publisher colophon

Notes

1. CAN’T WE ALL JUST GET ALONG?

1. US Bureau of Economic Analysis annualized quarterly GDP growth rates.

2. For updated data, see Emmanuel Saez’s website, http://elsa.berkeley.edu/~saez/.

3. State of Working America, http://stateofworkingamerica.org/chart/swa-wages-table-4-2-average-hourly-pay-inequality/.

4. State of Working America, http://stateofworkingamerica.org/chart/swa-wages-table-4-14-hourly-wages-education/.

5. Polling Report, www.pollingreport.com/CongJob1.htm.

6. In fact, when asked whether they had a higher opinion of Congress or a series of unpleasant or disliked things, voters said they had a higher opinion of root canals, NFL replacement refs, political pundits, used-car salesmen, cockroaches, head lice, and colonoscopies than of Congress (Easley 2013).

7. According to the Brookings Institute’s Vital Statistics on Congress, the legislative productivity of Congress—at least as measured by the number of public bills passed and signed into low—has fallen consistently over the past 50 years. In the 1950s and 1960s, in a typical two-year session of Congress, nearly 800 bills were passed. By the 1990s and 2000s, this had fallen closer to 400. In the 112th Congress of 2011–12, only 283 total public bills were passed and signed into law, and the 113th Congress did only slightly better with 296 (http://dailysignal.com/2014/12/30/turns-113th-congress-wasnt-least-productive/).

8. Newspaper Association of America, www.naa.org/Trends-and-Numbers/Readership/Age-and-Gender.aspx.

9. We calculated the 2012 share of voters in landslide counties using the same methods and data described in Bishop and Cushing (2008, 10), first calculating measures for 1988, 1992, and 2000 to insure that our new series would be consistent with what these authors report.

10. Pew Research Religion & Public Life Project, www.pewforum.org/Politics-and-Elections/Little-Voter-Discomfort-with-Romney%E2%80%99s-Mormon-Religion.aspx.

11. Scripps Survey Research Center at Ohio University, http://newspolls.org/articles/19604.

12. There is a now a vast literature on regional knowledge networks, especially as they relate to regional innovation systems (Markusen 1999). While we return to these issues at the end of the book, particularly the interconnection between such networks and epistemic communities, here the focus is on the impact on policy rather than on firm-level economic activity.

13. The elaboration is that we add a time dimension that was largely missing in Haas’s formulations but has been explored in subsequent work by Antoniades (2010), who distinguished between ad hoc communities focused on specific policy problems and those that develop a more constant and holistic character. For Antoniades, these more holistic communities—which are closer to our notion of diverse and dynamic epistemic communities—are aimed at the establishment and perpetuation of beliefs and visions as dominant social discourse, and are rooted more in social interactions and struggles than in particular policy problems.

2. DRIVING THAT TRAIN: CAN CLOSING THE GAP FACILITATE SUSTAINED GROWTH?

1. See also the working paper by Levine, Frank, and Dijk (2010), which looks at the effects of income inequality on “expenditure cascades” and finds that the US counties with the highest levels of income inequality were the most likely to experience financial distress.

2. While this is the full sample, we lacked key variables for all 181 regions. We were, for example, unable to calculate Gini coefficients for ten smaller CBSAs (less than 500,000 population), and unionization rates for fifteen smaller CBSAs are not reported. In regressions with those variables, we are actually analyzing 287 growth spells in 160 regions.

3. For the three cases without any growth spells, average employment and wage growth were relatively high—but that was due to averaging one spectacular performer, one dismal performer, and one middle-range performer.

4. Berg et al. (2012) report time ratios rather than hazard ratios; the two measures move in opposite directions. We report hazard ratios because they might be more familiar to readers and because the Cox procedure is built into SPSS, our program of choice for this exercise. To check our results, we also did all the regressions in Stata, using the Streg command with the accelerated-time-to-failure option (the exact method used in Berg et al. 2012). We report those results in a footnote when we consider the integrated model; suffice it to say here that everything moves in a very similar direction.

5. Another measure that we tried—and discarded—was a dummy variable indicating whether the regional growth spell ended during a period of national economic recession (54% of our growth spells did). As it turned out, this recession dummy variable was highly significant, entered either by itself or in conjunction with the percentage of time still growing while the nation was in recession, but the sign suggested that a growth spell that ends in a national recession is likely to be longer than one that doesn’t. While this seems like an anomaly, it isn’t. The average length of spells that ended in a recession is actually greater (26.77 versus 24.59) than those that ended outside of a national recession. The way to think about this is that a region that takes a national recession to get knocked off its growth spell is actually more shock-resilient, so its growth spells will be longer. Because a consideration of the features that explain this is exactly what we are trying to get at with our other variables, we do not include the recession-at-end variable either here or in our final multivariate specifications (although the results do not change much if we do).

6. In addition, the gross regional product measure used as the numerator is taken from another source, so we get some unrealistically high export shares. We logged the variable to reduce that problem and get a more normal distribution, but the result made little difference to the regression outcomes and was not parallel with the other share variables utilized later. Hence, we report the results for the straightforward share measure. Since we are not primarily concerned here with the size of the effect but its direction, we were not worried about what is likely a consistent overestimation of export shares.

7. Center for Metropolitan Studies, University of Pittsburgh, www.metrostudies.pitt.edu/Projects/MetropolitanPowerDiffusionIndex/tabid/1321/.

8. In particular, we used data on household income from the 1990 and 2000 5-percent Public Use Microdata Samples, applying trapezoidal integration to calculate the Gini coefficient for each year.

9. We used categorical household-income information by race of householder from the 1990 and 2000 censuses, and defined the middle class to include households with income between 80 and 120 percent of the overall regional median. All households falling in income brackets entirely contained within the resulting income boundaries for the middle class were included, and, for the income brackets “split” by the income boundaries, linear interpolation was used to estimate the number of households in such brackets falling in the middle class. This variable is a bit of a hang-over from our work on the determinants of equity and growth in Benner and Pastor (2012) and may be less reflective of the social-distance measures we are mainly exploring in this exercise, a point we mention in the discussion of the multivariate analysis.

10. The ratio is calculated for the so-called principal cities of a metro area relative to other areas.

11. Most economic institutions either do not vary substantially from region to region across the United States, or the nature of the differences is difficult to capture quantitatively (such as variances in economic development strategies). There are some state-level differences, such as minimum-wage or right-to-work legislation (used for example in Hill et al. 2012), but with multiple metro regions in single states, and some metropolitan regions crossing state boundaries, it seemed more appropriate to consider one economic institution that does vary by region: the percentage of unionization.

12. We reran the individual Cox regressions with the Gini coefficient and the Gini residual from this exercise; both were highly significant, but, as one might expect, the hazard ratio for the modified Gini coefficient on its own was lower (since some of the underlying explanatory power from the education structure—that is, its role in postponing the “hazard” of an end to a growth spell—has been set to one side).

13. Because we were worried about the impacts of the Great Recession on our results—after all, part of the reason we weren’t worried about right-censoring is that nearly all the growth spells were clipped by the end of the period we examined—we reran the full model with a dummy variable set to 1 if the growth spell ended in the official recession period (December 2007 to June 2009, so fourth quarter of 2007 to second quarter of 2009). The variable itself was not significant, and the only other variable that lost just a bit of its significance was the percentage of the workforce in construction, a sensible turn of events given the role that that sector played in the most recent turndown.

14. As mentioned in an earlier note, we also ran the model in Stata using Streg with an accelerated-time-to-failure option to obtain results more directly parallel to those in Berg et al. (2012). We specified the Weibull distribution, as did they. The only difference of note in the individual regressions is that the growth-shortening effect of the share of the population with a BA or better was more significant (although the high school-to-AA variable was still growth-promoting and much more significant). In the integrated model, virtually everything was identical in terms of significance, with a few minor exceptions. MPDI and the dissimilarity index were less significant, and the city–suburb poverty ratio was more significant, suggesting that the competition for significance of these similar variables winds up with slightly different results outside the Cox specification. The share of the workforce in construction also became insignificant, although it was signed as expected. As noted in the text, the time-ratio coefficient we obtain from this regression for our Gini measure (the only exactly parallel right-hand-side variable between the Berg et al. study and ours) is virtually identical to the value they obtain for the Gini.

15. To investigate that, we dropped the spatial-sorting variable, and the city–suburb poverty ratio did indeed return to significance.

3. WHERE TO GO, WHAT TO ASK: SELECTING AND DESIGNING THE CASE STUDIES

1. The reason is that we built up the various income ratios for the metro regions by adding up data from the census tracts. Because we worked with summary data, not micro-data, this data is actually recorded as the number of households in various preset income categories. To get at the exact income breaks for the 80/20 ratio at a regional level, we summed the number of households in each break across tracts for each metro area, approximating the distribution of household income. We then identified the income brackets containing the 80th and 20th income percentiles and applied a Pareto interpolation to choose the exact level of the 80th and 20th percentiles. This process helped us estimate the tails, but more effectively when there are many brackets; in 1980, the income categories were few in number, so we thought that estimates at the 90th and 10th percentiles would be less reliable than estimates at the 80th and 20th percentiles. The 80/20 ratio is also used in econometric work by Hill et al. (2012), and we took a similar approach in Benner and Pastor (2012).

2. We realize that including the whole time period as well as the various separate decades might seem like double-counting, but essentially we were trying to reward a region for highly consistent performance; see the discussion of the method in Benner and Pastor (2012).

3. While looking at 2010 made sense because it correlated with the time period being used for the trajectory analysis, we were concerned that this might be viewed as an odd and perhaps biased or unrepresentative ending point because of the ways in which the impacts of the Great Recession may have varied across different metropolitan economies. We therefore experimented with several other end points, including 2007 (right before the onset of the Great Recession) and an average of the years 2006–2010 (to smooth out business-cycle effects and perhaps get at a “true” value). As it turns out, no important differences in the characterizations of income and inequality resulted, so we stuck with the more intuitively obvious (and seemingly consistent) 2010. Also, while we considered both per capita income and median household income for our measure of end-point economic well-being, we decided to use only median household income. In part, this was because household income seems to be a better indicator of people’s access to economic resources. But this decision was also made in part because Latino households have a significantly higher-than-average household size compared with other racial groups, meaning that the per capita income figures shifted somewhat disproportionately in regions with a growing percentage of Latino households (a compositional effect). We could see no logical reason to introduce what seemed to us a bias against selecting Latinizing regions, particularly given that the demographic changes going on in the country make the examination of such regions especially important for lessons for the future. Our equity measure was an easier choice. Since we were already using income ratios in our measures of change over time, we decided to use the Gini coefficient of household income.

4. This process resulted in a number of other region types that we ended up not using in our selection process but which might be of interest in other research. These included valley, or up-down-up: “good” in at least three-quarters of changes in the 1980s, “bad” in at least three-quarters of changes in the 1990s, and “good” in at least three-quarters of changes in the 2000s; mountain, or down-up-down: “bad” in at least three-quarters of changes in the 1980s, “good” in at least three-quarters of changes in the 1990s, and “bad” in at least three-quarters of changes in the 2000s; and fall back: “good” in at least three-quarters of the changes in the 1980s and 1990s and “bad” in at least three-quarters of the changes in the 2000s, or “good” in at least three-quarters of the changes in the 1980s and “bad” in at least three-quarters of the changes in the 1990s and 2000s.

