Smart Work and Smart Cities Pay

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A week or so ago, I looked at the hardest-working versus highest-earning states using data from a new study (PDF) from the Bureau of Labor Statistics. The study by Dante DeAntonio used data from the Current Employment Statistics -- a monthly survey of more than 400,000 U.S. business establishments -- to generate estimates for employment, hours, and earnings for U.S. states. My MPI colleagues and I were curious about how data would play out for U.S. metropolitan regions, so we contacted DeAntonio and he graciously supplied the data.


Let's begin by looking at two key maps created by the MPI's Zara Matheson. The maps are based on data for all U.S. metros, but identify the 46 largest metros (those with more than one million people) by name.

The first map (above) shows average hours worked per week. Among large metros, Washington, D.C. tops the list with an average of 36 hours per week. Las Vegas is next, followed by San Antonio, Virginia Beach, and Seattle. Kansas City, Nashville, Miami, Phoenix, and San Jose round out the top ten. At the opposite end of the spectrum are Buffalo, Rochester, Minneapolis, Columbus, Philadelphia, and Pittsburgh.


The second map charts the highest-earning metros -- measured in terms of average hourly earnings. Among large metros, San Jose -- Silicon Valley -- tops the list, followed by San Francisco, D.C., New York, Minneapolis, and Philadelphia. The lowest-earning metros are Oklahoma City, Las Vegas, Louisville, Buffalo, Memphis, Jacksonville, Cleveland, and Pittsburgh.

What's most striking is how little overlap there is between the two maps. The locations where people put in longer hours are also the ones where workers earn less.

So, I decided to take a closer look at what might be behind these patterns. With the help of Charlotta Mellander, we ran a series of scatter-graphs and performed a simple correlation analysis to probe the effects of various demographic and economic factors on metro-level working hours and earnings. We ran the analysis both for all 340 U.S. metros and for the 46 large metros (again, those with more than one million people). As usual, I point out that our analysis points to association between variables only. It does not imply causation, and other factors may complicate the picture. Still, as with states, a number of patterns are striking.

First and foremost, we find a total lack of correlation between hours worked and earnings across U.S. metros. This holds true both for the largest metro areas and for all U.S. metros: there was no statistical significance at all for the correlation between these two variables in either case.

Second, hard work and long hours do not translate into economic wealth. There was no correlation whatsoever between working hours and economic output, measured as gross metropolitan product per capita, for either large metros or all metros.

Third, as we found in our earlier analysis of states, when it comes to earning power, working smarter trumps working harder across the board.


Source: Human capital data from the U.S. Census Bureau, 2006.

One way to measure smart work is by the level of human capital -- that is the percentage of a metro's workforce with a bachelor's degree and above. Human capital is closely associated with metro earnings. The correlation is .53 for all metros and even higher, .74, for large metros. Silicon Valley -- that is the San Jose-Sunnyvale-Santa Clara metro -- literally jumps off the chart. San Francisco, Seattle, D.C., greater New York, Minneapolis, and San Diego are all above the fitted line in the upper-right hand quadrant of the graph: they combine above-average human capital with above-average wages. Louisville, Oklahoma City, Memphis, Pittsburgh, Milwaukee, and Nashville are all below the line -- combining low levels of human capital with low wages -- wages, in fact, that are even lower than their human capital levels would predict. Interestingly, Detroit, New Orleans, Phoenix, and Las Vegas have significantly higher wage levels than their human capital levels would predict.


Source: Creative class data are from the Bureau of Labor Statistics, 2006. Creative Class definition as in Rise of the Creative Class.

Another way to gauge smart metros is by the share of their workforce in creative, professional, and technical jobs -- that is, metros with high percentages of workers in the creative class. Creative class metros have significantly higher average earnings. In fact, the correlation between earnings and the percentage of workers in creative class jobs is slightly higher than that for human capital -- .58 for all metros and .78 for large metros. The line on the scatter-graph runs quite steeply upward. Silicon Valley is again a huge outlier, way up in the right hand corner of the graph. San Francisco, Seattle, and greater New York are all above the line -- with high levels of creative class work and high average wages. D.C., interestingly enough, is slightly below the fitted line: its average wages are slightly less than its percentage of creative class workers would predict.


Source: Working-class data from Bureau of Labor Statistics, 2006. Working-class definition as in Rise of the Creative Class.

Earnings are far less in more traditional industrial economies. Metro earnings are negatively correlated with blue-collar, working-class jobs -- the correlation is -.31 for all metros and -.5 for large metros.

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Richard Florida is Co-founder and Editor at Large of CityLab.com and Senior Editor at The Atlantic. He is director of the Martin Prosperity Institute at the University of Toronto and Global Research Professor at NYU. More

Florida is author of The Rise of the Creative ClassWho's Your City?, and The Great Reset. He's also the founder of the Creative Class Group, and a list of his current clients can be found here
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