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Why Clinton Could Lose the Working Class in Ohio

In the latest Quinnipiac poll, Hillary Clinton and Donald Trump are tied in battleground Ohio. This suggests a very close race in Ohio in the fall. Economic issues, especially trade, led many former Democrats to cross party lines to support Trump in the Republican primaries. Many who hadn’t voted in recent elections joined them. We’re likely to see a repeat of this in November unless Democrats change their trade policies. None of this should surprise Democrats, especially those in Ohio.

As a professor of labor studies and co-director of the Center for Working-Class Studies at Youngstown State University for more than 30 years, I had many opportunities to talk politics with workers there. In 2000, many told me that, after voting for Democrats all their lives, they were choosing guns, gays and God over Al Gore, who had been a primary spokesman for the North American Free Trade Agreement (NAFTA) seven years earlier. In 2002, Northeast Ohio Democrats threw out eight-term congressman Tom Sawyer on the basis of his support for NAFTA, despite Sawyer having a 90 percent voting record on labor issues.

Since the passage of NAFTA, Ohio Republicans have controlled state government save for a brief interlude caused by Republican corruption in 2006. At the same time, two Democrats — Sen. Sherrod Brown and Rep. Tim Ryan, who replaced Sawyer — have been elected and re-elected in no small part due to their opposition to NAFTA and the pending Trans-Pacific Partnership (TPP). Clearly, trade policy poses a problem for Democrats and their presumptive candidate. Clinton has been tied to former President Bill Clinton’s NAFTA legislation and its Wall Street proponents. While she has stated that she is against TPP at this time, many Ohioans hear that as weasel words that only contribute to their distrust of Clinton.

It is widely speculated that the Obama administration will push for TPP acceptance in the lame-duck session following the 2016 general election. According to a tweet from CNN’s Dan Merica, Clinton says she will not lobby Congress on the issue. But this will only undermine her credibility and provide Trump with an incentive to continue to demagogue the issue.

In Ohio, about 60 percent of voters in 2012 did not have a college degree, one of the most commonly used (though problematic) proxies for identifying working-class voters. Slightly more than half of them voted for Obama, according to CNN exit polls. But while Obama won a majority of working-class votes in Ohio, he lost among whites, winning only 41 percent of their votes. This suggests that a significant portion of Obama’s working-class support in 2012 came from Ohio voters of color, not white voters. Four years later, the combination of white working-class support for Trump, as we saw in the primary, and expected lower African-American turnout — Clinton is unlikely to inspire the enthusiasm that Obama generated — may swing Ohio’s prized electoral votes to the presumptive Republican nominee.

Clinton needs the support of working-class Ohioans – the very people who have been hurt the most by trade policy. To do that, she needs to stop insisting that trade is good. Her current stance is similar to wooing West Virginia coal miners by touting the benefits of non-carbon fuels. Similarly, she should stop talking about retraining and promising high-tech jobs, which only reminds voters of how hollow such programs have been in the past.

Instead, Clinton should acknowledge that we have lost the trade war and pledge to use every legal means at her disposal to protect American workers and industries from the continued onslaught of imports. This would include initiating trade cases against countries that target American industries by subsidizing their exports, exploiting workers, manipulating their currencies, and polluting the environment.

She should threaten to impose tariffs on every imported product from countries that refuse to implement the same U.S. Occupational Safety and Health Administration and U.S. Environmental Protection Agency regulations and federal, state and local tax requirements that are imposed on American businesses.

At the very least, Clinton should do more than promise to build a strong infrastructure program. Such a program would put the skills, materials and physical strength of working-class Ohioans to work and improve Ohio’s competitive economic environment. Clinton has identified specific programs but she needs to do more to explain how she will pay for them. Otherwise, her campaign platform will sound too much like an echo of past hollow campaign promises.

Clinton should also stress making college affordable for the working class and those living in poverty. Not everyone wants a desk job in front of a computer, and older workers may not be interested in retraining for high-tech jobs. But they do want more education and training for their kids.

Finally, working people worry about how they will fare economically after retirement. They know that Wall Street oversold 401(k) plans and that traditional pensions are disappearing. Clinton needs to reject Wall Street’s calls for changes in Social Security and offer a specific program to maintain private pension plans without cutting benefits.

If Clinton does not develop a strong and believable working-class agenda, I predict that the Democrats will lose Ohio in November, and that would open the door to a Trump victory nationally.

This piece first appeared in the Plain Dealer on June 26,2016, and was re-posted at Working Class Studies blog.

John Russo is a visiting fellow at Kalmanovitz Initiative for Labor and Working Poor at Georgetown University and at the Metropolitan Institute at Virginia Tech. He is the co-author with Sherry Linkon of Steeltown U.S.A.: Work and Memory in Youngstown (8th printing).

Photo by Gage Skidmore from Peoria, AZ, United States of America – Hillary Clinton, CC BY-SA 2.0

July 26, 2016
The Future of Latino Politics

The sad decline in race relations has focused, almost exclusively, on the age-old, and sadly growing, chasm between black and white. Yet this divide may prove far less important, particularly in this election, than the direction of the Latino community.

This may be the first election where Latinos, now the nation’s largest minority group, may directly alter the result, courtesy of the race baiting by GOP nominee Donald Trump. If the GOP chooses to follow his nativist pattern, it may be time to write off the Republican Party nationally, much as has already occurred in California.

Today, Latinos represent 17 percent of the nation’s population; by 2050, they will account for roughly one in four Americans. Their voting power, as the GOP is likely to learn, to its regret this year, is also growing steadily, to 12 percent of eligible voters this year, and an estimated 18 percent by 2028.

Political geography may prove as critical here as rising numbers. African Americans, for historic reason, are heavily concentrated in deep blue cities, simply padding already existing Democratic supermajorities, or in the deep red South, where they are overwhelmed by a conservative white majority. In contrast, Latinos represent a growing constituency in critical swing states such as Florida, where they constitute one-fifth of the electorate, as well Virginia, Nevada, Colorado and, thanks to the genius of Donald Trump, perhaps even Arizona.

Read the entire piece at The Orange County Register.

Joel Kotkin is executive editor of NewGeography.com. He is the Roger Hobbs Distinguished Fellow in Urban Studies at Chapman University and executive director of the Houston-based Center for Opportunity Urbanism. His newest book, The Human City: Urbanism for the rest of us, will be published in April by Agate. He is also author of The New Class Conflict, The City: A Global History, and The Next Hundred Million: America in 2050. He lives in Orange County, CA.

Photo by chadlewis76

July 25, 2016

Surprising Ordos: The Evolving Urban Form

Ordos, in China’s Autonomous Region of Inner Mongolia (equivalent to a province) has received international notoriety as a "ghost city." I had already visited one other ghost city and found the reports considerably exaggerated (The Zhengzhou New Area in Henan, a commercial and residential district). But Ordos has received by far the most publicity.

It turns out that in reality the people far outnumber the ghosts, something I should have recognized when it was difficult to find a hotel room six months before my visit. Ghosts do not generally need hotel rooms. But the ghost city label is an exaggeration.

Defining Ordos

What is Ordos? Ordos (E’erduosi) is one of the more than 300 municipality level jurisdictions that constitute China and cover virtually all of its land area. Like other municipalities, Ordos is divided into districts which are translated broadly as county level jurisdictions. China has about 2,900 county level jurisdictions, compared to the 3,100 county level jurisdictions in the United States. There is an important difference, however. In the United States, with a few exceptions, municipalities are within counties and there may be many municipalities within counties. In China, counties are within municipalities.

Ordos is one of 12 municipalities in Inner Mongolia. Ordos is composed of eight districts. The Ordos metropolitan area is located in the urban district of Dongsheng and the "banner" (Inner Mongolian title for county) of Ejin Horo (the urbanized part of which is Azhen). The Kangbashi new area, to which the ghost city stories refer, is split between Dongsheng and Ejin Horo.

Contrary to the “ghost city” meme, population growth has been strong in these two districts. In Dongsheng, the 2010 census counted approximately 580,000 residents, an increase of 130 percent over the 2000 census. The population of Ejin Horo rose 53 percent to approximately 225,000 residents. Overall, these two adjacent districts represent a labor market (metropolitan area) of nearly 810,000 residents, which grew more than 100 percent between 2000 and 2010 (Image 1).

Ordos is located in the northern half of the Ordos Loop of the Yellow River, which with the Yangtze is one of China’s two great rivers. After passing Lanzhou (capital of Gansu), the eastward flowing river takes a sharp left turn to the north for approximately 600 kilometers (375 miles), then a sharp right turn back to the east for 300 kilometers (200 miles), turning south for 600 kilometers and finally turning east toward the Yellow Sea.

Overall, the population of Ordos was approximately 1.94 million in the 2000 census and had grown 42 percent since 2000. As a result, Ordos was by far the fastest growing of the 12 municipalities in Inner Mongolia. The municipality grew at more than double the rate of the capital, Hohhot (Huhehaote), and approximately seven times the overall rate of Inner Mongolia. The growth rate of Ordos was also five times China’s national 10 year growth rate of 7.8 percent.

The municipality covers a land area of 87,000 square kilometers, somewhat larger than Austria. Ordos Most of the population is in the more rural districts.

Genghis Khan

Genghis Kahn, founder of the Mongol empire (13th century), history’s largest contiguous empire plays importantly in the history of Ordos. Genghis Kahn is reputed to have been so impressed by Ordos that he wanted his personal effects buried here. The effects are buried at a mausoleum approximately 10 miles (25 kilometers) south of Kangbashi. The actual burial place of Genghis Kahn is not known, and consistent with Mongol tradition, is secret.

The So-Called "Ghost City:" Kangbashi New Area

The part of Ordos to which the "Ghost City" stories have referred is the Kangbashi New Area. It is adjacent to and north of the urbanization of Azhen in Ejin Horo. The Kangbashi new area covers approximately 350 square kilometers (135 square miles) in the Dongsheng and Ejin Horo districts at the time of the 2010 census. Thus, a census population count is not readily available. Informal estimates placed the population at under 30,000 in 2010. A more recent informal estimate by an Ordos municipal official indicated that the registered population had reached 72,000 in 2012 and would soon rise to 100,000.

Development of the Kangbashi New Area

Ordos is one of the most affluent municipalities of China. It is comparatively new wealth, which is the result of vast coal reserves that have been increasingly called upon since 2000 to support China’s spectacular growth. . According to People’s Daily, by 2012 the gross domestic product per capita of Ordos exceeded that of both Spain and South Korea.

With the huge natural resource revenue gains, municipal officials decided to build a new city approximately 16 miles (25 kilometers) south of the municipal seat in Dongsheng. In 2006, the municipal seat was moved from Dongsheng to the Kangbashi new area. Both the Kangbashi New Area and Ejin Horo are within commuting distance of much larger Dongsheng, via the Dongsheng Expressway. As of 2012, the municipality indicated that at least one half of the municipal functions had been moved to Kangbashi.

The Ordos Ceremonial Mall

Some national governments in the world have built new capital cities or districts and taken the opportunity to order them around what might be called ceremonial malls — government buildings, monuments and cultural institutions arranged around a central axis. Governments that build new capital cities have unique opportunities to build ceremonial malls. Perhaps the first of these was Washington, with its Capitol Mall (The Supreme Court to the Lincoln Memorial) and the later developed mall from the White House to the Jefferson Memorial.

Other particularly notable examples are Delhi, Canberra and Brasília. Perhaps the most famous such mall, though without the adjacent buildings and memorials, which had already been built elsewhere, is The Mall, running from Buckingham Palace to Trafalgar Square in London. This mall is somewhat different than the others, because it was built after most of the government buildings, which are located elsewhere.

Ceremonial malls can be built by local governments as well, and Ordos has built one of world-class dimensions. The table below compares the Ordos mall with other representative government malls. With a length of 2.7 miles (4.3 kilometers), the Ordos mall is approximately the equal of Washington’s Capitol Mall and Canberra’s middle mall, (Federation Mall/ANZAC Parade). The Ordos mall is somewhat shorter than the Delhi mall and less than one half the length of the Brasília mall, parts of which remain undeveloped. The Ordos mall is more than twice as long as The Mall in London.

The Ordos mall is more extensive, for example, than what may be the largest local government mall in the United States, in Los Angeles. This mall is shared by the city and the county of Los Angeles, with more than five times as many residents. In fact, the Ordos mall is of sufficient expanse and design to be mistaken for the centerpiece of a newly built national capital.