4. PARKS AND RECREATION: PLANNING THE EPISTEMIC COMMUNITY

1. We define the Salt Lake City region using the December 2003 US Census CBSA definition of Salt Lake City, UT, consisting of Salt Lake County, Summit County, and Tooele County. Note that this is different from Envision Utah’s definition, which covers the entire Wasatch Front, north and south of Salt Lake City, as well.

2. The Church of Jesus Christ of Latter-day Saints, www.lds.org/bc/content/shared/content/english/pdf/welfare/2011-welfare-services-fact-sheet.pdf.

3. As of 2014, only men are allowed to be ordained as bishops in the LDS Church, though there is a growing movement for more gender equality in the church (Ordain Women, http://ordainwomen.org).

4. Newsroom, Church of Jesus Christ of Latter-day Saints, www.mormonnewsroom.org/article/church-supports-principles-of-utah-compact-on-immigration.

5. Local Area Unemployment and Employment Statistics, US Bureau of Labor Statistics.

6. We define the Sacramento region using the December 2003 US Census CBSA definition of Sacramento–Arden Arcade–Roseville, CA, consisting of El Dorado County, Placer County, Sacramento County, and Yolo County.

7. Interviews, Bill Mueller, executive director, Valley Vision, March 18, 2011, and Barbara Hayes, president and CEO, Sacramento Area Commerce and Trade Organization, March 17, 2011, by C. Benner.

8. SACOG’s jurisdictional boundaries differ slightly from the CBSA definition we use in this book. In addition to El Dorado County, Placer County, Sacramento County, and Yolo County, SACOG also includes Yuba County and Sutter County.

9. Interview with Mike McKeever, SACOG, by Madeline Wander and Mirabai Auer, September 18, 2013.

5. BUSINESS KNOWS BEST: ELITE-DRIVEN REGIONAL STEWARDSHIP

1. Association of Chamber of Commerce Executives, www.acce.org/ars/about-the-alliance-for-regional-stewardship/.

2. We define the Grand Rapids region using the December 2003 US Census CBSA definition of Grand Rapids-Wyoming, MI, consisting of Barry County, Ionia County, Kent County, and Newaygo County.

3. We should note that the Brookings report defines the Grand Rapids region slightly differently than we do: it uses the federal definition of the Grand Rapids-Muskegon-Holland Combined Statistical Area, which includes the counties of Allegan, Barry, Ionia, Kent, Muskegon, Newaygo, and Ottawa.

4. Interviews, March 2013.

5. Interviews, March 2013

6. Interviews, March 2013.

7. We define the Charlotte region using the December 2003 US Census CBSA definition of Charlotte-Gastonia-Concord, NC-SC, consisting of Anson County, Cabarrus County, Gaston County, Mecklenburg County, and Union County in North Carolina, and York County in South Carolina.

8. “Regional leaders study best practices in Charlotte,” Tulsa Regional Chamber, http://site7.cubicdev.com/nlarchive2/15837/monday-memo#4.

9. In Regions that Work: How Cities and Suburbs Can Grow Together (Pastor et al. 2000), Charlotte’s strong regional identity and dual emphasis on equity and economic growth were lifted up as an example of an inclusive form of regionalism.

10. Center City Partners, www.charlottecentercity.org/live/neighborhoods/fourth-ward/ (accessed May 15, 2014).

11. Bank of America, http://newsroom.bankofamerica.com/press-releases/community/bank-america-announces-5-million-gift-foundation-carolinas; North Carolina Community Foundation, www.fftc.org/DukeGrants (accessed May 17, 2014).

12. As just one indicator, the 2012 national directory of the National Association of Latino Elected Officials lists only two Latino elected officials in the entire state of North Carolina, and both are in Raleigh, not Charlotte. The only states with fewer members are Arkansas, Oklahoma, and Vermont.

13. We define the Oklahoma City region using the December 2003 US Census CBSA definition of Oklahoma City, OK, consisting of Cleveland County, Grady County, Lincoln County, Logan County, McClain County, and Oklahoma County.

14. Oklahoman, “City Planning to be Aired,” December 11, 1958, http://dougdawg.blogspot.com/2008/12/oklahoma-city-area-history.html.

15. http://boathousedistrict.org/training-site/.

16. City of Oklahoma City, www.okc.gov/maps3/mapshistory.html, accessed May 20, 2014; Greater Oklahoma City, www.okcchamber.com/index.php?submenu=ChamberHistory&src=gendocs&ref=ChamberHistory&category=About, accessed May 23, 2014.

17. NewsOK, http://newsok.com/maps3 (accessed July 15, 2013).

18. We have been unable to find a study documenting what proportion of sales tax revenue in Oklahoma City comes from nonresidents, but in our interviews, multiple people said that 25–30 percent was a good rule of thumb.

6. STRUGGLE AND THE CITY: CONFLICT-INFORMED COLLABORATION

1. We define the Greensboro region using the December 2003 US Census CBSA definition of Greensboro-High Point, NC, consisting of Guilford County, Randolph County, and Rockingham County. The Piedmont Triad is a broader region of north-central North Carolina which includes the cities of Greensboro, High Point, and Winston-Salem.

2. “History of Greensboro,” www.greensboro-nc.gov/index.aspx?page=142 (accessed January 23, 2014). Since as far back as the 1850s, the region has been a logistics center thanks to the numerous railways (and later freeways) that cut through it.

3. “North Carolina in the Global Economy: Furniture,” www.soc.duke.edu/NC_GlobalEconomy/furniture/overview.shtml, accessed January 23, 2014.

4. “About Us,” Cone Denim, www.conedenim.com/about-us-2/, accessed January 23, 2014.

5. Greensboro Partnership, “Industry Clusters,” http://www.greensboropartnership.org/economic-development/industry-clusters, accessed January 23, 2014.

6. Achieve Guilford, www.achieveguilford.org/parents_community/ (accessed December 15, 2013).

7. Civil Rights Greensboro, “Black Power in Greensboro,” http://libcdm1.uncg.edu/cdm/essayblackpower/collection/CivilRights, accessed January 23, 2014.

8. Civil Rights Greensboro, “An Overview of Greensboro Race Relations, 1808–1980,” http://libcdm1.uncg.edu/cdm/essaygreensboro/collection/CivilRights, accessed January 23, 2014.

9. Civil Rights Greensboro, “The Greensboro Massacre,” http://libcdm1.uncg.edu/cdm/essay1979/collection/CivilRights, accessed January 23, 2014.

10. We define the Fresno region using the December 2003 US Census CBSA definition for Fresno, CA, which consists of Fresno County.

11. The strong agricultural presence in the region also contributes to other destructive social dynamics. The San Joaquin Valley has apparently now become the country’s center of the manufacture and use of methamphetamine, with an estimated 80 percent of the nation’s meth labs and 97 percent of “superlabs” located there, driven by the particular combination of rural and poverty-stricken conditions, the possibility of acquiring key toxic ingredients from the agriculture industry, and close access to major urban centers (Winter 2011).

12. American Factfinder, American Community Survey 2012 Five-Year file, http://factfinder.census.gov.

13. Community Alliance, http://fresnoalliance.com/wordpress/, accessed January 23, 2014.

14. We define the San Antonio region using the December 2003 US Census CBSA definition of San Antonio, TX, consisting of Atascosa County, Bandera County, Bexar County, Comal County, Guadalupe County, Kendall County, Medina County, and Wilson County.

15. San Antonio Economic Development Foundation, “Industry Clusters,” www.sanantonioedf.com/industry-clusters, accessed January 23, 2014; City of San Antonio, “San Antonio Industry Clusters,” www.sanantonio.gov/IID/IndustryClusters.aspx, accessed January 23, 2014.

16. Project QUEST, www.questsa.org, accessed January 23, 2014.

17. SA 2020 actually builds on Target ’90, a similar effort at citywide goal-setting undertaken in the 1980s under then-mayor Henry Cisneros.

7. THE NEXT FRONTIER: COLLABORATION IN THE NEW ECONOMY

1. We define the Silicon Valley region using the December 2003 US Census CBSA definition of San Jose–Sunnyvale–Santa Clara, CA, consisting of San Benito County and Santa Clara County.

2. Joint Venture Silicon Valley, www.jointventure.org/index.php?option=com_content&view=article&id=325&Itemid=330, accessed May 2, 2014.

3. Even before that rechristening in 1998, the organization was known for two decades as the Santa Clara Valley Manufacturing Group (www.bizjournals.com/sanjose/stories/1998/01/19/tidbits.html).

4. Silicon Valley Leadership Group, http://svlg.org/about-us/accomplishments, accessed May 19, 2014.

5. Silicon Valley Leadership Group, http://svlg.org/policy-areas/housing, accessed May 12, 2014.

6. The Living Wage Ordinance was passed in November 1998, making Silicon Valley the highest living wage city in the country at the time. In November 2012, San Jose voters passed the Minimum Wage Ordinance, which set a minimum wage of $10.00 an hour and required that it increase annually with the cost of living. By 2015, it had reached $10.30 an hour. The public-sector pension reform initiative was favored by approximately 70 percent of San Jose voters and approved in June 2012.

7. Moyers & Company, http://billmoyers.com/segment/bill-moyers-essay-the-united-states-of-inequality/.

8. Social Capital Community Benchmark Survey, www.hks.harvard.edu/saguaro/communitysurvey/ca5c.html.

9. We define the Raleigh-Durham region by combining the December 2003 US Census CBSA definitions of Raleigh-Cary, NC (consisting of Franklin County, Johnston County, and Wake County and Durham, NC (consisting of Chatham County, Durham County, Orange County, and Person County).

10. We define the Seattle region using the December 2003 US Census CBSA definition of Seattle-Tacoma-Bellevue, WA, consisting of King County, Pierce County, and Snohomish County.

11. Puget Sound Business Journal, www.bizjournals.com/seattle/subscriber-only/2013/07/26/boeing-tops-the-list-of-washington.html.

12. Puget Sound Business Journal, www.bizjournals.com/seattle/subscriber-only/2013/07/26/boeing-tops-the-list-of-washington.html.

13. See the November 13, 2009, KCTS interview with Larry Gossett, Roberto Maestas, and Bob Santos at www.youtube.com/watch?v=eCGeWRxEwxM.

14. Gossett, Maestas, and Santos interview.

15. Puget Sound Regional Council, www.psrc.org/growth/tod/, accessed May 8, 2014. The region received two federal grants to help integrate equity into regional planning: a Sustainable Communities Initiative grant and a Growing Transit Communities grant. It is also important to note that the region has an easier time than others planning for the integration of economic development and transportation because the Puget Sound Regional Council acts as both the region’s Metropolitan Planning Organization and the Economic Development District as the results of a memorandum of understanding developed in 2003. Also see the Puget Sound Regional Council, “History,” www.psrc.org/assets/3305/timeline.pdf.

16. Both out-of-state US-born and foreign-born residents of the Seattle region have higher levels of educational attainment (44 and 38 percent have at least a bachelor’s degree, respectively) than those Seattle residents born in Washington (only 30 percent of whom have a bachelor’s degree or higher). We should note, however, that Seattle’s foreign-born also exhibit lower levels of high school graduation than the Washington-born. The region’s immigrant population, it seems, works in both high-tech and low-wage service sectors.

