Introduction

The world economy is in the midst of an epochal transformation, from industrial capitalism to a new age of knowledge-based or creative capitalism. The canonical factors of production identified by the classical economists – land, labor, and capital – are no longer the principal drivers of economic growth. National and regional economies increasingly grow and thrive to the extent that they are able to harness science and technology, innovation, and human creativity as sources of economic and social progress.

Creativity differs in fundamental ways from more traditional, tangible factors of production. Unlike stocks of things that can be depleted or worn out, it is an infinitely renewable resource that is continually enlarging via education, job experience, and the spontaneous, often fortuitous combinations and recombinations of ideas that occur as people collaborate and compete.

Just as industrial capitalism gave rise to a new socioeconomic class of blue-collar workers, the creative economy has given rise to a new class of laborers who work with their minds and creativity. This creative class, which makes up between a third to nearly half of the workforce in the advanced economies, includes scientists and technologists; innovators and entrepreneurs; designers, musicians, entertainers, media workers, academics, and artistic and cultural creatives; and knowledge-based professionals in business, education, and health-care.

Like Marx’s (1887) working class, which was comprised of a number of very different occupations that all had physical skills in common, from skilled tradesmen to assembly-line workers, the diverse occupations that make up the creative class all draw upon an underlying set of creative skills.

This chapter, which draws off our previous work,¹ summarizes our rankings for 82 nations on a metric that we call the Global Creativity Index (GCI), a composite measure based on technology, talent, and tolerance, the 3Ts of economic development (Florida 2002, 2012).

Technology is the first T. As Marx (1887) and Schumpeter (1934, 1942, 1947a, 1947b) noted long ago, technology and technological innovation enable capitalism to generate new industries and spur new growth. Solow’s (1956) now classic residual formalized the role of technology in economic growth. An enormous body of recent literature confirms and elaborates on it (Nelson 1959, 1977; Nelson and Winter 1982; Rosenberg 1983; Mowery and Rosenberg 1989; Rosenberg and Nelson 1994).

Talent is the second T. All the way back to Adam Smith, economists have noted the role of human capital and skill in economic growth, which Smith defined as the “acquired and useful abilities of all the inhabitants or members of the society” (Spengler 1977). Becker (1964) and Mincer (1974) identified a direct link at the individual level between knowledge and skill on one hand and productivity levels on the other (captured by wage levels). There is an extensive literature linking human capital to national and regional growth (Barro 1991; Rauch 1993; Simon and Nardinelli 1996; Simon 1998; Glaeser 1999; Glaeser and Saiz 2003). Moreover, these links are most easily seen and felt in cities, which act as the key economic engines for the contemporary economy (Glaeser 2011).

Drucker (1969, 1993) and Machlup (1962) long ago noted the growing economic importance of knowledge workers and the knowledge economy. Knowledge workers not only invent new machines and processes that turn out old products more efficiently, they come up with completely new products that create whole new markets. Romer (1986) formalized the role of knowledge and connected it with technology in his theory of endogenous growth.

Tolerance is the third T. Both technology and talent are best conceptualized not as stocks but flows. Talent and technology flow not just to places with great universities or robust industrial structures but places which are open and have low barriers to entry (Florida 2002). Tolerance thus operates as a non-market factor that increases the efficiency of technology and talent. Page (2007) has shown that innovation is closely associated with diversity. Silicon Valley, perhaps the most innovative place in the world, exemplifies this. Between one-third and one-half of its new technology-based enterprises have a foreign-born person on their founding team (Wadhwa et al. 2007). Detailed cross-national studies (Noland 2005; Noland and Pack 2004) found that the economic performance of nations was positively associated with more open attitudes toward homosexuality, which are also correlated with globalization, controlling for other factors. Openness to diversity is also in line with the broad cultural shift from materialist values about money and things to newer “post-materialist” values, which favor self-expression and a wider quest for happiness and well-being, as identified by Inglehart (1989, 1997).

