Is a new world financial order emerging from the economic crisis? Will Asia’s rising financial centers displace the long-held dominance of New York and London?

The newly released edition of the Global Financial Centres Index (GFCI), an annual competitiveness ranking of the world’s leading financial centers, provides useful data with which to assess the evolving landscape of global finance.

The map above, prepared by Zara Matheson of the Martin Prosperity Institute (MPI), shows the world’s leading financial centers based on the GFCI ranking.

London and New York remain the world’s leading financial centers. But, Hong Kong and Singapore, which hold the third and fourth spots, are gaining ground, according to the report. Tokyo takes the fifth place and Shanghai has surged to sixth, giving Asia four of the top six spots overall. Chicago, Zurich, Geneva, and Sydney round out the top 10. Toronto, with its strong stable banks, ranks 12th overall but makes the list of the world’s eight leading “broad and deep” financial centers.

It’s true that big international economic crises – like the financial panic and Long Depression of the 1870s and the Great Depression of the 1930s — have a way of upending the geopolitical order and hastening the fall of old powers and the rise of new ones, and we may be in the early throes of such a shift now. But what factors can help us better understand the shifting landscape of global finance today?

In his magisterial book, Capitals of Capital, the economic historian Youssef Cassis writes that financial capitals, once established, “have incredible staying power.” The rise of major financial centers correlates with the rise of the nation’s economic power, but with a considerable time lag. The United Kingdom lost its position to the United States as the world’s largest economy in 1872 and the largest exporter in 1915. By the middle of the 20th century, America’s economic output was twice that of all of Europe combined. But it was only after the war that New York unseated London as the world’s financial center.

To get at this, my colleague Charlotta Mellander and I looked at the economic might of financial centers themselves and of the nations in which they are located. We ran a basic correlation analysis and generated a series of scattergraphs. The data cover 52 global financial centers. We stress that 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.

There is a clear connection between economic heft and global financial centers. This holds both for the financial center itself and the nation in which it is located. Our measure of metropolitan economic output comes from our research that uses satellite images of the world at night to derive a systematic measure of economic output based on light or energy emissions which we dub as our “light-based regional product” (LRP). A city’s global financial center score is reasonably associated with its LRP (.46) and slightly more so with its LRP per capita (.53). Both correlations are substantially higher than for a city’s population (.35).

The same basic pattern holds at the national level. Global financial centers are more likely in bigger economies. The GFCI is reasonably associated with economic output measured as gross domestic product per capita. The correlation was .28 when we used the combined score of all of a nation’s financial centers, and even higher (.51) when we used the score of its highest-ranked financial center.

Global financial centers are also more innovative. GFCI score is modestly correlated with patenting overall (.28) and slightly more so with patents per capita (.32).

The ability to attract talent has long been a defining characteristic of leading global financial centers. Cassis writes that: “The crucial contributory factor in the financial center’s development over the last two centuries, and even longer, is the arrival of new talent to replenish their energy and their capacity to innovate.” So we looked at the relationship between global financial centers and a proxy measure for openness to global talent  - data on attitudes toward racial and ethnic minorities from Gallup surveys. And we found a reasonably close association. The correlation was .4 when we used the combined score of all of a nation’s financial centers and .51 when we used its highest-ranked financial center.

We also looked at the relationship between global financial centers and another measure of openness – attitudes toward gay and lesbian people, also from Gallup surveys. And we again found a reasonably close association. The correlation was .32 when we used a nation’s highest-ranked financial center, and it was .27 (though not statistically significant) when we used the combined score of all the financial centers in a nation.

The economic crisis is altering the landscape of global finance, but change in the world financial order occurs slowly and gradually. Global financial centers have long staying power, and shifts at the very top follow long lag times. Asian financial centers, particularly Hong Kong and Singapore, have gained ground on London and New York, and Shanghai has risen rapidly to the top echelons of the global financial system. Our analysis finds a close association between global financial power and economic power more generally at the local and national level – and this bodes well for rising Asian centers, more so for Shanghai than for the smaller city-states of Hong Kong and Singapore. That said, their rise may be hindered from intense and growing regional competition among one another. Our analysis also finds a close association between openness and financial power which may hinder their rise to preeminence. Even though Asian centers, from Hong Kong and Singapore to Shanghai, are striving to improve their talent-attracting game, they still do not offer the level of openness, fluidity, and amenity found in London and New York. The interaction of these two factors – economic power and economic openness – will shape the evolution of the global financial system and its pinnacle powers for the foreseeable future.