In short, the Ordos mall is world class and already attracting tourists, principally from around China. As with the rest of China, international tourism is in its infancy and holds great potential for growth.

Selected Ceremonial Malls
	Dimensions (KM)		Dimensions (Miles)
Location	Length	Axis Width	Length	Axis Width	Government	Population (Millions)
Brasilia	9.7	0.21	6.0	0.13	National	195
Delhi	5.2	0.24	3.2	0.15	National	1225
Washington (Capitol Mall)	4.3	0.50	2.7	0.31	National	310
Ordos	4.3	0.18	2.7	0.11	Local	2
Canberra (Federation Mall/ANZAC Parade)	4.3	0.03	2.7	0.02	National	22
London (The Mall)	1.3	0.06	0.8	0.04	National	62
Los Angeles	1.1	0.08	0.7	0.05	Local	10
Axis width is minimum central area around which buildings and monuments are organized
Canberra & Washington have more than one mall
Some of Brasilia mall is undeveloped

Touring the Ordos Mall

The core of the mall is an axis, one large block wide, composed of greenery and statues (Images 2-13).

The mall stretches from municipal buildings at the north (Image 2) to a lake (Image 3), across which are skyscrapers, anchoring the mall on the south (Images 4 and 13). This interruption by a lake is similar to the Canberra mall described above

Near the north end of the mall is the Genghis Khan statue (Image 5).

The two horse’s statue is in the square to the south of the Genghis Khan statue (Image 6).

Each side of the mall is defined by one-way streets that are four lanes wide.

Among the two most important government buildings on the mall are the Library of Ordos and the Ordos Museum (to the left and right, respectively, in Image 9). Neither of these buildings will be pleasing to aficionados of traditional architecture, including the author. I agree with Chinese President Xi, who suggested that China needed no more weird buildings, referring to the CCTV Tower in Beijing, which local taxi drivers told me is referred to as the "underpants" building. Of course, architecture is a matter of taste.

Across the mall is the Ordos National Theatre and the Ordos Culture and Arts Center (Image 10, left and right). The circular and curved lines of these buildings offer a welcome refuge from the more courageous architecture of the Library and Museum, in the author’s view.

The mall also includes commercial buildings (Image 11). These buildings include a wide array of retail stores, such as large electronic and home appliance outlets, banks and other facilities. Within a one block walk of my hotel there were at least seven restaurants from which to choose. Generally, ghosts do not require this density of eating establishments.

Residential Areas

There are a variety of residential areas surrounding the mall on three sides (the south end of the mall is bordered by the urbanization of Ejin Horo). The residential buildings tend to be from 5 to 12 floors (Images 14 – 16), and include the equivalent of strip malls (Image 16) that are close at hand for residents and can have full parking lots. The residential areas also include some monumental treatments (Image 17).

Outside the Built-Up Urban Area

The built-up area of Kangbashi is relatively small, covering less than 10 square miles (25 square kilometers), or less than 10 percent of the Kangbashi new area.

Most of the "Ghost City" articles to limit their coverage to the small developed area. With the exception of a major roadway skeleton (along which there is virtually no development in many areas), much of the Kangbashi New Area is not a city at all. There are some small pockets of residential development spread throughout the area and a number of government buildings similarly dispersed beyond the built-up area (Images 18-24). At the same time, the parking lots were far from empty.

There are also a number of religious sites outside the built up urban area (Images 22 & 23) and three similarly designed sports facilities (Image 24).

The traffic volumes are well below the capacity of the more than ample arterial street system. But this is not unusual for newer suburban areas in China, where eight lane streets can be the rule.

What About the People?

Much of the ghost city coverage has been based on an assumption that few if any residents have arrived. A number of articles point out that the present development has been built for 300,000 residents and that the population is much less (above). Yet, the municipality indicates that the 300,000 resident projection is for 2020. Whether or not Ordos will reach that population by 2020 cannot yet be known.

Some of the ghost city articles have claimed that the Kangbashi new area was projected to have 1 million residents. However, the municipality’s website indicates that the 1,000,000 vision was for a much larger area than the Kangbashi new area. It also included Dongsheng, which alone already has nearly 600,000 residents as well as the urbanization of Ejin Horo. In other words, under the plan the area was already well on its way to achieving the eventual projection.

Other articles point out that there are few people walking on the streets. But, as Chai Jiliang, chief publicity officer of Kangbashi told China Daily in 2012: "So, why do local residents who mostly own private cars and have convenient public transportation have to walk on the streets if there are no major public events?" There is further evidence of people, the establishment of a campus of the Beijing Normal University in the Kangbashi new area. Indeed a recent article in The New York Times Style Magazine, by Jody Rosen, reported not only that there were people in the Kangbashi new area, but that they were generally happy with their city.

Ejin Horo

Perhaps the biggest surprise was the Ejin Horo urbanization (Azhen), immediately to the south of the Kangbashi New Area (Images 25-27). The tallest buildings are here and some of the most impressive commercial architecture. Just across the principal bridge from the Kangbashi New Area are two buildings resembling the One World Wide Tower on Eighth Avenue in New York (Image 25). Ejin Horo also has many condominium towers that are often taller than those in the Kangbashi New Area. Unlike the Kangbashi New Area, Ejin Horo appears to have grown more organically in response to market demand. The area’s international airport is also located in Ejin Horo.

Big Dreams, Big Challenges

The Kangbashi new area does face some problems. Like the rest of China, there are a number of uncompleted building projects, as the economy is not growing nearly as quickly as before. Though, again, I expected many more based on the negative published reports.

There has been a severe reduction in house prices, as the Chinese economy has gotten worse. There are reports that many of the apartments and condominiums are empty, though no information was found on the extent of unsold houses or the number that have been purchased simply as investments to hold (and have no residents). It is not unusual for Chinese buyers to invest in additional properties, leaving them empty, a situation that is also been reported in central Vancouver. However, there was no lack of cars in the parking lots of the residential districts.

Peoples Daily reports that coal extraction volumes are down significantly, which when combined with substantially lower coal prices in recent years has cut severely into the revenues of the Ordos municipality. The municipality is seeking additional revenue enhancing strategies, such as tourism (there are 9 million tourists annually), automobile manufacturing and solar power facilities.

Liu Qiang, a People’s Daily columnist noted that "There are worries that Ordos, with its huge debts and years of mismanagement, will repeat Detroit’s road to bankruptcy," While noting that Chinese municipalities are not permitted to file bankruptcy, the columnist suggests that " China’s local government debt, if not being better managed, might potentially pose a systematic risk greater than in Detroit."

Big dreams are not limited to cities in China. Ordos may have built civic monuments and infrastructure beyond its means. Only time will tell whether such visions can be sustained. The reality, however, is that Ordos, including the Kangbashi new area, is surprisingly vibrant and functioning with real people.

Note 1: Inner Mongolia is a part of China. Mongolia (often called "Outer Mongolia) is an independent nation located between China and Russia.

Note 2: The Evolving Urban Form is a newgeography.com metropolitan and urban area profiles from around the world. The more than 50 articles on in the series can be accessed here.

Photo: Genghis Kahn Mausoleum, Ordos, Inner Mongolia, China by Fanghong (Own work) [CC BY-SA 3.0 or GFDL], via Wikimedia Commons

Wendell Cox is principal of Demographia, an international pubilc policy and demographics firm. He is a Senior Fellow of the Center for Opportunity Urbanism (US), Senior Fellow for Housing Affordability and Municipal Policy for the Frontier Centre for Public Policy (Canada), and a member of the Board of Advisors of the Center for Demographics and Policy at Chapman University (California). He is co-author of the “Demographia International Housing Affordability Survey” and author of “Demographia World Urban Areas” and “War on the Dream: How Anti-Sprawl Policy Threatens the Quality of Life.” He was appointed to three terms on the Los Angeles County Transportation Commission, where he served with the leading city and county leadership as the only non-elected member. He served as a visiting professor at the Conservatoire National des Arts et Metiers, a national university in Paris.

July 23, 2016

Expo Line Expansion Fails to Stem L.A. Transit Loss

The long awaited and highly touted Santa Monica extension brought an approximately 50 percent increase in ridership of the Los Angeles Expo light rail line between June 2016 and June 2015. The extension opened in mid May 2016. In its first full month of operation, June 2016, the line carried approximately 45,900 weekday boardings (Note), up from 30,600 in June 2015, according to Los Angeles Metropolitan Transportation Authority (MTA) ridership statistics.

However MTA ridership continued to decline, with a 51,900 loss overall. Bus and rail services other than the Expo line experienced a reduction of 67,300 boardings (Figure).

Between June 2015 and June 2016, rail boardings rose 30,500, while bus boardings declined 82,400. In other words there was a loss of 2.7 bus riders for every new rail rider over the past year. Los Angeles transit riders have considerably lower median earnings than in the cities with higher ridership, and lower than the major metropolitan average (see the analysis by former Southern California Rapid Transit District Chief Financial Officer Tom Rubin and "Just How Much has Los Angeles Transit Ridership Fallen?" and ) here and here).

Note: A passenger is counted as a boarding each time a transit vehicle is entered. Thus, if more than one transit vehicle is required to make a trip, there can be multiple boardings between the trip origin and destination. Because the addition of rail services, as in Los Angeles, can result in forcing bus riders to transfer because their services can be truncated at rail stations, the use of boardings as an indicator of ridership can result in exaggeration, as the number of boardings per passenger trip is increased. This may have produced a decline of as much as 30 percent in actual passenger trips since 1985, as a number of rail lines have been opened in Los Angeles.

July 22, 2016
The U.S. Cities Creating The Most White-Collar Jobs, 2016
The information sector may have glamour and manufacturing, nostalgia appeal, but the real action in high-wage job growth in the United States is in the vast realm of professional and business services. This is not only the largest high-wage part of the economy, employing just under 20 million people at an average salary of $30 an hour, it’s also one the few high-wage sectors in which employment has expanded steadily since 2010, at more than 3% a year, adding nearly 3 million white-collar jobs.

In many ways, the business and professional service sector may be the best indicator of future U.S. economic growth. It is not nearly as vulnerable to disruption as energy, manufacturing or information employment, and more deeply integrated into the economy, including professions like administrative services and management, legal services, scientific research, and computer systems and design. In a pattern we have seen in other sectors, much of the growth is concentrated in two very different kinds of places: tech-rich metro areas and those that offer lower costs, and often more business-friendly atmospheres.

To generate our rankings of the best places for business services jobs, we looked at employment growth in the 366 metropolitan statistical areas for which BLS has complete data going back to 2005, weighting growth over the short-, medium- and long-term in that span, and factoring in momentum — whether growth is slowing or accelerating. (For a detailed description of our methodology, click here.)
Tech Strikes Again

There is a growing confluence between technology and business services, as more companies use the Internet to conduct commerce.

This can be seen in several of our top-ranked large cities. Business service employment in the San Francisco-Redwood City-South San Francisco MSA has grown a remarkable 45% since 2010, placing it second on our list, slightly faster than third-ranked Austin-Round Rock, which clocked 42% growth over the same span, and No. 4 San Jose-Sunnyvale-Santa Clara, where business services employment expanded 36%.

It’s questionable whether this pattern will continue, particularly in the high-cost Bay Area. There are signs of a slowdown in Silicon Valley and San Francisco, with more space being subleased and property prices seeming to have peaked, albeit at extraordinary high levels. In contrast the future for less expensive areas — increasingly attractive to millennials as well as companies — may be far brighter, as companies shift employment to places their employees can live decently.

Resurgence In Middle America

This pattern can be seen in the balance of our top-performing regions. It starts with our top-ranked metro area, Nashville, Tenn., which has seen business service employment grow 47.2% since 2010 to 152,700 jobs, with 7.7% growth last year alone. Some of this comes from the establishment of branch offices of Silicon Valley companies like Lyft and Everbright, as well as the expansion of the area’s strong health care and entertainment industries.

Nashville’s appeal to millennials is unsurpassed, with the strongest growth rate in net migration of college-educated people aged 25-34 of any metro area in the country, and the reasons are not hard to find. It’s a charming city located in a temperate part of the country, with both excellent, and affordable, urban and suburban options.

