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 absorbed by the “do-it-yourself” economy, i.e., diners serving themselves. Such are the paradoxes of progress that come with advanced economic restructuring, chiefly among them: economic and residential displacement.
Trends in Ohio are less advanced. Figure 13 shows Manufacturing as a percent of total GDP declining from 27.8% to 22.6%, whereas the Service economy is responsible for 64.1%. But Ohio’s Information Technology output (9.6%) is below the national rate of 15.9%, suggesting the state as a whole is not keeping up with subsequent waves of innovation.
Cuyahoga County’s Information Technology economy is performing a little better (13.4% of total output), but the county’s entrée into the Fourth Industrial Revolution is still nascent (See Figure 14). As expected, Manufacturing as a share is down from 19.7% to 14.4%, whereas the Service sector accounts for over 72% of total GDP.
But not all services are created equal. There are Blue-collar Services, like transportation and warehousing; Lower-wage Services, like accommodations, food service, and retail; and Knowledge-based Services, like healthcare and finance. To the extent Cuyahoga County is still economically advancing entails assessing how much of its services are increasingly knowledge-oriented. In 2001, 41.9% of the county’s total real GDP was from Knowledge-based Services (See Figure 15). By 2018 that number accounted for 47.4%.
Disaggregating, over 83% of Cuyahoga County’s Knowledge-based sector can be accounted for by two industries: finance, insurance, and real estate (FIRE) and healthcare (See Figure 16). Those two industries alone accounted for nearly 40% of the county’s total real GDP.
So, can Cuyahoga County be characterized as the proverbial knowledge economy? No doubt. A comparison to a service economy that is less knowledge-based is helpful. Las Vegas’ Clark County is nearly 82% Services and only 10.1% Information Technology (See Figure 17). The county’s Lower-wage Service sector, in turn, is nearly 32% of its total GDP (See Figure 18), compared to 13.2% for Cuyahoga County. This helps explain why Cuyahoga’s GDP per capita of $71.3K is nearly 25K more than Las Vegas’ Clark County ($48.3K), and why the latter’s annualized GDP per capita growth rate (0.68%) is a third of Cuyahoga’s (1.94%) since 201022.
It wasn’t supposed to be this way. In his 2002 book “Neon Metropolis”, UNLV historian Hal Rothman posited that Las Vegas provided a model for post-industrial American cities in the era of the service economy23. Las Vegas, he argued, was proliferated with unionized service jobs that employed the working class. But that model was based exclusively on “end-of-the-food-chain” activities: eating, drinking, construction, and hospitality. And as the old axiom goes, you cannot consume if you don’t produce. In fact, over-leveraged leisure and hospitality economies that require personal contact will bear a lot of the hurt for the foreseeable future due to contagion concerns related to COVID-19.
What big-city counties, then, offer a better roadmap of where Cleveland go in terms of an economic evolution? Boston’s Middlesex County and Pittsburgh’s Allegheny County are useful parallels. They are two regions with rich industrialist histories which, in turn, have given rise to world-class hospitals and universities. It’s a lineage that has spurred technological advance via a keen development and use of computer science departments in the likes of MIT and Carnegie Mellon24. Figure 19 shows Middlesex County is further along in its economic evolution, with 33.9% of its GDP from Information Technology. Allegheny County, too, has a rising Information Technology sector, up to 19.2% of total GDP.
Boston’s and Pittsburgh’s shared history and emergence is in fact playing out in the strategic policy space, with a partnership tentatively called the “AI Triangle” in the works. In accordance with the University of Montreal, the so-called “600-mile commute” is being billed as an affordable option to Silicon Valley25.
It’s a strategy that is increasingly making sense. Just as manufacturing deconcentrated from the Northeast and Midwest due to price constraints in the mid-20th century, so too is technology increasingly decamping from Northern California. “Our concentration in San Francisco is not serving us any longer,” noted Twitter CEO Jack Dorsey recently, “and we will strive to be a far more distributed workforce, which we will use to improve our execution26.”
Post-COVID-19, Dorsey doubled down, announcing in mid-May that the stay-at-home policies enacted for emergency purposes would be extended indefinitely for the vast majority of employees27. Other tech companies, such as Square, have followed suit, with Google’s work-from-home policies mandated until at least 202128.
Such trends are all part of the “death of distance” movement wherein where one works and lives has been decoupled29, with a place’s standard of living the “juice” for talent relocation. In this emergent movement, economic and community development are no longer mutually exclusive, but self-reinforcing. A better community attracts a teleworker whose firm exists in the ether of the internet, not in the bricks and mortar of space. Yet that salary of the telecommuter has the same multiplier effect in the local economy, supporting the likes of the mechanic, the butcher, and the cashier.
With the rise of COVID-19 and the social distancing practices becoming the new norm, expect these “death of distance” trends to advance, affecting dense, costly, technologically-oriented cities the hardest. The trend of inland Millennials, for instance, flocking to the coasts—one Brooklyn borough president recently remarked that NYC’s new arrivals needed to “go back to Iowa, go back to Ohio”30—is now an outdated migratory model. What’s was “in”—urban consumer amenities meant to attract newcomers—is now “out”; and what was “out”—a city and state’s capacity to mitigate global risk via quick-witted policy and logistics—is now “in”. Ohio’s reputation re: its COVID-19 response is not simply a public health policy, but an economic development policy as well.
This analysis has so far benchmarked the size of the economy, or total GDP, as well as GDP per capita, a standard-of-living measure31. What’s been less discussed is how and why GDP is an insufficient measure, if only because the end goal of growth is so limitingly conceived.
