Using American Community Survey Data to Expand Access to the School Meals Progams





Panel on Estimating Children Eligible for School Nutrition Programs
Using the American Community Survey

Allen L. Schirm and Nancy J. Kirkendall, Editors

Committee on National Statistics

Division of Behavioral and Social Sciences and Education

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Using American Community Survey Data to Expand Access to the School Meals Programs Panel on Estimating Children Eligible for School Nutrition Programs Using the American Community Survey Allen L. Schirm and Nancy J. Kirkendall, Editors Committee on National Statistics Division of Behavioral and Social Sciences and Education

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THE NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001 NOTICE: The project that is the subject of this report was approved by the Govern ing Board of the National Research Council, whose members are drawn from the councils of the National Academy of Sciences, the National Academy of Engineering, and the Institute of Medicine. The members of the committee responsible for the report were chosen for their special competences and with regard for appropriate balance. This study was supported by contract number AG-3198-C-09-0006 between the National Academy of Sciences and the U.S. Department of Agriculture. Support for the work of the Committee on National Statistics is provided by a consortium of federal agencies through a grant from the National Science Foundation (award number SES-1024012). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the organizations or agencies that provided support for this project. International Standard Book Number-13: 978-0-309-25720-6 International Standard Book Number-10: 0-309-25720-4 Additional copies of this report are available from the National Academies Press, 500 Fifth Street, NW, Keck 360, Washington, DC 20001; (800) 624-6242 or (202) 334-3313; http://www.nap.edu. Cover credit: The photo on the left is of 2-year-old Joaquin Sanchez as part of a campaign by the California Milk Processor Board Positivity Ambassador blogger campaign with NibblesandFeasts.com. The photo was taken October 4, 2011, by Joaquin's mother and blog author, Ericka Sanchez. Copyright 2012 by the National Academy of Sciences. All rights reserved. Printed in the United States of America Suggested citation: National Research Council. (2012). Using American Community Survey Data to Expand Access to the School Meals Programs. Panel on Estimating Children Eligible for School Nutrition Programs Using the American Community Survey, A.L. Schirm and N.J. Kirkendall, Editors. Committee on National Statis- tics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

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The National Academy of Sciences is a private, nonprofit, self-perpetuating society of distinguished scholars engaged in scientific and engineering research, dedicated to the furtherance of science and technology and to their use for the general welfare. Upon the authority of the charter granted to it by the Congress in 1863, the Academy has a mandate that requires it to advise the federal govern- ment on scientific and technical matters. Dr. Ralph J. Cicerone is president of the National Academy of Sciences. The National Academy of Engineering was established in 1964, under the charter of the National Academy of Sciences, as a parallel organization of outstanding engineers. It is autonomous in its administration and in the selection of its mem- bers, sharing with the National Academy of Sciences the responsibility for advis- ing the federal government. The National Academy of Engineering also sponsors engineering programs aimed at meeting national needs, encourages education and research, and recognizes the superior achievements of engineers. Dr. Charles M. Vest is president of the National Academy of Engineering. The Institute of Medicine was established in 1970 by the National Academy of Sciences to secure the services of eminent members of appropriate professions in the examination of policy matters pertaining to the health of the public. The Institute acts under the responsibility given to the National Academy of Sciences by its congressional charter to be an adviser to the federal government and, upon its own initiative, to identify issues of medical care, research, and education. Dr. Harvey V. Fineberg is president of the Institute of Medicine. The National Research Council was organized by the National Academy of Sciences in 1916 to associate the broad community of science and technology with the Academy's purposes of furthering knowledge and advising the fed- eral government. Functioning in accordance with general policies determined by the Academy, the Council has become the principal operating agency of both the National Academy of Sciences and the National Academy of Engineering in providing services to the government, the public, and the scientific and engineer- ing communities. The Council is administered jointly by both Academies and the Institute of Medicine. Dr. Ralph J. Cicerone and Dr. Charles M. Vest are chair and vice chair, respectively, of the National Research Council. www.national-academies.org

