Upcoming Events: August 5-8 - Logistics Development Forum

Event Date: August 5-8, 2014

Location: Stein Eriksen Lodge 7700 Stein Way Park City, UT 84060

The Logistics Development Forum addresses the importance of logistics in a corporation’s decision for a new manufacturing distribution and/or warehousing location. Through educational sessions and one-on-one meetings with Industry experts and extensive networking activities, attendees will have the opportunity to share ideas and learn how to become more competitive in the logistics supply chain market.

Conference Website: Logistics Development Forum

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Don't Mess with Texas - Federal Contract Spending Cuts and the Texas Economy

Today Chmura released the second in a series of white papers examining the reliance of states on federal contract spending. Download the full report.

Firms in Texas received $39.0 billion in federal contract awards in fiscal year (FY) 2013 in the United States[1]—more than all other states except Virginia ($51.2 billion) and California ($47.6 billion).[2] The Lone Star State boasts plentiful natural resources and an advanced industrial sector which are two of the reasons it is a large recipient of federal spending. Major metropolitan statistical areas such as Dallas, Houston, and San Antonio rely on this spending to support economic growth, particularly since the slow recovery from the Great Recession of 2007 to 2009.

Based on the latest data from the General Service Administration (GSA), $404.4 billion of federal contract spending went to firms in Texas from fiscal year FY 2003 through FY 2013. In FY 2013, the state received $39.0 billion, with over 80% of all federal contract awards coming from the Department of Defense (DoD).

Given the dependence of the Texas economy on federal government spending, it is important for federal, state, and local Texas representatives and citizens to understand the potential consequence for the economy during this period of defense downsizing. To learn more about the role federal procurement spending plays in the state of Texas as well as its impact by region and industry sector, download our full white paper here.

Federal Contract Spending in Texas


[1] Source: USASpending website, available at http://www.usaspending.gov/state-summary-tabular?tab=By+Location&contracts=Y&tabletype=statesummary.

[2] Federal government contracts are payments for goods and services rendered by the private sector. In this report, “federal contract spending,” “federal procurement spending,” and “federal contract awards” are interchangeable.

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Economic Impact: College students carrying high student loan debt and not buying homes

The national labor market continues to slowly improve, but so many people with work experience are still seeking jobs.

This is making it another tough summer for high school and college students who want to work as well as new college graduates entering the workforce.

Making matters worse, the mounting debt that college graduates are carrying is likely playing a role in the sluggish housing market.

Even though the national unemployment rate dipped to 6.1 percent last month compared with 7.5 percent for the same month a year ago, the Labor Department’s rate that accounts for people who have stopped looking for work or cannot find full-time jobs is almost double at 12.1 percent.

And while the number of people jobless for 27 weeks or longer fell to 3.1 million in June, it still made up 32.8 percent of the unemployed.

With so many experienced workers to choose from, it’s not surprising that the unemployment rate for 16- and 17-year-olds stood at 23.3 percent in June and was 19.3 percent for 18- and 19-year-olds, according to the Labor Department.

A quick review of openings on job posting sites for the Richmond area show students have plenty of job possibilities as receptionists, office clerks, cashiers and customer service representatives. One problem they may face, however, is that employers need someone year round rather than just during the summer.

College graduates who are looking for full-time employment might be having an easier time finding jobs based on the jobless rate for 20- to 24-year-olds that stood at 10.5 percent in June.

As usual, employability varies greatly based on skill sets. There are hundreds of job openings for registered nurses in the Richmond metro area, but only three postings for photographers.

The sluggish job market along with the debt college students have incurred is one factor contributing to slow growth in the housing market.

Based on data from the Federal Reserve Bank of New York, 43 percent of 25-year-olds had student debt in 2012 compared with 25 percent in 2003. The average debt carried by 25-year-olds was $20,326 in 2012, nearly double from the $10,649 in debt in 2003.

The average student loan per borrower for all ages in Virginia was $26,310 in 2012, compared with an average of $24,810 across the nation, according to the New York Fed.

Some troubling trends become apparent when we consider the percentage of borrowers with student loan debt who have home mortgage debt at age 30, which historically has been about the median age for first-time home buyers, according to the National Association of Realtors.

In 2003, for instance, a higher percentage of 30-year-olds with college debt were able to have a home mortgage as well a student loan, largely because the average student loan debt was much smaller than it is today.

This is not surprising because those who were college-educated in 2003 earned higher incomes on average than those without a college education, and student loan debt was low enough so that it did not deter a home purchase then.

But this past recession was a tipping point for the traditional 30-year-old first-time home buyer.

Educated consumers with student loan debt as well as buyers without student loans saw home ownership rates fall, but they fell more dramatically for those with a student loan. In 2013, 30-year-olds without student loans — presumably not college educated — were more likely to have purchased a home with a mortgage.

These trends suggest that for some individuals who are about 30 years old, the single biggest lifetime investment is not a home but an education.

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Growing Student Loan Debt and Its Impact on Housing

A recent report by the Brookings Institution has stirred up the debate about whether there is a looming student loan crisis. But there is no question that with a growing number of people in college and rising tuition costs, more and more students are taking on loans and face years of paying down that debt— whether they successfully graduate or not.

To illustrate a simple measure of the growing debt from student loans, Chmura built the maps below from Federal Reserve Bank of New York (“FRBNY”) Consumer Credit Panel data to show the balance of student loan debt per capita in each state between 1999 and 2012. 

Map - Student Loan Debt Balance Per Capita

To use Virginia as an example, student loans stood at $580 per capita in 1999; by 2012 that had increased 597% to $4,040 (note that the data are not adjusted for inflation). The average student loan per borrower in Virginia was $26,310 in 2012, compared with an average of $24,810 across the United States. As for fears about graduates not being able to pay off their debt, the percentage of student loan debt balance 90+ days delinquent in Virginia rose 2.79 percentage points between 2011 and 2012, but was still comparable to historic levels.  It is important to note in interpreting this data, though, that the percentage of delinquent loans may be similar to that of a decade ago, but with increasing enrollments a lot more people in the state are having difficulty paying off their student loans. In addition, many loans have six-month grace periods before requiring payments, and there are a variety of options to postpone or decrease payments that graduates who are unemployed or low-income can use to prevent default.

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So is this additional debt impacting any other financial decisions, especially among young households? Analysis released by the FRBNY using the same data series suggests a shift in homeownership among student borrowers age 30 and younger:

“Prior to the most recent recession, homeownership rates were substantially higher for thirty-year-olds with a history of student debt than for those without. This pre-recession pattern is typically explained by the fact that student debt holders have higher levels of education on average, and hence, higher income potential. Simply put, these more educated, often higher-earning, consumers were more likely to buy homes by the age of thirty.

 However, the recession brought a sudden reversal in this relationship. As house prices fell, homeownership rates declined for all types of borrowers, and declined most for those thirty-year-olds with histories of student loan debt. In last year’s blog, we reported that 2012 was the first time in at least ten years that thirty-year-olds with no history of student loans were actually more likely to have home-secured debt than those with a history of student loans… student loan holders were still less likely to invest in houses than nonholders in 2013, despite the marked improvements in the aggregate housing market.” (Emphasis added)

 

 

Chart - Proportion with Home-Secured Debt at Age 30

 

The data from FRBNY and Brookings Institute suggest that a crisis of young borrowers unable to pay off their student loans may not be a concern in the near future, but with over 40% of young adults (25 years old) carrying student debt, the rise in student debt over the past decade is likely preventing those households from contributing to a recovery in the housing market today.

 

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Virginia’s employment growth continues to lag

Virginia’s employment growth continues to lag the nation and it should remain subpar at least through next year.

The reason: federal spending cuts.

Stephen Fuller, an economist at George Mason University, recently pointed to a potential 4.5 percent decline in federal spending across the nation during the fiscal year that will end Sept. 30 and a further 9.2 percent decrease in the following fiscal year.

If Virginia businesses experience the same percentage decline in federal contract spending, Chmura Economics & Analytics estimates that could translate into a direct loss of 11,300 jobs in Virginia during calendar year 2014 and 21,600 jobs in 2015.

Based on those forecasts that would mean employment would grow 0.5 percent in Virginia in 2014 and 0.7 percent in 2015 — much lower than the national growth rate of 1.6 percent forecasted for 2014 and 1.1 percent in 2015.

The Richmond region should fare better. Employment in the area is forecast to grow at a faster rate of 1.4 percent in 2014 and 1.6 percent in 2015.

In the Richmond metro area, only 1.1 percent of its employment is dependent on defense contract awards compared to 11.3 percent in Northern Virginia, according to a model created by Chmura Economics & Analytics (www.chmuraecon.com/dodimpact).

Northern Virginia is bearing most of the brunt of the cuts in federal spending.

Employment grew 0.1 percent during the 12 months that ended in April and the region is forecast to see a 0.3 percent growth for all of 2014 and 0.5 percent in 2015.

Employment growth in the nation has been hovering around 1.7 percent on a year-over-year basis since January 2012 through April 2014, the latest data available.

By contrast, employment growth in Virginia decelerated over the same period. It grew 1.3 percent for much of 2011 but fell 0.1 percent two months ago April 2014 compared with April 2013.

The professional and business services sector, which is dependent on federal contracting, continues to show the largest losses with a contraction of 18,300 jobs during the 12-months that ended in April.

To put that into context, Virginia expanded by 29,000 jobs in 2012 and 43,000 in 2013. But before the Great Recession, Virginia added an average 67,000 jobs a year from 2004 through 2007.

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Upcoming Events: Jun 4 - Strategic Diversification in the Wake of Defense Cuts

Event Date: June 4, 2014
Location: Crystal Gateway Marriott, 1700 Jefferson Davis Highway Arlington, VA 22202

From this session you will learn how to use data about your labor market for workforce and economic development planning.  The panel will discuss the types of defense impacts occurring in the Oshkosh region and how the local communities are organizing, responding, and positioning for strategic transformation by leverage their assets. From the education pipeline to the dynamic labor shed, learn how to think about, act upon, and plan effectively in the intersection of labor and industry diversification. 

Moderator:

  • Dr. Chris Chmura, President and Chief Economist, Chmura Economics & Analytics 

Speakers:

  • Katherine Ahlquist, Economic Development Planner, East Central Wisconsin Regional Planning Commission
  • Leslie Peterson, Chief Operations Officer, Chmura Economics and Analytics 

Conference Website: Association of Defense Communities

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Virginia Department of Defense (DoD) Procurement Economic Impact Evaluation Model

Dr. Christine Chmura, Chmura Economics & Analytics, and Dr. Stephen Fuller, George Mason University, demonstrated the Virginia Department of Defense (DoD) Procurement Economic Impact Evaluation Model to the Virginia Deputy Secretary of Veterans and Defense Affairs and other public officials on May 20, 2014.  The evaluation model is made up of a supply-chain mapping of DoD contract awards, including sales and employment impacts from product service codes to industries and occupations.  The model, available for public use at www.chmuraecon.com/DoDimpact, is designed to provide clear and meaningful state, county, and metropolitan statistical area (MSA) level details about the current and projected economic impacts of DoD contract spending.