8. STEPPING BACK: THEORIZING DIVERSE AND DYNAMIC EPISTEMIC COMMUNITIES

1. A more recent faith-based initiative called MOSES (Metropolitan Organizing Strategy Enabling Strength) has explicitly engaged in a number of regional advocacy campaigns. Though it was founded in the late 1990s, its efforts have yet to result in substantial change in regional governance, which remains fragmented and divided on race and income lines.

9. LOOKING FORWARD: A BELOVED (EPISTEMIC) COMMUNITY?

1. US Department of Housing and Urban Development, http://portal.hud.gov/hudportal/HUD?src=/hudprograms/sci.

2. US Department of Housing and Urban Development, http://portal.hud.gov/hudportal/HUD?src=/program_offices/economic_resilience/sustainable_communities_regional_planning_grants.

APPENDIX B: DATA SOURCES AND METHODS FOR REGIONAL PROFILES

1. Available at http://socds.huduser.org/Census/Census_Home.html.

2. The formula used to calculate it is well established, and made available by the US Census Bureau at www.census.gov/hhes/www/housing/housing_patterns/app_b.html.

Notes

The principal cities–suburbs job distribution data is from the State of the Cities Data Systems (SOCDS), Decennial Census, and American Community Survey Data for 1980 through 2000.1 The SOCDS data includes employment counts on a place-of-work basis for CBSAs, principal cities, and suburbs (based on the OMB’s December 2003 definitions). Because the SOCDS data had not yet been updated with 2010 information at the time of writing, we drew 2010 data from the Longitudinal Employer-Household Dynamics, which uses a variety of data sources to generate estimates of total workers/jobs by census block (among other variables). We matched the block-level data to CBSAs and principal cities using GIS software to summarize the data as needed.

Segregation by race, and the poverty dissimilarity index, are measured using the “dissimilarity index,” which is calculated using two racial (or any other sort of) groups and can be construed as indicating the share of one group that would have to move to a new census tract to make the distribution of the two groups across all tracts in the region the same.2 Our method for measuring income segregation was derived from a 2010 report, Residential Segregation by Income, 1970–2009 (Bischoff and Reardon 2013). The only difference is that we focused on household income rather than family income so that we could generate measures of income segregation that cover the total population, including unrelated individuals, who are not included in family income measures. We organized census tracts within each region into six income categories, based on the ratio of tract-level median household income to the regional average (with the latter figured as a weighted average of the tract medians). The six income categories are defined as follows. Poor includes tracts with median household income of less than 67 percent of the regional average; low income means 67–79 percent of the regional average; low-mid income, 80–99 percent; high-mid income, 100–124 percent; high income, 125–149 percent; and affluent, income of 150 percent of the regional average or more. Once each tract’s income category was determined, population was summed by income category across all tracts in each region to get the distribution shown in the tables.

It also seems clear that our economy is experiencing not simply a jobs shortfall but also a dramatic period of economic restructuring, with some evidence that this is accompanied by a long-term slowdown in economic growth rates. In the decades of the 1950s and 1960s, the US economy experienced average annual growth rates of over 4 percent. This dropped to an average of 3 percent in the 1970s, ’80s, and ’90s. In the 2000s, average overall economic growth was only 1.6 percent a year, while in the first four years of the 2010s, it was 2 percent a year.1 Rapid population growth had something to do with these numbers—the baby boom was a substantial economic boost for the country in the 1950s and ’60s—but even adjusting for total population, per capita growth rates in recent decades have also slipped when compared to earlier decades.

Information fragmentation and spatial sorting has, we believe, eroded a common base of knowledge about the very nature of the problems we face as a nation—both in the political leadership and in the broader public that elects them. For example, in a July 2012 poll by the Pew Forum on Religion and Public Life, 30 percent of Republicans said that they thought that President Obama is Muslim—nearly double the percentage who thought so four years previously.10 Similarly, more than a third of respondents in a 2006 survey by Ohio University believed that federal officials either assisted in the 9/11 terrorist attacks or took no action to stop them so that the United States could go to war in the Middle East.11 While these examples could make for a lighthearted chuckle about the extremes in the political spectrum, we worry that they are evidence of a deeper challenge facing the nation. When we can’t agree on the basic facts, disagreement about appropriate solutions—and sharp but ill-informed ideological warfare—are sure to follow.

Information fragmentation and spatial sorting has, we believe, eroded a common base of knowledge about the very nature of the problems we face as a nation—both in the political leadership and in the broader public that elects them. For example, in a July 2012 poll by the Pew Forum on Religion and Public Life, 30 percent of Republicans said that they thought that President Obama is Muslim—nearly double the percentage who thought so four years previously.10 Similarly, more than a third of respondents in a 2006 survey by Ohio University believed that federal officials either assisted in the 9/11 terrorist attacks or took no action to stop them so that the United States could go to war in the Middle East.11 While these examples could make for a lighthearted chuckle about the extremes in the political spectrum, we worry that they are evidence of a deeper challenge facing the nation. When we can’t agree on the basic facts, disagreement about appropriate solutions—and sharp but ill-informed ideological warfare—are sure to follow.

So what’s new this go-around? In this effort, we expand our previous analysis in three ways. We stress more the process of community building than the impacts on growth and equity; we offer a fuller account of the ways in which conflict and collaboration can go together; and we add the characteristics of diversity and dynamism that our newest case-study research suggests are key to both sustainable communities and sustainable growth. While we explore the causal chains and the role of conflict below, it is useful to start by defining what we mean by a diverse and dynamic epistemic community at the metropolitan level.12

In the first column of Table 1.1, we summarize key characteristics of the more traditional conception of epistemic communities as originally proposed by Haas (with some elaboration).13 In the second column, we expand upon this original conception and identify key elements of what we call diverse and dynamic epistemic communities. The differences are rather straightforward. We see diverse and dynamic epistemic communities as having a broader membership base, an ability to accommodate multiple ways of knowing, a scope of action which stretches across multiple outcomes and conversational arenas, a desire to move beyond the episodic, and a capacity to handle conflict even as they facilitate a sense of common destiny.

We have also experienced a dramatic growth in income inequality in recent decades. Using data from the Internal Revenue Service, Emmanuel Saez and Thomas Piketty have demonstrated that from the 1940s up to the late 1970s, the proportion of total income in the United States captured by the top 10 percent of income earners consistently remained in the 33–35-percent range (Piketty and Saez 2003). Starting in 1979, however, upper income earners started gaining consistently higher proportions of total income, which rose to a peak of a full 50.4 percent of all income going to the top 10 percent of income earners in 2012. And much of this was concentrated in the top 1 percent, which saw its proportion of total US income rise from roughly 10 percent, between the 1940s and 1981, to a high of 23.5 percent in 2007 (with a slight fall to 22.5 percent in 2012; Atkinson, Piketty, and Saez 2011).2

This growth in inequality has many roots, including excessive CEO and executive compensation at the top of the income ladder, as well as excessive financialization, leading to outsize returns in the financial sector (Stiglitz 2012). But it is also due to stagnant and declining wages for large sectors of the workforce, partly because of large shifts in returns to education. While real hourly wages grew an average of 2.6 percent per year between 1948 and 1973, they grew only 0.2 percent per year in the 1970s, 0.8 percent per year in the 1980s, 0.3 percent per year in the 1990s, and 0.9 percent per year in the 2000s.3 Between 1973 and 2011, wages fell by more than 20 percent for workers with less than a high school degree, more than 7 percent for workers with only a high school degree, and nearly 5 percent for those with some college education. In 1973, these categories accounted for a full 95 percent of the labor force, and even by 2011, a full 66 percent of the labor force still had less than a college degree and was receiving wages that were lower in real terms than nearly 40 years previously.4

This growth in inequality has many roots, including excessive CEO and executive compensation at the top of the income ladder, as well as excessive financialization, leading to outsize returns in the financial sector (Stiglitz 2012). But it is also due to stagnant and declining wages for large sectors of the workforce, partly because of large shifts in returns to education. While real hourly wages grew an average of 2.6 percent per year between 1948 and 1973, they grew only 0.2 percent per year in the 1970s, 0.8 percent per year in the 1980s, 0.3 percent per year in the 1990s, and 0.9 percent per year in the 2000s.3 Between 1973 and 2011, wages fell by more than 20 percent for workers with less than a high school degree, more than 7 percent for workers with only a high school degree, and nearly 5 percent for those with some college education. In 1973, these categories accounted for a full 95 percent of the labor force, and even by 2011, a full 66 percent of the labor force still had less than a college degree and was receiving wages that were lower in real terms than nearly 40 years previously.4

Alongside these economic and distributional challenges has been a crisis in our political institutions that is nearly unparalleled in the nation’s contemporary history (Mann and Ornstein 2012). Prior to the November 2014 elections, approval ratings of President Barack Obama were nearly the lowest of his term. But most striking has been the long-term decline in the percentage of the American electorate approving of the way Congress is handling its job.5 In one poll conducted in early 2013, following the gridlock over the “fiscal cliff” and a particularly unproductive 112th congressional session, only 9 percent of respondents had a favorable opinion of Congress (Easley 2013).6 The Gallup Poll of Americans’ level of approval of Congress, probably the most reliable and consistent source of data to compare public opinion over time, found average approval ratings from 2011 to 2013 to be the lowest in the 40 years over which comparable data has been gathered, with consistently less than 15 percent of Americans approving of the way Congress was doing its job (Newport 2013).

Alongside these economic and distributional challenges has been a crisis in our political institutions that is nearly unparalleled in the nation’s contemporary history (Mann and Ornstein 2012). Prior to the November 2014 elections, approval ratings of President Barack Obama were nearly the lowest of his term. But most striking has been the long-term decline in the percentage of the American electorate approving of the way Congress is handling its job.5 In one poll conducted in early 2013, following the gridlock over the “fiscal cliff” and a particularly unproductive 112th congressional session, only 9 percent of respondents had a favorable opinion of Congress (Easley 2013).6 The Gallup Poll of Americans’ level of approval of Congress, probably the most reliable and consistent source of data to compare public opinion over time, found average approval ratings from 2011 to 2013 to be the lowest in the 40 years over which comparable data has been gathered, with consistently less than 15 percent of Americans approving of the way Congress was doing its job (Newport 2013).

One bit of evidence: Congress has become less and less effective at moving legislation, even as it has become more effective at partisan bickering (McCarty, Poole, and Rosenthal 2006).7 Party-unity scores, which measure the percentage of members voting with a majority of their party, have risen from levels of roughly 75 percent in the 1970s to around 90 percent in the most recent years (Ornstein et al. 2013). The polarization grows from—and feeds directly into—what we think is the most important underlying factor: a dramatic decline in consensus on basic facts needed for policymaking, such as the role of taxation in economic growth, the impact of immigrants on society, and even the nature of global warming.

Part of the reason for that increasing fragmentation of knowledge is an increase in narrowcasting in the media. Since the 1970s, we have experienced a growing customization of media channels and fragmentation of news sources, starting first with the growth in cable television and accelerating dramatically with the growth of the Internet (Owen 2012). Readership of daily newspapers has declined across all age groups; particularly striking is that less than 30 percent of adults age 18–34 read a daily newspaper, whether in print or on the Web.8 Meanwhile, with the acceleration and increasing sophistication of algorithm-based customization of Internet-based information—on sites as varied as Google, Facebook, Amazon, and the New York Times—information that is “unwanted” is increasingly filtered out without the consumer even knowing (Pariser 2011).