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PANEL ON ESTIMATING CHILDREN ELIGIBLE FOR SCHOOL NUTRITION PROGRAMS USING THE AMERICAN COMMUNITY SURVEY ALLEN L. SCHIRM (Chair), Mathematica Policy Research, Washington, DC DAVID M. BETSON, Department of Economics, University of Notre Dame MARIANNE P. BITLER, Department of Economics, University of California, Irvine F. JAY BREIDT, Department of Statistics, Colorado State University ROBERT E. FAY, Westat, Rockville, Maryland ALBERTA C. FROST, Alexandria, Virginia MICHAEL F. GOODCHILD, Department of Geography, University of California, Santa Barbara PARTHA LAHIRI, Department of Statistics, University of Maryland PENNY E. McCONNELL, Fairfax County Public Schools, Springfield, Virginia SARAH NUSSER, Department of Statistics, Iowa State University JOHN PERKINS, Perkins Consulting Group, Austin, Texas JAMES H. WYCKOFF, Currie School of Education, University of Virginia NANCY J. KIRKENDALL, Study Director ESHA SINHA, Associate Program Officer AGNES E. GASKIN, Administrative Assistant v

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COMMITTEE ON NATIONAL STATISTICS 2011-2012 LAWRENCE D. BROWN (Chair), Department of Statistics, The Wharton School, University of Pennsylvania JOHN M. ABOWD, School of Industrial and Labor Relations, Cornell University ALICIA CARRIQUIRY, Department of Statistics, Iowa State University WILLIAM DuMOUCHEL, Oracle Health Sciences, Tucson, Arizona V. JOSEPH HOTZ, Department of Economics, Duke University MICHAEL HOUT, Survey Research Center, University of California, Berkeley KAREN KAFADAR, Department of Statistics, Indiana University SALLIE KELLER, Provost, University of Waterloo, Ontario, Canada LISA LYNCH, The Heller School for Social Policy and Management, Brandeis University SALLY C. MORTON, Department of Biostatistics, University of Pittsburgh JOSEPH NEWHOUSE, Division of Health Policy Research and Education, Harvard University RUTH PETERSON, Criminal Justice Research Center, The Ohio State University HAL S. STERN, Donald Bren School of Computer and Information Sciences, University of California, Irvine JOHN THOMPSON, National Opinion Research Center at the University of Chicago ROGER TOURANGEAU, Westat, Rockville, Maryland ALAN ZASLAVSKY, Department of Health Care Policy, Harvard University Medical School CONSTANCE F. CITRO, Director vi

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Acknowledgments T he Panel on Estimating Children Eligible for School Nutrition Pro- grams Using the American Community Survey (ACS) wishes to thank the many people and organizations that contributed to the preparation of this report. Without their help, the panel could not have completed the report. As chair, I want to thank my fellow panel members for their commitment to the work under a demanding time schedule. They consistently provided insightful and constructive input under tight deadlines as we developed this report. I have appreciated their always good humor, and it has been a pleasure working with them. The panel thanks John Endahl, Jay Hirschman, and Cindy Long of the Food and Nutrition Service (FNS) for their patient explanation of the many rules, regulations, data sources, and evaluation studies pertaining to the school meals programs. We are also grateful for the expert advice of staff of the U.S. Census Bureau in helping us understand the data col- lected in the ACS and the estimates developed in the Small Area Income and Poverty Estimates (SAIPE) Program. Special thanks go to Wes Basel, Rick Denby, Doug Geverdt, and David Powers of the Census Bureau for participating regularly in our open meetings; offering advice; and, most important, performing the special tabulations of ACS data to derive the direct and model-based estimates we needed to conduct our work. The panel also extends special thanks to the food service authority directors in our case study districts: Christine Carillo-Spano of the Austin Independent School District, Texas; Altheria Maynard of the Savannah- Chatham County Public School System, Georgia; Nicole Meschi of the vii