Places that are open to new ideas, and that attract talented and creative people from across the globe, broaden both their technology and their talent capabilities, gaining an additional economic edge over and above those of technology and talent themselves. These places are increasingly found in and around major cities. Indeed, the economic activity produced within the world’s largest metropolitan areas accounts for a much greater economic value than their population sizes would suggest (Florida, Mellander, and Gulden 2010). Smaller urban areas that are rich in amenities and tolerance also punch well above their weight when it comes to technology-fueled growth.

The remainder of this chapter proceeds as follows. The next section outlines the data, variables, and methods used in our analysis. We then discuss the overall rankings of nations on the GCI and summarize the associations between the GCI and key indicators of economic and social performance. We conclude with a short summary of our key findings.

Methodology, Data, and Variables

This section discusses the methodology used in our analysis. The data covers 82 nations for the period 2000–2009. We sometimes use different years for different variables, and also utilize running averages, depending on data availability. The following describes the main variables and data sources used to construct the GCI.

The Global Creative Class

We now turn to our findings for the global creative class. The creative class is essentially a measure of skill based on occupations or the kinds of work people do. It thus differs from the more conventional measure of human capital that is based on educational attainment.

While there is considerable overlap between the two, they are not the same. In the United States, for example, nearly three-fourths of adults with college degrees are members of the creative class, but less than 60% of the people whose occupations qualify them as members of the creative class have college degrees (Currid-Halkett and Stolarick 2013). In Sweden, 90% of the highly educated hold creative class jobs while only one-fourth of the creative class have university degrees (Mellander 2009).

A significant body of research shows that the occupation-based creative class measure operates in addition to and through other channels than the standard education-based human capital variable. A large-scale study found that the creative class has a bigger effect on wages – a key element of regional productivity – whereas education tends to have a greater effect on income (Florida, Mellander, and Stolarick 2008). Independent research by Gabe shows that the creative class continues to have a substantial effect on regional economic growth when controlling for the effects of education and other factors (Gabe 2009). Having a creative class job brings economic benefits that extend beyond those of going to college. A college graduate working in the same occupation as a non-college graduate earns approximately 50% higher wages. But having a creative class job adds another 16%, about the same as another 1.5 years of additional education, according to Gabe’s research (Gabe 2009). A 2012 study used advanced statistical models to compare the effects of the creative class and human capital across the 257 EU regions. “Our results,” it concluded, “indicate that highly educated people working in creative occupations are the most relevant component in explaining production efficiency” (Marrocu and Paci 2012: 369).

McGranahan and Wojan (2007) used sophisticated statistical techniques to gauge the effects of the creative class versus human capital on regional growth. To do so, they used systems of simultaneous equations rather than the conventional simple regression models to control for population and employment growth as well as influences from a range of other local conditions and attributes. Their key findings overwhelmingly confirm the “strong independent influence on employment growth from both the initial share employed in the recast creative class occupations and its growth over the decade. By contrast, the statistical association with human capital variables is quite weak.” And they add: “the econometric test of the creative class thesis provides strong support for the notion that creativity has an effect on growth independent of the endowment of human capital” (McGranahan and Wojan 2007).

Another detailed study, this one investigating regional development in the Netherlands, also found that the creative class considerably outperformed the standard education-based human capital measure in accounting for employment growth. This led its authors to conclude that the creative class measure sets a “new standard” for measuring skill and talent, especially when considering regional labor productivity (Marlet and van Woerkens 2004). “With our Dutch data set we do find evidence that Florida’s creative class is a better predictor of city growth than traditional education standards,” they wrote. “Therefore we conclude that Florida’s major contribution is his successful attempt to create a population category that is a better indicator for levels of human capital than average education levels or amounts of highly educated people. The point is, as Florida stated, not which or how much education people can boast of, but what they really do in working life” (Marlet and van Woerkens 2004: 24).

