The overall level of world peace world fell for the third year in a row, according to the latest version of the Global Peace Index produced by the Institute for Economics and Peace. Most of this trend was driven by the increased “social and political turmoil in the Middle East and North African Nations during the early part of 2011,” the report notes.

But what are the factors that shape the relative peacefulness of nations?  And, what is the connection between peace – or its opposite – on their economic growth, well-being, and prosperity?

This map charts the Global Peace Index (GPI) scores for 153 countries worldwide. The GPI is based on 25 separate indicators of internal and external conflict, including wars and external conflicts, deaths from external conflicts, militarization, weapons exports, homicides, access to weapons, violent political demonstrations, prison populations, and police presence.

Iceland and New Zealand are the first and second most peaceful countries on the planet according to the GPI, followed by Japan, Denmark, and the Czech Republic. Canada is ranked eighth. Not surprisingly, the U.S. —with the world’s largest military, enmeshed in a seemingly “perpetual war” on terrorism, a large prison population,  high homicide rate, and relatively large domestic police presence —is ranked 82nd, between Gabon (81) and Bangladesh (83). The five least peaceful nations are North Korea, Afghanistan, Sudan, Iraq, and Somalia. War-ravaged Libya fell from 83 to 143.  It’s worth pointing out the considerable differences among the rising BRICs nations.  Two of them have  high GPI scores – Russia (147) and India (135) – and thus rank among the world’s least peaceful nations, while the other two – Brazil (74) and China (80) – rank in the neighborhood of the United States.

I thought it would be interesting to compare countries’ rankings on the GPI to their standings on a number of other economic, social, and demographic factors. With the help of my Martin Prosperity Institute colleague Charlotta Mellander, we ran the numbers and generated a series of scattergraphs, plotting GPI against these other metrics. Our analysis only covers approximately 75-80 countries due to data availability. Also note that the higher the GPI score, the less peaceful a country is. Though we found strong associations between a country’s prosperity and its GPI, we can’t say for certain whether peace promotes prosperity or prosperity promotes peace—or whether other factors that we haven’t considered play an equal or greater role. But the patterns that come up are intriguing enough to report here.

Generally speaking, peace follows the level of economic development.  A large number of affluent, advanced nations – Norway, Canada, Denmark, Japan and New Zealand – are among the most peaceful in the world.  The GPI is strongly associated with the level of economic development (a correlation of -.6—recall that the higher the GPI is, the less peaceful a nation is, hence the negative correlation).  There are also quite a few outliers, Russia, Israel and the United States among them.  Great powers, economically dominant countries like the U.S. today or the UK in the past, have also always developed large militaries. So have their rivals, like the USSR during the Cold War, or France and Germany on the continent during the days when England reigned supreme.

Peace is also a product of the type of development, not just its level.  The apex of economic development has shifted from resource extraction and manufacturing economies, with their large working classes, to more highly-educated and idea-driven post-industrial knowledge economies.

The GPI is closely associated with the share of workers in professional, technical and creative fields  (-.48) and also with human capital (-.45, see also the scatter-graph above). Russia, Israel and Pakistan are extreme outliers.

It almost goes without saying that peaceful countries have higher levels of happiness and well-being. When people don’t have to worry about sending their children off to war, being invaded by enemy armies, or terrorists with suicide bombs, they naturally experience higher levels of life satisfaction.  So it’s not surprising that the two are closely associated statistically, with a correlation of -.52 (once again, remember that a higher GPI score reflects a lower level of peace, hence the negative correlation). The U.S. is again something of an outlier here, situated near Mexico and Saudi Arabia – nations with significantly higher levels of well-being than their GPI scores might predict. Israel, Russia and Pakistan remain the extreme outliers.

And finally for the biggest takeaway:  There is a considerable positive correlation (.42) between the GPI and income inequality (see above).  Nations with less income inequality have higher levels of peace, while inequality is associated with peace’s opposite. Once more, Russia, Israel and Pakistan are the outliers. Central American and South American nations do particularly poorly on this measure—look at Honduras, El Salvador, and Ecuador, far out on the upper right quadrant. This tendency might be even more pronounced had we had the data for some African countries. Countries with strong inequality are often poorer nations with resource-based economies.  Such nations are more likely to be governed by the kinds of authoritarian regimes that are prone to strong man tactics internally and maintain tense relations with neighboring states.

