Check out the graph above via Catherine Rampell at The New York Times Economix (blog). It compares job losses in the current downturn to five previous recessions going back to the mid-1970s. The patten of job loss is far, far deeper than in any previous period and has extended for more than two years. By the looks of the chart, it appears that deep job losses may have ended and the pattern may be ticking up. Let’s hope so.

A week or so ago, Newsweek’s Julia Baird pointed to my analysis of the connection between national happiness and tolerance. That reminded me and my MPI colleagues of the Heritage Foundation’s Economic Freedom Index. The index, which covers 183 countries, is based on ratings for 10 specific factors: business freedom, trade freedom, fiscal freedom, government size, monetary freedom, investment freedom, financial freedom, property rights, freedom from corruption, and labor freedom. Hong Kong topped the list overall, followed by Singapore, Australia, New Zealand, Ireland, and Switzerland. Canada came in 7th, the highest among North American nations and slightly ahead of the United States which ranked 8th.

To what extent is economic freedom associated with tolerance and happiness? Are freer nations also more tolerant? Are their residents happier than those of other nations? To what extent is economic freedom also associated with other factors like affluence and material well‑being, the level of human capital, and the transition to postindustrial economic structures? And what is the relationship between freedom and economic inequality?

We decided to take a look. We ran some bivariate correlations and a series of scatter-graphs which I summarize below. Lack of matching data for some of our indicators caused us to leave out African nations from our analysis. As usual, I point out that our analysis points to association between variables only: It does not imply causation, and other factors may complicate the picture. Still, the findings are interesting.

Source: Economic Output is measured as gross domestic product per capita, World Bank, World Development Indicators

It’s not surprising that richer countries are, on average, freer – the correlation between the conventional measure of economic well-being (that is, gross domestic product per capita) and the Economic Freedom Index being .71. The scatter-graph above shows that Hong Kong, Australia, New Zealand, the United States, Canada, Denmark, the Netherlands, and Ireland all do very well on both measures.

Source: World Bank, World Development Indicators

Freer countries also have higher levels of human capital (that is, more highly educated populations) – the correlation here being .44. Again, Hong Kong, Singapore, Australia, New Zealand, the U.S., and Canada all do well on both.

Source: Global Competitiveness Index (PDF)

Freer countries score better on global competitiveness, according to the Global Competitiveness Index (PDF) developed by Harvard University professor Michael Porter for Davos. The correlation between the two is .7. Despite what some say about the rise of the Chinese state capitalist model, overall, economic freedom and competitiveness appear to go hand-in-hand

Source: Definition based on Rise of the Creative Class; data are from the International Labour Organization.

One thing that’s interesting: Economic freedom is closely associated with the transition to postindustrialism. A key indicator of that transition is the percentage of workers in the creative class which spans science and technology; arts, culture and entertainment; and the knowledge-based professions. The correlation between the creative class and economic freedom is .45. Hong Kong, Singapore, Australia, New Zealand, Ireland, the U.S., Canada, Denmark, and the Netherlands do well on both measures. On the other hand, the correlation between economic freedom and the working class is negative, though not statistically significant.

Source: Tolerance is a combined index of  attitudes toward racial and ethnic minorities and attitudes toward gays and lesbians. Data are from a Gallup Organization survey.

Freer countries are also more tolerant. We looked at the association between economic freedom and attitudes toward ethnic and racial minorities and gays and lesbians. Freer countries had more open-minded attitudes toward each. And, the association was even higher when we generated a combined measure of both – the correlation being .59.

Source: United Nations, Human Development Index

Freer countries also have higher levels of overall human development, as measured by the United Nations. The correlation here is .67.

Source: Gallup Organization

Freer countries, not surprisingly, are also happier. The correlation between economic freedom and Gallup’s measure of life satisfaction or happiness was .55.

Source:  World Development Indicators

The relationship between economic freedom and income equality is interesting. We measure inequality based on the conventional Gini coefficient of inequality. The overall correlation is negative (-.33). That means freer countries, on average, are more equal. Many of the countries with the highest levels of economic freedom are also among those with the highest levels of economic equality – Sweden, Denmark, Japan, and Finland. The most unequal countries – Bolivia, Brazil, Ecuador, and Argentina – on the other hand, have relatively low levels of economic freedom. The United States is one of several countries, along with the United Kingdom and Singapore, that have high levels of economic freedom alongside relatively high levels of inequality.

