Tag Archives: economic well being

Neighborhood Median Family Income: Measuring Economic Well-Being

.. Median Family Income ($MFI) and Median Household Income ($MHI) are two measures of economic well-being. Based on the 2018 American Community Survey 1-year (ACS) data, the U.S. 2018 $MFI was estimated to be $76,401 while the $MHI was estimated to be $61,937 .. both in 2018/current dollars. Create insights into patterns of well-being by neighborhood using geospatial analysis. $MFI patterns are illustrated by the following thematic pattern map.

Patterns of Economic Prosperity by Neighborhood/Census Tract
The following view shows patterns of $MFI by census tract for the inner beltway area of Houston/Harris County, TX. Income interval color patterns are shown in the inset legend. Tracts are labeled with $MFI. Click graphic for larger view. Expand browser window for best quality view. Larger view shows tracts labeled with tract code. It is easy to see how west Houston and east Houston areas differ.

– view developed with ProximityOne CV XE GIS software and related GIS project.
– these $MFI data are based on the 2018 ACS 5-year estimates.

This section focuses on $MFI but could just as well focus on $MHI and yet other related income measures. $MFI will almost always be greater that $MHI, generally by a large margin. See the U.S. 2018 $MFI and $MHI in context of related demographic-economic measure here. See more about the distinctions/definitions of families and and households below.

The ACS data are a unique source of income and related data at the neighborhood or sub-county level. View more about accessing and using the 2018 ACS 5-year estimates.

Family Definition
A family is a group of two people or more (one of whom is the householder) related by birth, marriage, or adoption and residing together; all such people (including related subfamily members) are considered as members of one family. The number of families is equal to the number of family households. However, the count of family members differs from the count of family household members because family household members include any non-relatives living in the household.

Related … an unmarried partner, also known as a domestic partner, is specifically defined as a person who shares a close personal relationship with the reference person. … Same-sex unmarried-partner families or households – reference person and unmarried partner are both male or female.

Household Definition
A household consists of all the people who occupy a housing unit. A house, an apartment or other group of rooms, or a single room, is regarded as a housing unit when it is occupied or intended for occupancy as separate living quarters; that is, when the occupants do not live with any other persons in the structure and there is direct access from the outside or through a common hall.

A household includes the related family members and all the unrelated people, if any, such as lodgers, foster children, wards, or employees who share the housing unit. A person living alone in a housing unit, or a group of unrelated people sharing a housing unit such as partners or roomers, is also counted as a household. The count of households excludes group quarters. There are two major categories of households, “family” and “nonfamily”.

Situation & Outlook Weekly Web Sessions
Join me in a Situation & Outlook web session to discuss more details about demographic-economic estimates and projections.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Regional Economic Information System; 2015 Annual Update

.. examining patterns of economic well-being among counties and regions … per capita personal income (PCPI) is the most comprehensive measure of individual economic well-being. PCPI estimates are developed/updated annually for counties, metros, states and the U.S. PCPI estimates, available as an annual time series 1969 through 2014, are developed as a part of the Regional Economic Information System (REIS). This section provides information on accessing and using the REIS data. See related Web section for more detail and data access tools.

Visual Analysis of Per Capita Personal Income Patterns
The following map shows the Houston metro (view profile) with bold brown boundary. Counties are labeled with county name and 2014 per capita personal income.

Click graphic for larger view. View developed with CV XE GIS software.

• See similar view of the San Francisco Metro/Area by County
• See this section to learn about making custom metro maps.

Important Features of the REIS Data
A distinguishing characteristic of the REIS data is that they are a set of multi-sourced data organized and used to estimate personal income. Personal income, unlike money income, is income received by all persons from all sources. A second characteristic is, unlike the American Community Survey (ACS), the REIS data are not based on a sample survey but rather employer-based data and other administrative data. Third, the lengthy annual time series lends itself to use in modeling and trend analysis based on a set of consistently defined subject matter.

Accessing & Using REIS Data
Resources to analyze regional economic patterns and characteristics:
1. Use the interactive table to examine characteristics of counties, metros and states. View/rank/compare per capita personal income over time.
2. Use the metro demographic-economic profiles. Examine REIS-based personal income components in context with other subject matter. Select/view any metro via interactive table.
3. Use the REIS datasets made available as a part of the ProximityOne Data Services Program (PDS).
4. Create thematic pattern maps & perform geospatial analysis of REIS data in ready-to-use GIS projects.
5. Use ProximityOne modeling tools to forecast personal income components; assess impact of change on your interests.

More About Using GIS Resources & Pattern Analysis
The following graphics illustrate use of the REIS GIS Project (details in Web section). These views show the change in per capita personal income during the period 2008 to 2014. The underlying datasets provide annual data in most cases from 1969 through 2014. Analyze patterns for only one/any selected/ year, change or percent change over time (an average of years or selected point in time). Zoom into regions of interest, set alternative pattern views, flexibly use labels, add data from other sources.

Per Capita Personal Income Change 2008-2014 by State

Click graphic for larger view. View developed with CV XE GIS software.

Per Capita Personal Income Change 2008-2014 by Metro

Click graphic for larger view. View developed with CV XE GIS software.
See this section to learn about making custom metro maps.

Per Capita Personal Income Change 2008-2014 by County

Click graphic for larger view. View developed with CV XE GIS software.

Per Capita Personal Income: County, Metro, State Interactive Table
  — top-ranked counties based on 2014 PCPI
Use of the interactive table is illustrated in the graphic below. The GeoType feature is used to select only counties. The table is then sorted in descending order on 2014 $PCPI.

Use the main Web section interactive table to examine areas of interest. Join me in a Data Analytics Lab session to discuss use of these data using analytical tools and methods applied to your situation.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.