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viiiACKNOWLEDGMENTS Pajaro Valley Unified School District, California; Helen Philips of the Norfolk Public Schools, Virginia; and Tammy Yarmon of the Omaha Pub- lic Schools, Nebraska. These individuals and their staff provided us with large amounts of data, answered many questions, and gave us valuable insights into the school meals programs. They are clearly dedicated to providing high-quality meals to as many children as possible. Special thanks go as well to Salvatore Saporito, Stuart Hamilton, and Ashwini Wakchaure of the College of William and Mary and the School Attendance Boundary Information System (SABINS) project for participating regularly in the panel's open meetings; making presenta- tions concerning the SABINS project and its progress; offering general advice concerning geographic issues; and, most important, creating the files containing boundary information for all schools in our case study districts in the form needed by the Census Bureau. In the process of preparing this report, the panel convened 10 meet- ings, 6 of them open meetings to benefit from presentations by many individuals. We would like to express our thanks for presentations con- cerning the school meals programs and administrative data by John Endahl, Ed Harper, Jay Hirschman, Cindy Long, Melissa Rothstein, Gary Vessels, and William Wagoner, FNS, and Christopher Logan of Abt Associ- ates; presentations concerning the ACS, SAIPE, and geographic issues by Mark Asiala, Wes Basel, Douglas Geverdt, Todd Hughes, David Johnson, Donald Lurey, Alfredo Navarro, David Powers, Michael Ratcliffe, and Sharon Stern of the Census Bureau; a presentation concerning measure- ment of income and program participation by John Czajka, Mathematica Policy Research; a presentation concerning income variability and its impact on eligibility for school meals by Constance Newman, Economic Research Service, U.S. Department of Agriculture; a presentation con- cerning the Access, Participation, Eligibility, and Certification Study by Michael Ponza, Mathematica Policy Research; and a presentation con- cerning relevant U.S. Government Accountability Office (GAO) studies addressing the school meals programs by Kay Brown, GAO. The panel would also like to express its appreciation to the staff of the National Center for Education Statistics for attending panel meetings, describing their data and the quality of those data, and facilitating our collaboration with the Census Bureau. The panel's final open meeting, held on March 3 and 4, 2011, was a workshop with school food service authority directors from our case study districts and selected other individuals from the school food com- munity with insights to offer about Provisions 2 or 3 and the school meals programs more generally. The purpose of the workshop was to help us better understand issues pertaining to a potential new provision of the school meals programs, as well as the information school dis-

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ACKNOWLEDGMENTS ix tricts would need to determine whether to adopt this special provision. Participants included Onetha Bonaparte, school meals program coordi- nator, Savannah-Chatham County Public School System, Georgia; Tim Cipriano, executive director of food services, New Haven Public Schools, Connecticut; Lyman Graham, food service director, Roswell Independent School District, New Mexico; Lynn Harvey, section chief, Child Nutri- tion Services, Division of School Support, North Carolina Department of Public Instruction; Leo Lesh, executive director, Enterprise Management, Denver Public Schools, Colorado; Terry Mendez, administrator for food and nutrition services, Brownsville Independent School District, Texas; Nicole Meschi, director of food and nutrition services, Pajaro Valley Uni- fied School District, California; Mary Jo Tuckwell, senior consultant, Food Services Group, inTEAM Associates, Wisconsin; and Tammy Yarmon, director, Nutrition Services, Omaha Public Schools, Nebraska. The infor- mation and insights provided by these individuals were tremendously helpful to the panel. The panel was assisted by a highly able staff. Our work could not have been completed without the extraordinary dedication, seemingly boundless energy, and many contributions of Nancy Kirkendall, the study director. She provided technical and substantive insights, conducted and oversaw many analyses, drafted and revised key sections of our reports, and kept the panel and project on track, all while remaining unfailingly upbeat. I very much enjoyed working with Nancy. We would like to acknowledge Linda Meyers and Lynn Parker of the Food and Nutrition Board of the Institute of Medicine for their help in identifying individuals knowledgeable about the school meals programs. We are also grateful for the consistently wise counsel provided by Connie Citro, director of the Committee on National Statistics, at critical points in our work when the path ahead was not clear; for the tabulations and analysis provided by Esha Sinha, associate program officer; for the assistance of Agnes Gaskin, administrative assistant to the panel, in handling our logistical arrangements and meetings and preparing the final manuscript; for the help of Kirsten Sampson Snyder in managing the report review process; for the skillful editing of Rona Briere; and for the management of the production process by Yvonne Wise. We would also like to thank stu- dents who assisted in data processing and analysis under the guidance of panel members or staff: Addison James, Colorado State University; Jeffrey Moon, Indiana University of Pennsylvania and a junior fellow of the Joint Program in Survey Methodology; John Michael Salas, University of California, Irvine; and Stephanie Zimmer, Iowa State University. In addition, we are grateful to Umet Ozek, National Center for Analysis of Longitudinal Data in Education Research (CALDER), American Institutes for Research, who provided aggregate District of Columbia Public Schools

