4
Conceptual Framework and Design of School District Case Studies

Our panel’s charge is not only to develop methods for using available data to estimate claiming percentages for reimbursement by the U.S. Department of Agriculture (USDA) for school breakfasts and lunches under a new Provision 4, but also to evaluate the usefulness of the resulting estimates for school districts that might want to consider adopting such a provision. There are well over 13,000 school districts in the United States, which are highly diverse in their size and the socioeconomic characteristics of their students. These differences will affect the attractiveness of Provision 4, in which data from the American Community Survey (ACS) and other sources would provide the basis for claiming percentages for a school, group of schools, or school district that provides free meals to all students in return for not having to accept or verify applications or categorize meals in the cafeteria line.

FRAMEWORK FOR CLASSIFYING SCHOOL DISTRICTS

To help understand school district differences more systematically in planning our technical approach and, specifically, designing case studies, we developed a framework for classifying school districts and identifying the geographic level of the estimates that would be needed to assess the impact of operating under Provision 4. At this point, we are focusing on just three school district characteristics that are relevant to our assessment: (1) students’ need for assistance (as measured by the percentage of students who are approved for free and reduced-price meals),



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4 Conceptual Framework and Design of School District Case Studies O ur panel’s charge is not only to develop methods for using avail- able data to estimate claiming percentages for reimbursement by the U.S. Department of Agriculture (USDA) for school breakfasts and lunches under a new Provision 4, but also to evaluate the useful- ness of the resulting estimates for school districts that might want to consider adopting such a provision. There are well over 13,000 school districts in the United States, which are highly diverse in their size and the socioeconomic characteristics of their students. These differences will affect the attractiveness of Provision 4, in which data from the American Community Survey (ACS) and other sources would provide the basis for claiming percentages for a school, group of schools, or school district that provides free meals to all students in return for not having to accept or verify applications or categorize meals in the cafeteria line. FRAMEWORK FOR CLASSIFyINg SCHOOL DISTRICTS To help understand school district differences more systematically in planning our technical approach and, specifically, designing case studies, we developed a framework for classifying school districts and identify - ing the geographic level of the estimates that would be needed to assess the impact of operating under Provision 4. At this point, we are focus - ing on just three school district characteristics that are relevant to our assessment: (1) students’ need for assistance (as measured by the per- centage of students who are approved for free and reduced-price meals), 4

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4 USING ACS DATA TO SUPPORT THE SCHOOL MEALS PROGRAMS (2) the heterogeneity of need across schools within the school district, and (3) school district size (as measured by enrollment). Key School District Characteristics Need: For a school district with very high need, the savings associ - ated with eliminating the application and verification processes and the process of distinguishing free, reduced-, and full-price meals in school cafeteria lines is likely to equal or exceed the additional costs of providing free meals to all students who take such meals.1 For such a school district, Provision 4 is likely to be attractive, assuming that the estimates of claim - ing percentages from the ACS and other sources satisfy other criteria, such as timeliness and accuracy. In contrast, a school district with very low need is not likely to be interested in Provision 4, regardless of the quality of the ACS-based estimates, because the savings in administrative costs are likely to fall far short of the added meal costs. “In between” school districts face less clear-cut decisions. Heterogeneity of need: In addition to the aggregate level of need within a school district, the heterogeneity of need across schools could affect a district’s decision regarding Provision 4. A school district might have an “in between” level of need because it has some schools with high levels of need and other schools with low levels of need. Such a district might want to adopt Provision 4 in the first group of schools, but not the second. To assess the attractiveness of Provision 4 for this type of school district would require estimates for groups of schools within the district. In contrast, a district-wide estimate would be adequate to assess the attractiveness of Provision 4 for a homogeneous school district. Enrollment size: The size of a school district will substantially affect the reliability of the estimate(s) on which to evaluate the attractiveness of Provision 4. For a large school district, the methods we set forth in Chapter 5 might yield reliable estimates for schools or groups of schools. For a small school district, however, it might not be possible to derive estimates with acceptable reliability below the school district level, even using statistical modeling. If that is the case, the attractiveness of Provi - sion 4 would have to be evaluated on the basis of an estimate for the entire school district, although that estimate might not be reliable. 1According to the Food Research and Action Center, “Schools that have implemented Provi - sion 2 or 3 have found that they can offset cost differentials with as few as 60 to 75 percent of students eligible for free or reduced-price school meals” (see http://www.frac.org/html/ federal_food_programs/cnreauthor/provision2.htm [accessed May 2010]).

