“Beautiful Places: The Role of Perceived Aesthetic Beauty in Community Satisfaction” is a new paper on regional studies that I wrote with my MPI colleagues Charlotta Mellander and Kevin Stolarick.

Here’s the abstract:

This research uses a large survey sample of individuals across United States locations to examine the effects of beauty and aesthetics on community satisfaction. The paper conducts these estimations by ordinary least-squares, ordered logit, and multinomial logit. The findings confirm that beauty is significantly associated with community satisfaction. Other significant factors include economic security, schools, and social interaction. Further, community-level factors are significantly more important than individual demographic characteristics in explaining community satisfaction.

Read the full paper here.

Just as people with higher levels of education have fared better during the Great Recession, cities and regions with higher levels of human capital have experienced lower rates of unemployment and higher wages. But human capital, which takes into account only the level of a worker’s education, is a crude measure – some of the world’s greatest entrepreneurs, like Bill Gates and Steve Jobs, are college dropouts.

A while back, I wrote about research done by my colleagues at the Martin Prosperity Institute (MPI), that took data provided by the Bureau of Labor Statistics’ O*NET Project on actual skills (physical skills of the sort used in manufacturing, analytical or cognitive skills, and social intelligence skills like the ability to direct teams, form entrepreneurial new businesses and organizations, and mobilize people and resources behind common causes and objectives) and charted their relations to the economic performance of cities and regions.

Now new MPI research charts the effects of these skills on regional wages. Each region is ranked three times: by its share of occupations requiring analytical skills, social intelligence skills, and physical skills. As the above chart shows, the two types of skills most closely associated with knowledge and creative work – social and analytical skills – add substantially to regional wages. Physical skills of the sort associated with blue-collar work do the opposite. The MPI study concludes that:

Some cities, like Huntsville or Houston, rely predominately on analytical skills. Most cities that draw highly on analytical skills also rely on social intelligence skills, like San Jose, Boston, Washington, Durham and Boulder. The cities that rely the most on physical skills are places like Dalton, GA, known for its carpet manufacturing, Toledo, OH, a center of glass manufacturing, and agricultural and food processing centers like Bakersfield, CA.

Employment in city regions is shifting away from physical skills and towards analytical and social intelligence skills. Regions that successfully increase their share of jobs relying on analytical and social intelligence skills are more likely to prosper than regions that rely on the physical skillset associated with manufacturing.

There is much more to be gained, both in terms of creating better jobs and making cities and regions more prosperous, from strategies that shift the skill mix of people and places toward social and analytical skills than those that seek to add more manufacturing jobs. Efforts to increase the social and analytical skills used in service jobs not only bolster productivity and spur innovation, but can improve wages and contribute to overall regional prosperity.

From Silicon Valley to Shanghai, cities are increasingly seen as engines of economic progress. Cities bring together diverse groups of people and companies in ways that increase productivity and create the networks, clusters, and chance interactions that lead to the discovery of new innovations and the creations of new entrepreneurial businesses. Up until now, the economic performance of cities has been gauged in terms of the education or human capital level of residents or the kinds of work they do.

But new research by my colleagues at the Martin Prosperity Institute sheds lights on the relationship between cities and three underlying types of workforce skills – physical skills of the sort used in manufacturing, analytical or cognitive skills, and social intelligence skills like the ability to direct teams, form entrepreneurial new businesses and organizations, and mobilize both people and resources behind common causes and objectives. The chart below plots the distribution of these three sets of skills by city size.

The relationship between skill and size is striking. On the one hand, smaller cities have more physical skill. As the chart shows, physical skills are negatively associated with city size. On the other hand, bigger cities account for more social and analytical skills. Both sets of skills increase with the size of cities. Social intelligence skills in particular are found in the largest cities and metro areas. And the largest cities have increased the proportion of social intelligence skills they account for over the past decade. Larger cities not only draw more educated and innovative people, but more people with the critical social skills required to turn new ideas into successful enterprises and industries.

