The United States has seen a steady erosion of blue-collar work over the past several decades. We define blue-collar, working class jobs as those which primarily make use of physical skill or manual labor. These occupations include not only factory work or production occupations, but jobs in construction, materials moving, transportation, installation, and repair. Blue-collar, working class jobs currently account for 23 percent of all U.S. employment. Blue-collar occupations and the regions that specialize in this kind of work have seen the highest levels of unemployment and the greatest vulnerability to the economic crisis. The decline of high-paying, blue-collar jobs for lower-skilled workers has caused considerable concern that the U.S. is losing an important source of good, family-supporting jobs, and that the American labor market is becoming more uneven and increasingly split between higher-paying knowledge work and lower-paying routine service work. But what will the geography of blue-collar work look like in the future?

The map above shows the metros with the biggest projected gains in blue-collar work. Not surprisingly, the biggest metros top this list. The biggest gainer is Greater New York which is projected to gain 41,084 working-class jobs followed by Houston (32,249), Chicago (30,482), Los Angeles (28,811), Phoenix (23,957), Atlanta (22,754), Washington, D.C. (21,387), Dallas (19,315), Riverside (16,755), and Las Vegas (14,781).

But job growth is a function of population size; it’s expected that large regions will dominate the list of the biggest job generators. So, the next map plots the projected percentage change in working-class jobs for U.S. metros. The regions with the largest projected working class increases are all in the Sun Belt: Pascagoula, MS (8.2 percent), Naples, FL (8.1 percent), Punta Gorda, FL (7.9 percent), Santa Fe, NM (7.4 percent), Cape Coral, FL (7.4 percent), Farmington, NM (7.4 percent), Jacksonville, NC (7.2 percent), Las Vegas ( 7.1 percent), Grand Junction, CO (7 percent), and St. George, UT (7 percent). The places with the slowest projected growth are mainly traditional manufacturing metros such as Elkhart (.2 percent) and Columbus, IN (1.06 percent), Muskegon, MI (1.16 percent), Oshkosh, WI (1.41 percent), Hickory, NC (.68 percent), and Dalton, GA (.46 percent), among others.

The next map shows the regions that are projected to have the highest share of their workforce doing blue-collar work out to 2018. Elkhart, IN, tops the list with nearly half (48.5 percent) of its workforce in blue-collar jobs – though that is down from 51.3 percent in 2008. It is followed by Dalton, GA (45.9 percent), Morristown, TN (40.9 percent), Houma, LA (40 percent), Decatur, AL (37.6 percent), Fort Smith, AR (36.5 percent), Hickory, NC (35.8 percent), Odessa, TX (35.4 percent), Gainesville, GA (35.3 percent), and Sheboygan, WI (34.3 percent).

The good news is that the U.S. will continue to create relatively high-paying working class jobs. These jobs will continue to provide good livelihoods for the workers fortunate enough to have them. The bad news is that their rate of growth will be sluggish and not nearly enough to provide the amount of good, family-supporting jobs required to undergird a middle class of lower-skilled workers. The harsh reality is that blue-collar, working class jobs in the U.S. are increasing slowly, and they will grow the slowest in traditional manufacturing and industrial regions and communities whose economic and social life has revolved around these jobs. There is little policy-makers can do – aside from declaring a trade war – to bring back large numbers of these high-paying jobs. But they can develop strategies to improve not just the wages but the content of blue-collar work, by engaging workers more fully and seeing them as a source of innovation. And they can help to infuse more creativity and design into manufacturing products, helping to broaden their market and counteract the trend toward declining prices. And policy-makers will have to develop strategies for improving wages and the content of work in other faster-growing segments of the economy, a point I will get to in my next post, which will cover the projected growth of service jobs.

Last Friday, my list of the 20 metros with the fastest-growing jobs was posted over at The Daily Beast. Jobs are the second-biggest issue facing the United States – second only to the economy, according to a recent Gallup poll – and a pending referendum on the Obama administration in the upcoming mid-term elections. As I noted:

The United States has lost an estimated 7.4 million jobs since the onset of the economic crisis. But, the economy is on track to create some 15.3 million new jobs looking out to 2018, according to projections done by the Bureau of Labor Statistics (BLS). And more than 50 million total jobs will come open, as older workers retire and many switch jobs and careers. Total U.S. employment is projected to grow by 10.1 percent over the period, according to the BLS forecast, considerably better than the 7.4 percent growth rate for previous decade (1998-2008), and roughly in line with population growth of 10.7 percent.

But where will the new jobs be located? Which places will grow the most jobs and, conversely, which will see the biggest job losses?