Whether prosperity breeds pacific attitudes or vice versa, there is little doubt that war is not just awful by every humane standard, it’s also a bad economic strategy—and one that, generally speaking, leads to much lower levels of happiness and well-being.

These days talk about taxes of any kind, unless cuts are being proposed, is the third rail of American politics.  Many business people and of course doctrinaire conservatives insist that lower tax rates create more incentives for investment, business formation and economic growth. Tax cuts, they continue, are thus a key mechanism for spurring economic growth. Though we haven’t seen much of the Laffer Curve since the heyday of Reaganism, a new generation of supply-siders is arguing for more tax cuts despite our already-staggering deficits.

A new study gives us a new tool with which to measure the effects of state taxes on economic development. “Competitiveness of State and Local Business Taxes on New Investment: Ranking States by Tax Burden on New Investment”, a collaboration between the Council on State Taxation (COST) and  Ernst & Young, ranks the 50 states according to a business tax competitiveness index. “The index includes all major state and local business taxes associated with new capital investments including corporate income and franchise taxes, real and personal property taxes and sales taxes on business input purchases,” notes the Executive Summary for the study. “It focuses on five types of mobile capital investments that states compete to attract: headquarters facilities; research & development facilities; office and call center facilities; durable and nondurable manufacturing facilities.”

The map above by Zara Matheson of the Martin Prosperity Institute maps the index across the 50 states. The average tax on new investments is 7.9 percent; obviously, there is tremendous variation from state to state. Maine imposes the smallest tax burden (just three percent) on new investment, followed by Oregon, Ohio, Wisconsin and Illinois, all with effective tax rates on new investment of five percent or less.  On the other side of the ledger, New Mexico’s state and local tax system imposes the highest tax burden, followed by the District of Columbia, Rhode Island, Kansas, and Louisiana, all of which have effective tax rates on new investments in excess of 10 percent.

But to what extent are the tax burdens that states impose on new investment associated with the differences in their economic performances? Do states with higher investment tax burdens show lower rates of innovation, economic output, and innovation compared to those with lower tax burdens? With the help of my colleague Charlotta Mellander, I took a quick look at the associations between state investment tax burdens and key measures of state economic performance.

The overwhelming result was that we could find no connection at all between a state’s investment tax burdens and a state’s economic performance. There was no statistical association between state investment tax rates and such key indicators of economic performance as economic output (gross state product) per capita, state income, or wages. Similarly, we found no association between state investment tax burdens and unemployment rates. Likewise, we discerned no association between a state’s investment tax burdens and its levels of human capital or knowledge based and creative class jobs. Nor was there any association between states’ investment tax burdens and the well-being and happiness levels of their citizens. Interestingly enough, there was no association between a state’s political complexion (whether it voted for McCain or Obama in 2008) and its investment tax burdens—in other words, conservative states did not have lower tax rates.

The scatter graph above, which shows the missing association between state investment tax rates and income, makes this plain. Look at states near the bottom of the graph, below the line for average income. In the right hand quadrant are states where low incomes match up with high investment tax burdens. New Mexico is the extreme outlier here, but Sunbelt states like Mississippi, South Carolina, Tennessee and Louisiana also fit this bill.  In the bottom left hand quadrant are states like Maine, Ohio, Oregon and Wisconsin, where incomes and tax burdens are both low. Now let’s look above the line, at high income states – a similar pattern is evident.  There are states like Delaware, New Hampshire and Illinois, where higher incomes match up to lower investment tax burdens. But there are also those, like Connecticut and Massachusetts where high incomes and high investment tax burdens go together. We ran many more of these, which all provide the same general picture, for example the scatter-graph for total factor productivity below.

Some might say those are broad economic indicators, and tax rates are designed to work in a more targeted way on business decision-making, encouraging more investment in plant and equipment for R&D, for example.  The COST/ Ernest & Young report makes this point as well, noting the two-stage nature of business location and investment decisions. In the first phase, companies orient their location decisions on key factors, such as the availability of talent and transportation costs, but then at the second stage, once the list is narrowed down, tax rates come more into play. As the study notes, “tax factors can be a determining factor between states with otherwise similar non-tax costs.”

If low tax burdens encourage more R&D investment and act to incentivize innovation, you’d expect to see more patents coming from the states with the lowest rates. But as the above scatter illustrates, the correlation between the Business Tax Competitiveness Index and patents per capita is actually negative (-.3, significant at the 5 percent level).

