Creative Class

Earlier this week, I discussed the new Gallup-Healthways Well-Being Index of happy cities. Today, with the help of my Martin Prosperity Institute colleague Charlotta Mellander, we take a look at some of the social, demographic, and economic factors that are associated with the happiness and well-being of cities.

There has been considerable debate on the factors that are associated with happiness and well-being at the national level. The well-known Easterlin Paradox suggested that happiness tends to level off after a certain income threshold. Psychologists, notably Edward Diener, have argued that factors such as health, challenging work, and close social relationships, among others, play a considerable role in happiness. Some have even made the case for instituting a new measure of gross national happiness to supplement conventional metrics like gross national product.

Recent studies by Princeton University’s Angus Deaton and Justin Wolfers and Betsy Stevenson of the University of Pennsylvania’s Wharton School question the Easterlin Paradox and indicate a closer link between happiness and income across nations. Carol Graham raises the enigma of the “happy peasant and the miserable millionaire” as a way to resolve this apparent paradox. Graham suggests that happiness is relative to one’s position in society. Take unemployment for example. Unemployment is crushing for previously employed people in places where gainful employment is the norm. But people in poor countries where unemployment is more the norm find other ways to be happy.

The Gallup-Healthways is the first comprehensive data set we know of that tracks happiness and well-being at the metropolitan level, providing data from a large-scale survey of individuals across 185 metro regions. We look at the associations between the Gallup-Healthways Metro happiness index and key social, demographic, and economic factors. Data-matching reduces the size of our sample to 170 metros – roughly half of all U.S. regions. As usual, we point out that 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.

Income, Wages, and Output: So what is the relationship between metro-level happiness and income, wages, and output? The correlation analysis suggests a moderate relation between wages (.45), income (.4), and economic output per capita. The scatter-graphs below show the relationships are reasonably linear, though there is a better fit for wages and income than for output per capita.

Unemployment: Conventional wisdom and academic studies suggest that a rising unemployment rate would take a big toll on happiness. We find a moderate effect across U.S. metros. The correlation between happiness and the unemployment rate is -.34 and between it and the year-over-year (December 2008 to December 2009) change in unemployment is -.3.

Post-Industrial Economic Structures: In ongoing research, we have been testing the notion that happiness and well-being may be more associated with key features of so-called post-industrial economic structures – namely the shift from physically oriented work to knowledge, professional, and creative occupations and industries – and from lower-skilled to more highly skilled and educated workforces. A large body of research has found a close association between human capital (measured as share of the population with a B.A. and above) and economic development across nations as well as regions; other research has found that human capital levels are becoming more divergent across regions over time. To get at this, we looked at the associations between happiness and human capital, as well as between it and creative-knowledge-professional occupations and blue-collar working class occupations.

Human Capital: Happiness at the city or metro-level is more closely associated with human capital with a correlation of .68 – the strongest correlation of any of the variables we looked at. The scatter-graph below shows a fairly linear relationship.

Creative Class: Happiness is also associated with the creative class, a correlation of .45. The scatter-graph below shows a fairly linear relationship.

High-Tech: Happiness is also associated with locations that have higher concentrations of high-tech industries. We find a correlation of .41 between it and the Milken Institute’s Tech-Pole measure.

Working Class: On the other hand, metro-level happiness is negatively associated with the working class, -.34, a finding which is similar to that for states.

It’s terrific to see unemployment rate dip below the 10 percent mark. But, unemployment in the Great Reset remains quite a bit deeper than in previous ones, as the NYT’s Catherine Rampell shows. The overall U-6 measure of unemployment – which includes discouraged workers – stands at 16.5 percent.

A close look at the numbers finds some groups are doing far better than others. Men continue to fare substantially worse than women:  The unemployment rate for adult men remains 10 percent, while the rate for women is now 7.9 percent.

The effects of the economic crisis continue to be extremely uneven. Unemployment remains much higher for the less educated. The unemployment rate for workers without a high school degree, 15.2 percent, is 50 percent higher than that for workers with a high school diploma, 10.1 percent, and three times higher than for college-educated workers, 4.9 percent.

Unemployment also varies substantially by industry. The unemployment rate for blue-collar workers remains quite high. The unemployment rate for manufacturing workers stands at 13 percent while construction workers face a staggering 24.7 rate. The rate for professional services workers has grown to 11.1 percent, but financial professionals have unemployment of 6.6 percent.  The rate for educational professionals stands at  5.5 percent, and that for government employees is 4.3 percent.

As one looks around the economic landscape I am struck by the devastation. One number stands out above all others. One in five males between the ages of 25 and 55 is out of work! That is a staggering number. The numbers are not going back to anything “normal” anytime soon according to the IMF. Financial crises followed by recessions do not return to normal levels of employment for over a decade. Why you might ask? The answer I guess is that the levels of debt need to be worked down. Everyone owes everyone money and none pay anyone. Second, the recession destroys real capital. In this situation it was housing. It will take years to work off the excesses of the housing crisis.

