About the Peer City Identification Tool
The Peer City Identification Tool (PCIT), developed by the Community Development and Policy Studies division (CDPS) of the Federal Reserve Bank of Chicago, is a data comparison and visualization instrument that can help policymakers and practitioners understand a municipality in the context of peer cities. Drawing on city-level indicators from the American Community Survey and historical Decennial Census records, the PCIT performs a cluster analysis to identify groups of similar cities along economic, demographic, social, and housing dimensions. The tool stems from the Industrial Cities Initiative (ICI), a study that profiled ten Midwestern cities with manufacturing legacies, describing how they have fared in socioeconomic terms over time.
The PCIT-960 includes 960 places from around the United States that meet one of the following criteria - these places were incorporated, with at least 25,000 population by the 1960 census; or these places were incorporated, with at least 50,000 population by the 2010 census.
To note: In seven cases, an incorporated place in 1960 deincorporated by 2010. These cities have current statistics reported for their township boundaries that exist today, which closely match their boundaries as incorporated cities in the past.
In eight cases, an incorporated place in 1960 annexed all or nearly all of the rest of their county. To simplify historical comparisons in these cities, the tool uses both current and historical statistics as reported for the county boundaries, not the more inconsistent boundaries of the specific place. These are listed by county name in the tool, with the principal city in parentheses. In addition, both the township and county statistical boundaries are noted in the download that is created by the table section of the tool, under the geoflag variable.
Finally, although it is not officially an incorporated place, the fiscal characteristics of Arlington, Virginia lead us to include it as a city in the tool, using statistics for Arlington County which is coextensive with Arlington as a census-designed place.
The seven cities that use township boundaries are:
Belleville, New Jersey
Bloomfield, New Jersey
Irvington, New Jersey
Montclair, New Jersey
Nutley, New Jersey
Orange, New Jersey
West Orange, New Jersey
The nine cities, including Arlington, that use county boundaries are:
Clarke County (Athens), Georgia
Richmond County (Augusta), Georgia
Fayette County (Lexington), Kentucky
Jefferson County (Louisville), Kentucky
Silver Bow County (Butte), Montana
First, select a city by entering the city name in the search bar, or clicking directly on the map. Next, select one of four themes off which to base the clustering: Equity, Affordability, Resilience and Outlook. The selected city and its peers, which usually number between five and fifteen, then appear highlighted on the map. While often peers are geographically proximate (i.e., within the same general region of the country), sometimes a peer search can yield surprising results. The PCIT will also present the user with data from the peer cities and a table of key variables that were used to identify the group. In addition, the tool generates peer group median values for each variable, as well as the median for all cities in the dataset, enabling comparison across and within the cities. This perspective can provide further context, especially in identifying areas in which the subject city might deviate from its peers, which can serve to highlight particular challenges or opportunities. Users can also select variables to graph or chart, providing a useful visual. All data and images can be exported, and the full underlying dataset of 960 cities and 28 indicators is available for download.
Select additional variables can be added by checking one or more of the boxes from the list that appears below the data table. This simply adds the data for the selected city and its peers; it does not change the peer cities.
Peer cities are grouped along four key themes, allowing users to explore a variety of potential peers. These themes are designed in response to key areas of concern voiced by city leaders following more than 200 interviews across ten cities as part of the Industrial Cities Initiative and other place-based research.
Equity addresses questions regarding inclusion, access, and diversity using the wage-based Gini coefficient, race and ethnicity-based dissimilarity indices, changes in poverty levels, and educational attainment. City leaders cited challenges of creating and implementing inclusive growth strategies that attract new businesses and jobs to their cities, while creating policies that allow marginalized populations to benefit from these new opportunities. The PCIT uses the wage-based Gini coefficient (as opposed to the income-based coefficient more frequently used) to focus on wage-earning workers who have been employed for the full year.
Resilience speaks to issues related to economic diversification by considering current conditions and trends in manufacturing employment, labor force participation, and unemployment. Many cities experienced economic shocks during the Great Recession, but had experienced decline along these measures during the preceding decades as well. Economic diversification and labor force conditions provide broad insights into areas of vulnerability and strength.
The Outlook theme explores signs of a city’s demographic and economic future by incorporating immigration, family composition, age structure and changes in total population. The age distribution of a population, net migration, and family composition provide clues about a city’s future. Cities experiencing unusual demographic shifts may look to peers undergoing similar shifts, and identify common non-demographic factors such as employment and educational opportunities.
