About this application: This application provides summary profiles showing frequently requested data items from various US Census Bureau programs. Profiles are available for the nation, states, and counties.
Per capita income in past 12 months (in 2018 dollars), 2014-2018 - (US Dollars)
County
Value
Adams
30,534
Allegheny
36,907
Armstrong
26,465
Beaver
30,016
Bedford
25,163
Berks
30,187
Blair
26,648
Bradford
27,319
Bucks
44,103
Butler
36,383
Cambria
25,779
Cameron
25,898
Carbon
26,867
Centre
29,432
Chester
48,225
Clarion
24,293
Clearfield
23,743
Clinton
23,780
Columbia
25,794
Crawford
25,731
Cumberland
36,012
Dauphin
32,485
Delaware
38,251
Elk
28,476
Erie
27,349
Fayette
25,876
Forest
15,360
Franklin
29,748
Fulton
26,082
Greene
26,902
Huntingdon
24,159
Indiana
25,276
Jefferson
24,974
Juniata
25,057
Lackawanna
28,427
Lancaster
30,778
Lawrence
27,734
Lebanon
28,560
Lehigh
32,252
Luzerne
27,919
Lycoming
26,867
McKean
25,517
Mercer
26,561
Mifflin
24,407
Monroe
28,486
Montgomery
46,776
Montour
34,095
Northampton
34,017
Northumberland
25,506
Perry
29,685
Philadelphia
26,557
Pike
33,111
Potter
24,087
Schuylkill
26,070
Snyder
26,415
Somerset
25,294
Sullivan
28,665
Susquehanna
29,630
Tioga
26,059
Union
25,606
Venango
25,837
Warren
27,593
Washington
34,244
Wayne
26,570
Westmoreland
32,916
Wyoming
28,810
York
31,468
Value for Pennsylvania (US Dollars): $32,889
Sources: U.S. Census Bureau, American Community Survey (ACS) and Puerto Rico Community Survey (PRCS), 5-Year Estimates. The PRCS is part of the Census Bureau's ACS, customized for Puerto Rico. Both Surveys are updated every year.
Definition
Per capita income is the mean income computed for every man, woman, and child in a particular group including those living in group quarters. It is derived by dividing the aggregate income of a particular group by the total population in that group. This measure is rounded to the nearest whole dollar. For the complete definition, go to ACS subject definitions "Income in the Past 12 Months, Per Capita Income."
Source and Accuracy
This Fact is based on data collected in the American Community Survey (ACS) and the Puerto Rico Community Survey (PRCS) conducted annually by the U.S. Census Bureau. A sample of over 3.5 million housing unit addresses is interviewed each year over a 12 month period. This Fact (estimate) is based on five years of ACS and PRCS sample data and describes the average value of person, household and housing unit characteristics over this period of collection.
Statistics from all surveys are subject to sampling and nonsampling error. Sampling error is the uncertainty between an estimate based on a sample and the corresponding value that would be obtained if the estimate were based on the entire population (as from a census). Measures of sampling error are provided in the form of margins of error for all estimates included with ACS and PRCS published products. The Census Bureau recommends that data users incorporate this information into their analyses, as sampling error in survey estimates could impact the conclusions drawn from the results. The data for each geographic area are presented together with margins of error at Using margins of error. A more detailed explanation of margins of error and a demonstration of how to use them is provided below.
For more information on sampling and estimation methodology, confidentiality, and sampling and nonsampling errors, please see the Multiyear Accuracy (US) and the Multiyear Accuracy (Puerto Rico) documents at "Documentation - Accuracy of the data."
Margin of Error
As mentioned above, ACS estimates are based on a sample and are subject to sampling error. The margin of error measures the degree of uncertainty caused by sampling error. The margin of error is used with an ACS estimate to construct a confidence interval about the estimate. The interval is formed by adding the margin of error to the estimate (the upper bound) and subtracting the margin of error from the estimate (the lower bound). It is expected with 90 percent confidence that the interval will contain the full population value of the estimate. The following example is for demonstrating purposes only. Suppose the ACS reported that the percentage of people in a state who were 25 years and older with a bachelor's degree was 21.3 percent and that the margin of error associated with this estimate was 0.7 percent. By adding and subtracting the margin of error from the estimate, we calculate the 90-percent confidence interval for this estimate:
Therefore, we can be 90 percent confident that the percent of the population 25 years and older having a bachelor's degree in a state falls somewhere between 20.6 percent and 22.0 percent.