But if Nashville is the belle of the business service ball, fifth-ranked Dallas-Ft. Worth is now the beast. The Texas powerhouse’s business services workforce has expanded 28.9% since 2010 to 458,200. The Dallas-Ft. Worth area has plenty of appeal to big companies with a large cohort of middle-income managers, as a paper to be published this fall by Southern Methodist University’s Klaus Desmet and Cullum Clark well describes. These jobs pay well enough to live well in Dallas’ nicer suburbs, such as Plano and Frisco, but not remotely enough to buy a house, or even a condo, in Los Angeles, San Francisco or New York.

This accounts, in part, for the relocation of Toyota America’s headquarters from Torrance, Calif., to the north Dallas suburbs, and likely plays a role in the plans of Jacobs Engineering, a longtime fixture in Pasadena, to relocate its headquarters to downtown Dallas.

In many ways, argues urban analyst Aaron Renn, Dallas is becoming the new Chicago. It is anchored by a large airport, a diverse economy and a location in the middle of country. Even as downtown Chicago has attracted some notable new corporate headquarters in recent years, these generally employ relatively few people, while companies that need access to a large white-collar workforce, like Toyota and Jacobs, have been gravitating to the Big D.

How About The Big Boys?

As manufacturing has declined in our largest cities, professional and business services have become the prime generator of high-end jobs. Yet among the country’s largest business service centers there is a growing divergence between the winners and laggards.

The most impressive performance among metro areas with over 500,000 business and professional service jobs has been New York. With 714,000 business service jobs, the Big Apple is without question the leader in the field, but more importantly it continues to grow. Since 2010, New York has grown its professional and business service employment by an impressive 22%, helping it rank 14th on our list. This reflects the city’s continued preeminence in such fields as law, design, marketing, public relations and advertising.

But the other traditional business service leaders have not fared nearly as well. Gotham’s traditional rival, Chicago-Naperville-Arlington Heights, still has 673,000 business service jobs but has seen only a modest growth just under 15%, ranking 43rd. Whatever may have been gained in generally small scale “executive headquarters” has not been enough to make the vast Chicagoland region a big winner.

Things are even less positive in 60th place Los Angeles-Long Beach-Glendale, the third largest business service area. Since 2010, its 13.8% growth is well below the national average. Nor is the slack in the Southland being picked up by the area’s sprawling suburbs, with Santa Ana-Anaheim-Irvine ranking a modest 39th and San Bernardino-Riverside clocking in at 52nd. The Bay Area business services world may be still booming, but south of the Tehachapi, progress is slow.

Will Business Services Continue To Disperse?

Those who suggest dense concentrations have efficiencies that overcome higher costs can take some solace from our numbers, but not too much. Many of the fastest growing business service centers are hardly paragons of dense urbanism, including No. 7 Orlando-Kissimmee-Sanford, Fla., and No. 8 Richmond, Va., where employment jumped 10% last year. Even sprawling Atlanta, which has lost some of its ‘90s era luster, is now growing its business service sector at a faster pace than New York and light years ahead of much denser Chicago and Los Angeles. It ranks 13th.

The shift to less expensive places seems certain to continue, in part due to the growing role of Internet communications, which breaks down formerly insurmountable distance barriers. Looking at the full list of the 366 metro areas we examined, the fastest-growers include many smaller communities, led by overall No. 1 New Bedford, Mass., where business services employment has grown 58.5% since 2010 to 6,200 jobs, as well as No. 3 Monroe, Mich., No. 4 Lake Charles, La., and No. 6 Lawton, Okla.

Essentially business service growth seems destined to break down into three types: (1) large and expensive metro areas — San Francisco-Silicon Valley and New York — whose economic dynamism is strong enough to counter high costs; (2) less expensive, but still large metros such as Nashville, Dallas-Ft. Worth, Richmond and a host of Florida cities that can be expected to garner a lion’s share of the new growth; and (3) smaller communities where business service sector jobs, particularly at the lower end, may be increasingly attracted as employers pursue an affordable quality of life. While the short term has favored the largest cities, the long term is pointing toward more migration to midsized and smaller destinations.
This piece first appeared at Forbes.

Joel Kotkin is executive editor of NewGeography.com. He is the Roger Hobbs Distinguished Fellow in Urban Studies at Chapman University and executive director of the Houston-based Center for Opportunity Urbanism. His newest book, The Human City: Urbanism for the rest of us, will be published in April by Agate. He is also author of The New Class Conflict, The City: A Global History, and The Next Hundred Million: America in 2050. He lives in Orange County, CA.

Michael Shires, Ph.D. is a professor at Pepperdine University School of Public Policy.