Figure 15.1 shows how the nations of the world stack up on the creative class shares of their workforces. The range on the map is quite large, from a high of nearly half to a low of just over 2%. Fourteen countries have 40% or more of their workforce in the creative class.

With nearly half (47.3%) of its workforce in the creative class, Singapore takes the top spot. Aside from Singapore and Australia in fourth place, the top-ranked countries are overwhelmingly Northern European and Scandinavian: the Netherlands (46.3%), Sweden (43.9%), Switzerland (44.8%), Belgium (43.8%), Denmark (43.7%), Finland (43.4%), Norway (42.1%), and Germany (41.7%). Ten of the top 15 countries for the creative class are European. With 35% of its workforce in the creative class the United States ranks 27th, below Slovakia (38.4%). Of the BRIC countries, Russia ranks highest at 20th (38.6%). Brazil is 57th (18.5%), and China 75th (7.4%).

Innovation and Cities

In the industrial age, the key role in innovation was played by nation-states, which subsidized large-scale research, the R&D departments of major corporations, and also clusters of smaller, more entrepreneurial firms which spearheaded and commercialized breakthrough technologies. With the rise of knowledge-based capitalism, cities and locations themselves have come to play a more central role. Marshall (1890) initially noted the role that agglomerations or clusters of firms played in spurring innovation and productivity improvement. Agglomeration is a way of organizing the division of labor horizontally between firms as posed to vertically within large firms.

Later Porter (1996, 1998), Piore and Sabel (1984) and others noted the rise of clusters of firms and of industrial districts as key features of knowledge-based capitalism. Saxenian (1994) applied the theory of industrial districts to leading innovative centers like California’s Silicon Valley. Jacobs (1969, 1984) identified the role of talent clustering in locations as the primary economic driver of activity. Her insights were later formalized by Lucas (1988, 2001) who identified the role of human capital externalities tied to location as the primary motor force or basic underlying “mechanism” of economic development. There is now a large body of literature on the higher rate of innovation and entrepreneurship and on the accumulation of talent and human capital in cities.

Studies of innovation identify the geographic concentration of innovation and university R&D and science and technology in cities, metro areas, and mega-regions. Florida notes that science and innovation are the spikiness of all economic activity, concentrated in an even smaller number of larger cities and metro areas than are population or economic activity (Florida 2005b).

A study of mega-regions (Florida, Gulden, and Mellander 2008), clusters of economically interconnected cities whose boundaries are delineated by night-time light emissions, identified 40 worldwide, including the Boston-Washington corridor, Greater London, Amsterdam-Brussels-Antwerp, Rome-Milan-Turin, Osaka-Nagoya, and Greater Tokyo. Those mega-regions house 23% of the world’s population but account for 59% of its economic output and 77% of patents globally. Another study (Florida et al. 2010) identified approximately 680 metropolitan regions with half a million inhabitants or more. Together, these regions account for more than 60% of the world’s economic activity, while housing just a quarter of the world’s population.

Elite academic activity, particularly in science and technology, is also spiky, concentrated and clustered in a small number of cities globally. Figure 15.2 (via Florida 2010 and based on the Academic Ranking of World Universities) charts the concentration of academic activity in metros around the world. The leading centers are Boston-Cambridge, New York, San Francisco, and Los Angeles in the United States, and London, Paris, and Zurich in Europe. In general, there is a strong geographic concentration of top universities on the East and West Coasts of North America, Western Europe, and a few places in Asia and Australia/New Zealand.

Venture capital and startup activity is also extremely concentrated across the globe. Figure 15.3 shows the location of start-up companies across the world (via Florida 2013a and SeedTable). The leading centers include the San Francisco Bay Area, New York, London, Los Angeles, Toronto, Boston-Cambridge, Chicago, Berlin, Bangalore, Austin, Seattle, and São Paulo. This is due to the fact that technology has a more complex set of inputs and is more closely tied to end users; also technology talent in general prefers urban locations. A similar pattern is revealed for innovation (Florida 2013b). In the period 2001–2011, the top 20 patent-producing metros accounted for close to 64% of all patent applications. Silicon Valley and the Bay area in California alone accounted for 13.6%.