“Our gross national product…counts air pollution and cigarette advertising and ambulances to clear our highways of carnage,” noted Robert Kennedy in a campaign speech 196832. “It counts special locks for our doors and the jails for the people who break them…Yet the gross national product does not allow for the health of our children, the quality of their education, or the joy of their play.”
The original architect of GDP, Simon Kuznets, forecasted these shortcomings when he was preparing the first national accounting system to the U.S. Congress in 1934. In his groundbreaking report, “National Income, 1929–1932”, Kuznets was simultaneously torn by a nation’s need to chart progress, i.e., “an index of productivity”33, and its ability to chart progress, even subtitling a section of his paper “Uses and Abuses of National Income Measurements”. Decades later, his concerns only grew. “Distinctions must be kept in mind between quantity and quality of growth…Goals for more growth should specify more growth of what and for what34.”
Part of the problem is that while GDP can chart the quantity of things produced—and GDP per capita can chart the average standard of living—what’s missing is a sense of how economic growth gets distributed. Equitably or not?
This partly depends on job access. Labor is the means by which goods are made and services are provisioned. But the rise of the Quaternary era has meant an increasing ability to automate tasks, from assembling to packaging to servicing to translation to diagnostics. The choice for execs, then, to increase output and reduce labor on one hand, or slow profit and bloat processes on the other, well, it often isn’t a choice. Efficiency reigns. “[E]xecutives are spending billions of dollars to transform their businesses into lean, digitized, highly automated operations,” explains Kevin Roose of the New York Times35. “They crave the fat profit margins automation can deliver, and they see A.I. as a golden ticket to savings…letting them whittle thousands of workers down to just a few dozen.”
But there are pains to said progress, particularly the disruption of labor markets. “As automation substitutes for labor across the entire economy,” explains Klaus Schwab, founder of the World Economic Forum36, “the net displacement of workers by machines might exacerbate the gap between returns to capital and returns to labor.”
What does this gap look like? A return look at Silicon Valley’s Santa Clara County is revealing. It is arguably the nation’s most advanced economy, a notion intimated by the popular moniker “becoming the next Silicon Valley”. Cities everywhere want massive GDP growth across a range of high-tech fields. What’s less alluring is that Silicon Valley’s growth is splitting from employment—a phenomena called the “great decoupling” by MIT’s Erik Brynjolfsson and Andrew McAfee37.
Figure 21 shows the GDP growth in Santa Clara’s Information Technology sector since 2001, gaining by a factor of 5. Meanwhile, employment in the sector remained relatively flat. Trends in the county’s Manufacturing sector were even more divergent: GDP growth grew by a factor of 3.5, whereas employment decreased in Santa Clara County by 70,500 (See Figure 22).
The socioeconomic result? A bifurcation of the labor market and attendant income inequality. A recent study noted that 9 out of 10 Silicon Valley workers earned less in 2017 than they did in 1997, with the only wage gains going to the top 10%38. Part of the reason is that the high-tech economy is a “winner-take-all” economy, wherein there are those who automate work and those whose work is automated—and only one of those groups gets paid well.
This is to say, then, that an evolving economy doesn’t preclude a devolving society. In fact, some of the most advanced U.S. economies are also the most unequal, according to data from the New York Fed39. The regions with the highest inequality include San Jose, CA, New York, Houston, San Francisco, and Washington D.C. This was due to flat wages for those region’s middle- and lower-income workers, coupled with rapid growth in wages for top earners.
Issues in the Rust Belt are also less than ideal, yet the extremes found in the likes of Silicon Valley are more muted. This is partly because the Rust Belt’s tech economies are less advanced; that is, while the economic growth trajectory of, say, Cleveland is flatter than Santa Clara County, so too are the “winner-take-all” effects. This doesn’t mean there aren’t trends of decoupling. Figure 23 shows that the total GDP from Cuyahoga County’s Information Technology sector increased from 2001, yet employment in that sector decreased.
There’s less of a decoupling in Cuyahoga’s Manufacturing sector (Figure 24), if only because there’s declines in both output and employment. Again, this coincides with Cuyahoga County’s evolution from goods- to knowledge-based economy. As to the extent that evolution is a net positive societally, well, it’s an open question. Yet one thing is certain: Becoming the “next Silicon Valley” is not a lofty ambition, if in fact societal well-being is central to that ambition. In light of COVID-19 and the boiling social unrest tied to the Black Lives Matter movement, how could it not?
Balancing the desires of firms with the needs of workers and their communities is hardly a new problem. “Technology is widely considered the main source of economic progress,” writes Joel Molkyr in “The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?”, “but it has also generated cultural anxiety throughout history40.” The famed economist David Ricardo put the issue simply some 200 years back, noting that “the substitution of machinery for human labour is often very injurious to the interests of the class of labourers . . . [It] may render the population redundant and deteriorate the condition of the labourer41.” More recently, philosopher Yuval Harari opined that the increasing automation of labor would mean a “rise of the useless class42”, a play off of Richard Florida’s ode to the knowledge worker dubbed the “rise of the creative class”43.
Enter the Rust Belt, a region whose label is central to the issue of economic dislocation. In his campaign for a second term, President Ronald Reagan coined the famous tagline “Morning in America”. It was a time when the Sun Belt was booming and when California was dreaming. Conditions in the Industrial Midwest, though, were brooding. In a campaign stop at a Cleveland steel mill, Reagan’s opponent, Walter Mondale, told the lunch-pail crowd that Reagan was "turning our great industrial Midwest and the industrial base of this country into a rust bowl44." The media reinterpreted Mondale’s comments as “Rust Belt”—a moniker that lives today.