Density is a key factor in innovation and regional economic growth. Over the past couple of weeks, I’ve looked at density of human capital, the creative class, and high-tech innovation. Instead of measuring these factors on a per capita basis, we looked at them in terms of land area, or per square kilometer.

The first map below plots the top 10 metros on each of the basic density measures, charting human capital, creative class workers, artistic and cultural creatives, patented innovations, and high-tech workers per square kilometer.

The second map plots the top 10 metros on human capital density, creative class density, artistic and cultural density, high-tech and innovation density, all relative to their population densities.

The maps are striking. They show how spiky and bicoastal this geography is. The highest-density places are clustered in the East Coast Bos-Wash region and in the West Coast in the regions around Silicon Valley and Greater Los Angeles. Outside of these locations, only Chicago, Boulder, and Ann Arbor rank highly on multiple measures. It’s worth pointing out the prominence of Ann Arbor on the list, the home of the University of Michigan located just outside Detroit. Its relative concentrations of creative class density and human capital density rival the most innovative and propulsive regions of the country. Clearly, there are bright spots within the Rustbelt economy, even right next to some of its most intractable problems of economic collapse. Detroit does not rank better than 100 on any of the measures conducted.

This analysis is only a starting point. There is much more to do. Metropolitan areas – which span core cites and their suburbs – come in all shapes and sizes. Some are more concentrated at the core, others more sprawling. Our ongoing research at the MPI is developing new 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. Stay tuned for more.

My past several posts have looked at the density of key economic and demographic factors across America’s metropolitan regions. Today, I turn to the density of high-tech industry and of innovation. Long ago, the great economist Joseph Schumpeter highlighted the role of innovation in powering the rise of new industries, the creative destruction of existing ones, and the growth in prosperity of economies. Robert Solow won the Nobel prize for identifying the role of technology in economic growth and development. Paul Romer has shown how the accumulation of scientific and technical knowledge is the central force in endogenous economic growth. Michael Porter and AnnaLee Saxenian, among others, have shown how clusters of high-tech companies and other economic assets have propelled the rise of new firms like Intel in semiconductors, Apple in computing, Genetech in biotech, Google in search, and countless others that have introduced not just new innovations but whole new industries and epochs of regional growth.

Patents are the conventional measure of innovation. Despite their various weaknesses, patents represent a systematic, quantitative measure of innovation and are used by economists as the single dominant measure of innovation. But, as with other measures, economists tend to measure them on a per capita basis.

Innovation Density: Our measure of innovation density is patents per square kilometer. The map below shows the density of innovation based on this measure. The median density of innovation is .008 patents per square kilometer. The densest metros have more than .4 patents per square kilometer, while the least dense have fewer than .001.

The chart below shows the top 10 metros in terms of density of innovation. The density of innovation in these metros ranges from 25 to more than 100 times the national norm.

Source: USPTO 2008.

It’s not surprising that San Jose (Silicon Valley) tops the list with .831 patents per square kilometer or that nearby San Francisco is second with .446 patents per square kilometer. Los Angeles is third with .41 patents per square kilometer, followed by Trenton, Bridgeport-Stamford, Connecticut, Greater Boston, Boulder, Greater New York, Ann Arbor, and New Haven.

The density of patents is closely associated with key regional economic outcomes such as regional wages (.668), regional incomes (.588), and regional economic output (.459). (As usual, I point out that these correlations only suggestion associations between variables. They do not specify any causation or make any claims about the direction of causality. Other intervening variables may come into play).

The scattergraph above plots the relationship between innovation density and regional wages – a key indicator of regional wealth and productivity. The close adherence of the observations to the fitted line suggests a close association between the two. Anchorage, Washington, D.C., Boulder, Trenton, Fremont, Napa, and, of course, San Jose are all located above the line – showing even higher wages than their density of  innovations would predict. Most of these places are among the most innovative in the country.

High-Tech Density: I now turn to the density of high-tech employment. Our measure of high-tech density is the  number of high-tech workers per square kilometer.

The next map shows the density of high-tech employment for U.S. metros. The median density of high-tech employment across all U.S. metros is less than one (.901) high-tech worker per square kilometer. The densest metro has nearly 40 high-tech employees per square kilometer, while the least dense have fewer than .1 high-tech workers per square kilometer.