We have also seen an increase in spatial sorting by both partisan ideology and social class. More people seem to be moving to areas with more homogeneous political and social circumstances, and thus are exposed to less diversity of opinions in their residential life as well (Chinni and Gimpel 2011). In 1976, for example, only about a quarter of America’s voters lived in a county where a presidential candidate won by a landslide (20 percent or more); by 2004, it had grown to nearly half (Bishop and Cushing 2008), and by 2012, more than half the voters lived in such landslide counties.9 As for class isolation, in 1970, only 15 percent of families were in neighborhoods that were classified as either affluent or poor. By 2007, this had more than doubled, to 31 percent of families (Reardon and Bischoff 2011).

While this notion of the complementarity of equity and growth has had some impact on the thinking and policies of multilateral institutions, it is only recently that the notion of a positive relationship between equity and long-term growth—beyond the usual Keynesian notions that placing money in the hands of less well-off consumers will yield a bigger economic bang for any stimulus dollar—has made its way into the discussion of the overall US economy (Boushey and Hersh 2012; Stiglitz 2012).1 To be sure, ground has been gained for this perspective, with authors like Nobel Prize winner Joseph Stiglitz arguing that highly unequal incomes can lead to excessive financialization of the economy and rent-seeking (that is, favor-seeking) by the wealthy in their dealings with government.

What about more core measures of social distance? Following Berg, Ostry, and Zettelmeyer (2012), we looked at the role of inequality in shaping growth spells, using a Gini coefficient measure derived from metropolitan household-income data from the decennial census.8 We also looked at the size of the “minority middle class,” that is, the proportion of African American and Latino households that are in the middle-income bracket for the region (first separately, then combined, although we present the results only for the combined measure to save space).9 We also wanted to look at other issues of social separation, so we considered a standard measure of residential segregation called the dissimilarity index, in this case calculated in terms of non-Hispanic whites versus everyone else, as well as the ratio of city to suburban poverty rates.10

We also looked at three broad measures of industrial structure in the region, namely the percentages of the workforce employed in construction, in manufacturing, and in public administration, as well as one set of economic institutions: the percentage of the workforce covered by unions.11 As shown in table 2.7, the percentage of employment in public administration is associated with longer growth spells; manufacturing and construction, each entered alone, do not have a significant impact on the length of growth spells. The unionization variable seems associated with shorter growth spells (perhaps squaring with the perspective that unions introduce labor market rigidity—but it is also important to remember that unionization and manufacturing tend to be correlated in these time periods), but the result is not statistically significant.

To deal with this issue, we ran a simple linear regression in which the dependent variable was the original Gini coefficient and the independent variable was the share of the population with at least a high school diploma and no more than either some years of college (but not a BA) or an AA degree, that is, our main education variable with the high correlation. With the regression weighted by metro population to give a better sense of the overall relationship, we took the residuals of the regression as a sort of detrended Gini coefficient—that part of inequality not directly explained by the single educational variable we are using in this exercise (and actually probably better capturing the political economy drivers of inequality).12

The Cox regression results with that modified Gini coefficient are shown in table 2.8. The first set of columns include all the variables tested above, while the second set of columns drops the three least significant measures. Note first that once we have accounted for all these structural variables, the percentage of the growth spell during which the nation has been in recession is no longer significant. The export variable is also insignificant, but, as we have suggested, this measure is imperfect anyway, given its timing. However, both the Metropolitan Power Diffusion Index (a measure of jurisdictional fragmentation) and higher levels of inequality are associated with shorter growth spells—and the effects are very significant.13

Of course, the big news is that the Gini coefficient remains highly significant—and, interestingly, the coefficient is essentially the same as before we did the detrending. (Every other non-education coefficient is stable as well, which makes sense since the “detrending” exercise was only done to separate out the education and Gini factors.) This suggests that inequality does indeed have a damping effect on growth spells. Moreover, one remarkable coincidence is that the time-ratio impact of the Gini measure on growth spells in the United States is almost the same as that found in the Berg, Ostry, and Zettelmeyer study on the Gini coefficient and cross-country performance.14

There are some interaction effects with the pre-existing variables which are perhaps best seen in the regression with all CBSAs. These generally involve slight shifts in coefficient values and significance levels, but the biggest shift is the city–suburb poverty ratio, which is after all likely to be correlated with political spatial sorting.15 We also see that as we move to the multi-county setting, several variables lose significance—not surprisingly, given the reduction in sample size. Here, the Metropolitan Power Diffusion Index has the most interesting change, losing significance in the multi-county sample. This makes intuitive sense given that it can capture fragmentation in the single-county cases included in the all-CBSA sample where the political spatial sorting variable has been set to 0, while the municipal fragmentation measure probably competes with the spatial sorting variable in the multi-county sample.

At the time of our analysis, we had the full set of employment data from 1990 to 2011, for a theoretical possible maximum growth spell (all job growth, all the time) of 84 quarters. While no one hit that stellar threshold, the resulting database included 324 growth spells in 181 of the 184 regions. There were three regions with no growth spell of at least 12 quarters in this time period, and while it might seem a bit cruel to call them out, here goes: Buffalo–Niagara Falls, NY; Merced, CA; and Sarasota-Bradenton-Venice, FL.2

So what does the data say? As it turns out, this is a debate that may be a bit moot: the length of growth spells and the overall growth rate are actually fairly well correlated. Table 2.1 takes the 181 regions which had growth spells and breaks them into categories based on the number of quarters in the overall period that a region was in a growth spell. The categories are chosen to create bands that are non-arbitrary but somewhat similar in terms of the number of regions that falls in each band (the basic results are not sensitive to our particular choice of breaks for the bands). Note that the minimum is 12 quarters—one needs that to have experienced a growth spell at all—and the maximum that any region spent in growth spells over the whole period is 70 quarters. We then calculate the growth in employment and real weekly earnings (also from the Quarterly Census of Employment and Wages data) over the whole period. The data suggests that more time in growth spells generates more overall employment growth and generally higher earnings (although the earnings effect seems to taper off in the higher bands).3

The testing technique specifically used in this exercise is a Cox regression, a particular type of survival analysis regression method. In our case, we are trying to see which factors are associated with an early exit from sustained growth. The reported coefficients are so-called hazard ratios that are always positive; when a coefficient is greater than 1, that means the variable being tested is associated with falling out of a growth spell; when the coefficient is less than 1, the variable being tested is associated with staying longer in a growth spell.4

An external shock, such as a national recession, is one of the factors most likely to end a growth spell. But in terms of considering the durability of a growth spell, the question is whether the region’s growth trajectory can withstand such shocks—and so we consider here the percentage of total quarters within the growth spell in which the national economy was in recession. Our notion is that the longer the spell has been impacted by the recession, the more likely it is to end—and the results for that hypothesis, reported in table 2.3, are significant at the .10 level, with the expected sign (in the tables that follow, any result that is significant at least at the .20 level is bolded). Again, recall how one should read these coefficients. The 1.018 coefficient indicates that, holding all other covariates constant, an increase of one unit (in this case a percentage-point increase in the share of the region’s growth spell that the nation is in an overall recession) is associated with a nearly 2-percent increase in hazard (or likelihood) of growth ending (with a coefficient less than 1 indicating a similarly figured percentage decrease in the likelihood of growth ending).5

Another way to get at external shocks and regional vulnerability is to consider the potential impacts of truly external factors, such as exports. To get at this, we calculated the proportion of gross regional product accounted for by international exports with data taken from the Department of Commerce. The first year for which we had the export data was 2005, and we averaged the years 2005 to 2010 to smooth out yearly variations and instead catch the overall structure. The basic notion is that a higher level of exposure to international trade could lead to less sustained growth. While the usual economic supposition is that more trade would be good for a nation as a whole, more susceptibility of one region’s industrial clusters to the rhythms of the international economy could bring risks as well as rewards. In any case, what we have is a highly imperfect measure of this trade openness, partly because it is taken from the end of the period rather than before, a failing to which we simply plead that we had no other such variable available to us for the earlier periods.6 The direction is as expected—a higher share of exports is associated with a greater hazard of falling out of a growth spell—and it is significant at the .03 level.

A third and newer approach, developed by David Miller of the University of Pittsburgh, builds on this Hirshmann-Herfindal Index approach but also incorporates the number of jurisdictions in the region (Hamilton, Miller, and Paytas 2004; Miller and Lee 2009).7 The resulting Metropolitan Power Diffusion Index (MPDI) is derived by using the square root of the percentage contribution to total regional expenditures, rather than the square, a process that gives greater mathematical value to the smaller units—and it is conveniently available for all metropolitan areas in 1987, 1997, and 2007.

What about more core measures of social distance? Following Berg, Ostry, and Zettelmeyer (2012), we looked at the role of inequality in shaping growth spells, using a Gini coefficient measure derived from metropolitan household-income data from the decennial census.8 We also looked at the size of the “minority middle class,” that is, the proportion of African American and Latino households that are in the middle-income bracket for the region (first separately, then combined, although we present the results only for the combined measure to save space).9 We also wanted to look at other issues of social separation, so we considered a standard measure of residential segregation called the dissimilarity index, in this case calculated in terms of non-Hispanic whites versus everyone else, as well as the ratio of city to suburban poverty rates.10

What about more core measures of social distance? Following Berg, Ostry, and Zettelmeyer (2012), we looked at the role of inequality in shaping growth spells, using a Gini coefficient measure derived from metropolitan household-income data from the decennial census.8 We also looked at the size of the “minority middle class,” that is, the proportion of African American and Latino households that are in the middle-income bracket for the region (first separately, then combined, although we present the results only for the combined measure to save space).9 We also wanted to look at other issues of social separation, so we considered a standard measure of residential segregation called the dissimilarity index, in this case calculated in terms of non-Hispanic whites versus everyone else, as well as the ratio of city to suburban poverty rates.10

To measure the income gap, or income inequality, we used the 80/20 household income ratio, which compares the 80th percentile of income earners with the 20th percentile, with higher ratios indicating more inequality. Recent attention has focused more on the tails of the income distribution, particularly the top 1 percent (Alvaredo et al. 2013). We focused on the 80/20 ratio, partly because we wanted to look at broader measures of social distance but also because calculations of a different and wider ratio (for example, the 90/10 ratio) were less reliable with the 1980 census data.1

From this process, we obtained sixteen z-scores for each region: one for each of the four variables in each of the four time periods (1980s, 1990s, 2000s, and the whole thirty-year period).2 We then computed the growth index as the mean of the eight growth-related z-scores, and the equity index as the mean of the eight equity-related z-scores (appendix A offers an account of those indices across all 192 regions, as well as a look at some of the initial and ending variables that go into the calculations and case selection). Finally, we ranked both the growth and equity indices into terciles (best, middle, and worst) across the entire 192-region sample, and mapped the results to observe the distribution of regions based on their relative scores on these indices (map 3.1). Together, the two tercile scores form a nine-cell grid, and the upper-right-hand cell of that grid is the “best,” in that it indicates both solid growth and relatively good trends in equity (meaning either actual upticks, or given the general trends in the United States during this time period, better than whatever dismal average was achieved by one’s particular census region).