The Virginia DoD Procurement Economic Impact Evaluation Model was developed by Chmura Economics & Analytics under the direction of the George Mason University Center for Regional Analysis.   Community and economic development leaders can use insights from the model to:

  • Focus economic development resources on at-risk industries and occupations.
  • Direct business retention efforts to firms likely to be affected by changes in spending patterns.
  • Provide valuable inputs to workforce organizations to identify or create programs to help unemployed workers.
  • Tabulate impact data to support applications for federal or state assistance programs.

To view the website, go to www.chmuraecon.com/DoDimpact. Please note you will be asked to provide your name, email address, and company affiliation (optional) the first time you access the site.

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The Reliance of the California Economy on Federal Contract Spending

California, the largest state economy in the nation, benefits from billions of dollars of federal contract spending each year. Chmura Economics & Analytics estimates that 1.8% or 280,364 of California’s jobs were directly supported by federal contract spending in fiscal year (FY) 2013. Metropolitan areas such as San Diego and industries such as manufacturing and professional and business services have a higher level of dependency on federal contract spending. Leaders in those regions and industries need to be prepared for future reductions in the federal budget.

Based on the latest data from the General Service Administration, total federal contract spending in California was $632.2 billion from FY 2000 to FY 2014—the largest amount for all states.1 Among all federal agencies, the Department of Defense (DOD) plays an oversized role in federal contract spending in California—in FY 2013, DOD contract awards amounted to $34.0 billion or 71.4% of all federal contract spending in the state.

State and community leaders of California need to understand the role of federal procurement spending in the state economy to prepare for future budget cuts. Since 2011, the threat of drastic cuts in the federal budget has surfaced several times, from the debt ceiling crisis in mid-2011 to the Budget Control Act of 2011, which gave rise to the “sequestration” cuts in 2013. The government shutdown in October 2013 further exacerbated the risk of budget uncertainty. To learn more about the role federal procurement spending plays in the state of California as well as its impact by region and industry sector, download our full white paper here: Whitepaper: The Reliance of the California Economy on Federal Contract Spending.

 

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1 Federal government contract awards represent awards granted to the private sector. Here we treat the phrases “federal contract spending,” “federal procurement spending,” and “federal contract awards” as interchangeable.

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Economic Impact: Richmond region’s job growth is better than state and nation

The Metro Richmond’s economy was hit harder during the Great Recession than the state and nation, in part, because of its dependence on finance and insurance industries. Employment growth in Richmond is now outpacing all of the metropolitan areas in the state and is growing slightly faster than the nation on a year-over-year basis.

The latest data for March shows Richmond employment is 1.8% above a year ago compared with 0.0% in Virginia and 1.7% in the nation.

As of November 2013, employment in Richmond passed the peak employment level of 635,541 that was reached prior to the last recession.  Since then, it has exceeded that peak by 8,599 jobs.

The nation will likely reach its previous peak this summer.  With Virginia job growth stalling mainly in Northern Virginia under the weight of Department of Defense cuts in spending; it may not reach its previous peak until 2015.

Of the ten major industry sectors that economists classify firms into, the information sector grew the fastest in the Richmond metro area.  Employment is up 8.8% from a year ago, but it is a small sector of only 8,700 workers. That growth translates into an additional 700 jobs over the last year. 

This sector includes firms that provide information such as newspapers, software publishers, sound recording studios, and satellite communications. According to the latest information from the fourth quarter of 2013, the average wages of this sector are $60,034.  That’s better than the average $46,962 for all industries in the metro area. 

The higher the wages of new jobs, the more it creates a ripple effect in the economy.  That is because those workers have more money to spend on goods and services in the area.

The retail sector is undoubtedly benefitting from the faster employment growth in Richmond.  In fact, it experienced the largest increase in jobs over the last twelve months ending with March by adding 3,900 jobs. 

Health and education services sector followed with 2,900 additional jobs. And, with the third largest increase of 1,400 jobs in the last twelve months, the finance, insurance, and real estate sector continues to recover.

The 11,600 gain in employment in Richmond over the last year occurred across most major industry sectors.  Such broad-based growth points to a healthy economy.  Clearly a lack of dependence on federal government spending is now favoring the RVA economy.

Christine Chmura is president and chief economist at Chmura Economics & Analytics. She can be reached at (804) 649-3640 or receive e-mail at chris@chmuraecon.com.

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Virginia Employment Growth Stuck In Neutral

This article was originally published in the Richmond TImes-Dispatch on April 2, 2014.

Employment in the nation is finally starting to pick up. Nonfarm payrolls rose by 192,000 in March, which is 1.7 percent higher than a year ago. The Richmond metropolitan area is seeing similar growth. Employment rose 1.5 percent over the year ending in February, which is the latest data available at the regional level.

In contrast, employment growth in Virginia has stalled. Employment declined in seven of the past 12 months. In fact, Virginia’s employment in February 2014 was 3.767 million — slightly below the February 2013 level of 3.769 million. This is unusual. Over the past two decades, Virginia’s employment growth typically outpaced that of the nation.

Even more unusual is the sector that is leading the decline. The professional and business services sector, which grew at a 2.2 percent annual average rate in the 10 years ending in 2013, contracted 3 percent in the year ending in February 2014.

The Northern Virginia metro area, which has been the engine of growth in the commonwealth, appears to have run out of steam. Employment fell 0.2 percent for the year ending in February; and similar to the state, the professional and business services sector contracted 2.5 percent over the same period.

Why is this happening? Chalk it up to cuts in defense spending.

There is no industry sector called “defense” because businesses that receive defense contracts range from manufacturers to retailers and service providers. However, almost half of the defense contracts awarded to firms in Virginia during the past two fiscal years were for professional and business services.

Defense contracts awarded to firms in Virginia more than doubled from $20 billion in fiscal year 2003 to a peak of $43.1 billion in fiscal year 2011. Over that period, Virginia’s economy became more dependent on defense contract awards as they made up 6.5 percent of gross state product in 2003 and rose to 9.9 percent of GSP in 2011. Contract awards to Virginia firms have since dropped to $33.5 billion in fiscal year 2013.

It stands to reason that just as Virginia benefited from the surge in defense spending, the drawdown will now dampen its growth.

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SleepBetter Lost-Hour Economic Index

These are the findings of Chmura Economics & Analytics in a study entitled “Estimating the Economic Loss of Daylight Saving Time for U.S. Metropolitan Statistical Areas” commissioned by the Carpenter Co. The study focused on only the aspects of economic losses where solid evidence from peer-reviewed academic journals could be obtained, showing how the DST change can lead to an increase in heart attacks, workplace injuries in the mining and construction sectors, and increased cyberloafing that reduces productivity for people who typically work in offices. A reasonable economic cost was then developed from the economic costs of heart attacks, workplace accidents and cyberloafing and applied to the more than 300 Metropolitan Statistical Areas (MSA) in the U.S.