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4 SCHOOL DISTRICT CASE STUDIES Matrix of need, heterogeneity of need, and enrollment size: Table 4-1 shows how the universe of U.S. school districts is distributed across com - binations of need, heterogeneity of need, and enrollment size, as defined below. We specified the boundaries of these categories pragmatically, with their primary purpose to partition school districts for purposes of this study. Need: • Low: Less than 50 percent of students are approved for free or reduced-price meals. • Medium: Between 50 and 75 percent of students are approved for free or reduced-price meals. • High: At least 75 percent of students are approved for free or reduced-price meals. Heterogeneity of need: • Heterogeneous: At least 25 percent of schools in the district have 75 percent or more of their students approved for free or reduced- price meals, and at least 25 percent of schools have less than 50 percent of their students approved for free or reduced-price meals. • Homogeneous: Not heterogeneous. Enrollment size: • Large: Student enrollment greater than or equal to 25,000. • Medium: Student enrollment greater than or equal to 12,000 and less than 25,000. • Small: Student enrollment less than 12,000.2 As shown in Table 4-1, almost 70 percent of school districts are in the “low-need, homogeneous” category for which Provision 4 is unlikely to be attractive. Very few school districts (less than 1 percent) are either “low need, heterogeneous” or “high need, heterogeneous,” which is not sur- prising given the definition of those categories. The remaining districts are distributed into 21 percent “medium need, homogeneous,” 6 percent “high need, homogeneous,” and 2 percent “medium need, heterogeneous.” 3 The categorization of school districts in Table 4-1 was created to guide 2 We note that 12,000 is roughly the median enrollment when school districts are weighted by enrollment; that is, school districts with greater than 12,000 enrollment cover about half of the students in the country. 3 The distribution in Table 4-1 is of districts, not enrollment; a table of enrollment would indicate that high-need districts, which are most likely to find Provision 4 attractive, enroll a significant proportion of students.

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0 TAbLE 4-1 Number and Percentage of School Districts in the United States by Need, Heterogeneity of Need, and Enrollment Size Need: School District Percentage Approved for Free or Reduced-Price Meals Low < 50% Medium 50-75% High ≥ 75% Enrollment Homogenous Heterogeneous Homogenous Heterogeneous Homogenous Heterogeneous Size Schools Schools Schools Schools Schools Schools 168 5 53 32 23 1 ≥ 25K (1.2%) (0.0%) (0.4%) (0.2%) (0.2%) (0.0%) 12-25K 265 8 83 33 33 0 (2.0%) (0.1%) (0.6%) (0.2%) (0.2%) (0.0%) < 12K 8,969 90 2,713 189 816 26 (66.4%) (0.7%) (20.1%) (1.4%) (6.0%) (0.2%) Total (%) 69.6 0.8 21.1 1.9 6.5 0.2 NOTE: See text for definitions of need, heterogeneity of need, and enrollment size. SOURCE: The universe for this table includes each school district on the 2007-2008 Common Core of Data (CCD) for which the Census Bureau prepared a Small Area Income and Poverty Estimate Program estimate for Title I allocations under the No Child Left Behind Act. There were 13,507 such school districts. The CCD data were used to classify the school districts (the data are available at http://nces.ed.gov/ccd/index.asp [accessed May 2010]).