The Rust Belt by and large is a term about loss: the loss of a way to upward mobility, the loss of a way of life. And as the age of industrialization gave way to the age of deindustrialization—a process denoting the deleveraging of manufacturing activities via automation and off-shoring—the pain was most acutely felt in Ohio, as well as other regions in the Great Lakes.
A few trend lines illustrate this point. In 1969, 43.4% of all private sector jobs in Ohio were in the Manufacturing sector (See Figure 25). These figures were 41.3% in Cuyahoga County and 35.1% nationally. By 2018, the percentage of jobs that were in Manufacturing was 17.5% in Ohio, 14.1% nationally, and 12.4% in Cuyahoga County.
Despite the declines, Ohio remains a top 10 state when it comes to proportion of private sector jobs that are in Manufacturing, along with four other Midwestern states (See Table 5). Note, though, the decline in share of manufacturing employment across the board, telling of the maturation of the industry.
Of course, the issue with deindustrialization is that one’s means to make a living impacts one’s standard of living, with income a mediating factor between (a) a better job and (b) a better life. Just how vital was manufacturing in the lining of pockets of Rust Belt workers? Very, as nearly 55% of all Ohioans earnings were from the Manufacturing sector in 1969. That figure was 51% for Cuyahoga County. Now, only 23.9% of Ohio’s earnings are derived from Manufacturing. In Cuyahoga County that figure dropped to 15.5% (See Figure 26).
With the fading of one epoch, however, another emerges. “As evidenced by the economic development history of mankind, evolution from an agricultural to an industrial and finally to a service economy is a natural and inevitable process for a specific country and even for the whole world,” explains one international economist, noting that the United States—though a later arrival to the Industrial Revolution compared to Europe—was “the first country to shift to a ‘service economy’ in the middle of the twentieth century”45. The reasons for this evolution are varied, yet include an outgrowth of specialized “white-collar” services selling to industrial firms—think Don Draper from Mad Men pitching to Chevrolet—as well as the emergence of a consumer class with disposable income which, in turn, demanded more services, thus inciting the need for more service occupations.
The easiest way to show this restructuring is to chart Manufacturing versus Service sector jobs across time. Between 1969 and 2000, the divergence between goods production and service provision is obvious (See Figures 27-29 below). Cuyahoga County, for instance, lost nearly 160,000 Manufacturing jobs (accounting for 60% of Ohio’s losses), yet gained nearly 216,000 Service jobs.
Focusing on Cuyahoga County, it’s important to show what types of Service jobs the county gained; that is, Blue-collar, Lower-wage, or Knowledge-based (See Figure 30). Since 2001, Cuyahoga County continued its decline in Manufacturing jobs (-41.4K), and it had a flattened Information Technology sector (-6.1K). The growth in Knowledge-based Service jobs was significant (62.4K). The county’s healthcare sector was responsible for nearly 50% of that growth. Lower-wage Service jobs declined by 10,500, but it still makes up the second largest sector of Cuyahoga County’s workforce.
Cuyahoga County has thus done well in evolving its knowledge economy. In 2001, the proportion of the private sector employment that was Knowledge-based (32%) was similar to the proportion that was Lower-wage (30.2%); meaning, there was an equal share in the likes of waitresses and cashiers as there was financiers and doctors. Those figures have since diverged, with 39.3% of Cuyahoga’s jobs in Knowledge-based Services, compared to 29.1% in Lower-wage Services (See Figure 31).
Comparatively, Knowledge-based Services make up 31.5% of national private sector employment, below the 33.5% share of Lower-wage Services. For Ohio, the proportion of Knowledge-based and Lower-wage jobs is 32.3% and 32.1%, respectively (See Figure 32 and 33).
Ohio’s evolution compares well to peer states, ranking 16th in Knowledge-based Service jobs as a percent of private employment. Figure 34 shows that the more knowledge jobs a state has the higher its real per capita income. Ohio is in the upper-right quadrant, clustering with states in the Northeast. The fast-growing jobs hubs in the Sun Belt states are mainly in the lower-left, echoing earlier findings that it’s the quality of jobs that matter, not the quantity.
Leading the charge in Ohio is Cuyahoga and Franklin Counties. Among counties with at least 1 million residents, Cuyahoga County ranks 7th in the proportion of jobs that are in Knowledge-based Services, behind Philadelphia, Brooklyn, Manhattan, the Bronx, Nassau, and Allegheny. Franklin County is 10th (See Figure 35).
Expectedly, Cuyahoga County lags in its proportion of private sector employment that is in the Information Technology sector (9.9%). Topping the list of big-city counties is Fairfax County in the D.C. metro, as well Santa Clara County and New York County. New York County is home to Manhattan, which houses the world’s largest Fin Tech scene. Leading in the Great Lakes is Oakland, Michigan, home to a growing Car Tech scene (See Figure 36).
Which brings us back to income. Deindustrialization’s impact on local income was noted. Increased earnings for Cuyahoga County’s knowledge workers, however, have helped pick up the slack. Earnings for Knowledge-based Services grew an inflation-adjusted 80% since 2001, and 45% for Information Technology. Those two sectors alone account for over half of earnings (55.3%) (See Table 6). The issue is with Lower-waged Service workers. They make up a big portion of the labor pool (29.1%), yet they get only 16.7% of private earnings.
