National Academies Press: OpenBook

The American Community Survey: Summary of a Workshop (2001)

Chapter: Appendix: An Example of Combining Information

« Previous: 8. Conclusion
Suggested Citation:"Appendix: An Example of Combining Information." National Research Council. 2001. The American Community Survey: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/10051.
×

Page 51

Appendix

An Example of Combining Information

The Panel on Estimates of Poverty for Small Geographic Areas is providing assistance to the Census Bureau in its development of model-based small-area estimates of the number of children living in poverty, which is needed for input to formulas allocating substantial funds to counties and school districts to address the needs of disadvantaged children under Title I of the Elementary and Secondary Education Act. Prior to this recent work, Title I had used the most recent census long-form (sample-based) counts to allocate funds, which produced estimates that were as much as 12 years out of date. Model-based estimates at the county level (for 1993 and every two years into the future) and at the school district level (for 1995 and every two years into the future) are now being used in place of the census long-form estimates. (Contemporaneous direct estimates cannot be supported with current survey or administrative data.)

The county-level model (used for both 1993 and 1995 estimates) is an excellent example of how current best practice permits one to combine data from various sources. These model-based estimates make use of a county-level regression model, which used as the dependent variable a logarithmic transformation of the current number of children in poverty, measured by a 3-year average (to reduce variance) from the Current Population Survey (CPS).1 This regression model makes use of (logarithmic transformations of)

1 Since the CPS does not have samples in all counties, the regression model was fit using only about 1,300 counties.

Suggested Citation:"Appendix: An Example of Combining Information." National Research Council. 2001. The American Community Survey: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/10051.
×

Page 52

the following covariates for a given county: the number of child exemptions reported by families in poverty on tax returns, the number of people receiving food stamps, the estimated population under age 18, the number of child exemptions on tax returns, and the number of poor school-age children in the county from the previous census. For counties with CPS sample households and with poor children in the sample, a linear combination (formally, empirical Bayes' shrinkage) of the direct estimate from the CPS and the model prediction from the regression model is computed; otherwise, the model prediction alone is used. After being transformed back to the original scale (assisted by an adjustment for transformation bias), the final county-level estimates of the number of poor school-age children are then ratio adjusted so that within each state the county-level estimates sum to a separately modeled state-level estimate.

The state-level model was developed in a similar manner to the county-level model. The state-level regression model uses as the dependent variable the estimated proportion of poor school-age children as measured by the CPS (using only a single year, given the larger sample size at the state level). The covariates used in this regression model are the proportion of child exemptions reported by families in poverty on tax returns, essentially the proportion of people receiving food stamps; the proportion of persons under 65 years of age who did not file a tax return; and the residual from the analogous census regression of the proportion of poor school-age children from the most recent census on the other three covariates contemporaneous with that time period. As in the county-level model, a linear combination (again based on empirical Bayes' methods) of the direct CPS estimate and the model prediction is used (though in practice, the estimated model error variance has been so low that the regression prediction has usually received the full weight).

For income year 1995, the requirement was to provide poverty estimates at the level of school districts. At this low level of geographic aggregation, the above approach based on regression modeling cannot be used, since corresponding data, especially for the covariates, does not now exist on a uniform basis. Therefore, the Census Bureau adopted a simple shares approach, distributing 1995 county-level estimates of the number of poor school-age children to school districts according to the school district to county poverty shares, measured using the 1990 census long form (ignoring some minor complexities).

In the future, the ACS is expected to play an important role in the estimation of the number of school-age children in poverty at the school district level, either by direct estimation based on aggregation of data over several years, or by combination in one of several ways, with other data series that are and might become available at the school district level (e.g., data on food stamp participation, data on school lunch participants, and poverty rates estimated from tax filers.) It is quite likely that even with the large sample size

Suggested Citation:"Appendix: An Example of Combining Information." National Research Council. 2001. The American Community Survey: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/10051.
×

Page 53

of the ACS, small-area estimation techniques will be required to combine information over time and geography to develop high-quality estimates. Issues of comparability of the decennial census, the CPS, and the ACS will need to be addressed, as will any changes in tax or welfare programs that affect data comparability over either time or geography.

Suggested Citation:"Appendix: An Example of Combining Information." National Research Council. 2001. The American Community Survey: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/10051.
×
Page 51
Suggested Citation:"Appendix: An Example of Combining Information." National Research Council. 2001. The American Community Survey: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/10051.
×
Page 52
Suggested Citation:"Appendix: An Example of Combining Information." National Research Council. 2001. The American Community Survey: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/10051.
×
Page 53
Next: References »
The American Community Survey: Summary of a Workshop Get This Book
×
Buy Paperback | $29.00 Buy Ebook | $23.99
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

The American Community Survey (ACS), to be run by the Census Bureau, will be a large (250,000 housing units a month), predominantly mailout/mailback survey that will collect information similar to that on the decennial census long form. The development of this new survey raises interesting questions about methods used for combining information from surveys and from administrative records, weighting to treat nonresponse and undercoverage, estimation for small areas, sample design, and calibration of the output from this survey with that from the long form. To assist the Census Bureau in developing a research agenda to address these and other methodological issues, the Committee on National Statistics held a workshop on September 13, 1998. This report summarizes that workshop.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    Switch between the Original Pages, where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

    « Back Next »
  6. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  7. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  8. ×

    View our suggested citation for this chapter.

    « Back Next »
  9. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!