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Metrics of Regional Equity

How do we measure success? As regional equity takes root in the next generation of practice, techniques and tools for measuring progress are critical to building momentum and gaining traction. Basic numerical analyses—whether counting a decreasing number of vacant properties in a neighborhood over a decade or comparing the number of jobs obtained through various CBAs in a year—bring precision and provide “hard data” to bolster arguments for regional equity policies. More subtle qualitative measures are also being developed. For example, we can now look at housing as not merely “affordable” but as existing within matrices of opportunities that include transportation to quality jobs, access to green public space, and proximity to healthful food.

A pioneer in the application of regional equity metrics for measuring and analyzing human activity and settlement patterns, urban expert and former Albuquerque Mayor David Rusk advocates using metrics to offer community leaders not only statistical indicators but also a means to interpret data. Rusk is not alone in this view. Redefining Progress (based in Oakland, California), Manuel Pastor (at the University of California at Santa Cruz) and john powell (with the Kirwan Institute)—among many others—are also part of this growing movement to establish community-defined indicators that “expose obstacles to a healthy quality of life, and illuminate economic, environmental and social trends.”[1]

Metrics also offer a way to keep multiple stakeholders committed to a plan of action without requiring congruence of motivation. Comparisons between regions that enable state or nationwide assessments are also possible with metrics. For example, Myron Orfield’s analysis of the fiscal capacities of jurisdictions illustrates compelling measurable disjunctions between affluent suburban communities and at-risk suburbs.


Racial segregation continues to be a significant factor that limits access by people of color to good jobs, good schools, increasing home equity, and many other economic goals. In his 2004 paper titled, “Regional Equity Metrics,” Rusk outlines specific regional equity goals and their corollary indices, which are based on measurements of both racial and economic segregation.[2]

Rusk writes that residential segregation indices are commonly measured in three ways: using dissimilarity indices, using isolation indices, and using exposure indices. “Dissimilarity indices measure the degree to which a minority population (e.g. blacks, Hispanics, poor persons) is set apart from the majority population (e.g. ‘whites,’ non-poor persons),” Rusk explains. “On a scale of zero to 100, an index of zero would indicate an even distribution of a minority group across all neighborhoods (census tracts) of a region; an index of ‘100’ would indicate total racial or economic apartheid. At an index of 100 for blacks, for example, all blacks and only blacks would live in certain neighborhoods and all whites and only whites would live everywhere else.”

Rusk’s use of segregation indicators in constructing a metric is a groundbreaking approach. In addition, a community’s voice, or lack thereof, can also be seen as a metric. One example of this can be seen in Thomas W. Sanchez’s study that quantifies the composition of metropolitan planning organization boards as a contributor to potential bias in allocating state and federal transportation funds nationwide.[3] (Also see Sanchez article on page 72 of this issue.) And the well-known Gini coefficient, an index that is a measure of inequality of distribution, has been used to quantify disparities in income distribution and has influenced the development of other useful metrics, including the Robin Hood index.[4]

Highly specialized and expensive research is not always required. Rusk has demonstrated this by developing metrics from United States census data collected from all jurisdictions across the country. At the Brookings Institution, 2000 census data has been the basis of several important studies, including Redefining Urban and Suburban America: Evidence from Census 2000, a three-volume study outlining the demographic trends defining our metropolitan regions.[5]

The Power of Images

Regional equity advocates like Myron Orfield, executive director of the Institute on Race and Poverty at the University of Minnesota, are using Geographic Information Systems (GIS) mapping tools to increase their impact. A GIS map explains a region’s complex social patterns in a way that is easy to grasp. While columns of numbers are mind numbing, a map illustrating impacts across a whole region helps people to understand key regional social and economic trends that affect their lives. GIS maps can be used to show economic inequalities among communities, or how new growth at a region’s edge undermines the inner city. Assisted by such maps, communities have achieved important gains in such areas as fair housing, more equitable school financing, reform of transportation spending, and brownfield remediation.

The forces of segregation and inequality are too large to confront in isolation. Activists tackling inner-city poverty often focus on one neighborhood at a time. Yet, the power of racial discrimination and fiscal inequality in a region undermines their efforts. Maps make larger patterns of inequity visible, both to citizens and to decision-makers.[6]

In the years ahead, metrics will play an increased role in defining outcome goals and will focus efforts to achieve greater regional equity for economically isolated and racially segregated residents. By using presentation strategies illustrated effectively with metrics, advocates will be in a better position to create compelling arguments to help reduce inequalities within regions and address the isolation of the poor from the rest of society.


1. See Rusk, D. Regional Equity Metrics, a report for the CORE group at the Ford Foundation, 2004.

2. Rusk notes that, statistically, the isolation and exposure indices are highly correlated with dissimilarity indices. Change the dissimilarity indices and the isolation and exposure indices will automatically follow.

3. Sanchez, T.W. An Inherent Bias? Geographic and Racial-Ethnic Patterns of Metropolitan Planning Organization Boards. Transportation Reform Series. Washington, D.C., Brookings Institution, 2006.

4. The Robin Hood index measures income inequality. The higher the index, the greater the inequality in income distribution.

5. Katz, B., Lang, R. and Berube, A. Redefining Urban and Suburban America: Evidence from Census 2000 (3 Volumes). Washington, D.C., Brookings Institution Press, 2006.

6. For more information, see page 27 of the summer 2005 issue of Yes! A Journal of Positive Futures. Issue includes “Social Patterns,” an article by Myron Orfield addressing the effectiveness of GIS Mapping. Bainbridge Island, Washington: Positive Futures Network.

This article is adapted from Breakthrough Communities: Sustainablility and Justice in the Next American Metropolis edited by M. Paloma Pavel, to be published in June 2009 by the MIT Press. © 2009 Massachusetts Institute of Technology. All rights reserved. (See page 32 for more information on the book).

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