Table 6: Cuyahoga County Employment versus Earning Share Source: BEA, 2018.
|Sector||Employment Share||Earning Share||Difference|
|Quaternary, Information Technology||9.9%||14.9%||5.0%|
Now, what do these trends mean? The takeaway? Well, we got an economic restructuring from Manufacturing to Services that began some time back, dislocating Rust Belt workers from living wages. MIT’s David Autor recently showed that much of the working class didn’t “graduate” into knowledge economy work, but instead became subsistent on lower-wage service work46. A “barbelling” of the labor market thus ensued, with knowledge workers on one end and service workers on the other. The father of term “knowledge economy”, Peter Drucker, envisioned such a scenario. “Knowledge workers and service workers are not ‘classes’ in the traditional sense,” Peter Drucker wrote in 199247. “But there is a danger that … society will become a class society unless service workers attain both income and dignity.”
Pre-COVID-19, the scholarly argument for service worker wage stagnation was that pay was commiserate with returns to skill. The wage premiums are for those in the techne class, or for the arbiters of knowledge services, as they provide the value-add in the current economic era. That’s true48. But only partly, as that valuation came to coincide with a devaluation of manual, last-mile work as an unglamorous, rudimentary endeavor49. Yet COVID-19 exposed that devaluation as self-serving. In fact, as knowledge workers work from home in the safety of physical separateness, they can only do so if the necessities brought to their doorstep—sanitation, food, utilities—are in fact brought. If not, the knowledge stops.
This reality is being realized. In his daily letters to his staff dated April 8th, 2020, Craig R. Smith, the Chair of the Department of Surgery in New York City’s Columbia Medical Center, discussed the risk associated with transporting COVID-19 patients from the ER in the infectious disease wing, noting the orderlies are selflessly stepping up50. His concluding paragraph reads:“Transport is just one reminder that every contribution matters. Consider this admirably prideful Tweet from “Jester D” on March 14: ‘I’m a garbageman. I can’t work from home and my job is an essential city service that must get done….Doctors and nurses are going to keep doctoring and nurse-ering. Us garbagemen are gonna keep collecting the garbage.’ Indeed, it must get done. Singer-songwriter John Prine died of COVID-19 yesterday, at age 73. He worked a day job as a mail carrier in Chicago for five years early in his career. John Prine wrote songs for common people… ‘The scientific nature of the ordinary man / Is to go out and do the best you can.’ The Post-COVID Break: Economic Dislocation of the Economically Displaced
Technology is not an industry per se. Rather, it’s deployed across industries, adding an element of “creative destruction”51 into how things have traditionally been done. The Quaternary sector, then, penetrates all economic activities, adding efficiencies with “PrecisionAg” in the Primary sector, robotics in the Secondary sector, and precision medicine and FinTech in the Tertiary sector.
“Once upon a time, it made perfect sense to talk about ‘the high tech industry’ in America,” explained technologist Anile Dash, noting that Fairchild Semiconductor and IBM were in the business of making tech for tech’s sake52. “But today, the major players in what’s called the ‘tech industry’ are enormous conglomerates that regularly encompass everything from semiconductor factories to high-end retail stores to Hollywood-style production studios.”
Prior to COVID-19, tech’s penetration in Tertiary, or Service, sector was deep, particularly in the arena of consumer goods, i.e., consumer tech. Netflix brought the cinema to the house. Amazon brought the mall to the house. Facebook brought the coffeeshop to the house. Google brought the library to the house. Apple brought the desktop to one’s hand from which entertainment, consumer goods, and social connection was brought to one’s house. Uber Eats brought the restaurant to the house. Beyond leisure and retail, knowledge services, too, are being brought to the house, via the likes of telemedicine, remote learning, and online banking and brokering.
This is not surprising. The whole rationale for creative destruction is to make what was harder easier, or what was scarce more abundant. The 2nd Agricultural Revolution was about making more food at less cost with fewer laborers. That happened. The 1st Industrial Revolution made the scarcity of human labor abundant by replicating it with steam and gears. Transportation, communication, domestic work: they were made easier by cars, phones, and appliances which, in turn, was only made possible by the advances in electrification in the 2nd Industrial Revolution. One critical raison d'être of the 4th Industrial Revolution is to make the scarcity of human attention less so via the advance of artificial intelligence.
Figure 37: A Timeline of Innovation. Source: Khazanah Research Institute
1st Agricutltural Revolution
(circa 10,000 B.C.)
Transition from hunting and gathering to domestication of animals and plants
2nd Agricutltural Revolution
(mid 17th - late 19th centuries)
Major development incldues crop rotation, imporved plough, individual enclosure, increase in farm size and selective breeding
1st Industrial Revolution
(end 18th - early 19th centuries)
Emergence of mechanization with the invention of the steam engine
2nd Industrial Revolution
(late 19th - early 20th centuries)
Electrification and mass production
3rd Agricultural Revolution
1930s - 1960s
High yielding varieties (HYVs), chemical fertilizers and argo-chemicals
3rd Industrial Revolution
1950s - 1970s
Shift from mechanical and analogue electronic technology to digital electronics
4th Industrial Revolution
Convergence of physical, digital, and biological spheres
The arrival of COVID-19 will only accelerate technology’s penetration into various industries. That means further automation of human tasks, and not simply for efficiency’s sake—i.e., robots doing more for less—but because human proximity is now a drag on productivity, not to mention a liability. It’s a scenario few saw. Worse, the endgame—how to do we work together without being together? —isn’t exactly foreseeable. There’s no playbook for this. “Everyone wants to know when this will end,” explained Devi Sridhar, a public health expert at the University of Edinburgh. “That’s not the right question. The right question is: How do we continue?”53
Economically, the most pressing issue at hand is what to do about the human contact issue. A vaccine is the obvious answer, yet that’s at least 12 months off—an eternity in the market. In the meantime, changes must be made, and companies hurting for cash will find the pressure to replace humans with machines intensifying. "The thing that we're hearing from customers is many of their budgets have been frozen except for budgets for automation," explained CEO Melonee Wise of Take Fetch Robotics54. The question their asking: “How can robots enable us to continue…while keeping social distancing?'"
