Poverty - Population Below 50% FPL

In the report area 3.88% or 5,776 individuals are living in households with income below 50% of the Federal Poverty Level (FPL). This indicator is relevant because poverty creates barriers to access including health services, healthy food, and other necessities that contribute to poor health status.
Report Area Total Population Population with Income at or Below 50% FPL Percent Population with Income at or Below 50% FPL
Franklin County, PA 148,872 5,776 3.88%
Pennsylvania 12,346,333 744,718 6.03%
United States 306,226,400 21,117,986 6.9%
Note: This Indicator is compared with the state average. Green - Better than state average, Red - Worse than state average.
Data Source: US Census Bureau, American Community Survey. 2010-14. Source geography: Tract
Website Updated August 2016

Poverty - Population Below 50% FPL

Data Background

The American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data. The ACS samples nearly 3 million addresses each year, resulting in nearly 2 million final interviews. The ACS replaces the long-form decennial census; however, the number of household surveys reported annually for the ACS is significantly less than the number reported in the long-form decennial census. As a result, the ACS combines detailed population and housing data from multiple years to produce reliable estimates for small counties, neighborhoods, and other local areas. Negotiating between timeliness and accuracy, the ACS annually releases current, one-year estimates for geographic areas with large populations; three-year and five-year estimates are also released each year for additional areas based on minimum population thresholds.

Citation: U.S. Census Bureau: A Compass for Understanding and Using American Community Survey Data (2008).

For more information about this source, including data collection methodology and definitions, refer to the American Community Survey website.


Population counts for demographic groups and total area population data are acquired from the U.S. Census Bureau’s American Community Survey. Data represent estimates for the 5 year period 2010-2014. Mapped data are summarized to 2010 census tract boundaries. Area demographic statistics are measured as a percentage of the total population based on the following formula:

Percentage = [Subgroup Population] / [Total Population] * 100

For more information on the data reported in the American Community Survey, please see the complete American Community Survey 2014 Subject Definitions.


Trends Over Time
The American Community Survey multi-year estimates are based on data collected over 5 years. For any given consecutive release of ACS 5-year estimates, 4 of the 5 years overlap. The Census Bureau discourages direct comparisons between estimates for overlapping periods; use caution when interpreting this data.

Race and Ethnicity
Race and ethnicity (Hispanic origin) are collected as two separate categories in the American Community Survey (ACS) based on methods established by the U.S. Office of Management and Budget (OMB) in 1997. Indicator race and ethnicity statistics are generated from self-identified survey responses. Using the OMB standard, the available race categories in the ACS are: White, Black, American Indian/Alaskan Native, Asian, and Other. An ACS survey respondent may identify as one race alone, or may choose multiple races. Respondents selecting multiple categories are racially identified as “Two or More Races”. The minimum ethnicity categories are: Hispanic or Latino, and Not Hispanic or Latino. Respondents may only choose one ethnicity. All social and economic data are reported in the ACS public use files by race alone, ethnicity alone, and for the white non-Hispanic population.

Data Limitations
Beginning in 2006, the population in group quarters (GQ) was included in the ACS. The part of the group quarters population in the poverty universe (for example, people living in group homes or those living in agriculture workers’ dormitories) is many times more likely to be in poverty than people living in households. Direct comparisons of the data would likely result in erroneous conclusions about changes in the poverty status of all people in the poverty universe.

Courtesy: Community Commons, <www.communitycommons.org>, August 2016