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 SCHOOL DISTRICT CASE STUDIES the development of our technical approach and, in particular, the selection of school districts for case studies. It was not designed to predict whether specific districts would adopt Provision 4. For determining how every school district would in practice approach its decision about adopting Provision 4, it is likely that our categorization would misclassify some districts. For example, some districts classified as homogeneous accord- ing to Table 4-1 might nonetheless want to adopt Provision 4 for only one or a few schools in the district. However, the panel requires only an approximate identification of school districts by need, heterogeneity of need, and enrollment size to develop its technical approach and select school districts for case studies. Information Needs for Assessment Table 4-2 shows, in a very simplified way, the geographic level of estimates that would be needed for different types of districts to assess the attractiveness of adopting Provision 4. For the sake of simplicity, we have assumed that only districts with medium need can be heterogeneous. Low-need and high-need districts are assumed to be sufficiently low and high, respectively, that there cannot be much variation in need across the schools within the district. We have also assumed that low-need districts are sufficiently low that they could not break even under Provision 4, and, thus, the provision would not be attractive to them. Consequently, there is no need to examine estimates of claiming percentages based on the ACS and other data sources for school districts in any of the low-need cells. For high-need districts, the attractiveness of Provision 4 can be assessed on the basis of a district-wide estimate of need because there is little within-district heterogeneity.4 Similarly, the attractiveness of Provision 4 for homogenous medium-need districts can be assessed on the basis of district-wide estimates. In contrast, a heterogeneous district with medium aggregate need is likely to require estimates for individual schools or groups of schools to assess the effects of heterogeneity on the attractiveness of adopting Pro- vision 4 for only some schools in the district. Obtaining such estimates would require input from the school district on attendance-area boundar- ies and other aspects of the district’s schools, making such districts ideal candidates for case studies in contrast to other districts for which district- wide estimates would suffice. Finally, reflecting the discussion of ACS sampling error in Chapter 5, Table 4-2 indicates that while it might be possible to derive reliable esti- 4 If a district is high need, Provision 4 is likely to be attractive from a financial point of view, as discussed above.

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 TAbLE 4-2 Geographic Level of Estimates Required to Evaluate Provision 4 for School Districts by Need, Heterogeneity of Need, and Enrollment Size Need: School District Percentage of Students Eligible for Free or Reduced-Price Meals Low Medium High Enrollment Homogenous Heterogeneous Homogenous Heterogeneous Homogenous Heterogeneous Size Schools Schools Schools Schools Schools Schools Large X — D S/G D — Medium X — D G D — Small X — D — D — D = District estimate. S = School estimate. G = School group estimate. X = No estimate (will not consider a special provision). — = (Nearly) empty cell (very few such districts). NOTE: See text for definitions of need, heterogeneity of need, and enrollment size. SOURCE: This table summarizes information displayed in Table 4-1 and reflects the panel’s interpretation.

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 SCHOOL DISTRICT CASE STUDIES mates for individual schools in some large, heterogeneous districts with medium need, it is more likely that reliable estimates could be derived for groups of schools in medium-sized and even many large districts. Although we have assumed, for the sake of simplicity, that small districts are too small to be heterogeneous, there are, in fact, close to 200 such districts (see Table 4-1). However, as discussed in Chapter 5, they are too small to make it likely that reliable estimates could be derived for groups of schools, let alone individual schools, in such districts. Consequently, the universe for case studies is the medium-sized and large school districts with medium aggregate need and heterogeneity of need across schools in the district. Such districts are likely to consider Provision 4 for a substantial fraction of schools—but significantly fewer than all schools—and therefore require estimates for individual schools and groups of schools. Although the analysis reflected in Table 4-2 may be oversimplified and might not accurately predict the behavior of individual school dis - tricts as they consider Provision 4, it has helped us in our immediate purpose of developing a technical approach. Specifically, it has helped guide our selection of districts for case studies. It also helps motivate the approach to exploring alternative methods for deriving estimates presented in Chapter 5 and, in particular, distinguishing the problem of deriving estimates for entire school districts from the problem of deriv - ing estimates for individual schools or groups of schools within a district. Next, we discuss how we selected districts for case studies, the informa - tion we will request from them, and how the data will be used. CASE STuDIES Selecting Case Study Districts As discussed in Chapter 3, data on school district boundaries are readily available from the School District Review Program managed by the Census Bureau in collaboration with the National Center for Educa - tion Statistics (NCES). Every 2 years, state officials are invited to review the Census Bureau’s school district information and to provide updates and corrections to the school district names, identification numbers, school district boundaries, and the grade ranges for which a school dis - trict is financially responsible. As a result, no new geographic information is needed for the Census Bureau to prepare estimates for school districts according to the methods described in Chapter 5. For levels of geography that are not included in its geographic data - base, the Census Bureau can provide estimates (subject to disclosure review) if the customer provides digitized boundary information that accurately aligns with the bureau’s mapping of streets and other features.