This is in line with Jane Jacobs’ early thinking that cities are containers for a diverse mix of people and skills. And it also helps us understand some of the deeper reasons for the success of larger cities, the plight of smaller ones, and our increasingly spiky economic landscape.

Here’s the long version of my column published in today’s The Globe and Mail.

Canadians often point to the angry red versus blue divide that is such a hallmark of American politics, with higher-income, more economically advanced places voting Democratic and less-affluent, more working class locales trending Republican, as a problem that Canada has risen above. But this same kind of cleavage has become increasingly apparent in Canada – glaringly so in Toronto’s upcoming mayoral election.

The most recent Nanos poll shows Rob Ford leading in Etobicoke, North York, and Scarborough, while George Smitherman leads in old Toronto. The conventional wisdom is that this is a product of amalgamation and the rise of the mega-city, which brought two distinct constituencies into one political jurisdiction in 1998. But it runs far deeper than that.

My Martin Prosperity Institute research team overlaid a map of the strongholds of the various mayoral candidates with another showing where creative, service, and blue-collar workers work. The division isn’t just urban-suburban.  Toronto’s economic and political geography takes the shape of a “T” that divides the city on an east and west as well as a north to south axis.  Higher-paying, higher-skill, creative class jobs – in fields spanning science and technology; business and management; arts, culture, and entertainment; health care and education – are concentrated along subway routes radiating out of the downtown core of the city in both directions. Lower-skill, lower-wage jobs are concentrated at the periphery of this T in both the core and more outlying areas. There are only a handful of districts left in the city where working class jobs predominate. One of them, up in the far left hand corner of the map, is Ford’s. Smitherman’s former riding and Pantalone’s ward are right smack in the middle of the T.

In the United States, that political divide is also a jurisdictional divide – pitting city against suburb. Joel Kotkin, who I have debated many times, has written about the increasing Balkanization of California along competing lines of class and occupation, with its affluent clustered along its coast and its farmers and middle class suburbanites concentrated in the state’s interior. The result, he writes, “is two separate…realities: a lucrative one for the wealthy and for government workers, who are largely insulated from economic decline; and a grim one for the private-sector middle and working classes, who are fleeing the state.” He might as well have been writing about Toronto.

But in Toronto it is taking place inside the city itself.  This inconvenient but unavoidable truth runs counter to a deep and long-standing perception: A social and political consensus – shared by NDPers, Liberals, and Conservatives alike – that Toronto, for all its demographic and economic variety, is at bottom “one” cit, and that it is a fair and equitable place. George Smitherman, Rocco Rossi, Joe Pantalone, and even John Tory, despite their differences, all reflect that same consensus — one that has streteched all the way from David Crombie and Mel Lastman to David Miller. The current election campaign shows how frayed that consensus has become. Ford, as Chris Hume wrote some time ago, “has tapped into a deep well of exurban fear and loathing… He personifies anti-urbanism, which makes him a hero.”

There has been considerable discussion in this campaign about whether we should concentrate on building more subways or light rail; much has been made of Ford’s adamant opposition to new bicycle paths. But these discussions ignore the basic fact that transit is bound up with Toronto’s class divide, as MPI research has shown. The members of Toronto’s struggling service and working classes – the ones who need public transit most — are woefully underserved. Forty-five percent of creative class members work within 500 meters of a subway line. Among the service class, that number drops to 31 percent. Buses could pick up some of the slack, but they don’t — they all too often run infrequently and on indirect routes. A five kilometer commute to the central city can take up to 40 minutes. Whether justly or not, Toronto’s working people feel that the city’s government is bloated and elitist and complacent, they believe that it benefits upscale urban dwellers and does not work for them.

Government did not cause Toronto’s (or for that matter, California’s) cleavages — they are the result of a fundamental economic restructuring that has brought enormous boons to some and left others out in the cold. As manufacturing shifts abroad and the technology and knowledge economy burgeons, innovative companies, highly skilled people, and the jobs that employ them have formed dense clusters. It is this very process which drives economic development forward, spurring innovation, generating new entrepreneurial firms, and creating new opportunities. But it also drives up housing values and splits up and sorts people by work and income.

