Methodology

The Distressed Communities Index (DCI) combines seven complementary metrics into a single holistic measure of community economic well-being. It can be calculated at multiple different scales: in this report at the zip code, city, county, and congressional district levels. In all, it captures 99 percent of the U.S. population and covers more than 26,000 zip codes and more than 3,000 counties (those with over 500 people) as well as nearly 800 cities (those with at least 50,000 people). The DCI is constructed using data from the U.S. Census Bureau’s American Community Survey 5-Year Estimates for 2011-2015 and Business Patterns data from the years 2011 and 2015.

The seven component metrics of the DCI are:

  1. No high school diploma: Percent of the population 25 years and older without a high school diploma or equivalent
  2. Housing vacancy rate: Percent of habitable housing that is unoccupied, excluding properties that are for seasonal, recreational, or occasional use
  3. Adults not working: Percent of the prime-age population (ages 25-64) not currently in work
  4. Poverty rate: Percent of the population living under the poverty line
  5. Median income ratio: A geography’s median income expressed as a percentage of its state’s median income
  6. Change in employment: Percent change in the number of jobs from 2011 to 2015
  7. Change in business establishments: Percent change in the number of business establishments from 2011 to 2015

Each component is intended to capture a distinct aspect of well-being. The first five indicators are relatively static and descriptive, while the latter two are more dynamic and directional. All are weighted equally in the index to reflect the multifaceted nature of prosperity, distress, and the states in between. Together, they provide a more complete picture of the economic state of a community than any single indicator could on its own.

Distress scores are calculated at each scale by ranking geographic units on each of the seven metrics, taking the average of those ranks, and then normalizing the average to be equivalent to a percentile. The result is a range of distress scores from 0 to 100, such that the zip code with the average rank of 13,000 out of 26,000 will register a distress score of 50.0. Given that the methodology requires ranking each geography among its peers (e.g. zip codes among zip codes and cities among cities), distress scores are not comparable across different tiers of geography. In other words, the underlying performance of a zip code and a city at the same percentile across the seven component metrics of the DCI may differ.

Geographical notes:

Zip codes as presented and discussed here should be considered approximations of geographies and communities. Zip codes represent postal routes defined by the U.S. Postal Service (USPS), not the U.S. Census Bureau, and their boundaries can and do change frequently. The U.S. Census Bureau builds its own proprietary approximations of zip codes called Zip Code Tabulation Areas (ZCTAs) from census blocks once after each Decennial Census. The DCI includes data tabulated by both zip code and ZCTA: Two of the underlying variables (those from Business Patterns) are defined by zip code and five (those from the American Community Survey) are defined by ZCTA. Since ZCTAs are static over each decade but zip codes may change, it is important to interpret the findings of the DCI as general trends for an approximate area rather than discrete developments within a clearly-defined set of lines. In addition, Business Patterns data are subject to errors that the Census Bureau does not go back to correct. Both boundary changes and these errors may affect change over time calculations.

Zip code-level estimates were used to create city- and congressional district-level estimates for change in employment and change in establishments. Zip code values were aggregated up to the city level using ZCTA-to-place relationship files provided by the Census Bureau and at the congressional district level using ZCTA-to-district relationship files for the 114th Congress provided by the Missouri Census Data Center. In instances where zip code boundaries cut across city or congressional district lines, zip code portions were attributed to cities and congressional districts according to the share of the associated ZCTA’s population falling within the boundaries of the larger geography using relationship files provided by the Missouri Census Data Center. Relationship files were also used to report the share of a city’s, county’s, state’s, or congressional district’s population residing in distressed and prosperous zip codes.

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