It is unlikely, for instance, that farmers will return to business as usual. The living and working conditions of seasonal migrant laborers makes their essential work susceptible to outbreaks, as is evidenced by the situation in Singapore wherein half of their cases can be tied to migrant dormitories55. To mitigate such risks, a considerable up-front investment is expected in “technologies like drones, autonomous tractors, seeding robots, and robotic harvesters [that] imply a dramatic reduction in farmers’ reliance on migrant labor,” so notes agricultural economist Wandile Sihlobo56.
And while American factories have been de-densifying for decades via the use of industrial robots, there’s a substantial amount of last-mile work that didn’t make financial sense to automate. Amazon fulfillment centers are filled with such workers. “But labor and robotics experts say social-distancing directives, which are likely to continue in some form after the crisis subsides, could prompt more industries to accelerate their use of automation,” so notes a New York Times piece “Robots Welcome to Take Over, as Pandemic Accelerates Automation”. The authors interview an executive of an AI robotics company that specializes in the recycling industry, saying orders have skyrocketed since the outbreak given that their robots will enable recycling facilities to space out their employees. Another benefit of the bots? “They can’t get the virus,” the executive said57. Amazon, too, is planning accordingly, after numerous COVID-19 cases among staff and subsequent staff protests58.
Perhaps the biggest effect—both economically and socioeconomically—is the fate of the Lower-wage Service worker, making up a third of the American workforce. Elaborating, if COVID-19 acts as an accelerant into Consumer tech’s penetration into the Service market, where do the economically displaced go if they are again economically displaced? Cashiers are at risk via automated kiosks, retail workers via on-demand ordering, waiters and waitresses via the rise of “dark kitchens”, or digital-only establishments that don’t need a dining room or waiters.
Outside of automation, how does a leisure and hospitality industry so reliant on face-to-face contact even continue?
Trends in the unemployment rate for leisure and hospitality workers show that it doesn’t, with Depression-level unemployment rates nearing 40% for the group (See Figure 38). Even after the lockdown, an already uphill climb for small businesses will be steep. “Keeping tables apart, socially distant service… that’s not a reality,” notes one Cleveland restaurateur, explaining his reason to close. “If you can’t make money turning every table twice and a half, how are you going to do it at half that59.”
Regionally, some states and cities are more susceptible to this reality than others. Figure 39 shows that the top 10 states with the highest concentration of Lower-wage Service jobs are primarily in the Sun Belt, led by Nevada (44%), Hawaii (44%), South Carolina (39%), Mississippi (39%), Florida (38%), and Alabama (38%). Ohio ranks 35th at 32.3%.
Figure 39: Proportion of Private Sector Jobs that are in Lower-wage Services by State, Source BEA, 2018.
Out of big-city counties over a million plus, the top 18 for the highest share of private jobs in Lower-wage Services are located in the Sun Belt, led by Clark County Nevada (47.1%). Nearly half of the counties in the top 10 are in Florida (See Figure 40).
Even after lockdowns soften with preventive measures, the likelihood people attend public gatherings is uncertain. Does this sound fun? “Every spectator goes through a kind of disinfection shower at the entrance of the theater,” explains a German theater director based in South Korea60. “You keep your clothes on, of course, but you don't really get wet either. It is a very thin spray. Every visitor has to have their temperature measured, everyone has to wear a protective mask over their mouth and nose for the entire stay in the theater.”
Regardless, back in late April there was an increasingly raging debate about the health of the economy versus the health of the people, particularly in population boomtown areas in which so much economic activity can’t be done virtually and at a distance. The Lt. Governor of Texas, Dan Patrick, recently made comments that went viral insinuating that the elderly would be willing to die if it meant Americans can “get back to work”61. Speaking with CNN’s Anderson Cooper, the Mayor of Las Vegas, Carolyn Goodman, offered her city as a “control group” to assess the extent fatalities will occur if social distancing measures are relaxed, if only so casinos and hotels can open.62 Georgia was the first state to relax social distancing measures so non-essential businesses like bowling alleys, tattoo parlors, and hair salons can open. This, despite the National Institute of Allergy and Infectious Diseases’ Dr. Anthony Fauci advising that Georgia shouldn’t be “going ahead and leapfrogging into phases where you should not be…” Not surprisingly, the effect of these efforts to open up early has led to a spike in COVID-19 cases in the Sun Belt, led by Texas, Florida, California, North Carolina, Arizona, and Georgia by late June63.
No doubt, the economic pain bearing down on the brunt of American firms and workers is unprecedented, as illustrated by national monthly initial unemployment claims across time (See Figure 41). But falsely framing a solution to COVID-19 as zero-sum dichotomy that means sacrificing the life of some for the livelihood of others, well, that neglects an awareness that it is in fact that kind of dichotomous thinking that got us into this mess in the first place.
COVID-19 didn’t create the urgencies we face as much as reveal them. The physical and mental well-being of Americans have been declining for some time64. This is related to how various economic restructurings has been good for some, and less good for most. And where one lands on that continuum of pain versus progress is ultimately felt in one’s flesh. After all, it all ends up in the body.
If there was one metric to measure it “all”, what would it be? Nothing captures everything, and there are many key measures like GDP, employment, and earnings that are necessary. Yet all of these are upstream to an outcome that’s arguably most definitive: life expectancy. In fact, so much of civilization’s race to progress is flattened into a Darwinian struggle to live longer with a higher quality of life. In Maslowian psychology, this is couched as going beyond the basic needs of food, water, shelter, and safety, to psychological needs of belongingness and esteem, and finally to self-actualization, or those pursuits of creativity and spirituality that lend themselves to “a life well lived”.