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4 USING ACS DATA TO SUPPORT THE SCHOOL MEALS PROGRAMS Although our panel could not collect digitized boundary information for school attendance areas from all school districts in the country, we can obtain such information for a small set of case study districts. For the selected school districts, the panel will evaluate the accuracy of the digi - tized boundary information provided by the districts and work with the Census Bureau to derive estimates for school attendance areas and groups of school attendance areas using the methods described in Chapter 5. Then, as discussed in Chapters 6 and 7, the panel will evaluate the esti - mates and assess the operational feasibility of the estimation methods. Consonant with the resources for our work, the panel proposes to invite six school districts to participate in our study as case studies. We will collect digitized school attendance boundary information, as well as information about program participation and costs for the school meals programs for each school in the school district. The panel will test its approaches for using ACS and other data to estimate eligibility and par- ticipation for each school in the district and for the school district as a whole. The panel will compare the ACS-based estimates to administrative data and will work with school and school district officials to evaluate the results and assess the potential costs and benefits of adopting Provi - sion 4. The panel will also work with school district officials to assess how schools might be grouped to improve the precision of estimates and oper- ate their school meals programs under Provision 4. The panel will work with the case study school districts to better understand the potential challenges associated with Provision 4. To ensure that estimates can be derived for school attendance areas and to facilitate evaluation of the estimates, the panel will select for case studies only school districts that satisfy the following requirements: • Must have taken applications for all schools in the district for the past 5 years (i.e., cannot already be under a special provision that eliminates taking applications), to allow comparisons to the 5-year ACS estimates. • Must have no outstanding counting/claiming issues—to enhance the accuracy of comparisons between survey and administrative estimates. • Must be willing and able to provide the following data for each school: o digitized school attendance-area boundaries of acceptable accuracy; o state/federal school identification number; o grade span; o total enrollment and enrollment in each grade;

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 SCHOOL DISTRICT CASE STUDIES o total students certified for free, reduced-, and full-price meals for the following categories: directly certified, categorically eligible but not directly certified, income eligible but not cat- egorically eligible; and o total meals (breakfast and lunch) claimed by category (free, reduced-, and full price). As described earlier, the panel decided that case studies should be selected from the medium-need, heterogeneous school districts in Table 4-1. We are interested in such school districts because they are likely to consider adopting Provision 4 for only some schools. From among the medium-need, heterogeneous school districts, we want to have districts that vary in terms of enrollment but are not so small that estimates for schools or groups of schools would be too imprecise. As a rough guide, we chose to consider only the 65 medium-need, heterogeneous school districts with enrollment greater than 12,000 students. Within this group, we planned to select four large school districts (enrollment of at least 25,000) and two medium-size school districts (enrollment between 12,000 and 25,000). The resulting list of potential case study districts was further refined on the basis of diversity in the aggregate level of need for free and reduced-price meals, diversity in the pattern of heterogeneity of need across schools, available information about state and district management and program operations, geographic diversity, and diversity in the race and ethnicity of students. The panel selected six case study districts and invited them to par- ticipate in the study. The school districts listed in Table 4-3 are the five that have agreed to participate as case study districts as of the date of publication of this report. It is expected that at least one case study district5 will have a sub- stantial fraction of students attending magnet or charter schools with attendance areas that might be district-wide or at least overlap the atten- dance areas of many neighborhood schools. Working with such a school district will enable the panel to consider alternative ways of accounting for charter and magnet school students when estimating eligibility and 5 Charter and magnet schools that draw from a district can be part of that school district, or they can be independent local education agency/school food authority. For example, based on information from the website of the Austin Independent School District (AISD) and the public charter school dashboard of the National Alliance for Public Charter Schools, it appears that the public charter schools in Austin have about 6 percent of public school students in Austin, although the charter schools are not part of the AISD.