Photograph: Downtown Nashville from BigStockPhoto.com
July 22, 2016

All Cities Business Services Jobs – 2016 Best Cities Rankings

Read about how we selected the 2016 Best Cities for Job Growth

2016 MSA Prof & Bus Svcs – Overall Ranking	Area	2016 Prof & Bus Svcs Weighted INDEX	2015 Prof and Business Services Emplmt (1000s)	Total Prof and Business Services Emplmt Growth Rate 2014-2015	2016 MSA Size Group	2015 MSA Prof & Bus Svcs Overall Ranking	Overall Rank Change 2014-2015
1	New Bedford, MA NECTA	97.1	6.2	58.5%	S	5	4
2	Nashville-Davidson–Murfreesboro–Franklin, TN	96.9	152.7	47.2%	L	38	36
3	Monroe, MI	95.0	4.9	44.6%	S	12	9
4	Lake Charles, LA	94.8	9.7	44.6%	S	99	95
5	San Francisco-Redwood City-South San Francisco, CA Metro Div	93.1	270.5	45.7%	L	15	10
6	Lawton, OK	92.6	4.9	37.4%	S	288	282
7	Elizabethtown-Fort Knox, KY	92.3	6.9	35.5%	S	111	104
8	Austin-Round Rock, TX	91.1	165.6	42.3%	L	44	36
9	Janesville-Beloit, WI	90.3	6.5	44.8%	S	7	(2)
10	Jackson, TN	89.9	6.7	39.3%	S	4	(6)
11	Sioux Falls, SD	89.0	15.5	33.2%	M	250	239
12	St. George, UT	88.8	4.7	34.6%	S	183	171
13	College Station-Bryan, TX	88.5	8.3	38.3%	S	53	40
14	San Jose-Sunnyvale-Santa Clara, CA	88.5	223.0	36.4%	L	13	(1)
15	Trenton, NJ	88.2	45.0	25.1%	M	269	254
16	Fayetteville-Springdale-Rogers, AR-MO	87.5	48.1	38.2%	M	26	10
17	Napa, CA	86.6	6.9	34.2%	S	94	77
18	Provo-Orem, UT	85.7	29.6	34.3%	M	9	(9)
19	Cape Coral-Fort Myers, FL	84.8	34.1	38.4%	M	41	22
20	Springfield, OH	83.4	5.0	20.8%	S	23	3
21	Dallas-Plano-Irving, TX Metro Div	82.8	458.2	28.9%	L	45	24
22	Raleigh, NC	81.2	113.5	27.9%	L	22	0
23	Laredo, TX	80.9	8.6	31.5%	S	66	43
24	Deltona-Daytona Beach-Ormond Beach, FL	80.4	22.7	33.7%	M	35	11
25	Charlottesville, VA	80.3	15.1	24.5%	S	76	51
26	Orlando-Kissimmee-Sanford, FL	80.3	201.2	26.8%	L	143	117
27	Waco, TX	80.1	11.5	36.5%	S	10	(17)
28	Richmond, VA	80.1	112.3	18.9%	L	260	232
29	Charlotte-Concord-Gastonia, NC-SC	79.8	188.2	25.3%	L	62	33
30	Portland-Vancouver-Hillsboro, OR-WA	79.4	172.9	24.9%	L	81	51
31	Stockton-Lodi, CA	79.1	20.5	35.2%	M	194	163
32	Bend-Redmond, OR	79.0	8.2	26.3%	S	47	15
33	Greeley, CO	78.1	9.9	38.3%	S	18	(15)
34	Tampa-St. Petersburg-Clearwater, FL	77.9	226.3	26.3%	L	154	120
35	Morgantown, WV	77.8	6.6	25.2%	S	30	(5)
36	Louisville/Jefferson County, KY-IN	77.7	88.4	21.9%	L	86	50
37	Salisbury, MD-DE	77.7	12.5	24.2%	M	128	91
38	Des Moines-West Des Moines, IA	77.2	47.4	24.2%	M	69	31
39	Bismarck, ND	76.9	8.4	29.1%	S	82	43
40	Greenville-Anderson-Mauldin, SC	76.6	71.4	23.8%	M	65	25
41	Fargo, ND-MN	76.6	16.7	25.6%	S	139	98
42	Tuscaloosa, AL	76.5	11.2	40.2%	S	2	(40)
43	Atlanta-Sandy Springs-Roswell, GA	76.2	485.1	23.9%	L	67	24
44	New York City, NY	76.0	714.0	22.0%	L	120	76
45	Auburn-Opelika, AL	76.0	7.4	37.7%	S	1	(44)
46	Greensboro-High Point, NC	74.7	53.6	19.4%	M	57	11
47	Kansas City, MO	73.9	90.4	18.3%	L	204	157
48	Redding, CA	73.8	6.4	33.3%	S	31	(17)
49	Flagstaff, AZ	73.7	3.3	35.6%	S	42	(7)
50	Gadsden, AL	73.5	4.4	37.5%	S	28	(22)
51	Denver-Aurora-Lakewood, CO	73.3	254.1	23.4%	L	145	94
52	Chambersburg-Waynesboro, PA	73.1	5.8	17.7%	S	132	80
53	Pueblo, CO	73.0	7.1	20.2%	S	104	51
54	Manchester, NH NECTA	72.9	16.4	20.2%	S	46	(8)
55	Las Vegas-Henderson-Paradise, NV	72.9	126.1	25.0%	L	97	42
56	Ogden-Clearfield, UT	72.5	28.4	31.3%	M	36	(20)
57	Asheville, NC	72.4	18.4	20.8%	M	152	95
58	Seattle-Bellevue-Everett, WA Metro Div	72.2	244.4	22.1%	L	125	67
59	Chattanooga, TN-GA	71.8	29.6	29.9%	M	173	114
60	Corvallis, OR	71.7	4.3	15.0%	S	73	13
61	Memphis, TN-MS-AR	71.6	99.9	25.5%	L	80	19
62	Knoxville, TN	71.5	63.2	15.8%	M	79	17
63	Fort Lauderdale-Pompano Beach-Deerfield Beach, FL Metro Div	71.3	144.0	21.6%	L	109	46
64	Port St. Lucie, FL	71.3	16.6	29.7%	S	6	(58)
65	Bowling Green, KY	71.3	9.1	20.7%	S	211	146
66	San Luis Obispo-Paso Robles-Arroyo Grande, CA	71.1	12.4	24.7%	S	108	42
67	Fond du Lac, WI	70.8	2.9	24.3%	S	72	5
68	Reading, PA	70.5	23.9	21.1%	M	21	(47)
69	Prescott, AZ	70.5	4.4	42.4%	S	71	2
70	Indianapolis-Carmel-Anderson, IN	69.8	162.5	26.2%	L	103	33
71	Spokane-Spokane Valley, WA	69.8	25.5	17.7%	M	199	128
72	Gainesville, FL	69.7	13.1	22.1%	S	34	(38)
73	Burlington-South Burlington, VT NECTA	69.7	14.2	18.9%	S	107	34
74	West Palm Beach-Boca Raton-Delray Beach, FL Metro Div	69.2	108.3	28.2%	L	51	(23)
75	North Port-Sarasota-Bradenton, FL	69.2	41.4	40.7%	M	17	(58)
76	Dover, DE	69.0	4.7	16.7%	S	310	234
77	Springfield, MO	68.8	25.2	27.3%	M	49	(28)
78	Haverhill-Newburyport-Amesbury Town, MA-NH NECTA Div	68.5	5.6	17.6%	S	278	200
79	Baton Rouge, LA	68.5	49.7	19.5%	M	142	63
80	Macon, GA	68.3	12.9	24.0%	S	52	(28)
81	Olympia-Tumwater, WA	68.2	10.3	32.2%	S	129	48
82	Hartford-West Hartford-East Hartford, CT NECTA	67.6	73.1	19.2%	L	92	10
83	Ann Arbor, MI	67.3	29.9	16.2%	M	165	82
84	Salt Lake City, UT	66.8	118.5	23.8%	L	102	18
85	Lubbock, TX	66.8	12.1	15.6%	S	241	156
86	Framingham, MA NECTA Division	66.7	36.9	15.3%	M	215	129
87	Saginaw, MI	66.0	11.9	18.5%	S	61	(26)
88	Burlington, NC	65.7	5.7	21.4%	S	11	(77)
89	Winston-Salem, NC	65.6	35.3	18.4%	M	146	57
90	Yuba City, CA	65.6	3.2	26.3%	S	37	(53)
91	Salinas, CA	65.5	13.6	15.9%	S	29	(62)
92	Columbus, IN	65.4	5.4	27.6%	S	20	(72)
93	Reno, NV	65.3	29.8	21.5%	M	246	153
94	Salem, OR	65.2	13.5	25.5%	M	177	83
95	Tacoma-Lakewood, WA Metro Div	64.8	27.5	16.2%	M	186	91
96	Athens-Clarke County, GA	64.7	7.8	19.5%	S	208	112
97	San Antonio-New Braunfels, TX	64.7	125.3	20.4%	L	117	20
98	Jacksonville, FL	64.6	102.8	19.9%	L	93	(5)
99	Elkhart-Goshen, IN	64.3	10.0	36.2%	S	19	(80)
100	Middlesex-Monmouth-Ocean, NJ	64.3	152.0	16.6%	L	244	144
101	Phoenix-Mesa-Scottsdale, AZ	64.1	336.7	21.9%	L	151	50
102	Panama City, FL	64.1	10.3	29.3%	S	96	(6)
103	Madison, WI	63.7	49.8	18.7%	M	136	33
104	Crestview-Fort Walton Beach-Destin, FL	63.3	14.3	14.1%	S	265	161
105	Harrisburg-Carlisle, PA	63.1	47.5	15.7%	M	236	131
106	Miami-Miami Beach-Kendall, FL Metro Div	63.1	162.5	24.4%	L	77	(29)
107	Lexington-Fayette, KY	62.6	40.3	33.9%	M	55	(52)
108	Oakland-Hayward-Berkeley, CA Metro Div	62.6	182.9	18.6%	L	105	(3)
109	Brownsville-Harlingen, TX	62.5	12.0	17.3%	S	153	44
110	Santa Rosa, CA	62.4	21.7	16.5%	M	188	78
111	Wausau, WI	62.2	5.3	22.1%	S	58	(53)
112	Portsmouth, NH-ME NECTA	62.2	11.8	10.9%	S	307	195
113	Naples-Immokalee-Marco Island, FL	62.1	14.9	25.5%	S	24	(89)
114	Savannah, GA	62.0	20.2	15.2%	M	85	(29)
115	Providence-Warwick, RI-MA NECTA	62.0	70.1	19.3%	L	112	(3)
116	Charleston-North Charleston, SC	60.7	49.4	15.0%	M	114	(2)
117	Punta Gorda, FL	60.7	4.6	27.8%	S	118	1
118	Taunton-Middleborough-Norton, MA NECTA Division	60.5	6.4	11.7%	S	268	150
119	Texarkana, TX-AR	60.1	4.4	-1.5%	S	362	243
120	Boston-Cambridge-Newton, MA NECTA Division	60.1	340.4	16.7%	L	190	70
121	Springfield, IL	59.7	11.9	-3.3%	S	352	231
122	Columbus, OH	59.6	178.8	20.1%	L	89	(33)
123	Wilmington, NC	59.3	15.1	15.3%	S	137	14
124	Scranton–Wilkes-Barre–Hazleton, PA	59.1	30.2	17.2%	M	252	128
125	Camden, NJ Metro Div	58.9	78.8	11.0%	L	140	15
126	Warren-Troy-Farmington Hills, MI Metro Div	58.3	267.4	24.7%	L	141	15
127	Houston-The Woodlands-Sugar Land, TX	58.3	465.4	21.7%	L	100	(27)
128	Omaha-Council Bluffs, NE-IA	58.3	74.6	15.6%	L	232	104
129	Topeka, KS	58.2	12.8	22.3%	S	48	(81)
130	Delaware County, PA	57.6	32.4	13.7%	M	209	79
131	Rockford, IL	57.6	17.0	12.8%	M	83	(48)
132	Grants Pass, OR	57.3	2.1	26.5%	S	3	(129)
133	Yuma, AZ	57.0	6.7	14.3%	S	337	204
134	Logan, UT-ID	56.7	6.0	8.4%	S	291	157
135	Kansas City, KS	56.4	93.5	19.6%	L	78	(57)
136	Lakeland-Winter Haven, FL	56.3	29.3	18.0%	M	182	46
137	Ithaca, NY	55.6	3.4	9.7%	S	75	(62)
138	Waterloo-Cedar Falls, IA	55.4	7.7	19.7%	S	88	(50)
139	State College, PA	55.4	6.2	12.0%	S	167	28
140	Yakima, WA	55.2	4.2	7.6%	S	339	199
141	Wichita, KS	55.1	33.5	13.8%	M	113	(28)
142	Eugene, OR	55.1	17.0	15.9%	M	303	161
143	Springfield, MA-CT NECTA	55.0	26.8	14.6%	M	161	18
144	Kalamazoo-Portage, MI	54.6	16.5	16.4%	S	84	(60)
145	Huntsville, AL	54.5	52.4	6.9%	M	311	166
146	Bellingham, WA	54.1	7.9	13.9%	S	159	13
147	Dover-Durham, NH-ME NECTA	54.1	4.0	14.4%	S	193	46
148	Nashua, NH-MA NECTA Division	54.0	15.1	16.2%	S	235	87
149	Pensacola-Ferry Pass-Brent, FL	53.9	22.8	14.2%	M	149	0
150	Roanoke, VA	53.9	22.7	10.6%	M	301	151
151	Modesto, CA	53.4	14.4	18.7%	M	70	(81)
152	Owensboro, KY	53.2	3.9	12.4%	S	304	152
153	Kahului-Wailuku-Lahaina, HI	53.1	7.2	12.0%	S	156	3
154	South Bend-Mishawaka, IN-MI	53.0	13.6	13.4%	S	119	(35)
155	York-Hanover, PA	52.4	20.8	14.9%	M	98	(57)
156	Pocatello, ID	52.4	4.0	27.7%	S	133	(23)
157	Myrtle Beach-Conway-North Myrtle Beach, SC-NC	52.3	12.7	18.3%	M	91	(66)
158	Killeen-Temple, TX	52.2	9.9	4.6%	S	346	188
159	Muskegon, MI	52.0	3.8	27.0%	S	90	(69)
160	Amarillo, TX	51.9	9.6	14.2%	S	281	121
161	Lima, OH	51.9	4.9	24.4%	S	243	82
162	Bangor, ME NECTA	51.9	6.5	14.8%	S	87	(75)
163	Midland, TX	51.6	9.4	19.5%	S	101	(62)
164	Lincoln, NE	51.5	19.6	12.2%	M	316	152
165	Bay City, MI	51.3	3.5	12.8%	S	60	(105)
166	New Haven, CT NECTA	51.1	30.2	15.5%	M	172	6
167	Durham-Chapel Hill, NC	51.1	38.9	9.2%	M	290	123
168	Iowa City, IA	51.1	6.9	15.7%	S	59	(109)
169	Anaheim-Santa Ana-Irvine, CA Metro Div	50.8	288.3	16.1%	L	121	(48)
170	Clarksville, TN-KY	50.8	9.1	11.9%	S	150	(20)
171	Chico, CA	50.5	5.8	16.0%	S	122	(49)
172	Leominster-Gardner, MA NECTA	50.4	3.8	10.8%	S	216	44
173	Victoria, TX	50.3	2.6	4.0%	S	64	(109)
174	Lewiston-Auburn, ME NECTA	50.0	7.0	11.6%	S	203	29
175	Pittsburgh, PA	49.8	178.1	10.6%	L	267	92
176	Montgomery Co-Bucks Co-Chester Co, PA Metro Div	49.7	198.7	10.1%	L	262	86
177	Jackson, MS	49.6	33.1	16.0%	M	180	3
178	Washington-Arlington-Alexandria, DC-VA-MD-WV Metro Div	49.5	604.6	7.7%	L	300	122
179	Columbia, SC	49.0	49.7	21.9%	M	231	52