As we mentioned above, we also wanted to go beyond trends over this thirty-year period and investigate whether the ending point on income levels and distributional measures was also above average. After all, if a region started off from a particularly low point in 1980, it could appear to improve dramatically over that period but still end up substantially below average—that is, the relative acceleration in its improvement may simply reflect a process of reverting to the mean (and maybe not even getting there). So, to make sure we were capturing regions that not only posted above-average improvements over time but also ended up at above-average levels of income and equity in the end, we looked at the median household income and the Gini coefficient of household income.3 In keeping with our trajectory analyses, we also normalized these endpoint scores by the broad census region, producing two z-scores (one for equity and one for growth) which could then be ranked into terciles. We show the results in map 3.2.

Specifically, we labeled each growth measure “good” if it was above the median value of that measure in its respective census-designated region, and “bad” if it was below the median. Conversely, we labeled each equity measure “good” if it was below the median of that variable in the respective census region (i.e. relatively less inequality and poverty), and “bad” if it was above the median (i.e. relatively more inequality and poverty). Using these tags, we identified whether a metro had improved or declined in terms of growth and equity in each of the four periods, and we created a typology to categorize the metros:4

To those who think that equity, growth, and sustainability must be the province of politically progressive locations, Salt Lake City might seem like an odd choice (although the central city has long had Democratic mayors, some with remarkably leftist politics).1 But the value of a case-selection process driven at least partly by quantitative considerations is that it can yield pleasant (or at least interesting) surprises—as well as pleasant greetings and rides from the airport.

One of the important influences of the church has to do with its role in addressing poverty in the region. While Mormons are generally quite conservative and have a strong suspicion of centralized government programs—a sentiment embedded in the larger Utah culture—the church has developed a quite substantial internal welfare structure that was first established in the 1930s. Mormons are encouraged to fast one day a month, and to donate at least the money that was saved on two missed meals, if not more, to the local church’s welfare fund. One hundred percent of these fast offerings are used to provide assistance to those in need (adminstrative costs associated with these programs are privately provided by the church through other channels). The church owns hundreds of thousands of acres of farmland and dairy operations. Food, including processed food products largely manufactured by the LDS-owned Deseret Industries and Deseret Manufacturing, are sent to the over 140 storehouses that the LDS Church operates. All told, some 10,000 volunteers work in these enterprises each year and in a range of humanitarian assistance efforts, and the total amount of humanitarian assistance provided between 1985 and 2011 was estimated at $1.4 billion.2

This important role of the local bishop highlights another feature of the LDS Church structure which becomes particularly important for our analysis: the personal contact with those less fortunate. The LDS Church has a lay clergy structure at the local level; bishops are called to service from among the members of a local congregation and serve without pay for a temporary period, typically three to seven years. Men who are called to be bishops are frequently among the more prominent and successful leaders in the community, including major business leaders.3 Thus, as we were told by a number of our key informants, many business executives and CEOs in the region have direct experience for an extended period of time acting essentially as social workers. While most of the assistance provided through these internal welfare structures benefits Mormons, bishops also frequently provide assistance to non-Mormons, reflecting the church’s commitment to helping those in need regardless of their beliefs.

Perhaps the most striking evidence of this different tone came in 2010 with the unveiling of the Utah Compact (4

Envision Utah originated from the creation of the Coalition for Utah’s Future in 1988. The coalition, which included political, business, and civic leaders, came together because of a growing concern about losing population, particularly younger people, to more prosperous states. The economic troubles facing Salt Lake City in that era ended up being quite short-lived; by the early 1990s, unemployment had dropped to 3.5 percent and employment growth rates averaged 4–5 percent a year for four years in a row, driven in part by a technology boom.5 Instead of population loss, by mid-decade the primary concerns were around quality-of-life issues, with growing air pollution and rapidly expanding sprawl, which was eating up farmlands and threatening neighboring canyons and mountainland.

Sacramento is another region where planning processes have been important for shaping the region’s trajectory. Though sometimes derisively referred to as Cowtown—the unsophisticated inland kid sister of California’s flashier coastal cities—Sacramento, the state’s capital, is now a dynamic region of over two million.6 Sacramento’s economy has traditionally drawn stability from its large public sector, anchored in middle-wage jobs. However, the closure of four large military bases in the late 1980s and early 1990s altered the structure of the region both economically and socially. Sacramento soon realized that weathering major economic shifts requires a broad-based strategy underpinned by a regional vision—and much of the last decade has included substantial regional initiatives intended to define and achieve that vision.

Housing and real estate prices were also key to attracting high-tech firms seeking affordable spaces for their operations and quality of life for their employees. Throughout the 1980s and 1990s, the region became an outpost for Silicon Valley firms, such as Hewlett Packard, Apple, and Intel. Beyond lower costs, the region’s seismic stability attracted firms looking to relocate away from the earthquake-prone environments of the San Francisco Bay Area.7 These industries contributed to a more egalitarian growth pattern by providing well-paying jobs for low- and middle-skilled workers. The defense industry, in particular, helped create a strong African American middle class in the region. At the close of the 1980s, average annual wages were relatively high, at $45,800 (inflation-adjusted to 2010 dollars), and unemployment was low, at 4.9 percent.

Unlike Salt Lake, in which the largely anti-government sentiment meant that regional planning was best launched from the nonprofit sector, in Sacramento the metropolitan planning organization, SACOG (Sacramento Area Council of Governments, consisting of twenty-two cities and six counties),8 stepped up to convene regional stakeholders and address the problems arising from sprawling development patterns. Starting in 2002, SACOG began a long-range regional planning process to engage residents in helping shape how their communities grow, through a common vision for land use and transportation infrastructure through 2050. As one might imagine in a relatively diverse region, there were many opposing views about the future of Sacramento. The first main conflict was between environmentalists and builders. Environmentalists were concerned about the continued negative impact of unbridled sprawl on the region’s environment and air quality, while builders were concerned about regulations that would jeopardize their livelihoods by limiting development.

Part of presenting the facts was providing statistics about the region. SACOG profiled the region’s projected employment and population growth and other demographic information to begin a conversation about expected growth in the future. Working with groups like the Urban Land Institute and the Metro Chamber, SACOG also did a survey of housing choice preferences and demonstrated the demand for more multi-family options—which served as helpful market information for developers and builders by demonstrating the existing and future market for denser development.9

In May 2000, fifty business and public-sector leaders from regions around the United States gathered in Kohler, Wisconsin, to explore creating a national network that would support regional initiatives.1 The result was the Alliance for Regional Stewardship. Recognizing limits to both federal power and local activism, and building on a growing regionalist movement across the country, the Alliance was committed to the idea that vibrant regions are built on the connections between an innovative economy, livable communities, social inclusion, and a collaborative style of governance. Since its founding, the organization has worked to develop regional leaders and support regional initiatives that advance these integrated and diverse goals (Henton, Melville, and Walesh 2003, 2004).

In addition to aggressively expanding the financial-services industry, business leadership also saw a vibrant downtown as a key ingredient for a successful future. An early planning document, the 1966 Odell Plan, called for a separation of uses downtown—a business and government hub flanked by separate high-rise residential towers areas bisected by a freeway. Over time, the business community would help champion a different vision: a 24-hour, pedestrian-friendly, mixed-use downtown. North Carolina National Bank, the predecessor to Bank of America, played a leading role here, including creating a Community Development Corporation to acquire property, provide loans, and encourage revitalization and preservation in the downtown area, paving the way for investment—and also for gentrification (Smith and Graves 2005). The Community Development Corporation has now evolved into Charlotte Center City Partners, a public–private partnership devoted to continuing the vision of Charlotte Center City as “viable, livable, memorable, and sustainable, with modern infrastructure, a tapestry of unique neighborhoods and a diversity of thriving businesses”10 (Atkins et al. 2011).

Corporate influence also extended strongly into local philanthropy and the arts. The Foundation of the Carolinas, a local leading philanthropy, has been heavily supported by corporate donations from Bank of America and Duke Energy, which were known for their responsive and deep pockets.11 Local museums have also benefitted. Bank of America, for example, donated a historic building to the Mint Museum of Craft and Design to house the Bank of America Gallery, a collection featuring American crafts (Nowell 1999).

Fraying does seem to have occurred. One reason is the growing in-migration of people from the Northeast and Midwest. These newer residents—and the newer businesses—have less sense of place and less pride in the hard-won compromises over schools. Another big shift has been the growth in the Latino population. Less than 1 percent of the region’s population in 1980, Latinos grew to 5 percent in 2000 and 10 percent by 2010. This complicates what has traditionally been a sort of social bargain between white corporate leaders and Black political activists, even though Latinos remain largely invisible in regional leadership circles in the region.12

In the mid-to-late 1980s, Oklahoma City13 was mired in an extended economic crisis, the result of a decline in the region’s core energy businesses and damage to the region’s banking and real estate sectors from the savings and loan meltdown. The region’s downtown area was hit especially hard, since the legacy of classic urban-renewal policy had accelerated the hollowing-out of the urban core. By 1988, Oklahoma City councilman I. G. Purser declared: “Downtown is dead and we helped kill it. There is no major retail, no major attraction and no place to eat” (Lackmeyer and Money 2006, i).

One factor that may have helped Oklahoma City stage this rapid turnaround is a relatively centralized and integrated regional governance structure. A classic example of an “elastic city” (Rusk 1993), Oklahoma City has expanded its boundaries over time rather than let growth be captured by newly incorporated suburbs. In 1953, Oklahoma City was about 56 square miles. In 1958, the Oklahoma City Chamber of Commerce sponsored an event in partnership with the mayor, focused on “Oklahoma City’s proposed metropolitan planning.”14 This conference led to a coordinated effort over the next fifteen years to rapidly annex land, with most of the increase coming quickly—by 1962 the city had encompassed more than 600 square miles. In effect, Oklahoma City created a strong regional government, similar to the city–county mergers in Nashville and Jacksonville in the 1960s, but in this case, through annexation that included expansion even beyond the county boundaries.

MAPS included a range of projects designed to cater to the needs of different constituencies while being part of a unified vision for improving quality of life in the region and renovating the urban core. Recreational projects included renovations to the Civic Center Music Hall, the Convention Center, and the Oklahoma State Fairgrounds; construction of a 20,000-seat indoor sports arena that eventually became the home of Oklahoma City’s first professional sports team, the Oklahoma City Thunder basketball team; the 15,000-seat Bricktown Ballpark, home of the Triple-A affiliate of the Houston Astros and frequent host of the Big 12 baseball tournament and periodic outdoor concerts. Other developments included a new public library; a trolley transit system; construction of the Bricktown Canal, which has become a major restaurant hub and entertainment attraction; and the transformation of a seven-mile stretch of the North Canadian River—which used to be derisively referred to by locals as “the river that needs mowing” due to its being choked by grass for much of the year—into a series of river lakes bordered by landscaped areas, trails, and recreational facilities. Renamed the Oklahoma River, this area has now become an attractive site for kayaking, canoeing, and sculling, and it was the first river to receive official designation by the US Olympic Committee as an Olympic and Paralympic Training Site.15 Funds raised by the tax over the five-year period totaled $350 million, and all the projects were completed debt-free.16

MAPS included a range of projects designed to cater to the needs of different constituencies while being part of a unified vision for improving quality of life in the region and renovating the urban core. Recreational projects included renovations to the Civic Center Music Hall, the Convention Center, and the Oklahoma State Fairgrounds; construction of a 20,000-seat indoor sports arena that eventually became the home of Oklahoma City’s first professional sports team, the Oklahoma City Thunder basketball team; the 15,000-seat Bricktown Ballpark, home of the Triple-A affiliate of the Houston Astros and frequent host of the Big 12 baseball tournament and periodic outdoor concerts. Other developments included a new public library; a trolley transit system; construction of the Bricktown Canal, which has become a major restaurant hub and entertainment attraction; and the transformation of a seven-mile stretch of the North Canadian River—which used to be derisively referred to by locals as “the river that needs mowing” due to its being choked by grass for much of the year—into a series of river lakes bordered by landscaped areas, trails, and recreational facilities. Renamed the Oklahoma River, this area has now become an attractive site for kayaking, canoeing, and sculling, and it was the first river to receive official designation by the US Olympic Committee as an Olympic and Paralympic Training Site.15 Funds raised by the tax over the five-year period totaled $350 million, and all the projects were completed debt-free.16

The success of the original MAPS and the MAPS for Kids programs paved the way for the passage of a third round of temporary sales tax increases, this time in 2008 (with a 54-percent majority) for the MAPS 3 initiative.17 Projects planned under the ten-year MAPS 3 initiative include a new downtown convention center; a downtown public park; a streetcar system; improvements to the Oklahoma River and Oklahoma State Fairgrounds projects; the construction of four new state-of-the-art senior health and wellness centers, designed to serve as gathering places for active seniors; and an expanded trail system and improvements to the city’s sidewalks, in efforts to promote a more walkable community. Again, the Chamber of Commerce played a strong role in advocating for the expanded taxes to support this major public investment.