Housing speaks to issues of affordability by incorporating data relating to home ownership (income-to-home value ratio and homeownership rate) and renting (rent burden), the quality and competitiveness of housing stock by using the age of housing as a proxy, and housing vacancies. Providing competitive housing affordable and attractive to both renters and buyers was a primary discussion point among surveyed community leaders. Demand for housing is captured indirectly by the vacancy rate and the age of housing stock, while the relationship between homeownership, rent burdens and housing affordability can be associated with broader economic conditions in a city.
The tool works by performing a hierarchical cluster analysis on all 960 cities, using the variables included in the selected theme. A cluster analysis is a way of grouping data based on the similarity of responses to several variables. A cluster analysis can be imagined as treating each subject city and its data as a "point" in space. The analysis then proceeds to identify "neighbors" for each city, and these are its peers. The clustering method used is Ward's method, which minimizes the variance across all variables in a given group.1 Specifically, Ward's method minimizes the sum of the squared errors across all variables within a cluster, at each step of the clustering procedure. Each variable in each theme is normalized to have a standard deviation of 1, and then weighted equally for the procedure.
If a cluster produces only a small number of results, the program instead uses the ranked values instead of the normalized values, which tends to produce more evenly distributed groups, but does not allow for easy distinction between extreme outliers and more typical cities. The cluster containing the focus city is expanded before the peer cities are presented for ease of explanation and verification, by including any other cities that have all variables fall between the cluster's maximum and minimum values on each variable.
1 For more information regarding Ward's Method, the original article detailing the method is publicly available at: http://homes.mpimf-heidelberg.mpg.de/~mhelmsta/pdf/1963%20Ward%20JASA.pdf.
Hispanic-White dissimilarity index
The Hispanic-White dissimilarity index measures the degree of segregation between persons of any race of Hispanic or Latino origin and persons of non-Hispanic or Latino origin who identify as White. The source data is drawn from the 2010 decennial census at the census tract level. The index reflects the proportion of members of one of the groups (Hispanic, or non-Hispanic White) that would need to move to a different census tract for both groups to be equally distributed within the city. Original calculations were performed by Brown University's "Diversity and Disparities" project, and the methodology, data, and limitations of the approach are discussed in greater length on their site.
Black-White dissimilarity index
The Black-White dissimilarity index measures the degree of segregation between persons identifying as Black in the 2010 decennial census and persons identifying as White in the 2010 decennial census. This index uses the same data and methodology as the Hispanic-White dissimilarity index above, and again relies on the work of Brown University’s “Diversity and Disparities” project.
The dissimilarity index measures whether one particular group is distributed across census tracts in the same way as another group. Values range from 0 to 100. A value of 60 (or above) is considered very high. It means that 60% (or more) of the members of one group would need to move to a different tract in order for the two groups to be equally distributed. Values of 40 or 50 are usually considered a moderate level of segregation, and values of 30 or below are considered to be fairly low. However, in cities where one race or ethnicity dominates the population, the dissimilarity index will be either skewed high or low. For this reason, the equity theme in the PCIT also includes the "percent White population" to provide this perspective. For further guidance on understanding and interpreting dissimilarity indices, please visit Brown University's "Diversity and Disparities" project.
Poverty rate is reported here as the poverty rate for families, using the 2012-2016 5-year American Community Survey estimates.
Change in poverty rate, 2000-2016
Change in poverty rate reflects the absolute, percentage-point change between the poverty rate for families as calculated by the 2000 decennial census, accessed via the National Historical Geographic Information System maintained by the Minnesota Population Center at the University of Minnesota, and the 2012-2016 5-year American Community Survey estimates for poverty rate for families.
Wage-based Gini coefficient
The wage-based Gini coefficient reflects inequality in wages earned by full-time, full-year employed workers in each city. The source data comes from the American Community Survey, years 2011 to 2014, via the Integrated Public Use Microdata System maintained by the Minnesota Population Center at the University of Minnesota. Because place is unavailable for public use of microdata, values are reported for place after interpolation from the Public Use Microdata Areas, with population weighting based on the 2010 decennial census via the Missouri Census Data Center’s Geographic Correspondence Engine. Calculations are performed by Community Development and Policy Studies division of the Federal Reserve Bank of Chicago.