If lower investment tax rates encourage new business investment, at the very least you’d expect to see more new firms in states with low investment tax rates.  Here again there is no relationship at all (see the scatter-graph above).

The bottom line is this: lower state investment tax burdens aren’t associated with stronger state economies, and higher investment tax burdens aren’t associated with worse ones.  Tax cuts may be an effective political strategy and lowering business and investment taxes may appeal to corporate interests and attract campaign contributions, but they have little relation to state economies. (A recent study I profiled here covers some five decades of data; it concluded that the impact of state taxes on economic growth is “quite variable” and “not always in the expected direction,” while  “expenditure impacts are more consistent.”)  When it comes to building robust, resilient and prosperous economies, more fundamental factors than taxes—like human capital, entrepreneurship, and innovation—come into play.

With the death of Osama Bin Laden, many believe that Al Quaeda was dealt a mortal blow. Time will tell, but as we learned from the Oklahoma City bombing and Nidal Malik Hasan’s rampage at Fort Hood, we have much to fear from our own home grown extremists. And not just from “lone nuts” acting on their own.

Since 2000, the number of organized hate groups – from white nationalists, neo-Nazis and racist skinheads to border vigilantes and black separatist organizations – has climbed by more than 50 percent, according to the Southern Poverty Law Center (SPLC). Their rise has been fuelled by growing anxiety over jobs, immigration, racial and ethnic diversity, the election of Barack Obama as America’s first black president, and the lingering economic crisis. Most of them merely espouse violent theories; some of them are stock-piling weapons and actively planning attacks.

But not all people and places hate equally; some regions of the United States—at least within some sectors of their populations—are virtual hate hatcheries. What is the geography of hate groups and organizations? Why are some regions more susceptible to them?

The SPLC maintains a detailed database on hate groups, culled from websites and publications, citizen and law enforcement reports, field sources and news reports. It defines hate groups as organizations and associations that “have beliefs or practices that attack or malign an entire class of people, typically for their immutable characteristics,” and which participate in “criminal acts, marches, rallies, speeches, meetings, leafleting or publishing.”  As of 2010, the SPLC documents 1,002 such hate groups across the United States.

The map below, by Zara Matheson of the Martin Prosperity Institute, graphically presents the geography of hatred in America today. Based on the number of hate groups per one million people across the U.S. states, it reveals a distinctive pattern.

Hate groups are most highly concentrated in the old South and the northern Plains states. Two states have by far the largest concentration of hate groups – Montana with 13.8 groups per million people, and Mississippi with 13.7 per million.  Arkansas (10.3), Wyoming (9.7) and Idaho (8.9) come in a distant third, fourth, and fifth.

Hate groups are much less concentrated in the Northeast, Great Lakes, and the West Coast.  Minnesota has the smallest concentration of hate groups, with 1.3 groups per million people, nearly ten times less than the leading state, followed by Wisconsin (1.4), New Mexico (1.5), Massachusetts (1.6), and New York (1.6). Connecticut (1.7), California (1.9), Rhode Island (1.9) all have less than two hate groups per million people.

But beyond their locations, what other factors are associated with hate groups? With the help of my colleague Charlotta Mellander, I looked at the social, political, cultural, economic and demographic factors that might be associated with the geography of hate groups. We considered a number of key factors that shape America’s geographic divide: Red state/ Blue state politics; income and poverty; religion, and economic class.  It is important to note that correlation does not imply causation —we are simply looking at associations between variables. It’s also worth pointing out that Montana and Mississippi are fairly extreme outliers which may skew the results somewhat. Nonetheless, the patterns we discerned were robust and distinctive enough to warrant reporting.

First of all, the geography of hate reflects the Red state Blue state sorting of American politics.

Hate groups were positively associated with McCain votes (with a correlation of .52).

Conversely, hate groups were negatively associated with Obama votes (with a correlation of -.54).

Hate groups also cleave along religious lines. Ironically, but perhaps not surprisingly, higher concentrations of hate groups are positively associated with states where individuals report that religion plays an important role in their everyday lives (a correlation of .35).

The geography of hate also reflects the sorting of Americans by education and human capital level. Hate groups were negatively associated with the percentage of adults holding a college degree (-.41).

The geography of hate also sorts across economic lines. Hate groups are more concentrated in states with higher poverty rates (.39) and those with larger blue-collar working class workforces (.41).