A third distinctive feature is the regional nature of the initiative. Much of this is driven simply by the sheer size of Oklahoma City in the region. Particularly because of the annexation powers described earlier, Oklahoma City is the third-largest city in the continental United States by land area (behind Jacksonville, Florida, and Houston, Texas). As a result, initiatives in the city immediately have a regional significance. But it’s also the case that the use of a sales tax (rather than for instance a property tax) ensures that suburban residents who shop in Oklahoma City are contributing to the core (which may be one reason why the MAPS for Kids initiative was sweetened by the inclusion of financing for suburban school districts).18 Perhaps most remarkably, the commitment to expanded taxes for public investment in this range of projects was led by a conservative Chamber of Commerce, in close cooperation with Republican mayors, and was supported by a majority of the predominantly Republican voters of the region.

Until recently, Grand Rapids was one of the better-performing regions in the Midwest.2 There is a long history here. In the mid-nineteenth century, Grand Rapids was one of the major hubs of the country’s timber industry, and the region established itself as the premier furniture manufacturing center in the country. Unlike the many other manufacturing-based regions that experienced significant deindustrialization in the 1980s, the “furniture city” was able to sustain a vibrant manufacturing sector, providing good middle-class job opportunities for non–college educated workers as late as the mid-2000s (Vande Bunte 2013). According to the Brookings Institution, while the entire nation lost almost a quarter of its manufacturing jobs between 1980 and 2005, manufacturing jobs in the Grand Rapids region increased by 28 percent (Atkins et al. 2011).3 Other sectors—particularly health care services—also grew during this time, resulting in an overall increase in jobs of 34 percent in the 1980s and 26 percent in the 1990s. During these two decades, the region’s average earnings increased by over 10 percent, and the proportion living in poverty decreased by 3 percent.

Until recently, Grand Rapids was one of the better-performing regions in the Midwest.2 There is a long history here. In the mid-nineteenth century, Grand Rapids was one of the major hubs of the country’s timber industry, and the region established itself as the premier furniture manufacturing center in the country. Unlike the many other manufacturing-based regions that experienced significant deindustrialization in the 1980s, the “furniture city” was able to sustain a vibrant manufacturing sector, providing good middle-class job opportunities for non–college educated workers as late as the mid-2000s (Vande Bunte 2013). According to the Brookings Institution, while the entire nation lost almost a quarter of its manufacturing jobs between 1980 and 2005, manufacturing jobs in the Grand Rapids region increased by 28 percent (Atkins et al. 2011).3 Other sectors—particularly health care services—also grew during this time, resulting in an overall increase in jobs of 34 percent in the 1980s and 26 percent in the 1990s. During these two decades, the region’s average earnings increased by over 10 percent, and the proportion living in poverty decreased by 3 percent.

Unlike other regions such as Silicon Valley, this group of private-sector elite did not come from elsewhere; rather, they made their fortunes largely by founding and growing family businesses right in Grand Rapids. With generations in the region, they have had a commitment to being stewards of place, and this was invaluable for the revitalization of downtown Grand Rapids, a process that began in the 1970s. As the story goes, the central city then was like many others in the Midwest: a large number of the downtown businesses had either closed or followed white flight to the suburbs. But in an incident that echoes a similar realization of the empty hole in the middle on the part of the Oklahoma City elite (which we elaborate on below), business leaders had a bit of consciousness-raising in 1976 when the city wanted to throw a welcome-home parade for hometown hero Gerald Ford after he lost his presidential bid to Jimmy Carter. The problem was that there were so many vacant buildings in downtown that the Secret Service didn’t have enough security personal to cover them all. The parade was only allowed to go ahead after a further mobilization of all available security personnel in the region—including off-duty sheriff’s deputies and law enforcement retirees—collected enough staff to police the parade (Emrich 2008). This incident struck a nerve with the business elite, and it helped motivate their investment in downtown revitalization.4

Most recently, another project that could have raised conflict did not. The Grand Rapids Downtown Market is a multifaceted development consisting of outdoor and indoor markets featuring local food and businesses as well as classes educating residents about preparing fresh and healthy foods. Since the market is adjacent to a concentration of low-income and homeless communities in downtown Grand Rapids, Grand Action approached the missions and social service organizations in the area—rather than the other way around, which is more typical—to collaboratively figure out ways to avoid displacement and leverage the local investment to benefit existing residents. As a result, for example, Grand Action established a food stamp program at the market.5

While this blueprint has had less lasting influence on planning processes in its region than Sacramento’s has (Dutzik and Imus 2002), one tangible outcome was the creation of an urban utility boundary around Grand Rapids, essentially drawing a line beyond which sprawl may not proceed. This has helped promote population growth in the central city, in contrast to the trend of other large Michigan cities in the 1990s. Additionally, in the early 2000s, the Grand Valley Metropolitan Council reportedly adopted a “fix it first” policy of spending on maintenance of existing transportation infrastructure before spending on any new road building, a strategy that tends to reduce sprawl and encourage denser development.6 A similar statewide policy was adopted in 2003, but only after community organizers and advocates, primarily in the Detroit area, organized for years to get it passed (Pastor, Benner, and Matsuoka 2009, ch. 3).

Although Charlotte was once known as a sleepy, second-tier city, today the Charlotte region epitomizes many of the qualities of the twenty-first-century Southern metro. It is anchored by a central city that has a reputation as a growing, economically and culturally vibrant hub, and its urban center, replete with a soaring and shiny skyline, well-used light rail system, and art museums, is the built representation of this retooled identity.7 During the 1980s and 1990s, strong economic growth coupled with equity improvements underpinned Charlotte’s positive transformation. Charlotte outpaced its urban counterparts in the South on many metrics and became somewhat of a “best practice” city, frequently visited by business and economic development professionals looking for ways to reinvigorate their own towns.8

Although Charlotte was once known as a sleepy, second-tier city, today the Charlotte region epitomizes many of the qualities of the twenty-first-century Southern metro. It is anchored by a central city that has a reputation as a growing, economically and culturally vibrant hub, and its urban center, replete with a soaring and shiny skyline, well-used light rail system, and art museums, is the built representation of this retooled identity.7 During the 1980s and 1990s, strong economic growth coupled with equity improvements underpinned Charlotte’s positive transformation. Charlotte outpaced its urban counterparts in the South on many metrics and became somewhat of a “best practice” city, frequently visited by business and economic development professionals looking for ways to reinvigorate their own towns.8

So, what happened? How did Charlotte—a “region that works”9—stop working quite so well? Some of the factors are structural. For example, many of the policies that tied together city and suburb fates—such as nearly automatic annexation of developing suburbs and relatively peaceful integration of schools through bussing—have ceased to exist. But, as we review in more detail in the next section, the nature of regional leadership is also important. Much of Charlotte’s development in the 1980s and 1990s was propelled by a remarkably coordinated group of corporate leaders who worked not just for the benefit of their own companies but also to stitch the region together, promote downtown development, and avoid the patterns of city–suburb division that have characterized so many other metropolitan areas.

The Greensboro region is located in the heart of North Carolina’s Piedmont Triad, and is known most prominently for its manufacturing legacy and its civil rights struggles.1 Although the formerly booming textile and furniture industries and the lunch-counter sit-in movement are still a source of pride for many in the region, deindustrialization and a history of social distrust and disconnection have contributed to poor performance on both growth and equity.

Despite Fresno being the country’s most fruitful region—literally, it is the most agriculturally productive county in the United States—it has become nationally known for its high levels of poverty and unemployment.10 Perhaps the most striking evidence of that came when the Brookings Institution issued a post–Hurricane Katrina study trying to explain the seemingly disparate impacts by race of the storm and its aftermath. In a comparison of concentrated poverty—or the proportion of all poor people in a city who live in extreme-poverty neighborhoods—in the largest fifty cities in the United States, New Orleans was naturally quite high on the list, hitting number two. In first place: Fresno (Berube and Katz 2005, 10).

Though agricultural interests are rarely directly represented in the city of Fresno’s politics, they fundamentally shape social and political dynamics in the region. Agriculture brings in large amounts of revenue, and directly provides 11 percent of the county’s jobs—yet the industry predominantly provides only seasonal employment with sub-poverty wages. In 2008, for example, the western valley’s 20th Congressional District had the distinction of being the poorest district in the country (Carter 2009, 7).11 Though labor laws exist to protect the workers, many sources claim that these rules are largely ignored. Farm workers receive sub-minimum wages and experience dangerous working conditions in which they are expected to work long days in some of the country’s hottest temperatures. Moreover, it is estimated that about half the agricultural workforce in California’s Central Valley—of which the San Joaquin Valley is a part—are undocumented, and thus are subject to harsh levels of exploitation with no protections (National Public Radio 2002; Pastor and Marcelli 2013).

One interviewee described the situation as a “tale of two cities,” in which the city is so segregated that rich Fresnans do not even see the poor ones, despite the region’s extremely high poverty rate (George 2013). And it’s not just the city but the region. Fresno’s northeastern suburb of Clovis is majority-white, with a median household income of $63,983, while Huron City, in the heart of the agricultural lands to the southwest of town, is 98.5 percent Latino, with a median household income of only $21,041 and a 46-percent poverty rate.12 The divide between rich and poor, white and non-white, north and south, exacerbates the downward spiral of economic polarization and stagnation the region has been experiencing for decades. The farther apart people grow, the less likely they are to see the value of investing in one another’s communities—and the more likely it is that conflict will not help produce new understandings, only new tensions.

