The Vitality of Dying Cities

The headline is dire: "Facing The Scenario Of Demographic Deserts In The EU." A desert connotes a void, the absence of life. Global cities, on the other hand, would be demographic rainforests. While geographic metaphors as heuristics abound, deserts can and do support a wealth of biodiversity. The same is true of places dealing with the shame of demographic decline.

At the scale of the nation-state, prosperity drives demographic desertification. Better educated women have fewer children. Smarter people tend to be more geographically mobile. Stuck in place is a symptom of socioeconomic malaise. Without immigration, many of the world's richest countries would be shrinking.

Demographic norms are currently caught between two economic eras. The industrial revolution pulled workers off of the farms and into the cities. The need for a large brood faded into the past. Both blue and white collars benefited from an income boost. In the knowledge economy, only the skilled get a lift and fortune favors a two-income household. While the number of people living within one home drops, the money earned (in real terms) grows. More people in Pittsburgh have jobs now than did during its population peak.

During the height of US manufacturing employment (as a % of the workforce) in the 1950s, the bulk of dependents were kids and women. Over the last few decades, more women have jobs as the number of retirees has exploded. Thus far, the typical answer to this policy problem amounts to a "Leave It to Beaver" approach. Have more children and attract more new residents. Educating children and immigrants is expensive as well as inefficient. Demographer Sarah Harper identifies the low-hanging fruit:

This idea that you need lots and lots of people to defend your country and to grow your country economically, that is really old thinking...It is much easier to enable older adults to stay upskilled and healthy and in the labour market than it is to say to women ‘oh you have got to have children’.

This idea that the 65+ can't work is really old thinking, too. Able-minded succeeds able-bodied, as the march of women into work demonstrates so well. We can't do much about unproductive toddlers. We haven't done enough for unproductive adults.

Geographic Heuristics: Rust Belt Is Rural

An Appalachian city is an oxymoron. Welcome to hillbilly urbanism. What about the cities of rural Iowa? Does not compute. There is rural. There is urban. Never the twain shall meet.

The US Department of Agriculture uses a similar dichotomy, metro and nonmetro. However, not all metro counties are alike. Some are large. Some are small. The same applies to nonmetro areas, many sporting urban areas of varying population size. Understanding the geographic variance within urban and rural results in a continuum of a "nine-part county classification":

Metro counties are straightforward, broken up into three population categories (large, medium, and small). Size matters for nonmetro, too. But there are six types, not three. The distinction concerns proximity to metro counties. Why would that matter? At some point, sprawl might graduate a nonmetro county to a metro one. In fact, a critical mass of commuters to a nearby metro area is part of the definition of adjacent counties.

The same could be said for smaller metro counties. Proximity to the largest metro counties confers similar spillovers, somewhat like the Census definition of combined statistical areas. The United States harbors a few urban galaxies such as Chicagoland. The end-all-be-all is the New York City agglomeration of agglomerations. Introducing the cosmopolitan Rust Belt:

“Allentown is the third-largest city in Pennsylvania and one of the youngest [in terms of population] in the state,” [Becky A. Bradley, executive director of the Lehigh Valley Planning Commission in Allentown] adds. “The whole downtown has been transformed within the past three years. We’re having an amazing renaissance. But the Lehigh Valley didn’t lose industries because we were never part of the Rust Belt. We were always linked to Philadelphia and New York because of our easy access, whether it was canals, roads, rails, or air.”

The Lehigh Valley was never part of the Rust Belt? “Well, we’re living here in Allentown and they’re closing all the factories down,” I yanked on my bow tie and squawked to my wife, and anyone else in ear shot, “Why does anyone think we want to hear that song here. We’ve spent the last 35 years working to overcome it!” Sure, Allentown was always linked to Philadelphia and New York. Both places were also part of the Rust Belt. Much of Philadelphia, like Baltimore, remains Rust Belt. And as New York has shed its economic malaise and Philadelphia revitalizes, so does Allentown.

The Rust Belt isn't so much spatial as it is temporal. Dig into the past of any city that employed substantial numbers in manufacturing and find at least one decade of Rust Belt. As economic restructuring filled out the five boroughs, globalization diffused to Philadelphia. It starts with a few neighborhoods until the impact shows up in the regional data. Bam! Philly is no longer a left behind place. By then, it's the turn of the Lehigh Valley. Is York next?

Migration Is Real Estate Development

People develop, not places. What does that mean? Economic development concerns a community or a region. Migration models (i.e. rational choice theory) predict that workers will move to areas doing better in terms of prosperity. You go where you grow, a heuristic that obfuscates how the act of moving from one to place to another results in economic development.

