Use and interpret Socioeconomic Status Index data

Esri's Data Development team produces demographic data (known as Updated Demographics) for the United States using a variety of sources to update small areas, beginning with the latest U.S. Census base along with a mixture of other private sources to capture demographic change. Alongside Updated Demographics, Esri provides U.S. Census Bureau and American Community Survey (ACS) demographics as a point of reference for understanding growth in an area and to provide additional community details. Data tutorials educate both the novice and the expert analyst to learn more about a topic to properly incorporate Esri Demographics that are accessible within various products. In this tutorial, you will learn about the following:

• How Socioeconomic Status is defined
• Why the Socioeconomic Status Index (SEI) can be impactful in your work
• How to use and interpret the SEI measure
• Important data considerations
• Additional resources

First, you'll learn how Esri defines Socioeconomic status.

Defining socioeconomic status (SES) is not as clear cut as one may expect. Although related concepts and hierarchies (e.g., upper class, middle class, lower class) are broadly understood, the term itself is not. This is due to the generalized nature of socioeconomic status and the contrasting ideas about what characteristics should be included when attempting to define it. While some may think income or wealth, employment, and education depict the best SES definition, others define it to include race, ethnicity, home ownership, family size, family types, health status, and even types of foods purchased. In the context of this new measure, Esri defines socioeconomic status as the intersection of sociological and economic characteristics that are indicative of social position relative to other people or areas.

Starting with Esri's 2023 Updated Demographics release, the Data Development team created a Socioeconomic Status Index, or SEI, that quantifies an area’s socioeconomic status using this definition and relevant variables.

"The combination of social and economic status can reveal a group or individual's unequal access to resources, privilege, power, and control in a society." [1]

Differences in socioeconomic status, background, and environment - among other factors - lead to inequalities. Socioeconomically disadvantaged neighborhoods have an impact on economic and social prosperity. Learning where and to what degree disparities exist in the U.S. is increasingly important for governments, hospitals, educational institutions, researchers, and more.

For example, analyzing socioeconomic data can help us better understand important disparities in social position and is shown to predict health, achievement, and mortality. For example, people living in areas determined to have low SES often have blue collar occupations, such as service industry jobs, income at or below the poverty level, and lower levels of formal education relative to the country at large. This means having limited access to the kinds of financial, educational, and social resources that could benefit their overall well-being. Individuals with high SES ratings, on the other hand, are likely to work in prestigious positions, such as in medicine or law, have higher salaries, and have attained high levels of education. This means people living in these areas generally have greater access to critical resources.

The SEI measure is derived from a mix of input variables from Esri Updated Demographics and the American Community Survey (ACS) on topics such as income and poverty, employment and occupation, educational attainment, and household characteristics. The SEI ranges from 0 to 100, where larger values indicate higher socioeconomic status. Reviewing and comparing SEI values can highlight important disparities in social position.

Similar to other measures offered by Esri, such as the Diversity Index, the SEI is useful as a comparative measure that allows data users to contrast geographic areas at the same level (e.g., two nearby counties), nested geographic areas (e.g., a tract and the county it is within), or the same area over time (e.g., the change in a county between years). This Index can also be used as an initial indicator to geographically identify where and to what degree disparities may exist.  View the Updated Demographics Methodology Statement for more information on Esri Demographic variables 

As a result, businesses and governing agencies can create policies and programs that can reduce poverty and inequality, address the needs of lower-income families, improve health conditions, address educational deficiencies, and foster improved economic opportunities.

It is helpful to couple this socioeconomic measure with additional demographic context such as characteristics from Tapestry Segmentation, indexes measuring local population diversity, or other similar variables to enhance your analysis.

The SEI is calculated as a weighted formula with variables selected based on a thorough review of the relevant literature. Factor Analysis was used to identify indicators that possess the highest explanatory power in the model. The SEI was developed for block group summary areas to take advantage of distinct, local neighborhood profiles. Transformations to inputs are made to ensure that the measure reflects an approximately normalized distribution (at local levels of geography such as block groups and tracts) within the bounds of the minimum and maximum values of 0 and 100, respectively. See the chart below for an illustration of this distribution. Naturally, however, variation in the values of this index is reduced when assessing broader geographic areas.

Based on the distribution of SEI values in all U.S. neighborhoods, the index can be partitioned for categorical analysis. SEI values that range from 0 to 44.9 can be classified as Low, while values between 45 to 65 can be classified as Moderate and those neighborhood SEIs at 65.1 or higher can be classified as High. For example, an SEI value of 35 would indicate that the area of interest has low SES relative to all neighborhoods in the U.S. However, it is important to keep in mind that these are large ranges, and there can be significant socioeconomic diversity between areas that fall within each.

You can access Esri Demographics using Esri software and through apps like ArcGIS  Business Analyst ,   ArcGIS for Excel , or ready to use maps from  ArcGIS Living Atlas of the World . For use outside of the Esri platform data files are available in CSV, dBase, Excel, shapefile, or file geodatabase formats.

 Explore a full listing of available data tables and variables in Esri data catalogs 

Contact an Esri data sales specialist with data questions at 800-447-9778 or send an email with your request to: datasales@esri.com.

Now that you have learned what the SEI measure is and how it's calculated, let's explore a "What if" business scenario that uses this indicator variable:

Measures such as the SEI are valuable tools for quickly identifying where disparities may exist in areas of interest. Comparing the data across multiple geographic levels can highlight how local communities compare to broader areas. Additional data like age, sex, employment status, as well as proximity measures like distance from the hospital or travel time by public transportation can be included to provide valuable context.

In the example above, the hospital administrator was able to define the program’s major market area and easily uncover the specific communities where additional resources will be needed to broaden awareness to boost participation rates for periodic mammogram screenings. Additional data was included to strengthen the final analysis which resulted in the identification of specific consumer markets to tailor and enhance the hospital’s outreach messaging.

The SEI ranges from 0 to 100, where larger values indicate higher socioeconomic status.

The SEI model was developed at the foundational block group level. This provides the most variation within the distribution of all estimated index values to best discern relative differences across neighborhoods.

The SEI is designed to have a more normalized distribution at lower levels of geography to better highlight areas that exhibit notable conditions in the tails of the distribution.

Like other measures, the calculation of the SEI is suppressed if an area contains insufficient input data to generate a reliable estimate.

In this tutorial, you learned about the basics of socioeconomic data, how to interpret the data, and the significant impact it has on U.S. neighborhoods. Additional data tutorials in two series are available. Click the links below for continued data exploration, learning, and ways to access the data.

Socioeconomic Status Index estimates are developed from a mix of input variables from Esri Updated Demographics and the American Community Survey (ACS) on topics such as income and poverty, employment and occupation, educational attainment, and household characteristics. Represented as point-in-time estimates as of July 1, the data is available by Esri’s standard geographic areas and for any user-defined polygon such as a ring or drive time.  Read the Esri Dependency Ratios Methodology Statement for more information  

Use our   data reference page  to help answer additional questions about Esri Demographics.

[1] U.S. Bureau of Labor Statistics, Spotlight/2018/race-economics-and-social-status

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