Bartlett et al., 2008). 

$0 -

<$1.40

$1.40 -

<$1.60

$1.60 -

<$1.80

$1.80 -

<$2.00

$2.00 -

<$2.20

$2.20 -

<$2.40

$2.40 -

<$2.60

$2.60 -

<$2.80

$2.80 -

<$3.00

$3.00 -

or more

0

5

10

15

20

25

30

1.0

12.7

5.7

2.9 2.3

5.6

17.8

13.7

Cost per lunch

26.8

11.8
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that breakfasts are a fixed fraction of the cost of a lunch across all SFAs. 

However, smaller studies (Hilleren, 2007; Sackin, 2008) indicate that cost 

per breakfast can vary depending on participation rates and style of service 

(in-classroom versus cafeteria, etc.). 

Bartlett’s assumptions may not fully account for SFA differences because 

not all SFAs serve a la carte foods or breakfasts or serve them in the same 

quantities. These assumptions could obscure differences associated with local 

input price differences. 
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Using a Statistical Model To Measure and 

Explain Geographic Cost Variation 

Cost-of-living indexes indicate substantial variation across the United States 

and suggest differences in labor and food costs for different locations. 

Foodservice costs may reflect these differences in the cost of living, but 

they also differ due to other factors. Those other factors include economies 

of scale based on the total number of meals served, the mix of student age 

groups who receive the meals, the mix of breakfasts and lunches served, 

types of food items served, or amounts and types of a la carte foods served, 

in addition to other management decisions. 

This section explains the econometric model we used to examine the effects 

of location after controlling for the many factors that can influence school 

meal costs. We then used these estimates and the underlying data to conduct 

simulations that identify which factors are the most important drivers of cost 

differences across locations. 

Developing the Cost Function Model

To measure the geographic variation in foodservice costs per meal after 

controlling for nongeographic factors, we used a multivariate translog cost 

function to examine the impact of the following variables on an SFA’s 

total school foodservice costs: (1) “output” as measured by total USDAreimbursable breakfasts and lunches served, (2) measures of input prices for 

labor, food, and supplies, and (3) several SFA characteristics. Translog cost 

functions have been used in a variety of empirical studies and can include 

one, two, or more measures of output as independent variables. As discussed 

previously, we used a single output cost function in which breakfasts and 

lunches were treated as one output, “meals.” This approach closely follows 

that used by many transportation economists (Allen and Liu, 1995; Baltagi 

et al., 1995; Caves et al., 1985) and MacDonald et al. (1999) and Ollinger et 

al. (2000), who examined meatpacking and poultry plants. In each of these 

studies, some firms produced many products and others only one. To deal 

with this discrepancy, the researchers treated all products as one common 

output and then accounted for product-specific differences with other variables. In this study, we apply this approach, for the first time, to the school 

meals setting. The single output specified here, the number of SFA “meals,” 

is the sum of the number of reimbursable breakfasts and lunches. The share 

of meals that are made up of breakfasts is accounted for in the model.5 This 

approach allows the model to accommodate both SFAs that serve breakfast 

and those that do not and relaxes the assumption that the cost of producing 

breakfast is a fixed fraction of the cost of producing lunch.

Due to limitations in the data set, the measure of the input price for food is 

constructed as total food expenditure per meal. This measure reflects both 

differences across SFAs in prices paid for individual food items and in food 

items served, making it difficult to separate these two influences on costs. 

To adjust for differences in food items served, we included a measure of 

meal “value” that captures the monetary value placed on the meal by the 

5We created three groups of breakfast 

service and account for them in our 

empirical model: no breakfasts, more 

than zero but less than one out of three 

meals is a breakfast, and more than 

one out of three meals are a breakfast. 

These groupings were selected based 

on model fit and are consistent with 

studies by Hilleren (2007) and Sackin 

(2008) in that they demonstrate that 

breakfast costs are high when few are 

served. We provide a detailed analysis 

of the contribution of breakfast volume 

to total cost per meal in appendix B.
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students and their parents. The measure of meal value is based on the quality/

appeal of meals and the resultant prices charged to students paying the full 

price, which is a price that the SFA establishes. Here, the concept of “highvalue” does not necessarily mean that the food is “healthier.”6 Instead, “highvalue” refers to meals that are more expensive to students and for which 

their parents are willing to pay a higher full price. For example, a high-value 

meal may include brand-name pizza, fresh salad, and tropical fruits rather 

than pizza made in the school or a central kitchen, canned peas, and canned 

peaches. The two meals have similar nutrient profiles but the high-value meal 

may be more appealing to both the student and/or the parents.

The underlying motivation for this approach was that SFAs that provide relatively more appealing foods can charge more to households paying the full 

out-of-pocket meal price than can SFAs that serve less appealing foods. We 

used a pair of variables to capture this “meal value” effect. The “high meal 

value” variable reflects the probability that an SFA is charging full price in 

the highest 10 percent of the distribution of SFA full prices (of those SFAs 

in the sample). The “low meal value” variable reflects the probability that an 

SFA is charging full price in the lowest 10 percent of the distribution of SFA 

full prices.7 Although this measure was developed to measure the effects 

of differences in food items on meal costs (which are then reflected in the 

SFA’s full price), to some extent, this measure can reflect differences in food 

item prices as well. 

SFA characteristics, particularly those related to breakfast servings, are 

important to note. About 10 percent of all SFAs do not serve any breakfasts 

(table 1). Breakfast accounts for fewer than one in three meals for most (75 

percent) SFAs. This result is important because Hilleren (2007) and Sackin 

(2008) found that costs per breakfast can be higher when few breakfasts are 

served. That is, previous research has found that there are economics of scale 

for serving breakfast. Initial analysis of this study’s sample of SFAs found 

similar results. 

Capital costs associated with producing school meals can include costs of 

building, equipment, and vehicles. Capital costs may be charged to the SFA 

only partially, if at all. Of the 1,655 SFAs in the original sample data set, 

nearly half (790) reported no capital costs at all, 1,285 identified no vehicle 

capital costs, and 1,607 had no building costs. In the final sample, about a 

third of the SFAs did not report any capital costs and the average capital cost 

for all SFAs was about 1.9 percent of total costs. These data suggest that 

many SFAs view themselves—or are viewed by the school district in which 

they operate—as providers of meal services in which schools districts furnish 

most capital investment and they incur none. 

Another set of variables captured whether or not: 

1. The SFA had a la carte sales per meal in excess of 10 cents.

2. The SFA reported any capital costs.

3. Less than 30 percent of all students enrolled in the SFA attended 

high school.

6Wagner et al. (2007) examined the 

relationship between school foodservice cost and compliance with USDA 

regulations, as a measure of nutritional 

quality. 

7Because the SFA’s foodservice costs 

can affect the SFA’s full price, we used 

a predicted probability that the SFA 

falls in the highest or lowest 10 percent 

of prices charged.
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4. More than 70 percent of students attended high school.

5. The SFA offered health benefits to foodservice workers.

6. The SFA used a foodservice management company for labor, 

purchasing food or supplies, or both.

7. The SFA used the universal free lunch option.

Table 1 provides descriptive statistics of the variables used in the model. 

Appendix table 1 provides detailed definitions of price measures, meals, 

and characteristics. Appendix A provides a more detailed discussion of our 

choice in methodology, model, variable definitions, and model selection.

In the year covered by the SFA Characteristics Survey, the average annual 

food service cost per meal across SFAs nationally was $2.70 (table 2). The 

average hourly wage (including fringe benefits) amounted to $11.72 across 

SFAs. Food expenditure per meal averaged $1.21. Other per meal costs, 

which averaged 25 cents per meal, include such costs as nonfood supplies 

and other miscellaneous items. The number of meals served varied widely, 

ranging from 2,700 meals per year to more than 143.6 million meals per year. 

The average annual number of meals served per SFA was about 500,000. 

Almost half (48 percent) of all SFAs were located in suburban areas, and 

about a fifth (19 percent) of all SFAs were in the Midwest.

After extensive testing, we selected a model that best fit the data. Each of the 

location and characteristics variables were subjected to a goodness-of-fit test 

(the Gallant-Jorgenson test), which showed that all of the variables significantly affected costs. Appendix A provides a detailed discussion of these 

statistical tests.8 Appendix A also provides the parameter estimates for the 

entire model, a detailed interpretation of the model results, estimated elasticities, and detailed discussions of how we controlled for meal-value. The 

model had good overall fit with an R2 value of 0.9817. 

The Gallant-Jorgenson test indicates that all variables included in the model 

significantly affect costs, but it does not indicate how much costs vary across 

locations. To examine cost differences, we needed to simulate foodservice 

costs under scenarios that varied one factor at a time. The next section uses 

such simulations to isolate geographic cost variation due to each input price 

and the characteristics. 

8We also tested numerous other 

variables, including central kitchens 

and meal planning methods, but these 

variables failed the Gallant-Jorgenson 

test for contribution to model fit. As 

suggested by an anonymous reviewer, 

we also examined the impact of State 

subsidies. The effects of these subsidies 

are statistically significant but insignificant in monetary terms. Because 

including the subsidies did not alter our 

substantive findings, we did not change 

our model.
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Table 1

Model variables and descriptive statistics, school year 2002-03

Model variables National average across SFAs

Cost per meal and input price measures: Dollars

Cost per meal (total annual foodservice cost divided by total reimbursable 

lunches and breakfasts served), 

2.70

Labor (average wage plus fringe benefits per hour per cafeteria worker) 11.72

Food (expenditures per reimbursable meal) 1.21

Other (expenditures per reimbursable meal) 0.25

Geography: Percent of all SFAs

Urbanicity—

Urban 13

Suburban 48

Rural 39

Food and Nutrition Service region—

Mid-Atlantic 12

Midwest 19

Mountain Plains 14

Northeast 11

Southeast 16

Southwest 14

Western 14

SFA characteristics: Number

Meals served per year, national average across SFAs 500,000 

Percent

SFA served no breakfasts 10

Between 0 and 33 percent of meals served are breakfasts 75

More than 33 percent of meals are breakfasts 15

Revenue from sales of a la carte items exceeds 10 cents per meal 64

Low meal value 9.4

High meal value 10.6

Reported capital costs 68

Less than 30 percent of SFA students attend high school 54

More than 70 percent of SFA students attend high school 2

SFA provides foodservice workers with health insurance 93

Foodservice management company provides some inputs 16 

More than 80 percent of schools are designated as universal free lunch 6

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the Economic 

Research Service, USDA.
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Isolating Geographic Cost Variation Due to 

Input Prices and Characteristics

The concern over geographic differences in foodservice costs focuses on 

differences in the cost of food and labor inputs, but other SFA characteristics also contribute to cost differences. We used simulations to isolate these 

effects and to measure the extent to which our measures of input prices and 

other SFA characteristics included in our model explain higher and lower 

costs across locations. To construct the estimates, we used the econometric 

model and characteristics of each location to compute “simulated per meal 

costs.” This estimated per meal cost includes the effects of only factors in the 

model. The set of simulated per meal costs then becomes the baseline against 

which other simulations are compared. For each simulation, we varied one 

factor at a time to examine the effect of that factor on estimated per meal 

costs (see box, “Simulating Contributions to Differences in Per Meal Costs”). 

The simulated per meal costs for each location are reported in table 2. The 

per meal costs in 13 of the 21 locations in table 2 are within 5 percent of the 

unadjusted average per meal cost, and only 4 locations had per meal costs 

that differed by more than 10 percent from the unadjusted average. 

Figure 3 illustrates how simulated per meal costs of rural, suburban, and 

urban areas of the seven FNS regions differ from the simulated national per 

meal cost. The Southeast, Southwest, and urban locations have per meal costs 

below the national average, and the Mid-Atlantic, Midwest, Northeast, and 

Western suburban locations have higher per meal costs (fig. 3). The difference in average per meal costs in these locations, compared with the national 

average, is due to the combined influence of differences in food, labor, 

supply prices, and characteristics included in the model across locations. 