Ultimately, the strike failed, but its legacy lingered. The region has continued to experience significant organizing around immigrants’ rights and other critical issues, including through a range of faith-based initiatives affiliated with PICO California.13 In recent decades, however, the environment has become an area of significant concern and organizing. Many factors contribute to the environmental problems: agriculture-linked industrial processes, automobiles traversing the valley between Northern and Southern California, heavy-duty diesel-fueled trucks transporting agricultural products out of the region, pesticides entering the air after use, and emissions from oil and gas fields. The resulting toxic soup has substantial health and welfare impacts (Alexeeff et al. 2012; Huang and London 2012; London, Huang, and Zagofsky 2011; Sadd et al. 2011). Indeed, in a statewide analysis of environmental burden and vulnerabilities conducted in 2013 by the California Office of Environmental Health Hazard Assessment, three of the state’s five worst zip codes were in Fresno.

If you talk to civic leaders in San Antonio today, they proudly boast of an increasingly multifaceted economy that has been able to move beyond reliance on military spending and now boasts of vibrant tourism, medical, energy, manufacturing, and professional-services sectors.14 They attribute that success—evident in jobs, earnings, and relative improvement in median household income and poverty—to a spirit of collaboration among government, business, universities, and community groups that has become part of the regional DNA (Benner and Pastor 2014).

What has been the record? From 1980 to 2010, jobs in the San Antonio region increased by 112 percent, and average earnings increased by 21 percent in inflation-adjusted dollars, both outperforming the averages for the top 192 metros. Part of the performance has been good fortune—or, shall we say, good energy. San Antonio is the headquarters of Tesoro and Valero, both Fortune 500 oil companies, and a range of oil-related firms have grown in recent years due to the fracking-related boom (though total oil output in Texas remains below its early 1970s peak). But San Antonio has seen employment growth across a range of other industries too, including bioscience, health care, financial services, call centers, tourism, and automobile-related manufacturing. Despite a decline in military-base employment with the closing of two of four Air Force bases in the region, military spending is still important for the region’s substantial IT/cybersecurity and aerospace clusters, also significant contributors to economic dynamism (Hernandez 2011; Thomas 2013).15

Project QUEST was designed to upgrade and reskill disadvantaged workers for good jobs in high-demand occupations. It does so by targeting a cluster of in-demand, well-paying, and growing occupations, and works with the community college system to develop degree and certificate programs suited to these occupations. Unlike many workforce development programs, Project QUEST requires that participants demonstrate economic need, defined as earning less than 50 percent of the area’s median household income. The organization links low-income individuals to training, but also links employers to its graduates. During the past twenty-one years, more than 80 percent of its entrants have graduated from the program, and 86 percent of those who graduated were placed into higher-paying occupations (Rodriguez 2013). Graduates enter the program with annual earnings hovering around $10,000, and leave earning on average $39,300 per year. In 2012, graduates earned an average hourly wage of $19.65.16

The region is also committed to crafting a shared vision of future regional prosperity. SA2020 is a regional visioning exercise and plan created with strong public participation (not unlike Envision Utah).17 The visioning process was led by the Jacksonville Community Council—a nonprofit group that works on developing vision and indicators documents for several regions, including Jacksonville, Florida, and Nashville, Tennessee. Both of these regions were profiled as strong performers on growth and equity in Benner and Pastor (2012), and the Jacksonville Community Council was praised for its role in creating knowledge networks across leadership silos. The city spearheaded the initiative, but foundations, businesses, and nonprofits have also adopted the principles. Out of this long public process, the city decided to focus on several areas—education, employment, environment, and health—and the city’s planning department is orienting its redevelopment plans around some of SA2020’s key goals.

Greensboro’s history is strongly rooted in its role as one of the largest textile manufacturing centers in the country. By the early 1830s, seventy-five mills were in operation and cotton material was being exported to neighboring counties and states.2 Shortly after, furniture manufacturing would take root in neighboring High Point and the areas westward.3 Both remained central to the local economy and employment for over a century, until the late 1990s, when the US economy shed much of its textile and apparel employment following the implementation of NAFTA (Scott 2003). According to many of our interviewees, the region has been slow to envision a broad, post-industrial future. Weak inter-regional collaboration, competition between cities, and changing leadership and organizational structures have hindered the process.

Greensboro’s history is strongly rooted in its role as one of the largest textile manufacturing centers in the country. By the early 1830s, seventy-five mills were in operation and cotton material was being exported to neighboring counties and states.2 Shortly after, furniture manufacturing would take root in neighboring High Point and the areas westward.3 Both remained central to the local economy and employment for over a century, until the late 1990s, when the US economy shed much of its textile and apparel employment following the implementation of NAFTA (Scott 2003). According to many of our interviewees, the region has been slow to envision a broad, post-industrial future. Weak inter-regional collaboration, competition between cities, and changing leadership and organizational structures have hindered the process.

The ups and downs—and ups—of companies like Cone Denim, the country’s oldest operating denim mill, shed light on the regional shifts in textile manufacturing and its role in the region’s economic consciousness.4 Founded in 1891, the company produces and supplies denim fabric to jeans manufacturers across the United States. In the 1970s, Cone was a regional economic staple, employing 2,800 loom operators, seamstresses, and patternmakers. In the ensuing decades, the company fell into decline, eventually filing for bankruptcy in 2003, as loom technology changed and production shifted to lower-wage countries. Demand has surged, however, for expensive denim, in particular for old-school, weathered-look fabrics, rejuvenating Cone’s potential market. In 2004, the company was purchased and revived by billionaire Wilbur Ross, known for his expertise in leveraged buyouts and restructuring failed companies. Today, Cone operates in a scaled-back, high-end market, producing fabric for high-end jeans using old Draper looms, but employing only 300 workers locally at its White Oak factory, a fraction of its former workforce (Burritt 2012).

Interviewees struggled to think of many examples of elite collaboration, beyond a limited number of business and governmental partnerships. Perhaps the most prominent example is the Greensboro Partnership, a multipronged entity providing business, economic, and community development in Greensboro through its member organizations: the Greensboro Economic Development Alliance (GEDA), Action Greensboro, Entrepreneur Connection, and the Greensboro Chamber of Commerce. Formed in 2005—relatively recently compared with similar groups in other regions—the Greensboro Partnership works on quality of life (“livability issues”) and economic development, with a focus on downtown redevelopment. Spearheaded by local philanthropy and the public sector, the partnership has helped align the activities and plans of the GEDA and the Chamber of Commerce—and often works in tandem with the local workforce investment board. The GEDA has recently released an economic development strategy in the form of cluster analysis, which is focused on high-growth, well-paying sectors, such as aviation, the supply chain/logistics industry, the life sciences, and innovative manufacturing.5

The partnership has helped elevate the issues of education and training. This is critical since there are several hurdles in the way of realizing a more vibrant regional economy, including skills gaps in older workforces, low retention of recent college graduates, low rates of high school graduation, and poor preparation of children to succeed in school. Action Greensboro, the community development arm of the partnership, has played a role in education policy and program development, especially in the development of Achieve Guilford, a K-12 education advocacy collaborative. The group has come together to create a common educational agenda, which stresses a “cradle to career” approach that lifts up key programs and milestones needed at each educational level.6 Action Greensboro is also involved in Opportunity Greensboro (http://opportunitygreensboro.com), a higher-education initiative seeking to deepen the connection between businesses and local colleges and universities and leverage the skills, resources, and talents of their 47,000 students to attract and grow industry.

Greensboro is well known as a central site in the civil rights struggle. In 1960, four North Carolina A&T students asked for coffee at the Woolworth’s whites-only lunch counter and gave birth to the sit-in movement in America. While these actions did result in the desegregation of department-store eateries, by 1968, civil rights organizers in Greensboro were more concerned about issues of job and educational discrimination, political underrepresentation, and poor housing, with the Black Power movement gaining adherents as issues continued to simmer. The Greensboro Association of Poor People was founded in 1968. Relying more on direct action and confrontation, it would become one of the largest sources of community activism in the city through the mid-1970s.7 During this time, some attempts at interracial cooperation and discussion in Greensboro were successful, including peaceful integration of the school system in 1971, the Chamber of Commerce’s Community Unity Division sponsoring weekly discussion meetings on racial conciliation between 1966 and 1976, and the city’s decision in the late 1970s to no longer pursue urban renewal because of its disparate impact on African Americans.8

Greensboro is well known as a central site in the civil rights struggle. In 1960, four North Carolina A&T students asked for coffee at the Woolworth’s whites-only lunch counter and gave birth to the sit-in movement in America. While these actions did result in the desegregation of department-store eateries, by 1968, civil rights organizers in Greensboro were more concerned about issues of job and educational discrimination, political underrepresentation, and poor housing, with the Black Power movement gaining adherents as issues continued to simmer. The Greensboro Association of Poor People was founded in 1968. Relying more on direct action and confrontation, it would become one of the largest sources of community activism in the city through the mid-1970s.7 During this time, some attempts at interracial cooperation and discussion in Greensboro were successful, including peaceful integration of the school system in 1971, the Chamber of Commerce’s Community Unity Division sponsoring weekly discussion meetings on racial conciliation between 1966 and 1976, and the city’s decision in the late 1970s to no longer pursue urban renewal because of its disparate impact on African Americans.8

As integration was taking root, however, the Greensboro Massacre shook the community to its core—and has remained an open wound. The massacre happened at a march against the Ku Klux Klan that was held on November 3, 1979. While the march was organized by the Communist Workers Party (CWP), long-time civil rights activist, founder of the Greensboro Association of Poor People, and CWP member Nelson Johnson was one of the key organizers, as the march was part of broader efforts to link together issues of race and poverty. During the demonstration, Ku Klux Klan and Nazi Party members, who had organized a counter-demonstration, opened fire and killed five protestors. The role of the police in this episode was controversial, since they were known to have anti-CWP sentiment and had only a light presence at the beginning of the march, despite knowing about the potential for violence to erupt. Though 14 KKK members were arrested for murder, a jury trial returned a not-guilty verdict in all cases (Magarrell and Wesley 2010; Waller 2002).9

Silicon Valley is well known as the global center of innovation in high-technology industries.1 The region has managed to maintain its innovative leadership through multiple rounds of economic restructuring; it has been consistently able to develop new technological innovations even as yesterday’s innovative technologies become more commoditized and globalized, and so migrate to other high-tech regions and lower-cost production centers. From the heart of the semiconductor and related integrated-circuit industry in the 1960s and 1970s, through the explosion of personal computers in the 1980s, through the software and Internet boom of the 1990s, and now into social media, the region has remained at the cutting edge of new technological innovations and related economic growth.

We end our tour of America’s high-tech regions in Seattle, the home of Microsoft, Amazon, and, as it turns out, an historical pattern of relatively inclusive growth.10 Between 1980 and 2010, Seattle’s earnings per job increased at a higher rate (29 percent) than the West as a whole (20 percent), and its income equality was substantially better than the rest of the West (though still getting worse—the 80/20 income ratio grew 5 percent between 1980 and 2010, compared to an 11-percent average for metro regions in the West). And this has happened even as Seattle has increased its global connections. Though it is only the fifteenth-largest metropolitan area in the country by population, Seattle has the sixth-highest export total, sending more than $47 billion in goods and services abroad in 2012 (Katz 2014).

How has Seattle been able to both grow faster and promote greater social equity? One obvious factor is simply the influence of a few major companies: Boeing, Microsoft, and Amazon. Boeing, the region’s largest employer, has 85,000 employees in Washington, mostly in the Seattle region.11 It has created a deeply rooted aerospace manufacturing presence that continues to provide middle-class opportunities for people without higher education—the types of jobs that have declined in many other regions of the country. Microsoft, the region’s second-largest private employer, has helped catalyze Seattle as a high-tech hub, providing over 40,000 jobs statewide.12 Similarly, Amazon has grown rapidly in the region since its founding in 1994.