180	Corpus Christi, TX	48.6	17.2	10.0%	M	324	144
181	Kingston, NY	48.5	4.6	15.8%	S	225	44
182	Atlantic City-Hammonton, NJ	48.4	10.0	4.9%	S	223	41
183	Santa Cruz-Watsonville, CA	48.3	10.0	8.3%	S	318	135
184	Spartanburg, SC	48.2	16.0	12.9%	S	163	(21)
185	Calvert-Charles-Prince George’s, MD	48.1	49.6	5.8%	M	247	62
186	Kankakee, IL	47.9	3.4	6.2%	S	39	(147)
187	Chicago-Naperville-Arlington Heights, IL Metro Div	47.8	672.7	14.9%	L	162	(25)
188	Urban Honolulu, HI	47.8	67.9	15.1%	L	191	3
189	Madera, CA	47.6	2.9	7.5%	S	226	37
190	San Diego-Carlsbad, CA	47.5	234.4	12.7%	L	160	(30)
191	Portland-South Portland, ME NECTA	47.5	26.8	9.2%	M	270	79
192	El Paso, TX	47.2	33.1	5.5%	M	323	131
193	Sacramento–Roseville–Arden-Arcade, CA	46.9	120.0	17.5%	L	116	(77)
194	Colorado Springs, CO	46.6	43.3	8.4%	M	289	95
195	Northern Virginia, VA	46.5	387.2	4.5%	L	325	130
196	Danbury, CT NECTA	46.4	9.3	14.3%	S	124	(72)
197	Walla Walla, WA	46.4	1.0	11.1%	S	313	116
198	Minneapolis-St. Paul-Bloomington, MN-WI	46.1	303.7	12.0%	L	174	(24)
199	Hagerstown-Martinsburg, MD-WV	46.1	9.4	15.0%	S	184	(15)
200	Philadelphia City, PA	46.0	91.2	9.9%	L	237	37
201	Bloomsburg-Berwick, PA	45.6	4.2	17.8%	S	198	(3)
202	Augusta-Richmond County, GA-SC	45.6	33.8	8.0%	M	218	16
203	Boulder, CO	45.6	32.9	14.0%	M	205	2
204	Tucson, AZ	45.4	52.2	12.2%	M	292	88
205	St. Louis, MO-IL	45.1	206.8	9.9%	L	200	(5)
206	New Orleans-Metairie, LA	44.9	74.7	9.6%	L	242	36
207	McAllen-Edinburg-Mission, TX	44.6	16.1	10.6%	M	258	51
208	Idaho Falls, ID	44.5	12.7	-1.8%	S	297	89
209	Riverside-San Bernardino-Ontario, CA	44.1	145.2	14.9%	L	54	(155)
210	Peoria, IL	44.1	22.4	-0.4%	M	351	141
211	Fort Collins, CO	44.0	19.3	10.5%	M	238	27
212	Orange-Rockland-Westchester, NY	43.5	87.7	11.6%	L	234	22
213	Detroit-Dearborn-Livonia, MI Metro Div	43.4	126.4	14.8%	L	196	(17)
214	Dayton, OH	43.3	51.0	10.9%	M	213	(1)
215	Beaumont-Port Arthur, TX	43.2	14.8	6.7%	M	40	(175)
216	Fresno, CA	43.1	30.9	16.3%	M	27	(189)
217	San Rafael, CA Metro Div	43.0	19.1	6.9%	S	341	124
218	Barnstable Town, MA NECTA	42.9	8.6	9.8%	S	95	(123)
219	Fort Worth-Arlington, TX Metro Div	42.8	111.0	13.8%	L	131	(88)
220	Brockton-Bridgewater-Easton, MA NECTA Division	42.6	7.4	-0.9%	S	130	(90)
221	Toledo, OH	42.6	37.1	16.3%	M	263	42
222	Lafayette-West Lafayette, IN	42.2	7.5	10.8%	S	16	(206)
223	Fort Wayne, IN	41.9	21.6	1.7%	M	254	31
224	Newark, NJ-PA Metro Div	41.6	221.2	6.7%	L	306	82
225	Milwaukee-Waukesha-West Allis, WI	41.4	125.2	10.3%	L	229	4
226	Vallejo-Fairfield, CA	41.0	9.8	12.7%	S	272	46
227	St. Cloud, MN	41.0	8.8	6.0%	S	344	117
228	Kennewick-Richland, WA	40.9	21.4	-15.9%	S	333	105
229	Akron, OH	40.9	52.3	6.3%	M	285	56
230	East Stroudsburg, PA	40.6	3.5	-4.6%	S	329	99
231	Nassau County-Suffolk County, NY Metro Div	40.3	171.1	11.0%	L	256	25
232	Oshkosh-Neenah, WI	40.2	11.3	12.2%	S	157	(75)
233	Abilene, TX	40.2	5.6	0.6%	S	170	(63)
234	Morristown, TN	40.1	3.4	14.8%	S	178	(56)
235	Glens Falls, NY	39.8	5.8	11.5%	S	212	(23)
236	Fort Smith, AR-OK	39.8	11.9	8.8%	S	214	(22)
237	Kingsport-Bristol-Bristol, TN-VA	39.8	10.1	4.5%	S	127	(110)
238	Little Rock-North Little Rock-Conway, AR	39.7	46.2	6.0%	M	319	81
239	Oklahoma City, OK	39.4	80.6	7.2%	L	144	(95)
240	Sebastian-Vero Beach, FL	38.9	5.1	7.8%	S	166	(74)
241	Cleveland, TN	38.7	8.4	178.9%	S	33	(208)
242	Eau Claire, WI	38.6	9.1	-1.8%	S	282	40
243	Los Angeles-Long Beach-Glendale, CA Metro Div	38.5	609.6	13.8%	L	181	(62)
244	Birmingham-Hoover, AL	38.2	64.9	9.6%	L	192	(52)
245	Lansing-East Lansing, MI	38.2	22.0	7.3%	M	273	28
246	Niles-Benton Harbor, MI	37.9	5.6	-5.1%	S	271	25
247	Davenport-Moline-Rock Island, IA-IL	37.9	24.4	10.9%	M	274	27
248	Lancaster, PA	37.6	22.8	13.6%	M	50	(198)
249	Wilmington, DE-MD-NJ Metro Div	37.3	55.6	12.6%	M	228	(21)
250	Appleton, WI	37.3	13.3	6.4%	S	158	(92)
251	Cincinnati, OH-KY-IN	37.2	167.0	11.1%	L	147	(104)
252	Albany, OR	36.7	3.3	6.5%	S	230	(22)
253	Michigan City-La Porte, IN	36.7	2.8	10.4%	S	56	(197)
254	Bergen-Hudson-Passaic, NJ	36.1	142.3	8.2%	L	275	21
255	Lake Havasu City-Kingman, AZ	36.0	3.5	-1.9%	S	25	(230)
256	Monroe, LA	35.6	7.9	7.3%	S	221	(35)
257	Santa Fe, NM	35.6	4.6	2.2%	S	348	91
258	Visalia-Porterville, CA	35.3	9.8	7.3%	S	338	80
259	Baltimore City, MD	35.3	46.3	20.4%	M	155	(104)
260	Lake County-Kenosha County, IL-WI Metro Div	34.9	67.3	13.0%	M	202	(58)
261	Rapid City, SD	34.8	5.0	4.2%	S	276	15
262	Elmira, NY	34.8	2.7	5.3%	S	123	(139)
263	Tallahassee, FL	34.6	19.3	5.5%	M	195	(68)
264	Battle Creek, MI	34.4	6.2	-1.6%	S	106	(158)
265	Longview, TX	34.3	8.9	1.5%	S	197	(68)
266	Ocala, FL	34.3	9.3	17.2%	S	63	(203)
267	Mobile, AL	34.3	22.5	-1.5%	M	335	68
268	Grand Forks, ND-MN	34.2	3.0	-2.2%	S	207	(61)
269	Grand Rapids-Wyoming, MI	34.0	74.2	9.9%	L	68	(201)
270	Johnson City, TN	33.8	8.2	-1.2%	S	189	(81)
271	Huntington-Ashland, WV-KY-OH	33.8	12.1	5.5%	S	164	(107)
272	Boise City, ID	33.3	41.1	8.3%	M	308	36
273	Great Falls, MT	32.5	3.1	-4.1%	S	347	74
274	Altoona, PA	32.2	5.5	8.6%	S	175	(99)
275	Peabody-Salem-Beverly, MA NECTA Division	31.7	9.9	8.8%	S	296	21
276	Danville, IL	31.7	2.0	-4.8%	S	305	29
277	Cleveland-Elyria, OH	31.6	147.2	10.1%	L	220	(57)
278	Columbus, GA-AL	31.6	13.2	0.8%	S	342	64
279	Virginia Beach-Norfolk-Newport News, VA-NC	31.6	103.9	5.9%	L	264	(15)
280	Fairbanks, AK	30.8	2.2	-5.6%	S	298	18
281	Dalton, GA	30.7	6.1	-10.2%	S	185	(96)
282	Medford, OR	30.6	7.0	6.1%	S	148	(134)
283	Lafayette, LA	30.5	21.7	6.9%	M	283	0
284	Rochester, NY	30.4	66.0	6.5%	L	286	2
285	Pittsfield, MA NECTA	30.4	3.8	5.6%	S	317	32
286	Greenville, NC	30.2	6.8	10.9%	S	110	(176)
287	Grand Junction, CO	30.0	5.3	6.7%	S	251	(36)
288	Fayetteville, NC	29.9	12.5	-9.0%	S	349	61
289	Weirton-Steubenville, WV-OH	29.6	1.9	1.8%	S	261	(28)
290	Gary, IN Metro Div	29.6	22.3	9.3%	M	126	(164)
291	Elgin, IL Metro Div	29.3	34.9	18.4%	M	138	(153)
292	Evansville, IN-KY	29.0	18.1	1.5%	M	179	(113)
293	Santa Maria-Santa Barbara, CA	29.0	22.4	5.7%	M	222	(71)
294	Decatur, IL	28.2	3.2	-17.4%	S	361	67
295	Cedar Rapids, IA	28.1	13.6	0.5%	S	353	58
296	Terre Haute, IN	28.0	5.5	2.5%	S	343	47
297	Johnstown, PA	27.6	5.7	1.2%	S	253	(44)
298	Erie, PA	27.2	10.1	-5.0%	S	201	(97)
299	Tyler, TX	27.1	8.5	-3.0%	S	171	(128)
300	Silver Spring-Frederick-Rockville, MD Metro Div	27.0	124.3	-0.5%	L	336	36
301	Hanford-Corcoran, CA	27.0	1.3	5.4%	S	233	(68)
302	Allentown-Bethlehem-Easton, PA-NJ	26.9	47.5	4.6%	M	245	(57)
303	Jackson, MI	26.9	4.1	8.0%	S	345	42
304	Bremerton-Silverdale, WA	26.9	7.1	-4.5%	S	321	17
305	Bakersfield, CA	26.8	25.4	4.1%	M	299	(6)
306	Albany-Schenectady-Troy, NY	26.7	52.3	3.2%	L	332	26
307	Charleston, WV	26.7	14.4	2.1%	S	302	(5)
308	Duluth, MN-WI	26.4	8.0	0.4%	S	248	(60)
309	Lynn-Saugus-Marblehead, MA NECTA Division	26.3	2.7	8.1%	S	8	(301)
310	Lowell-Billerica-Chelmsford, MA-NH NECTA Division	26.0	20.8	-2.8%	S	240	(70)
311	Flint, MI	26.0	15.5	4.0%	S	134	(177)
312	Rocky Mount, NC	25.6	5.5	-13.2%	S	334	22
313	Montgomery, AL	25.5	20.5	1.0%	M	169	(144)
314	Norwich-New London-Westerly, CT-RI NECTA	25.3	8.9	-2.9%	S	279	(35)
315	Racine, WI	25.1	6.4	1.1%	S	287	(28)
316	Hickory-Lenoir-Morganton, NC	24.8	13.2	3.9%	S	32	(284)
317	Palm Bay-Melbourne-Titusville, FL	24.8	29.5	-8.6%	M	280	(37)
318	Albuquerque, NM	24.3	57.6	-0.9%	M	312	(6)
319	Canton-Massillon, OH	23.9	14.1	2.2%	M	168	(151)
320	Anchorage, AK	23.6	20.0	0.5%	M	340	20
321	Lawrence-Methuen Town-Salem, MA-NH NECTA Division	23.4	10.1	-2.6%	S	331	10
322	Las Cruces, NM	23.4	7.0	-6.3%	S	315	(7)
323	Tulsa, OK	23.3	57.5	6.1%	M	219	(104)
324	San Angelo, TX	23.3	3.8	6.5%	S	239	(85)
325	El Centro, CA	23.1	2.2	-13.2%	S	356	31
326	Sheboygan, WI	23.0	4.3	4.0%	S	277	(49)
327	Waterbury, CT NECTA	22.8	5.2	9.1%	S	115	(212)
328	Lawrence, KS	22.7	5.2	6.9%	S	74	(254)
329	Florence-Muscle Shoals, AL	22.4	4.0	-11.2%	S	328	(1)
330	Watertown-Fort Drum, NY	22.1	2.2	-3.0%	S	358	28
331	Sherman-Denison, TX	22.0	2.9	4.8%	S	14	(317)
332	Bridgeport-Stamford-Norwalk, CT NECTA	22.0	64.9	2.7%	M	294	(38)
333	Green Bay, WI	21.7	20.0	1.5%	M	320	(13)
334	Youngstown-Warren-Boardman, OH-PA	21.6	21.9	3.6%	M	259	(75)
335	Odessa, TX	21.3	4.2	-1.6%	S	43	(292)
336	Rochester, MN	21.3	5.5	8.5%	S	224	(112)
337	Missoula, MT	21.2	6.2	-11.5%	S	255	(82)
338	Vineland-Bridgeton, NJ	20.4	3.6	1.9%	S	364	26
339	Champaign-Urbana, IL	20.1	7.8	1.7%	S	266	(73)
340	Oxnard-Thousand Oaks-Ventura, CA	19.6	34.5	3.5%	M	309	(31)
341	Decatur, AL	19.2	5.4	3.2%	S	284	(57)
342	Casper, WY	19.2	2.9	3.6%	S	227	(115)
343	Worcester, MA-CT NECTA	18.4	25.9	-2.3%	M	249	(94)
344	Cheyenne, WY	18.2	3.2	2.2%	S	135	(209)
345	Buffalo-Cheektowaga-Niagara Falls, NY	17.9	69.0	-2.8%	L	330	(15)
346	Dothan, AL	17.8	4.3	-12.2%	S	176	(170)
347	Mansfield, OH	17.0	5.1	-9.4%	S	187	(160)
348	Lewiston, ID-WA	16.8	1.3	-11.6%	S	360	12
349	Binghamton, NY	16.8	9.2	3.4%	S	210	(139)
350	Coeur d’Alene, ID	16.7	6.0	-6.3%	S	206	(144)
351	Shreveport-Bossier City, LA	16.3	17.0	-5.4%	M	257	(94)
352	Dutchess County-Putnam County, NY Metro Div	16.1	11.5	-7.0%	S	350	(2)
353	Utica-Rome, NY	16.1	8.2	-7.2%	S	363	10
354	Lynchburg, VA	16.1	11.9	-7.0%	S	295	(59)
355	Sioux City, IA-NE-SD	16.0	7.8	-12.4%	S	355	0
356	Carson City, NV	11.6	1.9	-5.1%	S	217	(139)
357	Wichita Falls, TX	11.6	3.7	-13.4%	S	326	(31)
358	La Crosse-Onalaska, WI-MN	11.0	6.0	-6.7%	S	314	(44)
359	Bloomington, IL	10.4	9.7	-11.3%	S	357	(2)
360	Bloomington, IN	10.2	4.4	-26.4%	S	366	6
361	Sierra Vista-Douglas, AZ	10.2	3.8	-32.4%	S	293	(68)
362	Gulfport-Biloxi-Pascagoula, MS	10.0	14.9	-14.2%	M	359	(3)
363	Billings, MT	10.0	8.2	-6.8%	S	327	(36)
364	Syracuse, NY	9.4	31.1	-6.3%	M	322	(42)
365	Merced, CA	8.1	3.5	-15.2%	S	354	(11)
366	Anniston-Oxford-Jacksonville, AL	7.0	4.4	-21.3%	S	365	(1)