Higher income states with greater concentrations of creative class workers (creative class occupations span science and technology; business and management; education, law, and medicine; and arts, culture, and entertainment) provide a less fertile medium for hate.  Hate groups were negatively correlated with state income levels (-.36), and the percentage of workers in creative class occupations (-.48).

Hate groups also reflect the underlying openness, tolerance and diversity of an area.  Hate groups are negatively correlated with concentrations of gay and lesbian households (-.37) and even more so where there are larger concentrations of immigrants (-.53).

Hate groups also reflect the underlying openness, tolerance and diversity of an area.  Hate groups are negatively correlated with concentrations of gay and lesbian households (-.37) and even more so where there are larger concentrations of immigrants (-.53).

The geography of hate groups follows the more general sorting of America by politics and ideology, religion, education, income levels and class.  But the presence of hate groups does not necessarily lead to hate crimes.  A 2010 study of “Hate Groups and Hate Crimes” by economists Matt Ryan and Peter Leeson found no empirical connection between the two. Tracing the association between hate groups and hate crimes between 2002 and 2006, they found that while the number of hate groups grew substantially, the number of hate crimes did not—in fact such crimes actually decreased slightly.  But they found a strong connection between hate crimes and adverse economic conditions, particularly unemployment and to a lesser extent poverty.  They suggest that hate crimes follow the pattern outlined long ago in the classic frustration-aggression thesis which, as its name implies, links aggression to high levels of frustration.  “[W]hen people endure economic hardship they get frustrated,” write Ryan and Leeson. “They take their frustration out on vulnerable social groups, such as ethnic, sexual and religious minorities.”

Even if hate groups are not directly connected to hate crimes, they arise from the same underlying economic factors that are dividing Americans by class, ideology and politics. Hate groups, like hate crimes, are strongly associated with want.  The geography of hate in America reflects and reinforces its deepening geography of class.

Boxer Manny Pacquiao and baseball star Alex Rodriguez top the list of the world’s highest paid athletes, according to new data compiled by ESPN.

ESPN tracked annual salaries—the base pay the players received for their most recent season or calendar year (endorsements and other sources of income were excluded) across 182 nations and 17 sports, from baseball and basketball to badminton and cricket. Salary data was collected from “multiple sources, including leagues, agents, consulates, embassies, sports federations, cultural centers and the U.N.”

Wake up America: It’s not football, baseball, basketball, or even NASCAR that accounts for the lion’s share of sports superstars: 114 of the 184 best-paid athletes in the world play soccer—almost seven times more than the next runner up (basketball, with 18 uber-rich players). For the rest, there are 12 baseball players, six auto-racers, five golfers, five football players, four cricketeers, three boxers, and three track and field contestants. Rugby and tennis each contribute two competitors and there is one representative each from badminton, cycling, motorcycle racing, sumo wrestling, and yachting.

The first map, above, by Zara Matheson of the Martin Prosperity Institute shows which countries these athletes live in. The richest two players, Pacman the Destroyer and A-Rod (both earned $32,000,000), live in the Philippines and the U.S. respectively (A-Rod spent much of his childhood in the Dominican Republic, but was born in New York City). Finland’s Kimi Raikonnen ($26.3 million) and Spain’s Fernando Alonso ($22.7 million), both auto racers, are the third and fourth highest paid. Venezuela’s Johan Santana clocks in at $21 million and some change; Italy’s Valentino Rossi, a motorcycle racer, earns $20,800,000.

The second map shows the ratio of the highest paid athletes’ salaries to a proxy measure for average pay – a country’s Gross Domestic Product or GDP per person. A-Rod’s $32 million salary, for example, is about 715 times the size of the per capita U.S. GDP of $44,872 per person.  Jason Bay, who hails from Canada, makes $18.1 million playing for the New York Mets – 455 times Canada’s per capita GDP. That sounds like – and is – a big difference. But Manny Pacquiao makes 18,000 times the per capita GDP of the Philippines and two other athletes’ salaries are even higher multiples.  Samuel Dalembert of the Sacramento Kings hails from Haiti; he makes more than 19,000 times Haiti’s per capita GDP. And the salary of Emmanuel Adebayor, who plays soccer for Manchester United, is a staggering 24,000-plus times more than the per capita GDP of his homeland of Togo.

