Small-Area Income and Poverty Estimates Priorities for 2000 and Beyond (2000) / Chapter Skim
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5 Future Model Development: The Role of Administrative Records
5 Future Model Development: The Role of Administrative Records
Pages 125-149
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From page 125... ...
If appropriate variables could be found to use in regression models to predict poverty or income for subcounty areas, such models would likely be better than the current shares procedure. The difficulty is that no administrative records data sources currently exist that can provide consistently measured, updated predictor variables for a subcounty model, in the way that tax return and food stamp data are used in the state and county models.
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From page 126... ...
In this chapter we first review the advantages and problems of developing two possible data sources for predictor variables for subcounty income and poverty regression models: IRS tax return records, which could be used in modeling both income and poverty, and food stamp records, which could be used in modeling poverty. Both of these sources currently provide significant variables in the Census Bureau's state and county models; their use for subcounty models would require further development of the Census Bureau's capabilities for geocoding addresses to small geographic areas.
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From page 127... ...
Alternatively, school lunch data might be used as a predictor variable in a regression model for school district poverty estimates. We then discuss data needs for improved population estimates, which are required for many uses of small-area income and poverty estimates from SAIPE: for example, in fund allocation formulas for which SAIPE estimates of numbers of poor need to be converted to poverty rates.
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From page 128... ...
Currently, they are being used to form predictor variables in the SAIPE income and poverty models for states and counties. The process of assigning poverty status to the IRS records, in general terms, involves comparing adjusted gross income for families on tax returns to a poverty threshold that corresponds to the number of adult and child exemptions reported on the return (including exemptions reported for children away from home)
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From page 129... ...
To use IRS tax return data to form predictor variables for an income or poverty model for school districts or other subcounty areas, or, alternatively, to form within-county shares or proportions for subcounty areas, the Census Bureau will need to further refine its geocoding capabilities so that addresses can be assigned geographic codes below the county level. As discussed below (see "Geocoding with TIGER and MAF")
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From page 130... ...
Based on that research, the Bureau decided to use the monthly counts averaged over a 12-month period centered on lanuary 1 of the calendar year subsequent to the income reference year for the poverty estimates. The Census Bureau further refines the food stamp counts in three ways: it subtracts counts by state of the numbers of people who received food stamps due to specific natural disasters from the counts of the total number of recipients; it uses the results of time-series analysis of monthly state food stamp data from October 1979 through September 1997 to smooth outliers; and it adjusts the counts of food stamp recipients in Alaska and Hawaii downward to reflect the higher eligibility thresholds for those states.
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From page 131... ...
Another problem with the use of food stamp data in the county models concerns the time that is required to obtain the data, which are not available until almost 2 years after the year to which they refer. This delay is one of the reasons that the Census Bureau currently produces county poverty estimates with a minimum 3-year time lag (e.g., estimates for income year 1995 were completed in fall 1998~.
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From page 132... ...
The benefits to the Census Bureau and to the Department of Education and other federal agencies of having food stamp data available for use in poverty models are clear, provided that the costs are bearable. State agencies perhaps could benefit from having geocoded food stamp records available for such purposes as streamlining the enrollment of children in school lunch and breakfast programs who are automatically eligible because their families are en
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From page 133... ...
However, TIGER cannot now be used for geocoding addresses to 6For this purpose, each school district in a state might be provided with the list of families participating in food stamps whose addresses were geocoded to that district, provided that such a procedure is compatible with the state s confidentiality provisions for food stamp data. Because TIGER generates public use products, it contains address ranges and not individual street addresses; the census Bureau regards the latter as confidential under Title 13 of the u.s.
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From page 134... ...
When new street names are identified from the DSF (e.g., a new subdivision) , the Census Bureau's regional offices check them using local maps or in the field, and the map locations and address ranges for the segments are added to TIGER.
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From page 135... ...
To update governmental unit boundaries in TIGER, the Census Bureau every year conducts a Boundary and Annexation Survey to ascertain boundary changes for counties, cities, townships, and American Indian areas. In addition, beginning with the 1995-1996 school year, the Department of Education is providing funding for school district boundaries to be updated and put into TIGER every 2 years.
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From page 136... ...
or for someone's business may be geocodable, but not to the appropriate residential address. Indeed, changes to administrative records (e.g., a requirement to list residential addresses on tax returns)
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From page 137... ...