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xACKNOWLEDGMENTS data for use in the panel's analyses of the effects of school choice. Finally, we would like to thank several staff of Mathematica Policy Research: Kai Filion and Mary Grider, who devoted numerous hours to constructing the district-level evaluation database that supported many of our key analyses; Frank Yoon, who helped us analyze differences between ACS and administrative estimates; and Esa Eslami, Bruce Schechter, Joel Smith, and Carole Trippe, who prepared estimates of Supplemental Nutrition Assistance Program (SNAP) recipients from ACS and SNAP Quality Con- trol data. This report has been reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise, in accordance with procedures approved by the Report Review Committee of the National Research Council (NRC). The purpose of this independent review is to provide candid and critical comments that will assist the institution in making its published report as sound as possible and to ensure that the report meets institutional standards for objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the delibera- tive process. We wish to thank the following individuals for their review of this report: Marc P. Armstrong, professor and CLAS collegiate fellow, chair, Department of Geography, interim chair, Department of Commu- nication Studies, dean's administrative fellow, College of Liberal Arts and Sciences, The University of Iowa; Marilyn Briggs, co-director, Center for Nutrition in Schools, Department of Nutrition, University of Cali- fornia, Davis; Kathy F. Kuser, consultant, Lithia, Florida; Jean Opsomer, Department of Statistics, Colorado State University; Joseph Salvo, Popu- lation Division, New York City Department of City Planning; Eric Slud, area chief of mathematical statistics, Center for Statistical Research and Methodology, U.S. Census Bureau, and professor, Statistics Program, Mathematics Department, University of Maryland; and Grant I. Thrall, Department of Geography, University of Florida. Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the report's conclusions or recommendations, nor did they see the final draft of the report before its release. The review of this report was overseen by V. Joseph Hotz, Department of Economics, Duke University and Charles Phelps, university professor and provost emeritus, University of Rochester. Appointed by the NRC's Report Review Committee, they were responsible for making certain that an independent examination of this report was carried out in accordance with institutional procedures and that all review comments were carefully considered. Responsibility for the final content of this report rests entirely with the authoring panel and the institution.

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ACKNOWLEDGMENTS xi Finally, we recognize the many federal agencies that support the Committee on National Statistics directly and through a grant from the National Science Foundation. Without their support and their commit- ment to improving the national statistical system, the work that served as the basis for this report would not have been possible. Allen L. Schirm, Chair Panel on Estimating Children Eligible for School Nutrition Programs Using the American Community Survey

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Tables, Figures, and Boxes TABLES 2-1Percentage of Enrolled Students by Approval Status for School Meals Programs, Fiscal Years (FY) 2005-2010, 22 2-2Numbers of Students Eligible for the National School Lunch Program (NSLP) from Two Sources: (1) Current Population Survey (CPS) Estimates Based on Annual Income and (2) NSLP Certifications for Free and Reduced-Price Meals (in thousands), 1993-1999, 23 2-3Official National School Lunch Program (NSLP) Participation (average daily meals, divided by 0.927) and Percentage of Lunches Served by Approval Category, Fiscal Years (FY) 2005- 2010, 25 2-4National School Lunch Program (NSLP) Participation Rates by Approval Category, Fiscal Years (FY) 2005-2010, 26 2-5Target Day Participation Rates in the National School Lunch Program (NSLP) (percentage of enrolled students) from the School Nutrition Dietary Assessment Study-III, by Income Level, Meal Category, and School Level, 27 2-6Federal Reimbursement Rates for 2010-2011 School Meals Programs by Eligibility Category, 29 3-1Number and Percentage of U.S. School Districts by Size and Percentage Approved for Free or Reduced-Price Meals, 51 3-2Case Study Districts, 58 xvii