Florida (2002, 2012) suggests that places spur innovation not just through their ability to accumulate technology and talent, but also by the ability to engender tolerance and open-mindedness, which enables them to attract a broader range of talent. Roughly a third to a half of entrepreneurial companies in Silicon Valley have a foreign-born founder on their team (Wadhwa et al. 2008). Related studies (e.g., Inglehart 1989, 1997) found that talent is drawn to the combination of economic opportunity, tolerance and open-mindedness, and quality of place provided by certain locations.

For all these reasons, global cities and mega-cities have emerged as the loci of talent and knowledge accumulation, technological innovation, and entrepreneurial business formation in knowledge-based capitalism, taking on roles previously played by corporations and the nation-state (Florida, Gulden et al. 2008). In the long run, this shift from national to regional and local will affect the way we think about innovation and innovation systems.

The Global Talent Index

We now turn to the broader Global Talent Index which combines two separate measures of talent: creative class share and Human Capital as measured by the share of adults who have completed tertiary education.

Figure 15.4 shows how countries fare on talent. Scandinavian countries rank high – Finland and Sweden are in first and second place; Denmark and Norway are fourth and sixth; Singapore ranks third; New Zealand is fifth; and Australia seventh. The United States is eighth, just ahead of Greece and Slovenia in the ninth and tenth spots. Of the BRIC countries, Russia ranks highest at 13th, with Brazil in 66th, India in 75th, and China in 76th place.

The Global Technology Index

The Global Technology Index combines three measures: R&D Investment as a percent of GDP; the number of professional researchers in a country as a share of population; and the number of patents granted per capita. The first two measure critical inputs to the process of technology generation and come from the World Bank; the third is a measure of innovative output and is from the US Patent and Trademark Office.

Figure 15.5 shows how the nations of the world stack up on the Global Technology Index. Finland, the home of Nokia, ranks first in researchers, third in R&D investment, and fourth in innovation. Japan ranks fourth in R&D investment, third in researchers, and second in innovation. Japanese companies have not only consistently pushed the technology envelope, they have been if anything even better at building reliable, follow-on generations of products, from high-quality cars to flat panel displays.

The United States ranks third, finishing sixth in R&D investment and seventh in researchers, but solidly in first place for innovation. With its infrastructure for entrepreneurial venture capital finance in Silicon Valley and elsewhere, the United States has seen a long list of high-tech start-ups turn into global giants, such as Microsoft, Apple, Google, and Yahoo.

Fourth-ranked Israel has the highest concentration of engineers in the world – 135 per 10,000 people, compared to 85 per 10,000 people in the United States (Senor and Singer 2009). Sweden takes fifth place and Switzerland, Denmark, Korea, Germany, and Singapore round out the top ten. Canada ranks 11th. While much has been made of the ascendance of the BRIC countries, they do not rank highly on our technology measure. The highest ranking of them is Russia, in the 28th spot. China ranks 37th, about the same as Latvia and Bulgaria. Brazil takes 48th place and India 49th, just behind Serbia and Croatia.

The Global Tolerance Index

The Global Tolerance Index combines two measures of tolerance, both from the Gallup World Poll. The first captures a country’s openness to ethnic and racial minorities; the second its openness to gay and lesbian people.

Figure 15.6 shows how the nations of the world stack up on global tolerance. Canada ranks first of the 82 countries and Ireland second. The Netherlands ranks third: it is the only country among the top five that is more open to gay and lesbian people (83%) than it is to racial and ethnic minorities (73%). New Zealand ranks fourth, followed by Australia in fifth place. Spain is in sixth place, followed by Sweden and the United States.