SCHOOL LUNCH DATA Another possible source of information on poverty from administrative records that is available specifically for school districts comprises counts of students who are approved to receive free meals under the National School Lunch Program. School lunch data have the advantage that they are compiled for schools and school districts and, hence, do not require geocoding of individual addresses.l° 9The proposed study would update the results of a study of geocoding 1995 tax returns to census blocks that was conducted in 1998.
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From page 138... ...
· Participation in the school lunch program is voluntary and may be MINCES is the only federal agency that attempts to obtain school lunch data for school districts. The Department of Agriculture obtains aggregate counts each October at the state level of the number of children approved for free lunches and reduced-price lunches in both public and participating private schools.
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From page 139... ...
If the relationship between students approved for free lunches and poor school-age children varies across jurisdictions, it would not be possible to use school lunch data to estimate school-age poverty for school districts directly (e.g., by applying a constant factor to the school lunch counts to obtain estimated numbers of poor school-age children)
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From page 140... ...
to form within-county shares. Alternatively, changes in school lunch counts, instead of shares, could be applied to updated county estimates.l3 Yet another alternative is the possibility of developing a school district poverty model similar to the state and county regression models, and using school lunch counts, or year-to-year changes in those counts, as a predictor variable in the model (assuming comparability of school district school lunch data over counties and states)
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From page 141... ...
the 1990 census comparison estimates, neither set of school lunch-based estimates was much more accurate in either state than the estimates that were based on 1980 census data, which were 10 years out of date. Looking at both overall differences and differences for categories of school districts, the use of the number of students approved for free lunches as the basis for estimates of poor school-age children was marginally more accurate than the other two methods that were evaluated.l6 These results are not encouraging for the use of school lunch data as a consistent measure of poverty for school-age children.
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From page 142... ...
The reason for this result is that school lunch counts include children in families with incomes that are near but not below the poverty threshold. Adjusting the school lunch data to add up to county estimates of poor schoolage children-that is, using the school lunch data to form within-county shares-greatly improved the accuracy of estimates of districts that were eligible for Title I concentration grants.
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From page 143... ...
For school districts, population estimates of children aged 5-17 are needed to convert SAIPE model estimates of numbers poor to proportions poor for determining eligibility for Title I concentration grants. Also needed for Title I allocations are school district estimates of total population-due to a provision in the legislation whereby states can use estimates other than SAIPE estimates to allocate funds to school districts with fewer than 20,000 people.
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From page 144... ...
This magnitude of coverage increase would likely improve the quality of the migration estimates-and, in turn, the population estimates-derived from the tax files. Methods and procedures for regularly using information returns-which amount to some 1 billion documents annually-are yet to be developed, but they warrant the Census Bureau's close attention.
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From page 145... ...
If the information documents (1099 forms) could be merged with the 1040 tax files and the population coverage of the files thereby increased significantly, it would be possible to develop simpler methods with which 17Files the Census Bureau plans to use include IRS 1040 tax returns and 1099 information documents, the SSA Numident file, Medicare enrollment records, Selective Service registration files, Department of Housing and Urban Development tenant rental assistance certification files, and Indian Health Service patient registration files.
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From page 146... ...
Data from IRS tax files could likely contribute to improved school district population estimates if the individual records could be geocoded to school districts, as they are for other levels of geography. We recommend that the Census Bureau assign high priority to evaluating the extent of geocoding of tax records to school districts that can be achieved with the TIGER system after the 2000 census.
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From page 147... ...
Department of Education's Common Core of Data school enrollment information may be useful, although the data pertain only to public school enrollment. For school districts, it could be possible to estimate within-county changes over time in contrast to the current system of maintaining the relative distribution from the last decennial census.
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From page 148... ...
RECOMMENDATIONS 5-1 The Census Bureau and other agencies that produce small-area estimates by using administrative records, such as tax returns and food stamp data, should regularly devote resources to reviewing the quality, comparability, and timeliness of those administrative data for their use in estimation. The review should consider possible changes to administrative records systems that would benefit estimation without undue cost to the data collection agency or undue burden on respondents.
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From page 149... ...
5-3 The Census Bureau should consider conducting evaluations of the possible uses of National School Lunch Program data to develop improved estimates of poor school-age children for school districts. 5-4 The Census Bureau should conduct research on improved data and methods for small-area estimates of total population and population by age.
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