MSATotal Cost of
Lost Hour
Population
in 2010
Index RankPer Capita
Cost
% Difference
From Nat'l Avg.
MSA Rank
National $433,982,548.00 260,392,304   $1.65449    
Morgantown, WV $445,685.00 129,709 1 $3.37947 104.26% 293
Huntington-Ashland, WV-KY-OH $930,759.00 287,702 2 $3.18188 92.32% 162
Parkersburg-Marietta, WV-OH $519,472.00 162,056 3 $3.15273 90.56% 245
Charleston, WV $973,594.00 304,284 4 $3.14694 90.21% 156
Kingsport-Bristol-Bristol, TN-VA $925,487.00 309,544 5 $2.94061 77.74% 151
Lakeland, FL $1,582,213.00 602,095 6 $2.58458 56.22% 87
Tampa-St. Petersburg-Clearwater, FL $7,283,123.00 2,783,243 7 $2.57369 55.56% 18
Ocala, FL $863,182.00 331,298 8 $2.56256 54.89% 147
North Port-Bradenton-Sarasota, FL $1,822,027.00 702,281 9 $2.55172 54.23% 73
Punta Gorda, FL $404,984.00 159,978 10 $2.48982 50.49% 247
Scranton--Wilkes-Barre, PA $1,412,054.00 563,631 11 $2.46403 48.93% 90
Myrtle Beach-Conway-North Myrtle Beach, SC $662,576.00 269,291 12 $2.41994 46.27% 168
Pittsburgh, PA $5,794,723.00 2,356,285 13 $2.41877 46.19% 22
Palm Bay-Melbourne-Titusville, FL $1,336,302.00 543,376 14 $2.41877 46.19% 96
Evansville, IN-KY $873,111.00 358,676 15 $2.39418 44.71% 142
Tulsa, OK $2,277,053.00 937,478 16 $2.38892 44.39% 54
Sebastian-Vero Beach, FL $334,825.00 138,028 17 $2.38584 44.20% 283
Bloomington, IN $464,931.00 192,714 18 $2.37282 43.42% 218
Chattanooga, TN-GA $1,268,241.00 528,143 19 $2.36178 42.75% 98
Deltona-Daytona Beach-Ormond Beach, FL $1,187,424.00 494,593 20 $2.36128 42.72% 103
Bangor, ME $368,824.00 153,923 21 $2.35671 42.44% 259
Alexandria, LA $367,169.00 153,922 22 $2.34615 41.81% 257
Palm Coast, FL   $227,338.00 95,696 23 $2.33651 41.22% 347
Muncie, IN $279,430.00 117,671 24 $2.33557 41.17% 317
Crestview-Fort Walton Beach-Destin, FL $428,270.00 180,822 25 $2.32946 40.80% 224
Cape Coral-Fort Myers, FL $1,455,015.00 618,754 26 $2.31281 39.79% 84
Mobile, AL $967,710.00 412,992 27 $2.30459 39.29% 126
Jacksonville, FL $3,146,585.00 1,345,596 28 $2.29993 39.01% 40
Toledo, OH $1,523,209.00 651,429 29 $2.29976 39.00% 82
Lafayette, IN $471,375.00 201,789 30 $2.29752 38.87% 210
Canton-Massillon, OH $943,763.00 404,422 31 $2.29519 38.72% 128
Johnson City, TN $461,978.00 198,716 32 $2.28654 38.20% 214
Dayton, OH $1,952,542.00 841,502 33 $2.28210 37.93% 62
Knoxville, TN $1,617,373.00 698,030 34 $2.27890 37.74% 74
Pensacola-Ferry Pass-Brent, FL $1,037,154.00 448,991 35 $2.27193 37.32% 110
Terre Haute, IN $394,960.00 172,425 36 $2.25291 36.17% 233
Miami-Fort Lauderdale-Miami Beach, FL $12,725,469.00 5,564,635 37 $2.24919 35.95% 8
Port St. Lucie-Fort Pierce, FL $969,275.00 424,107 38 $2.24782 35.86% 117
State College, PA $351,910.00 153,990 39 $2.24765 35.85% 256
Asheville, NC $969,459.00 424,858 40 $2.24427 35.65% 116
Lewiston-Auburn, ME $243,661.00 107,702 41 $2.22512 34.49% 334
Lexington-Fayette, KY $1,066,009.00 472,099 42 $2.22084 34.23% 106
Jackson, TN $260,264.00 115,425 43 $2.21771 34.04% 321
Michigan City-La Porte, IN $251,036.00 111,467 44 $2.21503 33.88% 329
Cheyenne, WY $206,604.00 91,738 45 $2.21503 33.88% 354
Naples-Marco Island, FL $723,778.00 321,520 46 $2.21405 33.82% 149
Bowling Green, KY $282,675.00 125,953 47 $2.20733 33.42% 302
Kokomo, IN $221,462.00 98,688 48 $2.20711 33.40% 345
South Bend-Mishawaka, IN-MI $715,354.00 319,224 49 $2.20402 33.21% 150
Hickory-Lenoir-Morganton, NC $818,612.00 365,497 50 $2.20285 33.14% 141
Anderson, IN $294,790.00 131,636 51 $2.20256 33.13% 295
Atlantic City, NJ $612,213.00 274,549 52 $2.19317 32.56% 169
Columbus, IN $170,968.00 76,794 53 $2.18967 32.35% 362
Johnstown, PA $318,637.00 143,679 54 $2.18119 31.83% 275
Harrisburg-Carlisle, PA $1,215,731.00 549,475 55 $2.17610 31.53% 94
Lawton, OK $274,358.00 124,098 56 $2.17442 31.43% 307
Louisville, KY-IN $2,826,250.00 1,283,566 57 $2.16562 30.89% 42
Morristown, TN $300,143.00 136,608 58 $2.16094 30.61% 286
Wheeling, WV-OH $323,960.00 147,950 59 $2.15361 30.17% 268
Wichita Falls, TX $331,130.00 151,306 60 $2.15244 30.10% 266
Youngstown-Warren-Boardman, OH-PA $1,236,309.00 565,773 61 $2.14919 29.90% 91
Owensboro, KY $249,981.00 114,752 62 $2.14258 29.50% 322
Shreveport-Bossier City, LA $868,062.00 398,604 63 $2.14190 29.46% 129
Gainesville, FL $574,037.00 264,275 64 $2.13636 29.13% 173
Altoona, PA $275,914.00 127,089 65 $2.13528 29.06% 305
Williamsport, PA $251,895.00 116,111 66 $2.13372 28.97% 319
Panama City-Lynn Haven, FL $364,847.00 168,852 67 $2.12517 28.45% 236
Detroit-Warren-Livonia, MI $9,268,747.00 4,296,250 68 $2.12188 28.25% 13
Birmingham, AL $2,431,810.00 1,128,047 69 $2.12028 28.15% 50
Elizabethtown, KY $257,362.00 119,736 70 $2.11403 27.78% 313
Buffalo-Niagara Falls, NY $2,440,468.00 1,135,509 71 $2.11384 27.76% 49
Ocean City, NJ $207,878.00 97,265 72 $2.10205 27.05% 351
Fort Wayne, IN $888,743.00 416,257 73 $2.09993 26.92% 122
Steubenville-Weirton, OH-WV $265,544.00 124,454 74 $2.09854 26.84% 311
Huntsville, AL $890,913.00 417,593 75 $2.09832 26.83% 119
Erie, PA $598,252.00 280,566 76 $2.09720 26.76% 164
Montgomery, AL $798,346.00 374,536 77 $2.09646 26.71% 136
Gulfport-Biloxi, MS $528,298.00 248,820 78 $2.08825 26.22% 185
Carson City, NV $116,608.00 55,274 79 $2.07490 25.41% 366
Anniston, AL $250,003.00 118,572 80 $2.07373 25.34% 316
Mansfield, OH $261,914.00 124,475 81 $2.06951 25.08% 310
Sandusky, OH $161,894.00 77,079 82 $2.06578 24.86% 363
Elkhart-Goshen, IN $414,780.00 197,559 83 $2.06496 24.81% 216
Florence, AL $308,807.00 147,137 84 $2.06422 24.76% 206
Auburn-Opelika, AL $293,975.00 140,247 85 $2.06161 24.61% 276
Lafayette, LA $573,540.00 273,738 86 $2.06072 24.55% 167
Lima, OH $221,940.00 106,331 87 $2.05289 24.08% 336
Clarksville, TN-KY $571,732.00 273,949 88 $2.05264 24.07% 166
Lebanon, PA $278,565.00 133,568 89 $2.05123 23.98% 292
Reading, PA $857,038.00 411,442 90 $2.04871 23.83% 125
Gadsden, AL $217,236.00 104,430 91 $2.04596 23.66% 338
York-Hanover, PA $904,776.00 434,972 92 $2.04583 23.65% 113
Barnstable Town, MA $447,310.00 215,888 93 $2.03784 23.17% 198
Ann Arbor, MI $713,184.00 344,791 94 $2.03440 22.96% 146
Springfield, OH $285,322.00 138,333 95 $2.02862 22.61% 285
Wilmington, NC $747,299.00 362,315 96 $2.02861 22.61% 139
Dothan, AL $299,824.00 145,639 97 $2.02478 22.38% 270
Monroe, LA $362,816.00 176,441 98 $2.02244 22.24% 229
Hattiesburg, MS $293,083.00 142,842 99 $2.01802 21.97% 272
Lancaster, PA $1,064,016.00 519,445 100 $2.01465 21.77% 99
Decatur, AL $313,503.00 153,829 101 $2.00444 21.15% 258
Oklahoma City, OK $2,550,073.00 1,252,987 102 $2.00169 20.99% 43
Pascagoula, MS $330,101.00 162,246 103 $2.00107 20.95% 242
Hagerstown-Martinsburg, MD-WV $546,398.00 269,140 104 $1.99674 20.69% 172
Casper, WY $153,138.00 75,450 105 $1.99624 20.66% 364
Greenville, SC $1,289,377.00 636,986 106 $1.99086 20.33% 83
Greensboro-High Point, NC $1,464,797.00 723,801 107 $1.99044 20.31% 71
New Orleans-Metairie-Kenner, LA $2,360,696.00 1,167,764 108 $1.98827 20.17% 46
Las Vegas-Paradise, NV $3,941,238.00 1,951,269 109 $1.98658 20.07% 30
Indianapolis, IN $3,537,009.00 1,756,241 110 $1.98081 19.72% 35
Missoula, MT $219,657.00 109,299 111 $1.97660 19.47% 332
Ithaca, NY $203,399.00 101,564 112 $1.96970 19.05% 342
Dover, DE $324,756.00 162,310 113 $1.96789 18.94% 241
Cleveland-Elyria-Mentor, OH $4,151,800.00 2,077,240 114 $1.96580 18.82% 28
Allentown-Bethlehem-Easton, PA-NJ $1,614,902.00 821,173 115 $1.93420 16.91% 64
Prescott, AZ $414,603.00 211,033 116 $1.93229 16.79% 203
Providence-New Bedford-Fall River, RI-MA $3,141,889.00 1,600,852 117 $1.93032 16.67% 38
Worcester, MA $1,565,876.00 798,552 118 $1.92861 16.57% 67
Portland-South Portland-Biddeford, ME $1,005,605.00 514,098 119 $1.92385 16.28% 101
Billings, MT $308,857.00 158,050 120 $1.92200 16.17% 249
Springfield, MA $1,353,410.00 692,942 121 $1.92098 16.11% 77
Ames, IA $174,611.00 89,542 122 $1.91794 15.92% 356
Fayetteville, NC $713,043.00 366,383 123 $1.91413 15.69% 137
Fort Smith, AR-OK $580,963.00 298,592 124 $1.91364 15.66% 158
Houma-Bayou Cane-Thibodaux, LA $403,825.00 208,178 125 $1.90787 15.31% 205
Rapid City, SD $245,126.00 126,382 126 $1.90763 15.30% 300
Lewiston, ID-WA $118,048.00 60,888 127 $1.90686 15.25% 365
Lake Charles, LA $386,936.00 199,607 128 $1.90657 15.24% 213
Akron, OH $1,359,432.00 703,200 129 $1.90138 14.92% 75
Baton Rouge, LA $1,550,929.00 802,484 130 $1.90084 14.89% 66
Jackson, MS $1,040,664.00 539,057 131 $1.89874 14.76% 95
Cleveland, TN $223,348.00 115,788 132 $1.89718 14.67% 318
Iowa City, IA $293,659.00 152,586 133 $1.89286 14.41% 255
Richmond, VA $2,419,992.00 1,258,251 134 $1.89163 14.33% 44
Baltimore-Towson, MD $5,209,507.00 2,710,489 135 $1.89034 14.26% 20
Blacksburg, VA $312,735.00 162,958 136 $1.88752 14.08% 244
Orlando, FL $4,092,620.00 2,134,411 137 $1.88588 13.99% 26
Great Falls, MT $155,815.00 81,327 138 $1.88437 13.89% 359
Charlottesville, VA $384,484.00 201,559 139 $1.87614 13.40% 208
Farmington, NM $247,716.00 130,044 140 $1.87350 13.24% 301
Winston-Salem, NC $909,101.00 477,717 141 $1.87168 13.13% 105
Greenville, NC $359,766.00 189,510 142 $1.86714 12.85% 220
Pittsfield, MA $248,995.00 131,219 143 $1.86631 12.80% 296
Sioux City, IA-NE-SD $271,947.00 143,577 144 $1.86290 12.60% 273
Reno-Sparks, NV $805,515.00 425,417 145 $1.86230 12.56% 115
Columbia, MO $327,147.00 172,786 146 $1.86219 12.55% 232
Saginaw-Saginaw Township North, MI $378,070.00 200,169 147 $1.85766 12.28% 215
Davenport-Moline-Rock Island, IA-IL $716,738.00 379,690 148 $1.85661 12.22% 134
Kalamazoo-Portage, MI $614,919.00 326,589 149 $1.85185 11.93% 148
St. Louis, MO-IL $5,295,893.00 2,812,896 150 $1.85172 11.92% 19
Jackson, MI $300,340.00 160,248 151 $1.84336 11.42% 251
Columbus, OH $3,441,849.00 1,836,536 152 $1.84324 11.41% 32
Coeur d'Alene, ID $259,303.00 138,494 153 $1.84148 11.30% 280
Cincinnati, OH-KY-IN $3,984,972.00 2,130,151 154 $1.83994 11.21% 27
Battle Creek, MI $254,031.00 136,146 155 $1.83515 10.92% 288
Bay City, MI $201,047.00 107,771 156 $1.83479 10.90% 335
Niles-Benton Harbor, MI $292,506.00 156,813 157 $1.83460 10.89% 254
Albany-Schenectady-Troy, NY $1,621,964.00 870,716 158 $1.83212 10.74% 59
Amarillo, TX $462,864.00 249,881 159 $1.82184 10.11% 184
Roanoke, VA $570,732.00 308,707 160 $1.81834 9.90% 153
Harrisonburg, VA $230,573.00 125,228 161 $1.81091 9.45% 306
Burlington, NC $277,822.00 151,131 162 $1.80802 9.28% 261
Trenton-Ewing, NJ $673,675.00 366,513 163 $1.80780 9.27% 140
Jefferson City, MO $275,316.00 149,807 164 $1.80754 9.25% 265
Waterloo-Cedar Falls, IA $307,311.00 167,819 165 $1.80106 8.86% 237
Vineland-Millville-Bridgeton, NJ $287,125.00 156,898 166 $1.79988 8.79% 253
Hot Springs, AR $175,556.00 96,024 167 $1.79815 8.68% 349
Cumberland, MD-WV $188,630.00 103,299 168 $1.79599 8.55% 339
Binghamton, NY $458,868.00 251,725 169 $1.79288 8.36% 186
Augusta-Richmond County, GA-SC $1,015,110.00 556,877 170 $1.79285 8.36% 92
Dubuque, IA $170,614.00 93,653 171 $1.79177 8.30% 353
Rocky Mount, NC $277,596.00 152,392 172 $1.79160 8.29% 263
Flint, MI $775,183.00 425,790 173 $1.79060 8.23% 120
Springfield, IL $382,338.00 210,170 174 $1.78923 8.14% 204
Lynchburg, VA $459,487.00 252,634 175 $1.78884 8.12% 183
Glens Falls, NY $234,236.00 128,923 176 $1.78696 8.01% 299
Utica-Rome, NY $543,419.00 299,397 177 $1.78516 7.90% 160
Nashville-Davidson--Murfreesboro, TN $2,880,135.00 1,589,934 178 $1.78166 7.69% 37
Muskegon-Norton Shores, MI $311,558.00 172,188 179 $1.77962 7.56% 234
Danville, VA $192,681.00 106,561 180 $1.77840 7.49% 337
Goldsboro, NC $221,405.00 122,623 181 $1.77585 7.34% 309
Florence, SC $370,844.00 205,566 182 $1.77431 7.24% 267