July 21, 2016

Large Cities Business Services Jobs – 2016 Best Cities Rankings

Read about how we selected the 2016 Best Cities for Job Growth

2016 MSA Prof & Bus Svcs Ranking among Large MSAs	Area	2016 Prof & Bus Svcs Weighted INDEX	2015 Prof and Business Services Emplmt (1000s)	Total Prof and Business Services Emplmt Growth Rate 2014-2015	2015 MSA Prof & Bus Svcs Ranking among Small MSAs	Overall Rank Change 2014-2015
1	Nashville-Davidson–Murfreesboro–Franklin, TN	96.9	152.7	47.2%	4	3
2	San Francisco-Redwood City-South San Francisco, CA Metro Div	93.1	270.5	45.7%	2	0
3	Austin-Round Rock, TX	91.1	165.6	42.3%	5	2
4	San Jose-Sunnyvale-Santa Clara, CA	88.5	223.0	36.4%	1	(3)
5	Dallas-Plano-Irving, TX Metro Div	82.8	458.2	28.9%	6	1
6	Raleigh, NC	81.2	113.5	27.9%	3	(3)
7	Orlando-Kissimmee-Sanford, FL	80.3	201.2	26.8%	35	28
8	Richmond, VA	80.1	112.3	18.9%	59	51
9	Charlotte-Concord-Gastonia, NC-SC	79.8	188.2	25.3%	9	0
10	Portland-Vancouver-Hillsboro, OR-WA	79.4	172.9	24.9%	15	5
11	Tampa-St. Petersburg-Clearwater, FL	77.9	226.3	26.3%	40	29
12	Louisville/Jefferson County, KY-IN	77.7	88.4	21.9%	16	4
13	Atlanta-Sandy Springs-Roswell, GA	76.2	485.1	23.9%	10	(3)
14	New York City, NY	76.0	714.0	22.0%	29	15
15	Kansas City, MO	73.9	90.4	18.3%	50	35
16	Denver-Aurora-Lakewood, CO	73.3	254.1	23.4%	37	21
17	Las Vegas-Henderson-Paradise, NV	72.9	126.1	25.0%	20	3
18	Seattle-Bellevue-Everett, WA Metro Div	72.2	244.4	22.1%	31	13
19	Memphis, TN-MS-AR	71.6	99.9	25.5%	14	(5)
20	Fort Lauderdale-Pompano Beach-Deerfield Beach, FL Metro Div	71.3	144.0	21.6%	25	5
21	Indianapolis-Carmel-Anderson, IN	69.8	162.5	26.2%	23	2
22	West Palm Beach-Boca Raton-Delray Beach, FL Metro Div	69.2	108.3	28.2%	7	(15)
23	Hartford-West Hartford-East Hartford, CT NECTA	67.6	73.1	19.2%	18	(5)
24	Salt Lake City, UT	66.8	118.5	23.8%	22	(2)
25	San Antonio-New Braunfels, TX	64.7	125.3	20.4%	28	3
26	Jacksonville, FL	64.6	102.8	19.9%	19	(7)
27	Middlesex-Monmouth-Ocean, NJ	64.3	152.0	16.6%	57	30
28	Phoenix-Mesa-Scottsdale, AZ	64.1	336.7	21.9%	39	11
29	Miami-Miami Beach-Kendall, FL Metro Div	63.1	162.5	24.4%	12	(17)
30	Oakland-Hayward-Berkeley, CA Metro Div	62.6	182.9	18.6%	24	(6)
31	Providence-Warwick, RI-MA NECTA	62.0	70.1	19.3%	26	(5)
32	Boston-Cambridge-Newton, MA NECTA Division	60.1	340.4	16.7%	45	13
33	Columbus, OH	59.6	178.8	20.1%	17	(16)
34	Camden, NJ Metro Div	58.9	78.8	11.0%	33	(1)
35	Warren-Troy-Farmington Hills, MI Metro Div	58.3	267.4	24.7%	34	(1)
36	Houston-The Woodlands-Sugar Land, TX	58.3	465.4	21.7%	21	(15)
37	Omaha-Council Bluffs, NE-IA	58.3	74.6	15.6%	53	16
38	Kansas City, KS	56.4	93.5	19.6%	13	(25)
39	Anaheim-Santa Ana-Irvine, CA Metro Div	50.8	288.3	16.1%	30	(9)
40	Pittsburgh, PA	49.8	178.1	10.6%	62	22
41	Montgomery Cnty-Bucks Cnty-Chester Cnty, PA Metro Div	49.7	198.7	10.1%	60	19
42	Washington-Arlington-Alexandria, DC-VA-MD-WV Metro Div	49.5	604.6	7.7%	65	23
43	Chicago-Naperville-Arlington Heights, IL Metro Div	47.8	672.7	14.9%	42	(1)
44	Urban Honolulu, HI	47.8	67.9	15.1%	46	2
45	San Diego-Carlsbad, CA	47.5	234.4	12.7%	41	(4)
46	Sacramento–Roseville–Arden-Arcade, CA	46.9	120.0	17.5%	27	(19)
47	Northern Virginia, VA	46.5	387.2	4.5%	67	20
48	Minneapolis-St. Paul-Bloomington, MN-WI	46.1	303.7	12.0%	43	(5)
49	Philadelphia City, PA	46.0	91.2	9.9%	55	6
50	St. Louis, MO-IL	45.1	206.8	9.9%	49	(1)
51	New Orleans-Metairie, LA	44.9	74.7	9.6%	56	5
52	Riverside-San Bernardino-Ontario, CA	44.1	145.2	14.9%	8	(44)
53	Orange-Rockland-Westchester, NY	43.5	87.7	11.6%	54	1
54	Detroit-Dearborn-Livonia, MI Metro Div	43.4	126.4	14.8%	48	(6)
55	Fort Worth-Arlington, TX Metro Div	42.8	111.0	13.8%	32	(23)
56	Newark, NJ-PA Metro Div	41.6	221.2	6.7%	66	10
57	Milwaukee-Waukesha-West Allis, WI	41.4	125.2	10.3%	52	(5)
58	Nassau County-Suffolk County, NY Metro Div	40.3	171.1	11.0%	58	0
59	Oklahoma City, OK	39.4	80.6	7.2%	36	(23)
60	Los Angeles-Long Beach-Glendale, CA Metro Div	38.5	609.6	13.8%	44	(16)
61	Birmingham-Hoover, AL	38.2	64.9	9.6%	47	(14)
62	Cincinnati, OH-KY-IN	37.2	167.0	11.1%	38	(24)
63	Bergen-Hudson-Passaic, NJ	36.1	142.3	8.2%	63	0
64	Grand Rapids-Wyoming, MI	34.0	74.2	9.9%	11	(53)
65	Cleveland-Elyria, OH	31.6	147.2	10.1%	51	(14)
66	Virginia Beach-Norfolk-Newport News, VA-NC	31.6	103.9	5.9%	61	(5)
67	Rochester, NY	30.4	66.0	6.5%	64	(3)
68	Silver Spring-Frederick-Rockville, MD Metro Div	27.0	124.3	-0.5%	70	2
69	Albany-Schenectady-Troy, NY	26.7	52.3	3.2%	69	0
70	Buffalo-Cheektowaga-Niagara Falls, NY	17.9	69.0	-2.8%	68	(2)

July 21, 2016

Mid Sized Cities Business Services Jobs – 2016 Best Cities Rankings

Read about how we selected the 2016 Best Cities for Job Growth

2016 MSA Prof & Bus Svcs Ranking among Midsized MSAs	Area	2016 Prof & Bus Svcs Weighted INDEX	2015 Prof and Business Services Emplmt (1000s)	Total Prof and Business Services Emplmt Growth Rate 2014-2015	2015 MSA Prof & Bus Svcs Ranking among Small MSAs	Overall Rank Change 2014-2015
1	Sioux Falls, SD	89.0	15.5	33.2%	61	60
2	Trenton, NJ	88.2	45.0	25.1%	67	65
3	Fayetteville-Springdale-Rogers, AR-MO	87.5	48.1	38.2%	4	1
4	Provo-Orem, UT	85.7	29.6	34.3%	1	(3)
5	Cape Coral-Fort Myers, FL	84.8	34.1	38.4%	9	4
6	Deltona-Daytona Beach-Ormond Beach, FL	80.4	22.7	33.7%	6	0
7	Stockton-Lodi, CA	79.1	20.5	35.2%	43	36
8	Salisbury, MD-DE	77.7	12.5	24.2%	24	16
9	Des Moines-West Des Moines, IA	77.2	47.4	24.2%	15	6
10	Greenville-Anderson-Mauldin, SC	76.6	71.4	23.8%	14	4
11	Greensboro-High Point, NC	74.7	53.6	19.4%	13	2
12	Ogden-Clearfield, UT	72.5	28.4	31.3%	7	(5)
13	Asheville, NC	72.4	18.4	20.8%	29	16
14	Chattanooga, TN-GA	71.8	29.6	29.9%	36	22
15	Knoxville, TN	71.5	63.2	15.8%	17	2
16	Reading, PA	70.5	23.9	21.1%	3	(13)
17	Spokane-Spokane Valley, WA	69.8	25.5	17.7%	45	28
18	North Port-Sarasota-Bradenton, FL	69.2	41.4	40.7%	2	(16)
19	Springfield, MO	68.8	25.2	27.3%	10	(9)
20	Baton Rouge, LA	68.5	49.7	19.5%	26	6
21	Ann Arbor, MI	67.3	29.9	16.2%	32	11
22	Framingham, MA NECTA Division	66.7	36.9	15.3%	50	28
23	Winston-Salem, NC	65.6	35.3	18.4%	27	4
24	Reno, NV	65.3	29.8	21.5%	58	34
25	Salem, OR	65.2	13.5	25.5%	37	12
26	Tacoma-Lakewood, WA Metro Div	64.8	27.5	16.2%	41	15
27	Madison, WI	63.7	49.8	18.7%	24	(3)
28	Harrisburg-Carlisle, PA	63.1	47.5	15.7%	56	28
29	Lexington-Fayette, KY	62.6	40.3	33.9%	12	(17)
30	Santa Rosa, CA	62.4	21.7	16.5%	42	12
31	Savannah, GA	62.0	20.2	15.2%	19	(12)
32	Charleston-North Charleston, SC	60.7	49.4	15.0%	22	(10)
33	Scranton–Wilkes-Barre–Hazleton, PA	59.1	30.2	17.2%	61	28
34	Delaware County, PA	57.6	32.4	13.7%	48	14
35	Rockford, IL	57.6	17.0	12.8%	18	(17)
36	Lakeland-Winter Haven, FL	56.3	29.3	18.0%	40	4
37	Wichita, KS	55.1	33.5	13.8%	21	(16)
38	Eugene, OR	55.1	17.0	15.9%	80	42
39	Springfield, MA-CT NECTA	55.0	26.8	14.6%	31	(8)
40	Huntsville, AL	54.5	52.4	6.9%	82	42
41	Pensacola-Ferry Pass-Brent, FL	53.9	22.8	14.2%	28	(13)
42	Roanoke, VA	53.9	22.7	10.6%	79	37
43	Modesto, CA	53.4	14.4	18.7%	16	(27)
44	York-Hanover, PA	52.4	20.8	14.9%	20	(24)
45	Myrtle Beach-Conway-North Myrtle Beach, SC-NC	52.3	12.7	18.3%	20	(25)
46	Lincoln, NE	51.5	19.6	12.2%	84	38
47	New Haven, CT NECTA	51.1	30.2	15.5%	35	(12)
48	Durham-Chapel Hill, NC	51.1	38.9	9.2%	75	27
49	Jackson, MS	49.6	33.1	16.0%	39	(10)
50	Columbia, SC	49.0	49.7	21.9%	55	5
51	Corpus Christi, TX	48.6	17.2	10.0%	89	38
52	Calvert-Charles-Prince George’s, MD	48.1	49.6	5.8%	59	7
53	Portland-South Portland, ME NECTA	47.5	26.8	9.2%	68	15
54	El Paso, TX	47.2	33.1	5.5%	88	34
55	Colorado Springs, CO	46.6	43.3	8.4%	74	19
56	Augusta-Richmond County, GA-SC	45.6	33.8	8.0%	51	(5)
57	Boulder, CO	45.6	32.9	14.0%	47	(10)
58	Tucson, AZ	45.4	52.2	12.2%	76	18
59	McAllen-Edinburg-Mission, TX	44.6	16.1	10.6%	64	5
60	Peoria, IL	44.1	22.4	-0.4%	92	32
61	Fort Collins, CO	44.0	19.3	10.5%	57	(4)
62	Dayton, OH	43.3	51.0	10.9%	49	(13)
63	Beaumont-Port Arthur, TX	43.2	14.8	6.7%	8	(55)
64	Fresno, CA	43.1	30.9	16.3%	5	(59)
65	Toledo, OH	42.6	37.1	16.3%	66	1
66	Fort Wayne, IN	41.9	21.6	1.7%	62	(4)
67	Akron, OH	40.9	52.3	6.3%	73	6
68	Little Rock-North Little Rock-Conway, AR	39.7	46.2	6.0%	85	17
69	Lansing-East Lansing, MI	38.2	22.0	7.3%	69	0
70	Davenport-Moline-Rock Island, IA-IL	37.9	24.4	10.9%	70	0
71	Lancaster, PA	37.6	22.8	13.6%	11	(60)
72	Wilmington, DE-MD-NJ Metro Div	37.3	55.6	12.6%	54	(18)
73	Baltimore City, MD	35.3	46.3	20.4%	30	(43)
74	Lake County-Kenosha County, IL-WI Metro Div	34.9	67.3	13.0%	46	(28)
75	Tallahassee, FL	34.6	19.3	5.5%	44	(31)
76	Mobile, AL	34.3	22.5	-1.5%	90	14
77	Boise City, ID	33.3	41.1	8.3%	80	3
78	Lafayette, LA	30.5	21.7	6.9%	72	(6)
79	Gary, IN Metro Div	29.6	22.3	9.3%	23	(56)
80	Elgin, IL Metro Div	29.3	34.9	18.4%	25	(55)
81	Evansville, IN-KY	29.0	18.1	1.5%	38	(43)
82	Santa Maria-Santa Barbara, CA	29.0	22.4	5.7%	53	(29)
83	Allentown-Bethlehem-Easton, PA-NJ	26.9	47.5	4.6%	57	(26)
84	Bakersfield, CA	26.8	25.4	4.1%	78	(6)
85	Montgomery, AL	25.5	20.5	1.0%	34	(51)
86	Palm Bay-Melbourne-Titusville, FL	24.8	29.5	-8.6%	71	(15)
87	Albuquerque, NM	24.3	57.6	-0.9%	83	(4)
88	Canton-Massillon, OH	23.9	14.1	2.2%	33	(55)
89	Anchorage, AK	23.6	20.0	0.5%	91	2
90	Tulsa, OK	23.3	57.5	6.1%	52	(38)
91	Bridgeport-Stamford-Norwalk, CT NECTA	22.0	64.9	2.7%	77	(14)
92	Green Bay, WI	21.7	20.0	1.5%	86	(6)
93	Youngstown-Warren-Boardman, OH-PA	21.6	21.9	3.6%	65	(28)
94	Oxnard-Thousand Oaks-Ventura, CA	19.6	34.5	3.5%	81	(13)
95	Worcester, MA-CT NECTA	18.4	25.9	-2.3%	60	(35)
96	Shreveport-Bossier City, LA	16.3	17.0	-5.4%	63	(33)
97	Gulfport-Biloxi-Pascagoula, MS	10.0	14.9	-14.2%	93	(4)
98	Syracuse, NY	9.4	31.1	-6.3%	87	(11)