The Global Creativity Index

The 3Ts, technology, talent, and tolerance, work together in mutually reinforcing ways. Any one T is a necessary but in itself insufficient condition for economic success. For a nation or region to effectively compete in the creative economy, all three Ts have to work together. The GCI combines all three indexes into an integrated measure of a nation’s overall creative economic potential.

Figure 15.7 charts how the nations of the world compare on the overall GCI. Sweden is first, topping the United States in second place. Finland takes third place, followed by Denmark in fourth, Australia in fifth, and New Zealand in sixth place. Canada and Norway take seventh and eighth place and Singapore and the Netherlands round out the top ten. Of the BRIC nations, Russia scores the best in 36th place, followed by Brazil in 46th, India in 50th, and China in 58th place.

Creativity and National Economic Performance

We now turn to the associations between creativity measured by the GCI and economic and social performance. Our analysis is structured around four key questions.

• Are more creative economies also more productive and competitive?
• Are more creative nations associated with higher levels of human development more generally?
• Do more creative economies generate higher levels of happiness for their residents?
• Are creative economies more or less equal?

Creativity and Economic Output

We begin with the relationship between creativity as measured by the GCI and economic output per capita.

Figure 15.8 shows the relationship between the GCI and gross domestic product per capita. There is a considerable correlation between the two (.84); there are only a few extreme outliers at the top and the bottom of the chart. Nations above the fitted line have higher gross domestic product per capita than their GCI scores would predict, while those below the line have lower economic output than predicted. On the one hand, the United States, Norway, Switzerland, Japan, Hong Kong, the United Kingdom, Israel, Austria, Germany, and Korea all have levels of gross domestic product per capita which are slightly higher than their GCI scores would predict. On the other hand, Canada, the Netherlands, Finland, Australia, and New Zealand have levels of gross domestic product per capita which are slightly lower than their GCI scores would seem to warrant. Perhaps more significantly, very low GCI scores appear to be associated with even lower levels of economic output per capita, as the cases of Nicaragua, Mongolia, Kyrgyzstan, Uganda, and Madagascar indicate.

Creativity and Economic Competitiveness

We now turn to the relationship between the GCI and a well-established measure of global competitiveness: the Global Competitiveness Index developed by Michael Porter et al. (2000) for the World Economic Forum.

Figure 15.9 plots the relationship between the GCI and the Global Competitiveness Index. The correlation between the two is again substantial (.79), however there is more “scatter” about the line. The United States, Singapore, Switzerland, the United Kingdom, Japan, Hong Kong, Germany, and Denmark all perform better in terms of competitiveness than their GCI scores would lead one to expect. Canada performs just slightly better than its GCI scores predict. China and India both perform significantly better on competitiveness than their GCI scores suggest they should. On the other hand, New Zealand, Ireland, and Spain perform lower on competitiveness than their GCI scores would seem to predict.

Creativity and Entrepreneurship

Schumpeter (1934, 1942, 1947a, 1947b) long ago showed how innovation and entrepreneurship come together to set in motion the “creative destruction” that drives economies forward. Research in psychology finds close connections between creative and entrepreneurial people (Sternberg and Lubart 1999). We chart the relation between the GCI and the Global Entrepreneurship Index (Acs and Szerb 2010), a measure of entrepreneurial activity which covers 54 nations worldwide.

Figure 15.10 plots the relationship between the GCI and the Global Entrepreneurship Index. The correlation between the two is strong (0.81). The fit is good but there are a large number of countries above and below the line. On the one hand, New Zealand, Australia, Sweden, Denmark, the United Kingdom, and Hong Kong all perform better on the Global Entrepreneurship Index than their GCI scores would predict. Canada is just slightly above the fitted line, while the United States, perhaps surprisingly, is just below it. On the other hand Germany, France, Belgium, and Singapore have lower levels of entrepreneurial activity than the GCI would predict.

Creativity and Human Development

We now look at the connection between creativity and underlying human development based on the United Nations Human Development Index, which takes a wide variety of human development factors into account, from health conditions and life expectancy to education levels and standards of living.