Kingston, NY $329,107.00 182,493 183 $1.77370 7.21% 226
Monroe, MI $273,894.00 152,021 184 $1.77202 7.10% 264
Lansing-East Lansing, MI $833,169.00 464,036 185 $1.76592 6.74% 109
Holland-Grand Haven, MI $473,601.00 263,801 186 $1.76574 6.72% 174
Pine Bluff, AR $179,721.00 100,258 187 $1.76307 6.56% 344
Texarkana, TX-Texarkana, AR $243,750.00 136,027 188 $1.76242 6.52% 287
Springfield, MO $782,413.00 436,712 189 $1.76210 6.50% 112
Salisbury, MD $224,297.00 125,203 190 $1.76197 6.50% 308
Spartanburg, SC $509,124.00 284,307 191 $1.76127 6.45% 163
College Station-Bryan, TX $408,296.00 228,660 192 $1.75620 6.15% 193
St. Joseph, MO-KS $227,058.00 127,329 193 $1.75388 6.01% 303
Syracuse, NY $1,180,655.00 662,577 194 $1.75257 5.93% 80
Rochester, NY $1,877,514.00 1,054,323 195 $1.75146 5.86% 51
Elmira, NY $157,948.00 88,830 196 $1.74881 5.70% 357
Memphis, TN-MS-AR $2,337,491.00 1,316,100 197 $1.74683 5.58% 41
Winchester, VA-WV $227,756.00 128,472 198 $1.74361 5.39% 298
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $10,561,043.00 5,965,343 199 $1.74125 5.24% 6
Anderson, SC $331,261.00 187,126 200 $1.74111 5.24% 222
Jacksonville, NC $314,639.00 177,772 201 $1.74076 5.21% 227
Jonesboro, AR $213,245.00 121,026 202 $1.73297 4.74% 312
Sumter, SC $188,886.00 107,456 203 $1.72886 4.49% 333
Bloomington-Normal, IL $297,450.00 169,572 204 $1.72524 4.28% 235
Bismarck, ND $190,424.00 108,779 205 $1.72174 4.06% 330
Champaign-Urbana, IL $404,833.00 231,891 206 $1.71704 3.78% 192
Bremerton-Silverdale, WA $438,118.00 251,133 207 $1.71584 3.71% 182
Lawrence, KS $192,383.00 110,826 208 $1.70732 3.19% 328
San Antonio, TX $3,716,198.00 2,142,508 209 $1.70595 3.11% 24
Des Moines, IA $986,927.00 569,633 210 $1.70404 3.00% 88
Spokane, WA $815,061.00 471,221 211 $1.70120 2.82% 108
Tyler, TX $362,634.00 209,714 212 $1.70071 2.79% 200
Athens-Clarke County, GA $332,237.00 192,541 213 $1.69713 2.58% 219
Wichita, KS $1,073,694.00 623,061 214 $1.69488 2.44% 86
Poughkeepsie-Newburgh-Middletown, NY $1,154,051.00 670,301 215 $1.69334 2.35% 79
Joplin, MO $301,371.00 175,518 216 $1.68877 2.07% 231
Abilene, TX $282,987.00 165,252 217 $1.68426 1.80% 240
Boston-Cambridge-Quincy, MA-NH $7,793,647.00 4,552,402 218 $1.68380 1.77% 10
San Angelo, TX $191,352.00 111,823 219 $1.68303 1.73% 325
Topeka, KS $400,150.00 233,870 220 $1.68282 1.71% 189
Pocatello, ID $155,071.00 90,656 221 $1.68238 1.69% 355
Madison, WI $971,417.00 568,593 222 $1.68033 1.56% 89
Virginia Beach-Norfolk-Newport News, VA-NC $2,848,285.00 1,671,683 223 $1.67579 1.29% 36
Lubbock, TX $483,463.00 284,890 224 $1.66908 0.88% 161
Tuscaloosa, AL $371,157.00 219,461 225 $1.66337 0.54% 195
Sherman-Denison, TX $203,597.00 120,877 226 $1.65660 0.13% 314
La Crosse, WI-MN $224,725.00 133,665 227 $1.65357 -0.06% 290
Grand Forks, ND-MN $165,417.00 98,461 228 $1.65236 -0.13% 346
Waco, TX $394,549.00 234,906 229 $1.65195 -0.15% 188
Longview, TX $359,095.00 214,369 230 $1.64754 -0.42% 197
Beaumont-Port Arthur, TX $650,601.00 388,745 231 $1.64604 -0.51% 132
Cape Girardeau-Jackson, MO-IL   $160,928.00 96,275 232 $1.64403 -0.63% 350
Kansas City, MO-KS $3,396,584.00 2,035,334 233 $1.64133 -0.80% 29
Fayetteville-Springdale-Rogers, AR-MO $772,340.00 463,204 234 $1.63993 -0.88% 107
Peoria, IL $631,004.00 379,186 235 $1.63670 -1.07% 135
Corpus Christi, TX $710,406.00 428,185 236 $1.63179 -1.37% 114
Sioux Falls, SD $378,499.00 228,261 237 $1.63088 -1.43% 191
Decatur, IL $183,380.00 110,768 238 $1.62828 -1.58% 331
Manchester-Nashua, NH $663,068.00 400,721 239 $1.62744 -1.63% 130
Boise City-Nampa, ID $1,017,199.00 616,561 240 $1.62263 -1.93% 85
Chicago-Naperville-Joliet, IL-IN-WI $15,568,610.00 9,461,105 241 $1.61844 -2.18% 3
Eau Claire, WI $265,021.00 161,151 242 $1.61748 -2.24% 243
Oshkosh-Neenah, WI $274,532.00 166,994 243 $1.61690 -2.27% 238
Savannah, GA $570,207.00 347,611 244 $1.61335 -2.49% 143
Little Rock-North Little Rock, AR $1,147,493.00 699,757 245 $1.61285 -2.52% 72
Rome, GA $157,878.00 96,317 246 $1.61216 -2.56% 352
Burlington-South Burlington, VT $346,073.00 211,261 247 $1.61116 -2.62% 201
Warner Robins, GA $229,125.00 139,900 248 $1.61081 -2.64% 274
Victoria, TX $188,788.00 115,384 249 $1.60923 -2.74% 320
Macon, GA $378,813.00 232,293 250 $1.60391 -3.06% 190
Columbus, GA-AL $480,773.00 294,865 251 $1.60364 -3.07% 157
Midland, TX $221,613.00 136,872 252 $1.59247 -3.75% 281
Charlotte-Gastonia-Concord, NC-SC $2,845,880.00 1,758,038 253 $1.59213 -3.77% 33
Valdosta, GA $225,264.00 139,588 254 $1.58721 -4.07% 278
Kankakee-Bradley, IL $182,897.00 113,449 255 $1.58561 -4.16% 324
Brunswick, GA $181,130.00 112,370 256 $1.58537 -4.18% 326
Milwaukee-Waukesha-West Allis, WI $2,506,228.00 1,555,908 257 $1.58426 -4.24% 39
Danville, IL $131,406.00 81,625 258 $1.58337 -4.30% 360
Albany, GA $253,037.00 157,308 259 $1.58206 -4.38% 252
Logan, UT-ID $201,329.00 125,442 260 $1.57853 -4.59% 304
New Haven-Milford, CT $1,384,050.00 862,477 261 $1.57832 -4.60% 60
Killeen-Temple-Fort Hood, TX $649,274.00 405,300 262 $1.57558 -4.77% 127
Green Bay, WI $490,506.00 306,241 263 $1.57533 -4.78% 152
Rockford, IL $559,542.00 349,431 264 $1.57493 -4.81% 145
Fond du Lac, WI $161,165.00 101,633 265 $1.55964 -5.73% 341
Manhattan, KS   $201,274.00 127,081 266 $1.55775 -5.85% 297
Cedar Rapids, IA $408,023.00 257,940 267 $1.55581 -5.96% 177
Odessa, TX $216,475.00 137,130 268 $1.55262 -6.16% 282
Albuquerque, NM $1,399,881.00 887,077 269 $1.55210 -6.19% 57
Wenatchee, WA $174,873.00 110,884 270 $1.55112 -6.25% 327
New York-Northern New Jersey-Long Island,NY-NJ-PA $29,682,674.00 18,897,109 271 $1.54489 -6.62% 1
Sheboygan, WI $181,234.00 115,507 272 $1.54320 -6.73% 323
Norwich-New London, CT $429,665.00 274,055 273 $1.54199 -6.80% 170
Wausau, WI $210,056.00 134,063 274 $1.54105 -6.86% 291
Gainesville, GA $280,222.00 179,684 275 $1.53385 -7.29% 225
Appleton, WI $350,752.00 225,666 276 $1.52871 -7.60% 194
Hartford-West Hartford-East Hartford, CT $1,877,815.00 1,212,381 277 $1.52336 -7.93% 45
Omaha-Council Bluffs, NE-IA $1,333,208.00 865,350 278 $1.51529 -8.41% 58
Racine, WI $301,037.00 195,408 279 $1.51519 -8.42% 217
Janesville, WI $246,458.00 160,331 280 $1.51187 -8.62% 250
Las Cruces, NM $321,250.00 209,233 281 $1.51009 -8.73% 199
Mankato-North Mankato, MN $147,728.00 96,740 282 $1.50192 -9.22% 348
Grand Rapids-Wyoming, MI $1,178,645.00 774,160 283 $1.49741 -9.49% 69
McAllen-Edinburg-Pharr, TX $1,179,244.00 774,769 284 $1.49700 -9.52% 68
Duluth, MN-WI $425,512.00 279,771 285 $1.49589 -9.59% 165
Yakima, WA $369,819.00 243,231 286 $1.49541 -9.61% 187
Mount Vernon-Anacortes, WA $177,569.00 116,901 287 $1.49396 -9.70% 315
Dalton, GA $215,804.00 142,227 288 $1.49234 -9.80% 277
Los Angeles-Long Beach-Santa Ana, CA $19,390,317.00 12,828,837 289 $1.48658 -10.15% 2
Brownsville-Harlingen, TX $612,204.00 406,220 290 $1.48226 -10.41% 124
Fairbanks, AK $147,010.00 97,581 291 $1.48174 -10.44% 343
Riverside-San Bernardino-Ontario, CA $6,352,361.00 4,224,851 292 $1.47881 -10.62% 12
Longview, WA $153,768.00 102,410 293 $1.47677 -10.74% 340
Bellingham, WA $301,501.00 201,140 294 $1.47428 -10.89% 209
Rochester, MN $278,633.00 186,011 295 $1.47328 -10.95% 223
Columbia, SC $1,148,180.00 767,598 296 $1.47118 -11.08% 70
El Paso, TX $1,192,338.00 800,647 297 $1.46470 -11.47% 65
Minneapolis-St. Paul-Bloomington, MN-WI $4,842,583.00 3,279,833 298 $1.45216 -12.23% 16
Hinesville-Fort Stewart, GA $114,891.00 77,917 299 $1.45025 -12.34% 361
Raleigh-Cary, NC $1,663,591.00 1,130,490 300 $1.44734 -12.52% 47
St. Cloud, MN $277,151.00 189,093 301 $1.44155 -12.87% 221
Laredo, TX $366,846.00 250,304 302 $1.44147 -12.88% 181
San Luis Obispo-Paso Robles, CA $393,949.00 269,637 303 $1.43698 -13.15% 171
Sacramento--Arden-Arcade--Roseville, CA $3,125,517.00 2,149,127 304 $1.43037 -13.55% 25
Hanford-Corcoran, CA $221,963.00 152,982 305 $1.42702 -13.75% 260
Houston-Baytown-Sugar Land, TX $8,591,542.00 5,946,800 306 $1.42095 -14.12% 5
Lincoln, NE $435,900.00 302,157 307 $1.41888 -14.24% 154
Charleston-North Charleston, SC $958,180.00 664,607 308 $1.41799 -14.29% 78
Olympia, WA $361,151.00 252,264 309 $1.40807 -14.89% 180
Corvallis, OR $122,493.00 85,579 310 $1.40778 -14.91% 358
Fargo, ND-MN $298,526.00 208,777 311 $1.40634 -15.00% 202
Atlanta-Sandy Springs-Marietta, GA $7,462,139.00 5,268,860 312 $1.39295 -15.81% 9
Santa Cruz-Watsonville, CA $371,038.00 262,382 313 $1.39083 -15.94% 175
Chico, CA $310,836.00 220,000 314 $1.38963 -16.01% 196
Eugene-Springfield, OR $496,260.00 351,715 315 $1.38774 -16.12% 144
Santa Barbara-Santa Maria-Goleta, CA $595,677.00 423,895 316 $1.38211 -16.46% 118
Redding, CA $249,004.00 177,223 317 $1.38190 -16.48% 228
Napa, CA $191,518.00 136,484 318 $1.38012 -16.58% 284
Santa Rosa-Petaluma, CA $677,960.00 483,878 319 $1.37803 -16.71% 104
Colorado Springs, CO $897,143.00 645,613 320 $1.36672 -17.39% 81
San Diego-Carlsbad-San Marcos, CA $4,279,163.00 3,095,313 321 $1.35970 -17.82% 17
Idaho Falls, ID $177,313.00 130,374 322 $1.33764 -19.15% 294
San Francisco-Oakland-Fremont, CA $5,859,991.00 4,335,391 323 $1.32941 -19.65% 11
Ogden-Clearfield, UT $737,490.00 547,184 324 $1.32560 -19.88% 93
San Jose-Sunnyvale-Santa Clara, CA $2,472,499.00 1,836,911 325 $1.32385 -19.98% 31
Vallejo-Fairfield, CA $554,586.00 413,344 326 $1.31961 -20.24% 123
Oxnard-Thousand Oaks-Ventura, CA $1,102,236.00 823,318 327 $1.31673 -20.41% 63
Denver-Aurora, CO $3,395,406.00 2,543,482 328 $1.31296 -20.64% 21
Medford, OR $268,465.00 203,206 329 $1.29939 -21.46% 207
Durham, NC $666,098.00 504,357 330 $1.29894 -21.49% 102
Salinas, CA $544,618.00 415,057 331 $1.29055 -22.00% 121
Dallas-Fort Worth-Arlington, TX $8,355,402.00 6,371,773 332 $1.28972 -22.05% 4
Anchorage, AK $499,298.00 380,821 333 $1.28952 -22.06% 133
Austin-Round Rock, TX $2,235,782.00 1,716,289 334 $1.28124 -22.56% 34
Bend, OR $205,348.00 157,733 335 $1.28044 -22.61% 248
Kennewick-Richland-Pasco, WA $325,878.00 253,340 336 $1.26515 -23.53% 176
Portland-Vancouver-Beaverton, OR-WA $2,855,767.00 2,226,009 337 $1.26179 -23.74% 23
Salem, OR $500,670.00 390,738 338 $1.26025 -23.83% 131
Madera, CA $191,973.00 150,865 339 $1.25153 -24.36% 262
Boulder, CO $374,413.00 294,567 340 $1.25013 -24.44% 159
Modesto, CA $653,370.00 514,453 341 $1.24912 -24.50% 100
Grand Junction, CO $186,143.00 146,723 342 $1.24778 -24.58% 269
Fresno, CA $1,179,885.00 930,450 343 $1.24720 -24.62% 55
Pueblo, CO $200,950.00 159,063 344 $1.24254 -24.90% 246
El Centro, CA $220,389.00 174,528 345 $1.24198 -24.93% 230
Yuba City, CA $210,683.00 166,892 346 $1.24160 -24.96% 239
Stockton, CA $862,343.00 685,306 347 $1.23761 -25.20% 76
Bakersfield, CA $1,051,318.00 839,631 348 $1.23150 -25.57% 61
Washington-Arlington-Alexandria, DC-VA-MD-WV $6,970,911.00 5,582,170 349 $1.22822 -25.76% 7
Salt Lake City, UT $1,401,163.00 1,124,197 350 $1.22585 -25.91% 48
Greeley, CO $314,296.00 252,825 351 $1.22267 -26.10% 179
Tallahassee, FL $453,660.00 367,413 352 $1.21441 -26.60% 138
Seattle-Tacoma-Bellevue, WA $4,226,240.00 3,439,809 353 $1.20840 -26.96% 15
Merced, CA $308,988.00 255,793 354 $1.18807 -28.19% 178
Bridgeport-Stamford-Norwalk, CT $1,105,338.00 916,829 355 $1.18576 -28.33% 56
Visalia-Porterville, CA $531,838.00 442,179 356 $1.18296 -28.50% 111
St. George, UT $165,901.00 138,115 357 $1.18140 -28.59% 279
Santa Fe, NM $164,210.00 144,170 358 $1.12025 -32.29% 271
Fort Collins-Loveland, CO $340,812.00 299,630 359 $1.11871 -32.38% 155
Provo-Orem, UT $517,472.00 526,810 360 $0.96610 -41.61% 97
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Economic Impact: Jobless numbers are key in the economy