These results suggest that average per meal cost differences across locations 

still exist even after controlling for SFA characteristics. For each location, 

the question then becomes which particular factors drive cost variation.

Table 2

Simulated per meal costs by urbanicity and Food and Nutrition Service (FNS) region, school year 2002-03

FNS region

Urbanicity Mid-Atlantic Midwest

Mountain 

Plains Northeast Southeast Southwest Western

------------------------------------------------------Dollars per meal--------------------------------------------

Rural 2.49 2.49 2.29 2.65 2.31 2.15 2.72

Suburban 3.16 3.05 2.47 2.84 2.55 2.48 2.78

Urban 2.30 2.10 2.09 2.02 2.38 1.94 2.17

Average1 2.93 2.74 2.34 2.70 2.40 2.22 2.64

1Weighted by the share of school food authorities (SFA) from each region. 

Assumptions: Total foodservice cost per meal is simulated by using the estimated cost equation together with mean location-specific values 

for wages and benefit rates, food expenditures per meal, supply expenditures per meal, SFA size (annual reimbursable breakfasts and 

lunches served), and other characteristics included in the model. 

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the Economic Research 

Service, USDA.
14

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Simulating Contributions to Differences in Per Meal Costs

We conducted 4 separate simulations of average per meal costs for each of the 21 locations. First, we substituted the 

location’s average values for input prices and characteristics into the econometric model and came up with an estimated 

cost per meal per location (see table 2). Then, we substituted one national average input price (there are three input 

prices) or national average characteristics into the model and estimated cost per meal again for each of the three input 

prices or national average characteristics. This gives us four estimated costs per meal with each one based on all but one 

of the location’s average values and one national average value. We then subtract these four estimated costs per meal 

containing one national average value from the estimated per meal cost based only on the location’s input prices and 

characteristics. The results show the contribution of each component to differences in per meal costs across locations. 

The table below summarizes the design of the four simulations.

A = Simulated per meal costs using location-specific averages for our measures of input prices and SFA characteristics.

B = Simulated per meal costs using national averages for our measures of input prices and SFA characteristics. 

C = Simulated per meal costs using location-specific averages for our measures of input prices and the national average 

for SFA characteristics.

D = Simulated per meal costs using the national average for food expenditure per meal and location-specific averages 

for SFA characteristics, wage and benefit rates, and supply expenditures per meal. 

E = Simulated per meal costs using the national average for wage and benefit rates and location-specific averages for 

SFA characteristics, food expenditures per meal, and supply expenditures per meal. 

F = Simulated per meal costs using the national average for supply expenditures per meal and location-specific averages 

for SFA characteristics, food expenditures per meal, and wage and benefit rates. 

Five measures of contributions to per meal costs follow from the five simulations:

A – B: Total cost difference due to all factors included in the cost function model (fig. 3 and table 4).

A - C: Cost differences due to location-specific differences in SFA characteristics (fig. 6 and table 4).

A – D: Cost differences due to location-specific food expenditure per meal (fig. 4 and table 4).

A – E: Cost differences due to location-specific wage and benefit rates (fig. 5 and table 4).

A – F: Cost differences due to location-specific supply expenditures per meal (table 4). 

Cost simulation specifications

Components of per meal cost differences 

Simulation

Food expenditures 

per meal

Wage and benefit 

rates 

Supply expenditures 

per meal

School food authority 

(SFA) characteristics

A Location Location Location Location

B National National National National 

C Location Location Location National

D National Location Location Location

E Location National Location Location

F Location Location National Location
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The second stage of the simulation was based on answering a series of 

“what if” questions. For example, to isolate the role of, say, wage rates on 

(simulated) per meal costs, one would ask the counterfactual question “What 

would per meal cost be if all SFAs had the same wage rate?” Simulated 

figures for each location are developed by replacing the wage rate for each 

location with a single national wage rate and then re-simulating per meal 

cost. The difference between the two simulations—one setting the wage 

rate at the location-specific level and the other using a single wage rate—

measures the contribution of differences in wage rates to differences in per 

meal cost. This methodology is used to derive the four separate contributions 

to cost differences: food expenditures per meal, labor prices (wage rates 

and fringe benefits on an hourly basis), supply expenditures per meal, and 

SFA characteristics (see box, “Simulating Contributions to Differences in Per 

Meal Costs”). The results are shown in figs. 4-6 and table 3.

When we considered the contribution of per meal food expenditures to 

differences in per meal costs for each location,9 we found that differences 

across SFAs in food expenditures may reflect different input prices for the 

same products or differences in food items served that are not captured 

by the study’s measure of meal value (fig. 4). Differences due to per meal 

food expenditures vary from about 38 cents below the national average for 

Southwestern urban SFAs to about 35 cents above the national average for 

Mid-Atlantic suburban SFAs (fig. 4). In addition, meal costs in suburban 

areas differ by about 50 cents between high-cost Mid-Atlantic SFAs and relatively low-cost Southeast SFAs. Finally, per meal food expenditures in rural 

and urban locations are relatively low.

Differences due to wage and benefit rates are shown in figure 5. In the case 

of labor, our data set contained detailed data on wage and benefit rates for 

different job categories in each SFA. Therefore, differences due to wages and 

benefits reflect only differences in input prices for labor and not differences 

in a mix of staffing across SFAs (see appendix A for details on wage and 

9 Due to data limitations, the study’s 

measure of food input price is the proxy 

total food expenditures per meal. See 

the section on “Developing the Model” 

and appendix A for details.

Mid-Atlantic Midwest Mountain Plains Northeast Southeast Southwest West

Figure 3

Simulated differences in per meal costs from the national average by location, school year 2002-03

Dollars per meal

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the Economic 

Research Service, USDA. See box, “Simulating Contributions to Differences in Per Meal Costs,” for an explanation of simulations.

Rural Suburban Urban

0.80

-0.80

0.60

-0.60

0.40

-0.40

0.20

-0.20

0
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benefit rates in our model). Mid-Atlantic, Midwestern, and Western urban 

and suburban locations have relatively high labor costs. Meal costs attributable to wage and benefit rates differ by about 50 cents between high-cost 

Western suburban SFAs and low-cost Southwestern rural SFAs. 

In addition to food expenditures and labor, SFA characteristics contribute 

substantially to cost variation (fig. 6). For example, in the Northeast, per meal 

costs differ very sharply between suburban/rural SFAs, with SFA character0.40

-0.40

0.30

-0.30

0.20

-0.20

0.10

-0.10

0

Mid-Atlantic Midwest Mountain Plains Northeast Southeast Southwest West

Figure 4

Contribution of per meal food expenditures to differences between locations’ simulated per meal costs 

and the national average, school year 2002-031

Dollars per meal

1

Due to data limitations, food expenditures per meal are used as a proxy for food input prices. This measure reflects both differences in food 

prices and food items served in a location.

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the Economic Research 

Service, USDA. See box, “Simulating Contributions to Differences in Per Meal Costs,” for an explanation of simulations.

Rural Suburban Urban

-0.40

0.30

-0.30

0.20

-0.20

0.10

-0.10

0

Mid-Atlantic Midwest Mountain Plains Northeast Southeast Southwest West

Figure 5

Contribution of wage rates and fringe benefits to differences between locations’ simulated per meal costs 

and the national average, school year 2002-03

Dollars per meal

Source: Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the 

Economic Research Service, USDA.

Rural Suburban Urban
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istics adding about 40 cents per meal to the national average cost per meal, 

and urban SFAs, with SFA characteristics subtracting about 20 cents from 

the national average cost per meal.

Four major SFA characteristics drive these cost differences within the 

Northeast across urban, suburban and rural areas: SFA size, as measured by 

the number of meals served; breakfast volume; meal value; and sales of a 

la carte foods. Northeastern urban SFAs are, on average, seven times larger 

than the national average, whereas Northeastern rural and suburban SFAs are 

one-tenth and one-fifth the size of the national average. The sizes of the SFAs 

are associated with economies of scale. Thus, costs per meal are lower in the 

far larger SFAs and higher in the smaller SFAs.10 In the urban Northeast, 

20 percent of SFAs serve more than 33 percent of their meals as breakfasts. 

In contrast, about 10 percent of Northeastern rural SFAs serve more than 

33 percent of their meals as breakfasts and no Northeastern suburban SFAs 

serve more than 33 percent of its meals as breakfast.

The other two main drivers of cost differences are meal values, as reflected in 

prices paid by full-price students, and a la carte revenues. Northeastern urban 

SFAs tend to serve lower value meals, while Northeastern suburban SFAs 

tend to have higher value meals. Northeastern rural SFAs serve meals with a 

meal value at about the average across SFAs. Estimates based on our model 

suggest that high-value meals were 23-27 cents more costly to produce than 

low-value meals. In addition, about 85 percent of Northeastern rural and 

suburban SFAs have a la carte sales that are more than 10 cents per meal, 

whereas 65 percent of Northeastern urban SFAs have a la carte sales that are 

more than 10 cents per meal.11

In the other locations as well, much of the cost difference due to SFA characteristics can be attributed to these four major drivers. Southwestern urban 

SFAs, for example, serve about half as many meals as Northeastern urban 

10See appendix A for a discussion 

of the econometric model and its 

estimates for economies of scale 

and other cost-influencing factors 

highlighted in this section.

11The costs of a la carte foods are 

included in our measure of per meal 

costs.

0.30

0.40

0.50

-0.30

0.20

-0.20

0.10

-0.10

0

Mid-Atlantic Midwest Mountain Plains Northeast Southeast Southwest West

Figure 6

Contribution of school food authority characteristics to differences between locations’ simulated per 

meal costs and the national average, school year 2002-03

Dollars per meal

Source: Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the 

Economic Research Service, USDA.

Rural Suburban Urban
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SFAs do but also serve more breakfasts and lower value meals, giving them 

even lower costs than urban Northeastern SFAs.

Other characteristics contribute to costs, but do not vary by location as much 

as the major drivers do. Health benefits, for example, contribute substantially 

to costs, but most SFAs offer them.

Table 3

Major contributors to differences in per meal costs from the national average vary by location, 

school year 2002-03

Contributors to meal cost differences

Region Urbanicity

Input price measures 

School food 

authority (SFA) 

characteristics

Total

difference

Food expenditures 

per meal1

Wage and 

benefit rates

Supply expenditures per 

meal

-----------------------------------Dollars per meal-----------------------------------

Mid-Atlantic

Rural -0.14 -0.03 -0.03 0.03 -0.17

Suburban 0.35 0.09 -0.02 0.13 0.55

Urban -0.08 0.12 0.00 -0.16 -0.12

Midwest

Rural -0.05 0.08 -0.01 0.16 0.18

Suburban 0.32 0.12 0.04 0.15 0.63

Urban -0.15 0.09 0.01 -0.03 -0.08

Mountain Plains

Rural -0.09 -0.17 -0.02 0.15 -0.13

Suburban 0.11 -0.07 -0.02 0.04 0.06

Urban -0.19 -0.04 0.01 -0.06 -0.28

Northeast

Rural 0.05 -0.04 -0.07 0.41 0.35

Suburban 0.17 0.03 -0.02 0.36 0.54

Urban -0.11 0.03 -0.10 -0.17 -0.35

Southeast

Rural -0.23 -0.14 -0.01 -0.09 -0.47

Suburban -0.15 0.01 0.05 -0.01 -0.10

Urban -0.12 -0.01 0.05 -0.17 -0.25

Southwest

Rural -0.22 -0.29 -0.04 -0.14 -0.69

Suburban -0.02 -0.20 -0.02 -0.08 -0.32

Urban -0.38 -0.10 -0.01 -0.26 -0.75

Western

Rural 0.06 0.18 0.00 0.16 0.40

Suburban 0.09 0.20 0.03 0.13 0.45

Urban -0.20 0.15 0.05 -0.11 -0.11

Mean absolute 

difference2 0.151 0.104 0.029 0.143 0.332

Note: For each row, the highlighted cell identifies the factor contributing the most to the difference between that location’s simulated costs per 

meal and the national average.