But one’s life expectancy is not fully in the hands of the individual. If one conceives of the body as a geography—and it is—then it’s easy to see how a mind and body is influenced by the household it lives in. That influence, then, broadens at geographic scale: a household is tied to a neighborhood, a neighborhood to a city, a city to a region and state, all of which are interlocked globally. COVID-19 makes this nesting of space plain as day, fast-tracking the global-to-local-to-individual process in a breathtaking manner. But while the contagion is fast, the systemic structures that make it land harder on the vulnerable have been a long time coming.
Let’s start with the basics. Overall, life expectancy in the U.S. has not only plateued, but it’s declined for three consecutive years65. Among the developed world, the nation is trending the wrong way (See Figure 42), assaulting the notion of American exceptionalism.
What’s happening? Deindustrialization’s impact in particular—and the erosion of middle-income employment more generally—has had an explanatory effect66. In their recent study "Life Expectancy and Mortality Rates in the United States, 1959-2017" in JAMA, the authors find that stalled longevity is a function of Americans aged 25 to 64 dying of drug overdose, alcohol abuse, and suicide. Importantly, the deaths are geographically specific, with 8 of the 10 states with the largest number of excess deaths in the Rust Belt or Appalachia, particularly in the communities of the Ohio Valley. "The notion that U.S. death rates are increasing for working-age adults is particularly disturbing because it is not happening like this in other countries," said Steven Woolf, the lead author67. "This is a distinctly American phenomenon."
Echoing these findings, Angus Deaton and Anne Case, authors of the new book “Deaths of Despair and the Future of Capitalism”, found that “deaths of despair” have increased four- to five-fold for white, non-college-educated middle-age adults since the 1990’s. For instance, 30-year-olds died by suicide, drugs, or alcohol at a rate of 31 per 100k deaths in 1992, increasing to 147 per 100k in 2017. For 53-year-olds, the increase went from 32 per 100k to 131 per 100k. Explaining their findings, Case noted68: "We don’t think [American capitalism] is working for people without a four-year college degree — and that’s two-thirds of Americans between the ages of 25 and 64."
One key trend supporting this thesis is the falling male labor force participation rate, going from 86.6% of men employed in 1948 to 66.7% in May of 2020 (See Figure 43). It’s a sobering stat. The Philadelphia Fed recently showed that it was men with a high school diploma or less that are dropping out of the labor force the most, and that this exit was not “a transitory event, in that the majority of men who reported not working in a given month had also not worked over the previous year.”69
Such trends, however, are hidden in plain sight, given the growth pundits’ laudation of a Dow Jones Index that keeps breaking glass ceilings. But “the stock market is not the economy,” notes economist Paul Krugman70. Or as was shown prior: growth doesn’t equal development, particularly when it’s being decoupled from opportunity. What’s key at this point is that the delta between growth and opportunity gets played out societally, especially at the level of the community. From there, it gets absorbed individually.
A “walk” down the scale of economic restructuring will prove helpful. Figure 44 shows counties in Ohio with the highest proportion of jobs in Knowledge-based Services and Information Technology combined. Unsurprisingly, the knowledge economy job hubs are located in the largest urban centers, led by Delaware (49.8%), Hamilton (49.2%), Cuyahoga (49.1%), and Franklin (46.9%).
Figure 44: Proportion of Private Sector Jobs in Knowledge-based Services and Information Technology by County. Source: BEA, 2018.
Meanwhile, areas where Lower-wage Service jobs are most concentrated are in South Central Ohio (See Figure 45). These counties also have the lowest life expectancies at around 72 (See Figure 46). By contrast, Columbus’ Delaware county’s life expectancy is 82.4 and Cleveland’s Geauga County is 81.3.
Figure 45: Proportion of Private Sector Jobs in Lower-wage Services by County. Source: BEA, 2018.
Figure 46: Life Expectancy by County. Source: County Health Rankings, 2020.
The geographic split evident between counties is also evident within a given county. Figure 47 shows Cuyahoga County’s Lower-wage Service workers are most concentrated in predominantly Black neighborhoods just north and south of Cleveland’s Health Tech Corridor: home to the region’s “eds and meds” institutions. That said, the bifurcation of the regional labor market is manifested as a bifurcation of the local housing market, a phenomenon called “residential sorting” by urban economists71.
Figure 47: Where Lower-skill Serivce Workers Live in Cuyahoga County. Soruce: LODES, 2017.
The sorting, then, gets played out in the way amenities flow. Knowledge- and tech-worker neighborhoods are flush with investment, manifest as a cornucopia of goods and services that check-off Maslow’s hierarchy of needs: physical safety, healthy food, clean air and water, quality housing, good schools and healthcare, pretty aesthetics and parks, a strong social fabric and concomitant information access, not to mention the freedom from scarcity that allows the luxury of aspiration. Meanwhile, disamenities grow in areas of isolation: violence and trauma, dirty air and water, deteriorating housing, poor schools and health services, a social bond break with less information and support, and a lack of a psychological and spiritual reprieve that comes with perpetually existing without enough.
The spatial split brings to mind a book by Thomas P.M. Barnett's called “The Pentagon's New Map”72. For the expert geostrategist, the world is divided between two types of geographies: the “Core”, where "globalization is thick with network connectivity, financial transactions, liberal media flows, and collective security," and the “Gap”, or areas disconnected from globalization and defined by poverty, low education rates and "the chronic conflicts that incubate the next generation" of instability. While Barnett’s “haves and have nots” was conceived at the level of the nation-state, it need not stay there. There is a Core and Gap between American regions, within regions, and even within neighborhoods. "We ignore the Gap's existence at our own peril," concludes Barnett.