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xviii TABLES, FIGURES AND BOXES 3-3Illustrative Approximate Standard Errors of ACS Direct Estimates by Type of ACS Release, School Enrollment, and Estimated Fraction of Free and Reduced-Price Eligible Students, 90 4-1 Average Differences Between ACS 5-Year Estimates for 2005-2009 and CCD Estimates for 2009-2010, 102 4-2 Average Differences Between ACS 3-Year Estimates and CCD Estimates for Last School Year in ACS Reference Period, 103 4-3 Average Differences Between ACS 1-Year Estimates and CCD Estimates, Large Districts Only, 104 4-4 Average Across Years of Average Differences Between ACS Estimates and CCD Estimates for Very High FRPL and High FRPL Districts, 104 4-5 Average Across Years of Average Differences Between ACS Estimates and CCD Estimates for Low to Moderate FRPL Districts, 105 4-6 Potential Effects of Certification Errors on the Distribution of Students Under Various Assumptions, 108 4-7 BRRs Based on Monthly and Annual Income Estimates: Bias and Ratio, 113 4-8 Intertemporal Variability of ACS 5-Year Estimates by Enrollment, 123 4-9 Average Differences Between ACS Direct and Model-Based Estimates and CCD Estimates for Small Districts, 126 4-10BRRs Based on Certified Students Versus BRRs Based on Meals Served: Illustration with Case Study Districts, 132 4-11Use of National Participation Rates to Take Participation into Account: Illustration with Case Study Districts, 134 4-12Use of State Participation Rates to Take Participation into Account: Illustration with Case Study Districts, 135 4-13Alternative BRRs for Case Study Districts, 136 4-14Alternative BRRs for States, 138 4-15BRRs Based on Certified Students Versus BRRs Based on Meals Served: Illustration with Case Study District Schools, 140 4-16Illustration of Potential Participation Effects of Universal Free Meals Under the AEO in Case Study Districts, 142 4-17Illustration of Potential Participation Effects of Universal Free Meals Under the AEO in Case Study District Schools, 144 4-18BRRs for Case Study Districts Based on Certified Students Versus Meals Served Under Traditional Operating Procedures and the AEO, 146

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TABLES, FIGURES AND BOXES xix 4-19BRRs for Case Study District Schools Based on Certified Students Versus Meals Served Under Traditional Operating Procedures and the AEO, 148 5-1 Districts Operating Under Provision 2 or 3 in 2009-2010, Not in a Base Year, 153 5-2 Number and Percentage of School Districts in the United States in 2009-2010 by Need, Heterogeneity of Need, and Enrollment Size, 164 5-3 Step 1a: Calculate Averages of ACS Eligibility Percentages for Preliminary Benchmarking, 174 5-4 Step 1b: Calculate Averages of Administrative Certification Percentages for Preliminary Benchmarking, 174 5-5 Step 1c: Calculate Preliminary Benchmarking Adjustments, 175 5-6 Step 1d: Calculate Preliminary Benchmarked Eligibility Percentages, 175 5-7 Step 1e: Calculate Preliminary BRRs, 176 5-8 Step 2: Preliminary Assessment of the AEO: Simulate Reimbursements, 177 5-9 Step 2: Preliminary Assessment of the AEO: Calculate BRRs Based on Administrative Certification Percentages, 178 5-10Step 2: Preliminary Assessment of the AEO: Compare BRRs Based on Benchmarked ACS Estimates with BRRs Based on Administrative Estimates, 179 5-11Step 4a: Calculate Averages of ACS Eligibility Percentages for Final Benchmarking, 180 5-12Step 4b: Calculate Averages of Administrative Certification Percentages for Final Benchmarking, 180 5-13Step 4c: Calculate Final Benchmarking Adjustments, 181 5-14Step 4d: Calculate Benchmarked Eligibility Percentages, 182 5-15Step 4e: Calculate BRRs, 183 5-16Step 5: Final Assessment of the AEO: Simulate Reimbursements, 184 5-17Step 5: Final Assessment of the AEO: Calculate BRRs Based on Administrative Certification Percentages, 185 5-18Step 5: Final Assessment of the AEO: Compare BRRs Based on Benchmarked ACS Estimates with BRRs Based on Administrative Estimates, 186 5-19Step 6: Calculate Initial Claiming Percentages and Make Final Decision About Adopting the AEO, 187 5-20Step 7a: Benchmark Newly Released ACS Eligibility Percentages, 187 5-21Step 7b: Update AEO Claiming Percentages, 188