Brain drain is economic development. In place-based parlance, that statement looks absurd. But from the perspective of the migrant, it could not be more obvious. The most common rationale for relocation is financial gain. The migrant also benefits from living in a new place, experiencing ideas and perspectives otherwise unavailable in a hometown. Consider the epiphany of Quality Dairy champion Craig Terrill:

Terrill went to Michigan State University and stayed in the area for a few years after college. Then he left.
He worked as a communications point person for the city of Takoma Park in Maryland for six years before returning to Lansing two years ago. A big part of the job was running the social media accounts for the city, which borders Washington, D.C.
"One thing about Takoma Park is it’s really a quirky, crunchy, granola kind of city, like a Berkeley, California vibe, or Portland," Terrill said. "Everyone’s kind of weird, artsy, at least that’s the vibe it gives off.
"When I moved back to Lansing, I wasn’t getting that anymore. I was like, 'What if I did that here, but it was fictional and there was no filter.'".
So after some bar conversations and encouragement from friends, he launched Lansing Facts and it "went nuts."
Terrill says that, in D.C., the economy was surging and everything about the city was really expensive and fancy. It made him nostalgic for QD and places that seemed more down to earth.

Via Terrill, brain drain is economic development for Lansing, Michigan. Upon his return, he brought back a bit of Takoma Park, Maryland. He catalyzed what he liked best about Takoma Park in a place he liked better. It is the best of both worlds. The weird and artsy in the down to earth Rust Belt.

Terrill elevated his social media skills in a globally significant metro. He applied those talents in the left behind part of the country. All of this was made possible because he left Lansing, which needn't be in the orbit of Washington, DC thanks to the miracle of brain drain.

Brain drain is revitalizing urban Lansing one repat at a time. In fact, journalist Nona Willis Aronowitz worries that her own return migration is pushing out tenured residents in Harlem, "A year and a half later, in seemingly direct response, enterprising storeowners are serving salted caramel lattes and selling dry-aged picanha around the corner from us, ignoring the huge chunk of Harlem residents living in subsidized housing and making new residents like us look like jerks."

Global labor commands a global wage. Wherever such migrants end up, housing prices will go up. Goods and services will gentrify. Migration is real estate development.

Irrational Choice Migration: Geographic Heuristics and Superstar Cities

Why does the best talent move to the most expensive cities? One theory posits that the returns to skill are so great that the migration is a rational choice. The large salary justifies the exceptionally high cost of living.

The draw of superstar talent to a superstar city also would seem to overcome distance. Most relocation is to a nearby locale, with massive urban agglomerations acting as the exception to Ravenstein's rule. As a result, a place such as New York City will have stronger relationships outside of the country (e.g. London) than inside it (e.g. Buffalo).

The absolute distance between New York and London is much longer than between NYC and Buffalo. But the cognitive distance, informed by globalization, turns the relationship on its head. From the vantage point of Manhattan, London is much closer.

In psychology, the heuristic of distance is used to understand how humans perceive similarity. Then along came Amos Tversky, who annihilated the theory. As captured in The Undoing Project, human subjects wouldn't conform to the hypothesized symmetry of similarity judgments:

When people compared one thing to another - two people, two places, two numbers, two ideas - they did not pay much attention to symmetry. To [psychologist Amos Tversky] - and to no one else before Amos - it followed from this simple observation that all the theories that intellectuals had dreamed up to explain how people made similarity judgments had to be false. "Amos comes along and says you aren't asking the right question," says University of Michigan psychologist Rich Gonzalez. "What is distance? Distance is symmetric. New York to Los Angeles has to be the same distance as Los Angeles to New York. And Amos said, 'Okay, let's test that.'" If, on some mental map, New York sits a certain distance from Tel Aviv, Tel Aviv must sit precisely the same distance from New York. Yet you needed only to ask people to see that it did not: New York was not as much like Tel Aviv as Tel Aviv was like New York. "What Amos worked out was that whatever is going on is not a distance," says Gonzalez. "In one swoop he basically dismissed all theories that made use of distance. If you have a distance concept in your theory you are automatically wrong."

From New York, Tel Aviv seems a world away, if even on the horizon. In Tel Aviv, New York is a familiar presence, like a neighboring town or suburb. If you have a distance concept in your migration theory (e.g. distance decay) you are automatically wrong?