To get at this, my Martin Prosperity Institute (MPI) team applied the detailed employment projections of the BLS to U.S. metro regions. The BLS forecasts job trends across 22 major occupational groups which include more than 822 specific job categories for the decade 2008 through 2018. My MPI team used these BLS national forecasts to generate similar estimates for each of America’s 392 metro regions. Essentially, we used the BLS overall estimations to predict job growth in each region based on its current mix of jobs.

Over the next couple of weeks, I’ll be posting the results of our analysis here. I’ll get us started today with a series of maps and analyses of the metros that stand to gain the most jobs overall.

The map above shows the metros with the biggest projected gains in total employment out to 2018. New York is projected to add 578,974 jobs, the most in the country. It is followed by Los Angeles (405,392), Chicago (344,740), Washington, D.C. (261,465), Atlanta (235,036), Houston (232,001), Philadelphia (202,970), Dallas (203,202), Phoenix (191,210), and Boston (186,457).

But job growth is a function of population size; it’s expected that large regions will dominate the list of the biggest job generators. So, the next map (below) plots the projected percentage change in overall employment for U.S. metros.

Rochester, Minnesota, is the biggest percentage gainer, with projected job growth of 12-plus percent. The major hub cities of the Bos-Wash corridor do well with Greater Washington in second place, Greater New York in 15th, and Boston 19th. The D.C. suburb of Bethesda and Trenton-Ewing – a suburb of both New York and Philadelphia – also number among the top 10. College towns like Charlottesville, VA, Gainesville, FL, Ithaca, NY, Boulder, CO, and Corvallis, OR, which have performed well over the course of the economic crisis, number among the nation’s top 20 projected job gainers.

The biggest projected job losers are mainly older manufacturing communities. Elkhart, Indiana, a region which currently ranks near the top of this list in terms of unemployment, also ranks last in terms of projected job gains, adding just 6,639 total jobs – a 5.4 percent gain. Next is Dalton, GA (5.6 percent), Morristown, TN (7 percent), Visalia, CA (7.3 percent), Columbus, IN (7.7 percent), Decatur, GA (7.7 percent), Cleveland, TN (7.8 percent), Hickory, NC (7.8 percent), Holland, MI (7.8 percent), and Gainesville, GA (7.9 percent).

But what accounts for this growth? We ran correlations for key economic, social, and demographic variables and the percentage change in employment from 2008 and 2018. These are preliminary, exploratory analyses that simply point to associations between variables. We don’t make any claims here about the direction of causality, and we acknowledge that intervening variables may come into play. Several key factors emerge as driving forces in the regional jobs equation.

Size matters, but only to a very slight degree. Population is only very slightly correlated with percentage change in employment (.17).

Characteristics of the labor force and of metro economies matter much more. Human capital is very closely associated with percentage change in metro level employment (.56). The scattergraph above plots the association.

The nature of the economy is also closely associated with metro job growth. The percentage change in metro employment is closely associated with the percentage of the workforce in the creative class (.64).

But it is even more strongly associated with the share of the labor force in blue-collar, working class jobs, where the correlation is high and negative (-.84).

This suggests that the structural forces that are reshaping the U.S. economy from an industrial to a more idea, knowledge, and human capital driven post-industrial economic system will continue to deepen. Left unchecked, these forces will continue to divide the U.S. economy and U.S. society by skill-level, occupation, and economic class – the kind of work people do. And this rising economic divide of work and class will also continue to be reflected in and overlaid by a deepening geographic divide, as the geography of class compounds economic and social inequality. Public policy then will have to focus not just on generating jobs but on improving the content of many of those jobs, especially in the service class, increasing innovation and productivity, more fully utilizing and engaging workers’ capabilities and talents, and improving wages.

My next post will look at the projections for blue-collar factory work. Future posts will cover projections for the growth of lower-skill, lower-pay service work and the projected increase in higher-wage knowledge and creative work.

Here’s some video from my CNBC StreetSigns appearance with Robert Shiller.

Check out my new piece in The New Republic:

Speaking at a health care reform rally in Raleigh, North Carolina, in July 2009, President Obama declared that the worst of the recession was over. “We have stopped the free-fall. The market is up and the financial system is no longer on the verge of collapse,” he said proudly.

A year or so later, with midterm elections looming and an electorate that is as fearful and angry as any in memory, the stock market has risen, but even a breath of bad news can send it tumbling. As dismal as housing prices continue to be, they have yet to hit bottom in some places. Unemployment remains frozen at an overall level of nine-plus percent, and job creation has been anemic. If the crisis belonged to George W. Bush, the recovery has been Obama’s—and it has been a fragile and tentative one at best. Along with billions of dollars in stimulus payments, the president has spent down most of his political capital. So what is his next step?

Read the full article here.