1Due to data limitations, per meal food and supply expenditures were used as proxies for food input prices and supply input prices. The measure for food reflects both differences in location-specific prices for food items and differences in food items served. 

2These are means of the absolute values of table cell values. The total of the differences in each column exceeds the average difference because components may be of different signs. For example, wage and benefit rates contribute positively to cost in Western urban SFAs, while the 

total cost difference is negative.

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the Economic Research 

Service, USDA.
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Relative Contribution of Cost-Influencing Factors

In table 3, we examine the relative contribution of each factor to cost variation in each of the 21 locations. We also examine the overall contribution 

of the location-specific differences in food expenditures per meal, wage and 

hourly benefits, and supplies relative to the impact of SFA characteristics on 

costs. 

Table 3 allows us to distinguish between location-specific food, labor, 

and supply costs from nongeographic characteristics. After accounting for 

nongeographic characteristics of SFAs, average foodservice costs per reimbursable meal (including all breakfasts and lunches) in 21 locations (rural, 

urban, and suburban areas in each of 7 U.S. regions) range from 21 percent 

below the national average for the rural Southwest to 19 percent above in the 

suburban Midwest. The Southwest and Southeast regions had average costs 

per meal below the national average, and urban locations had lower average 

costs per meal than their rural and suburban counterparts. 

Any one location can differ from others in terms of which factor(s) make its 

per meal costs different from the national average. For example, one location 

may have relatively high wage costs that result in a relatively high per meal 

costs, while another may have especially small SFAs or some other SFA 

characteristic driving its costs. For each row in table 3, the highlighted cells 

identify the factor that contributes the most to the difference between that 

location’s simulated per meal costs and the national average per meal cost. In 

11 of the 21 locations, food expenditures per meal are the largest drivers of 

differences in simulated per meal costs. Wage and benefit rates are the largest 

contributors in five other locations. SFA characteristics, particularly total 

number of meals served, the study’s measure of meal value, and the presence 

of a la carte foods, were the most important drivers in five locations. Supply 

costs per meal contribute minimally to cost differences.

Table 3 can also be used to derive a measure of the overall impact of differences in location-specific food expenditures per meal, wage and hourly benefits, and supplies relative to the impact of SFA characteristics. The bottom 

row of the table reports the mean absolute difference for each input price and 

the characteristics. The mean absolute difference is defined as the average 

(or mean) of the absolute values of the table cells. For food, the mean absolute difference is plus or minus 15 cents, indicating that, on average across 

the Nation, differences in food expenditures per meal contribute about this 

much to the location’s difference from the national average per meal cost. 

The other mean absolute differences are 10 cents for labor (wage and benefit 

rates), 3 cents for supplies, and 14 cents for SFA characteristics. The sum of 

differences associated with our measures of input prices is plus or minus 28 

cents, twice the difference associated with SFA characteristics.

Note that, although due to data limitations, we may not have been able to 

control for all factors, particularly those related to meal values and breakfasts, the results do indicate that, even after controlling for SFA characteristics, location-related input prices have a significant effect. 
20

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Conclusions

SFAs that participate in USDA’s National School Lunch and School 

Breakfast Programs are required to be nonprofit but are generally expected 

by local school districts to cover their variable costs. Some SFAs may have 

difficulty reaching this goal because of differences in local prices for food, 

labor, and supplies or because of their own operational characteristics. 

Budgetary issues can become issues of student participation in school meals 

depending on how SFAs adjust to close gaps between costs and revenues and 

the mix of low-cost healthy foods available to the SFA. 

The purposes of this study were (1) to measure how large the differences are 

in school meal costs across locations, regions, and levels of urbanicity, after 

accounting for SFA characteristics other than prices, and (2) to measure the 

contributions of factors that help explain which locations have higher costs 

and which have lower costs. 

In answer to the first issue, we found that average adjusted per meal foodservice costs (including all breakfasts and lunches) in SFAs grouped into 21 

locations (rural, urban, and suburban areas in each of 7 U.S. regions) differed 

by as much as 20 percent above or below the national average. The Southeast 

and Southwest regions had consistently lower adjusted per meal costs on 

average, and urban locations had lower adjusted per meal costs on average 

than their rural and suburban counterparts. 

We used an econometric model to account for SFA characteristics and estimate how much of the cost variation across locations is due to differences in 

our measures of input prices versus SFA characteristics. Although the SFA 

Characteristics Survey offered a large national sample stratified by region 

and urbanicity, data limitations posed challenges. In particular, labor wage 

and benefits rates were available, but food prices were not, forcing us to use 

food expenditures per meal as a proxy.

The study results show that the main drivers of cost differences varied by 

location. Labor costs were the largest contributors in five locations. SFA 

characteristics (particularly the total number of meals served), the study’s 

measure of meal value, and the presence of a la carte foods were the most 

important drivers in five locations. In the remaining 11 locations, the largest 

contributor to cost variation was differences in total food expenditures per 

meal, which include differences in food item prices and food items served. 

Overall, location-associated differences in labor costs and per meal food 

and supply expenditures outweighed cost differences associated with SFA 

characteristics.

The survey-based estimates of per meal costs and the simulations do not 

directly assess the adequacy of a reimbursement rate for NSLP lunches or 

SBP breakfasts, the issue addressed by the SLBCS-II study. Because we 

combine lunches and breakfasts to generate an overall per meal cost estimate, 

results are not directly comparable to those of the SLBCS-II. Neither do the 

findings answer the question of whether the USDA reimbursement is sufficient to produce a nutritious meal because the data used in the study did not 

include information on which SFAs produced meals that met USDA nutrition 

standards. The findings do not imply that higher cost SFAs are operating at 
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a loss. Higher cost SFAs may also be obtaining higher revenues from such 

sources as higher meal prices charged to students paying full price for meals, 

increased sales of a la carte foods, or State or local subsidies to the SFA. 

More research that includes data on revenues as well as costs is necessary to 

answer the question of whether SFAs in some locations of the country are 

more likely to operate at a loss. 

Although the findings do not answer all of the complex questions about reimbursement rates, nutrition, and the likelihood of SFAs operating at a loss, the 

findings do provide information on the two issues the study was designed 

to address—the sizes of differences of per meal cost across SFAs and the 

factors that help explain those differences. Complementing this analysis with 

more data and research on school revenues and meal quality would further 

enhance understanding of school food finances and their implications for 

meeting Federal child nutrition policy objectives.
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References

Allen, W. Bruce, and Dong Liu, “Service Quality and Motor Carrier Costs: 

An Empirical Analysis,” The Review of Economics and Statistics 77(3): 

499-510, August 1995.

Antle, John M. “No Such Thing as a Free Safe Lunch: the Cost of 

Food Safety Regulation in the Meat Industry,” American Journal of 

Agricultural Economics 82(2):310-22. May 2000.

Baltagi, Badi H., James M. Griffin, and Daniel P. Rich. “Airline 

Deregulation: The Cost Pieces of the Puzzle,” International Economic 

Review 36(1):245-60, February 1995.

Bartlett, Susan, Frederic Glantz, and Christopher Logan. School Lunch and 

Breakfast Cost Study-II: Final Report, Special Nutrition Programs Report 

No. CN-08-MCII, U.S. Department of Agriculture, Food and Nutrition 

Service, Office of Research, Nutrition and Analysis, Project Officer: 

Patricia McKinney and John R. Endahl, April 2008.

Baumol, William J., John C. Panzar, and Robert D. Willig. Contestable 

Markets and the Theory of Industry Structure, New York: Harcourt Brace 

Jovanovich, June 1982.

Berndt, Ernst R. The Practice of Econometrics: Classic and Contemporary, 

New York: Addison Wesley Publishing, 1991.

Blackorby, C., and R.R. Russell. “The Morishima Elasticity of Substitution: 

Symmetry, Consistency, Separability, and its Relationship to the Hicks 

and Allen Elasticities,” Review of Economic Studies 48(1):147-58, 

January 1981.

Blackorby, C., and R.R. Russell. “Will the Real Elasticity of Substitution 

Please Stand Up? A Comparison of the Allen/Uzawa and Morishima 

Elasticities,” American Economic Review 49(4):882-88, September 1989.

Caves, Douglas W., Laurits R. Christensen, Michael W. Tretheway, and 

Robert J. Windle. “Network Effects and the Measurement of Returns 

to Scale and Density for U.S. Railroads,” Andrew F. Daugherty, ed., 

Analytical Studies in Transport Economics, Cambridge, UK: Cambridge 

University Press, 1985.

Gallant, A. Ronald, and Dale W. Jorgenson. “Statistical Inference for a 

System of Simultaneous, Non-Linear, Implicit Equations in the Context of 

Instrumental Variable Estimation,” Journal of Econometrics 11(2-3):275-

302, October-December 1979.

Glantz, Frederic B., Christopher Logan, Hope M. Weiner, Michael Battaglia, 

Ellen Gorowitz. School Lunch and Breakfast Cost Study, U.S. Department 

of Agriculture, Food and Nutrition Service, Office of Analysis and 

Evaluation, Project Officer: John R. Endahl, October 1994.
23

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Hilleren, Heather. School Breakfast Program Cost/Benefit Analysis: 

Achieving a profitable SBP, University of Wisconsin Extension Family 

Living Program, Madison, WI, 2007.

Mathematica Policy Research, Inc. School Food Authority Characteristics 

Survey of 2002-03, 2004

MacDonald, James M., Michael Ollinger, Kenneth Nelson, and Charles 

Handy. Consolidation in U.S. Meatpacking, Agricultural Economics 

Report No. 785, U.S. Department of Agriculture, Economic Research 

Service, March 1999.

Missouri Economic and Research Information Center, Missouri Department 

of Economic Development. Cost of Living Indices, 2009.

National Center for Education Statistics, U.S. Department of Education. The 

Common Core of Data, http://nces.ed.gov/ccd/, 2004.

Oliveira, V. The Food Assistance Landscape, FY 2009 Annual Report, 

Economic Information Bulletin No. 6-7, U.S. Department of Agriculture, 

Economic Research Service, March 2010.

Ollinger, Michael, James MacDonald, and Milton Madison. Structural 

Change in U.S. Chicken and Turkey Slaughter, Agricultural Economic 

Report No. 787, U.S. Department of Agriculture, Economic Research 

Service, November 2000.

Ponza, Michael. NSLP/SBP Access, Participation, Eligibility, and Certification Study—Erroneous Payments in the NSLP and SBP, Volume I: 

Study Findings, Special Nutrition Programs Report No. CN-07-APEC, 

U.S. Department of Agriculture, Food and Nutrition Service, Office 

of Research, Nutrition and Analysis, Project Officer: John R. Endahl, 

November 2007.

Sackin, Barry. “Analysis of a Proposed Breakfast Mandate for California,” 

submitted to the California School Nutrition Association, 2008.

St. Pierre, Robert, Mary Kay Fox, Michael Puma, Frederic Glantz, and Marc 

Moss. Child Nutrition Program Operations Study: First Year Report, U.S. 

Department of Agriculture, Food and Nutrition Service, Office of Analysis 

and Evaluation, Project Officer: John R. Endahl, August 1991.