The jobless rate is falling, and that’s good news. But it is declining for the wrong reasons, and that’s not good for a variety of reasons.

The rate has fallen significantly since the recession ended. It stood at 6.6 percent in January. The government will release the February figures Friday.

The Federal Open Market Committee set a 6.5 percent jobless rate as its target to start raising the federal funds rate. But recently, the Fed said it also will consider “additional measures of labor market conditions” before raising the funds rate.

The committee is downplaying the unemployment rate because it is falling for the wrong reasons.

The jobless rate is declining, in part, because people who can’t find jobs have stopped looking for work. These “discouraged workers” are no longer counted in the official unemployment rate.

An alternative measure of unemployment would count people currently not looking for work but would take a job if offered and they have looked for a job in the past 12 months. The alternative measure also would take into account those who are working part time but would prefer full-time work.

Add those into the definition, and unemployment soared to 12.7 percent in January.

The participation rate, which has been falling nationally, also points toward an increase in discouraged workers.

The percentage of people 16 years and older who are working fell from 66 percent when the recession started in December 2007 to 63 percent in January — a level not seen since 1978.

In fact, if people were not discouraged and the participation rate still stood at 66 percent today, the official unemployment rate would be 10.9 percent.

Economists debate whether the downward trend in the participation rate is a lingering result of the recession that might change when growth picks up. Besides, there has been a larger percentage point drop in the participation rate among the relatively young population rather than the baby boomers.

