Using American Community Survey Data to Expand Access to the School Meals Programs (2012) / Chapter Skim
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3 Technical Approach
3 Technical Approach
Pages 49-92
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From page 49... ...
Estimates that can be computed from the ACS are eligibility rates (with eligibility determined using ACS variables) , while estimates that can be computed from administrative data are certification rates that reflect students applying and being approved or directly certified through the application, certification, and verification processes.
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in addition to the BRR and claiming percentages to help them assess whether to adopt the AEO.1 The panel's analytical results are focused throughout on school districts in which more than 75 percent of students were eligible for free or reduced-price meals in any school year from 2004-2005 through 2009-2010 because these districts are most likely to be interested in the AEO districtwide. We call these districts "very high FRPL [free or reduced-price lunch]
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* All school districts in the United States with Common Core of Data (CCD)
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This section begins with a description of the ACS direct and model-based estimates and then describes the other data sources the panel compared with the ACS: the administrative data collected by the Food and Nutrition Service (FNS) in support of the school meals programs, administrative information about schools and school districts collected and provided by the National Center for Education Statistics (NCES)
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Thus of the 13,777 school districts for which ACS estimates were released in fall 2011, only 985 had 65,000 or more residents according to the July 2010 Census Bureau population estimates, and only 3,411 had more than 20,000 residents.4 Moreover, even in medium-sized and large school districts, attendance areas for individual schools or groups of schools are small. Because ACS estimates are not provided for school attendance areas, estimates for these areas would need to be based on boundary information or lists of census blocks provided to the Census Bureau by a state or local education agency.
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This timing is only a few months later than the release of direct ACS estimates. As a result, SAIPE estimates are considerably more timely than the 5-year ACS estimates, the only other available option for small school districts.
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The panel collaborated with the Census Bureau, which agreed to adapt the SAIPE approach and provide model-based ACS estimates of the percentages of students eligible for free and reduced-price meals in each school district in the United States and in the school attendance areas in the case study districts. The methodology developed to provide these model-based estimates is described in Appendix C, and the estimates are evaluated in Chapter 4.
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The CCD, a program of NCES, conducts five census operations annually to collect fiscal and nonfiscal data on all public schools, public school districts, and state education agencies in the United States. It provides an official listing of public elementary and secondary schools and school districts in the nation, which is used to select samples for other NCES surveys, and it provides basic information and descriptive statistics on public elementary and secondary schools and schooling in general.
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that were used in the panel's analysis. Case Study Districts The panel invited six school districts to participate in this study as case studies, and five agreed.
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In addition to providing data and collaborating with the panel, the school food authority directors of the case study school districts were invited to participate in a workshop held in Washington, DC, in March 2011. The agenda for the workshop is provided in Appendix E, Part 2.
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However, not all school districts are included in the Census Bureau's Topologically Integrated Geographic Encoding and Referencing (TIGER) files.10 Additionally, 41 districts had ACS direct estimates but were not in the CCD, and 227 districts had ACS model-based estimates but no 5-year ACS estimates.11 Merging with form FNS-742 data was more challenging because the ID numbers in that file vary by state and over time and are often different from NCES IDs.
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The three sources were a workshop with selected school food authority directors, a survey of Provision 2 and 3 school districts, and a wealth of information from the school food authority directors of the case study districts. 13Data set named District_ACS_SAIPE_CCD_schools_Master.V2.xlsx.
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Typically, they do so to increase participation. The Chatham County and Denver school districts have implemented universal free feeding in some schools.
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. At or above that level, the additional costs of feeding all students for free are expected to be offset by savings associated with elimination of administrative processes associated with the traditional school meals programs.
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. Participants said that school districts would decide whether to adopt the AEO by "doing the math." Districts would first determine whether the AEO might increase participation in targeted schools of interest to them.