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 USING ACS DATA TO SUPPORT THE SCHOOL MEALS PROGRAMS TAbLE 4-3 Case Study Districts School District Number of Schools Number of Students (in thousands) Austin, TX 119 83 Chatham, GA 49 34 Norfolk, VA 52 35 Omaha, NE 92 48 Pajaro Valley, CA 33 19 SOURCE: Data from NCES Common Core of Data, 2007-2008, see http://nces.ed.gov/ccd/ [accessed May 2010]. participation based on the geographic residence of a student rather than on information about the specific school the student attends. 6 Recruiting Case Study Districts The panel contacted state directors of the potential case study districts to describe our project and to ask for their assistance. A copy of the letter was also sent to the appropriate regional office of the Food and Nutrition Service (see Attachment A). With the approval of state directors, the panel contacted school district staff. To facilitate the development of our case studies, the panel obtained the support of the School Nutrition Associa - tion (SNA) for this project. The incoming president of SNA wrote a letter that was included with the panel’s letters to state directors and to school district officials (see Attachment B). Case Studies Data Collection and Analysis Plan As noted above, the panel will obtain from each case study district digitized boundaries for school attendance areas and detailed data for each school on enrollment, students approved for free and reduced-price meals, and reimbursable meals served. Furthermore, using a protocol such as that in Attachment C, the panel will collect additional informa- tion, including information pertaining to school food service revenues and the procedures and costs for operating the school meals programs. The first part of the analysis of case studies will be to evaluate the accuracy of school district attendance-area boundaries. The digitized boun- daries provided by school districts may not correspond to the Census Bureau’s defined blocks or block groups, which are the basic units for geo- 6 Other situations that draw students from neighborhood schools include home-schooling, open enrollment, and other school choice programs.

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 SCHOOL DISTRICT CASE STUDIES graphic aggregation. There are, however, well-defined methods for inter- polating statistical estimates that cut standard reporting zones based on a range of possible assumptions (Goodchild, Anselin, and Deichmann, 1993). This activity will involve collaboration between the panel, school district officials, and geographers at the Census Bureau. The second part of the analysis will involve obtaining estimates of eligibility and participation from the ACS and other sources using the methods outlined in Chapter 5 for school attendance areas and whole school districts. These estimates will be compared with school-level and school district-level administrative data and assessed for bias and precision as described in Chapter 6. The panel will also consider esti - mates for groups of schools as defined in collaboration with school district officials. The third phase of the analysis will be to use the estimates of eligibil - ity and participation as the basis for hypothetical claiming percentages for reimbursement under Provision 4. The implied hypothetical reimburse - ments will be assessed relative to reimbursements under the traditional approach (which the case study school districts are using now). Based on estimates provided by the districts for the costs of administrative processes that would be eliminated under Provision 4 (certification, veri - fication, and meal counting by category), differences between costs and reimbursements can be compared for the traditional approach and Provi- sion 4. The panel hopes to use this information to describe situations that render Provision 4 more or less attractive to school districts. Finally, the panel will consult with case study districts and states to identify current uses of data on the numbers of students who are approved for free and reduced-price meals to further illuminate the potential impact of Provi- sion 4. In addition to providing information to and collaborating with the panel, the case study school districts will be invited to participate in a workshop to be held in Washington, DC, in October or November 2010. This workshop will provide staff from each case study school district with the opportunity to present information about special features of the district and reactions to Provision 4, while interacting with staff from the other case study districts, panel members, and other attendees.