The drop in participation among people ages 15 to 24 appears to be happening, in part, because a larger percentage of that age group is staying in school than occurred before the recession.

Furthermore, more than 3.6 million people have been unemployed for 27 weeks or more – another statistic that points to weakness in the labor market. That figure represented 52.3 percent of the unemployment in January compared with 17.4 percent before the recession started.

Two groups that remain especially hard hit are those 16- to 19-year olds where the jobless rate is 20.7 percent.

The Fed’s shift away from the unemployment rate as a target for lifting its accommodative policy reflects what the man on the street still feels: The economy is not growing fast enough to generate strong job growth.

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Recent Industry Reclassifications Have Major Impact on Analysis in Health Care, Finance Sectors

In the first quarter of 2013, many establishments that provide home care for the elderly were reclassified from NAICS 814110 (private households) to 624120 (services for the elderly). This reclassification, while appropriate according to the BLS, may cause problems for anyone analyzing the health care industry, especially in the regions that were most affected.

Nationwide, this reclassification greatly contributed to the large jump in health care and social assistance (NAICS 62) employment over the quarter, from a post-recession average year-over-year growth rate of 1.7% to 4.3% growth in the first quarter of 2013. The effect is even more pronounced in sub-industries of health care, as illustrated in the table below.

Some description

 

Industry

Employment growth 2012 Q4

Employment growth 2013 Q1

Health Care and Social Assistance (NAICS 62)

1.8%

4.3%

Social Assistance (NAICS 624)

2.4%

19.6%

Individual and Family Services (NAICS 6241)

4.6%

36.8%

Individual states most affected by this reclassification are California, Massachusetts, Missouri, Nebraska, and Washington, with California and Washington as the top two. California recorded 21.4% growth in the health care and social assistance sector; Washington recorded 11.5% growth. The map below illustrates the regions with the largest percentage change in employment in the health care sector for the first quarter of 2013.

Some description

This change also has a corresponding deflationary effect in the “other services” sector (NAICS 81). Nationally, year-over-year employment growth in that sector went from a positive 3.4% in the fourth quarter of 2012 to a negative 8.5% in the first quarter of 2013. There is a similar magnification of the effect in sub-industries of other services as well. Again, California and Washington experienced the greatest percentage change in this sector, with Massachusetts, Missouri, and Nebraska rounding out the top five. California recorded 41.4% loss and Washington 35.3% loss.

Some description

Industry

Employment growth 2012 Q4

Employment growth 2013 Q1

Other Services (except Public Administration) (NAICS 81)

3.4%

-8.5%

Private Households (NAICS 814 & 8141)

15.9%

-61.7%

Some description

The BLS published a note on their Business Employment Dynamics (BED) website regarding the change:

First quarter 2013 data were affected by an administrative change to the count of establishments in the education and health services industry. A review of the administrative data from which the BED data are derived revealed that certain establishments that provide non-medical, home-based services for the elderly and persons with disabilities had been misclassified in the private households industry (NAICS 814110)…. These establishments are now…classified in services for the elderly and persons with disabilities (NAICS 624120.) This non-economic industry code change artificially inflates the data for gross job gains, openings, births, and the net employment change for the following data series: national total private, state total private, the education and health services sector, and firm size class.

The note makes it clear that the change is a result of a misclassification in prior periods, rather than a fundamental change in the industry. That does not diminish the inflationary effect for the first quarter of 2013, however, and this effect is important to take into consideration when performing any analysis on employment in the health care or other services industries.

Another important change in the first quarter of 2013 is that to the funds, trusts, and other financial vehicles sub-sector (NAICS 525). This is a sector that should typically have little to no employment, according to the census definition:

… These entities earn interest, dividends, and other property income, but have little or no employment and no revenue from the sale of services.

The adjustment that occurred in this period reassigned many employees from these types of funds to other, more appropriate industries in the finance sector. Typically, employees who manage financial vehicles of these types are recorded as part of the other financial investment activities industry group (NAICS 5239). Much of the employment loss from the funds, trusts, and other financial vehicles sub-industry was moved to that industry in this quarter, but some also went to other industries in the finance sector (NAICS 52), depending on firm and region.

Some description

The nationwide adjustment in the first quarter of 2013 caused a reduction in employment in the funds, trusts, and other financial vehicles sub-industry in the majority of regions in the United States. The reduction varies, however, depending on the level of employment prior to the adjustment. Connecticut, Nevada, and Virginia all recorded more than 98% employment loss in this sub-industry. In Virginia, 88% of employment affected by this change moved from other insurance funds (NAICS 525190) to direct property and casualty insurance carriers (NAICS 524126). Other states experienced other movements, depending on the employment landscape prior to the reclassification. See the map below for more information on how individual states were affected.

Some description

Again, this is an adjustment in reporting practice, not a fundamental change in the classification of the industry. However, as with the changes to the health care and other services sectors, the deflationary effect of reclassifying a large percentage of employees of the sub-sector must be taken into consideration when performing any analysis of the finance sector.

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Improving Unemployment Claims Data Masks Decade's High Variance between the States

Over the past several months the labor market has been sending out some mixed signals in terms of its relative strength. For instance, initial unemployment claims data—typically a reasonable signal of the overall labor market—has been trending downward and is not far off from a new 35-year low. Once adjusted for the size of the current population, the September figure of the seasonally adjusted 4-week moving average of 305,000 initial claims would “traditionally” be associated with a very strong labor market. In October the number of claims have edged upwards, but still remain fairly low.

Recently several economists & bloggers—Marginal RevolutionThe Money AllusionCalculated Risk, and others—have noted the disconnect between the recent unemployment claim numbers and the rest of the labor market—job creation, the unemployment rate, and wage growth. Some have taken it as a sign that the labor market is not really as weak as the headline unemployment rate suggests, while others see it as a symptom of the decline of manufacturing, which frequently relies on mass layoffs to adjust output. At its lowest in early 2000 there were 0.94 claims per 1,000 people in the country. The only other periods to approach this—1989 and in 2006—the same metric was 1.16 and .96 respectively. As of the end of September, the current rate was .96 claims per 1,000 citizens—given a U.S. population estimate of 316.4 million in 2013.

National Initial Unemployment Claims 1989-2013

Table

However, masked in these national numbers is the marked difference in claims data between the states. Similar to our analysis that showed great disparity in job gains by state, the claims data shows a very different picture when examined at the state level. While some states are at or near record lows in terms of the level of unemployment claims per 1,000 residents, other states are still well above the number of monthly unemployment claims that would signal a return to a healthy labor market.

Claims per 1,000 Residents (September-2013)

In contrast to the past two recoveries where a vast majority of states had also seen their claim data nadir in close correlation with the national job market, this recovery has seen several states lag well behind the leaders in terms of layoff declines. One simple measure is to see how the average state was doing in terms of its claims per 1,000 residents when the national economy experienced a new low in initial claims. The elevated figure for this simple average among the 50 states and the District of Columbia in 2013 also features a higher weighted variance for these claims per 1,000 people currently among the states as compared to the corresponding periods in 2000 and 2006.

 200020062013
National Low in 52-Week Moving Average of Initial Claims per 1,000 People 0.94 0.96 0.96
Average of All States 52-Week Moving Average of Initial Claims per 1,000 People (same week as national low) 1.01 1.04 1.11
Source: Federal Reserve Bank of St. Louis and Chmura Economics & Analytics

Another way to examine this is look at the 52-week moving average of claims by state to see in each of these periods—1989, 2000, 2006, and 2013—how close each state was to its lowest ever ratio of claims per 1,000 residents. For instance in 2000 and 2006, on average when the nation claims rate bottomed out in terms of initial claims the average state was within 14 and 17 percentage points respectively above its historic low of number of claims per 1,000 residents. In contrast in 2013, as the nation approaches its historic low for initial claims per 1,000 residents, on average the states are 26 percentage points above their previous historic low.

Essentially many states approached their historic lows in 2000 and in 2006, but much fewer states are close to their historic lows currently despite the healthy national numbers in terms of initial claims. Another way to think about it would be to posit that the economic expansions in the late 1990s and mid 2000s were rising tides that spread business activity relatively broadly across most states and MSAs. Whereas today’s expansion is a more of story of states that have and have not, where several states are beginning to forge new records for employment and many other states lag significantly behind in terms of their labor market.

Distribution of State Claims per 1,000 Residents in Terms of State Historic Low 1988-2013

While it is possible that the average of initial claims per 1,000 will narrow across many more states as the nation actually reaches new 35-year low of claims per 1,000 citizens, it is unlikely that the states which have the highest current claims rate will make enough progress in the next several months to fundamentally change the shape of this distribution. This would require large drop of in claims in Alaska, Wisconsin, Pennsylvania, Oregon, and New Jersey all of which have more been averaging more than 1.5 claims per 1,000 residents for the past year. Furthermore, we find little preliminary evidence lately that the level of initial unemployment claims are associated with the size of the manufacturing sector—as gauged by the location quotient, but we will explore this relationship more fully in subsequent blogs.

Does Manufacturing Matter?

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Regional Effects of Government Shutdown on Private Sector Industries: Examples from 1995-1996

Written by Patrick Clapp and Chris Chmura.

The impact of shutting down the U.S. government is much larger than the 800,000 federal workers who are staying at home and not spending money on commuting and lunch. Even though Congress has agreed to pay these workers, the reduction and shutdown of certain services is rippling through the economy.

The Wall Street Journal cites an estimate from J.P Morgan Chase economists that “each week of a shutdown would reduce the annualized pace of fourth-quarter economic growth by 0.12 percentage point.” However, the forecast only looks at effects on government workers, not the private sector or consumer confidence.

One question missing from the various lists of FAQs about the shutdown is whether any private sector industries could be affected. Fewer people are making retail purchases, going out to eat, enjoying arts or entertainment, and/or traveling and staying at hotels when tourist attractions like the Grand Canyon or Smithsonian museums are closed. They might also delay big purchases, like a new house, even if they could get a loan from an understaffed Federal Housing Administration. That gives us four North American Industrial Classification System (NAICS) sectors for closer inspection: retail trade (NAICS code 44), real estate and rental and leasing (53), arts, entertainment and recreation (71), and accommodation and food services (72).

Next, we need to look at a comparable period. The last time the government shut down was between December 16, 1995 and January 6, 1996, a total of 21 days. We consider the effects on our selected sectors around that period for metropolitan statistical areas (MSAs). We also consider the percentage of federal employees in the area during that time period—areas with a higher percentage of federal workers implies those areas would have more furloughed workers, and likely larger impacts on local industries. Data are obtained from Chmura’s database powering the EQSuite®.