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Survey of Provision 2 and 3 Districts The panel conducted a survey of school food authority directors in school districts that reported operating under Provision 2 or 3. The purpose was to ascertain the advantages and disadvantages of these provisions from their point of view and to see whether they had data they were willing to share that would help us identify changes in participation because of providing universal free meals.
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Respondents indicated that the percentage of students certified for free and reduced-price meals that triggered the adoption of Provision 2 was high. One district used the severe need breakfast cut-off (60 percent)
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, and claiming percentages being fixed at the base-year level and not reflecting changes in participation or demography. There were also comments about problems in obtaining completed applications in nonprovision schools and the resulting difficulty of collecting meal charges from parents who had not filed applications but whose children ate the meals.
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Additional Information from Case Study Districts In addition to providing the information formally requested of the case study districts and participating in the panel's workshop, the case study school food authority directors responded to many additional questions we posed as we attempted to understand the data on and processes of the school meals programs. We are grateful for their assistance.
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While this list of error sources may appear extensive, the current procedures for certification and meal counting in the school meals programs are subject to their own errors associated with administrative processes that 20While the panel compared ACS data with administrative data, it should be noted that the administrative data also are subject to error.
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Geographic areas that are available in TIGER include blocks, block groups, census tracts, school districts, small cities, towns, counties, and states. The Census Bureau routinely provides detailed demographic data for school districts, as well as for higher levels of geography.
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The panel considered several approaches by which school districts could transfer information on school attendance area boundaries to the Census Bureau as part of the AEO, with a view to determining which approach would be most accurate, easiest for school districts, and most efficient for the Bureau to use in tabulating data for schools. We determined that the best approach would be block rectification, the method adopted by SABINS.
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collected maps for the schools in the 21 largest school districts and computed estimates for race and ethnicity (available at the block level) and for income eligibility for free and reduced-price school meals (available only at the block group level)
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NOTE: School attendance boundaries are shown in red. SOURCE: Prepared by the panel.FIG3-2.eps bitmap
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The SABINS project provides block-rectified lists for the school attendance areas in many of the country's districts for school year 20092010. If available for a district, these are sufficiently accurate for use in the school meals programs and would be an easy way for a district to obtain the needed geographic data.
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Although the ACS includes a question on SNAP participation during the past year, public assistance programs providing cash income are lumped into a single question, and only some of those programs confer categorical eligibility for free meals. There is also evidence that program participation is underreported in the ACS.22 A key task for the panel was to determine how data collected in the ACS can be used to reflect the eligibility criteria of the school meals programs.
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Income Eligibility Guidelines Income eligibility guidelines are prescribed annually by the secretary of agriculture for use in determining eligibility for free and reduced-price meals and for free milk.23 These guidelines differ by the size of the family or economic unit and whether the student lives in Alaska or Hawaii. Eligibility for free meals is based on income at or below 130 percent of the federal poverty guidelines, while that for reduced-price meals is based on income between 130 and at or below 185 percent of the federal poverty guidelines.
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Workshop participants further commented that possible reasons for later applications include downturns in the local economy that result in job losses, an influx of migrant workers, or attempts to obtain benefits for summer programs. Definition of Income In applying to receive benefits under the school meals programs, the "household must report current income on a free and reduced price application.
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price adjustment to reflect differences in consumer prices between the 12-month period that was covered by the respondent's answers to the income questions and the calendar year of the interview.25 Differences in the timing of income measurement between the ACS and applications for the school meals programs, combined with challenges in determining which school year should apply to a given public school student's record, contribute to specification error. Another challenge in using the ACS data on benefit receipt and, more generally, income is reporting error.
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The Census Bureau defines all related individuals as a "family" and all persons who live in the housing unit as a "household."28 The Healthy, Hunger-Free Kids Act of 2010 specifies that foster children are categorically eligible for free meals. The panel's definition of 26See http://www.fns.usda.gov/cnd/frp/2010_application.doc.