So what does entrepreneurship have to do with the recession? If we take what we know today, entrepreneurs and innovation play a vital role in the economy. But can they help us in the great recession? In other words, what policy should we be pursuing to move the unemployment rate below 10 percent and back into the neighborhood of 5 percent? We know that new firms are important. They create most of the net jobs.  However, only a small percent, perhaps 4 percent, create almost all of the jobs in any given four-year period. And this seems to hold up in different times, different countries, and different industries.

So how do we forge a policy? Two stories are told out there. First we know that age and size are important variables. And we know that age appears to be more important than size. In other words, we should target firms based on age not size. The two stories out there are one by Zoltan Acs and the other by Carl Schramm. In a highly influential study, Acs found that the average high impact firm was about 20 years old and came in all sizes, small, medium, and large. Schramm, on the other hand, using a Census Bureau study, found that firms less than five years old created almost all of the jobs independent of size.  They both cannot be right.

However, if we are interested in short-term policy solutions and not real economic growth, we should help stimulate solo self-employed. They have a start-up rate that is three times as large as firms with employees. They start easily but also go out of business quickly. So an effective policy would be to make it easier for them to stay in business longer.

A simple policy would be to cut the self-employment tax, not over 15 percent of all new solo self-employed firms to zero for three years. If they hired any employees we should cut the employer share 7.5 percent for three years also. This would greatly increase the survival rate for these new firms. Of course this is not a long-term solution because many of these firm will contribute very little to productivity, economies of scale, or wealth creation. But they will pull down the unemployment rate.

The impact on the deficit would not be great since many of these people would not have survived to pay payroll taxes anyway. Once the economy picks up the issue of long-run growth can be addressed. But in the short run, let’s get people working.

If a picture’s worth a thousand words, this chart has to be valued more than 10 times that.  Detroit is literally off the chart. The big losers, other than the Motor City, are sprawling Sun Belt metros: the Miami, Tampa, Orlando, So-Flo Triangle; So-Cal’s once-vaunted Inland Empire, L.A. and San Diego; Las Vegas; Portland; and Rustbelt cities Buffalo, Rochester, and St. Louis.

The big winners: D.C. and Baltimore. NY and Boston also do well, along with Silicon Valley and San Francisco, Austin, Seattle, and Denver. Salt Lake City and Oklahoma City are also in very good shape, as well as, surprisingly, certain Rustbelt Cleveland and Milwaukee.

Chart from Paul Kedrosky, original data from Indeed.

The U.S. unemployment rate is 9.7 percent, the highest in some time, but the burden of unemployment is  spread unevenly across the economy. Production workers face a 15.1 percent unemployment rate, while unemployment among construction and extraction workers stands at 17 percent. But unemployment among management and professional workers is only 5.4 percent. Researchers at the Martin Prosperity Institute (MPI) previously identified long-run differences in the unemployment rates faced by industrial workers and knowledge, professional, and creative workers.

New analysis by the MPI team tracks unemployment among management and professional – or creative class – workers from 1983 to the present. While unemployment among creative class workers as a whole is far below the rate faced by production and construction workers, there is considerable variation in unemployment among the various occupations, professions, and job types that make up the creative class.

Creative workers in arts, design, and entertainment occupations consistently face higher unemployment rates and significant spikes during recessions. In contrast to other creative fields, the unemployment rate for arts, design, and entertainment workers sometimes runs higher than the overall unemployment rate.

Computer, sciences, and engineering professionals experience lower rates of unemployment than arts, design, and entertainment workers. But the lowest rates of unemployment and the most stable employment are found in meds and eds occupations – health and education – where unemployment stays consistently low, even during downturns.

The full analysis is here.

Been busy with the final push on the book and also tweeting up a storm (so do follow me there). Hope to get back to more regular posting after the holiday. But news of rising unemployment and the two-track economy got me thinking about the changing nature of jobs and work and my favorite books on employment, labor, and work.

Ben Hamper’s Rivethead, a scorching account of mind-numbing, dehumanizing work on the assembly line.

Harry Braverman’s Labor and Monopoly Capital, the seminal analysis of the separation of mental and manual labor, conception from execution.

David Montgomery, The Fall of the House of Labor, a labor history classic.

In an age when the tendency to separate mental and manual labor has reached an extreme, it’s important to value all work and remember that each and every worker – in the laboratory, artist studio, factory floor, and retail shop – is creative. It’s also important that we continue to strive to improve the pay, working conditions, and creative content of all work.  We must do more than just create jobs. Our task is to create better, more fulfilling, more productive, and more humanizing kinds of work. The productivity of our society and well-being as individuals can be greatly enhanced by creating work that employs our full productive capabilities, enables us to use our chosen talents, and fully express who we are and who we want to be. In an era when so many people are alienated by work and choose to express themselves through consumption and lifestyle, work remains a key element – if not the key factor – of our identity.

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.

The U.S. economy has shed 7.2 million jobs since the onset of the recession. But the economic pain of unemployment has not been spread equally, according to a new analysis by my colleagues at the Martin Prosperity Institute.

The graph below, compiled by Ulrich Atz, tracks the unemployment rate for three broad groups or classes of employment – the working class, the service class, and the creative class from 1971 to May 2009.

