GREATER CHARLOTTE IN THE GLOBAL ECONOMY BENCHMARKING THE REGION’S GLOBAL COMPETITIVENESS ASSETS

Brookings Metropolitan Policy Program March 23, 2016

1

Major forces necessitate global economic engagement Intensifying Globalization The cross-border flow of goods, capital, and services has exploded in recent decades…

Significant Global Demand …and foreign markets continue to drive global economic growth

$26 trillion

36% of global GDP $3 trillion

1980

86% of global economic growth will occur outside the United States from 2015 to 2020

2012

Sources: James Manyika and others, “Global Flows in a Digital Age,” McKinsey Global Institute, 2014; World Economic Outlook, International Monetary Fund, 2016.

2

Metropolitan economies must compete globally Taking part in global markets is no longer a choice for metropolitan leaders. City and regional leaders can either seize the opportunities afforded by the global dynamics or risk falling victim to the downsides of globalization. This benchmarking study analyzes Greater Charlotte’s competitive position through four factors—trade and investment, innovation, talent, and infrastructure—compared to 19 other city-regions that most closely resemble Greater Charlotte’s size, wealth, productivity, industrial structure, and competitiveness factors.

Regional Competitiveness Framework Trade

Innovation

Talent

Infrastructure

3

Peer metro areas

Minneapolis Denver Phoenix

Austin

San Antonio

Montreal Cleveland Indianapolis

Birmingham Frankfurt

Stockholm Copenhagen Hamburg Munich Zurich

Kansas City Dallas Atlanta

Melbourne

4

Bottom Line – Charlotte’s competitive position Economic performance: Overall economic growth has been robust over the past decade, but on metrics of inclusion Greater Charlotte has lagged. Trade and Investment: Greater Charlotte is very globally-oriented. Exports and foreign direct investment account for a disproportionate share of the regional economy, led by tradable anchors like machinery, transportation equipment, and financial services. But the region is at risk of losing ground to peer metropolitan economies unless it shores up its competitive drivers: •

Innovation: Build up very low levels of research and development, technology commercialization, and venture capital investment;

•

Talent: Help employers overcome challenges in filling job vacancies, especially occupations that require STEM skills; and

•

Infrastructure: Address lagging broadband speeds and disparities in broadband access by income. 5

1 | Economic Performance

Greater Charlotte is a wealthy region

$126.2 billion 2.4 million

Metropolitan GDP 107th largest metro in the world

$53,142 Metropolitan GDP per Capita 61st wealthiest metro in the world

Definition: 10-county metropolitan statistical area Source: Brookings analysis of Moody’s Analytics data.

7

Metro Monitor: 3 elements of economic performance Growth indicators capture net change in the total size of a metropolitan area’s economy. We measure growth through the change in total jobs, change in gross metropolitan product (GMP), and change in aggregate wages.

Prosperity refers to the wealth and income produced by an economy on a per-capita or per-worker basis. We measure prosperity through the change in GMP per job, change in the average annual wage, and change in GMP per capita.

Inclusion indicators measure how the benefits of growth and prosperity in a metropolitan economy—specifically, employment and income—are distributed among people. We measure inclusion by the change in the median change, change in the relative income poverty rate, and the change in the employment-to-population ratio.

Source: Richard Shearer and others, “Metro Monitor 2016” (Washington: Brookings Institution, 2016).

8

Greater Charlotte’s economy has grown rapidly

Jobs

1 year

5 years

10 years

+3.6% (13th)

+11.3% (14th)

+15.3% (16th)

+3.0% (22nd)

+15.6% (17th)

+23.5% (18th)

+5.1% (17th)

+18.1% (10th)

+23.3% (20th)

17th

10th

(100 metro rank)

Gross Metro Product (GMP) (100 metro rank)

Aggregate Wages (100 metro rank)

OVERALL GROWTH (100 metro rank)

18th Source: Brookings Metro Monitor.

9

But growth in prosperity metrics has slowed

Average Wage

1 year

5 years

10 years

+1.4% (50th)

+6.1% (8th)

+6.9% (34th)

+1.1% (51st)

+6.6% (35th)

-2.5% (73rd)

(100 metro rank)

GMP Per Capita (100 metro rank)

GMP Per Worker

-0.6% (74th)

+3.9% (36th)

59th

21st

+7.1% (33rd)

(100 metro rank)

OVERALL PROSPERITY (100 metro rank)

50th Source: Brookings Metro Monitor.

10

And growth has not necessarily been inclusive

Employment/ Population

1 year

5 years

10 years

+1.4% (39th)

+1.2% (44th)

-4.6% (82nd)

-1.3% (68th)

-7.5% (84th)

-10.0% (86th)

(100 metro rank)

Median Wage (100 metro rank)

Rel. Poverty Rate

+1.2% (73rd)

-2.0% (36th)

65th

59th

+12.9% (92nd)

(100 metro rank)

OVERALL INCLUSION (100 metro rank)

95th Source: Brookings Metro Monitor.

11

Bottom Line – Economic performance Growth has been strong. Since 2004 and in the aftermath of the recession, overall growth in jobs, output, and aggregate wages has been among the top fifth of U.S. metropolitan areas. Prosperity has improved as well, but not as rapidly as overall growth. Output per capita, output per worker, and average yearly wages have all expanded, but less so in the past year. Inclusive growth remains elusive, however. Over the past 10 years, the employment-to-population ratio and the median wage have both declined, and the relative income poverty rate has increased.