The twenty-five athletes with the highest ratio of salary to GDP are overwhelmingly soccer players from African and Latin American countries. But the $17.5 million salary that China’s Yao Ming earns is 4600 times more than the per capita GDP of his home country; Peja Stojakovic, who plays for the Dallas Mavericks, earns 2700 times what the average Serbian does. On the low end of the scale, there’s the track star Michael Junior Jackson, whose $5000 per season is enough to make him his country’s highest-paid athlete, but is still $800 less than an average worker from the Pacific island nation of Niue makes.

The ratio of CEO pay to that of the average worker has become a common benchmark for massive rises in economic inequality.  According the just-released 2011 edition of Executive Paywatch, the CEOs of S&P 500 companies took home an average of $11.4 million in 2010, up 23 percent from the previous year, and roughly 250 times America’s per capita GDP.  According to The New York Times Economix blog, this amounts to “28 times the pay of President Obama, 213 times the median pay of police officers, 225 times teacher pay, 252 times firefighter pay, and 753 times the pay of the minimum-wage worker.” They are enormous gaps to be sure, but they’re a far cry from the difference between A-Rod’s and the average American’s salary and not even in the same universe as the light year-sized differences between the salaries of superstar athletes and the average citizens of their developing nations.

These are terrific athletes for sure, but do their talents justify such incredible rewards? I love sports, but do they add anything substantial to economic prosperity? What kind of signal does such a highly skewed reward system send about the kinds of talents and skills that really matter for economic growth?

More than 15 years ago, the economist Robert H. Frank famously critiqued America’s emerging Winner Take All Society, in which the super-successful take a bigger and bigger share of the riches, leaving the rest of us to fight over scraps. Things have only gotten worse in the intervening years; the economic crisis has done little, if anything, to abate this runaway trend.

Outlandish sports stars’ salaries illuminate the grotesqueries of  the current age of super-star capitalism, and of our increasingly inequitable, irrational, and unconscionably unequal global economy.

Denmark, Sweden and Canada lead the world in high-well being – the percent of people who say they are “thriving” in life, according to a newly released survey by the Gallup Organization. The U.S. ranked 12th, behind Panama and Venezuela. Nineteen countries registered high rates of well-being, with more than half of their populations reporting that they are thriving in their day to day lives.

The world is sharply divided by levels of well-being, according to the same survey. In 67 nations, less than 25 percent of respondents reported that they are thriving in their lives. In 22 countries, less than 10 percent reported that they are thriving.  Just one percent of people in Chad, and two percent in the Central African Republic and Haiti reported that they are thriving.  While African nations reported the lowest levels of thriving, low levels were also found in Middle Eastern and South East Asian nations.

Of the BRICs nations that are so often lauded for their economic progress, only Brazil reported a high level of thriving (57 percent). Russia’s (24%), India’s (17%) and China’s (12%) levels were not so robust.

Clearly a good part of this is related to income: Poor countries report lower levels of happiness than more affluent ones, as a number of happiness studies show. But there are other factors at work as well, as my own research with Charlotta Mellander and Jason Rentfrow reveals. Our results indicate that the factors that contribute to happiness differ in high- and low income countries. In low-income nations (defined as those with less than $11,000 in per capita GDP), happiness turns on income.  But it matters much less in high-income countries. In these countries, people are affluent enough to cover the basics which are essential to a base level of happiness. Two factors matters in particular over and above income in these more well-off nations. The first is the nature of the job market: people are happier in affluent nations where more of them work in knowledge-based, creative and professional jobs and less work in blue-collar working class jobs. Values matter too, especially openness and tolerance toward ethnic and racial minorities and toward gay and lesbian people.

High levels of well-being and of thriving seem to turn not just on income but on the ability to find work that is challenging, purposeful and enjoyable and the freedom one has to be one’s self.

Earlier this week, I wrote about the new rankings of happy cities based on the 2010 edition of the Gallup-Healthways Well-Being Index. The map below shows how 185 of America’s largest metros stack up on this happiness index.

In his book, Stumbling on Happiness, Harvard psychologist Daniel Gilbert notes that there are three great decisions in life that affect your happiness: “Where to live, what to do, and with whom to do it.”  The second two have been examined in great depth; the third, up until now, not so much.

Happiness – or what students of the subject refer to as “subjective well-being”—is typically charted and studied at the national level.  But we can use the detailed data and rankings provided by this new Well-Being Index to better understand the factors that make people happier in some cities than others.  (I’d be remiss if I failed to point out that, as with everything else that is subjective, not everyone is happy with the same kind of city – a point that I ruminate upon at some length in my book Who’s Your City?).

