The official poverty measure, the Supplemental Poverty Measure, and Census's SAIPE program all tell different stories. Here's how to read poverty data on CensusDepth.
The Census Bureau produces multiple poverty measures, and understanding the differences between them is essential to interpreting poverty data correctly. CensusDepth draws primarily on ACS data for comparative geographic poverty analysis, supplemented by SAIPE estimates for county-level precision.
The official poverty measure was designed in the early 1960s by economist Mollie Orshansky. It defines poverty thresholds based on family size and composition, originally calculated as three times the cost of a minimum food diet. In 2023, the poverty threshold for a family of four is approximately $31,200. The threshold is adjusted annually for inflation but has not been fundamentally revised since the 1960s, which many researchers argue makes it outdated.
The ACS reports income-to-poverty ratios for individuals and families. The most commonly cited figure is the percentage of the population below 100% of the federal poverty level. CensusDepth's state poverty rankings, county poverty rankings, and city poverty rankings all use this ACS-derived measure.
Key ACS poverty variables:
Child poverty rates tend to be higher than overall poverty rates because children lack independent income. In the 2023 ACS, national child poverty is approximately 16%. The child poverty rankings by state show significant geographic variation — Mississippi, New Mexico, and Louisiana consistently have the highest child poverty rates; New Hampshire, Connecticut, and Utah have the lowest.
For county-level poverty analysis, CensusDepth also incorporates SAIPE data. The Small Area Income and Poverty Estimates program uses a model-based approach that combines ACS survey data with administrative data from tax records and food stamp program records to produce more statistically reliable estimates for small counties. SAIPE is especially valuable for rural counties where ACS sample sizes are small and margins of error are large.
Official poverty statistics have well-documented limitations. They don't account for regional cost-of-living differences (being "poor" in Mississippi is materially different from being "poor" in San Francisco). They measure income but not assets or wealth. And they don't fully capture the role of non-cash benefits like SNAP, Medicaid, housing assistance, and the Earned Income Tax Credit in supplementing household budgets.
Despite these limitations, ACS poverty rates remain the most consistently measured, widely available, and geographically granular poverty indicators available for the entire country. Use the county comparison tool to compare poverty rates alongside income and housing cost data for a fuller picture of economic hardship.
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