12

2 | Global Trade and Investment

Two components of global economic orientation

1

Exports are sales of goods and services to foreign

2

Foreign direct investment arises when a foreign entity

entities (people or companies).

invests in a business enterprise in the United States.

Source: Emilia Istrate and Nicholas Marchio, “Export Nation 2012: How U.S. Metropolitan Areas are Driving National Growth” (Washington: Brookings Institution, 2012).; Dwight Perkins, Steven Radelet, and David Lindauer, “Investment, Productivity, and Growth.” In Economics of Development: Sixth Edition. (New York: W.W. Norton & Company, 2006).

14

Why exports matter Exports are driving economic growth and wage gains. 27.0% 20.0% 17.0%

8.1%

Share of Job Growth ‘09-’14

Share of GDP Growth ‘09’14

Services Trade Wage Premium

Manuf. Trade Wage Premium

Middle market firms benefit from exports. More likely to grow revenue (76 percent of exporters vs. 64 percent of nonexporters) More likely to add employees (51 percent of exporters vs. 39 percent of nonexporters). Source: Moody’s Analytics; Brookings, “Export Nation,” 2014. J. Bradford Jensen, “Global Trade in Services,” Petersen Institute for international Economics, 2011; David Riker, “Do Jobs in Export Industries Still Pay More?”, International Trade Administration, 2010. Brookings, National Association for the Middle Market, “Accelerating Exports in the Middle Market,” 2014. The Middle Market is defined as companies with annual revenues between $10 million and $1 billion.

15

Greater Charlotte’s economy is export-intensive Greater Charlotte relies upon exports for a greater share (14 percent) of its economic output than any U.S. peer metro area, as exports have grown faster than the economy as a whole since 2003. Exports supported more than 110,000 jobs in 2014. Exports, GDP, and jobs, Greater Charlotte and peer regions

Metro Area

Change in Export Export share of Share GDP, 2003GDP, 2014 2014

Gross exports (USD billion), 2014

Annualized Export Growth, Annualized GDP Growth, 2003-14 2003-14

Total Jobs Supported by Exports, 2014

Change Total Export Jobs, 2003-2014

Charlotte

14.0%

5.1%

17.7

5.7%

2.3%

110,043

40,973

Indianapolis

13.3%

4.0%

15.0

4.1%

1.7%

81,619

18,693

Dallas

13.2%

4.3%

54.9

7.0%

3.0%

315,544

106,507

Cleveland

11.7%

3.2%

13.6

2.6%

0.5%

81,371

9,007

Kansas City

9.6%

3.4%

10.2

4.8%

1.1%

64,588

18,235

Minneapolis

9.6%

2.5%

20.3

4.0%

1.1%

132,698

21,655

Austin

9.2%

-0.7%

10.0

6.3%

4.2%

62,113

10,956

Atlanta

9.0%

3.1%

26.6

4.6%

1.3%

188,807

56,222

San Antonio

8.9%

4.1%

9.2

8.2%

2.8%

57,160

26,061

Phoenix

8.9%

-1.6%

18.4

1.4%

1.7%

123,392

-18,814

Denver

8.0%

2.7%

13.6

5.6%

2.2%

94,701

29,740

Source: Brookings analysis of data from Census, BEA, Moody’s analytics, BLS, NAFSA, IRS, EIA, and Sabre.

16

Adv. manufacturing/financial services drive exports Machinery, chemicals, transportation equipment, and financial services contributed 63 percent of export growth between 2008 and 2014. Greater Charlotte exports by sector, 2014, and contribution to export growth, 2008-2014

Source: Brookings analysis of data from Census, BEA, Moody’s analytics, BLS, NAFSA, IRS, EIA, and Sabre.

17

Why foreign direct investment matters Global investment flows are growing. Worldwide foreign direct investment has increased from just over $200 billion in 1990 to $1.2 trillion in 2014. Foreign-owned firms pay higher wages. Average wages in foreign-owned firms are $77,000 versus $60,000 in domestic firms. Foreign-owned firms contribute disproportionately to export and innovation capacity by transmitting new technologies and corporate best practices. 20.3% 18.9%

5.0%

Foreign firms share of:

U.S. workers

U.S. corporate R&D

U.S. goods exports

Source: Brookings analysis of UNCTAD, WTO and ITC, based on Eurostat, OECD, IMF, UNSD, and other international and national sources; Bureau of Economic Analysis.