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 USING ACS DATA TO SUPPORT THE SCHOOL MEALS PROGRAMS ATTACHMENT A ExAMPLE OF LETTER TO STATE OFFICIAL Dear Texas State Director: The Food and Nutrition Service (FNS) of the U.S. Department of Agri- culture has asked the National Academy of Sciences (NAS) to establish an expert panel to study the possibility of using data from the U.S. Census Bureau’s American Community Survey (ACS) to estimate the percentage of children eligible for the free and reduced-price school meals programs within school districts and school attendance areas. Descriptions of the panel’s technical approach and membership are attached. FNS seeks to determine whether the estimates will be sufficiently accurate to be used as the source of meal claiming percentages for a voluntary universal feed- ing provision. The findings of this study will be used to inform Congress and policy makers as they explore options for expanding alternatives to paper applications. One of the tasks of the panel will be to develop illustrative estimates of eligibility rates for school attendance areas in six case study districts in the United States, to compare them to school-level data, and to evaluate costs and benefits of the methodology. Results of the analysis will be shared with the case study districts. The panel believes that the state of Texas has a number of districts that are good candidates to serve as a case study. The panel would like your support in selecting a Texas case study school district and gaining the district’s cooperation. The panel is for- tunate to have John Perkins, former senior director of the Child Nutri- tion Programs Division with the Texas Education Agency and Assistant Commissioner for Food and Nutrition with the Texas Department of Agriculture, as a member. He has agreed to help us work with a Texas school district in obtaining the data we need, and to provide coordination, collaboration, and feedback to the district. As NAS study director, I will also be working with individuals from the selected district. Advantages to District Case study districts will be the first in the nation to fully understand what an ACS-based approach to eligibility determination might mean for them specifically. They will receive eligibility estimates for their schools and groups of schools prepared in support of this study that have passed the confidentiality review of the U.S. Census Bureau. They will inform and receive the results from an assessment of costs and benefits. Someone from the district will be invited to participate in a workshop to be held in Wash- ington, DC, to discuss the project and its implications for the district.

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 SCHOOL DISTRICT CASE STUDIES Requirements for Case Study District A school district must satisfy the following requirements: • Be willing to collaborate with the panel. • Have no outstanding counting and claiming issues. • Have taken applications for free/reduced-price meals for the last 5 years and be able to provide data for each of those years on: o number of students certified by meal category as of October (directly certified, categorically eligible but not directly certi- fied, and income eligible but not categorically eligible), and o number of meals claimed by category by month. • Be able to provide digitized school boundary maps, the dates for which they are applicable, and a description of the methodology used to create them. • Be able to provide a description of each public school (grade range, NCES ID, enrollment overall and by grade). • Be able to provide the number of students not attending local public schools (charter and magnet, other public). It would be helpful if districts can provide the number of students residing in the district that attend private schools. The panel is particularly interested in selecting as case study districts those for which participation in existing special provisions (such as Pro - visions 2 and 3) is not a clear choice. In particular, we thought districts with groups of schools with few students eligible for free or reduced-price meals and also with groups of schools with many students eligible for free or reduced-price meals would be good candidates. With that as a criterion we tentatively selected the Austin school dis- trict to serve as the case study district from Texas; however, we are open to suggestions of another district. We are also interested in interviewing someone from the San Antonio school district to explore their reasons for ending the use of Provision 2. Next Steps The panel will be assessing costs and benefits associated with the ACS-based approach to determining eligibility. We would be interested in any information you may have at the state level concerning costs of the administrative processes (applications, verification, and meal counting) as well as expected changes in participation associated with providing uni - versal feeding. John Perkins has provided some information to the panel on the latter. Perhaps you could direct us to knowledgeable individuals to talk to about these topics.