The next chart shows the top 10 metros in terms of density of high-tech workers. The density of high-tech workers in these metros ranges from 15 to 42 times the median for all metros.

Source: Bureau of Labor Statistics 2008.

Los Angeles tops the list with 39 high-tech workers per square kilometer. San Francisco is next with 30, followed by Trenton (29), San Jose (Silicon Valley) (24), New York (23), Bridgeport-Stamford (22), Greater Boston (21), Greater Washington, D.C. (19), Boulder (17), and New Haven (14).

The next map shows the density of high-tech employment compared to what we’d expect given their population density based on a residual analysis.

The  next chart shows the 10 metros which have the highest density of high-tech employment compared to what their population density would predict.

Source: Bureau of Labor Statistics 2008.

Now San Jose (Silicon Valley) tops the list, followed by Boulder – which BusinessWeek recently named the best place for high-tech startups – and Greater San Francisco. Trenton is fourth followed by Greater Washington, D.C.; Greater Boston; Los Angeles; Huntsville, Alabama; Bridgeport-Stamford; and Seattle.

High density is closely correlated with regional wages (.604), regional income (.509), and regional economic output (.372). (As usual, I point out that these correlations are only a sign of associations between variables. They do not necessarily mean there is causation and I do not make any claims about direction of causality. And, of course, other intervening variables may come into play).

The scattergraph below plots the relationship between high-tech density and wages. San Jose, Washington, D.C., and San Francisco are all well above the line.

My next post sums up the key findings of our density analysis.

My past several posts have looked at the density of key variables across America’s metropolitan regions. Today, I turn to the density of a subset of the creative class – the density of artists and cultural creatives. My own earlier research, which landed me on “The Colbert Report” of all places, showed that metros with higher proportions of employed artistic and cultural workers also have higher incomes, higher rates of innovation, and higher housing prices. The reason is not that artistic and cultural creatives are more likely to launch new businesses or invent new products, but that their location in an area signals that a community is open to diverse groups of people who are open to new ideas and self-expression. The concentration of artistic and cultural creatives in a place is a sign of a local ecosystem that is more conducive to generating new ideas and mobilizing resources around them.

Our measure for the density of artistic and cultural creatives is the number of artistic and cultural creative workers per square kilometer. The map below shows the density of artistic and cultural creatives across U.S. metro regions. The median density of artistic and cultural creatives across all U.S. metros is only .08 per square kilometer. The densest metros have more than four artistic and cultural creatives per square kilometer, while the average metro has less than a tenth of a cultural worker.

The chart below shows the 10 densest metros in terms of artistic and cultural creatives. The density of these metros ranges from 12 to almost 40 times the national norm.

Source: Bureau of Labor Statistics 2008.

Topping the list is Los Angeles with 4.7 artistic and cultural creatives per square kilometer, followed closely by New York with 4.4 per square kilometer. San Francisco (2.2), Bridgeport-Stamford (1.6), Greater Boston (1.5), Trenton (1.5), Greater Washington, D.C. (1.4), Honolulu (1.3), Milwaukee (1.2), and Chicago (1.2) round out the top 10.

The next map shows the density of artistic and cultural creatives compared to what their population density would predict based on a residual analysis.

The next chart lists the 10 metros which have the highest density of artistic and cultural creatives compared to what we’d expect based on their population density.

Source: Bureau of Labor Statistics 2008.

Los Angeles again tops the list, followed closely by greater New York. Greater Washington, D.C. is third, followed by San Francisco and Minneapolis. But now Santa Fe (a center for visual artists), Nashville (a center for music), Madison, and – surprisingly – Salt Lake City enter the top 10 alongside Greater Boston.

So to what degree is the density of artistic and cultural creatives associated with key regional economic outcomes? Our correlation analysis suggests that the connection is strong. The density of artistic and cultural creatives is closely correlated with regional income (.614), wages (.622), output (.533), and innovation (.431). (As usual, I point out that these correlations point only to associations between variables. They do not specify any causation or make any claims about the direction of causality. And, of course, other intervening variables may come into play).

The scattergraph above plots the relationship between the density of artistic and cultural creatives and regional wages – a key indicator of regional wealth and productivity. The fitted line is reasonably steep and suggests a close association between the two. San Jose (Silicon Valley), San Francisco, Greater Washington, D.C., the Trenton area of Central New Jersey, Boulder, Ann Arbor, and L.A. all are located above the line – showing even higher wages than their high density of artistic and cultural creatives would predict.