The Gini coefficient is calculated by comparing the cumulative wage income of all individuals across the sample with the overall cumulative wage income. The Gini coefficient originated with Corrado Gini in the 1912 paper "Variability and Mutability" as a measure of income inequality. It is usually computed by taking the ratio of two areas on an indexed plot of cumulative income. The numerator is the area between the linear "Line of Equality" and the Lorenz Curve which describes the proportion of total income earned by the poorest fraction of the population, for each fraction between 0 and 1. The denominator is the area between the Line of Equality and the x-axis, which necessarily equals ½.
For more on uses and interpretations of the Gini coefficient see for example "On the measurement of inequality" by Atkinson (Journal of economic theory, 1970), "Relative deprivation and the Gini coefficient" by Yitzhaki (The quarterly journal of economics, 1979), or "A Study of the Best Theoretical Value of Gini Coefficient and Its Concise Calculation Formula" by Zuguang (Economic Research Journal, 2004).
Note on interpreting the Gini coefficient: Users should keep in mind that the Gini coefficient by definition measures inequality, and as such is a measure of dispersion (in this case wage dispersion) across a place or population. Therefore, in places where there is little dispersion, that is minimal difference between high and low, inequality will be low. Thus, in places of concentrated poverty (or concentrated wealth) there will be little inequality and therefore low Gini coefficients. In places with a low Gini coefficient, users should also take into account poverty levels and perhaps income levels to add nuance to interpreting the context of the coefficient. While low inequality may be a desired state, low inequality can also exist in places of deep, concentrated poverty. In particular, when calculating a Gini coefficient for wages, low inequality in a low income area suggests that there is little variation in wages because many workers are earning minimum wage.
We selected a wage-based Gini coefficient for inclusion in the tool because the values of the Gini coefficient for wages frequently serves as an indicator of two common scenarios that community development professionals and policy experts may hope to resolve. As mentioned above, low wage inequality alongside low household incomes points to a population that is highly dependent on low-wage, especially minimum wage jobs. On the other hand, high wage inequality in a place with medium household incomes suggests that job polarization may be occurring, as described by David Autor and others previously middle-wage jobs have disappeared or transitioned to low-wage jobs, while higher-wage, white collar jobs are also growing. This scenario is particularly likely if high wage inequality appears along with a major loss of manufacturing jobs.
Change in inequality index, 2008-2014
The change in inequality index, 2008-2014 reports the absolute change in the wage-based Gini coefficient between the wage-based Gini coefficient calculated using American Community Survey microdata from the years 2005-2008 and the wage-based Gini coefficient calculated using American Community Survey microdata from the years 2011-2014.
Note on interpreting change in inequality index: Users should take into account the note on interpreting the Gini coefficient, above. A decline in the inequality index, particularly in places with already low Gini coefficients, may indicate a trend towards concentration of wages at the low or high end of the wage spectrum.
Percent White measures the proportion of a city's population identifying as White in the 5-year American Community Survey estimates for years 2012-2016. It is included in the PCIT to provide context for interpreting the dissimilarity index (see note for the Dissimilarity Index, above.)
Percent with a bachelor's degree
Percent with a bachelor's degree measures the proportion of a city’s residents age 25 and older that hold a bachelor’s degree or other advanced degree, based on estimates by the 2012-2016 5-year American Community Survey.
In the context of the PCIT equity theme, obtaining a bachelor's degree is a proxy for access to higher-paying, quality jobs.
The unemployment rate used by this tool is the unemployment rate at the place (city) level among the population age 16 or older, as estimated by the 2012-2016 5-year American Community Survey.
Labor force participation rate
The labor force participation rate used by this tool is the labor force participation rate at the place level among the population age 16 or older, as estimated by the 2012-2016 5-year American Community Survey.
Change in labor force participation rate, 2000-2016
The change in labor force participation rate, 2000-2016 reports the absolute, percentage-point change in the labor force participation rate as published by the 2000 decennial census and the 2011-2015 5-year American Community Survey. The 2000 census data is drawn from the National Historical Geographic Information System of the Minnesota Population Center at the University of Minnesota.
