income, numbers of poor people, poor children under age 5 (states only), poor children aged 5-17 in families, and poor people under age 18. The estimates released in early 1999 (for income year 1995) also included the numbers of poor school-age children in families for more than 14,000 school districts. The U.S. Department of Education has used the SAIPE estimates for Title I allocations since 1997, and some other programs use them as well.

Because there is no one data source that can provide the SAIPE estimates, the Census Bureau develops them by using statistical modeling techniques that combine data from household surveys, the decennial census, and administrative records. The SAIPE estimates, consequently, are “ indirect,” and, as such, their quality depends on the choice of a suitable statistical model.

In the coming decade, it should be possible to develop more accurate and timely income and poverty estimates for small areas by using new and improved sources of data from household surveys and administrative records. However, none of the existing or planned surveys or administrative records sources can, by itself, provide direct estimates of sufficient reliability, timeliness, and quality of responses for all of the SAIPE income and poverty estimates. Therefore, the panel concludes that the SAIPE program must to rely primarily on models that combine data from more than one source to produce indirect estimates.

The use of small-area income and poverty estimates for allocating funds or related program purposes imposes significant requirements if the estimates are to satisfy the intentions of program legislation. Such requirements include the desired concept or definition of poverty or income measured, the level of geographic detail, the level of population or demographic detail, the timeliness of production and updating, and the accuracy of measurement. The selection of a set of estimates to use in a given program will generally involve tradeoffs among competing goals. For fund allocation, it is important to consider features of the specific allocation formula, some of which may be sensitive to the level of accuracy in the estimates. For example, if a formula has a threshold for eligibility for funding, an area that is erroneously estimated to be below the threshold will not receive any funds, even if the degree of underestimation is small.

For program use, policy makers should consider the advantages and

Front Matter (R1-R14)

Executive Summary (1-10)

1 Introduction (11-17)

2 Needs for Small-Area Income and Poverty Estimates (18-43)

3 Current SAIPE Models (44-81)

4 Future Model Develpment: The Role of Surveys (82-124)

5 Future Model Development: The Role of Administrative Records (125-149)

6 Using Estimates in Allocation Formulas (150-160)

7 Recommendations for Producers and Users (161-166)

Appendix: Interactions Between Survey Estimates and Federal Funding Formulas (167-190)

References and Bibliography (191-200)

Biographical Sketches of Panel Members and Staff (201-206)