Children in Poverty

Data Source

Small Area Income and Poverty Estimates; American Community Survey, five-year 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.

The American Community Survey (ACS) is a nationwide survey designed to provide communities with a fresh look at how they are changing. It is a critical element in the Census Bureau's reengineered decennial census program. The ACS collects and produces population and housing information every year instead of every ten years, and publishes both one-year and five-year estimates. The County Health Rankings use American Community Survey data to obtain measures of social and economic factors.

Website to download data
For more detailed methodological information

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.

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

We report Children in Poverty using the ACS race data categories: American Indian & Alaska Native, Asian & Pacific Islander, Black, Hispanic, and non-Hispanic White. The race and ethnicity data come from the American Community Survey using combined five-year estimates.

Caution should be used when comparing these estimates across states

Caution should be used when comparing SAIPE model-based estimates for different states or different counties in the same year. For more information please visit General Cautions About Comparing Estimates.

Caution should be used when comparing these estimates across years

Caution should be used when comparing SAIPE model-based estimates for different years. For more information please visit General Cautions About Comparing Estimates.

Numerator

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.

Denominator

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

Modeled estimates have specific drawbacks with their usefulness in tracking progress in communities. Modeled data may not capture the effects of local conditions, such as health promotion policies. In order to better understand and validate modeled estimates, it can be helpful to supplement with additional local data. Additionally, it is important to examine national trends as you are assessing change in your own community.

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. For many communities, you can access the same tables at the census tract or census block level.

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