The logic of capitalism is filled with contradictions. Those contradictions create new wealth and simultaneously bring new divisions and new social costs. Toronto, like virtually every other major city in North America, stands at a critical inflection point. Its recent economic success has, in effect, split it right down its middle.

When I first moved to Toronto, I believed we still had a chance to avoid the fraying of the social compact that is eating away at the states. But it has happened here too and it will not go away.

This, it seems to me, is the real subtext of this election, the understory that we have not wanted to acknowledge. Toronto has fallen victim to the same spiky structural forces that are concentrating economic assets and dividing communities across the globe, here in North America and right here in our very own city. We cannot push this under the rug. Regardless of what happens on election day, we must all face up to the fact that we are no longer one Toronto. That is the central challenge that the next mayor and all of us will be dealing with long after this election is past.

That’s the title of an important new study by my Martin Prosperity Institute colleague Kevin Stolarick. Economists have long used the firm as their basic unit of analysis. But Marx long ago said it was the kind of work people do that not only defined their class position but is what really propelled the economy. The study makes the case for the emerging field of “occupational organization” as a complement and counterpoint to the more established field of industrial organization. Here’s the abstract.

Industrial Organization studies the behavior of firms, markets and economies through the lens of industry.  With the transition to a knowledge or creative economy, occupation has become an equally important consideration for understanding regional economies and markets. Industry alone is no longer a sufficient discriminator to understand regional markets and structures.  This paper discusses the rising importance of occupation as a unit of analysis for understanding regional economies and economic structures. However, occupation does not supplant industry as occupation alone is also not sufficient.  Rather, an understanding of the organization of occupations and industries across regions and occupations within industries is required. The study of Occupational Organization is needed.

The full study is here.

Everyone interested in urban and regional economic development must check out this new MPI study, ”Knowledge in Cities.” Using data from the U.S. Department of Labor’s Occupational Information Network database – O*NET – it identifies 11 key types of regions by the knowledge, skill, and work they do. Here are some examples of the regional types it defines.

• Enterprising Regions like Chicago, L.A., Miami, and Toronto have high knowledge about sales and marketing, economics and accounting, customer and personal service, and information technology and telecommunications.
• Engineering Regions like San Jose (Silicon Valley) and Calgary are high in engineering and IT; low knowledge about physical and mental health.
• Thinking Regions like New York, Philadelphia, San Diego, and Portland, Maine, have high knowledge about arts, humanities, IT, and commerce, and low knowledge about manufacturing.
• Making Regions like Detroit have high knowledge about manufacturing, but very low knowledge about commerce and the humanities.
• Building Regions have very high knowledge about construction and transportation.
• Understanding Regions – mainly college towns like Charlottesville, VA, and Iowa City, IA – have very high knowledge about arts, science, humanities, and IT but very low knowledge about manufacturing.

The study also finds that three types of regions – Engineering, Enterprising, and Building Regions – have higher levels of productivity and earnings per capita, while Teaching, Understanding, Working, and Comforting Regions have lower levels of economic development.

My research team at the Martin Prosperity Institute just released a new report on Toronto in the Creative Age. Several things really stand out.

First, Toronto is still coming into its own as a major North American metro. U.S. metros in the Northeast and Midwest had significant expansion a whole lot earlier, but Toronto has seen substantial growth since the 1950s, sort of like a Sunbelt metro. Since then, it has transformed from a sleepy metro into a large and rapidly growing one, fueled by massive immigration and growing across almost every knowledge and creative  industry.

Second, Toronto performs extremely well – compared to its benchmark peers – on two of the 3Ts of economic development (talent and tolerance), ranking third in talent and fifth in tolerance (see the table above).