18

Greater Charlotte’s economy is FDI-intensive Greater Charlotte relies upon foreign firms for a greater share (6.8 percent) of its employment base than any peer metro area. However, Charlotte is one of only two U.S. peer metros where FDI’s share of employment has declined since 1991. Jobs in foreign-owned establishments (FOEs), Greater Charlotte and peer regions, 2011

Metro Area

Share of Jobs in FOEs

Jobs in FOEs

Charlotte

6.8%

48,810

Atlanta

6.8%

134,611

Indianapolis

6.5%

49,910

Kansas City

5.6%

47,371

Dallas

5.2%

134,111

Minneapolis

5.0%

75,593

Change in share of jobs in foreign-owned establishments (FOEs), Greater Charlotte and peer regions, 1991-2011

Charlotte 8.2%

6.8% 6.8% 6.5%

Atlanta 5.6%

5.6%

Cleveland 5.6% 5.2%

Denver

4.8%

50,099

Dallas 4.8%

Austin

4.6%

29,180

Denver 4.5% Minneapolis 4.2%

Cleveland

3.9%

34,010

Phoenix 3.8%

Phoenix

3.7%

55,742

Indianapolis 3.7%

San Antonio

3.0%

21,576

Kansas City 3.2%

5.0% 4.8% 4.6%

3.9% 3.7%

3.0%

Austin 2.7% San Antonio 2.4%

Source: Brookings analysis of D&B / NETS, BEA, and Moody’s Analytics data

1991

2011

19

Charlotte FDI historically skews toward greenfield Greenfield foreign direct investment accounts for one-third of Greater Charlotte’s investment, the second highest share among peer U.S. peer metro areas. Nearly half (48 percent) of FDI comes from just four European countries, led by Germany. Share of FOE jobs by mode of entry, Greater Charlotte and peer regions, 1991-2011 San Antonio

Charlotte

36.9%

28.2%

33.3%

27.8%

39.0%

29.8%

Phoenix

29.4%

Kansas City

28.0%

Indianapolis

27.0%

28.4%

44.5%

Cincinnati

26.9%

30.1%

43.0%

Dallas

26.3%

Austin

25.4%

Atlanta

24.4%

Minneapolis

17.7%

36.5% 25.8%

33.0%

Greenfield

37.1%

England

12.9%

Belgium

10.9%

France

8.3%

48.8% 36.5% 55.5%

M&A

15.4%

41.8%

39.0% 26.8%

Germany

38.2%

37.7% 30.2%

Share of FOE Jobs

35.0%

Denver

32.1%

FOE jobs by source country, thousands, Greater Charlotte, 2011

Before 1991

Canada

7.8%

0

2000

4000

6000

8000

Total FOE Jobs

Source: Brookings analysis of D&B / NETS, BEA, and Moody’s Analytics data

20

But recent greenfield FDI flows are about average Since 2009, Charlotte ranks 10th of 20 global metros in total Greenfield FDI flows per capita, led by sectors such as automotive, engines/turbines, industrial machinery, renewable energy, and textiles. Greenfield foreign direct investment per capita, Greater Charlotte and peer regions, 2009-2015 Austin

$5,511

Birmingham (UK)

$3,563

Melbourne

$2,867

Zurich

$2,614

Montreal

$2,462

Frankfurt

$1,887

Stockholm

$1,688

Atlanta

$1,662

Copenhagen

$1,348

Munich

$1,170

San Antonio

$1,065

Indianapolis

$896

Hamburg

$882

Dallas

$787

Kansas City Denver Minneapolis Cleveland

Rest 50%

$1,456

Charlotte

Phoenix

Greenfield FDI, top sectors, 2009-2015

$673 $603 $567 $443 $386

Textiles 7% Renewables 8% Machinery 8% Turbines 11% Automotive 16% Source: Brookings analysis of fDi Intelligence data.

21

Bottom Line – Trade and Investment Exports and foreign direct investment account for a significant share of Greater Charlotte’s economy. The region is the most export-intensive and FDI-intensive metro area among its U.S. peer group. Key traded sectors like financial services, management consulting, and advanced manufacturing anchor exports and FDI. However, in the global competition for Greenfield FDI, Greater Charlotte is about average compared to all its global peer metro economies. Greater Charlotte’s unique reliance on global markets demands a sharp focus on the assets that determine global competitiveness: innovation, talent, and infrastructure. Trade and Investment

CLT ATL ATN BIR

CLE CPN DAL DEN FRT HAM IND

KC

MEL MPLS MON MUN PHX

SA

STK ZUR

Export share of GDP, 2014

1

8

7

-

4

-

3

11

-

-

2

5

-

6

-

-

10

9

-

-

% change in export share of GDP, 2003-2014

1

7

10

-

6

-

2

8

-

-

4

5

-

9

-

-

11

3

-

-

Annualized export growth, 2003-2014

4

7

3

-

10

-

2

5

-

-

8

6

-

9

-

-

11

1

-

-

Share of jobs in foreign-owned establishments (FOEs), 2011

1

2

8

-

9

-

5

7

-

-

3

4

-

6

-

-

10

11

-

-

Change in share of jobs in FOEs, 1991-2011

10

4

3

-

11

-

7

8

-

-

1

2

-

5

-

-

9

6

-

-

Greenfield FDI per capita, 2009-2015

10

8

1

2

20

9

15

18

6

14

13

16

3

19

5

11

17

12

7

4

Peer Rank

Lower

Higher

CLT = Charlotte

CPN = Copenhagen

IND = Indianapolis

MUN = Munich

ATL = Atlanta

DAL = Dallas

KC = Kansas City

PHX = Phoenix

ATN = Austin

DEN = Denver

MEL = Melbourne

SA = San Antonio

BIR = Birmingham (UK)

FRT = Frankfurt

MPLS = Minneapolis

STK = Stockholm

CLE = Cleveland

HAM = Hamburg

MON = Montreal

ZUR = Zurich

22

3 | Innovation

Why innovation matters Innovative capacity matters because it is the most durable economic advantage for firms and regions in advanced economies. It is a critical input to competitiveness, and an important driver of rising productivity which, in turn, generates increases in living standards. Innovation takes many forms, and includes improvements in products, services, processes, and management techniques. It can be hard to measure and even harder to predict. But research suggests that a few things influence the quality of innovation ecosystems: research and development (R&D), commercialization of that R&D into new products and services (e.g. patents), the presence of entrepreneurial activities that are linked to technology development, and advanced industrial production.