Figure 15.11 plots the GCI against the Human Development Index for the nations of the world. The fit is good, with outliers mainly at the lower left hand quadrant of the graph, where some of the least developed nations of the world are found. The overall correlation between GCI and the Human Development Index is again substantial (0.82). The United States performs considerably less well on the Human Development Index than its GCI score would predict; Canada performs slightly better. Of the BRIC countries, India performs significantly worse on Human Development Index than its GCI score would seem to warrant. Four less developed nations, Cambodia, Pakistan, Madagascar, and Uganda, lag significantly on Human Development when their GCI score is taken into account.

Creativity and Well-Being

There is a considerable ongoing debate concerning the relationship between economic development and subjective well-being, much of it revolving around the effects of money or material well-being on happiness. While it was initially found that the relationship between income and happiness only holds within and not across countries – the so-called “Easterlin effect” (Easterlin 1995) – more recent econometric studies by Deaton (2008) and Stevenson and Wolfers (2008) have challenged this view, finding that income exerts strong effects on happiness across the board.

We examine the relationship between the GCI and a comprehensive measure of happiness and life satisfaction collected by the Gallup Organization’s World Poll (2010), using a standard set of core questions that asks individuals to rank their satisfaction with aspects of their lives.

Figure 15.12 plots the GCI against life satisfaction. The correlation between the two is substantial (0.74). The fit is reasonably good, with outliers mainly at the bottom quadrants of the graph, that is, among the less developed nations. The relationship between the GCI and life satisfaction is strongest among the more advanced nations. Denmark, Finland, the Netherlands, Ireland, Switzerland, New Zealand, and Canada have higher levels of life satisfaction than their GCI scores would predict. The United States has a level of life satisfaction that is roughly in line with its GCI score. Singapore, the United Kingdom, Taiwan, Hong Kong, and Korea have lower levels of life satisfaction than their GCI scores would predict. Among the BRIC countries, Brazil has a significantly higher level of life satisfaction than its GCI score would predict, while Russia’s is considerably lower. Both India and China have lower levels of life satisfaction than the GCI would predict.

Creativity and Inequality

It is widely believed that the shift from an industrial to an innovative, knowledge, and creativity-driven economy exacerbates inequality, as former high-paying, family-supporting manufacturing jobs decline and the labor market is bifurcated into higher-pay, higher-skill knowledge and professional jobs and lower-pay, lower-skill service jobs. A series of studies document the growth in income inequality across the advanced world (e.g., Sherman and Stone 2010). But do higher levels of creative performance and higher levels of income inequality necessarily go together?

Figure 15.13 plots the association between the GCI and the basic measure of income inequality, the Gini coefficient. The line slopes downward and the correlation between the two is negative (–0.43); in other words, the GCI is systematically associated with lower levels of income inequality – and hence greater equality – across the nations of the world.

A closer look at the chart reveals two distinctive trajectories for the relationship between creativity and inequality. On the one hand, there are countries like the United States, the United Kingdom, Singapore, and to a lesser extent, Australia and New Zealand, in which high levels of creativity as well as productivity and economic competitiveness go hand in hand with higher levels of inequality. But there are also a substantial number of countries in which high levels of creativity, competitiveness, and productivity combine with much lower levels of inequality. These are largely Scandinavian and Northern European countries, including Sweden, Denmark, Finland, Norway, the Netherlands, and Germany. Japan is represented as well. Among the less developed nations, we find high levels of inequality in South American nations like Paraguay, Bolivia, Panama, Brazil, Honduras, Ecuador, and Argentina. Of the BRIC nations, China, Russia, and especially Brazil all exhibit much higher levels of inequality than their GCI scores would predict.

