Abstract
What drives economic growth in our communities and how can we ensure that more people benefit from that growth? While economic growth has been the focus of many U.S. cities and regions since the Great Recession, it is the second question that is gaining much-needed attention in recovery years. Answering either question is complicated by the lack of ability to access, analyze and apply data across diverse stakeholders and geographies. This chapter is for practitioners and policymakers interested in coordinating data across multiple stakeholders and geographies, and is particularly relevant for those interested in addressing inequality through more equitable economic development efforts. The chapter surfaces one example of a model in which cross-sector partners identified ways to improve labor market outcomes for all residents, especially lower income residents, across an 18-county region: first by using data and research to identify economic challenges and opportunities, and second by coordinating a plan of action across diverse sectors and jurisdictions. The chapter discusses the process that Northeast Ohio, and specifically the Fund for Our Economic Future, experienced as one example of cross-sector partners struggling to build—and re-build—a competitive economic base that benefits all people in its various communities. Its lessons have relevance for others trying to do the same in their own local, national or global contexts.
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Notes
- 1.
Refers to the 2007–2009 recession as defined by the National Bureau of Economic Research (NBER), available at http://www.nber.org/cycles.html.
- 2.
The best source I have found for U.S. metro area economic trends is the Brookings Institution’s Metropolitan Monitor, from which many of the trends referred to here are derived. In its latest version (Shearer et al. 2016), data are available between 2009 and 2014 for variables related to growth (jobs, gross product (GMP) and aggregate wages), prosperity (averages wages per job, GMP, productivity) and inclusion (share of the population employed, median wages and poverty rates relative to local area income). See also Berube and Holmes (2016) for income inequality; EIG (2016) for spatial inequality across cities and neighborhoods; and Dourado and Koopman (2015) for growth of the independent workforce.
- 3.
See brief reflection by Shepherd (2016) on the potential and risk of big data influence decision-making at both micro and macro levels.
- 4.
See also Lui (2016), which highlights five principles for “remaking economic development,” the first being to set the right goals (pp. 20–21).
- 5.
- 6.
Previous editions can be accessed at www.thefundneo.org/what-matters. The Dashboard of Economic Indicators was originally designed by Randall Eberts, George Erickcek, and Jack Kleinhenz in 2006 as a working paper for the Federal Reserve Bank of Cleveland. Subsequent refinements are largely attributable to Ziona Austrian, Iryna Lendel, Afiah Yamoah and Merissa Piazza of the Cleveland State University, with the latest analysis [retitled What Matters to Metros (2013)] authored by Emily Garr Pacetti. Deviations from past models include the period of growth, defined here as change over time between 1990 and 2011, in place of a subset of growth years as the dependent variable; and an extended variable list including indicators related to health, the arts, housing, and sustainability that had not been considered in previous iterations. For a detailed methodology, please refer to The Dashboard of Economic Indicators (Austrian et al. 2009).
- 7.
Originally envisioned as a “dashboard” from which to track the region’s progress year to year, the research contained many indicators that were, by their nature, slow to change. This prompted the Fund to focus more on its usefulness as a tool to help identify what is important to the economy in a given period of time, i.e. “what matters” to metros.
- 8.
- 9.
Initially referred to as “Voices and Choices,” this engagement and feedback effort evolved from a broad-based community campaign to understand the public’s priorities, to a more targeted outreach exercise with key stakeholders, communities, academics and community leaders, who helped guide the research year-to-year. Note: There was and is no silver-bullet engagement strategy that the Fund employed, and there was broad recognition that engagement activities could always be more robust, more long-term and more directly applied to resulting strategies. Resource constraints tend to complicate this task. For more discussion and examples of failed and successful community engagement efforts, see Barnes and Schmitz (2016).
- 10.
Through a series of discussion forums, the Fund’s research reached more than 800 regional and national civic leaders. The discussions focused on the observation that job growth cannot be a region’s only measure of success and led to additional conversations and strategic planning about how to better link economic growth and equitable opportunity. Ultimately, the research led the Fund, in partnership with the Federal Reserve Bank of Cleveland and others, to a “Growth and Opportunity” agenda (Pacetti 2014; Schweitzer et al. 2014), that reinforced connections among workforce and training efforts (“job preparation”), employer demand (“job creation”) and the spatial and social disconnect between jobs and workers (“job access”). For more information, see http://www.thefundneo.org/growth-opportunity.
- 11.
A Conversation between Paul Krugman and Janet Gornick, Equality Indicators Conference, City University of New York (CUNY), Institute for State and Local Governance. October 1, 2015. An alternative vision is offered in Treuhaft et al. (2011).