Source: For a full review of the role of innovation in metropolitan growth, see George Washington Institute of Public Policy and RW Ventures, “Implementing Regionalism.”

24

R&D expenditures significantly trail peer metros A wide literature confirms that research activities help advance economic development. Greater Charlotte ranks second to last among its global peers in university R&D expenditures, both overall and business-funded. All R&D Conducted at Universities per $1,000 of GDP, 2013 or latest year available Copenhagen

$10.42

Montreal

$7.86

Stockholm

$7.47

Austin

Austin

$0.70

Denver

$0.30

Atlanta

$0.27

$6.54

Melbourne

$6.11

Atlanta

$4.93

Hamburg

$4.25

Cleveland

$4.03

Minneapolis

$3.79

Munich

$3.68

Frankfurt

$3.67

Birmingham (UK)

$3.00

Denver

$2.73

Indianapolis

$2.63

San Antonio

Indianapolis

$0.13

Minneapolis

$0.13

San Antonio

$0.12

Cleveland

$0.10

Dallas

$0.09

Phoenix

$0.06

Charlotte

$0.01

Kansas City

$0.01

$2.39

Phoenix

$1.93

Dallas Charlotte Kansas City

Business-Funded R&D Conducted at Universities per $1,000 of GDP, 2013

$1.69 $0.28 $0.25 Source: NSF Higher Education Research and Development Survey data.

25

Charlotte lacks a major scientific research institution Unlike its peer metros, Charlotte does not house a research institution ranked in the top 750 in the world by CWTS and Leiden University in terms of scientific impact. UNCCharlotte is the region’s most significant research asset. Number of universities in CWTS/Leiden University Top 750 List, 2010-2013

5 4

Melbourne Montreal Stockholm

Dallas

0

3

Birmingham Munich San Antonio Zurich

2

Atlanta Copenhagen Frankfurt

1

Charlotte

Austin Cleveland Denver Hamburg Indianapolis Kansas City Minneapolis Phoenix

Higher education R&D expenditures, top source by metro area, FY2013 Rank

Institution

Metro Area

R&D expenditures (millions USD)

15

University of Minnesota

Minneapolis

$858,378

23

Georgia Institute of Technology

Atlanta

$730,488

31

University of Texas

Austin

$634,132

45

University of Texas Southwestern Medical Center

Dallas

$440,620

50

Case Western Reserve University

Cleveland

$425,788

52

University of Colorado

Denver

$409,443

53

Arizona State University

Phoenix

$405,154

70

Indiana University-Purdue University

Indianapolis

$332,760

113

University of Texas Health Science Center

San Antonio

$175,983

236

University of Missouri

Kansas City

$28,829

246

University of North Carolina

Charlotte

$24,764

Source: Brookings analysis of Centre for Science and Technology Studies (CWTS) and Leiden University data and NSF Higher Education Research and Development Survey data.

26

Charlotte’s patenting intensity lags peers Total patenting has increased in Greater Charlotte over time, but the region still trails global peers in commercialization. In the 2008 to 2012 period, Charlotte produced 3.9 patents per 10,000 residents, third to last among peers. Total patents, Greater Charlotte,1977-2012

Patents per 10,000 inhabitants, 2008-2012 Stockholm

24.8

Minneapolis

22.4

Copenhagen

19.7

Munich 1,010

17.4

Frankfurt

14.2

Zurich

13.9

Austin

829

11.4

Indianapolis

10.8

Cleveland

10.6

Hamburg 504

7.0

Denver

6.4

Dallas 299

92 20

27

1977-1982

1983-1987

1988-1992

1993-1997

1998-2002

2003-2007

2008-2012

5.8

Phoenix

5.2

Atlanta

5.1

Montreal

5.0

Birmingham (UK)

4.8

Melbourne

4.8

Charlotte Kansas City San Antonio

3.9 2.8 2.0

Source: Brookings analysis of OECD REGPAT data.

27

IT and manufacturing generate most patents Charlotte’s largest technology families are information technology, advanced manufacturing, life sciences, and energy. Top patenting technology groups, Greater Charlotte Region, 2008-2012 Rank

Tech Subgroup

Tech Family

Patents

1

IT methods for management

Information technology

121

2

Computer technology

Information technology

110

3

Medical technology

Life sciences

69

4

Civil engineering

Energy and infrastructure

47

5

Materials, metallurgy

Advanced manufacturing

44

6

Control

Precision systems

44

7

Other special machines

Advanced manufacturing

41

8

Chemical engineering

Advanced manufacturing

40

9

Optics

Precision systems

33

10

Measurement

Precision systems

33

Source: Brookings analysis of OECD REGPAT data.