For each of the four sector categories in every MSA, we first calculated the year-over-year percent change in employment by month and wages by quarter to filter out some seasonal fluctuations. The graphs later in this post shows the percentage point changes in growth rates month to month or quarter to quarter relative to the number of people employed by the federal government in each region as a percent of total regional employment. With less demand during the shutdown, we would expect firms in these industries to cut back employees’ hours, which would show as a decrease in the growth rate of wages. To a lesser extent, they might cut their staff size, which would appear as a negative growth rate in employment.

Now for some results: The clearest story is in the retail sector, where there was a strong negative relationship between the percentage of federal employees in a region and changes in the growth rate of wages quarter to quarter in the time periods surrounding the last government shutdown. Employment in the retail sector also declined, but not as dramatically as wages, implying that businesses cut hours, rather than laying off workers to cope with the government shutdown.

The Washington, D.C. metro area ranked fifth of 353 MSAs in the percentage of federal employees in the workforce. The year-over-year growth rate in wages in the retail sector slowed 0.6 percentage points between the last quarter of 1995 and the first quarter of 1996. In the metro area with the largest percentage of federal workers, Warner Robins, Georgia, the year-over-year growth rate of retail wages fell 4.5 percentage points over that time.

Tourist areas such as national parks and museums also suffered. The year-over-year growth rate of retail wages in the Flagstaff, Arizona MSA, near the Grand Canyon, fell 1.7 percentage points between the last quarter of 1995 and the first quarter of 1996.

The effects of furloughed workers rippled through the arts, entertainment, and recreation sector as well. As federal employees were unsure if they would get paid, metro areas with a large percentage of federal employees experienced sharper drops in wages for this sector over the shutdown period and a similar drop in employment. In the Jacksonville, North Carolina MSA, home of Marine Corps Base Camp Lejeune, the year-over-year employment growth rate in the arts, entertainment, and recreation sector fell over 16 percentage points between December and January.

The year-over-year growth rate of employment and wages in the real estate sector and accommodation and food services sector were not negatively affected, suggesting they are more resistant to uncertainty caused by a government shutdown. This makes intuitive sense for the real estate sector in particular—if the only thing keeping someone from purchasing a house is delayed paperwork at the FHA, they are still going to buy a house, just somewhat later than expected.

Percentage Point Change in Year-Over-Year Growth Rate of Wages in Select Sectors,
Fourth Quarter of 1995 to First Quarter of 1996
 % Federal
Employees
Retail TradeReal EstateArts
Ent
& Rec
Accom
& Food Svcs

Warner Robins, GA MSA

28.1%

-4.5

4.9

-14.0

-0.4

Bremerton-Silverdale, WA MSA

24.0%

-6.5

10.0

-6.1

3.1

Washington-Arlington-Alexandria, DC-VA-MD-WV MSA

14.9%

-0.6

*

-3.2

-1.2

Jacksonville, NC MSA

13.3%

-2.5

-10.8

-6.3

-3.9

Lawton, OK MSA

10.8%

-1.0

-1.6

8.9

1.1

Fairbanks, AK MSA

10.5%

2.0

27.1

2.0

-5.7

Fort Walton Beach-Crestview-Destin, FL MSA

10.1%

-4.2

3.5

16.6

12.8

Huntsville, AL MSA

9.9%

-0.5

-1.4

-10.5

-4.4

Anniston-Oxford, AL MSA

9.8%

3.3

3.6

1.1

-6.1

Clarksville, TN-KY MSA

8.7%

1.6

-2.9

2.0

7.8

Anchorage, AK MSA

8.2%

-3.2

-3.4

6.7

-2.1

Cheyenne, WY MSA

7.5%

0.8

-14.6

-17.9

-0.7

Honolulu, HI MSA

7.3%

1.9

4.3

-1.9

1.4

Las Cruces, NM MSA

7.2%

6.0

-44.7

-8.3

8.2

Lebanon, PA MSA

6.8%

-5.8

-4.9

-0.3

-0.5

Bakersfield, CA MSA

6.7%

-1.3

-8.0

0.2

2.6

Texarkana, TX-Texarkana, AR MSA

6.7%

-4.7

2.9

-1.0

-4.3

* Data for the real estate sector of the Washington, DC metro area is unavailable prior to 2001

Similar to the last shutdown, Congress said it will pay the furloughed employees even though they did not work, and employment and wages in these industries quickly bounced back—and we would expect the same when the current shutdown ends. Continuing to look at the retail sector, year-over-year growth in wages in the quarter following the shutdown was positively correlated with the percentage of federal workers in a region, implying renewed demand from now-working government employees rippled out to more hours in the retail sector.

However, that quick return to growth took place in a strong mid-1990s economy. In the midst of our current slow recovery, a similarly long shutdown could be a much larger and longer setback for workers in both the public and private sectors.

The above chart shows the MSAs most likely to be impacted by the federal shutdown based on the most recent data available. The size of each bubble represents total MSA employment, and the color of a bubble indicates the political party affiliations of the senators representing that state. MSAs closer to the top right of the chart have higher unemployment rates and a larger percentage of federal workers in their local workforce, and are expected to be hit harder by the shutdown.

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Reflections on the 2013 Workforce Boardroom Conference

Chmura Economics & Analytics recently presented its second annual Workforce Boardroom Conference. The conference theme was Illuminate to Innovate, and it brought thought leaders from a variety of disciplines together to discuss smart solutions to some big challenges facing our regions, states, and nation.

The event kicked off with a presentation from Marge Connelly, who brought her unique insight and private sector perspective on customer-centered innovation from her career in financial services and higher education. Following are some other highlights from the event.

Federal Issues, Sequestration and Your Economy

Chmura has been researching defense spending, the sequestration, and their impacts on state and regional economies. The team has developed one of the most complete datasets on federal defense spending ever constructed, and Chris Chmura’s remarks were designed to identify, inform, and inspire defense-dependent communities to action. Data-Driven Decision Making

Jay Dougherty, partner at Mercer and co-founder of the Workforce Sciences Institute, shared his candid experience with companies making location decisions today. He emphasized the use of quantitative data analysis to optimize the location, labor, cost of doing business, and business climate of a corporate decision.

Chmura team member Dan Meges joined Bruce Stephen of Monster Government Solutions to talk about workforce planning and the use of Real-Time Labor Intelligence. Monster, which started the online job posting revolution, is now manipulating its massive database of candidates and openings to help create a clearer picture of labor supply and demand. This perspective is invaluable for communities and higher education working to better align their program offerings with market trends.

Chmura’s chief statistician, Greg Chmura, took the stage to share some interesting analysis on the connection between education, innovation, and job growth. His research built on findings of the Fund for Our Economic Future in northeastern Ohio. The research is surprising - innovation and job growth don’t necessarily go hand in hand.

Measuring What Matters – New Paradigms for Economic Development

One of the goals of the conference was to challenge traditional thinking about economic development and Ed Burghard of the Burghard Group’s talk on the American Dream Composite Index (ADCI) did not disappoint. Ed suggested the audience push away from traditional outcome measures such as job creation and capital investment and instead focus on the well-being and satisfaction of the populace.

Ed has partnered with Xavier University to advance the ADCI, which uses household survey data to measure the pulse of the American sentiment. It’s a holistic measure that includes five components, including economic, a well-being, societal, diversity, and environmental indexes.

Four Business Perspectives

The conference brought four unique and valuable business perspectives to the forefront. The first was from a return presentation by David B. Trebing, General Manager, State and Local Relations for Daimler. David shared the new reality for manufacturers operating in a competitive, dynamic, global marketplace. This reality includes the need to locate production facilities in growing and emerging markets, a push towards cost reduction and efficiency, and the need for a qualified workforce.

The second business topic was presented by Carlos Solari, VP at Wilkitech. Carlos’s remarks enlightened the group on emerging trends in the cyber security industry and what communities need to know to attract and retain businesses in that sector.

Jeff Harris with Xerox Services spoke on the relationship between innovation, people, and the environment, and what that means for business and communities today. His futuristic remarks about the shifting trends in the definitions of work and the workplace prompted healthy discussions over lunch about how economic developers will adapt.

The conference closed with some practical public relations strategies from Hope Katz Bibbs, founder and president of The Inkandescent Group, LLC. Hope has helped many entrepreneurs and early-stage companies transform their innovative ideas into growth, and she shared her ideas about what it takes to help a new enterprise launch successfully and get the attention it deserves.

What’s Next?

It was an exciting two days in Richmond and Chmura thanks the engaged attendees and speakers. Now, we’re busy planning for 2014. If you would like to learn more about the 2014 conference, contact Maggie Bishop at mbishop@chmuraecon.com.

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Advanced Education - Beneficial to Job Growth or No?

The benefit of higher education for an individual is well established: higher wages and lower unemployment rates among other things. But what about the benefits for communities? A recent study suggested a surprising answer.

What Matters to Metros, published by the Fund for Our Economic Future, is an intriguing look at the factors that influence the economies of U.S. metropolitan areas. It found that higher education is linked positively with income, output, and productivity growth, but its data did not support a positive link with employment growth.

Furthermore, the data show that metropolitan areas which have a large percentage of workers with low educational attainment (no high school diploma or equivalent) more often have faster job growth.

This latter fact, actually, should not be so surprising. After all, since businesses want to minimize costs it is reasonable that high-labor-intensive industries would relocate or expand in areas with a large number of low-skilled workers for filling low wage jobs.

Furthermore, as we pointed out in a previous blog, middle-skilled jobs in aggregate have been declining over the past decade while lower-skilled jobs have been on the upswing.

But the same blog also showed growth among the jobs requiring the most skills. Because of this, shouldn’t we see a positive link between higher educational attainment and regional job growth?

For this answer, we must return to the Fund for Our Economic Future data. A first look shows that, indeed, there appears to be virtually no relationship between employment growth (1990-2011) and the percentage of population with educational attainment of a four-year degree or higher (which we’ll call “advanced education”).

Each dot in the below chart represents a metropolitan area (encompassing 115 “mid-sized” regions). The dotted line represents the trend line best fitting the data.

Poking around in the details, however, turns up some curiosities. In certain subsets (such as metro areas with a small percentage of foreign born or metro areas in the Northeast and Midwest), we find a positive correlation between advanced education and job growth. Could the variables be interacting in complex ways so that the positive job growth effects of advanced education in communities are obscured by other factors?

Apparently so. If we adjust job growth against expectations based upon some other variables, the effect of advanced education is clearer to see.

For this purpose, I’m not going to attempt to adjust job growth for all possible variables, but will just look at two that have big impacts: (1) the industry mix—that is, the share of jobs in manufacturing, government, retail, and other sectors—and (2) the business climate. This latter factor was explored and defined nicely in the Fund for Our Economic Future work and comprises three pieces: tax costs, the rate of unionization, and energy costs.