Traditionally, the United States has worried about the offshoring of manufacturing jobs. But concern is mounting that as the rest of the world becomes increasingly well-educated and competitive, the desirable and high-paying service jobs previously considered immune to moving overseas are becoming increasingly vulnerable.

In a widely cited 2007 study, economist Alan Blinder estimated that between 22 and 29 percent of U.S. jobs were vulnerable to offshoring as of 2004. In a sobering Wall Street Journal op-ed published earlier this month (via Economist’s View), economists Martin Neal Bailey, Matthew Slaughter, and Laura Tyson provide evidence that the global competition for jobs is heating up considerably. They note that multinationals have long been among the most important economic drivers of the U.S. economy, accounting for an estimated 19 percent of all private jobs, 25 percent of all private wages, and 41 percent of the increase in private labor productivity since 1990. Then they cite a McKinsey study based on personal interviews with senior executives from 26 of America’s largest companies that finds the U.S. faces an unprecedented level of competition to “attract, retain and grow the operations of multinational companies that it’s never faced before.”

A 2008 Bureau of Labor Statistics report (via Derek Thompson) focused on the potential offshoring of service occupations and found that over 30 million of these jobs, about one-fifth of the total, were at risk of offshoring. The BLS definition of service jobs includes both high-paying, highly skilled, knowledge work – from scientists and engineers to architects and fashion designers, managers, business analysts, and CEOs – as well as lower-skilled, lower-paid service work like telemarketers and data entry clerks. The study found that the at-risk service jobs were, on average, higher-paying ($61,473 vs. $41,610) and more highly skilled – more than half of them (54 percent) required college degrees and eight in 10 required “some college.”

But how might the offshoring or service work affect cities and metropolitan regions? Which cities and regions are most vulnerable to potential offshoring? And which have economies that are more insulated from the possible offshoring of service work?

To look more closely at how these trends might impact U.S. cities and regions, my team at the Martin Prosperity Institute used the BLS data and methodology to estimate the vulnerability of different metropolitan regions to offshoring. The BLS study used detailed data on job content and skill, spanning the years 2001-2007, to identify the characteristics of 515 U.S. occupations that make them vulnerable to offshoring and to rate them on their susceptibility to being moved offshore. They used four criteria, below:

The study ranked these occupations according to three categories of susceptibility to offshoring. The highest-ranked or most susceptible service jobs spanned 33 occupations – such as data entry clerks, telemarketers, tax preparers, pharmacy technicians, and paralegals – employing 9.5 million workers. The middle-ranked jobs spanned 94 occupations – including engineers, scientists, economists, fashion designers, writers and editors, medical scientists – and employed 14.3 million workers. The lowest-ranked or least at-risk service jobs included 33 occupations – CEOs, financial analysts, environmental engineers, art directors, and landscape architects – that employed 6.5 million workers. The chart below from the BLS study provides key data on the profile of each tier of at-risk service occupations.

My colleague Charlotta Mellander used the BLS ranking system to generate estimates of susceptibility to offshoring for U.S. regions, matching the at-risk service occupations in the BLS analysis to the occupational structures for all 392 U.S. metropolitan areas.

Across all metros, 22.5 percent of service jobs fell into the three at-risk categories. But there was considerable variation across regions. The most vulnerable regions had more than 30 percent of jobs at risk, compared to the least vulnerable regions where 7.5 to 8 percent of service jobs were at risk. I want to make it clear – there is virtually no chance that all the vulnerable jobs will be lost.  In fact, it is likely that only a small percentage will actually move offshore, particularly those in the lowest-risk category.

The chart below lists the 20 U.S. metro regions most susceptible to the offshoring of service work across all three categories of jobs: low, medium, and high-risk. These top 20 regions had between a quarter and more than a third of their service jobs at risk of offshoring.

Source: Regional data are from the BLS‘ Occupational and Employment and Wage Estimates for metropolitan areas, 2008.

The regions with the greatest percentage of at-risk jobs across all three BLS risk-categories are chiefly those with high percentages of higher-skill knowledge work. These include three regions in the Greater Boston area (Framingham, Boston, and Lowell), greater Washington, D.C. (the D.C./Northern Virginia area and Bethesda, Maryland, and its surroundings), San Jose (Silicon Valley) and San Francisco, and Greater New York. Seattle and Atlanta also number among these top 20 most vulnerable. While these overall figures are daunting, it is unlikely that some of the lower-risk jobs will be offshored in any significant or meaningful way. Clearly, some categories of service jobs are more likely to face offshoring than others, as the BLS methodology explicitly notes.

So now let’s look at metros with the largest concentrations of their job structures in the highest-rated or most-vulnerable categories identified in the BLS study. Using this more realistic metric, 6.9 percent of service jobs across all U.S. metros are at high risk to offshoring. Again, there is considerable variation across regions from 12 percent of service jobs at the high end to roughly 3 percent at the low end.