Economic freedom, according to our analysis, is not just a lofty goal or something that can be imposed on societies. Rather, as we’ve seen, economic freedom is tied to material economic and social conditions. Economic freedom, not surprisingly, reflects levels of affluence and economic development: Richer countries are, on average, freer. But economic freedom is also tied to postindustrial economic structures. It is considerably higher and more widespread in nations with a larger creative class, a smaller working class, and more highly educated people. Freer countries are also more tolerant, with more open social attitudes toward minorities and gays.  This is in line with Ronald Inglehart’s decades-long observation of the shift from materialist to post-materialist values. And economic freedom goes hand in hand with higher levels of happiness and life satisfaction.

Perhaps the most interesting finding to emerge from our analysis concerns the relationship between economic freedom and inequality. The relationship is often seen in terms of a trade-off. The ability to pursue one’s economic dreams unencumbered by social or institutional restraint is seen as conditioning higher levels of inequality. But that’s not what we found. In fact, our analysis indicates that the relationship between economic freedom and income inequality is negative. That is, freer countries are on balance more equal. Many of the countries with the highest levels of economic freedom also have the highest levels of economic equality.

The United States is one of a relatively small number of nations, along with the United Kingdom and Singapore, that have relatively high levels of economic inequality alongside high levels of economic freedom. Americans frequently see this pattern as reflecting the country’s individualistic ethos that spur hard work, individual ambition, and unbridled entrepreneurship. Darwinian as it may be, inequality is seen as a necessary, if negative, byproduct of this ethos which is what in the end makes America richer, happier, and freer than other nations. That may have been true in the past. But today, the U.S. pattern is far from the dominant one: In fact, it’s rather unique. Economic freedom in general is associated with relatively low levels of income inequality.

All of this points to the fact that it’s not just greater affluence that leads to freedom and happiness but the combination of greater wealth with relative economic equality. Freer, happier societies reflect the old adage of a rising tide that lifts all boats. That’s something Americans keep in mind as the nation grapples with how best to generate economic growth, create jobs, and deal with rising social and economic inequality in the ongoing era of the Great Reset.

Earlier this week Catherine Rampell posted this map over at Economix. It shows the percentages of workers who drove to work alone by state and is based on U.S. Census data.

D.C. has the lowest rate – a fact which was not lost on D.C. blogging circles. NY did well too.  The worst performers were Alabama, Tennessee, and Ohio, where about eight in 10 workers drive alone -  more than double that of D.C.

With the help of my colleague Charlotta Mellander, we took a quick look at some factors that might be associated with this geographic pattern. It’s not an exhaustive list: We examined some key economic factors like income and economic output, human capital and the creative class, and psychological ones like happiness, stress, and personality. We removed D.C. from the analysis because it was such an extreme outlier. We did not develop or run any serious multivariate analysis – just simple correlations, or associations, between variables.

Still the findings point to some reasonably clear patterns.

Income and Economic Output: The richer the state, the less likely people were to drive alone. Driving alone was negatively correlated with state income levels (-.46) and output per capita (-.41).

Class and Human Capital: States with higher percentages of college graduates (-.47) and the creative class (-.43) were less likely to have people driving alone. Driving alone was much more likely in states with large working class concentrations (.62).

Professional and Creative Jobs: Driving alone was less likely in states with high concentrations of virtually every type of professional, knowledge-based and creative jobs. But it was least likely in states with large concentrations of artists, designers, and entertainers (-.63), architects and engineers (-.61), scientists (-.56 ), and lawyers (-.55).

Diversity – Immigrants and Gays: Driving alone was less likely in states with high concentrations of immigrants (-.51) and gays (-.41).

Happiness: Happiness research tells us that commuting is one of life’s least pleasurable activities.  Driving along was negatively associated with state levels of happiness and well-being (-.46) and positively associated with states with higher levels of stress (.29).

Personality: Psychologists identify five main personality types. Driving alone was more likely in states with high levels of three of them: extroverts (.29), conscientiousness (.36), and agreeableness (.44). Interestingly, there was no association between driving alone and the two other types – neurotic and openness to experience, which some might say makes it harder to explain New York.

This map is cool (via Catherine Rampell at Economix and based on Census data).

Yikes: More than 100 million American workers drive to work alone. Rampell, one of my fave economics bloggers, explains:

About three-quarters of American workers drove to their jobs alone in 2008. The least carpool-friendly states appeared to be Alabama, Tennessee and Ohio, where about 83 percent of workers drove alone. The District of Columbia and New York — whose residents are heavily dependent on public transportation — had the lowest rates of solo commuters, at 37.2 percent and 53.7 percent.

Anybody have the stats for Toronto?

Here’s the map from the Social Science Research Council’s American Human Development Project.

The pattern is more or less what you would think. Catherine Rampell from Economix notes that:

Connecticut, which has the highest development of all American states, is roughly comparable with Ireland (the fifth most-developed country worldwide). But Mississippi has an H.D.I. level roughly on par with that of Turkey (#76 in the international development rankings).