July 21, 2016

Small Cities Business Services Jobs – 2016 Best Cities Rankings

Read about how we selected the 2016 Best Cities for Job Growth

2016 MSA Prof & Bus Svcs Ranking among Small MSAs	Area	2016 Prof & Bus Svcs Weighted INDEX	2015 Prof and Business Services Emplmt (1000s)	Total Prof and Business Services Emplmt Growth Rate 2014-2015	2015 MSA Prof & Bus Svcs Ranking among Small MSAs	Overall Rank Change 2014-2015
1	New Bedford, MA NECTA	97.1	6.2	6.3%	5	4
2	Monroe, MI	95.0	4.9	11.5%	11	9
3	Lake Charles, LA	94.8	9.7	5.8%	59	56
4	Lawton, OK	92.6	4.9	8.9%	151	147
5	Elizabethtown-Fort Knox, KY	92.3	6.9	10.8%	66	61
6	Janesville-Beloit, WI	90.3	6.5	9.0%	7	1
7	Jackson, TN	89.9	6.7	4.1%	4	(3)
8	St. George, UT	88.8	4.7	10.2%	99	91
9	College Station-Bryan, TX	88.5	8.3	6.0%	35	26
10	Napa, CA	86.6	6.9	3.5%	56	46
11	Springfield, OH	83.4	5.0	8.6%	17	6
12	Laredo, TX	80.9	8.6	5.3%	43	31
13	Charlottesville, VA	80.3	15.1	5.9%	49	36
14	Waco, TX	80.1	11.5	-4.4%	9	(5)
15	Bend-Redmond, OR	79.0	8.2	5.6%	32	17
16	Greeley, CO	78.1	9.9	-0.3%	14	(2)
17	Morgantown, WV	77.8	6.6	2.1%	22	5
18	Bismarck, ND	76.9	8.4	3.3%	50	32
19	Fargo, ND-MN	76.6	16.7	5.0%	82	63
20	Tuscaloosa, AL	76.5	11.2	-2.0%	2	(18)
21	Auburn-Opelika, AL	76.0	7.4	-1.8%	1	(20)
22	Redding, CA	73.8	6.4	9.1%	23	1
23	Flagstaff, AZ	73.7	3.3	10.0%	29	6
24	Gadsden, AL	73.5	4.4	6.5%	20	(4)
25	Chambersburg-Waynesboro, PA	73.1	5.8	0.6%	77	52
26	Pueblo, CO	73.0	7.1	3.4%	61	35
27	Manchester, NH NECTA	72.9	16.4	2.7%	31	4
28	Corvallis, OR	71.7	4.3	4.0%	46	18
29	Port St. Lucie, FL	71.3	16.6	2.5%	6	(23)
30	Bowling Green, KY	71.3	9.1	4.6%	113	83
31	San Luis Obispo-Paso Robles-Arroyo Grande, CA	71.1	12.4	3.0%	64	33
32	Fond du Lac, WI	70.8	2.9	4.8%	45	13
33	Prescott, AZ	70.5	4.4	14.9%	44	11
34	Gainesville, FL	69.7	13.1	3.2%	26	(8)
35	Burlington-South Burlington, VT NECTA	69.7	14.2	4.1%	63	28
36	Dover, DE	69.0	4.7	8.5%	163	127
37	Haverhill-Newburyport-Amesbury Town, MA-NH NECTA Division	68.5	5.6	4.4%	145	108
38	Macon, GA	68.3	12.9	7.5%	34	(4)
39	Olympia-Tumwater, WA	68.2	10.3	0.3%	75	36
40	Lubbock, TX	66.8	12.1	8.7%	130	90
41	Saginaw, MI	66.0	11.9	3.5%	40	(1)
42	Burlington, NC	65.7	5.7	2.4%	10	(32)
43	Yuba City, CA	65.6	3.2	12.9%	27	(16)
44	Salinas, CA	65.5	13.6	14.2%	21	(23)
45	Columbus, IN	65.4	5.4	0.6%	16	(29)
46	Athens-Clarke County, GA	64.7	7.8	4.0%	111	65
47	Elkhart-Goshen, IN	64.3	10.0	2.0%	15	(32)
48	Panama City, FL	64.1	10.3	-1.9%	58	10
49	Crestview-Fort Walton Beach-Destin, FL	63.3	14.3	5.7%	138	89
50	Brownsville-Harlingen, TX	62.5	12.0	5.9%	85	35
51	Wausau, WI	62.2	5.3	1.3%	37	(14)
52	Portsmouth, NH-ME NECTA	62.2	11.8	4.7%	162	110
53	Naples-Immokalee-Marco Island, FL	62.1	14.9	0.9%	18	(35)
54	Punta Gorda, FL	60.7	4.6	2.2%	68	14
55	Taunton-Middleborough-Norton, MA NECTA Division	60.5	6.4	1.6%	140	85
56	Texarkana, TX-AR	60.1	4.4	5.6%	199	143
57	Springfield, IL	59.7	11.9	6.6%	190	133
58	Wilmington, NC	59.3	15.1	2.7%	81	23
59	Topeka, KS	58.2	12.8	-2.0%	33	(26)
60	Grants Pass, OR	57.3	2.1	0.0%	3	(57)
61	Yuma, AZ	57.0	6.7	-3.4%	177	116
62	Logan, UT-ID	56.7	6.0	7.1%	152	90
63	Ithaca, NY	55.6	3.4	0.0%	48	(15)
64	Waterloo-Cedar Falls, IA	55.4	7.7	1.3%	53	(11)
65	State College, PA	55.4	6.2	5.1%	93	28
66	Yakima, WA	55.2	4.2	16.5%	179	113
67	Kalamazoo-Portage, MI	54.6	16.5	3.3%	51	(16)
68	Bellingham, WA	54.1	7.9	-0.4%	89	21
69	Dover-Durham, NH-ME NECTA	54.1	4.0	3.5%	104	35
70	Nashua, NH-MA NECTA Division	54.0	15.1	-1.3%	126	56
71	Owensboro, KY	53.2	3.9	2.6%	160	89
72	Kahului-Wailuku-Lahaina, HI	53.1	7.2	2.9%	86	14
73	South Bend-Mishawaka, IN-MI	53.0	13.6	4.1%	69	(4)
74	Pocatello, ID	52.4	4.0	5.3%	78	4
75	Killeen-Temple, TX	52.2	9.9	4.9%	185	110
76	Muskegon, MI	52.0	3.8	0.0%	54	(22)
77	Amarillo, TX	51.9	9.6	2.1%	147	70
78	Lima, OH	51.9	4.9	2.8%	131	53
79	Bangor, ME NECTA	51.9	6.5	0.5%	52	(27)
80	Midland, TX	51.6	9.4	-7.8%	60	(20)
81	Bay City, MI	51.3	3.5	7.1%	39	(42)
82	Iowa City, IA	51.1	6.9	-2.4%	38	(44)
83	Clarksville, TN-KY	50.8	9.1	1.5%	84	1
84	Chico, CA	50.5	5.8	7.4%	70	(14)
85	Leominster-Gardner, MA NECTA	50.4	3.8	4.6%	116	31
86	Victoria, TX	50.3	2.6	0.0%	42	(44)
87	Lewiston-Auburn, ME NECTA	50.0	7.0	0.0%	108	21
88	Kingston, NY	48.5	4.6	6.9%	121	33
89	Atlantic City-Hammonton, NJ	48.4	10.0	5.6%	119	30
90	Santa Cruz-Watsonville, CA	48.3	10.0	4.9%	168	78
91	Spartanburg, SC	48.2	16.0	4.3%	90	(1)
92	Kankakee, IL	47.9	3.4	10.8%	28	(64)
93	Madera, CA	47.6	2.9	3.6%	122	29
94	Danbury, CT NECTA	46.4	9.3	3.0%	72	(22)
95	Walla Walla, WA	46.4	1.0	0.0%	164	69
96	Hagerstown-Martinsburg, MD-WV	46.1	9.4	-1.7%	100	4
97	Bloomsburg-Berwick, PA	45.6	4.2	0.0%	106	9
98	Idaho Falls, ID	44.5	12.7	9.8%	156	58
99	San Rafael, CA Metropolitan Division	43.0	19.1	5.9%	180	81
100	Barnstable Town, MA NECTA	42.9	8.6	3.6%	57	(43)
101	Brockton-Bridgewater-Easton, MA NECTA Division	42.6	7.4	11.6%	76	(25)
102	Lafayette-West Lafayette, IN	42.2	7.5	-10.7%	13	(89)
103	Vallejo-Fairfield, CA	41.0	9.8	1.4%	142	39
104	St. Cloud, MN	41.0	8.8	4.3%	183	79
105	Kennewick-Richland, WA	40.9	21.4	2.9%	175	70
106	East Stroudsburg, PA	40.6	3.5	6.1%	173	67
107	Oshkosh-Neenah, WI	40.2	11.3	0.3%	87	(20)
108	Abilene, TX	40.2	5.6	-3.5%	94	(14)
109	Morristown, TN	40.1	3.4	3.1%	98	(11)
110	Glens Falls, NY	39.8	5.8	-3.3%	114	4
111	Fort Smith, AR-OK	39.8	11.9	-0.3%	115	4
112	Kingsport-Bristol-Bristol, TN-VA	39.8	10.1	4.1%	73	(39)
113	Sebastian-Vero Beach, FL	38.9	5.1	1.3%	92	(21)
114	Cleveland, TN	38.7	8.4	-8.4%	25	(89)
115	Eau Claire, WI	38.6	9.1	1.9%	148	33
116	Niles-Benton Harbor, MI	37.9	5.6	0.0%	141	25
117	Appleton, WI	37.3	13.3	-0.7%	88	(29)
118	Albany, OR	36.7	3.3	4.2%	124	6
119	Michigan City-La Porte, IN	36.7	2.8	0.0%	36	(83)
120	Lake Havasu City-Kingman, AZ	36.0	3.5	9.4%	19	(101)
121	Monroe, LA	35.6	7.9	-0.4%	118	(3)
122	Santa Fe, NM	35.6	4.6	3.7%	187	65
123	Visalia-Porterville, CA	35.3	9.8	0.0%	178	55
124	Rapid City, SD	34.8	5.0	-0.7%	143	19
125	Elmira, NY	34.8	2.7	-1.2%	71	(54)
126	Battle Creek, MI	34.4	6.2	-0.5%	62	(64)
127	Longview, TX	34.3	8.9	-6.9%	105	(22)
128	Ocala, FL	34.3	9.3	-1.4%	41	(87)
129	Grand Forks, ND-MN	34.2	3.0	4.6%	110	(19)
130	Johnson City, TN	33.8	8.2	3.8%	103	(27)
131	Huntington-Ashland, WV-KY-OH	33.8	12.1	-4.0%	91	(40)
132	Great Falls, MT	32.5	3.1	-1.1%	186	54
133	Altoona, PA	32.2	5.5	-1.2%	96	(37)
134	Peabody-Salem-Beverly, MA NECTA Division	31.7	9.9	1.0%	155	21
135	Danville, IL	31.7	2.0	-10.4%	161	26
136	Columbus, GA-AL	31.6	13.2	3.1%	181	45
137	Fairbanks, AK	30.8	2.2	0.0%	157	20
138	Dalton, GA	30.7	6.1	2.2%	101	(37)
139	Medford, OR	30.6	7.0	-1.9%	83	(56)
140	Pittsfield, MA NECTA	30.4	3.8	0.9%	167	27
141	Greenville, NC	30.2	6.8	-2.4%	65	(76)
142	Grand Junction, CO	30.0	5.3	-5.9%	134	(8)
143	Fayetteville, NC	29.9	12.5	2.5%	188	45
144	Weirton-Steubenville, WV-OH	29.6	1.9	0.0%	137	(7)
145	Decatur, IL	28.2	3.2	6.7%	198	53
146	Cedar Rapids, IA	28.1	13.6	-3.1%	191	45
147	Terre Haute, IN	28.0	5.5	0.0%	182	35
148	Johnstown, PA	27.6	5.7	0.0%	135	(13)
149	Erie, PA	27.2	10.1	-0.3%	107	(42)
150	Tyler, TX	27.1	8.5	-3.4%	95	(55)
151	Hanford-Corcoran, CA	27.0	1.3	-2.5%	125	(26)
152	Jackson, MI	26.9	4.1	0.0%	184	32
153	Bremerton-Silverdale, WA	26.9	7.1	4.9%	169	16
154	Charleston, WV	26.7	14.4	-5.1%	158	4
155	Duluth, MN-WI	26.4	8.0	-4.7%	132	(23)
156	Lynn-Saugus-Marblehead, MA NECTA Division	26.3	2.7	-9.1%	8	(148)
157	Lowell-Billerica-Chelmsford, MA-NH NECTA Division	26.0	20.8	-2.2%	129	(28)
158	Flint, MI	26.0	15.5	-2.1%	79	(79)
159	Rocky Mount, NC	25.6	5.5	1.9%	176	17
160	Norwich-New London-Westerly, CT-RI NECTA	25.3	8.9	2.7%	146	(14)
161	Racine, WI	25.1	6.4	0.5%	150	(11)
162	Hickory-Lenoir-Morganton, NC	24.8	13.2	1.3%	24	(138)
163	Lawrence-Methuen Town-Salem, MA-NH NECTA Division	23.4	10.1	-2.3%	174	11
164	Las Cruces, NM	23.4	7.0	-3.2%	166	2
165	San Angelo, TX	23.3	3.8	-0.9%	128	(37)
166	El Centro, CA	23.1	2.2	-5.7%	194	28
167	Sheboygan, WI	23.0	4.3	0.8%	144	(23)
168	Waterbury, CT NECTA	22.8	5.2	-3.7%	67	(101)
169	Lawrence, KS	22.7	5.2	-12.4%	47	(122)
170	Florence-Muscle Shoals, AL	22.4	4.0	0.0%	172	2
171	Watertown-Fort Drum, NY	22.1	2.2	3.2%	196	25
172	Sherman-Denison, TX	22.0	2.9	-7.4%	12	(160)
173	Odessa, TX	21.3	4.2	-9.3%	30	(143)
174	Rochester, MN	21.3	5.5	-2.9%	120	(54)
175	Missoula, MT	21.2	6.2	-3.6%	136	(39)
176	Vineland-Bridgeton, NJ	20.4	3.6	1.9%	201	25
177	Champaign-Urbana, IL	20.1	7.8	-1.3%	139	(38)
178	Decatur, AL	19.2	5.4	-5.8%	149	(29)
179	Casper, WY	19.2	2.9	-3.4%	123	(56)
180	Cheyenne, WY	18.2	3.2	-3.1%	80	(100)
181	Dothan, AL	17.8	4.3	-0.8%	97	(84)
182	Mansfield, OH	17.0	5.1	-1.9%	102	(80)
183	Lewiston, ID-WA	16.8	1.3	-2.6%	197	14
184	Binghamton, NY	16.8	9.2	-4.2%	112	(72)
185	Coeur d’Alene, ID	16.7	6.0	-5.3%	109	(76)
186	Dutchess County-Putnam County, NY Metropolitan Division	16.1	11.5	0.0%	189	3
187	Utica-Rome, NY	16.1	8.2	-0.8%	200	13
188	Lynchburg, VA	16.1	11.9	-1.1%	154	(34)
189	Sioux City, IA-NE-SD	16.0	7.8	-2.1%	193	4
190	Carson City, NV	11.6	1.9	-3.4%	117	(73)
191	Wichita Falls, TX	11.6	3.7	-2.7%	170	(21)
192	La Crosse-Onalaska, WI-MN	11.0	6.0	-3.7%	165	(27)
193	Bloomington, IL	10.4	9.7	-3.3%	195	2
194	Bloomington, IN	10.2	4.4	-5.1%	203	9
195	Sierra Vista-Douglas, AZ	10.2	3.8	-10.9%	153	(42)
196	Billings, MT	10.0	8.2	-6.5%	171	(25)
197	Merced, CA	8.1	3.5	-2.8%	192	(5)
198	Anniston-Oxford-Jacksonville, AL	7.0	4.4	-3.6%	202	4

