Children in Poverty

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Percentage of people under age 18 in poverty. The 2023 County Health Rankings used data from 2021 for this measure.

Children in Poverty captures an upstream measure of poverty that assesses both current and future health risk. Poverty and other social factors contribute a number of deaths comparable to leading causes of death in the United States like heart attacks, strokes, and lung cancer.1 While repercussions resulting from poverty are present at all ages, children in poverty may experience lasting effects on academic achievement, health, and income into adulthood. Children living in low-income households have an increased risk of injury as a result of unsafe environments and are susceptible to more frequent and severe chronic conditions and their complications, such as asthma, obesity, diabetes, ADHD, behavior disorders, and anxiety, than children living in high income households.2-4

Find strategies to address Children in Poverty

Data and methods

Data Source

Small Area Income and Poverty Estimates

The US Census Bureau, with support from other federal agencies, created the Small Area Income and Poverty Estimates (SAIPE) program to provide more current estimates of selected income and poverty statistics than those from the most recent decennial census. The main objective of this program is to provide updated estimates of income and poverty statistics for the administration of federal programs and the allocation of federal funds to local jurisdictions. These estimates combine data from administrative records, intercensal population estimates, and the decennial census, along with direct estimates from the American Community Survey, to provide consistent and reliable single-year estimates. These model-based single-year estimates are more reflective of current conditions than multi-year survey estimates. At the county level, SAIPE provides estimates on children ages 5-17 in families in poverty, children under age 18 in poverty, all people in poverty, and median household income. Estimates are created for school districts, counties, and states.

Website to download data
For more detailed methodological information

Key Measure Methods

Children In Poverty is a percentage

Children in Poverty is the percentage of people under age 18 living in poverty.

Children In Poverty is created using statistical modeling

Data come from the Small Area Income and Poverty Estimates program, which uses data from the American Community Survey; estimates are produced using complex statistical modeling. Using modeling allows the generation of more stable estimates for places with small population or survey counts. For more technical information on Children in Poverty estimates, please see their methodology.

Children In Poverty by race and ethnicity uses a different data source than overall county estimates

In the data table for Children in Poverty, we report rates for American Indian & Alaska Native, Asian & Pacific Islander, Black, Hispanic, and non-Hispanic white children. The rates for race and ethnic groups come from the American Community Survey using combined five-year estimates.


The numerator is the number of people under age 18 living in a household whose income is below the poverty level. Poverty status is defined by family; either everyone in the family is in poverty or no one in the family is in poverty. The characteristics of the family used to determine the poverty threshold are: number of people, number of related children under 18, and whether or not the primary householder is over age 65. Family income is then compared to the poverty threshold; if that family’s income is below that threshold, the family is in poverty. For more information, please see Poverty Definition and/or Poverty.


The denominator is the total number of people under age 18 in a county.

Can This Measure Be Used to Track Progress

This measure can be used to track progress with some caveats. Modeled estimates have specific drawbacks with regard to their usefulness in tracking progress in communities. Modeled data are not particularly good at incorporating the effects of local conditions, such as health promotion policies or unique population characteristics, into their estimates. Counties trying to measure the effects of programs and policies on the data should use great caution when using modeled estimates. In order to better understand and validate modeled estimates, confirming this data with additional sources of data at the local level is particularly valuable.

Nationally, the rates of children in poverty have changed pretty dramatically over the last decade. The percent of children in poverty rose significantly from 2008 to 2012 but has been falling since. It is important to note these national trends as you are assessing change in your own community.

Finding More Data

Disaggregation means breaking data down into smaller, meaningful subgroups. Disaggregated data are often broken down by characteristics of people or where they live. Disaggregated data can reveal inequalities that are otherwise hidden. These data can be disaggregated by:

  • Age
  • Race
  • Subcounty Area

We recommend starting with the Small Area Income & Poverty Estimates website, which contains information on poverty by age and gender. Another resource is the Community Commons Health Equity Assessment Report, which maps children in poverty at the census tract level. You will need to log in to access the Health Equity Assessment Report, but the registration process is simple.

You can calculate poverty status by age and race using tables B17020A-G. These tables can be accessed at For many communities, you can access the same tables at the census tract or census block level.


1 Galea S, Tracy M, Hoggatt KJ, DiMaggio C, Karpati A. Estimated deaths attributable to social factors in the United States. American Journal of Public Health. 2011; 101(8):1456-1465.

2 McCarty AT. Child poverty in the United States: A tale of devastation and the promise of hope. Sociology Compass. 2016; 10(7):623-639.

3 Hair NL, Hanson JL, Wolfe BL, Pollak SD. Association of child poverty, brain development, and academic achievement. Journal of the American Medical Assocation - Pediatrics. 2015; 169(9):822-829.

4 Dreyer BP. To create a better world for children and families: the case for ending childhood poverty. Academic Pediatrics. 2013; 13(2):83-90.