So with the help of my Martin Prosperity Institute colleague Charlotta Mellander, I looked at some of the key characteristics of cities that are associated with greater happiness across these U.S. metros. A good deal of the research on happiness finds that income plays a crucial role. While there was some debate on this early on – note the eponymous Easterlin Paradox, which says that income and happiness don’t relate at the national level—there is now a broad consensus that happiness and income are closely associated. We look at the effects of income, as well as other key variables – such as education levels or human capital, and the nature of the job market and workforce (that is, the share of jobs in knowledge-based, professional and creative industries as opposed to blue-collar working class fields).  As usual, our analysis points only to associations between variables. It does not specify causation or the causal direction of those associations, which are questions for future research. Still, the results are interesting across several dimensions.

Are wealthier cities, like wealthier nations, also happier cities?  Indeed they are. We find reasonably strong correlations between well-being and several measures of income and wealth – hourly wages (.41) and income (.38), and economic output per capita (.38).

Conventional wisdom and academic studies alike suggest that levels of happiness would fall as unemployment rises. And this is what we find. There is a significant negative correlation between unemployment and happiness across U.S. metros (-.33).

Education levels, or human capital, have a huge effect on city economies, being closely associated with city incomes.  They play a substantial role in reported levels of well-being as well.  There is a close association between human capital (measured as the percentage of adults with a college degree or higher) and city happiness (.69) – considerably stronger than that for income.

And what about the kind of work we do?  My own research has documented the connection between a large-scale presence of the creative class of workers – people who work in science and technology; business and management; arts, culture and entertainment; medicine and education – and the prosperity of cities. But it’s about more than prosperity. Once a certain threshold of income is met, our research finds, the work people do plays a substantial role in their happiness, over and above the effect of income at the national, state, and city levels. Our findings here reinforce and confirm this conclusion.  There is a substantial positive correlation between city happiness and the share of creative class jobs (.5) and a significant negative one between well-being and the share of working class jobs (-.4).

The composition of city job markets plays a considerable role in our sense of well-being as well. Cities with more knowledge, professional and creative jobs have less unemployment (the rate of unemployment in these fields barely crossed five percent during the worst of the downturn and is now back down below it) and higher levels of income. And creative work is intrinsically satisfying; it makes better use of our social and cognitive skills. Having creative and purposeful work to do is in itself a key factor in happiness, as detailed studies by the psychologist Mihaly Csikszentmihalyi have noted.

Conversely, cities with more blue-collar economies have been among the hardest hit by the economic crisis. Unemployment is high, incomes are lower.  Workers in these kinds of jobs have faced much greater trouble finding new jobs (the unemployment rate for production workers is 10 percent; for construction workers it tops 20 percent). Not only do these workers have skills and incomes which are tied to their specific jobs, many in these areas are trapped in underwater homes and unable to relocate to areas with more work and greater opportunity:  Hardly a recipe for happiness.

It’s increasingly apparent that America is becoming more and more divided and unequal—by income, by the kind of work we do, by our levels of education, by our politics and culture. And perhaps most dangerously, Americans are divided by their sense of happiness and well-being as well. Along with everything else that polarizes us, America increasingly faces an increasingly unequal geography of class and happiness.

Click here to listen to the fun segment on today’s show.

As NPR describes it:

Professor Richard Florida, director of the University of Toronto’s Martin Prosperity Institute, has released his Trick or Treat Index for 2010. It’s a kind of Lonely Planet guide for hobgoblins. He gives us his top-five list of the best U.S. cities for Halloween trick-or-treating.

Here’s the original list, a map, and the list for Canada.

How has the economic crisis affected the happiness and well-being of Americans? Newly released data from the Gallup-Healthways Well-Being Index enables us to take a look.

At the national level, not so much: The mid-year 2009 score is 65.1, a moderate decline from 65.5 in 2008. (Catherine Rampell of Economix provides a nice summary of the survey methods, indicators, and key findings.)

But, rising unemployment appears to have a significant relationship to the happiness of states, according to our analysis of the Gallup-Healthways data.

Not surprisingly, the biggest declines in overall happiness occurred in work-related well-being. The Gallup-Heathways Well-Being Index is made up of six separate sub-indexes – life evaluation, emotional health, work environment, physical health, healthy behavior, and access to basic necessities. Five of these indexes fell between 2008 and 2009, with the biggest decline occurring in the work environment index: More than three-quarters of states saw their work environment score fall in 2009.