For Amazon, Seattle is like New York City and Washington, DC. We go where we know. Proximity matters. The line between the Pacific Northwest and the Northeastern superstars is shorter than the one to Chicago or Dallas. However, for superstars residing in the Big Apple, Seattle is no substitution for London. The migration is an irrational choice.

Consumer University: Winner-Take-All Eds & Meds

In Rust Belt cities such as St. Louis, neighborhoods tied to anchor institutions are doing relatively well. Neighborhoods tied to manufacturing experience terminal decline. Optics at the regional scale will depend upon preponderance of neighborhoods associated with the respective economic eras. This apparent divergence is a wedge between low-skill and high-skill jobs that structures much of the political discourse today:

Figure 2 shows that cross-MSAs wage convergence rates between 1940 and 1980 were the same for high-skill and low-skill workers. But, they differ strongly post 1980. Between 1980 and 2010, wage convergence rate occurs only among low-skill workers not for high-skill workers.

The 1980 rupture only concerns high-skill workers with wealth concentrating in select metros. Over time, more and more regions join Club Divergence. How? There is no theory of change offered, only fear about places left behind.

The granularity of geographic scale is not fine enough. Economic restructuring (the process of deleveraging from the high concentration of manufacturing employment) happens neighborhood by neighborhood, not in certain cities. A toehold of globalization on a few blocks next to a research university is lost in the noise of continuing low-skill economic convergence. To the extent local and state leadership cling to yesterday will serve to prolong the pain and perpetuate the Rust Belt stereotype.

But the embrace of a hospital or a university comes with its own peril. Chasing bodies instead of knowledge enters the arena of superstar cities and winner-take-all divergence. Given the diffusing pressures of demographic decline, how can Flyover Country compete with the likes of Oxford for tuition dollars?

Campus building has become an arms race among top business schools around the world as they seek to beat competition from cheaper online executive education courses with the lure of high-end training facilities in world renowned locations.

A few institutions will scale and gobble up all the inexpensive market for MBAs. While a few other institutions will be able to promote themselves as a luxury good (on the meds side, see Cleveland Clinic). This recipe for success is not one that most places can follow. Esports at the University of Akron won't serve as a catalyst for desperately needed urban revitalization and economic convergence will continue to dominate the landscape there.

Rust Belt Heuristics

A common demographic heuristic is loss aversion. Better to stop one resident from leaving than attract one newcomer. Rust Belt shame is all about loss aversion. If anyone leaves, whether for the suburbs or another state, then something is wrong with a place. A recent article in the Washington Post rekindles the dying cities geographic stereotype:

In America’s Rust Belt and parts of the Northeast, millennials and young professionals are leaving rather than moving in, and populations there are dwindling. Among those who remain, both the residents and the houses are aging.

The journalist (unwittingly) creates a new type of human, the net migrant. Negative migration indicates an exodus, not a lack of people moving into the city. The percentage (or absolute number) of the population actually leaving might be much lower than those exiting cool coastal cities. All that matters is whether or not there is a - or a +. Our loss aversion tendency compels us to fix the neighborhood and seduce the net migrant to stay.

Loss aversion at the regional or municipal scale turns a blind eye to strong neighborhoods in cities with lousy (i.e. Rust Belt) reputations. Quoting research from the St. Louis Fed, "Even in the most distressed older industrial cities some neighborhoods are doing quite well."

Rust Belt St. Louis is the case study. 35 tracts out of 218 total qualify as rebound neighborhoods (see Figure 1 above). With the 1970s (the nadir of urban America) as the baseline, Rust Belt cities have substantial areas of improvement as well as deepening distress. But the latter receives all the coverage and whitewashes a huge part of the United States with one broad brush stroke.

Indeed, in unpacking monolithic Rust Belt St. Louis, the Fed finds use for affordable housing efforts, "Subsidized housing also plays an important role in the continued economic diversity of rebound neighborhoods." Near anchor institutions reside young adults with enough earning power to drive up real estate prices beyond the means of the lowest wages. Regardless, the population drop defines the city. Loss aversion wins again, data be damned.

Trends of Regional Divergence and Convergence

All hail economic divergence. Amazon, the iconic company of the cloud computing era, staked its second headquarters flag in the US twin towers of domestic hegemony. Adding another jewel to the NYC-DC crown, economists and policy analysts warily eyed the deepening divide of two Americas. What to do with the "left-behind places"?