The report finds that:

Unemployment for all three groups has spiked since the onset of the recession.  But the downturn has hit hardest on working class. . . The working class has been hard hit by every downturn since 1971. Working class unemployment spiked from 6.2 percent in 1973 to 14.5 percent in the 1975 downturn.  It spiked again from 7.7 percent in 1979 to 16.8 percent in 1983.  It reached 12.0 percent in 1992.

In contrast, the unemployment rate for the creative class has hardly ever reached the 4 percent mark.  Unemployment rates among the working and service class are typically about 3-4 and 2-3 times respectively the rate of those in the creative class.

A closer look at monthly data (available starting in 2000) reveals that unemployment rates among the working and service classes typically move together while creative class unemployment lags the other two by several months.

The full analysis is here.

The impacts of the economic crisis continue to be felt unevenly across the country. I’ve previously looked at the factors associated with higher rates of regional unemployment. But which places have seen the biggest jumps in unemployment since the crisis hit?

To get at this, my colleague Charlotta Mellander conducted a straightforward statistical exercise called a “residual analysis.” It’s a simple way to track how a location performs relative to the performance of all other locations. Basically, the analysis examines to what extent the initial unemployment rate in May 2008 seems to have had an impact on the change in unemployment over the last year. Technically speaking,  Mellander ran a regression analysis predicting change in unemployment over this last year (May 2008 to May 2009) as a function of the initial level of unemployment at the beginning of the period (May 2008). She then compared the predicted values to the actual values.

The first graph shows the pattern for U.S. states.

The hardest hit states are ones that were doing badly even before the crisis hit. The fitted line is steep; the correlation between the two is 0.59 and significant; and the R2, 0.345. States below the line experienced a smaller than predicted increase in unemployment levels, while those above the line saw a larger than predicted increase.

Michigan has the highest unemployment rate, but Oregon (+3.0) has taken the biggest relative hit. Alabama (+1.8), Indiana (+1.6), South Carolina (+1.6), and Wisconsin (+1.4) have also taken bigger than expected hits. North Dakota has the lowest rate of unemployment but Alaska (-2.8), Mississippi (-2.1), Arkansas (-1.2), Connecticut (-1.2), Iowa (-1.1), and Nebraska (-1.2) have done better than expected.

The second graph repeats the analysis for U.S. metropolitan regions. It excludes two extreme outliers in California – Yuma and El Centro – which started the period with 20 percent plus rates of unemployment.

The hardest hit metros are also those that were doing badly before the crisis. The fitted line is again steep; the correlation coefficient is high, 0.59; and the R2, 0.351.

The crisis has hit hardest at smaller Rustbelt metros, especially those in Indiana: Elkhart-Goshen, IN; (+7.3); Kokomo, IN (+7.2); Decatur, GA (+3.2); Sheboygan, WI (+2.7); Fort Wayne, IN  (+2.3); and Youngstown, OH (+2.2).

While Detroit has faced staggering unemployment, the difference between its actual and predicted unemployment is +1.6. Among large metros, Portland (+3.1), Charlotte (+2.2), and, San Jose (+1.9) experienced even bigger than expected increases in unemployment. Las Vegas (1.5), Boise (1.29), and Orlando (+1.29) have also been hard hit. San Francisco (+.93), Miami (+.49), L.A., Chicago (+.31), Atlanta, and San Diego (+.21) also performed worse than their May 2008 unemployment levels predicted.

Several Oregon metros took worse than expected hits: Bend-(+4.6), Eugene-Springfield (+3.8), Portland (+3.0), Salem (+2.5), Medford (+2.4), Corvallis(+1.9). Metros that border Oregon like Spokane, Washington (+0.8) and Boise, Idaho (+1.3) also have high differentials.

Three Texas cities – Dallas (-1.0), Houston (-0.9), and Austin (-1.0) – performed considerably better than expected. Minneapolis-St. Paul (-0.4) did too. Cities along the Bos-Wash mega-region – Boston (-0.4), D.C. (-0.3), New York (-0.1), and even Philadelphia (-0.3) – also did better than predicted. Surprisingly, Phoenix also outperformed expectations (-.2), albeit modestly.

College towns number among the best performers, doing much better than predicted: Champaign-Urbana, Illinois, home to University of Illinois (-2.2); Iowa City, University of Iowa (-1.81); Manhattan Kansas, Kansas State University (-1.82); College Station, Texas, Texas A&M (-1.74); New Haven, Connecticut, Yale University (-1.54); State College, Pennsylvania, Penn State University (-1.47); Boulder, Colorado, University of Colorado (-.93); Austin, Texas, University of Texas (-1.0); Ann Arbor, Michigan, University of Michigan (-.94); and Ithaca, New York, Cornell University (-.97), among others.

Check out this map of job postings by metro area (h/t: Steven Pedigo).

The map controls for population.

D.C. has the most openings, and Baltimore is second. San Jose, Austin, Hartford, Seattle, Salt Lake City, Denver, Boston, Las Vegas, Charlotte, and San Francisco all are doing reasonably well, relatively speaking.

Detroit comes in dead last, with the fewest openings Miami. Buffalo, Rochester, L.A., and Chicago are doing poorly.

An interactive version and the full list of cities is here.