But it performs  poorly on the first T – technology, ranking 11th, or last – among  its North American peers. Toronto actually did fairly well on our measure of high-tech industry, scoring fourth behind only Los Angeles, New York, and Seattle  based on our North American Tech-Pole Index which was adapted from the Milken Institute’s Tech-Pole Index. The MPI research shows that patents in particular are a trouble spot. Not only did Toronto rank third to last in patents – ahead of only Montreal and Vancouver – it also had the lowest score on patent growth, with the region filing 8.3 percent fewer patents in 2005 than in 2000. And it’s not due to a lack of high-tech workers – Toronto ranked fourth of its 11 peers on the North American Tech-Pole Index.

Toronto should be proud of  its strengths in talent and tolerance but it also needs to address its weakness in technology and innovation. One way to do that is to leverage the strengths of surrounding areas like Waterloo, home to RIM, Ottawa and Rochester, home to a considerable tech cluster, and Montreal, home to Cirque de Soleil and aerospace, software, and video game firms. Toronto can take the lead in building and capitalizing on the strength of “Tor-Buff-Loo-Mont-Owa” mega-region by strengthening the physical and institutional connections across the mega-region. The mega-region does not currently offer the seamless connections between cities found in “Bos-Wash” or greater Tokyo. But faster rail connections and more seamless border crossings would increase the scale and market size of the mega-region while increasing the velocity of moving people, goods, and ideas across it.

Density is a key factor in innovation and economic growth. The dense geographic clustering of economic activities was true of the industrial behemoths of the past – steelmaking in Pittsburgh and automotive production in Detroit. And, despite advances in communications technology, it applies even more so today: from high-tech firms in Silicon Valley to film producers in Los Angeles and recording studios and record labels in Nashville. There’s no doubt: The geographic concentration of firms, industries, technologies, people, and other economic assets plays a powerful role in innovation and economic growth.

The great economist Alfred Marshall long ago outlined the dynamic of agglomeration – that is, the process by which co-location of related economic activities and assets shapes industries and economic development. Jane Jacobs showed us how the clustering of diverse groups of people, firms, and industries in cities provides the basic engine of innovation and new product development. Harvard’s Michael Porter has shown how clusters of related industries, customers, and suppliers power innovation and growth. Density makes it easier for people and firms to interact and connect with one another, and it reduces the effort, friction, and energy that’s used to make these connections. Density increases the speed at which new ideas are conceived and diffused across the economy, accelerating the speed with which new enterprises and new industries are created.

The curious thing is that most of our key economic and innovation measures don’t take density explicitly into account. Economists, economic geographers, and other social scientists tend to normalize the numbers they’re interested in by population, representing the data on a per person or per capita basis. This approach has led to all sorts of important empirical insights and findings. But since density itself is an important factor in certain kinds of economic growth, it’s useful and important to develop indicators that take it explicitly into account. For that, we need to look at the distribution of activities and key variables across space. So instead of measuring them on a per capita basis, we can examine them on the basis of land area or per square kilometer.

A while back, I posted about this analysis by Rob Pitingolo (h/t: Don Peck) which looked at the density of human capital. Pitingolo developed an intriguing metric that he called “educational attainment density.” Instead of measuring human capital or college degree holders as a function of population, he measures it as a function of land area – that is, as college degree holders per square kilometer. He did this for the primary urban centers of metropolitan areas.

Inspired by this, I worked with my Martin Prosperity Institute colleague Charlotta Mellander to build indicators of density for a wider range of key economic and demographic variables. We conduct our analysis at the metropolitan level. It’s important to point out that there are limits to using the metropolitan area as a unit of analysis. Metropolitan areas combine core cities with their suburbs and come in all different shapes and sizes. Some are more concentrated at the core (like Portland), others more sprawling (like Phoenix). Examining the distribution of key economic, social, and demographic variables at the metro scale is admittedly crude. But it is also a useful and important starting point, since the metro level is by far the most common unit of analysis in studies of regional economic development. In our research on the subject, we’re interested in developing new, more precise metrics and indicators of density within metropolitan areas – comparing central cities or urban centers to suburbs and probing the distribution of density across Census tracts and zip codes, which I will report in future posts.