The sorting between amenity-rich and -poor communities ultimately ends up affecting longevity. This is evidenced by Figure 48 showing the spatial inheritances of life expectancy in Cuyahoga County. Premature death clusters in space as if they were a contagion. In many respects, they are. The field of epigenetics, for example, shows how the environment, or the outside, modifies a person’s DNA, or the inside73. Elaborating, structural economic changes globally manifest as socioeconomic inequalities locally, igniting psychosocial stress that changes the body’s biology.
Figure 48: Life Expectancy in Cuyahoga County. Source: U.S. Small-area Life Expectancy Estimates Project-USALEEP, 2010-2015.
It’s a sequence that lingers intergenerationally. “Each exposure has effects that may persist across the life course and in some instances may be transmitted to offspring via epigenetic inheritance,” notes the authors of the essay “Biological memories of past environments: Epigenetic pathways to health disparities.”74 “Since epigenetic markings provide a ‘memory” of past experiences, minimizing future disparities in health will be partially contingent upon our ability to address inequality in the current environment.”
For the most part, this is not being done. Healthcare is often an “after the fact” industry, treating bodily disease as oppose to the “upstream” impacts on the body. That’s not surprising. Health practitioners can only do so much. They can treat sick individuals, but sick societies? That’s not up them. It’s up to “us”.
Regardless, what’s occurring isn’t exactly working. In 1970, life expectancy in the U.S. ranked 18th out of 43 peer nations. With the latest figures from the OECD, U.S. life expectancy today ranks 28th. This, despite the U.S. spending $10,206 per capita annually in healthcare services—the most among developed nations. This discordance is plotted in Figure 49. Most countries with high spends have higher life expectancies. Not in America. The nation stands exceedingly alone, despite having the best medical institutions in the world.
Now, the nation is awash with the dying, with 200,000 coronavirus deaths expected by September75. COVID-19 sped up the slowing-moving car crash of the economically displaced and their diseases of despair and their chronic conditions. “When we someday tally up all the casualties of the coronavirus,” writes Joe Pinsker in the Atlantic76, “the high number of older Americans among the dead will reflect the sad, universal fact of physical decline. But for many of those who had underlying health conditions, inequality will be the actual cause of death.”
Still, pandemics don’t afford the luxury of looking back. What it does offer is a respite from the delusion that economic progress has not meant societal costs. For months, almost everyone was home and almost everything stopped. The plagued grabbed us by the proverbial jowl and made us look in the mirror, reflecting the fact that it’s the most economically and physically vulnerable that are out about, tending to a capitalism that hasn’t been tending to them.
In the “The Rise of the Knowledge Society”, management theorist Peter Drucker explained that knowledge had historically been applied to being, or that manner of learnedness77. “Then it came to be applied to doing. It became a resource and a utility.” Drucker noted that as the meaning of knowledge changed so did the value society placed on it. “Knowledge had always been a private good,” Drucker wrote. “Almost overnight it became a public good.”
Knowledge as a public good cut several ways. A quality public educational system ensured there would be a supply of qualified workers going forward. Then there was the demand side of the equation, with federal R&D funding seen as key to the formation of new industries that would not only birth companies and grow jobs, but also better the quality of life. It was an effort jumpstarted just after World War II.
In a letter dated November 17th, 1944, President Roosevelt addressed Vannevar Bush, the Director of Office of Scientific Research and Development, recognizing his group’s success in applying scientific knowledge to “the technical problems paramount in war”78. “There is, however, no reason why the lessons to be found in this experiment cannot be profitably employed in times of peace,” President Roosevelt continued, noting that knowledge can be generated to improve national health and raise the standard of living. Roosevelt concluded: “New frontiers of the mind are before us, and if they are pioneered with the same vision, boldness, and drive with which we have waged this war we can create a fuller and more fruitful employment and a fuller and more fruitful life.” Makes sense. If knowledge can be federally-marshalled for security purposes, so can it for purposes of prosperity.
A year later a report was produced called “Science: The Endless Frontier”, which conceived of the National Science Foundation: an effort to fund the production of knowledge for the public good79. That report kicked off an era in the 50s and 60s in which nearly 70% of all R&D was funded by the federal government (See Figure 50). It was a time of discovery that laid the foundation for computer science, and eventually today’s technological age.
While that effort to tie science to economic development ushered in remarkable advances on numerous fronts, a split was nonetheless created in the institutions where most public-facing knowledge was produced, particularly academia. By the mid-20th century, scientists were given near-exclusive control over the assertion of truths, explained Yale sociologist Immanuel Wallerstein80. Meanwhile, the practitioners of humanistic knowledge—those seekers of “the good and the beautiful”—had to cede standing. “Never before in the history of the world had there been a sharp division between the search for the true and the search for the good and beautiful.”
The fault line in this split was in the field of economics. Though economics—as an abstraction of human motivation and behavior in the milieu of the market —is by its nature a “soft” science, its practitioners aimed toward making it empirically known81. So arose the notion of “homo economicus”, or the idea that the “economic man” was a behavioral blank slate who was consistently rational and pursued their self-interests optimally. Such assumptions were needed if economics was to be squeezed of its uncertainty. After all, irrationality abhors a mathematical constant, and despite how much irrationality is baked into the marketplace82—in form of “bubbles”, biases, fear, greed, misinformation, or just dumb luck—the notion that the economy is just a sum of its predictable parts remains stubbornly unbending.