St. Pierre, Robert, Mary Kay Fox, Michael Puma, Frederic Glantz, and Marc 

Moss. Child Nutrition Program Operations Study: Second Year Report, 

U.S. Department of Agriculture, Food and Nutrition Service, Office of 

Analysis and Evaluation, Project Officer: John R. Endahl, June 1992.

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Costs in the National School Lunch Program, GAO/RCED-94-32BR, 

December 1993.

Wagner, Barbara, Benjamin Senauer, and C. Ford Runge. “An Empirical 

Analysis of and Policy Recommendations to Improve the Nutritional 

Quality of School Meals,” Review of Agricultural Economics 29(4):672-

88, December 2007.
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Appendix A: The Model and Model 

Diagnostics

Econometric Model

Partial and total cost function analyses have been used to examine costs. 

Partial cost analyses are models like Bartlett, Glanz, and Logan (2008) in 

which costs are regressed on a group of variables thought to affect costs. The 

model may or may not be grounded in theory. Three types of commonly used 

total cost functions are the Cobb-Douglas, Constant Elasticity of Substitution 

(CES), and translog cost functions. 

Only the translog cost function allows for more than two inputs, places no a 

priori restrictions on substitution elasticities—i.e., the ratio at which inputs, 

such as capital and labor, substitute for each other—and is consistent with 

constraints typically assumed by economists (Berndt, 1991). In addition, 

this second-order Taylor expansion in log form is very general and permits 

a variety of possible production relationships, including returns to scale, 

optimal input shares that vary with the level of output and characteristics, and 

nonconstant elasticities of input demand.12 Different specifications allow for 

alternative ways in which characteristics can be combined to examine their 

impact on costs, which is important because it allows us to accommodate the 

diverse production practices followed by SFAs across the United States. 

The translog cost function can be adapted for either single or multiple products. A single product cost function assumes that one product may or may 

not have slight variations. Product variations are accounted for by model 

characteristics. In the context of this study, breakfasts and lunches could be 

described as generic meals with different characteristics. A multiple-product 

(or multiproduct) cost function (Baumol, Panzar, and Willig, 1982) allows 

for two or more distinct products. 

The school meal program includes three types of meals: breakfasts, lunches, 

and after-school snacks. SFAs may offer only one meal (e.g., lunches), all 

three meals, or any combination of two meals. School lunches are by far 

the most popular meal in terms of meals served, but breakfasts must be 

accounted for because a substantial number of them are served. After-school 

snacks are a much less popular item and are generally very low cost. These 

were dropped after they were shown to be insignificant to model fit. 

We use a single-product cost function for several reasons. First, this analysis 

requires a model that is applicable both to SFAs serving only lunch as well 

as those serving multiple meal types. Further, multiproduct cost functions 

are most applicable for distinct products, so determining whether lunches 

and breakfasts are types of one product (meals) or two very distinct meals 

is important. SLBCS-II results suggest that meal preparation costs differ 

greatly, but these cost differences melt away when breakfast participation 

rates are low, which is important because most SFAs in our sample serve 

mostly lunches (Hilleren, 2007; Sackin, 2008). 

12 The use of production functions in economic analyses date to 

the 1920s when economists used 

them to estimate labor productivity. Since then, there have been a 

number of refinements, including 

the introduction of the dual of the 

production function called the cost 

function. These cost functions 

have evolved as a way to explain 

average labor productivity, quantify 

relationships among inputs and 

outputs in agriculture, estimate 

substitution elasticities among 

inputs, and estimate economies of 

scale. The translog cost function 

was seen as an advancement of 

production theory from estimation 

of labor productivity from production functions to estimation of cost 

functions with no prior restrictions 

on substitution elasticities.
25

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Lunch and breakfast costs can also vary widely. Cost differences between 

lunches and breakfasts at SFAs may be smaller if they serve high-cost breakfasts and low-cost lunches. Intuitively, this makes sense. A breakfast can 

be a sandwich, potatoes, orange juice and milk, while a lunch could be a 

hamburger, French fries, a vegetable, and milk. These differences suggest a 

continuum of costs in which breakfast and lunch costs may be interchangeable. We confirmed this hypothesis with regression analyses that showed 

no difference in costs between breakfasts and lunches unless breakfasts 

comprised more than one-third of all meals. 

The single product translog cost function with product characteristics 

describing variations of one single product has been used by economists 

under different conditions. Output was defined as ton-miles for hauling 

freight in trucking (Allen and Liu, 1995) and railroads (Caves, Christensen, 

Tretheway, and Windle, 1985), passenger-miles in airlines (Baltagi, Griffin, 

and Rich, 1995), and pounds of meat in meat and poultry (MacDonald et al., 

1999; Ollinger, MacDonald, and Madison, 2000). Output characteristics were 

used to distinguish variations in the common output (e.g. processed pork 

rather than raw pork in hog slaughter or size of shipments, types of routes, 

locations served, etc., in transportation industries).

Model Details

The econometric model (equation A.1) is quite long but actually quite 

simple:

•	ln is the natural log operator

•	Ci

 is the cost of school meals in SFAi 

•	Pi represents prices for labor (PLAB), food (PFOOD), and nonfood supplies 

(PSUPPLY)

•	MEALS refers to the total of USDA-reimbursable breakfasts and lunches

•	CURBANICITY represents the type of metropolitan statistical area of the 

SFA—CSUBURB and CRURAL

•	CREGION represents the Food Nutrition Service region of the SFA—

CATLANTIC , CMIDWEST, CMOUNTAIN, CNORTHEAST, CSOUTHEAST, 

CSOUTHWEST, and CWEST. 

The other C variables are additional dummy variables reflecting SFA characteristics as defined in appendix table 1 and then discussed further. 
26

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2

1 ( .1) ln ln ln ln ln 2

1 ln ) ln *ln *ln 2

*ln *ln

i

I i i i

I

ij i j m

i i j

Mi i URBANICITY ij URBANICITY j MM i i i j

MA URBANICITY i REGION ij REGION j

i i i j

MR RE

 A C = + + P P * P MEALS 0 i i i

( MEALS MEALS P C C P

C MEALS C C P

C

α β β γ

γ σ

σ π π

π

γ

+

+ +∑ + + +

+ + +

∑ ∑∑

∑ ∑ ∑

∑ ∑ ∑ ∑

*ln *

*ln *ln *

*ln *ln

i j i

i i i

i

GION ARi URBANICITY REGION B BFAST

j i i i

Bij BFAST i BMi BFAST ij BFASTi URBANICITY L lacarte

i j i i j

lacarte i lacarte cap cap capP cap il Ml i i

MEALS C C C

w C P C MEALS C C C

C P C MEALS C C

π ψ

ω ω φ

δ δ

Σ Σ

Σ Σ

Σφ φ Σ

+ + +

∑ +∑ + + +

+ + +

∑ ∑

∑

_ _

_ _

_

*ln

*ln *ln

*ln *

* *ln

*ln

i i

i

i

capM cap HSi HIGH SCHOOL HSij HIGH SCHOOL j

i i j

HSMi HIGH SCHOOL HSiK HIGH SCHOOLi cap

i i

HSiS HIGH SCHOOLi SERVICE H HEALTH Hi HEALTH i

i i

HM HEALTH

P

C MEALS C C P

C MEALS C C

C C C C P

C ME

δ ψ ψ

ψ ψ

ψ ψ ψ

ψ

+ +∑ +∑Σ

+∑ +∑ +

∑ + +∑ +

*

*ln *ln

*ln *ln

ln *ln ln *ln

i

i i

i i iI i

HRi HEALTH REGION S SERVICE

i

Si Service i SM Service

i

F FREE F FREE i FM FREE

i

i Value ij Value j MV Value i

i i j i

ALS C C C

C P C MEALS

C C P C MEALS

C w C P C MEALS

π υ

υ υ

ψ ψ ψ

ω ω ε

Σ

+ + +

+ +

+∑ + +

+ + +

∑

∑ ∑ ∑ ∑
27

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Appendix table 1

Definitions of cost function variables

Variable Definition

Cost Total wage and fringe benefit, food, including donated food, and supply costs

PLAB _ _ _ _ LAB P =average SFA wage average SFA benefits +

where

22 3 4 _ * tan _ * _ *sup _ 29 29 29 Mean wage assis t wage cook wage ervisor wage = + +

( _ _ ) _ _ *[ ] ( _ _ _ )SFA fringe benefits Mean benefits Mean wage SFA wages SFA fringe benefits = +

PFOOD _

_ FOOD

FOOD COST P NUMBER REIMBURSABLE MEALS = −

where FOOD-COST = purchased food plus donated commodities used + State and processor charges 

related to donated commodities + foodservice management fees. Note that food costs include expenditures needed to prepare a la carte and snack foods in addition to expenditures for reimbursable school 

meals. 

PSUPPLY _ _

_ _ SUPPLY

NON FOOD COST P NUMBER REIMBURABLE MEALS =

NON_FOOD_COST = supplies and expendable equipment + utilities + other contracted or purchased 

services + other direct costs + indirect costs charged to SFA account.

MEALS Number of reimbursable breakfasts and lunches served by the SFA

Location Includes region and urbanicity variables

CSUBURB One if Common Core data indicate that SFA is a suburban area. Zero otherwise.

CRUR One if Common Core data indicate that SFA is a rural area. Zero otherwise.

CATLANTIC One if SFA located in FNS “Mid-Atlantic” region and zero otherwise.

CMIDWEST One if SFA located in FNS “Midwest” region and zero otherwise.

CMOUNT One if SFA located in FNS “Mountain” region and zero otherwise.

CNORTHEAST One if SFA located in FNS “Northeast” region and zero otherwise.

CSOUTHWEST One if SFA located in FNS “Southwest” region and zero otherwise.

CWEST One if SFA located in FNS “Western” region and zero otherwise.

SFA characteristics 

CBFAST0 One if SFA served no breakfasts and zero otherwise.

CBFAST33 One if breakfasts as share of all meals is greater than 33 percent and zero otherwise. Meals served 

equals number of breakfasts plus lunches.

CVALUE_LO Probability that meal value fell in the 10 percentile or lower of the value distribution.

CVALUE_HI Probability that meal value fell in the 90 percentile or higher of the value distribution.

CLACARTE One if revenues from a la carte food exceeds $0.10 per meal, zero otherwise. A la carte foods come 

from survey question asking for food sales, such as a la carte foods. 

CCAP One if SFA had capital costs and zero otherwise.

CHIGH_SCHOOL_LO One if the number of high school students enrolled in NSLP program as a share of all students (elementary, middle and high school) enrolled in the NSLP program is less than 30 percent. It is zero otherwise.

CHIGH_SCHOOL_HI One if the number of high school students as a share of all students is more than 70 percent. It is zero 

otherwise.

CHEALTH One if SFA provides workers with health insurance and zero otherwise.

CFOOD_SERVICE One if service management company provides some or all (1) workers, (2) food or supplies purchasing, 

or (3) food or supplies purchasing and labor. Zero otherwise.

CFREE One if more than 80 percent of schools in the SFA are designated as free lunch for all schools and zero 

otherwise.
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Input Price Measures

Input prices include labor, food, and supplies—PLAB , PFOOD , and PSUPPLY. 

The price of labor is the average wage per hour plus average employee benefits per hour. Average wage equals the weighted average wage of a typical 

kitchen staff that includes 22 assistants, 3 cooks, and 4 supervisors for a total 

of 29 workers and average employee benefits are average wages times fringe 

benefits as a share of total labor costs, where total labor costs are the sum of 

wage and benefit costs. 

The wages of kitchen assistants (assistant_wage), cooks (cook_wage), and 

supervisors (supervisor_wage) come from the survey and are specific to each 

SFA. The mix of assistants, cooks, and supervisors are averages across SFAs. 

Fringe benefit (cost_fringe_benefits) and wage (cost_wages) costs come from 

the survey and are specific for each SFA. 

