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Small Area Income and Poverty Estimates (SAIPE): 2009 Highlights

KaNin Reese and Alemayehu Bishaw
Component ID: #ti121645973


This document presents 2009 data from the Small Area Income and Poverty Estimates (SAIPE) program of the U.S. Census Bureau. The SAIPE program produces poverty estimates for the total population and median household income estimates annually for all counties and states. SAIPE data also produces single-year poverty estimates for the school-age population (age 5-17) for all school districts in the U.S. 

The main objective of the SAIPE program is to provide timely, reliable estimates of income and poverty statistics for the administration of federal programs and the allocation of federal funds to local jurisdictions. Some state and local programs also use SAIPE inco me and poverty estimates to distribute funds and manage programs

Component ID: #ti168344658

The following estimates are produced for 2009 from the Small Area Income and Poverty Estimates (SAIPE) program:

Counties & States School districts
all people in poverty total population
children under age 18 in poverty children ages 5 to 17
related children ages 5 to 17 in families in poverty related children ages 5 to 17 in families in poverty
children under age 5 in poverty (for states only)
median household income

These model-based estimates are produced for school districts, counties and states of all population sizes. They are produced by combining data from administrative records, intercensal population estimates, and the decennial census with direct estimates from the American Community Survey to provide consistent and reliable single-year estimates.

Tables of highest and lowest SAIPE estimates for:

Component ID: #ti177247378


Component ID: #ti358627674

General Cautions

Estimates for counties or states in the sorted tables may not be statistically different from estimates for other counties or states in the tables due to uncertainty in the estimates. Please see uncertainty for information associated with model-based estimates, and also general cautions about comparing model-based estimates.

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