In my next post, I turn my attention to the density of high-tech innovation.

In this, the third in my series of posts on density, I look at the density of the creative class. More than 35 million Americans are members of the creative class, making up roughly a third of the workforce. The creative class is a measure of human capital that looks at what occupations people work at rather than whether they earned a college degree. The creative class includes workers in science and technology, business and management, health care and law, and arts, culture, design, media, and entertainment.

The map below shows the density of the creative class across U.S. metros. The median density across all U.S. metros is roughly 8.4 creative class workers per square kilometer. The densest metros have more than 140 creative class workers per square kilometer, while the least dense have less than one.

The chart below shows the 10 metros with the highest densities of creative class. The creative class density of these metros ranges from eight to almost 20 times the national norm.

Source: Bureau of Labor Statistics 2008.

Topping the list is the greater Trenton area of central New Jersey (Princeton is within commuting distance to New York and Philadelphia) with 149 creative class workers per square kilometer. Greater New York is second with 147 creative class workers per square kilometer. Los Angeles (145), San Francisco (109), and Boston (100) each have 100 or more creative class workers per square kilometer. Bridgeport-Stamford (93), Greater Washington, D.C. (81), Honolulu (77), Philadelphia (75), and Chicago (74) round out the top 10.

The next map shows creative class density compared to what we’d expect based on population density. It’s based on a residual analysis which compares creative class density to population density.

The next chart shows the 10 metros with the highest creative class densities relative to their population density.

Source: Bureau of Labor Statistics 2008.

The Trenton area of Central New Jersey again tops the list, but now it is followed by New York, Los Angeles, Boston, Washington, D.C., San Jose (Silicon Valley), Boulder, San Francisco, Bridgeport-Stamford, Ann Arbor, Durham, and Minneapolis. This chart again shows the high ratio of creative class workers to population in leading college towns.

So to what degree is creative class density associated with key regional economic outcomes? Our correlation analysis suggests that creative class density is closely correlated with regional wages (.59), income (.57), economic output (.496), and innovation (.405). (As usual, I point out that these correlations point only to associations between variables. They do not specify any causation or make any claims about the direction of causality. Other intervening variables may come into play).

The scattergraph above plots the relationship between creative class density and regional wages – a key indicator of regional wealth and productivity. The fitted line is reasonably steep and suggests a close association between the two. San Jose (Silicon Valley), San Francisco, Greater Washington, D.C., Greater New York, Seattle, Boulder, Ann Arbor, Denver, and Durham all are located above the line – showing even higher wages than their creative class density would predict.

My next post turns to a subset of the creative class, examining the density of artistic and cultural creatives for U.S. metros.

It’s now well-accepted that the concentration of highly skilled people or of human capital is a key element of economic growth and development. Jane Jacobs argued that the clustering of talented and energetic people in cities is the fundamental driving force of economic development. The Nobel prize-winning University of Chicago economist Robert Lucas formalized Jacobs’ insights, showing that human capital externalities, or what have been called Jane Jacobs externalities, are indeed the key factor in economic growth and development.

But most economists measure human capital on the basis of population – the conventional measure being the percentage of adults with a bachelor’s degree or above. Our analysis here takes a different approach, getting at the density of human capital by looking at the number of adults with a bachelor’s degree per square kilometer.

The map below shows the human capital density of U.S. metros. The median human capital density across all U.S. metros is roughly 7.4 people per square kilometer. The densest metros have more than 100 degree holders per square kilometer, while the least dense have less than one.

The chart below shows the 10 densest metros in terms of human capital. The human capital density of these metros ranges from 10 to 20 times the national norm.

Source: U.S. Census Bureau 2008.

It’s not surprising that Greater New York tops the list with 156 college graduates per square kilometer. The figure is not just for Manhattan or New York City where such density would be expected, but for the entire metro area, including suburbs in Long Island and New Jersey. Los Angeles places second, surprisingly, with 123 college graduates per square kilometer. San Francisco is a close third with 122 college graduates per square kilometer. Two additional metros have 100 or more degree holders per square kilometer: Bridgeport-Stamford, Connecticut (104), and the Trenton area in central New Jersey (100), which is the state capital, includes Princeton and is approximately equidistant between the Philadelphia and New York-Northern New Jersey commuter sheds. Greater Boston (84), New Haven (75), Greater Washington, D.C. (74), Honolulu (73), and Chicago (69) round out the top 10.