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xx TABLES, FIGURES AND BOXES B-1Percentage of Total Students in the United States by Group (related, unrelated, foster, and group quarters), 248 B-2Percentage of Related Students Income-Eligible for School Meals by Economic Unit, 248 B-3Relationships Reported for Students Who Are Not Related to the Householder, 249 B-4Household Composition of Unrelated Students Who Are Not Unmarried Partners, 249 B-5Assignment of Unrelated Students Who Are Not Unmarried Partners to Economic Units for Sensitivity Analysis, 250 B-6Income Eligibility Distribution of Public School Students in the United States: Groups of Students and Economic Units, 253 B-7Percentage of Students in the United States Eligible for School Meals Under Definitions EU4 and EU5, 256 C-1Regression Results for 2009, 272 C-2Distribution of Estimates, Relative SEs (2009) and 5-Year RMSDs for Free and Reduced-Price Eligibility Rates Estimated by the Model and from the 1-Year ACS, 277 E-1Case Study Districts, 288 E-2Data Received from Case Study Districts, 290 E-3Counts of Schools in Case Study Districts, 297 E-4Comparison of CCD and District Data on Enrollment and Numbers of Students Certified as Eligible for Free and for Reduced-Price Meals in Case Study Districts (percentage difference of enrollment-based weighted average over schools), 299 E-5Comparison of CCD and District Data on Enrollment and Numbers of Students Certified as Eligible for Free and for Reduced-Price Meals in Case Study Districts (percentage difference of districtwide totals), 300 E-6Ratio of Average Percentage Free and Reduced-Price-Eligible Students from the CCD to the Same Average Percentage from District Data by Case Study District (average taken over schools), 300 F-1Average Differences Between ACS 5-Year Estimates and 5-Year Averages of CCD Estimates, 317 F-2Average Differences Between ACS 3-Year Estimates and 3-Year Averages of CCD Estimates, 318 F-3Average Differences Between ACS 5-Year Estimates of Enrollment and Various CCD Estimates, 320

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TABLES, FIGURES AND BOXES xxi F-4 Average Differences Between ACS 3-Year Estimates of Enrollment and Various CCD Estimates, 322 F-5 Average Differences Between ACS 1-Year Estimates of Enrollment and CCD Estimates, 322 F-6 Average Differences Between ACS 5-Year Estimates and CCD Estimates for Low to Moderate FRPL Districts, 323 F-7 Average Differences Between ACS 3-Year Estimates and CCD Estimates for Low to Moderate FRPL Districts, 324 F-8 Average Differences Between ACS 1-Year Estimates and CCD Estimates for Low to Moderate FRPL Districts, 324 F-9 Model Versus Empirical Estimates for Variances of Year-to-Year Changes, Large Districts Only, 331 F-10Model Versus Empirical Estimates for Variances of Year-to-Year Changes, Medium Districts Only, 332 F-11Model Versus Empirical Estimates for Variances of Year-to-Year Changes, Small Districts Only, 333 F-12Intertemporal Variability of ACS 5-Year Estimates, by Enrollment, 336 F-13Standard Deviation, Bias, and RMSE for ACS 1-Year, 3-Year, and 5-Year Estimates at Lags of 0 and 2 years, 337 F-14Results for Various Models Predicting Differences Between ACS 5-Year Estimates for 2005-2009 and CCD Estimates for 2009- 2010, 339 G-1Comparison of Counts of Households and Individuals Receiving SNAP Benefits at the National Level, ACS Versus SNAP QC, 2009, 345 G-2State Level Counts of School-Age Children (aged 5-17) in Households Receiving SNAP Benefits, 2009, 346 G-3Percentage Eligible by Category for Various Demographic Characteristics Using Monthly and Annual Income, 354 G-4Percentage of Schools in Omaha, Nebraska, According to Whether Free or Reduced-Price Eligibility Percentage is Over or Under 75 Percent, by Measure, 360 G-5Share of Public School Enrollment by Choice Status, 363 G-6Eligibility Distribution for Households with Students, Selected Characteristics, 365 G-7Certification Category and Correct Eligibility Category in School Year 2005-2006, 366 G-8Illustration of Impact on BRR of Two Assumptions: (1) 10 Percent of Students Who Must Pay Full Price Applied for but Were Denied Approval for Free or Reduced-Price Meals and (2) Students Who Did Not Apply Were Not Eligible for Free or Reduced-Price Meals, 368