The below chart illustrates the strong correlation between job growth and a composite index of industry mix and business climate.

Using this relationship to set expectations, we can now examine how metros perform against this benchmark. Do metros with higher rates of advanced education more often perform above expectations? The answer is yes.

The next and final chart shows job growth beyond expectations on the vertical axis versus population with advanced education along the horizontal. The data show a significant, positive link between advanced education and job growth.

It looks like higher education is, indeed, correlated with job growth! There are, however, a couple of items that need to be highlighted.

First, you may notice the horizontal scale on the last chart is irregular. This is because a stronger link between advanced education and job growth was found according to an exponential relationship rather than linear. Essentially, incremental changes in advanced education were found to have a more pronounced effect on job growth where education was higher to begin with. While certainly interesting, the exact nature of this relationship should be explored further before concluding that this non-linear relationship is the best, most accurate model.

Also, it should be noted that while rates of advanced education do positively influence job growth, the data show that other factors are more closely related. For example, industry mix, business climate, and immigration each appear to be more strongly linked with job growth than the rate of advanced education.

Furthermore, having a high percentage of population with advanced education in addition to a high percentage with low education still tests out as having a stronger connection with job growth than only having a high percentage of population with advanced education. Again, this is not terribly surprising given the occupation trends we referenced earlier.

This last point does, however, raise some questions about the community benefits of increasing education and skill levels from the lowest skill levels to mid-skill levels. It stands to reason that we should see benefits in terms of income and productivity. Perhaps we can find job growth benefits as well at this level, but that will have to wait for another blog.

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Research Finds $17 Billion in Business Lost Due to Defense Cuts

Reveals Top Ten Most Impacted Metros in the Nation

Nearly three years of continuous budget wrangling in Congress has left the fragile U.S. economic recovery limping along, in many respects, instead of galloping. Sequestration and the Budget Control Act have put the squeeze on both defense and non-defense spending. In this environment, Chmura has spent a great deal of time helping state lawmakers, city officials, and other civic stakeholders understand the economic impact of these defense cuts at the community level. What does it mean to your community if a specific defense contract gets cut back or cancelled altogether? To answer these questions, Chmura mapped the supply chain of defense contractors in the nation and analyzed defense spending contract data over the past thirteen years.

First, defense spending is by design a bit opaque as credible and specific national security considerations oftentimes keep U.S. policy makers from telegraphing openly the true nature or extent of specific programs. Second, many defense contracts are multiyear projects, but public documents, such as contract award notices, make it difficult to see how payments to contractors are set to be dispersed or spent in detail. Third, many government contractors, who competitively bid and win large contracts, subcontract many aspects of the work to other firms. Thus, a single contract can impact several disparate communities at different times with different intensities over the life of the contract. Chmura has dealt with these issues by analyzing typical contract payouts and by adjusting these figures to more accurately model the flow of funds to contractors and subcontractors.

To begin answering questions regarding the previous and looming defense cuts, one must first determine how much of the budget is being cut by region. The defense industry is big, and as President Eisenhower famously noted in 1961, the military industrial complex has political momentum all its own that can alter spending based upon the peculiarities of power and influence. The Department of Defense’s spending has declined substantially from its peak in 2010. By 2012, defense spending was already cut by close to 6% (without adjusting for inflation). While these cuts are large and further cuts are expected in 2013 and 2014, they are not unprecedented. In a recent study, the Center for Strategic and International Studies examined real (adjusted for inflation) defense spending cuts since World War II and found that in the aftermath of the Korean and Vietnam wars, and at the end of the Cold War, defense spending cuts were more severe in each case than in the current environment. (For more see the full CSIS report here: http://csis.org/publication/defense-budgets-double-whammy-drawing-down-while-hollowing-out-within)

While the cuts in defense spending are real, they vary greatly across the military by branch and function. For instance, from 2013 to 2014, Army procurement is being cut 3% and Marine procurement will be down 14% while Navy procurement spending is set to rise to $39 billion—a 13% increase from the year before—and Air Force procurement spending is set to increase by 1%. Moreover, the Army’s and the Navy’s Operation and Maintenance budgets will both be cut by 4%, while the Marine’s Operation and Maintenance budget is set to expand by 4% and the Air Force’s by 5%. A public advocacy infographic shop, Timeplots, assembled an impressive infographic depicting the size and scale of the changes in government spending from 2013 through 2014, including defense spending by spending category. See the full infographic here: http://visual.ly/death-and-taxes-2014-us-federal-budget

In order to help make sense of the community impact of the recent pending defense cuts, Chmura created the following analysis to see which metropolitan statistical areas (MSAs) have been most impacted by the recent defense spending cuts. At the aggregate level, some of the largest MSAs have seen the most dramatic cuts in the period from fiscal year 2010 to fiscal year 2012. However, after adjusting for the size of the MSA, several much smaller areas stand out for the level of cuts they have experienced over this period. Similarly, by aggregate dollar figure, a few of the largest MSAs have gained the largest increases in government contracts over this period, but after adjusting for the size of the labor market in these metro areas, several much smaller U.S. metros stand out in terms of the contractual gains.

MSATotal Defense Contract Cuts 2010 to 2012

New Orleans-Metairie-Kenner, LA MSA

-$2,205,619,764

Oshkosh-Neenah, WI MSA

-$1,894,064,198

Washington-Arlington-Alexandria, DC-VA-MD-WV MSA

-$1,486,275,593

St. Louis, MO-IL MSA

-$1,452,512,297

Tucson, AZ MSA

-$1,383,208,070

Memphis, TN-MS-AR MSA

-$1,375,595,134

San Antonio, TX MSA

-$1,207,102,665

New York-Northern New Jersey-Long Island,NY-NJ-PA MSA

-$1,099,061,559

Riverside-San Bernardino-Ontario, CA MSA

-$1,074,130,554

Hartford-West Hartford-East Hartford, CT MSA

-$1,073,509,712

MSATotal Defense Contract Cuts 2010 to 2012$ Cut per Capita

Oshkosh-Neenah, WI MSA

-$1,894,064,198

-$11,342

Johnstown, PA MSA

-$782,445,252

-$5,446

Hinesville-Fort Stewart, GA MSA

-$187,835,293

-$2,411

Manhattan, KS MSA

-$289,552,359

-$2,278

Crestview-Fort Walton Beach-Destin, FL MSA

-$367,857,608

-$2,034

New Orleans-Metairie-Kenner, LA MSA

-$2,205,619,764

-$1,889

Columbus, GA-AL MSA

-$438,654,422

-$1,488

Tucson, AZ MSA

-$1,383,208,070

-$1,411

York-Hanover, PA MSA

-$572,827,209

-$1,317

Binghamton, NY MSA

-$308,778,050

-$1,227

MSATotal Defense Contract Gains 2010 to 2012

Seattle-Tacoma-Bellevue, WA MSA

$2,040,368,382

Portland-South Portland-Biddeford, ME MSA

$1,610,361,678

Phoenix-Mesa-Scottsdale, AZ MSA

$1,581,407,851

Amarillo, TX MSA

$1,311,645,313

Norwich-New London, CT MSA

$1,244,943,132

MSAFederal Contract Gains 2010 to 2012$ Gains Per Capita

Amarillo, TX MSA

$1,311,645,313

$5,249

Norwich-New London, CT MSA

$1,244,943,132

$4,543

Portland-South Portland-Biddeford, ME MSA

$1,610,361,678

$3,132

Bellingham, WA MSA

$476,416,342

$2,369

Huntsville, AL MSA

$584,875,448

$1,401

The labor market impact of these spending cuts can vary widely depending on the type and nature of the defense spending. Every industry in the area will have a different economic impact based on the size of its local supply chain and the spending spillover from its directly employed workers. However, it stands to reason that these spending cuts, as steep as they are, can be a driving force to upset labor markets in many of the nation’s MSAs, both big and small. To learn more about Chmura’s expertise and research regarding defense spending and supply chain mapping, contact us here.

Contract Dollars Gain/Loss per Capita by MSA, 2010 to 2012

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The Jobs Are Coming Back, Just Not Always Where They Were Lost

The latest U.S. jobs report from early July indicates the national economy continues to add jobs at a slow but steady pace. The latest data indicates the economy has added approximately 195,000 jobs each month for the past three months. If we go back to January 2011, the nation added on average close to 185,000 jobs per month. The U.S. economy is gradually recovering and now the labor market may even be gaining some momentum.

In February 2010, the U.S. job market had hit rock bottom. From the pre-recession high of 138 million people employed, the economy had shed some 8.7 million jobs—more than 6% of total nonfarm employment had vanished. Since then, total nonfarm employment has grown to almost 136 million, which is only 1.5% below the former peak. It is likely—assuming the current pace of about 180,000 new jobs created per month continues—that the U.S economy will reach its previous peak of employment in mid-2014. At that point, the U.S. economy will be deemed to have fully “recovered” from the previous recession’s job losses.

How Long Will the Recovery Take?

However, while the jobs are coming back, they are not where we left them. As of the first quarter of 2013, in fact, a small number of states have already fully recovered the number of jobs lost during the last recession and have begun to set new records of employment. These states are Texas, North Dakota, Alaska, South Dakota, West Virginia, and Utah as well as the District of Columbia. Close behind these areas are another 14 states that have recovered between 98% and 99% of the jobs they lost during the previous recession—states like Massachusetts, New York, and Nebraska. These states, along with the U.S. economy as a whole, will likely regain their previous employment peak within the next 12 months.

For the remainder of the country, however, the recovery of lost jobs has been much less robust and many states remain well below their pre-recession employment levels. For instance, California’s economy was hit much harder in the previous recession than the nation; at its nadir, California had lost nearly 9% of nonfarm employment. California’s labor market is rebounding, but it still remains as of roughly 4% below its previous employment peak. It is likely that it will take California well into 2015 to recover its previous peak employment, whereas by then many other states will be setting new employment records. States like Arizona and Florida are more than 7% below their previous employment peaks, while Nevada and Michigan are more 10% below their peak nonfarm employment.

In many previous recessions, employment bounced back quickly in the nation, typically within two years of the recession starting and many times in the very same places that lost employment in first place. This recession has seen the slowest employment recovery since the Great Depression, and the jobs that are being created today are in different sectors of the economy and in different localities. The map below depicts by county how employment levels have recovered from the previous pre-recession peaks across the nation. While some areas of the country enjoy new employment records, many others are still years away from recovering all the jobs that were lost, and in some locations it is likely that the job market may never recover back to previous peak. The jobs are coming back, just not where we left them.

Peak Employment Map

Source: JobsEQ®

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