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0 USING ACS DATA TO SUPPORT THE SCHOOL MEALS PROGRAMS I very much hope that you can support this important project on behalf of the nation’s schools and school children. I will call you in the next few days to set up a time to answer your questions, obtain your input, and discuss the best way to proceed. Alternatively, please phone or e-mail me using my contact information below. I am especially interested in determining the best contact procedure to reach the selected school district—whether you prefer to forward my letter to the district or to talk with the district directly. USDA and NAS see this as a very important public policy issue for the National School Lunch Program and School Breakfast Program. We recognize that states and school districts have many demands placed upon them, but hope that you will be able to work with us on this project, which has great potential for improving program access and reducing the paperwork burden on schools and parents. Thank you so much for your time and attention to this important matter. Sincerely, Nancy J. Kirkendall, Ph.D. Senior Program Officer Committee on National Statistics National Academy of Sciences (202) 334-2303 nkirkendall@nas.edu copy to: Cindy Long, John Endahl, FNS Director, SW Regional Office

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 SCHOOL DISTRICT CASE STUDIES ATTACHMENT b LETTER OF SuPPORT FROM THE SCHOOL NuTRITION ASSOCIATION January 8, 2010 Dear School Nutrition Director: The SNA leadership has reviewed a description of USDA and the National Academy of Sciences Committee on National Statistics’ proposed program eligibility study and hereby registers its support for the collec - tion of this information. Your school nutrition program has been selected as one of six school districts to serve as a case study for this project. Convened at the request of the USDA Food and Nutrition Services (FNS), a panel of experts is charged with determining how to make use of the American Community Survey (ACS), a yearly census form, to estimate eligibility for the school meals programs. The purpose of these estimates is to develop percentages by which USDA would reimburse school districts for their expenses in providing free breakfasts and lunches to all children attending specified schools. The panel will consider the ability of the ACS to provide estimates for school attendance areas, built by aggregating esti- mates for census tracts and block groups. If such estimates are accurate, FNS may offer a new universal feeding provision that will make use of survey estimates for claiming percentages. SNA believes that the data collected by this study will be of signifi- cant practical use in developing measures to expand access and program participation. We expect that the results of this study will contribute to efforts to streamline the programs, improve efficiency, and ensure that all children who are eligible for school meals receive them. The National Academy of Sciences assures us that they have taken steps to minimize the reporting burden placed on districts participating in the study. Sincerely, Dora Rivas, R.D., S.N.S. President

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 USING ACS DATA TO SUPPORT THE SCHOOL MEALS PROGRAMS ATTACHMENT C PROTOCOL FOR CASE STuDy DISTRICTS SCHOOL DISTRICT AND SCHOOL DATA 1. District name, address, state ID, federal ID. 2. Contact person: Name, title, address, phone, e-mail. 3. For each school (campus) in the district for 2003-2004 through 2008- 2009 provide data as of October 31 of the school year. a. School name, address, state ID, federal ID. b. Digital attendance areas (boundaries). c. Grade levels. d. Official enrollment by grade. e. Official attendance by grade. f. Is this school a severe-needs school under the School Breakfast Program (SBP)? g. Is this school eligible for the 2 cent incremental reimbursement for the National School Lunch Program (NSLP)? h. Number of students approved as free eligible and number approved as reduced-price eligible. i. Number of students directly certified for free meals, number categorically eligible but not directly certified and not requir- ing verification, and number categorically eligible identified by application. j. Number of students approved as free eligible and number approved as reduced-price eligible based on income and household size information submitted on an application. k. Meal count by free, reduced price, and full price for the month of October, separate for SBP and NSLP. Average daily participation for NSLP and SBP for October. l. Meal prices for NSLP and SBP paid meals. 4. Revenue that accrues to school food services for the district. a. School district annual revenue from reduced-price and full-price students for NSLP and SBP. b. School district annual revenue from students from à la carte snack bar, and other sales that are not part of SBP or NSLP. c. School district annual revenue from U.S. Department of Agricul- ture (USDA) reimbursements for NSLP and SBP. d. School district annual revenue from state (not USDA reimburse- ments) for NSLP or NSB. e. School district annual revenue from local sources (neither USDA nor state reimbursements) for NSLP and NSB. f. If there are other sources of revenue, please describe.