The hand-wringing concerning Flyover Country misunderstands the impact of globalization. The shocks to the world system during the 1970s and 1980s signaled an end to economic divergence. The declining manufacturing advantage informed the emerging trend of regional divergence in the United States. Fortune favored those areas transitioning out of the production of goods towards the export of knowledge. Today's observed divergence was and is but a time lag in the course of economic restructuring.

As more US regions deleverage from global divergence in this era of global economic convergence, the domestic trends of convergence will become apparent. The pressing problem will be, as already the case in Silicon Valley, divergence within regions. In fact, that labor market bifurcation is a strong indicator of globalization penetrating a left-behind place. Those who toil in legacy tradable industries or non-tradable services will look across a gaping chasm of economic restructuring at knowledge workers spending big city wages in markets well down the urban hierarchy.

The same forces reshuffling America are at work in China. The power of artificial intelligence rests on the low-paid efforts of humans, signalling a shift from divergence to convergence:

The data factories are popping up in areas far from the biggest cities, often in relatively remote areas where both labor and office space are cheap. Many of the data factory workers are the kinds of people who once worked on assembly lines and construction sites in those big cities. But work is drying up, wage growth has slowed and many Chinese people prefer to live closer to home.

China is deleveraging from global economic convergence. The age of rural-to-urban migration is at an end. Mechanical Turks can reside wherever one can access the cloud, teaching machines how to produce knowledge. Only the highly skilled need to move, which is already the case in the United States.

The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis

The title of the paper is the title of the post. I am taking notes as I read the analysis. When assessing the importance of research, I turn to Michel Foucault. I am interested in the breaks of history. At these moments, the delineation is between the history that was and the history that will be. How does economic history change? The abstract piques such interest:

"We find strong evidence of a 'shift' in the importance of application-oriented learning research since 2009 (relative to developments in robotics and symbolic systems research), and that a significant fraction of this upswing in application-oriented learning research was initially led by researchers outside the United States."

The suggested inflection point is 2009. That works on a number of levels. Economically, that's post-Great Recession. Technologically, that's post-smart phone and Amazon Web Services (i.e. cloud computing). Echoing the "Second Machine Age", the way the world worked changed. In conventional understanding, 2009 ushers in a new round of economic restructuring. As I continue through the paper, I will be searching for evidence of this epochal break (episteme in Foucault lingo).

I hope that the abstract itself, particularly the second half, has your full attention.

Bigger than a steam engine: "artificial intelligence also has the potential to change the innovation process itself, with consequences that may be equally profound, and which may, over time, come to dominate the direct effect."

That's an impressive introduction and a big contract to fulfill with the reader. The authors posit a positive Riemann sum, the slope of the innovation slope will change. It has changed. Fair to ask how do they know this to be true?  The boldness is gobsmacking.  Empirically (albeit anecdotally) they deliver.

I can vouch for the anecdotes. I've seen the same in Loudoun County. What we know is transforming because how we know is different. This is a change on par with the Enlightenment, not the Industrial Revolution.

"But whether or not Atomwise delivers fully on its promise, its technology is representative of the ongoing attempt to develop a new innovation 'playbook', one that leverages large data sets and learning algorithms to engage in precise prediction of biological phenomena in order to guide design effective interventions."

As a result, scientific inquiry itself will change to take advantage of the new tools. Cleveland should take a leadership role in developing such tools. This is the knowledge society, not the knowledge economy.

Take a gander at this sentence:

"while some applications of artificial intelligence will surely constitute lower-cost or higher-quality inputs into many existing production processes (spurring concerns about the potential for large job displacements), others, such as deep learning, hold out the prospect of not only productivity gains across a wide variety of sectors but also changes in the very nature of the innovation process within those domains"

If you will, the knowledge economy application of AI is the more efficient production processes. The knowledge society is the change "in the very nature of the innovation process". The knowledge society is the invention of a method of invention: As the Enlightenment is to the Industrial Revolution; artificial intelligence is to ?

Coming to my mind is Theodore Porter's book, "Trust in Numbers." AI gives social scientists the power they thought they had during the era of logical positivism. We didn't have the right telescope to match our ambition. Now we do.

Betting this part toward the beginning will resonate:

"We focus on the interplay between the degree of generality of application of a new research tool and the role of research tools not simply in enhancing the efficiency of research activity but in creating a new 'playbook' for innovation itself."

A new playbook for innovation itself is Enlightenment 2.0.

I'm five pages in and completely dazzled.

Brilliant: "while developments in robotics have the potential to further displace human labor in the production of many goods and services, innovation in robotics technologies per se has relatively low potential to change the nature of innovation itself."