How has Seattle been able to both grow faster and promote greater social equity? One obvious factor is simply the influence of a few major companies: Boeing, Microsoft, and Amazon. Boeing, the region’s largest employer, has 85,000 employees in Washington, mostly in the Seattle region.11 It has created a deeply rooted aerospace manufacturing presence that continues to provide middle-class opportunities for people without higher education—the types of jobs that have declined in many other regions of the country. Microsoft, the region’s second-largest private employer, has helped catalyze Seattle as a high-tech hub, providing over 40,000 jobs statewide.12 Similarly, Amazon has grown rapidly in the region since its founding in 1994.

During this time, movement builders from these various struggles—ranging from the Black Panthers to the Blackfeet Indians of Montana to the Asian Coalition for Equity to the United Farm Workers—would hold meetings at St. Peter Claver Church, which allowed them to use the space cost-free (Santos 2005). As the church became a hub of civil rights activists across multiple movements, leaders from different constituencies realized that despite the specifics of their individual communities’ plights, their struggle for social justice and equity was the same.13 Four leaders in particular formed a uniquely close bond, helping unite their communities across difference. These leaders became known as the Four Amigos and included Larry Gossett, a Black student activist; Roberto Maestas, a Latino leader involved in the farmworker movement; Bob Santos, a Filipino leader in the anti-displacement movement; and Bernie Whitebear, a Native American leader in the indigenous rights movement (Santos 2005).

With the understanding that they were much stronger together than they ever were apart, showing up to each other’s fights became second nature. For instance, in 1970, the federal government decided to reduce the size of the Fort Lawton army post in northwest Seattle, freeing up land that had once belonged to American Indians. Severely lacking services, the American Indian community—led by Bernie Whitebear—requested that the city dedicate a portion of the land to an Indian Cultural Center as part of the original treaty the government had used to take the land in the first place. When discussions with the city failed, Bernie Whitebear led an organized group of community members called the American Indian Fort Lawton Occupation Forces in a three-month occupation of Fort Lawton, which eventually led to a negotiation with the city—mediated by the federal government—to grant the Indians a 99-year lease on twenty acres (what eventually became Discovery Park). The city also gave $600,000 to the American Indian Women’s Service League to help build a social services center (Whitebear 1994). However, there was a secret ingredient in this victory: allies outside of the American Indian community. Before the occupation, Whitebear called on his friends—the rest of the Four Amigos—to rally their communities and participate in the occupation. So, alongside the American Indians were Black students and Filipino and Chicano leaders—and even some white progressives.14

This sense of inclusion extends to regional planning efforts, too. While most of the growth in the 1980s and 1990s was in the suburbs, in the 2000s, growth in the urban cores and in the suburbs was about equal: 12 percent and 14 percent, respectively. Now, low-income communities of color in more urban areas of the Seattle region face the threat of gentrification. Partly in response, local and regional government agencies, including the Puget Sound Regional Council, have collaborated to develop a Growing Transit Communities initiative, in concert with the region’s voter-approved $25 billion transit build-out, in an effort to locate housing, jobs, and services close to transit, with a focus on ensuring affordability.15 For instance, the city of Seattle is granting transit-oriented development acquisition loans that help developers purchase vacant land near light rail stations to build mixed-use projects that include affordable housing and commercial space for small businesses and community facilities (City of Seattle 2014b). Indeed, the city of Seattle has required the development of affordable housing for decades. Starting in 1981, Seattleites voted to tax themselves to fund affordable housing for low-income workers, seniors, and homeless people; through this levy, the city has funded over 10,000 affordable units (City of Seattle 2014c).

Despite this fascinating history of incorporating equity into governance and planning, we also heard concerns that regional discussions can be too shallow—able to bring stakeholders together to find common interests, but less effective in dealing with bigger substantive differences in interests or perspectives. As the region becomes less white and more racially diverse—and as that population shifts southward through the region, particularly because of high housing costs in the central city—the Seattle process will need to adapt, and broaden the leadership at the table. In addition to concerns about housing costs, transportation options, and employment opportunities, a major concern of regional leaders moving forward is the level of preparedness—or lack thereof—among Seattle-born youth to participate in the region’s booming STEM sectors.16 All that said, Seattle offers a remarkable example of a region at the leading edge of America’s knowledge economy that has also built a set of knowledge communities where listening to others is valued, collaboration is second nature, and equity is at the very least an actively voiced concern.

Why didn’t the economic growth of the Internet boom in the late 1990s translate into more broadly shared opportunity? Why wasn’t the region better able to respond in a positive way to the economic challenges following the dot-com collapse? After all, Silicon Valley was the home to Joint Venture Silicon Valley, founded in 1993 specifically to “provide analysis and action on issues affecting [the] region’s economy and quality of life.”2

Business leadership in the region over the past three decades has been somewhat fragmented as well, further weakening the region’s ability to respond to these challenges. The Silicon Valley Leadership Group (SVLG), formerly known as the Silicon Valley Manufacturing Group (which suggests something that many observers forget—there was once manufacturing in the Valley that helped develop and sustain a middle class), represents the largest employers in the region, with a strong emphasis on high-tech industries.3 The organization was founded in 1977, when David Packard (of Hewlett-Packard fame) brought together a number of fellow CEOs with the premise that local employers should be actively working with government to find solutions to regional issues, like transportation, housing, permit streamlining, education, and the environment.4 As of 2013, the organization had over 375 member companies, which purportedly account for one out of three private-sector jobs in the region.

Business leadership in the region over the past three decades has been somewhat fragmented as well, further weakening the region’s ability to respond to these challenges. The Silicon Valley Leadership Group (SVLG), formerly known as the Silicon Valley Manufacturing Group (which suggests something that many observers forget—there was once manufacturing in the Valley that helped develop and sustain a middle class), represents the largest employers in the region, with a strong emphasis on high-tech industries.3 The organization was founded in 1977, when David Packard (of Hewlett-Packard fame) brought together a number of fellow CEOs with the premise that local employers should be actively working with government to find solutions to regional issues, like transportation, housing, permit streamlining, education, and the environment.4 As of 2013, the organization had over 375 member companies, which purportedly account for one out of three private-sector jobs in the region.

The housing policy of the SVLG seems to have narrowed in its orientation as well. The work is avowedly motivated by the barrier high housing costs create to “recruiting and retaining top talent to Silicon Valley”5 rather than a concern for the extremely high housing-cost burden facing existing low- and moderate-income residents of the Valley. Meanwhile, those same high housing costs have contributed to levels of homelessness that are truly shocking in such a wealthy region, including a long-standing 68-acre homeless camp, dubbed the Jungle, which was widely regarded as the largest homeless camp in the country until it was cleared out in early December 2014 (Campbell and Flores 2014; Grady 2014; Nieves 2000).

While the Silicon Valley Leadership Group has become the most prominent voice for large business in the region, the San Jose Silicon Valley Chamber of Commerce remains the largest and oldest business chamber in the region. Founded in 1886, the chamber has over 1,500 members and includes many more of the region’s small, medium, and family-owned businesses. Like many other chambers, it has been largely reactive and traditional in its policy stances, partly reflecting the small and local businesses that are the largest source of its membership. For example, it strongly opposed both San Jose’s 1998 Living Wage Ordinance and a 2012 city-wide minimum-wage proposal, while the SVLG took a neutral stance on both campaigns. A controversial public-sector pension reform initiative on the 2012 ballot, seen by public-sector unions as a direct attack on their very existence, was also pushed by the chamber while the SVLG remained on the sidelines.6

Ultimately what has emerged is a region that our key informants almost universally described as fragmented and divided, with the high-tech community largely isolated from the broader region and particularly those parts of the region that are less fortunate. The “tale of two valleys,” which had been a minor (though important) theme in media coverage and academic accounts of the Valley’s development in the 1970s and 1980s, started becoming more prevalent in the 1990s and by 2013 had reached major national prominence. The Valley was highlighted in a Bill Moyers special called “The United States of Inequality,”7 as well as in a devastating portrayal in the New Yorker of the high-tech industry’s myopia with regard to social problems (Packer 2013).

It is that sort of interaction with the “other” that can lead to trust, collaboration, and concern about equity and fairness. This is of course difficult in the highly atomized world of Silicon Valley, and appearances—in this case, of an easy acceptance of talented engineers from around the world—can be deceiving. Indeed, a key survey of social capital conducted in 2000 in communities across the United States found that people in Silicon Valley were more likely than in comparable regions to have friendships that crossed racial lines, but less likely than elsewhere to have friendships that crossed lines of income and class.8

Raleigh-Durham provides an important comparison with Silicon Valley because it is a place where technology-driven growth and social equity have gone together in a more sustained way (though with some slippage in recent years) and where knowledge networks in and about the region seem to be more diverse and more intentional.9 At the same time, Raleigh-Durham is home to a vibrant high-tech industry, a trio of world-class universities, and a renowned public school system, luring businesses and residents from across the country to relocate there. Indeed, over the last thirty years, Raleigh-Durham has easily outperformed its counterparts in the American South.

There are certainly cases where social-movement activities have not led to the development of diverse epistemic communities. In Detroit, for example, equity advocates in the union movement, which has been dominated by the United Auto Workers, tended to focus more on holding on to whatever wage and benefit premiums they could derive from the auto industry and less on broader debates about regional development patterns. African American political activists—both those engaged in formal electoral processes and those in community organizing—have tended to focus on dynamics in the city of Detroit itself, rather than challenge white flight or organize for regional tax-base sharing, for example (Pastor and Benner 2008).1 For social-movement activism to stimulate the development of diverse epistemic communities probably also requires some level of governance opportunity or champion to help translate between the worldviews of activists and elite leadership, a role Henry Cisneros played in San Antonio with great impact.

Sustainable Communities Regional Planning Grants support metropolitan and multijurisdictional planning efforts that integrate housing, land use, economic and workforce development, transportation, and infrastructure investments. The Regional Planning Grant Program places a priority on investing in partnerships that direct long-term regional development and reinvestment, demonstrate a commitment to addressing issues of regional significance, utilize data to set and monitor progress toward performance goals, and engage stakeholders and citizens in meaningful decision-making roles.1

As of early 2015, Sustainable Communities Regional Planning grants had been awarded to 74 regional grantees in 44 states, including some of the regions mentioned in the case studies.2 In 2010—as noted in chapter 4—Salt Lake County was awarded $5 million to continue Envision Utah’s work around regional transportation and affordable housing planning; Salt Lake was one of only two regions awarded the maximum grant that year. And as mentioned in chapter 7, the Puget Sound Regional Council was awarded nearly $5 million to support its Growing Transit Communities project, which built a partnership of cities, counties, and public and nonprofit partners with a vision to connect jobs to where people live. The Sustainable Communities Initiative has included the creation of new civic conversations in metropolitan regions, annual conferences with representatives from multiple regions, and a slew of technical-assistance efforts that aim to lift up broad issues of sustainability and equity as well as economic development. This is exactly the sort of community-building we see in our cases, and it is heartening to see federal incentives for replication.

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Additional Information

ISBN
9780520960046
Related ISBN
9780520284418
MARC Record
OCLC
1111944452
Launched on MUSE
2019-08-15
Language
English
Open Access
Yes
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