Cleveland and Ohio’s progress can be intimated through a variety of topics. Some topics—like “globalization,” “innovation,”, “deindustrialization”, and “pandemic”—seem big and distant, while others are less abstract and more local, such as “jobs,” “income,” “housing,” “policing”, “education,” and “health”. Then there are some topics that are viscerally personal, if only because the direct impact they have on the mind and body. These include “foreclosure,” “lead,” “infant mortality,” “opioids,” “police brutality”, and “pneumonia”.
While these issues are of topical concern in their own right, their vastness in scope can be disorienting to those charged with guiding progress. No doubt, a multitude of efforts exist to gather data and distill information on Ohio, be they academic, non-profit, or journalistic. In turn, initiatives are kicked off to spur solutions in receipt of said information. But these initiatives often struggle to find footing. This is partly because we lack a theory of change that ties various threads of information into a body of knowledge, and ultimately a collective vision.
Put another way, everything is connected: the global, the local, and the individual. The COVID-19 pandemic is a hard-charging testament to that fact, as is the murder of George Floyd in which an assault on a Minneapolis street rippled into marches on the world’s streets. And the quicker we can elucidate those connections, the faster that collective efforts can move beyond intention and action and into individual impact.
This is no small task. Problems, after all, are structural, while solutions are local. Care is thus needed in the analysis of each, if only because many of our problems are not in our control, yet the “fix” to those problems must be.
The following is a policy white paper called “The Future of Growth”, created through a partnership between Cleveland State’s Center on Urban Theory and Analytics and Rust Belt Analytica. The goal is to simplify the complex of how and why the global economy changes, examining how those changes impact regional economies, neighborhood conditions, and ultimately individual well-being. Along the way, assumptions are challenged. For example, is population and total job growth an accurate way to measure progress? Or do measures of productivity and longevity offer a better strategic approach? The former presumes that it’s the quantity of lives that matters, whereas the latter intimates it’s about the quality of life.
With this and other data in hand, the intent is to scaffold the information it into a collective awareness of where Cleveland and Ohio was, where it is, and where we need to be. Importantly, the result of this effort is intended to go beyond an ability to make better-informed decisions via the stacking of facts. Progress is less linear than that. It is equally about busting out of old paradigms of thought. As the theoretical physicist David Bohm put it: “The ability to perceive or think differently is more important than the knowledge gained.” If current events teach us anything, it’s that—thinking differently when the choice to think differently was made for us.
The “front door” to any economic inquiry is through the lens of Gross Domestic Product (GDP). The Bureau of Economic Analysis (BEA) defines GDP as “a comprehensive measure of U.S. economic activity” that calculates “the value of the goods and services produced” in a given time at a specific place1. That said, GDP is an imperfect measure, as are all standalone measures. Why that is will be unpacked. Consider this initial section a level-setting. One that’s necessary, if rudimentary.
Nationally, Ohio’s economic output ranks 7th. While the state is no California, Texas, or New York, having a top 10 economy in the world’s hegemonic power is significant at face value, and it represents as an opportunity geography for the majority of the global population.
Drilling down only enriches this perspective, especially regarding the economic impact of Ohio’s big cities. The metropolitan statistical areas (MSAs) of Cincinnati ($124.95 billion), Cleveland ($119.33 billion), and Columbus ($114.68 billion) rank as the 28th, 33rd, and 35th largest in the nation respectively (out of 384 MSAs). What’s more, the GDP for the MSAs of Cincinnati, Cleveland, and Columbus as a group equals $358.95 billion, making up 59.3% of the state’s total economic output.
A last slice of the data further disaggregates where Ohio’s economic output is sourced. The BEA just released new figures showing total real GDP at the county level. They were the first of their kind. Cuyahoga County’s total real GDP of $87.7 billion ranked as the 31st largest economy in the nation. That puts Cuyahoga in the top one percentile (See Figure 2).
In Ohio, Cuyahoga makes up nearly 15% of Ohio’s GDP. Along with Columbus’ Franklin and Cincinnati’s Hamilton counties, the three big-city counties represent over 40% of the state’s total output (See Figure 3).
The question now to turns to: “So what?” Will economic growth as a concept of magnitudes stand the test of time? It’s a big question. One that will echo throughout.
Figure 3: Percent of Real GDP for State of Ohio by County. Source: BEA, 2018.
What makes a place successful? The answer depends on the definition of “success”. One benchmark, total GDP, was just examined. GDP, in turn, is a function of two factors: a growing labor force and a more productive labor force. The former implies quantity, the latter quality. When we talk quantity in this respect one thing inevitably comes to mind: population growth. The logic isn’t faulty. Figure 4 shows there is a positive correlation between a metro’s population growth and total GDP. This makes sense: grow people, grow consumption, grow growth.
Figure 4: Annualized Population Growth Rate v. Annualized Total GDP for all MSAs. Source: BEA, 2001 and 2018.