Ideally, the cost function would be estimated using actual food prices similar 

to those we have for labor, but we lack these data. Hence, prices of food and 

supplies are defined as total expenditures in those categories divided by the 

total number of USDA breakfasts and lunches. Note that expenditures for 

food and supplies are influenced by SFA choices and thus our price measures 

for these inputs are endogenous. Further, a la carte foods contribute to food 

and supply expenditures but are not counted in reimbursable meals. We 

control for these issues by including indicators of meal value and a la carte 

sales volume described below.

Characteristics of School Food Authorities

Breakfasts: All SFAs offer the USDA reimbursable-type school lunch (that 

is, one that meets FNS nutrient and serving size standards), and many also 

provide other offerings. About 90 percent of all SFAs in our sample serve 

breakfasts as part of the School Breakfast Program, but they account for only 

a small share of all meals. Preliminary tests suggested that there is no difference in costs between lunches and breakfasts when relatively few breakfasts 

are served (see discussion in appendix B). This suggested that one important 

characteristic is whether SFAs serve no, few, or many breakfasts. 

We examined several variables that could describe different characteristics of 

breakfasts. We settled on two dummy variables: CBFAST0 and CBFAST33—no 

breakfasts and more than one out of three meals served is a breakfast. About 

75 percent of all SFAs report that they serve some breakfasts but that breakfasts account for less than one out of three meals; about 10 percent of the 

total number of SFAs offer no breakfasts at all (see table 1). Producing just 

a few breakfasts is costly per unit because there are setup, preparation, and 

cleanup costs regardless of the number of breakfasts served. Hilleren (2007) 

and Sackin (2008) confirm that breakfast costs are high when few are served. 

We provide a detailed analysis of the contribution of breakfast volume to 

total cost per meal in appendix B.

A la Carte Foods: A la carte foods are particularly vexing because they can 

be a substantial cost to SFAs, yet the SFA Characteristics Survey did not 

indicate the number of students purchasing them. The only available data 
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indicating their importance to the SFA are revenue data. This is probably 

because those foods are sold in more flexible quantities than USDA meals, 

acting as snacks, as whole meals, or as additional side items to a USDA 

meal. In general, they appear to be snacks or side items. More than a third of 

all SFAs report less than 10 cents per meal in a la carte revenue, and more 

than half of SFAs obtain less than 20 cents per meal.13 A variable representing after-school snacks was also tested but was dropped because it was 

not significant. 

Meal Value: SFAs may incur higher costs by serving higher priced, more 

popular foods, such as fresh vegetables, while others may serve lower priced 

foods, such as canned vegetables. We need to account for these higher costs 

driven by the use of high-value inputs because assuming that all SFAs use 

the same type of inputs would bias the results. Despite its importance, only 

one previous study has examined the effect of quality on foodservice costs. 

That study focused on compliance with USDA regulations as a measure of 

nutritional quality (Wagner et al., 2007). 

Previous economic analyses of other industries provide a strategy for 

accounting for value (quality). Economists, such as Antle (2000), interpret 

prices as measures of quality. Making this assumption about the link between 

prices and quality and using a consumption model of the demand for food, 

we regress meal prices on income, wealth, and variables representing types 

of inputs and school meal and SFA characteristics.

Two measures of meal value were estimated: the probabilities of an SFA 

falling in the 90-99th percentile of food prices (high value) and the probability of an SFA falling in the 0-10th percentile of food prices. Estimation 

proceeded in the following way. First, we ranked the average price paid for a 

full-priced meal by each SFA from highest to lowest price. Then, we recognized that truly high-value meals exist at the 90th percentile or higher of all 

average prices paid for a school lunch and truly lower value meals occupy the 

10th percentile or lower of all average prices of school lunches. Next, for the 

higher value group, we set a dependent variable equal to one if it fell in the 

90-99th percentile and zero otherwise, and, for the lower value group, we set 

the dependent variable equal to one if it fell in the 10th percentile or lower 

and zero otherwise. After that, we defined the variables shown in appendix 

table 2 as independent variables and, using a probit regression, estimated the 

probabilities of an SFA serving high-value or low-value meals—i.e., falling 

in the 10th or lower percentile or falling in the 90th or higher percentile. We 

label the predicted probabilities of a meal price in the 10th or lower or 90th 

or higher percentile as CVALUE_LO and CVALUE_HI in appendix table 2. 

Capital Costs: Most SFAs did not report any capital costs and, of those 

that did, most indicated those costs were quite small. The school districts of 

these SFAs likely covered most or all capital costs—i.e., the school district 

provides a place to eat and the necessary facilities, and the SFA staffs those 

facilities. However, since some SFAs report some capital costs and they 

are a real cost of providing a meal, we include a dummy variable (CCAP) to 

account for differences in accounting for capital costs.14

13The cutoff values for breakfasts 

and a la carte revenues were chosen 

by trial and error. That is, these cutoff values gave the best model fit.

14No SFAs accounted for cafeteria 

space and other major capital costs 

that would be considered a capital 

cost by restaurants.
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Appendix table 2

Probit estimates of high and low meal value indicators

Variable

90th or 

higher 

percentile

10th or 

lower 

percentile Definition

Intercept -3.875***

(0.367)

2.224***

(0.377) Intercept term

INPUTS

Commodities as a share of all 

food costs 0.619

(0.402)

-1.882***

(0.402)

Cost of commodities as a share of all food costs. 

Food cost equals (purchased food + donated commodities used + State 

and processor charges related to donated commodities)

Purchased food as share of food 

costs

0.905***

(0.292)

-1.071***

(0.166) Value of purchased food divided by the value of all food

Average wage of an assistant 0.082***

(0.010)

0.048***

(0.012) Average pay rate for a foodservice assistant

DEMAND

Median family income in SFA 0.0105***

(0.0028)

-0.0491***

(0.0060)

Median family income in SFA in thousands

Poverty level 3.735***

(0.358)

1.173**

(0.361) Poverty level of the SFA

Median housing value 0.0042***

(0.005)

-0.0054***

(0.0010) Median housing value in SFA in thousands

Share of students not eligible to 

pay reduced or free rates paying 

for full price meal

-0.0011*

(0.0006)

0.0025***

(0.0006)

Students paying full price per meal divided by all students not eligible for 

reduced or free rates

Share of students receiving free 

lunches

-2.351***

(0.213)

0.951***

(0.187)

Students approved for free lunch as a share of all students

Salaries and wages per student 0.0012**

(0.0005)

0.0007

(0.0004)

Salaries and wages divided by total number of enrolled students

Unspecified food payments 0.0035***

(0.0006)

0.0001

(0.0005) Unspecified food payments as a share of all students

State reimbursement per lunch -0.0435

(0.057)

0.060**

(0.019)

Number of lunches reimbursed by the State as a share of all lunches 

served

SCHOOL MEAL PROGRAM

Share of schools preparing food 

offsite

0.864***

(0.072)

-0.745***

(0.101)

Schools preparing food offsite divided by sum of schools preparing offsite, 

schools preparing onsite, and schools preparing on and offsite

After-school snack 0.207***

(0.058)

-0.179**

(0.057) One if SFA offers after-school snack and zero otherwise

Share of schools using atypical 

menus

-0.372

(0.393)

0.985***

(0.209)

Number of SFAs using school menus other than the typical school menus 

as a share of all SFAs using any school menu plan

SFA CHARACTERISTICS

Percent of 4th grade above 

proficient

-0.0099**

(0.0043)

-0.039***

(0.0045)

Percent of fourth grade students achieving at or above proficient level in 

math

Urban -0.319**

(0.099)

0.221**

(0.090) Common Core data indicate that SFA in an urban district. Zero otherwise.

Rural -0.236***

(0.052)

0.299***

(0.058) Common Core data indicate that SFA in a rural district. Zero otherwise.

Observations 1,612 1,612

Log likelihood -2300 -2163

Change in log likelihood 1172*** 672***

SFA = School food authority.

Notes:*, **, *** significant at the 0.10, 0.05, and 0.001 levels. Standard errors in parentheses.

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the Economic Research Service, 

USDA.
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Share of High School Students: High school food service offers larger 

portion sizes and often offers more variety. Thus, SFAs with either a large or 

small share of students attending high schools should have different costs and 

must be accounted for in the model. 

Health Insurance: Labor costs can vary substantially with employee benefits. Health insurance is a particularly high labor cost that may attract higher 

quality workers and can substitute for higher wages. 

Foodservice Management Company: Perhaps to reduce labor costs, some 

SFAs opt to have meals supplied by a private foodservice management 

company. This shifts costs from labor to another account—food—meaning 

that the labor share of costs should drop and the food share of costs should 

rise. Table 2 indicates that about 6 percent of all SFAs contract out work to 

foodservice management companies.

Universal Free Meals: Another potentially cost-reducing mechanism for 

schools that serve mainly free and reduced-price meals is offering free meals 

for all students under “universal free meal” provisions of the NSLP (Ralston 

et al., 2008). From a cost and revenue perspective, this option reduces the 

administrative costs of encouraging families to apply for free meals and 

collecting money from paying students at a relatively small loss in revenue 

because most students in these schools would have received free meals 

anyway. Table 2 shows that use of these provisions is modest. 

Estimation

The cost function can be estimated directly, but parameter estimates are often 

inefficient because of multicollinearity among explanatory variables. Gains 

in efficiency can be realized by estimating the input demand equations (costshare equations) jointly with the cost function. The equations are obtained 

from the derivatives of the total cost function with respect to each price.

_

.2 ln ln i i

i i i i

i i i

i i

i ij j Mi ij URBANICITY ij REGION

i j i j i j

Bi BFAST L LACARTE cap CAPi HSi HIGH SCHOOL i Hi HEALTH i i i

Si SERVICE Fi FREE Vi VALUE

lnC P X A ) = = + P + M + C C

lnP C

w C C C C C

i

C C w C 

i

β β γ σ π

κ δ ψ ψ

υ ψ

+ Σ

∂

+

∂

+ + ∑ + +

+ ++ +∑

∑ ∑ ∑ ∑ ∑

∑

The shortrun cost function (equation A.1) is estimated jointly in a multivariate regression system with three input demand equations. To account for 

likely cross-equation correlation in the error terms, we used a nonlinear iterative, seemingly unrelated regression procedure. Since the input shares add to 

one, we dropped the demand for supplies equation to avoid a singular covariance matrix.

All variables are normalized (i.e., divided by their mean values before estimation); thus, the first-order terms (the bs) can be interpreted as the estimated cost-share of input i at mean values. The other coefficients capture 
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changes in the estimated input shares with changes in other prices, number of 

meals served, and other model components. 

Symmetry and homogeneity of degree one are imposed on the cost function in 

order to gain improvements in efficiency and reduce the number of parameters 

estimated (Berndt, 1991). Symmetry means that the coefficients on all interaction terms with identical components are equal (that is, the coefficients 

bij=bji = γiM, dcap,i = di,cap, wi,j = wj,i , ψHS,i = ψi,HS , ψH,i = ψi,H, υS,i = υS,i, 

ψF,i = ψMC,F , σU,i = σi,U, and πi,j = πj,i ). The omitted variables are not 

reported because they are implied.

Homogeneity of degree one means that if all inputs are doubled, then output 

(meals served) also doubles. Systems that are homogeneous of degree one 

have the following properties: ∑βi =1, ∑βi,j =0, ∑ γMi =0, ∑ δcap,i =0, 

∑ ωi,j =0, ∑ ψHS,i = 0, ψH,i =0, ∑υS,i =0, ∑ ψF,i =0, ∑ σU,i =0, ∑ πi,j =0. Since 

some parameters equal combinations of other variables, some parameters 

could be dropped. In this analysis, we dropped the price of supplies and all of 

its interaction terms.