Meanwhile, as the notion of economic man became ideologically presumed, less knowledge was being applied to understanding the values of the free market— or the “why” of profit—with more energy devoted to understanding the process of valuation itself—or the “how” of profit. This is evidenced in a shift in how R&D was funded. In 1980, both government and the private sector funded R&D at a 1:1 ratio. Today, over two-thirds of research is funded via business. This pivot has been referred to as the “privatization of the public good”83.
It is a pivot that has been greased and grown by the field of management consulting, arguably the purveyors of knowledge in today’s knowledge economy. Think McKinsey, Deloitte, Bain, etc. Such firms exist to perfect the how of profit. McKinsey, for instance, does “execution, not policy”84, with various consultancy tactics executed including outsourcing, lay-offs, and automation on the stakeholder-side; and mergers and stock buybacks on the shareholder-side.
This “leaning-and-meaning” of the private sector was motivated by an aggressively normative belief that executive managers are “the agent of the individuals who own the corporation…and his Primary responsibility is to them,” so notes Milton Friedman in his famed 1970 New York Times piece “The Social Responsibility of Business is to Increase Its Profits.”85 That dogma has since became doctrine, made real by the most standard of consultancy playbooks: efficiency as a means and profit as an end.
The execution of this worldview has had a profound impact on how and where money flows. The top 1% of earners take home 20.5% of the national income, levels not seen since the Great Depression (See Figure 51).
Meanwhile, the salary and wages of all workers as a percent of GDP is down to 43.2%, after peaking at 51.7% in 1969 (See Figure 52). Illustrating these effects locally, 71.7% of Ohioan’s incomes were from salary and wages in 1969. In Cleveland, it was nearly 74%. By 2018, the percent of income coming from the salaries and wages decreased to 51.2% in Ohio and 54.2% in Cleveland (See Figure 53).
What’s picking up the slack? Personal transfer receipts, defined as income payments to persons for which no current services are performed and net insurance settlements. Sources include government aid from Medicare, food stamps, unemployment benefits, and Social Security. Over 18% of Ohioan’s incomes comes from such receipts today, up from 6.6% in 1969. In Cleveland, the increase went from 6.5% to 18.4%. If this is a rational market, one would loathe to see an irrational one (See Figure 54).
Income from dividends, interest, and rent is also rising. This is money paid to select individuals, particularly stockholders, lenders, and landowners. In 1969, 15% of total income in the U.S. was via these sources. That’s up to nearly 21%. In the Cleveland metro, the increase went from 14% to 19.4% (See Figure 55). Dubbed “financialization”, this is essentially the process of money making money. The scholar Gerald Epstein defines financialization as “the increasing role of financial motives, financial markets, financial actors and financial institutions in the operation of the domestic and international economies.”86 Epstein explains that the financialization of an economy—historically symptomatic of a declining hegemonic power—is part and parcel with a shift in money “between capital and labor on the one hand, and between management and workers on the other hand.” It thus partly explains the decline in income from salary or wages across time.
As efficiency—or the how of profit—squeezed workers out of a livable wage, another negative externality took hold: the system became so lean that it became gaunt, susceptible to natural forces, like contagions and natural disasters, that don’t operate on timetables as a stock and flow. “[I]f there is a single economic policy lesson to learn from the coronavirus pandemic, it is that the United States’ obsession with efficiency over the last half century has brutally undermined its capacity to deal with such a catastrophic event,” notes management theorist Roger Martin in his Washington Post essay “The virus shows that making our companies efficient also made our country weak.”87
Medicine, cotton swabs, meat, testing kits, reagents, sanitizer, toilet paper, ventilators, facial masks—these necessities were unavailable when we needed them most, exposing the insufficiency with efficiency. That’s because firms and cities become specialists in the production of few things, a concept called “comparative advantage” by economists88. But these “things” are just bits in a broader supply chain, and if something goes wrong in a given city—e.g., a region in Italy was one of a few that manufactured cotton swabs for COVID-19 testing and was no longer able to make them89—then bottlenecks occur. Bottlenecks, in turn, ignite chain reactions that only let the contagion compound, because the virus couldn’t be tracked and traced.
A converse to efficiency is resiliency. Whereas efficiency is the continued “leaning-and-meaning” of processes in an existing environment, resilience requires the ability to adapt to the unknown. “Resilient systems are typically characterized by the very features—diversity and redundancy, or slack—that efficiency seeks to destroy,” explains Martin90.
How do nations and cities become resilient? There are tactical approaches—which are fairly easy to execute—and ideological approaches—which are not.
As for the tactical, there’s existing ways to build redundancies, or slack, in supply chains. The U.S.’ Strategic Petroleum Reserve, for instance, is well-stocked, with 635 million barrels of oil in salt caverns available on the Texas and Louisiana coasts91. Conversely, the Strategic National Stockpile, or the national repository of antibiotics, vaccines, chemical antidotes, antitoxins, and other critical medical supplies, isn’t well-stocked. By April 9th, 90% of the federal stockpile of N95 masks were gone, with the remaining 10% being withheld from States for federal healthcare workers92. This led States to outbidding each other on the private market, with one medical supply company in New Jersey selling N95 masks at 500% over the list price, leading a competitor, 3M, to file a price-gouging lawsuit93. It’s a devastating commentary on the economics of public health.
The solution, of course, is to buffer public health from the private market, which means understanding that livelihood is an upstream driver of firm profit. No health, no workers, no customers, no profit. Knowing this would provide the impetus needed to build the Strategic National Stockpile so resiliency is baked into the system. It would also mean reshoring the production of goods that are of vital interest to American well-being, particularly pharmaceuticals and other forms of medical manufacturing. Figure 56 shows significant declines in the output of pharmaceutical firms based in the U.S.