28

Startup activity trails most U.S. peer metros Entrepreneurship enables innovation by translating new ideas into market-ready products and services. Charlotte ranked 25th (of 40 metros) in the Kauffman Foundation’s 2015 Startup Activity Index. Startup activity rank, Kauffman Foundation, 2015 Austin Denver San Antonio Atlanta Phoenix Dallas

Components of startup activity index The Kauffman Index Startup Activity ranks forty large metro areas in 2015 based on three metrics:

1 5 10

•

Rate of new entrepreneurs—percent of adult population that became entrepreneurs in a given month calculated as a 3-year moving average;

•

Opportunity share of new entrepreneurs—percent of new entrepreneurs who were not unemployed before starting their business calculated as a 5-year moving average;

•

Startup density—number of startup firms per 100,000 resident population defining a startup as firms less than one-year old employing at least one person besides the owner.

13 14 15

Charlotte Indianapolis Kansas City Cleveland Minneapolis

25 28 29 35 37

Source: The Kauffman Index: Startup Activity 2015.

29

Very few Charlotte firms attract venture capital Venture capital (VC) provides funds for innovative enterprises positioned for high growth and the potential to create and capture new markets. VC firms stimulate local economies; they are three to four times more patent-intensive than other firms, and are much more likely to translate R&D into high-growth ventures. Total venture capital investments per capita, Greater Charlotte and peer metros, 2005-2014 Austin Denver Minneapolis Stockholm Atlanta Dallas Copenhagen Montreal Indianapolis Cleveland Zurich Kansas City Phoenix Munich San Antonio Charlotte Frankfurt Melbourne Hamburg Birmingham (UK)

$4,084 $1,471 $1,085 $1,056 $919 $882

Top industries, by venture capital investment, Greater Charlotte, 2005-2014

$766 $605

Rank

Industry

Total VC Investment (millions USD)

$583

1

Software

$72.37

$527

2

Healthcare Services

$59.93

$510

3

Commercial Products

$58.30

$478

4

Other Financial Services

$44.09

$474

5

Commercial Services

$42.94

6

Media

$36.38

7

Chemicals

$19.95

8

Services (Non-Financial)

$16.27

9

Pharma and Biotech

$16.07

10

Consumer Durables

$11.70

$407 $293 $174 $86 $85 $67 $42

Source: S. Kortum and J. Lerner, “Assessing the Contribution of Venture Capital to Innovation.” Rand Journal of Economics 31(2000), 674-92; Dirk Engel and Max Keilbach, “Firm Level Implications of Early Stage Venture Capital Investment—An Empirical Investigation.” (Max Planck Institute of Economics, 2002). Brookings analysis of Pitchbook data.

30

Advanced industries: America’s tech super-sector Advanced industries are technology-intensive manufacturing and services industries, and their profile and performance provide a helpful measure of the nation’s “tech” sector at its broadest. Defining advanced industries

Why advanced industries matter

50 industries across manufacturing, services, and energy that meet two criteria:

50 industries across manufacturing, services, and energy, account for the U.S. share of: 90.3%

$450

81.2%

Minimum industry R&D per worker, 80th percentile of industries

57.5%

2.7

21 percent

17.9%

8.9%

Minimum share of workers in industry whose occupations require a high degree of STEM knowledge

Jobs

GDP

Exports

Patents

R&D

Source: Mark Muro and others, “America’s Advanced Industries” (Washington: Brookings Institution, 2015).

31

Charlotte has a diversity of advanced industries Charlotte’s advanced industries range from high-end services to advanced manufacturing to energy generation and distribution. This mix provides a range of employment opportunities at different wage and education levels. Ten largest advanced industries by employment, Greater Charlotte, 2014 Rank

Share of advanced Annual compensation industry jobs per worker ($)

Advanced Industry

Jobs

1

Management and Technical Consulting

15,250

15.9%

85,931

2

Computer Systems Design

10,100

10.5%

106,348

3

Architectural and Engineering

9,020

9.4%

78,724

4

Motor Vehicle Parts Manufacturing

7,590

7.9%

52,601

5

Data Processing and Hosting

6,800

7.1%

121,391

6

Electric Power Generation and Distribution

3,410

3.5%

136,079

7

General Purpose Machinery Manufacturing

3,030

3.1%

68,132

8

Engine and Turbine Manufacturing

2,480

2.6%

98,093

9

Other Miscellaneous Manufacturing

2,420

2.5%

37,595

10

Medical Equipment Manufacturing All advanced industries

2,390 96,210

44,854 86,370

All industries

1,082,870

2.5% 100% --

54,763

Source: Brookings analysis of Moody’s Analytics data.