Survey Weights

In a memo dated August 6, 2004, Hall and Zheng (Mathematica Policy 

Research, Inc.) assert that survey data used in the analysis should reflect 

the population of local public SFAs in the 50 States and the District of 

Columbia. Survey data used in the analysis were a representative sample, but 

it is still necessary to use weights to account for differences in the probability 

of selection associated with sample design, nonresponse, and ineligibility. 

These weights were provided by Mathematica Policy Research, Inc. 

Tests for Model Selection

Gallant-Jorgenson (G-J) likelihood ratio tests are commonly used to assess 

model fit in cost function analyses—i.e., whether a selected variable or group 

of variables affect production costs (Gallant and Jorgenson, 1979). A likelihood ratio test is preferable to single-variable statistical significance because 

translog cost functions have many interaction terms for each explanatory 

variable, making any single variable a poor measure of variable importance. 

Hypotheses are tested by comparing a reference model containing a variable of interest to a model in which that variable is excluded (the restricted 

model). If the difference in the G-J statistic exceeds a critical value, then the 

hypothesis that the test variable does not affect costs is rejected.

Appendix table 3 contains the model numbers and descriptions of the models 

to be examined, the G-J statistic, number of parameters estimated, models 

tested (test versus reference models), number of restrictions (the number of 

variables left out of the test model), the critical chi-square value (significance 

at the 0.01 level), and the model chi-square statistic (the difference between 

the G-J statistics of the two models).15

We proceed with the test as follows. For each test, we compare the fit of the 

restricted model (all models in which at least one variable is left out of the 

reference model) to that of the reference model. The reference model is the 

full model. If the difference in the G-J—a chi-square statistic—exceeds the 

15The difference in the values of the 

objective function equals N*S(a, v)

R - N*S(a1, v1)u, where S(a, v)R is 

the minimum value of the objective 

function of the restricted model, S(a1, 

v1)u is the minimum value of the 

objective function of the unrestricted, 

reference model, and N is the number 

of observations. The value of the 

objective function is printed as output 

from the nonlinear estimation of the 

seemingly unrelated regression model 

in the SAS statistical package.
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critical value, then the restricted model is rejected in favor of the reference 

model. 

Model I is a very basic, 10-parameter model that accounts for input prices 

and meals produced. Model II (the reference model) is the final model 

containing 118 parameters representing input prices, meals, metropolitan 

area, regional variables, breakfasts, a la carte revenues, capital costs, share of 

high school students, health care for workers, the use of foodservice companies, free meals for all students, and meal values. The third last column 

shows that there are 108 restrictions and the second last column shows that 

the critical chi-square statistic is 145. The critical chi-square is the minimum 

value needed to conclude that the model is significantly different from the 

reference model. The last column indicates that the model chi-square easily 

surpasses the critical chi-square, meaning that the variables are significant to 

model fit.

Appendix table 3

Model selection tests for school meal cost functions

Test statistics1

Model Description

G-J

 statistic

Parameters

estimated Test Restrictions

Critical chi-square

at 0.01 level

Model 

chi-square 

I Translog input prices and 

output 4025 10 - - - -

II Full, reference model2 3442 118 II vs I 108 145 583***

III Removes areas (rural and 

suburban) from II

3486 94 III vs II 24 45 44**

IV Removes region from II 3534 76 IV vs II 42 71 92***

V Removes breakfasts from II 3520 102 V vs II 16 34 78***

VI Removes a la carte revenues from II

3516 114 VI vs II 4 15 74***

VII Removes capital costs from 

reference model

3464 112 VII vs II 6 18 22***

VIII Removes shares of high 

school students from II

3496 106 VIII vs. II 10 25 28***

IX Removes health care for 

workers from II

3475 108 IX vs II 10 25 33***

X Removes foodservice companies from II

3483 112 X vs II 6 18 40***

XI Removes free meals, all 

students from II

3455 114 XI vs II 4 15 13*

XII Removes high, low value 

meals from II

3470 104 XII vs II 14 31 28*

XIIIa Adds enhanced menus to II 3448 122 XIII vs II 4 15 -6

XIIIb Imposes homotheticity on II 3504 114 XIII vs II 4 15 -62

*** , **,* significant at the 0.01, 0.02, and 0.05 levels of confidence.

1Chi-square statistics are the difference in G-J statistics between the test and reference models. Restrictions are the differences in the number of parameters estimated between the two models.

2The full model includes variables for our input price measures, output, and meal value and dummy variables for determining if the SFA had 

capital expenditures, if schools served no breakfasts or if schools that served breakfasts accounted for 33 percent or more of all meals, if 

ratios of high school students as shares of all students exceeded two different limits, if a la carte revenues exceeded a critical amount, if the 

SFA offered free meals to all students, if health care was provided to workers, if the SFA uses a foodservice companies, and for types of 

metropolitan areas and regions.
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In subsequent tests, one optional characteristic (any variable except for prices 

and meals) is removed and that restricted model is compared against the 

reference model. For example, we removed the two variables representing 

breakfasts from the reference model and determined model fit by subtracting 

the G-J statistic of the reference model from this model. Since the model chisquare surpasses the critical chi-square statistic, breakfasts do significantly 

affect meal costs. All of the remaining tests proceeded in the same fashion, 

and all of these tests, except for the universal free meals, demonstrated that 

the restricted variables should be included in the model. We kept the variable 

for universal free meals in the model because it still contributed to explaining 

model fit.

In the second last test (second last row of the table), a variable representing 

enhanced menus was added to the model. Although nutrition standards and 

meal reimbursements are consistent across schools, USDA offers SFAs 

the flexibility to use either a food-based or a nutrient-based meal planning 

approach in creating meals that meet these nutrition standards. This flexibility allows SFAs to choose a meal planning option that best suits their 

needs. 

As shown, it was not significant and even detracts from the model, so we 

dropped it. Other variables tested at earlier stages of modeling included 

whether the SFA participated in the school snack program and whether the 

SFA used a central cooking facility, but they proved to be not significant and 

were dropped.

In the last test (last row of the table), we tested for homotheticity. This test 

evaluates whether labor, food, and supply prices vary with the number of 

meals served by eliminating the interaction terms between factor prices and 

output volume (forcing factor shares to be invariant with respect to output). 

The test indicates that these input prices vary with the number of meals 

served. Thus, we reject Model XIIIb in favor of Model II.

Model Diagnostics and Discussion of Model Estimates 

Appendix table 4 contains the estimated coefficients from equation A.1. The 

23 terms after the intercept are all of the first-order coefficients. Next, there 

are six quadratic terms and then all of the interaction terms. All of the input 

price and meal variables are interacted with all other variables. There are 

also a few other interaction terms: the breakfast variables with the meal value 

variable, breakfasts with urbanicity variables, the high school dummy variables with dummy variables for capital and foodservice companies, and each 

regional variable with dummy variables for health care, suburban SFAs, and 

rural SFAs.

The model R2 was typical for cost functions—about 0.9829. The first-order 

coefficients of input prices can be interpreted as cost shares at sample means 

and should always be positive to meet regularity conditions. All three coefficients are positive, satisfying this condition. A negative value for one of 

the input share coefficients would imply that an input has a negative price 

(someone would have to pay the user to use the input). A large number of 

estimated negative values could imply a poorly estimated regression. There 

were few violations. Only 3.0 percent of all observations for supplies were
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Appendix table 4

Translog cost function estimates for school meals, school year 2002-03, continued

Variable Coefficient t-statistic Variable Coefficient t-statistic

Intercept -0.008 -0.09 PSUPPLY*MEALS 0.001 0.89

PLAB 0.327*** 19.39 PSUPPLY*CSUBURB 0.005 0.74

PFOOD 0.608*** 36.34 PSUPPLY*CRUR 0.004 0.54

PSUPPLY 0.065*** 5.39 PSUPPLY*CATLANTIC -0.013* -1.67

MEALS 0.966*** 38.18 PSUPPLY*CMIDWEST -0.026*** -3.40

PSUPPLY*CMOUNT -0.022** -3.01

CSUBURB 0.027 0.43 PSUPPLY*CNORTHEAST -0.037*** -4.61

CRUR 0.079 1.25 PSUPPLY*CSOUTHWEST -0.026*** -3.70

CATLANTIC -0.189* -1.78 PSUPPLY*CWEST -0.007 -0.86

CMIDWEST -0.236** -2.55 PSUPPLY *CBFAST33 -0.002 -0.50

CMOUNT -0.193** -1.98 PSUPPLY*CBFAST0 -0.003 -0.56

CNORTHEAST -0.178* -1.81 PSUPPLY* CLACARTE 0.017*** 4.79

CSOUTHWEST -0.105 -1.02 PSUPPLY*CCAP -0.001 -0.31

CWEST -0.311*** -2.91 PSUPPLY*

CHIGH_SCHOOL_LO

-0.006* -1.75

CBFAST33 -0.049 -1.35 PSUPPLY *

CHIGH_SCHOOL_HI

0.030*** 3.26

CBFAST0 0.091 1.12 PSUPPLY *CHEALTH 0.016*** 3.08

CLACARTE 0.075*** 4.53 PSUPPLY*CSERVICE 0.012*** 2.70

CCAP 0.033* 1.66 PSUPPLY*CFREE 0.016*** 2.63

CHIGH_SCHOOL_LO 0.007 0.32 PSUPPLY* CVALUE_LO -0.002** -2.51

CHIGH_SCHOOL_HI 0.248** 2.41 PSUPPLY* CVALUE_HI 0.005*** 3.09

CHEALTH 0.049 0.76 MEALS *CSUBURB -0.004 -0.34

CSERVICE -0.068*** -2.86 MEALS *CRURAL -0.020 -1.42

CFREE -0.011 -0.39 MEALS *CATLANTIC 0.012 0.68

CVALUE_LO -0.022*** -4.23 MEALS *CMIDWEST 0.016 0.92

CVALUE_HI -0.008 -0.74 MEALS *CMOUNT -0.003 -0.14

PLAB * PLAB 0.148*** 20.10 MEALS *CNORTHEAST -0.017 -0.88

P FOOD * PFOOD 0.162*** 32.31 MEALS *CSOUTHWEST 0.014 0.86

PSUPPLY*PSUPPLY 0.064 - MEALS *CWEST -0.009 -0.50

MEALS*MEALS 0.007* 1.60 MEALS*CBFAST33 -0.017** -2.05

CVALUE_LO *CVALUE_LO -0.0003** -2.59 MEALS*CBFAST0 -0.025** -2.03

CVALUE_HI *CVALUE_HI -0.002 -1.41 MEALS * CLACARTE -0.007 -1.02

 PLAB * PFOOD -0.123*** -20.47 MEALS *CCAP -0.008 -1.22

PLAB * PSUPPLY -0.025*** -5.17 MEALS*

CHIGH_SCHOOL_LO

0.011* 1.68

PLAB * MEALS -0.011*** -4.98 MEALS*

CHIGH_SCHOOL_HI

0.047* 1.66

PLAB * CSUBURB 0.048*** 5.23 MEALS *CHEALTH 0.017* 1.60

PLAB * CRUR 0.063*** 6.69 MEALS *CSERVICE -0.009 -1.06

PLAB *CATLANTIC 0.013 1.16 MEALS*CFREE -0.015 -1.46

PLAB *CMIDWEST -0.007 -0.67 MEALS * CVALUE_LO -0.002 -1.18

continued
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Appendix table 4

Translog cost function estimates for school meals, school year 2002-03, continued