So the shift noted is network analysis. That's cool in and of itself as a way to identify epochal breaks. Something to keep in mind going forward. Finishing Section I, we are on solid footing for understanding this round of economic restructuring.

The heart of section 2 is the following distinction:

"'GPTs' are usually understood to meet three criteria that distinguish them from other innovations: they have pervasive application across many sectors; they spawn further innovation in application sectors, and they themselves are rapidly improving."

From the literature on general purpose technologies, that's the litmus test. Deep learning, not robotics, passes. Therefore, deep learning can drive economic restructuring.

Another important concept  is the "invention of a method of inventing" (IMI). This "economics of research tools" is a useful way to think about knowledge society. GPTs cause breaks in economic history. IMIs cause breaks in knowledge history. To be an IMI, AI should change the paradigm of basic research. That's where "The Book of Why" might help with your conceptualization of knowledge society. Back to the paper:

"The invention of optical lenses in the 17th century had important direct economic impact in applications such as spectacles. But optical lenses in the form of microscopes and telescopes also had enormous and long-lasting indirect effects on the progress of science, technological change, growth, and welfare: by making very small or very distant objects visible for the first time, lenses opened up entirely new domains of inquiry and technological opportunity."

How does AI allow us to see things we've never seen before? My Janelia Research Campus anecdotes answer that question in an affirmative way. A bacterial infection is a general condition that we can now associate with specific organisms and target treatment (i.e. precision medicine). This is a paradigmatic shift in well-being knowledge. The same could be said for identify the specific causes of health disparities.

On a sour note, we are out on own defining the knowledge society:

"Mokyr (2002) points to the profound impact of IMIs that take the form not of tools per se, but innovations in the way research is organized and conducted, such as the invention of the university. GPTs that are themselves IMIs (or vice versa) are particularly complex phenomena, whose dynamics are as yet poorly understood or characterized."

That we will understand these dynamics as no one has done is daunting. Onto section 3 ...

The authors work through the history of AI. Symbolic systems is the AI that has been around for decades. The paper has already covered the other two categories by distinguishing between robotics and neural networks. The break in history:

"in the mid-2000s, a small number of new algorithmic approaches demonstrated the potential to enhance prediction through back propagation through multiple layers"

That's a major breakthrough for the neural networks category of AI, which had been around for about 25 years with disappointing results. This is the birth of AI as a GPT and IMI. Conveniently, they present a matrix:

Deep learning is characterized as both a GPT and an IMI. I should say, it could be a "general purpose IMI". Section 5 attempts to find evidence in support of that bit of speculation. I didn't read through the methodology closely since I'm not concerned with replication. And guess what? That's all section 5 is, a description of the methodology with the raw results.

Section 6 evaluates deep learning as an empirical GPT:

"The first insight is that the overall field of AI has experienced sharp growth since 1990."

"there is a steady increase in the deep learning publications relative to robotics and symbolic systems, particularly after 2009"

"there does seem to be an acceleration of learning-oriented patents in the last few years of the sample"

"By the end of 2015, we estimate that nearly 2/3 of all publications in AI were in fields beyond computer science."

Again, the time line with breaks fits my Foucault model. It also shows the attributes of a GPT. But we are still very early in the game.

Section 7 evaluates the IMI impacts. What's at stake:

"If it is also a general purpose IMI, we would expect it to have an even larger impact on economy-wide innovation, growth, and productivity as dynamics play out—and to trigger even more severe short run disruptions of labor markets and the internal structure of organizations."

The paper goes on to describe these disruptions to what I would now comfortable identify as the knowledge society. This is what you must help the new President of CSU to understand.

Section 8 is the conclusion. Since it is only two paragraphs in length, I will post the second one here:

"Our preliminary analysis highlights a few key ideas that have not been central to the economics and policy discussion so far. First, at least from the perspective of innovation, it is useful to distinguish between the significant and important advances in fields such as robotics from the potential of a general-purpose method of invention based on application of multi-layered neural networks to large amounts of digital data to be an 'invention in the method of invention'. Both the existing qualitative evidence and our preliminary empirical analysis documents a striking shift since 2009 towards deep learning based application-oriented research that is consistent with this possibility. Second, and relatedly, the prospect of a change in the innovation process raises key issues for a range of policy and management areas, ranging from how to evaluate this new type of science to the potential for prediction methods to induce new barriers to entry across a wide range of industries. Proactive analysis of the appropriate private and public policy responses towards these breakthroughs seems like an extremely promising area for future research."