32

But trails peers in advanced industries intensity Charlotte’s share of employment and output generated by advanced industries trail most peer metros, but advanced industries employment growth has been fifth highest since 2008. Advanced industries employment and output profile, Charlotte and peer regions Rank Metro Area

Share of total workforce, 2014

Jobs, 2014

Annualized job growth rate, Share of GDP, GDP (USD billion), 2008-2014 2014 2014

1

Austin

12.8%

119,480

2.9%

25.6%

26.7

2

Dallas

10.4%

344,650

0.7%

23.9%

95.8

3

Denver

10.3%

139,690

1.8%

19.2%

30.0

4

Kansas City

9.6%

99,850

1.5%

14.4%

14.5

5

Minneapolis

9.5%

181,990

0.3%

16.4%

33.3

6

Indianapolis

9.1%

91,920

0.5%

23.5%

23.8

7

Cleveland

8.9%

93,520

-0.9%

13.7%

14.8

8

Charlotte

8.9%

96,210

1.4%

16.6%

19.1

9

Atlanta

8.8%

221,320

0.7%

14.5%

39.8

10

Phoenix

8.2%

154,900

-0.1%

14.9%

28.0

11

San Antonio

6.5%

65,440

2.9%

15.9%

16.0

Source: Brookings analysis of Moody’s Analytics data.

33

Bottom Line – Innovation Fast growth in advanced industries employment since 2008 shows that Greater Charlotte does house innovative firms in management and technical consulting, advanced manufacturing, and computer systems design. But very low levels of research and development, technology commercialization, and venture capital reveal that Greater Charlotte’s innovation ecosystem is not yet at the same level as global peers. Unlike its peer metro economies, Greater Charlotte does not house major research universities or medical centers, major engines of the innovation economy. UNC-Charlotte is the region’s most significant research asset, but it is not currently ranked among the top 750 universities with the greatest scientific impact. Innovation R&D conducted at universities per $1,000 of GDP, 2013 Business-funded R&D conducted at universities per $1,000 GDP, 2013 Percentage of university scientific publications cited in top 10 percent, 2010-2013 Percentage of university scientific publications conducted with industry, 2010-2013 Patents per 10,000 inhabitants, 2008-2012 Startup activity rank, Kauffman Foundation, 2015 Venture capital investments per capita, 2005-2014 Share of jobs in advanced industries, 2014 % change in advanced industries employment, 2008-2014

Peer Rank

Lower

Higher

CLT ATL ATN BIR CLE CPN DAL DEN FRT HAM IND 18 6 4 12 8 1 17 13 11 7 14 10 3 1 7 8 2 4 20 7 4 9 5 11 8 2 15 10 13 20 5 6 16 7 1 10 2 8 12 3 18 14 7 16 9 3 12 11 5 10 8 7 4 1 10 6 2 8 16 5 1 20 10 7 6 2 17 19 9 8 9 1 7 2 3 6 5 7 1 11 6 3 8

KC MEL MPLS MON MUN 19 5 9 2 10 11 5 19 12 3 18 6 18 19 14 17 9 19 17 2 15 4 9 11 12 18 3 8 14 4 5 4 9 -

PHX 16 9 16 15 13 5 13 10 10

SA 15 6 17 11 20 3 15 11 2

STK ZUR 3 14 1 4 13 1 6 4 11 -

CLT = Charlotte

CPN = Copenhagen

IND = Indianapolis

MUN = Munich

ATL = Atlanta ATN = Austin BIR = Birmingham (UK) CLE = Cleveland

DAL = Dallas DEN = Denver FRT = Frankfurt HAM = Hamburg

KC = Kansas City MEL = Melbourne MPLS = Minneapolis MON = Montreal

PHX = Phoenix SA = San Antonio STK = Stockholm ZUR = Zurich

34

4 | Talent

Why Talent Matters Human capital—the stock of knowledge, skills, expertise, and capacities embedded in the labor force—is of critical importance to enhancing productivity, raising incomes, and driving economic growth. Producing, attracting, and retaining educated workers; creating jobs for those workers; and connecting those workers to employment through efficient labor markets all matter for regional competitiveness and ensuring broad-based economic opportunity.

As labor markets have become more global, skilled immigrants have become an increasingly important source of talent for U.S. metro economies. Foreign students studying at U.S. universities—the well-educated workers of tomorrow—are a new and growing input to metropolitan economic competitiveness and cultural diversity.

Source: For a full review of the role of human capital in metropolitan growth, see George Washington Institute of Public Policy and RW Ventures, “Implementing Regionalism.”

36

Charlotte’s workforce is well-educated About 35 percent of the region’s workforce has obtained a post-secondary degree, placing Charlotte in the middle of its highly educated peer group. Percentage of population above 15 years old with tertiary education, 2013 Minneapolis

43.3

Denver

42.6

Zurich

42.0

Stockholm

42.0

Austin

41.2

Copenhagen

39.5

Kansas City

36.9

Atlanta

36.6

Melbourne

36.0

Charlotte

35.1

Munich

34.1

Dallas

33.8

Phoenix

33.3

Indianapolis

33.3

Cleveland

33.0

Birmingham (UK)

32.2

Frankfurt

32.1

Montreal San Antonio Hamburg

31.2 28.9 28.7

Source: Brookings analysis of U.S. Census Bureau data.

37

Yet regional employers struggle to fill job vacancies Despite its educated workforce, Charlotte’s employers nevertheless face challenges in filling job vacancies. Among U.S. peers, Charlotte’s online job postings had the longest median duration (11 days) before being filled. Median duration of job openings (days), Greater Charlotte and peer regions, 2013 11 10 9 8

8 7

7 6

4

Charlotte

Indianapolis

Austin

Dallas

Phoenix

Kansas City San Antonio

Atlanta

Cleveland

1

1

Denver

Minneapolis

Source: Brookings analysis of Burning Glass data.