For lay people and policymakers alike, population growth has become the default metric of success. If a place is growing it’s succeeding, if a place is shrinking it’s not. This belief is undergirded by a bigger-is-better bias that guides so much of human decision-making1. “[P]eople in the United States tend to have an implicit association in memory that bigger is better,” note the authors of the study “When Bigger Is Better (and When It Is Not): Implicit Bias in Numeric Judgments”. This leads people to associate higher numbers with higher quality even in situations where it should not. The writer Elias Canetti referred to this as the “modern frenzy of the increase2.”
A query of big city paper’s after the annual Census tally is illustrative. A bigger-is-better bias bleeds through the finger-wagging headlines that follow:
Meanwhile, a sense of assuredness comes through in the headlines of fastest-growing places:
But growth does not equal development. A place can add a quantity of people, yet still lose out on quality of life; and vice versa: a place can lose a quantity of people yet gain on quality of life. It’s important, then, to disaggregate from broad-brushed measures of success, such as population growth and total GDP. In his Quartz piece “Stop obsessing about GDP growth—GDP per capita is far more important”, economist reporter Dan Kopf discusses how total GDP growth is a misleading measure11. “A country’s aggregate economic growth is not what matters,” he writes. “What matters is whether the people living in a country are getting wealthier”. A better, albeit imperfect, measure is real GDP per capita, calculated as total economic output divided by total population. It’s the most commonly accepted measure of a place’s standard of living12.
A simple trend analysis demonstrates this point. Regions in the South and Southwest lead the way in terms of a growing population. The top seven fastest-growing big-city metros from 2001 to 2018 are in the Sun Belt: Austin, Las Vegas, Orlando, Houston, Charlotte, Phoenix, and San Antonio. The slowest-growing? They are in the Rust Belt: Cleveland, Pittsburgh, Detroit, Providence, R.I., Chicago, St. Louis, and Milwaukee.
But in terms of real GDP per capita, a different reality comes into focus (See Figure 6). In 2001, the regions’ GDP per capitas were nearly equal. By 2007, the Sun Belt ($56,188) overtook the Rust Belt ($54,762). Since the Great Recession, though, the regions’ paths have diverged. Today, the Rust Belt’s real GDP per capita ($59,073) is nearly $4.5k more than the Sun Belt’s ($54,604).
Figure 5: Map of the Rust Belt and Sun Belt.
A similar story unfolds when looking at Cleveland and Cuyahoga County. The metro’s real GDP per capita increased from $49,280 to $58,010, just above the nation’s ($56,968). At the county level, Cuyahoga County’s real GDP per capita increased from $57,518 to $71,325, ranking 67th out of the nation’s most populous 500 counties.
A last slice of the data looks at how real GDP per capita is growing across time. Figure 8 plots the annualized growth rates of GDP per capita and total population for all 384 metros since the Great Recession. There is no correlation between population growth and productivity increases (R2 = .138). For instance, the metro of Myrtle Beach, S.C.—which ranked near the top in total population growth—had a declining real GDP per capita since 2010 (from $32.4K to $30.9K). Such are the “growth-without-prosperity” cities (lower-right quadrant). Cleveland, however, is in the upper-left quadrant, characterized by metros that lag in population growth but lead in productivity growth. Cleveland’s population growth rate of -0.11% is far below the average of all metros (0.83%), but its real per capita GDP growth rate of 1.47% is higher (1.38%). Other big-city metros in this category include Pittsburgh, Chicago, Detroit, and Los Angeles.
Figure 8: Annualized Population and Real GDP Per Capita Growth for all MSAs, 2010-2018. Source: BEA
County-level trends paint a similar picture (See Figure 9). Cuyahoga County’s annualized population growth rate since 2010 was -0.3%. That’s below the national rate of 0.7%. But its real GDP per capita growth (1.92%) was higher than the national rate (1.54%).
Figure 9: Annualized Population and Real GDP Per Capita Growth for Big-City Counties, 2010-2018. Source: BEA
The literature on population versus productivity growth—or quality versus quantity—isn’t new. In a 2002 Brookings paper, economist Paul Gottlieb analyzed whether it was possible for regions to “grow without growth”13. His investigation answered in the affirmative. A decade later urbanist Richard Florida updated a version of the analysis for the Atlantic14. His takeaway?“A rising population can create a false illusion of prosperity, as it did in so many Sunbelt metros, which built their house-of-cards economies around housing construction and real estate development…The south and the west may be winning the demographic race, but America’s economic winners are the places that have improved their productivity—something which doesn’t turn on the sheer numbers of workers they have on tap, but rather on how skilled and innovative they are.”
Knowledge drives economic growth. It does so two ways: by being applied to existing processes so there’s efficiencies in the making of goods and delivery of services (think robots and car making); and by fueling innovation, leading to next-order processes (think artificial intelligence and driverless cars). Successful cities have economies that are knowledge-based. The regions with the nation’s highest real GDP per capita, for instance, are all recognized as knowledge economy “hotspots” (See Table 2).