2009 is the temporal break. IMI concerns the knowledge society (the economy of basic science). GPT is the knowledge economy.

The Two Tomorrows

The report starts out with the good news, the "icing". Beneath the surface, the region is merely average. That's a far cry from Jon Pinney's Rust Belt shaming of Cleveland at the City Club. I figure the Fund must be politically polite. The report is not as frank as the introduction claims it is. That said, this is a damning statement: "We hope the greater Northeast Ohio community will undertake an honest reckoning, too. Together, we can do better than just icing." Ouch.

The summary of the headwinds the region faces is weak. I don't see much evidence of understanding how disparities, for example, are connected to economic restructuring. The main initiative is job creation, but in terms of quality employment. The goal isn't more jobs. Training and access follow from that. Hopefully the report addresses bifurcation. The executive summary does not. Job creation itself can be the cause of "systemic race-based inequities".

Off the bat, in the meat of the report, bifurcation is raised. The authors seem to lay this at the feet of the legacy economy. The only ding for the emerging industries is an over-reliance on one or two. The prescription for that is economic diversification. But what about bifurcation in regions that have chosen to be "extraordinary"? Perhaps that comes later with workforce development and access.

The brief section on digitization is promising. The emerging economy is connected with bifurcation of wages, much like Drucker does. The last paragraph is a giant nothing-burger:

"Northeast Ohio must prioritize driving innovation into existing industries, foster flexible and responsive job preparation activities that can keep up with new market demands and purposefully build in digital access for disconnected residents currently cut off by a growing digital divide."

Is everyone going to get a high tech job? I don't see how this would solve the bifurcation problem stemming from digitization. I would like to see some regional examples of what success looks like.

I'm not going to go into racial inclusion discussion unless I see it integrated into the other parts of the report. So far, it's just in there to be in there. I do appreciate the traded sector discussion. Dovetails nicely with our ironic demography lens. Lots common ground, common language here.

The interrogation of the job growth metric is also good. Pittsburgh is the tortoise. Charlotte is the hare. We all know who wins the race.

I gather the Fund is tapping the expertise at Brookings for this report. I won't make a normative judgment on that other than to point out that bifurcation will likely get worse with such prescriptions.

The job growth strategy is a grab-bag of the usual suspects. Nothing new there. Just stay the course and mind the conventional wisdom. More or less, the same could be said of the approaches to job preparation. The region just has to execute better? There aren't any new ideas here. Nor are there new lenses to understand the problem.

I'm wary of the job hubs approach. Exogenous shocks could render large transit infrastructure investments moot. I'm looking at you, Denver. I think the region would be better served by first understanding what is causing the spatial mismatch.

I like the call for better, more appropriate metrics of success. Brookings is providing the heft here. I would have looked at Fed stuff. This should be a regional conversation, not a canned product.

Lastly, after perusing the end notes, I can confirm the heavy hand of Brookings. This is their baby. One note that caught my eye: "Peer economies include Baltimore, Buffalo, Cincinnati, Detroit, Milwaukee, and Pittsburgh MSAs. All GMP data from the Bureau of Economic Analysis." That looks like a cohort of demographic decline large metros. The goal appears to be better than the average peer.

Produce and Export

Some cities, like Pittsburgh during the heyday of steel, produce. Other cities, such as no-other-reason-to-exist Las Vegas, are palaces of consumption. Most places are somewhere in between. Better to produce than consume is the idea behind Producer Cities.

As manufacturing employment waned, while output kept growing, legacy producer cities imploded. The Baby Boom 1950s were not forever. Demographic decline would change how we understand economic development:

“Pittsburgh is not a region with a lot of population growth,” said Chris Briem, regional economist at the University of Pittsburgh University Center for Social and Urban Research. “Two-thirds of the economy is made up of jobs providing goods and services to the local population. So comparing Pittsburgh, a place without a lot of demographic growth, to places that are experiencing demographic growth, you’re going to get different pictures—and not a picture that’s saying one region is doing better than another in terms of its fundamental economic competitiveness.”

When children made up the lion's share of population change, job growth is tied at the hip with consumption. But consumption economies are, by definition, local. The geography of wages for local services is small in area and therefore available to anyone living there. The competition for minding the store till is fierce. Paychecks for such a job are low.

Which jobs demand a high paycheck? Careers that provide goods and services outside the regional market. The world competes for your work.

Cities that capture more labor that the world wants is a producer. Cities that capture more labor that the town wants, is a consumer. Producer Cities will bring macro wealth to micro well-being.