The chart below lists the 20 metros with the largest shares of service jobs at high risk to offshoring. These 20 regions had between 8.2 and 12.5 percent of their jobs at risk.

Source: Regional data are from the BLS‘ Occupational and Employment and Wage Estimates for metropolitan areas, 2008.

This list is considerably different than the earlier one. Far fewer knowledge-based regions make this narrower list of jobs that are at high risk to offshoring. Sioux Falls, South Dakota, has the highest percentage of these highly at-risk jobs, followed by Omaha, Dallas, Phoenix, and Jacksonville. Among large regions (those with more than one million people), Kansas City, San Antonio, Philadelphia, Atlanta, Charlotte, Hartford, Tucson, Pittsburgh, Austin, Houston, and Cincinnati also had relatively large shares of their service jobs (between 7.5 and 8.2 percent) in this higher at-risk category. The regions with the smallest percentages of highly at-risk jobs are mainly smaller places including a good number of college towns, from Corvallis, Oregon, and Ames, Iowa, to Lawrence, Kansas, State College, Pennsylvania, and Ithaca, New York. Among large regions, Detroit, New Orleans, San Jose, Las Vegas, San Francisco, San Diego, Memphis, Baltimore, Washington, D.C., Portland, and Seattle had the smallest percentages of service jobs that were highly at risk to offshoring.

That said, the United States can ill afford more job losses due to any circumstance. As Bailey, Tyson, and Slaughter note, the world is entering into a period of increased global competition for economic activity and jobs. Multinational firms under increased pressure will search out the most competitive and productive locations to do business – from manufacturing products to carrying out research and generating new ideas to performing services.

Even when we look only at the highest-risk, most vulnerable service occupations, nearly one in 10 service jobs in large regions like Dallas, Boston, Phoenix, and Salt Lake City are at risk. Furthermore, regions which have been among the most economically resilient over the course of the economic crisis like Omaha, Tulsa, and Oklahoma City, have employment structures that are among the most vulnerable to offshoring.

These trends are critically important to consider at a time when job creation is a national and global priority. It is not only important to create jobs, but also to create “stickier” jobs – better-paying, family-supporting jobs that are more strongly rooted in place and less likely to be shuffled around to chase lower costs. In order to create sustainable prosperity, it’s imperative to increase both the number and quality of these sticky, locationally rooted jobs.

A good deal of the ongoing national conversation on jobs has focused on either the need to retain higher-paying manufacturing jobs or to shift the structure of the economy toward higher-end, higher-skilled work while upgrading the educational and skill levels of the workforce. But it is perhaps even more imperative to upgrade the sticky jobs our economy already has. Among the fastest-growing jobs categories, according to the BLS, are what are called personal services – everything from day care and elder care to home health aides, food preparation, hair-cutting, home maintenance, and the like. These jobs are nearly impossible to offshore as they involve direct face-to-face human contact. A key platform in a national jobs strategy must include increasing the quality of these currently low-paying service jobs, turning them into higher-paying, family-supporting jobs, as I have elsewhere argued.

It’s more critical now than ever before to develop a national strategy for upgrading the millions upon millions of low-skill but locationally sticky jobs, as well as to add more high-skill, high-wage jobs and better prepare the workforce more broadly. Our economic future depends on it.

More and more economic experts are saying the U.S. economy is headed for a “double-dip” recession. But actually it’s much more – and much more serious than that. Earlier this week, Paul Krugman speculated that the U.S. is headed for a Third Great Depression, noting that while recessions are relatively common and depressions quite rare, he fears our current economic circumstance is coming to look more like the Great Depression of the 1930s or the Long Depression of the late 19th century.

The first chart below from David Leonhardt of The New York Times shows the recent downturn in private-sector unemployment.

The second chart from Leonhardt’s Times colleague Catherine Rampell (one of the most statistically savvy reporters around) compares the current economic downturn to previous ones. Krugman’s point taken: This doesn’t look like any run-of-the-mill downturn.

While some do not want to face the looming reality, it is becoming clearer every single day that what we face is not any typical recession but a full-blown economic reset. My own look at the previous two similar crises shows that Great Resets like what we are now going through are generation-spanning events which require deep changes in economic, institutional, and spatial structures.

Are our economic policy-makers ready for the enormity of the challenges we face - the deep and fundamental changes in our economic system, from what we produce to what we consume – required to restore economic prosperity?

The new state unemployment stats for May are out from the Bureau of Labor Statistics. Michigan no longer leads the nation in unemployment – that distinction now goes to Nevada. Calculated Risk provides this chart of state unemployment rates.

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.