Table 2: Top 5 Real GDP Per Capita, Largest 40 MSAs. Source: BEA, 2018.
|San Jose, CA||$159.6|
|San Francisco, CA||$105.1|
|New York, NY||$79.4|
That a city can be successful or not implies their economies evolve, or else get left back. There are numerous theories describing this evolution, but one theory in particular is helpful. It’s an “evolutionary economics”15. concept called the “Four Sector Theory”16. It explains that the global economy had a “Primary” stage that was natural resource-driven (circa 1800s), leading to a “Secondary” stage that was industrially-driven (circa 1940s), followed by a “Tertiary” stage which is one of service provision. In Cleveland this meant an economy backstopped by the likes of Standard Oil in the late 1800s, to Ford in the mid-20th century, to Cleveland Clinic today.
The latest, most emergent stage, “Quaternary”, is all about the cutting-edges of technology. Think big data, computer processing, and artificial intelligence, and the resultant impact they have not only on market activities, but on human well-being. To date, so-called “big tech” firms are the purveyors of the Quaternary era, as are the academic entities that drive the R&D that advances techne’s commercial use17.
Table 3 illustrates tech’s rising dominance, showing the top firms by market capitalization over a hundred-year period. Note the evolution of the highest-valued firms and their position in the marketplace: going from natural resource (Primary) and raw materials (Secondary) in 1919, to tech’s capital accumulation via the likes of Apple, Alphabet, Microsoft, and Amazon (Quaternary).Table 3: Top firms by market capitalization, 1917 and 2017. Source: S & P 500
|U.S. Steel (Steel)||Apple (Tech)|
|American Telephone & Telegraph (Telecom)||Alphabet (Tech)|
|Standard Oil of N.J. (Oil)||Microsoft (Tech)|
|Bethlehem Steel (Steel)||Amazon (Tech)|
|Armour & Co. (Food)||Berkshire Hathaway (Conglomerate)|
Just as firms rise and fall in harmony with economic change, so do the cities where these firms exist. Where a given city rests on this evolutionary continuum can be gauged by looking at what industries drive their GDP18. The BEA segments GDP into industries using the North American Industry Classification System (NAICS)19, which can then be classified into the Primary, Secondary, Tertiary, and Quaternary sectors (See Table 4). The more an economy is driven by Tertiary and Quaternary sectors, the more they have gone through “economic restructuring”20—a term denoting an evolution from a labor- to knowledge-based activities.
Table 4: Industry NAICS Codes Classified into Sectors
|Primary||Natural resources and mining|
|Transportation and warehousing|
|Accommodation and food services|
|Other services (except government and government enterprises)|
|Administrative and support and waste management and remediation services|
|Educational services and Health care|
|Management of companies and enterprises|
|Finance, insurance, real estate, rental, and leasing|
|Arts, entertainment, and recreation|
|Professional, scientific, and technical services|
The evolution of the national economy since 2001 is shown in Figure 10. The nation’s compositional share of GDP from the Natural Resources, or Primary, sector has stayed low: from 3.6% to 4.3% (See Figure 10). The share of GDP from the Manufacturing, or Secondary, sector has declined, going from 20.7% to 17.2%. Note, though, the emergence of the Information Technology, or Quaternary, sector, accounting for 15.9% of GDP, up from 11.5% in 2001. Still, the U.S. is a predominantly service-oriented economy, comprising 62.8% of GDP. These trends are in line with the evolutionary theory espoused above.
Many places, including Cleveland, have gone through the first economic restructuring from Manufacturing to Services. Fewer places have gone through the second economic restructuring from Services to Information Technology. The latter restructuring has been dubbed “The Fourth Industrial Revolution”, described as a set of technologies “such as artificial intelligence, genome editing, augmented reality, robotics, and 3-D printing, [that] are rapidly changing the way humans create, exchange, and distribute value.”
Though the U.S. is just entering the most emergent era, there are select places, like Santa Clara County— home to Silicon Valley—and San Francisco County that are already there. Figure 11 shows the GDP composition by sector for Santa Clara. Information Technology accounts for 41.4% of GDP, surpassing the Service sector (31%). Manufacturing, due to tech-driven products like semiconductors and robotics, remains strong, up to 28% of GDP.
San Francisco County trends are not dissimilar, outside of Manufacturing (See Figure 12). The compositional share is shifting from the Service economy to Information Technology, with Services going from 68.6% of total GDP to 50.7%, whereas Information Technology growth continues apace, now up to 42.6%.
What these economic shifts in Northern California mean “on the ground” are varied in their ramifications. A recent New York Times piece “San Francisco Restaurants Can’t Afford Waiters. So They’re Putting Diners to Work” is fitting21. Rising technologist incomes has meant a white-hot real estate market, which has had a trickling down effect on how San Franciscans consume goods and get services. “Commercial rents have gone up,” notes the New York Times’ Emily Badger. “Labor costs have soared. And restaurant workers, many of them priced out by the expense of housing, have been moving away.” The economic output, then, that would otherwise come from waiters and waitresses is being partly abs