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xxii TABLES, FIGURES AND BOXES G-9 Illustration of Impact on BRR of Two Assumptions: (1) 25 Percent of Students Who Must Pay Full Price Applied for but Were Denied Approval for Free or Reduced-Price Meals and (2) Students Who Did Not Apply Were Not Eligible for Free or Reduced-Price Meals,369 G-10Illustration of Impact on BRR of Two Assumptions (1) 40 Percent of Students Who Must Pay Full Price Applied for But Were Denied Approval for Free or Reduced-Price Meals and (2) Students Who Did Not Apply Were Not Eligible for Free or Reduced-Price Meals, 370 G-11Illustration of Impact on BRR of Two Assumptions: (1) 10 Percent of Students Who Must Pay Full Price Applied for but Were Denied Approval for Free or Reduced-Price Meals and (2) 9.5 Percent of Students Who Did Not Apply Were Eligible for Free Meals and 8.3 Percent for Reduced-Price Meals, 371 G-12Illustration of Impact on BRR of Two Assumptions: (1) 25 Percent of Students Who Must Pay Full Price Applied for but Were Denied Approval for Free or Reduced-Price Meals and (2) 9.5 Percent of Students Who Did Not Apply Were Eligible for Free Meals and 8.3 Percent for Reduced-Price Meals, 372 FIGURES 2-1School meals process and distributions of enrolled students and meals served across free, reduced-price, and full-price categories: Traditional approach and universal free meals, 17 3-1Illustration of split blocks: School attendance areas and census blocks in Austin, Texas, 72 3-2Illustration of split blocks: Aerial view of school attendance areas in Austin, Texas; close-up of areas surrounding airport, 72 4-1Comparison of ACS 5-year (2005-2009) and CCD (2009-2010) estimates for very high FRPL districts: Percentage of students eligible for free meals, 97 4-2Comparison of ACS 5-year (2005-2009) and CCD (2009-2010) estimates for very high FRPL districts: Percentage of students eligible for reduced-price meals, 99 4-3Comparison of ACS 5-year (2005-2009) and CCD (2009-2010) estimates for very high FRPL districts: Percentage of students eligible for free or reduced-price meals, 100 4-4Comparison of ACS 5-year (2005-2009) and CCD (2009-2010) estimates for very high FRPL districts: BRR, 101

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TABLES, FIGURES AND BOXES xxiii 4-5Intertemporal variability of ACS 5-year estimates: Squared coefficient of variation of year-to-year change in blended reimbursement rate versus enrollment, 123 B-1Impact of alternative economic unit definitions by state, 258 B-2Impact of alternative economic unit definitions by school district, 258 B-3Impact of alternative economic unit definitions on state-level eligibility (EU4-EU5) without and with categorical eligibility, 259 B-4Impact of alternative economic unit definitions on district-level eligibility (EU4-EU5) without and with categorical eligibility, 260 B-5Impact of categorical eligibility at the state level with EU4 and EU5 (EUX with categorical eligibility-EUX), 262 B-6Impact of accounting for categorical eligibility at the district level with EU4 and EU5 (EUX with categorical eligibility-EUX), 262 C-1Median free and reduced-price eligibility rates estimated by the models over time, 273 C-2Average 5-year ACS eligibility rates for free and reduced-price meals by size of school district, 273 C-3Median of relative standard errors for percentages eligible for free meals estimated by the model and from the 5-year and 1-year ACS by size of school district, 274 C-4Median of relative standard errors for percentages eligible for reduced-price meals estimated by the model and from the 5-year and 1-year ACS by size of school district, 274 C-5Median of root mean squared differences (RMSDs) for free- eligible percentages estimated by the model and from the 1-year ACS by size of school district, 275 C-6Median of root mean squared differences (RMSDs) for reduced- price-eligible percentages estimated by the model and from the 1-year ACS size of school district, 275 C-7Median of relative standard errors for model-based and 1-year and 5-year ACS-based free eligibility percentages by size of school district, 278 C-8Median of root mean squared differences (RMSDs) for model- based and 1-year ACS-based free eligibility percentages by size of school district, 278