38

Employer demand for STEM skills is high About one in five Charlotte workers work in STEM occupations. Nearly half of all job ads are in STEM occupations, the third highest share in the nation. Share of workers in STEM occupations, Greater Charlotte and peer regions, 2013 25.4%

23.9%

22.7%

22.5%

22.2%

21.9%

21.7%

21.5%

21.3%

20.8% 18.3%

Denver

Austin

Indianapolis

Minneapolis

Cleveland

Charlotte

Kansas City

Dallas

Atlanta

Phoenix

San Antonio

Share of job ads in STEM occupations, Greater Charlotte and peer regions, 2013 48.3%

Charlotte

47.4%

Austin

44.9%

Denver

42.9%

Atlanta

42.7%

Dallas

42.1%

Kansas City

41.7%

Minneapolis

41.5%

Cleveland

39.3%

Phoenix

37.4%

San Antonio

35.4%

Indianapolis

Source: Brookings analysis of Burning Glass data.

39

And STEM jobs are among the hardest to fill The median duration for STEM job ads are higher (14 days) than job ads overall (11 days), and middle-skill STEM jobs—those requiring more than a high school degree but less than a bachelor’s degree—have the longest fill times. Median duration of STEM job openings (days), Greater Charlotte and peer regions, 2013 Austin

17

San Antonio

32

15 14

Charlotte Kansas City

13

Phoenix

12

Dallas

12

Indianapolis

18

Median duration STEM jobs overall: 14 days 10

11

Atlanta

7

Cleveland

5

Denver Minneapolis

Median duration of STEM job openings (days), by education requirement, Greater Charlotte, 2013

2 1

High School or Less

Some college or Associate's degree

Bachelor's Degree

STEM Jobs Source: Brookings analysis of Burning Glass data.

40

Meanwhile the labor force participation rate is falling Charlotte’s labor force participation rate has declined by a larger margin than all global peer metros except Atlanta and Phoenix since 2000, exacerbating labor supply-demand mismatches. Labor force participation rate, 2000-2014 San Antonio Birmingham (UK) Phoenix Atlanta

2000 72.8

69.6

70.6 72.2 79.3

71.3

80.4

71.9

Charlotte

81.2

73.7

Indianapolis

75.7

Cleveland

80.7 79.4

76.4

Austin

82.5

76.8

Montreal

78.2

77.3

Melbourne

75.0

Munich

74.4

Kansas City

77.4 78.5 82.1

78.7

Denver

79.7

Minneapolis Hamburg

80.7

74.5

Dallas

Frankfurt

82.3 86.8

80.0 71.0

80.0 72.3

83.5

Copenhagen

81.6

Zurich Stockholm

2014

83.8 84.6 86.3

78.8

92.6

Source: Brookings analysis of OECD data.

41

Immigration can help address skills demands Immigrants further economic growth by matching their skills with the demands of local employers. While the share of its population born abroad is relatively low, Charlotte is an emerging immigrant gateway, passing 250,000 immigrants in 2014. And in-migrants, whether from other states or abroad, are relatively well-educated. Foreign-born share of total population, Greater Charlotte and peer regions, 2013 Melbourne Zurich Montreal Munich Stockholm Frankfurt Dallas Austin Copenhagen Phoenix Hamburg Birmingham (UK) Atlanta Denver San Antonio Minneapolis Charlotte Kansas City Indianapolis Cleveland

Educational attainment, by migration status, Greater Charlotte, 2014 37.4%

25.9% 25.2%

Overall population

13.1%

30.4%

25.1%

31.4%

21.4% 20.0% 19.6%

Migrated from:

17.5% 14.8%

Another U.S. state

8.0%

28.6%

20.4%

43.0%

14.5% 14.4% 14.2% 13.4%

Abroad

14.8%

20.8%

20.6%

43.8%

13.2% 12.1% 11.8% 9.7% 9.4% 6.4%

Less than high school High school graduate Some college or associate's degree Bachelor's degree or higher

6.0% 5.7% Source: Brookings analysis of U.S. Census Bureau data.

42

And employers are demanding international talent Greater Charlotte’s employers exhibit strong demand for highly skilled foreign workers, applying for the fourth most H-1B visa applications per 1000 workers among U.S. peers. Nearly 81 percent of those applications are positions that demand STEM skills, the second highest share in this analysis. H-1B guest worker visas requested per 1,000 workers, Greater Charlotte and peer regions, 2010–2011 Austin

3.94

Share of H-1B applications for positions demanding STEM skills, Greater Charlotte and peer regions, 2010-2011 Minneapolis

80.8% 80.7%

Dallas

3.65

Charlotte

Atlanta

3.63

Phoenix

75.7%

Kansas City

75.6%

Austin

75.6%

2.72

Charlotte

Minneapolis

2.44

Phoenix

1.86

Denver

74.5%

Denver

1.79

Atlanta

74.2%

Cleveland

1.78

Dallas

73.7%

Kansas City

1.53

69.0%

Indianapolis

Indianapolis

1.43

San Antonio

San Antonio

1.39

Cleveland

66.2% 60.0%

Source: Brookings analysis of U.S. Department of Labor, Labor Condition Application data.