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 SCHOOL DISTRICT CASE STUDIES 5. Alternative to 4. Total school district revenue that accrues to school food services. a. Percent of revenue that comes from student payments (reduced price and full price) for NSLP and SBP. b. Percent of revenue that comes from à la carte, snack bar, and other sales that are not part of NSLP or SBP. c. Percent of revenue that comes from USDA reimbursements for NSLP and SBP. d. Percent of revenue that comes from state reimbursement (not USDA) for NSLP and SBP. e. Percent of revenue that comes from local sources (neither USDA nor state). f. Percent of revenue from other sources (please describe). SCHOOL DISTRICT DESCRIPTIvE QuESTIONS Answer the following program questions for school year 2008-2009 only. (Note: this information may be collected in a telephone interview, perhaps sharing the questions in advance.) If there is a question that you cannot answer, please provide the name and contact information for the person who should know the answer. 1. How is direct certification done for your district? Do you use com- puterized matching, or some other process? Is matching done locally or by the state? What percent of Supplemental Nutrition Assistance Program (SNAP) (formerly food stamp) students are identified by direct certification? 2. Are free and reduced-price applications processed centrally or by each school? 3. How many person-days are spent processing free and reduced- price applications each year? What is the annual cost of application processing? 4. How many person-days are spent verifying free and reduced-price applications each year? What is the annual cost of verification? 5. Have you considered adopting NSLP Provision 2 or 3? Why or why not? What factors caused you to not adopt? 6. Does the district (or state) participate in other special pilots or pro - visions, such as the elimination of reduced-price fees? If so, please describe. 7. Provide a summary of findings on your last Coordinated Review Effort (CRE), and the Corrective Action Plan (CAP). 8. Please provide food service profit and loss reports for the school district, if available. Are profit and loss statements also available by school?

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4 USING ACS DATA TO SUPPORT THE SCHOOL MEALS PROGRAMS 9. How are the digitized school areas (boundaries) determined and how frequently are they updated? 10. Would you be willing to provide the panel with results of geocoding student address lists using TIGER line files to evaluate an alternative approach to obtaining geographical information about and deriving estimates for school attendance areas? 11. Does your district use the data on numbers of children certified for free or reduced-price meals for other purposes? If so, please list pro - grams, how much funding is involved, and the source of the funding (state, local, and other). 12. Does your district have up-to-date information about the number of charter and magnet school students and their participation in the school meals programs? Do you have data about the number of chil- dren in home-schooling? Do you have information about students attending schools outside the school attendance boundaries because of open enrollment or public school choice programs? STATE QuESTIONS State level questions to be asked during telephone conversations with case study states. Initial contact is via letter to the state director who over- sees school nutrition (chief state school officer). 1. At the state level, how do you use the data on free and reduced-price students? 2. Do you use the numbers in state allocation formulas? If so, what do you use in Provision 2 and Provision 3 districts where they no longer take applications? 3. What would be the impact if a district no longer took applications and relied on American Community Survey (ACS) data for claiming percentages? 4. Does your state maintain a database with the information from schools or school districts that are rolled up to complete the Form FNS-10 for the state? If so, for how many years are the data available? Can you separately identify the Provision 2 or Provision 3 schools or districts in the database? Can you identify the base years? 5. Has your state conducted an analysis to help schools determine whether they would benefit from Provision 2 or Provision 3? If so, can the panel obtain a copy?