The unemployment rate, labor force participation rate, and change in labor force participation all provide context for the health of the labor market in these areas. A low unemployment rate and high labor force participation rate are indicators of a tight labor market and median incomes should be higher and demonstrate modulated decline, as a result. Conversely, low labor force participation rates are often seen with high unemployment rates and low incomes demonstrating low-demand employment conditions.
Labor share of manufacturing
The labor share of manufacturing is the percentage of all employed workers in a city employed in manufacturing, as estimated by the 2012-2016 5-year American Community Survey.
Change, labor share of manufacturing, 1970-2016
The change in labor share of manufacturing, 1970-2016 reports the absolute, percentage-point change in the labor share of manufacturing as reported in the 1970 decennial census and the 2012-2016 5-year American Community Survey. The 1970 census data is drawn from the National Historical Geographic Information System of the Minnesota Population Center at the University of Minnesota.
The PCIT grew out of the "Industrial Cities Initiative" that explored economic trends across 10 industrial cities. The extent of manufacturing employment decline and a place’s ability to diversify its economy following that decline, were both indicators of resilience and vulnerability. See note on interpreting median family income below.
Median family income
Median family income is drawn from 2012-2016 5-year American Community Survey estimates.
Change in median family income, 2000-2016
The change in median family income, 2000-2015 is the percent difference between the median family income as indicated in the 2000 decennial census, inflation-adjusted to be in 2016 constant dollars, and the median family income as estimated in the 2012-2016 5-year American Community Survey.
In observing how and whether a place has diversified away from manufacturing employment (see note on interpreting manufacturing employment, above), the level of median family income and respective changes can provide insight into the quality of jobs that are now available.
Median household income
Median household income is drawn from 2012-2016 5-year American Community Survey estimates.
Percent foreign-born measures the percentage of residents of a place born outside of the United States or its territories, and reflects the estimates published by the 2012-2016 5-year American Community Survey.
Operating under the assumption that people generally immigrate to a place for economic opportunity, the percent of a population that is foreign born can be an indicator of actual or anticipated opportunity.
Percent change in population, 2000-2016
Percent change in population, 2000-2015, is the percent difference between the population of a place as reported in the 2000 decennial census and the population estimated for the 2011-2015 5-year American Community Survey.
Population trends are a primary indicator of a places’ economic health. Losses that deviate from peer trends can indicate an underlying weakness.
Percent of households with children
Percent of households with children is the percentage of all households which include members of the household under the age of 18, which are related to the head of household. These estimates are drawn from the 2012-2016 5-year American Community Survey.
Decisions about where to raise children are informed by many factors, including school quality and public safety. The extent to which a place’s population consists of households with children can be an indicator of how basic services are managed and delivered.
Percent of population 20-64
The percent of population 20-64 is the percentage of the population between the ages of 20 and 64, inclusive. Estimates are those published by the 2012-2016 5-year American Community Survey.
The percent of the population aged 20-64 is a proxy for the working age population and provides insight into the balance between an independent and dependent population.
The population of the city as estimated by the 2012-2016 5-year American Community Survey.
Percent of housing units built pre-1980
Percent of housing units built pre-1980 measures the portion of all housing units identified by the 2012-2016 5-year American Community Survey as being constructed before the year 1980.
Within the PCIT, the percent of housing units built pre-1980 is a proxy for housing demand. High percentages of housing built before 1980 can be an indicator of little demand for new builds. It should be noted, however, the cities in the northeast often by definition have an older housing stock due to the overall age of the city. Here, geographical peers can be useful in determining what should be expected.
The vacancy rate measures the percentage of all residential units which are unoccupied, as estimated by the 2012-2016 5-year American Community Survey.
The vacancy rate can be another indicator of housing demand. High vacancy rates may point to a housing market in distress. However, low vacancy rates across a city, may mask pockets of housing distress present at the neighborhood level.
Home value to income ratio
The home value to income ratio is the ratio of the median value of owner-occupied homes, as estimated by the 2012-2016 5-year American Community Survey, to the median household income, also as estimated by the 2012-2016 5-year American Community Survey.
This is a measure of home purchase affordability. However, in interpreting this variable, users should take into account vacancy rates, as well as percent rent burdened rates. In situations where the home value to income ratio is low, but high vacancy or high rent burden percentages exist, low housing values may be driven by overall lack of demand compounded by low incomes. Users may want to bring in additional income variables to explore this context.
The homeownership rate is the percentage of householders which own their residence, as estimated by the 2012-2016 5-year American Community Survey.