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Five different methods for arranging related and unrelated individuals into economic units in a household were specified and compared at the national level, at the state level, and for the 115 school districts that are coterminous with (that is, occupy the entire same geographic territory as) one or more Public Use Microdata Areas (PUMAs)
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This section examines how categorical eligibility can increase the estimated percentages of school children who are eligible for free school meals. Students are categorically eligible for free meals if someone in the family participates in certain means-tested public assistance programs targeting the low-income population.
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do not match the time frame of the administrative data (indicating current participation) used to conduct direct certification or otherwise identify categorically eligible students in the school meals programs.
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may qualify for noncash TANF benefits despite having income that exceeds the eligibility guidelines for SNAP or the school meals programs. The panel compared ACS estimates of eligibility using our preferred definition of an economic unit and considering the household to be a single economic unit in order to evaluate the contribution of receipt of SNAP benefits and public assistance income to the percentages of children eligible for free, reduced-price, and full-price school meals.
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For purposes of this study, ACS data must provide estimates of eligibility for the school meals programs for small geographic areas defined by individual schools or school districts. All children attending these schools are eligible to obtain school meals for free or at the reduced or full price whether they live in traditional housing units or group quarters.
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School districts that plan to use this approach should evaluate blocks at the borders to ensure that large population groups are not assigned incorrectly. Conclusion 2: An appropriate definition of a public school student in the ACS is a person aged 20 or younger with no high school diploma or GED who attended public school within the past 3 months and was in a grade included in the school or school district.37 Conclusion 3: The appropriate income eligibility guidelines to use with ACS data are those for the school year that began in the last half of the past calendar year referenced by the ACS data.
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Conclusion 5: Based on the analysis performed by the panel and our interpretation of the school meals programs' definition of an economic unit, an appropriate definition of an economic unit for determining eligibility for free or reduced-price school meals should allow for multiple economic units in an ACS household. Conclusion 6: ACS data on the receipt of SNAP benefits and public assistance income should be used to account for categorical eligibility when deriving eligibility estimates for the school meals programs.
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From page 86... ...
The ACS has relatively high coverage and response rates, and processing errors in an ongoing survey tend to be small because of the repeated use of systems developed for the survey. Also important to consider, as indicated in the previous section on limitations, are errors in the panel's specifications for using the ACS data to estimate eligibility for the school meals programs.
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Are ACS estimates for schools consistent with administrative estimates provided by the case study school districts and administrative estimates from the CCD? These comparisons would identify whether there are systematic differences between estimates from the survey and administrative data sources.
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Variation over time will be important for school districts considering a new pro vision because such variation causes changes in reimbursement from year to year, some of which are desirable and some of which are not from a district's perspective. Finally, what is the trade-off between temporal stability and responsiveness to real changes in socioeconomic conditions?
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From page 89... ...
As noted previously, Table 3-1 shows the population of school districts categorized according to free and reduced-price percentage and district population size. As discussed above, we had available five 1-year ACS estimates for the large school districts, three 3-year estimates for the medium districts, and only one 5-year estimate for the small districts.
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Systematic Differences To address the question of consistency between estimates from the ACS and alternative administrative data sources, the panel evaluated the difference between an ACS estimate (enrollment, percentage free, percentage reduced price, percentage free or reduced price, BRR) and the corresponding estimate from an alternative data source computed for each school district or school in our evaluation database.39 If the average of these differences over a large group of districts (or schools)
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From page 91... ...
Data on school district reimbursements under the school meals programs were not available to the panel, so there is no way to compare ACS estimates with actual reimbursement data. Participation For the case study districts and schools within those districts, the panel compared BRRs based entirely on distributions of students with BRRs based on distributions of meals served.
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If there are districts in which ACS eligibility estimates fluctuate excessively in ways that are not consistent with real changes in socioeconomic conditions, there will be little a district can do other than decide not to adopt the AEO. If ACS estimates are fairly stable but differ systematically from administrative estimates, however, a procedure for benchmarking the ACS estimates to the administrative estimates could provide the best way to use ACS data in support of the school meals programs.
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