This is broadly in line with happiness research. It had been long thought that happiness essentially levels off after a moderate income level is crossed. But an influential study by Betsy Stevenson and Justin Wolfers found a strong association between happiness and economic conditions. A 2005 study found that a significant increase in Finland’s unemployment rate (from three to 17 percent) did not produce a significant drop in overall well-being.

The new Gallup-Healthways Index also covers the 50 states. Interestingly enough, the “happiest states” in 2009 – Hawaii, Utah, and Montana – were more or less the same as in 2008; the same is true of the “unhappiest states” – West Virginia, Kentucky, and Arkansas. Drilling down a little further, Utah topped the list in life evaluation, Hawaii in emotional health, Idaho in work environment, North Dakota in physical health, Vermont in healthy behavior, and Iowa in basic access.

Still, it’s clear that the economic crisis has been harder on some states that others. Older industrial states of Michigan, Indiana, and Ohio have seen their unemployment rates soar in the double digits, while the housing crisis has wreaked havoc on once fast-growing states like Florida and Arizona.

The availability of state-level data for before (2008) and after (2009) the crisis provides a useful lens for examining the effects of worsening economic conditions on state happiness.

So my collaborator, regional economist Charlotta Mellander, looked at the relationships between happiness and economic factors like output, income, and unemployment. Let me emphasize that what I am reporting here are correlations or associations. While these findings do not imply causation, they remain interesting nonetheless.

First off, the relationship between happiness and economic output has apparently become weaker. The relationship between the two which was correlated (.33) and statistically significant in 2008, is no longer so (.27 and not statistically significant in 2009).

Second, the relationship between income on happiness also seems to have weakened (falling from a correlation of .43 in 2008 to .30 in 2009 – both significant at the .01 level).

Third, unemployment appears to be the biggest short-run factor affecting state happiness. Two measures of unemployment – a higher state unemployment rate and a bigger increase in that rate between 2008 and 2009 – were associated with both lower levels of state well-being and a bigger drop in state well-being between 2008 and 2009.

The first chart graphs the relationship between 2009 state unemployment rate and state well-being. Hawaii and Utah, above the line; and West Virginia, Kentucky, and Arkansas below it, are clearly outliers. Still, the fitted line shows a reasonably close association between unemployment and happiness among states. The correlation coefficient of -.44 between the two (statistically significant at the .01 level) lends additional support to this.

The second chart graphs the relationship between state happiness and the change in the unemployment rate between 2008 and 2009. Hawaii and Utah, and West Virginia, Kentucky, and Arkansas are again outliers. But the fitted line shows a clear association between the two. And while the correlation coefficient between the two is weaker than above (-.34 and statistically significant at the 0.05 level) it nonetheless supports the association.

The connection between state happiness and unemployment also came through when we looked at the relationships between the change in state well-being between 2008 and 2009 and the two measures of unemployment – the 2009 unemployment rate and change in unemployment between 2008 and 2009. The correlations for each are statistically significant (-.30 for the 2009 unemployment rate and -.34 for change in the unemployment rate between 2008 and 2009, both significant at the .05 level).

Given all of this, it’s safe to say that unemployment plays a reasonably big role in the happiness – or should I say, unhappiness – of states.

I continue to be astounded by the unrelenting interest in “singles maps” and singles ratios. A bluntly titled blog devoted to San Francisco’s lop-sided gender ratio cites this 2007 study (and an earlier 1991 one) which identified a singles’ tipping point of sorts. The study found that:

[A]s the sex ratio augmented in favor of women, at first, as you would expect, the women simply turned fussy and went for richer and more powerful men. But at a certain point a curious thing happened: the amount of socioeconomic status a guy needed to get girl increased way more than the math would predict. Specifically when the ratio was tilted in favor of women by 10%, low status men became not 10% less likely to get a girl but 200-300% less likely and high status men 30% less likely.

In other words, increase the number of males in a system too much and the number of females interested in pairing up GOES DOWN, due to some mysterious psychological trigger. Women won’t pick and choose, they won’t choose at all. They abandon the enterprise. Romance dies. Society crumbles. Imagine a bar with 100 girls and 100 guys. The bouncer admits 10 more guys and competitively speaking it’s as if, for the low status guys, 130 guys walked into the room (and for the high status guys, 30 guys). The bar might as well close for the night.

The original paper is here. Map at Gene Expression via Marginal Revolution.