Homeownership rates are often indicators of neighborhood/community stability, as homeowners are likely to stay and invest in the places where they own homes. Low home-ownership rates combined with high percentages of rent burden can indicate a community that is struggling to provide homeownership opportunities for its residents, and at the extreme, an indicator of an appraisal gap.
Percent rent-burdened households
The percent rent-burdened households is the percentage of renter households which spend more than 30 percent of their gross monthly income on rent each month, as estimated by the 2012-20156 5-year American Community Survey.
One of the primary responsibilities of a municipality is to provide affordable rental housing for residents. While many places struggle with high percentages of rent burdened populations, a review of a city’s peers can provide insight into where this challenge is particularly acute.
Median monthly housing costs
Median monthly housing costs are estimates published by the 2012-2016 5-year American Community Survey.
Monthly housing costs as included in the PCIT represent both ownership and rental housing costs. Users may want to include income variables to provide context for interpreting housing costs and assessing overall affordability.
Lake Havasu City
North Little Rock
South San Francisco
District of Columbia
Port St. Lucie
West Palm Beach
Clarke County (Athens)
Richmond County (Augusta)
East St. Louis
West Des Moines
Fayette County (Lexington)
Jefferson County (Louisville)
St. Clair Shores
St. Louis Park
Silver Bow County (Butte)
North Las Vegas
West New York
North Richland Hills
Salt Lake City
West Valley City
Fond du Lac
Following is a non-exhaustive list of other free, web-based tools that provide insights into places. The data provided in these tools complements that available through the PCIT and can help expand analyses.
By clicking on the links below, you will be taken to an external website and all descriptive language used below is taken directly from the host sites.
Federal Reserve Bank of Atlanta: Small City Economic Dynamism Index
“Small cities are like nerve centers connecting the regional economy. They are the hearts of their respective counties and metropolitan areas as well as hubs of employment, retail, health care, and education for people living in surrounding rural areas. Many small cities are growing and attracting new investments, but unemployment, poverty, vacant buildings, and economic distress are pronounced in some small metros. We've compiled a dataset and created the Small City Economic Dynamism Index to help policymakers and practitioners gain more nuanced perspectives. The index ranks 245 small U.S. cities across 14 indicators of economic dynamism in four categories: demographics, economics, human capital, and infrastructure.”
Federal Reserve Bank of St. Louis: Community Investment Explorer
"The Community Investment Explorer aggregates customizable data from the Community Development Financial Institution (CDFI), New Markets Tax Credit (NMTC) and Low Income Housing Tax Credit (LIHTC) programs to show geographic comparisons and trends over time. Developed by the St. Louis Fed, this interactive tool draws on publicly available data from over 500,000 community development transactions."
HUD's Community Assessment Reporting Tool (CART)
“CART is a reference tool designed by the US Department of Housing and Urban Development to display HUD's investments in communities across the United States. Use the Search bar to enter a name of a city, county, metropolitan area or state to see many of HUD's investments or use the Advanced Search for more options. CART can easily generate reports in PDF and Excel.”
“Community Commons is a place where data, tools, and stories come together to inspire change and improve communities. We provide public access to thousands of meaningful data layers that allow mapping and reporting capabilities so you can thoroughly explore community health. As a mission driven organization, we aim to make our custom tools publicly available whenever possible and our partners understand and support this public-good mission. The goal of Community Commons is to increase the impact of those working toward healthy, equitable, and sustainable communities. We believe this happens when Commons users access our tools to gain a deeper understanding of community assets and opportunities and then use data visualizations to convey that knowledge through partnerships and collaboration.”
EJSCREEN: Environmental Justice Screening and Mapping Tool
“EJSCREEN is an environmental justice mapping and screening tool that provides EPA with a nationally consistent dataset and approach for combining environmental and demographic indicators. EJSCREEN users choose a geographic area; the tool then provides demographic and environmental information for that area. All of the EJSCREEN indicators are publicly-available data. EJSCREEN simply provides a way to display this information and includes a method for combining environmental and demographic indicators into EJ indexes.”
“PolicyMap offers easy-to-use online mapping with data on demographics, real estate, health, jobs and more in communities across the US. From the classroom to the boardroom, thousands of organizations trust PolicyMap to find the right data for their research, market studies, business planning, site selection, grant applications and impact analysis.”