- 12.
Benner and Pastor (2013) conducted an exercise for 184 metro areas with a population of 250,000 or above, and found that the capacity of regions to maintain growth and withstand recessionary shocks was positively associated with various measures of equity (lower racial segregation, lower income inequality and less political fragmentation). The data are backed up by previous empirical investigations (Benner and Pastor 2012; Carlson et al. 2012) and reinforced in their recent book (Benner and Pastor 2015).
- 13.
Analysis by Shierholz (2016), based on Bureau of Labor Statistics data between 2007 and 2015. The analysis compares job losses and gains during the recession (2007–2009) to those in the recovery (2009–2013) by pay per hour. It finds that during the Recovery period, low wage jobs (jobs that pay $10 per hour or less) and high wage jobs (jobs that pay between $47 and $50 per hour) increased disproportionately to middle wage jobs. The exception was jobs paying $51 per hour or higher.
- 14.
See Shepherd (2016) for micro and macro examples.
- 15.
For this reason, the best proxy we have for economic regions, or market areas, is at the metropolitan level. A metropolitan statistical area (“metro area”) is defined by the Office of Management and Budget as a geographical region with a relatively high population density at its core (minimum population of 50,000 in core urban area) and close economic ties throughout its surroundings. It constitutes one or more counties with a high degree of social and economic integration (as measured by commuting to work) with the urban core.
- 16.
Based on latest estimate from Moodys.com, as reported by Team NEO (2016).
- 17.
See Pacetti et al. (2015) for a detailed analysis of job growth in Northeast Ohio, highlighting the outward growth of jobs away from city centers over the last two decades and the increasing disconnect between jobs and workers. Such disconnects in cities, as measured by commute times, are associated with a significant decrease in workers’ economic mobility (Chetty et al. 2014). For more on the importance of connecting regional and local economic development efforts generally, see Weissbourd (2004), Weissbourd et al. (2009), Carlson et al. (2012), Lynch and Kamins (2012), Pacetti (2013).
- 18.
The group was made up of representatives of small and large philanthropic organizations, community foundations, hospitals, educational institutions, banks, and leading companies. It included representation from non-profit intermediaries such as NorTech (focused on innovation), JumpStart, Inc. (entrepreneurship), MAGNET (advanced manufacturing), BioEnterprise (biotechnology), Team NEO (business development) and other business development organizations throughout an 18 county region that focused on business retention and attraction. Team NEO worked in parallel with a state-led effort called JobsOhio.
- 19.
Population was limited to residents between the ages of 25–64 in order to provide an accurate assessment of those who were working age without confounding them with retirees and/or students. Standard labor force participation rates typically measure the population 16+ and may skew the perception of communities with disproportionately high or low student or elderly populations (the latter of which is the case for Northeast Ohio, which has a disproportionately older population). A notable drawback of this measure is its inability to measure progress year-to-year due to its dependence on smaller geographic data -census blocks or tracts that require an aggregation of (pooled) data over two, three or five years from the American Community Survey. A benefit is that even as residents may “move out” of distressed neighborhoods—presumably moving on to better opportunities, the tracking of “number of distressed areas” would adjust accordingly, as tracts are periodically readjusted based on population—the focus being on the share of the overall population who lives in these places.
- 20.
Examples of community-specific profiles and maps of economically distressed areas in Northeast Ohio are available at: http://www.thefundneo.org/growth-opportunity/neighborhood-profiles.
- 21.
Collective Impact was first introduced in a 2011 and is based on the premise that large-scale social change requires broad cross-sector coordination, rather than isolated interventions of individual organizations. The article describes five conditions for collective success: a common agenda, shared measurement systems (emphasized here), mutually reinforcing activities, continuous communication, and backbone support organizations. See Kania and Kramer (2011).
- 22.
See Russell (2016). “In An Improving Economy, Places in Distress,” New York Times. February 24, 2016. The data represents aggregate trends over the 2010–2014 time frame.
- 23.
See Irons and Berube (2016), based on the Rockefeller Foundation framework, currently in development.
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Pacetti, E.G. (2017). Aligning Local and Regional Data to Achieve a More Inclusive Economy: A Northeast Ohio Model. In: Holden, M., Phillips, R., Stevens, C. (eds) Community Quality-of-Life Indicators: Best Cases VII. Community Quality-of-Life and Well-Being. Springer, Cham. https://doi.org/10.1007/978-3-319-54618-6_5
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