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xxiv TABLES, FIGURES AND BOXES C-9 Median of relative standard errors for model-based and 1-year and 5-year ACS-based reduced-price eligibility percentages by size of school district, 279 C-10Median of root mean squared differences (RMSDs) for model-based and 1-year ACS-based reduced-price eligibility percentages by size of school district, 279 F-1Regression fit of log(design variance) versus log (enrollment), 335 F-2Squared coefficient of variation of year-to-year change in ACS 5-year estimate of BRR versus inverse of enrollment, 336 G-1Out-of-district public enrollment, Washington, DC, public schools, 2008: School catchment-based and enrollment-based free or reduced-price-eligible percentages, 357 G-2Within-district open enrollment in Omaha public schools, 2008-2009: School catchment-based and enrollment-based free or reduced-price-eligible percentages, 359 G-3Five-year (2005-2009) ACS-estimated and 2008-2009 actual enrollment by free or reduced-price eligibility percentages, Omaha public schools, 361 BOXES 2-1Special Provisions, 31 5-1Calculating ACS and Administrative Averages and Benchmarking Adjustments, 167 5-2Preliminary Benchmarking of ACS Estimates, 168 5-3Calculating Preliminary Blended Reimbursement Rates Based on Benchmarked ACS Estimates and a District's Most Recent Participation Rates, 169 5-4Calculating AEO Claiming Rates for Use in First Year after Base Year, 171 5-5Benchmarking Future ACS Eligibility Estimates, 172 5-6Updating AEO Claiming Rates, 173 5-7Calculating ACS and Administrative Averages and Benchmarking Adjustments Provision 2 or 3 Districts, 192 5-8Benchmarking of ACS Estimates Provision 2 or 3 Districts, 193 5-9Calculating Blended Reimbursement Rates Based on Benchmarked ACS Claiming Percentages in Provision 2 or 3 Districts, 194

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TABLES, FIGURES AND BOXES xxv 5-10Calculation of AEO Claiming Rates for Use in 2014-2015 Provision 2 or 3 Districts, 196 5-11Benchmarking Future ACS Estimates and Updating of AEO Claiming Rates Provision 2 or 3 Districts, 197 5-12Adjustment of ACS Eligibility Percentages to Account for Students Who Live in Nontraditional Housing, 198 5-13Other Uses of Data on Students Certified for Free and Reduce- Price Meals, 200 B-1ACS Questions on Schooling, 237 B-2ACS Questions on Achievement, 238 B-3ACS Question on Age, 239 B-4Income as Defined by FNS "Eligibility Manual for School Meals," 240 B-5ACS Questions About Income, 242 B-6ACS Question About Relationship to Respondent, 246 B-7Definition of Economic Units for Sensitivity Analysis, 251 B-8ACS Questions Related to Categorical Eligibility, 255 F-1SAS Code for Analysis of Variability, 325 F-2SAS Proc Mixed Output: The Mixed Procedure, 326 F-3SAS Output Proc Mixed: Estimated R Matrix for Large Districts, 327 F-4SAS Output Proc Mixed: Estimated R Correlation Matrix for Large Districts, 328 F-5SAS Output Proc Mixed, 329 F-6SAS Proc Mixed Output, Fit Statistics, 330 F-7SAS Proc Mixed Output, Least Squares Means, 330 F-8Covariates Used in Regression Analysis, 340

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