Variable Coefficient t-statistic Variable Coefficient t-statistic

PLAB *CMOUNT 0.005 0.53 MEALS * CVALUE_HI -0.001 -0.39

PLAB *CNORTHEAST 0.009 0.82 CSUBURB*CATLANTIC 0.067 0.89

PLAB *CSOUTHWEST 0.029*** 2.94 CSUBURB *CMIDWEST 0.098 1.32

PLAB *CWEST -0.002* -0.14 CSUBURB *CMOUNT 0.007 0.08

PLAB *CBFAST33 -0.024*** -3.90 CSUBURB*CNORTHEAST -0.038 -0.48

PLAB * CBFAST0 -0.007 -0.96 CSUBURB*CSOUTHWEST 0.034 0.46

PLAB * CLACARTE 0.020*** 4.06 CSUBURB*CWEST 0.018 0.25

PLAB *CCAP 0.0003 0.64 CSUBURB*CBFAST33 -0.0001 -0.00

PLAB *CHIGH_SCHOOL_LO 0.002 0.50 CSUBURB*CBFAST0 -0.224*** -2.88

PLAB *CHIGH_SCHOOL_HI -0.014 -1.05 CRURAL*CATLANTIC 0.039 0.50

PLAB *CHEALTH 0.042*** 5.64 CRURAL *CMIDWEST 0.002 0.03

PLAB *CFOOD_SERVICE -0.076*** -11.96 CRURAL *CMOUNT -0.037 -0.44

PLAB * CFREE -0.004 -0.39 CRURAL*CNORTHEAST -0.100 -1.18

PLAB * CVALUE_LO -0.0025*** -2.64 CRURAL*CSOUTHWEST -0.002 -0.02

PLAB * CVALUE_HI -0.006** -2.31 CRURAL*CWEST -0.038 -0.51

PFOOD * PSUPPLY -0.039*** -8.74 CRURAL *CBFAST33 -0.039 -1.06

PFOOD * MEALS 0.010*** 4.39 CRURAL*CBFAST0 -0.209*** -2.68

PFOOD*CSUBURB -0.053** -5.75 CHEALTH *CATLANTIC 0.114 1.41

PFOOD *CRUR -0.067*** -7.05 CHEALTH *CMIDWEST 0.051 0.76

PFOOD *CATLANTIC 0.0002 0.02 CHEALTH *CMOUNT 0.095* 1.60

PFOOD *CMIDWEST 0.034*** 3.12 CHEALTH *CNORTHEAST 0.076 1.01

PFOOD *CMOUNT 0.016 1.55 CHEALTH *CSOUTHWEST 0.134* 1.61

PFOOD *CNORTHEAST 0.028** 2.49 CHEALTH *CWEST 0.177** 1.99

PFOOD*CSOUTHWEST -0.003 -0.034 CBFAST33* CVALUE_LO 0.004 0.63

PFOOD *CWEST 0.009 0.76 CBFAST33* CVALUE_HI 0.011 1.26

PFOOD*CBFAST33 0.026*** 4.29 CBFAST0* CVALUE_LO -0.009*** -2.31

PFOOD*CBFAST0 0.009 1.36 CBFAST0* CVALUE_HI -0.016 -1.39

PFOOD * CLACARTE -0.037*** -7.48 CCAP*CHIGH_SCHOOL_LO -0.006 -0.40

PFOOD *CCAP -0.002 -0.42 CCAP*CHIGH_SCHOOL_HI -0.111* -1.87

PFOOD *CHIGH_SCHOOL_LO 0.003 0.73 CHIGH_SCHOOL_LO*

CFOOD_SERVICE

-0.007 -0.35

PFOOD*CHIGH_SCHOOL_HI -0.016 -1.23 CHIGH_SCHOOL_HI*

CFOOD_SERVICE

-0.102** -1.91

PFOOD*CHEALTH -0.058*** -7.91

PFOOD*CFOOD_SERVICE 0.064*** 10.11

PFOOD* CFREE -0.013 -1.45

PFOOD* CVALUE_LO 0.004*** 4.45

PFOOD * CVALUE_HI 0.0003 0.12

Notes: *,**, *** significant at the 0.10, 0.05 and 0.001 levels. All variables are standardized at their means, so first-order coefficients can 

be interpreted as elasticities at the sample means. Dummy variable capture shifts in costs. There were 1,432 observations taken from the 

2002-03 SFA Characteristics Survey on the costs of producing school meals at the school food authority level. The model R2 was 0.9817.

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the Economic Research 

Service, USDA.
37

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negative and no observations for food or labor were negative. 

Parameter values for the first-order input price terms provide estimates of the 

share of costs devoted to labor (PLAB), food (PFOOD), and supplies (PSUPPLY). 

Food inputs account for about 61 percent of meal costs, while labor and 

supplies comprise about 33 and 6 percent of costs. These cost shares apply 

only to the reference category, which is Southeastern SFAs in an urban 

setting that claim no capital costs, do not provide health care to foodservice 

staff or free meals to all students, serve breakfasts that account for between 0 

and 33 percent of total meals served, serve an average number of elementary 

and high school students, prepare average-value meals that generate less than 

10 cents per meal in revenue from a la carte sales, and do not contract with 

foodservice companies. 

The interaction terms show how cost shares vary from the reference SFA 

value. For example, the coefficients on the interactions of the urbanicity 

variables (CSUBURB and CRURAL) with labor and food input prices (PLAB and 

PFOOD) show how labor and food cost shares change from urban to suburban 

and rural settings.

There is a sizable change if the Southeastern SFA was a suburban SFA rather 

than an urban one. In this case, the suburban dummy variable would equal 

one. For a suburban, Southeastern SFA, the labor share would rise to about 

37 percent, since the value of the interaction of the suburban dummy variable 

with the price of labor (PLAB *CSUBURB) is added to the coefficient on the 

price of labor (PLAB) to determine the labor share. The food share (PFOOD + 

PFOOD*CSUBURB) would drop to about 55 percent.

There is also a substantial difference for an urban, southeastern SFA that 

offered health care. In that case, the labor share rises from 33 to about 37 

percent (PLAB + PLAB*CHEALTH) and the food share drops to about 55 

percent (PFOOD + PFOOD*CHEALTH). Finally, if the southeastern SFA offered 

health care and was located in a suburban area, then the labor share would 

rise to about 41 percent (PLAB + PLAB *CSUBURB+ PLAB*CHEALTH) and 

the food share would drop to about 50 percent (PFOOD + PFOOD*CSUBURB+ 

PFOOD*CHEALTH).

The coefficients shown in appendix table 4 indicate sizable drops (more than 

1 percent) in the labor share from the reference case for SFAs that serve no 

breakfasts, serve meals to a high proportion of high school students, or SFAs 

that use a foodservice company. In a similar way, there would be a substantial increase in the labor share (and usually a decrease in the food share) for 

SFAs that are smaller than average, generate more than 10 cents per meal 

from a la carte items, offer health care to their workers, are either suburban or 

rural, and are located in either the Mid-Atlantic or Southwestern regions. 

The a la carte and labor share findings are of particular interest. SFAs that 

have a la carte services devote 2 percent more of each dollar spent for labor 

and spend about 3.7 percent less for food. This means that non-a la carte 

foods—i.e., NSLP meals—provide more food per dollar spent than a la carte 

foods.
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Elasticities

The Allen cross price elasticity indicates the degree of substitutability among 

inputs—i.e., how a change in the use of one input affects usage of a different 

input. The Morishima cross-price elasticity indicates how a change in the 

price of one input affects use of another input. Positive values for either of 

the cross elasticities indicate substitutability between inputs, and negative 

values indicate that the inputs are complements. 

The own- and cross-price elasticities of input demand and the Allen elasticities of input substitution indicate the degree of responsiveness to changes 

in input prices. The own-price input demand elasticity shows how a given 

change in prices for food or other input affects demand for that input. A 

cross-price elasticity shows how a change in the price of food or another 

input affects demand for a different input. A positive sign means that the two 

inputs are complements, and a negative sign indicates that they are substitutes. Equations A.3 and A.4 define mathematically own- and cross-price 

elasticities.

The input demand own- and cross-price elasticities for any inputs i and j are 

equal to:

 

2

( )

.3 jj j j

jj

j

A S S

S

φ

ε

+ − =

and

 

( )

.4 jk j k

jk

j

A S S

S

φ

ε

+ =

The Allen elasticity of input substitution indicates the degree to which a 

given percentage change in input k—labor—can substitute for a percentage 

change in input j—food. A higher positive number indicates greater substitutability. The Allen partial cross elasticity of input substitution can be written 

as

 

( )

.5 jk j k

jk j

i

A S S S S

φ

ε

+ =

where the S represents input shares of jth or kth input and comes from the 

first-order price coefficients; φjk is the coefficient on the kth input price for 

the jth input and is also the coefficient on the interaction term between the 

jth and kth input prices in equation A.1; φjj is the coefficient on the jth input 

price in the demand equation for that input and is also the coefficient on the 

squared input in equation A.1.16

Appendix table 5 contains the own-price and the Allen and Morishima cross 

elasticities for the major inputs of labor, food, and supplies. Model coefficients and the equations shown in the appendix are used to estimate the elasticities. Own-price elasticities for labor imply that a 10-percent increase

16We use fitted input shares with representative data to estimate equations 

A.5-A.7 because predicted input shares 

may vary with output, input prices, 

and/or plant characteristics. Reported 

elasticities should also use representative values.
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in the price of labor leads to a 2.2-percent decline in demand for that input. 

The change is much stronger for food—about a 4.7-percent decrease in 

food with a 10-percent increase in the price. The own-price elasticity for 

supplies is highly inelastic—i.e., there is very little change in input usage 

with a change in prices. The positive sign is the opposite from what would be 

expected, but it is of such low magnitude that it really is reflecting a lack of 

responsiveness and not a shift in usage as prices change. 

The Allen cross elasticity implies that a 10-percent increase in labor usage 

results in a 3.81-percent decrease in food inputs. For example, SFAs may 

begin making pizza from scratch, which would raise labor requirements, but 

would also reduce food costs since prepared pizzas cost more than the cost of 

the ingredients. The negative sign on labor and supplies indicates that a rise 

in the use of labor inputs results in an increase in the use of supplies. 

Blackorby and Russell (1981, 1989) argue that Morishima elasticities have 

a bias toward showing inputs as substitutes and that Allen elasticities are 

biased toward showing inputs as complements. Not surprisingly, appendix 

table 5 shows that Morishima elasticities are higher than Allen elasticities for 

comparable cells.

The elasticity of total costs with respect to output (meals) provides a natural 

measure of scale economies by showing how costs change as SFA size 

changes. A number less than one means that the percentage rise in costs is 

less than the percentage rise in meals, or it becomes less costly to prepare 

the next meal. For example, a value of 0.90 indicates that costs increase by 

0.9 percent for every 1-percent increase in the number of meals produced 

(average costs fall as the number of meals rise). Because the variables are all 

divided by their sample means before estimation, the first-order term, γM, 

can be interpreted as estimated scale economies for SFAs at the sample mean 

Appendix table 5

Input demand estimates and elasticities

Input price variables

Item PLAB PFOOD PSUPPLY

Estimated input shares 0.327 0.608 0.065

εii (own input price) -0.220 -0.468 0.050

Aij (Allen cross elasticities)

 PLAB - 0.381 -0.177

 PFOOD - - 0.025

 PSUPPLY - - -

Mij (Morishima cross elasticities)

 PLAB - 0.452 0.208

 PFOOD 0.593 - 0.470

 PSUPPLY -0.108 -0.035 -

= ?

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. 

(2004), and analysis by the Economic Research Service, USDA.
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size. The cost elasticity equation is defined as the derivative of the cost function with respect to output:

, _

.6 ln ln

ln I I i

i

I i

CM M MM Mi i MU URBANICITY MR REGION i i

BMi BFAST LM LACARTE Cap Mc HSM HIGH SCHOOL i i

HM HEALTH SM SERVICE FM FREE MV VALUE

i

lnC A ) = = + M + P C C

M i

C C + C

C C C + C

ε γ γ γ σ π

ω κ δ ψ

ψ υ ψ ω

∂

+∑ +∑ ∂

+∑ + +∑

+ + +

∑

∑

where values of the cost elasticity, εCM, that are less than 1 imply scale economies and values above 1 imply scale diseconomies. 