July 21, 2016

So You Want a Revolution

You say you want a revolution
Well you know
We’d all want to change the world.____ The Beatles (1968)

Apparently not. Not any more. Not everyone wants to change the world. To the Beatles in 1968, when young people aged less than 30 added up to 52% of the US population, it might have looked like everyone wanted a revolution and that a nascent movement had a deep reserve of younger cohorts ready to push for change. But the percentage of the population aged less than 30 today is only 39% and falling. If 39% vs. 52% does not look like a big difference, consider that 13% of the US population is equivalent to 42 million additional young people who would be among us, if the percentage was the same as in 1968. A quarter to a third (10 to 14 million) would be in their 20s.

At the same time, because older more conservative generations would weigh less in the total population mix, their moderating influence would be less effective at deterring the young. This shift in the age distribution of the population explains why the youth revolt gained traction in 1968 but more recent attempts such as Occupy Wall Street turned to farce and fizzled out.

Meanwhile the over-45 age bracket now accounts for 41% of the US population (vs. 31% in 1968), its highest level ever and a level that explains the elevation of the two oldest presidential nominees in US history, Hillary Clinton and Donald Trump. It also helps explain why the nostalgia-powered Trump is still a contender while the youth-oriented Bernie Sanders has withdrawn. At this stage of the process in 1970, Sanders would have been the nominee while Clinton and Trump would have already left the scene.

Speaking of revolutions, a recent op-ed in the Wall Street Journal draws an analogy between Iran in 1979 and Turkey today in the immediate aftermath of the aborted Turkish coup d’etat. Writes the author:

Revolutions don’t require majorities, but rather angry and excited minorities that are willing to act violently to take power.

Undoubtedly true, but they also require a critical mass of young people combined with fairly dismal economic conditions which Turkey does not have now to the same extent as Iran in 1979. In 1979 in Iran, the under 30 accounted for a huge 71% of the population and Iranian GDP per capita on a PPP basis was about $2,000 (in 2013 dollars). By contrast, in Turkey today, the under 30 are only 50% and GDP per capita is in excess of $10,000. That is enough young people to shake things up as the young did in the West in 1968 but probably not enough to impose a lasting change as the young did in Iran in 1979.

A general hypothesis therefore is that the danger of civil unrest grows when per capita GDP is low and the population is young. Looking at successful uprisings in Algeria (1962), China (1949), Cuba (1952) and Iran (1979), we note that the under 30 numbered more than 60% in every case. Meanwhile revolts failed in Hungary (1956) and Czechoslovakia (1968) where the under 30 were less than 50% of total population. Of course, this is not a comprehensive list and there may be examples that refute the hypothesis. In addition, foreign interference as in Hungary and Czechoslovakia renders the age distribution less relevant to the outcome of a revolt. But it is a fair bet that a larger young population in a lower-income country heightens the risk of unrest.

The graph below shows for each country the per capita GDP in 2014 dollars and the percentage of people aged less than 30. The US is shown in red. The cutoff levels are set at $5000 for GDP per capita and at 60% for population aged under 30. Countries in the upper left quadrant are wealthier and have fewer young people and are as a result at lower risk of civil unrest. Countries in the lower right are younger and poorer and have in theory a higher risk of civil unrest. Iran in 1979 was clearly in the lower right high-risk quadrant. Turkey today is in the upper left lower-risk quadrant.

Readers of this site may be familiar with this graph from a previous post discussing the relationship of fertility and national income. It is worth revisiting the earlier post to understand why some countries are outliers on the graph.

So what are the countries that fall in the lower right quadrant? These countries have an under 30 population of 60% or more of total, and a GDP per capita of $5,000 or less. Here is the list.

At the other extreme, if we look at Brexit and the nomination of Donald Trump as examples of a new form of revolt that we may call ‘older age populism’, here are the countries that are exposed to it, using as cutoffs $20,000 for GDP per capita and 40% for population aged less than 30. Not surprisingly, most of these countries are part of the West and most enjoyed a significant demographic dividend in the three decades 1975-2005.

Of course, most revolutions end badly, and many end very badly. On the revolution train, idealists sit in the front and present in the early days the benign and seductive case for change. Radicals bide their time while sitting in the back and later take over with their nefarious plans. The Beatles knew it:

But when you talk about destruction
Don’t you know that you can count me out

Full lyrics here.

Read more about why Occupy Wall Street failed.

Sami Karam is the founder and editor of populyst.net and the creator of the populyst index™. populyst is about innovation, demography and society. Before populyst, he was the founder and manager of the Seven Global funds and a fund manager at leading asset managers in Boston and New York. In addition to a finance MBA from the Wharton School, he holds a Master’s in Civil Engineering from Cornell and a Bachelor of Architecture from UT Austin.

July 21, 2016