Each high-creativity, high-inequality nation has a high-creativity, low-inequality counterpart. This is a likely reflection of these countries’ differing levels of social welfare. Though more systematic study is needed before we can draw any firm conclusions, this finding gives reason for optimism. At the same time, it suggests that sustainable, long-term prosperity requires a significant investment in education and skill development.

Conclusion

This chapter has charted the role of creativity in the economic and social performance of 82 nations across the advanced and developing worlds, using both the Global Creativity Index, a composite measure of the technology, talent, and tolerance capabilities of nations based on Florida’s (2002, 2012) theory of the creative class and the 3Ts of economic development, and the three indexes that make it up. It has also systematically examined the relationship between the GCI and key measures of economic output and competitiveness, as well as a broader set of measures of human development, subjective well-being, and income inequality.

The creative class makes up more than 40% of the workforce in 40 nations, but our analysis suggests that the creative economy is emerging unevenly across the world. The creative economy is most established in Scandinavia and Northern Europe, the United States and Canada, and Australia and New Zealand. Moreover, the creative economy has emerged unevenly within countries as well as between it. Factors like amenities, quality of place, and openness and tolerance play an increasingly important role in attracting people to certain cities and creative centers. Larger cities in particular allow for the iterative and cumulative processes of creativity and skill-accumulation that make them the preeminent spatial homes of technology, talent, tolerance, and economic growth within the nation state. These larger cities are emerging as economic winners in an increasingly spiky world.

When it comes to the overall GCI, Scandinavian and English-speaking nations dominate the top ten spots. Sweden takes first place on the overall GCI. The United States is second, followed by Finland, Denmark, and Australia; Canada ranks eighth; New Zealand, Norway, Singapore, and the Netherlands round out the top ten. Despite their rapid economic rise, the BRIC nations still do not crack the upper tiers on the GCI: Russia ranks 31st, Brazil 46th, India 50th, and China 58th.

The GCI is closely associated with key measures of economic and social progress. Nations that score high on the GCI have higher levels of economic output, entrepreneurship, and overall economic competitiveness. Nations that invest in creativity and that achieve on the 3Ts of economic development also have higher levels of human development, life satisfaction, and happiness.

This chapter suggests that the economic, technological, and entrepreneurial performance of nations is due to a set of factors that extend beyond just science and technology. In line with other research at the national and subnational levels (see Barro 1991; Rauch 1993; Simon and Nardinelli 1996; Simon 1998; Glaeser 1999; Glaeser and Saiz 2003), we find that talent plays a substantial role in national economic performance. Our research further suggests that talent spans more than the conventional educational measure of human capital, but also includes occupational skill based on the kind of work people do. Overall, our research suggests that technology and talent are mutually reinforcing and complementary factors in cross-national economic performance.

This chapter further suggests that non-market factors, which we refer to as tolerance and openness to human capital, play an additional complementary role in the economic performance of nations. Nations that are more open to talent, which have lower barriers to entry for talent, benefit from the ability to attract and to mobilize a wider pool of talented individuals. This is in line with other research that finds that tolerance, diversity, and openness are associated with higher rates of innovation (Page 2007) and higher levels of national economic performance (Noland 2005; Noland and Pack 2004). Generally speaking, technology, talent, and tolerance operate as capabilities that reinforce each other.

This chapter also sheds light on the complex relationship between creativity, economic progress, and inequality. Generally speaking we find creativity (measured via the GCI) to be negatively associated with income inequality. While some countries, like the United States and the United Kingdom, achieve high GCI scores alongside relatively high levels of inequality, in general elevated levels of global creativity are associated with lower levels of inequality. We identify two paths here: a low road path where higher creativity is associated with greater levels of inequality, and a high road path where higher levels of creativity are associated with lower levels of inequality. This is at odds with the notion that it is large disparities in income that create the incentives and motivations that drive economic progress.

This provides some cause for optimism, even in the wake of the global economic crisis, as it suggests that economic growth and development can go along with lower levels of inequality – and that our future prosperity increasingly turns on the full development of each and every human being.

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