MapScroll and Economix clear up any remaining confusion about an earlier, problematic map. Check out the project’s website and terrific interactive maps.

Andrew Sullivan points to Ed Glaeser’s Economix  post on the geography of unemployment and finds a common thread: “Edward Glaeser compares city to city unemployment numbers and affirms Richard Florida’s thesis.” Glaeser writes:

While the disparity in unemployment rates is enormous, it isn’t random. Some areas aren’t just miraculously better able to handle the downturn. Long-standing features of the urban landscape can explain the bulk of the variation in today’s unemployment rates.

Given the enormous gap in unemployment between skilled and unskilled workers, it isn’t surprising that skills best explain today’s metropolitan unemployment rates. The share of adults with college degrees in 2000 can, on its own, explain about one-half of the variation in the unemployment rate.Somewhat remarkably, the educational level of the metropolitan area before World War II can do almost as well.

Here’s his scatter-plot.

Source: Edward L. Glaeser, via Economix

Glaeser also finds that regional unemployment is “strongly linked to manufacturing.” Here’s his plot of the correlation between current unemployment and manufacturing’s share of the labor force in manufacturing in 1970.

Source: Edward L. Glaeser via Economix

Regional unemployment is also related to density, finding that “unemployment is lowest in those areas that are most centralized.” Sprawling places appear less resilient economically.

Source: Edward L. Glaeser via Economix

His conclusions: invest in skills and human capital; “beware industrial policies aimed at keeping America tied to heavy industry;” stop trying to breathe life back into declining regions; and encourage mobility.

While the regional diversity within the United States might prompt politicians to pursue policies that target aid to distressed regions, that seems likely to be counterproductive. America has always dealt with regional economic disparities through migration. … Today’s recession will also prompt mobility, probably toward more skilled, more centralized cities with less historical commitment to manufacturing.”

I could not agree more. Our urban policy, such that it is, is decidedly backward looking. It’s high time urban policy focus on leveraging the three key things – mobility, density, and human capital accumulation – that are real engines of prosperity.

From the New York Times Economix:

Over at Economix, Princeton economist Alan Krueger weighs in on how to measure and evaluate the popularity or success of popular musicians.

To an economist, the most popular artist is the one who would sell the most tickets at a given price. That is, if all artists charged the same price, whoever would attract the largest audience would be the most popular. Likewise, the most popular author is the one who sells the most books at a given price. Demand is higher for some artists and authors than others.

But herein lies the rub: The price of concerts (and books) varies. If Bruce Springsteen charges a lower price than Madonna, his revenues may well be lower even if he draws more fans.

Only in the special case where an increase in price exactly offsets a reduction in the number of tickets sold would gross revenue measure the popularity of an artist. That is, if artist A sells 10 percent fewer tickets by charging 10 percent more, gross revenue could be used as a measure of popularity.

Another consideration is the number of concerts the performers are willing to supply. Celine Dion may have been willing to perform more shows than Bruce Springsteen, accounting for her higher revenues.

Finally, unlike book sales, concert fans cannot always buy a ticket at the list price. Ideally, the revenue collected in the secondary resale market — by scalpers, for example — should be included in the rankings of artists as well.

For those so inclined, a longish but very interesting paper on the subject with Marie Connolly is here.

There’s a great post by Edward Glaeser (in the Economix blog of the New York Times), titled “New York, New York: America’s Resilient City.”

In it, he describes how New York has managed to avoid the decay that has afflicted many large older cities, and, after a brief downturn in the 1970’s, came roaring back as arguably the most influential single city in the world.

His explanation? In a word – “smart people.”

“New York still has an amazing concentration of talent. That talent is more effective because all those smart people are connected because of the city’s extreme population density levels. Historically, human capital — the education and skills of a work force — predicts which cities are able to reinvent themselves and which ones are not. Those people who are continuing to pay high prices for Manhattan real estate are implicitly betting that New York’s human capital will continue to come up with new ways of reinventing the city. “

Glaeser continues, describing why dense cities succeed…

“They thrive by enabling us to connect with each other, which then promotes learning and innovation. The current downturn will only increase the returns to being smart, and you get smart by hanging around smart people. As long as New York continues to attract and connect those people, the city will continue to thrive.”

Now here’s what every city planner wants to know. Is this replicable? Can this success be engineered or encouraged, and are the effects measurable in 10 years, 20 years, a lifetime?

Does anyone have successful examples of campaigns and projects to replicate this resilient infrastructure? Or perhaps, examples of some cautionary unsuccessful attempts?

Best wishes to everyone for a creative and fruitful New Year!