We also compare our density measures to population density, to see which metros over- and under-perform relative to their populations. To get at this, Mellander performed a residual analysis – a statistical procedure which systematically compares how metros perform on a given factor compared to what we’d expect based on their population density. We also look at the associations between our various density measures and key metrics for regional economic development – wages, incomes, innovations, and regional economic output. As usual, I’ll point out that these are preliminary, exploratory analyses that simply point to associations between variables. We don’t make any claims here about the direction of causality, and we acknowledge that intervening variables may come into play.

Over the next couple of weeks, I’ll report the key findings from our analysis. Later this week, I’ll look at density of human capital – based on the conventional measure of people with a bachelor’s degree and above. Then, I’ll turn to the density of the creative class – that is, of people employed in science and engineering, business and management, health care and law, and arts, culture, design, media, and entertainment. The fourth post in this series will look at the density of a subset of this group – artistic and cultural creatives. In the fifth post, I’ll share our findings on density of innovation and high-tech industry. And, in the final post in the series, I’ll bring it all together and sum it up with maps of these density measures.

A new report from our Martin Prosperity Institute team charts the geography of class in Toronto. The map below shows the deep underlying economic – class - divisions of the city and can also help us understand the current polarized mayor’s race.

The map shows the concentration of three broad classes of work across the city’s census tracts. The kind of work people do is the hallmark of social-economic class and the map shows a city where the dominant classes occupy, literally, two different social, economic, and geographic spaces. This segmented pattern mirrors the same trend identified by earlier research on the worsening residential segmentation of the city highlighted by my University of Toronto colleague David Hulchanski.

Higher-paying, higher-skill creative class jobs – in fields spanning science and technology; business and management; arts, culture, and entertainment; health care and education – are concentrated in a T-shape pattern radiating out of the downtown core of the city. Lower-skill, lower-wage jobs surround the creative class T and are concentrated in more outlying areas. Toronto’s geography reflects a city that has become almost completely post-industrial: There are very, very few districts left in the city where working class jobs are the dominant concentration. But where those jobs are can help us understand the mayor’s race and Toronto’s increasingly class-polarized politics.

The two leading candidates come from completely different economic and geographic worlds. The only working class concentrations in the upper left-hand quadrant of the map are in or very close to Rob Ford’s city council riding. Prior to running for mayor, George Smitherman represented the Toronto Centre Provincial riding, an area that is at the virtual apex of the creative class zone.

Toronto needs to come to grips with its growing class divide, and to develop strategies that can begin to address it if it wants to retain the tolerance, social cohesion, and commitments to social justice which have so long been its hallmarks.

My new paper on Canada’s creative class, done in collaboration with my MPI colleagues Kevin Stolarick and Charlotta Mellander, is out. It’s titled “Talent, Technology and Tolerance in Canadian Regional Development” and is published in the latest issue of the Canadian Geographer.

Here’s the abstract:

This article examines the factors that shape economic development in Canadian regions. It employs path analysis and structural equation models to isolate the effects of technology, human capital and/or the creative class, universities, the diversity of service industries and openness to immigrants, minorities and gay and lesbian populations on regional income. It also examines the effects of several broad occupations groups—business and finance, management, science, arts and culture, education and health care—on regional income. The findings indicate that both human capital and the creative class have a direct effect on regional income. Openness and tolerance also have a significant effect on regional development in Canada. Openness towards the gay and lesbian population has a direct effect on both human capital and the creative class, while tolerance towards immigrants and visible minorities is directly associated with higher regional incomes. The university has a relatively weak effect on regional incomes and on technology as well. Management, business and finance and science occupations have a sizeable effect on regional income; arts and culture occupations have a significant effect on technology; health and education occupations have no effect on regional income.

The full paper is here.