The next map shows the human capital density of U.S. metros compared to what their population density would predict, based on a residual analysis.

The next chart shows the 10 metros with the highest residual value – that is, the highest human capital density compared to what we’d expect based on their population density.

Source: U.S. Census Bureau 2008.

Now San Francisco tops the list. Bridgeport-Stamford is second – a long-time upper-crust commuter suburb of New York, it now increasingly finds itself home to high-end financial operations. It’s followed by Greater Washington, D.C. and Greater Boston. Silicon Valley now makes the list – in seventh place. But New York falls from first to eighth. A number of college towns make the list – Boulder and Ann Arbor – as well as Barnstable (a community composed chiefly of highly educated retirees and commuters on Cape Cod) and the Trenton area of New Jersey.

So to what degree is human capital density associated with key regional economic outcomes? Our correlation analysis suggests that human capital density is closely correlated with regional income (.615), regional wages (.619), regional economic output (.475), and innovation (measured as patents, .464). (As usual, I point out that these correlations point only to associations between variables. They do not specify any causation or make any claims about the direction of causality. And, of course, other intervening variables may come into play).

The scattergraph above plots the relationship between human capital density and regional wages – a key indicator of regional wealth and productivity. The fitted line is reasonably steep and suggests a close association between the two. San Jose (Silicon Valley), San Francisco, Washington, D.C., Greater New York, Boulder, Ann Arbor, and Durham are all located above the line – showing even higher wages than their human capital density would predict.

In my next post, I turn to a related measure for human capital. But instead of measuring it based on educational attainment, we look at the work people actually do, examining the density of the creative class of people employed in science and technology, business and management, health care and law, and arts, culture, design, media, and entertainment.

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.

Ever wonder where the highest-paying jobs in your field are? Now, courtesy of the Bureau of  Labor Statistics (BLS), we have some answers.

Late last week, the BLS released a report (via Catherine Rampell of The New York Times Economix) showing the U.S. metros with the highest-paying jobs in nine major occupational categories including: business, finance, and management; professional and technical work; service; office work; construction; and blue-collar production jobs, among others. The BLS measures what it calls ”average pay relative” which includes wages, salaries, commissions, and bonuses. And, its calculations control for differences in the composition of jobs, industry, firm-level, and occupational characteristics, and the fact that data are collected at different times during the year. As the BLS defines it: “The average pay relative for all occupations and each occupational group equals 100.” A figure above 100 reflects the percentage above the national norm, while values below 100 reflect the percentage below that norm.

The chart below shows the pay profile for a series of U.S. metro regions. San Francisco is highest, followed by Greater New York, Salinas, California, and Greater Boston, which are all above the U.S. norm.

The second chart below shows the two highest-paying metros for each of the nine major occupational groupings in the BLS analysis. The San Francisco Bay Area has one of the two top-paying metros in six categories. Salinas, California, numbers among the top two in four job categories. Greater New York ranks in three. Chicago, Seattle, Minneapolis-St.Paul, Sacramento, and Providence each have one.

The upshot of all of this is that America is splitting into two distinct kinds of regional economies – a small set of affluent regions with relatively large concentrations of well-paying occupations, and a much larger set of less-advantaged regions with less-skilled, more poorly paying ones. In this kind of world, the ability to achieve the American Dream of economic opportunity and upward socioeconomic mobility increasingly turns on where you live.

This overlays the nation’s worsening income inequality with a glaring geographic dimension. America is becoming increasingly sorted not just by culture and attitudes, political party, or favorite candidate, but by powerful economic  forces - human capital level, skill, type of work, and, ultimately, pay and income. Given this underlying economic geography of work, money, and class, it’s little surprise that America’s politics continues along its current polarized and rancorous path.

Economic crises like the current one have devastating economic and social costs, but they also give rise to major rounds of technological innovation. That’s why I call them Great Resets. There was a significant spike in patents in the wake of the Panic and Long Depression of 1873 – and subsequent decades saw the rise of major new innovations from the light bulb, phonograph, and telephones to systems innovations like electric power, telephone systems, and urban transit (i.e. street cars, cable cars, and subway systems). The Great Depression was far and away the most “technologically progressive decade of the 20th century,” according to the detailed research of economic historian Alexander Field, outpacing the high-tech boom of the late 20th century by a considerable margin.