Equation A.6 allows the estimated cost elasticity to vary with output, prices, 

capital costs, urbanicity, region, breakfasts served, a la carte foods, share of 

high school students, health benefits, foodservice management companies, 

share of schools with free meals for all students, and meal value. The parameters, γMM and γMi and the other coefficients show the dimensions along 

which scale economies vary.

Values of the cost elasticity that are less than 1 indicate economies of scale. 

For example, a value of 0.90 indicates that costs increase by 0.9 percent 

for every 1-percent increase in output (in turn, average costs fall as output 

increases). Values in excess of 1 show diseconomies of scale.

Appendix table 6 shows how cost elasticities and costs change at SFA sizes 

equal to one-fourth, one-half, one, and two times the regional mean size. 

Overall, the table shows that the cost elasticity is less than one when evaluated at sample mean prices and that costs drop as SFA size increases. Large 

SFAs produce meals that are anywhere from 10 to 27 cents less costly than 

small SFAs. The average cost difference across the 7 regions due to an eightfold increase in size is 19 cents per meal. 

The eightfold difference in size and the associated meal costs illustrated 

in appendix table 6 actually understate the economies of scale that exist 

between urban SFAs, which are the largest SFAs, and rural SFAs, which are 

the smallest. Northeastern urban SFAs are about 7 times the mean size SFA, 

whereas Northeastern rural SFAs are 0.09 times the mean size SFA or, on 

average, about 80 times smaller than an average-sized Northeastern urban 

SFA. Overall, urban SFAs are about 3.7 times the sample mean size SFA, 

and rural SFAs are about 0.21 times the sample mean size SFA.

The derivative of the second-order output term in the cost elasticity equation indicates the change in per meal cost as the number of meals served 

increases. A negative sign indicates that the cost of preparing and serving the 

next meal is less than the cost of the previous meal; a positive sign means the 

opposite. Since the coefficient on the second-order output term is positive 

(appendix table 4), the cost of preparing the next meal is more than the cost 

of preparing the previous one. However, since the cost elasticity is less than 

one for all the regions, the cost of preparing the next meal is still below the 

average.17

17Typically, cost functions are Ushaped—the cost of producing the first 

unit is very high and the costs of later 

units are lower. At some point, it becomes more costly to produce the next 

unit. The cost of production at this point 

is actually below the average cost of 

production since average costs include 

the first, very high-cost, items. Thus, 

costs can be increasing in the presence 

of economies of scale.
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Appendix table 6

Simulated per meal costs by region and school food authority (SFA) size

Region

One-fourth 

mean sizea

One-half 

mean size Mean size

Twice mean 

size

Regional size relative to sample 

mean sizes

Regional cost 

elasticity at sample 

mean output/prices

----------------------------Dollars----------------------------

Mid-Atlantic $2.97 $2.90 $2.85 $2.81 0.856 0.978

Midwest $2.69 $2.63 $2.58 $2.54 0.489 0.982

Mountain Plains $2.27 $2.19 $2.12 $2.06 0.439 0.963

Northeast $2.61 $2.51 $2.42 $2.34 1.100 0.949

Southeast $2.52 $2.44 $2.38 $2.32 1.500 0.966

Southwest $2.25 $2.21 $2.17 $2.15 0.816 0.980

Western $2.72 $2.63 $2.56 $2.49 1.325 0.957

--------------------------Number--------------------------

Meals served 413,250 826,000 1,653,000 3,306,000 NA NA

----------------------Cost elasticity----------------------

Reference cost 

elasticity 0.956 0.961 0.966 0.971 NA NA

NA = Not applicable.

Note: Assumptions used in simulations: All input price measures and SFA characteristics are set at regional averages, except size, which is 

set at one-fourth, one-half, one, and two times sample average. 

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the Economic Research 

Service, USDA.
42

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Appendix B: School Breakfasts 

Rigorous cost accounting methods used to estimate school breakfast and 

lunch meal preparation costs in SLBCS-II indicated that it cost much less to 

prepare a breakfast than a lunch. However, costs rise when few breakfasts 

are served (Sackin, 2008; Helleren, 2007). Results from our cost analyses 

indicate breakfasts are less costly than lunches only if more than one of every 

three meals served by the SFA are breakfasts. If fewer breakfasts are served, 

then breakfast costs are the same as lunch costs. Below, we examine whether 

breakfasts, even at low volumes, are the same cost as lunches by examining 

regional regressions and testing for differences in costs due to share of meals 

comprised of breakfasts. Then, we consider why costs might be the same.

Examination of Costs by Region

In this analysis, we investigate whether the conclusions reached about breakfasts from the larger model hold up in more simplified analyses that focus 

directly on the impact of breakfasts on costs. To do this, we stripped the cost 

function (equation A.1.1) of all explanatory variables except for the expressions for input prices, number of meals, and dummy variables for 33-percent 

SFAs and lunch-only SFAs and their interactions with input prices and 

number of meals served (equation B.1): 

2

1 ( .1) ln ln ln ln 2

ln ln ) ln *ln

*ln *ln

ij i j

i i j

m Mi i MM i

B Bij BFAST i BFAST i BM BFAST j

 B C = + + 0 Pi P * P i i

MEALS ( MEALS MEALS P

C w C P C MEALS i i i i i i i

α β β

γ γ

ψ ω ε

γ

Σ

+ + +∑

+ + ∑ ∑ +∑ +

∑ ∑∑

where the variables are defined as above.

We ran 21 regressions in total (3 urbanicities and 7 regions) and examined 

signs on the lunch-only and 33-percent breakfast dummy variables (the reference category is 0-33 percent breakfasts). If breakfasts are less costly than 

lunches to produce, the dummy variable for lunch-only SFAs should be positive and the dummy variable for the 33-percent SFAs should be negative. 

Since each region/urbanicity regression has two comparisons (signs on the 

lunch-only and 33-percent parameters) of dummy variables, 42 comparisons 

are possible. Of the comparisons, 14 have outcomes that differ from expectations, 15 have outcomes consistent with expectations, and the remaining 

either could not be estimated or were biased.

An even simpler model that excludes the interactions of prices with the 

breakfast dummy variables and meals with the breakfast dummy variables 

was also tested. Results indicated that that 18 of 42 comparisons differed 

from their expected outcomes. These results indicate that breakfasts cost as 

much or more to produce as lunches in about one-half the cases in which 

there was a direct comparison between the two meals. Since costs are indistinguishable, it is valid to treat breakfasts and lunches as similar meals. 
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Breakfast Costs and Why They May Be 

the Same as Lunch Costs

Appendix table 7 provides a comparison of costs by the number of breakfasts 

served, as a percentage of total meals. The three panels compare estimated 

meal costs for urban, rural, and suburban SFAs serving different numbers of 

breakfasts. Each panel compares estimated costs for SFAs in which breakfasts account for 33 percent or more of all meals, lunch-only SFAs, and SFAs 

in which 0-33 percent of all meals served are breakfasts (0-33 percent SFAs). 

One might expect SFAs that serve more breakfasts would have lower costs 

since SLBCS-II indicated that breakfasts have lower costs of production 

than lunches. This lower cost relationship is true for urban 33-percent SFAs, 

which have much lower costs than either the urban lunch-only or urban 0-33 

percent SFAs. The cost difference ranges from 34 cents for Western SFAs to 

54 cents for Southeastern SFAs, and the cost difference between 33-percent 

SFAs and 0-33 percent SFAs ranges from 17 cents for Midwestern SFAs to 

26 cents for Southeastern SFAs.

The cost-breakfast relationship breaks down for suburban and rural SFAs. 

Lunch-only SFAs have lower costs than both 33-percent and 0-33 percent 

SFAs in suburban SFAs across all regions. The breakdown of the costbreakfast relationship is not as strong for rural SFAs as it is for suburban 

SFAs, but it still exists. The 33-percent SFAs have the lowest costs in all 

regions, but the difference between them and lunch-only SFAs varies from 

only 2 cents for Western SFAs to 13 cents for Southeastern SFAs. Lunchonly SFAs have lower estimated costs than 0-33 percent SFAs for every 

region except the Mid-Atlantic. 

An examination of the cost function results suggests that high setup costs 

and weak economies of scale may explain the results. Consider setup costs. 

Coefficients are lower for urban 33-percent SFAs (CBFAST33) and lunchonly suburban and rural SFAs (interactions of CBFAST0 with CSUBURB and 

CRURAL). Second, 0-33 percent SFAs have weaker economies of scale than 

lunch-only and 33-percent SFAs (coefficients on MEALS*CBFAST0 and 

MEALS*CBFAST33 are both negative in appendix table 4). 

Results also show that 0-33 percent SFAs have a high labor share of costs, 

perhaps due to meal participation imbalances. The coefficients on the labor 

cost shares (PLABOR and its interactions) show that 33-percent SFAs have 

labor cost shares that are about 2.5 percent lower than either the 0-33 percent 

share or lunch-only SFAs.18

Meal participation imbalances mean that all facilities and resources may 

not be used, raising average costs. Under unbalanced conditions, managers 

may have to schedule excess labor to serve lower volume meals (typically 

breakfasts) in order to have sufficient workers to serve higher volume meals 

(typically lunches). Alternatively, a school may have to purchase additional 

equipment to accommodate a different meal. 

Results for breakfasts are consistent with research showing that per meal 

costs can be high when the number of breakfasts served is small. In an 

18Coefficients on PLab*CBFAST33 and 

PLab*CBFAST0 indicate changes in labor 

share for high-breakfast-share and 

lunch-only SFAs.
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School Foodservice Costs: Location Matters / ERR-117

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analysis for the California State Nutrition Association, Sackin (2008) showed 

that schools need to serve at least 91 breakfasts to cover the cost of hiring 

the minimum 2 extra hours of labor required to prepare and serve the meal. 

In smaller schools, this may be a problem. A University of Wisconsin study 

(Helleren, 2007) asserts that participation rates must be high enough to overcome setup, service, and equipment costs.

Appendix table 7

Cost comparison by the share of meals that are breakfasts: Simulations using location-specific means for 

input price measures and school food authority (SFA) characteristics

Urbanicity of SFAs and share of 

meals that are breakfasts

MidAtlantic MidwestMountain

Plains Northeast Southeast Southwest Western

-----------------------------------------------Dollars/meal-----------------------------------------------

Urban SFAs:

No breakfasts served 2.58 2.29 2.39 2.24 2.71 2.28 2.35

Breakfasts less than 33 

percent of meals

2.36 2.11 2.13 2.07 2.43 2.05 2.20

Breakfasts more than 33 

percent of meals

2.14 1.94 1.95 1.86 2.17 1.83 2.01

Suburban SFAs:

No breakfasts served 2.99 2.91 2.31 2.67 2.31 2.31 2.57

Breakfasts less than 33 

percent of meals

3.24 3.10 2.49 2.83 2.58 2.52 2.82

Breakfasts more than 33 

percent of meals

3.05 3.00 2.35 2.75 2.34 2.35 2.66

Rural SFAs:

No breakfasts served 2.41 2.42 2.26 2.60 2.28 2.15 2.61

Breakfasts less than 33 

percent of meals

2.41 2.52 2.32 2.67 2.41 2.23 2.77

Breakfasts more than 33 

percent of meals

2.31 2.36 2.15 2.50 2.15 2.04 2.59

Source: School Food Authority Characteristics Survey, Mathematica Policy Research, Inc. (2004), and analysis by the Economic Research 

Service, USDA.