Joseph Schumpeter long ago showed how economic crises give rise to the gales of creative destruction – as new entrepreneurial individuals and enterprises seize the opportunity to forge new business models, and new industries revolutionize and transform the economy. The British economist of innovation, Christopher Freeman, found evidence that innovations not only accelerate but bunch up during economic downturns only to be unleashed as the economy begins to recover, ushering in powerful new waves of technological change.

A study released today by the Kauffman Foundation (h/t: Ian Swain) provides additional evidence that our current crisis takes the form of a Great Reset. According to the study, 2009 was a banner year for new business startups. As the graph above shows, more than 550,000 new businesses were started over the course of the year. The report found that “the 340 out of 100,000 adults who started businesses each month represent a 4 percent increase over 2008, or 27,000 more starts per month than in 2008 and 60,000 more starts per month than in 2007.”  This represents the highest rate of new business startups in 14 years. The startup rate for African-Americans also surged to record levels, according to the study.

Last Saturday, I posted the Global Well-Being Map from the Gallup Organization which showed how the countries of the world stack up on an index of well-being which runs from “suffering” to “thriving.”

The Gallup study found a “clear well-being divide between the wealthier countries of northern, western, and central Europe and some poorer countries within eastern and southern Europe.”

Economic development clearly plays a role in levels of  national well-being or happiness. A major cross-national study by University of Pennsylvania economists Justin Wolfers and Betsey Stevenson found a close association between income and the happiness of nations. As Wolfers writes:  “1) Rich people are happier than poor people. 2) Richer countries are happier than poorer countries. 3) As countries get richer, they tend to get happier.”

But other factors also appear to be at play. It is not just the level of economic development that matters, but its nature and type. The past several decades have seen a shift from older industrial-style economies and societies to newer post-industrial ones characterized by higher levels of knowledge-based, professional, and creative jobs, more highly educated populations, and a shift toward what Ronald Inglehart has dubbed “post-materialist values” – a diminished commitment to traditional authority and institutions and a shift toward self-expression, openness, and tolerance. My recent research with Charlotta Mellander and Jason Rentfrow finds evidence that post-industrial socioeconomic structures and post-materialist values matter to the happiness of nations, especially of the most advanced nations, in addition to the effects of income and the level of economic development.

With Mellander’s help, I took a quick look at the factors which might be associated with this new Gallup well-being index of the well-being of nations. Not surprisingly, the level of economic output plays a key role, but post-industrial structures and post-materialist values do as well. Before going any further, let me point out that this analysis is based on simple correlations which identify associations between variables but do not specify causality.

“Thriving” is positively correlated with the level of economic output, measured as GDP per capita (.65).

“Thriving” is also associated with the level of education (.45) and share of the workforce in knowledge-based, creative, and professional occupations (.42).

And it is also correlated with attitudes toward racial and ethnic minorities (.44) and gays and lesbians (.7).

“Struggling” is negatively correlated with the level of economic output (-.57).

It is also associated with the share of the workforce in knowledge-based jobs (-.5), the level of education (-.4), and with attitudes toward racial and ethnic minorities (-.38) and gays and lesbians (-.63).

“Suffering” is negatively correlated with the level of economic output (-.5).

It is also negatively correlated with attitudes toward ethnic and racial minorities (-.34) and even more so with attitudes toward gays and lesbians (-.56).

This is just a first cut. We are actively probing these issues in further detail and with more advanced statistical techniques in our ongoing research. But for now, we can say that while income and the level of economic development play an important role in the happiness of nations, well-being is also related to the type and nature of economic development and the values it engenders. There is something in the nature of post-industrial economies and in their values that appears to affect the happiness of their people over and above the effects of income. Perhaps it is that people with higher levels of education have more flexibility or choice in pursuing their dreams, building families and social relationships that are more fulfilling, or simply in their ability to adjust to misfortune or bad times. Perhaps it is that knowledge-based jobs are more challenging and fulfilling. It’s also clear that  the most troubled societies – those with the highest reported levels of suffering – also, generally speaking, face the highest levels of intolerance. While income and the level of economic development certainly need to be top of mind considerations when we think about or attempt to act on the happiness of nations, one simply cannot neglect the effects of economic and social structure and of values in social well-being.