43

Foreign students are another potential talent source Foreign students enhance an economy’s global engagement by injecting spending into the local economy, establishing connections with their home market, and staying to local to work upon graduation. Currently, Charlotte has fewer foreign students at its colleges and universities than many of its peer regions. F1 student visas approved per 1,000 higher education students, Greater Charlotte and peer regions, 2008–2012 Dallas

Top origin markets for F1 student visas, Greater Charlotte, 2008–2012

22.5

Denver

21.3 Canada - 162

Austin

16.9

Atlanta

16.1 Saudi Arabia - 464

Minneapolis

14.5

Cleveland

14.0

Indianapolis

Phoenix San Antonio

China - 641 India – 742

12.7

Charlotte

Kansas City

South Korea - 239

12.3 11.9 11.3 10.6

Source: Brookings analysis of Immigration and Customs Enforcement data.

44

Bottom Line – Talent Greater Charlotte’s workforce falls in the middle of its peer group in terms of tertiary educational attainment. Charlotte’s employers nevertheless face challenges in filling job vacancies, especially middle-skill occupations that demand STEM skills. Among U.S. peers, Charlotte’s online job postings had the longest median duration before being filled. As workers have aged, Charlotte’s labor force participation rate has declined, potentially exacerbating these skills deficiencies as STEM-intensive advanced industries expand. Greater Charlotte’s educational institutions will be critical in filling this gap. Employers are turning to in-migrants—both from abroad and the rest of the U.S.—to fill skills needs. Charlotte is not yet a major immigration hub, but its foreign-born population is growing quickly. In-migrants tend to be disproportionately well-educated. Talent Share of 15+ population with tertiary education, 2013 Median duration of job openings, 2013 Share of workers in STEM occupations, 2013 Median duration of STEM job openings, 2013 Labor force participation rate, 2014 Change in labor force participation rate, 2000-2014 Foreign-born share of total population, 2013 Share of foreign-born population with at least some college or associate's degree, 2013 H-1B guest worker visas per 1,000 workers, 2010-2011 F1 student visas approved per 1,000 higher education students, 2008-2012

Peer Rank

Lower

Higher

CLT ATL ATN BIR CLE CPN DAL DEN FRT HAM IND 10 8 5 16 15 6 12 2 17 20 14 11 4 9 3 8 2 10 6 9 2 5 8 1 3 9 4 11 3 6 2 5 16 17 12 19 13 3 14 7 5 4 15 18 20 15 9 11 6 14 10 3 2 16 17 13 8 12 20 9 7 14 6 11 19 8 7 4 6 10 9 2 4 3 1 8 2 7 10 7 4 3 6 1 2 8

CLT = Charlotte ATL = Atlanta ATN = Austin BIR = Birmingham (UK) CLE = Cleveland

CPN = Copenhagen DAL = Dallas DEN = Denver FRT = Frankfurt HAM = Hamburg

KC MEL MPLS MON MUN 7 9 1 18 11 6 1 7 4 8 1 8 10 6 11 9 13 5 17 8 4 18 1 16 3 4 3 5 9 5 9 5 -

IND = Indianapolis KC = Kansas City MEL = Melbourne MPLS = Minneapolis MON = Montreal

PHX 13 7 10 7 18 20 10 11 6 10

SA 19 5 11 10 20 12 15 1 11 11

STK ZUR 4 3 1 2 1 7 5 2 -

MUN = Munich PHX = Phoenix SA = San Antonio STK = Stockholm ZUR = Zurich

45

5 | Infrastructure

Why infrastructure matters Firms rely upon global access points like airports and ports and digital infrastructure to move their products, services, and people to markets outside the region in the most efficient manner possible. The competitiveness of a metropolitan economy also hinges on its ability to effectively connect its people and physical assets to their best use within the region—what planners and economic developers call “spatial efficiency.”

Source: For a full review of the role of infrastructure in metropolitan growth, see George Washington Institute of Public Policy and RW Ventures, “Implementing Regionalism.”

47

CLT is a fast-growing, well-connected airport In 2015, the Charlotte-Douglas International Airport moved 44.9 million passengers between Charlotte and 147 destinations, the 24th highest total in the world. Passenger growth was among the fastest in its global peer group between 2004 and 2014.

44.9 million

Origin-destination aviation passenger growth, Greater Charlotte and peer regions, 2004-2014 72.7% 65.4% 65.1%

CLT’s total passengers, 2015

59.9% 55.5% 48.0% 46.0% 45.3% 42.5%

th 24

42.0% 33.1% 21.7% 19.4% 16.2%

CLT’s global rank, 2015

12.2% 10.2% 3.6% -3.8% -7.2% -20.3%

Melbourne Copenhagen Stockholm Charlotte Zurich Austin Hamburg Birmingham (UK) Denver Montreal Munich Frankfurt San Antonio Atlanta Minneapolis Dallas Phoenix Kansas City Indianapolis Cleveland

Source: Brookings analysis of Sabre aviation data.

48

Int’l passenger connections strongest with Europe International origin-destination flows are largest with the rest of North America (Toronto, Mexico City, Montreal); Europe (London, Frankfurt, Munich, Paris); and Asia (Shanghai, Tokyo, Delhi, and Seoul). Growth has been fastest with South America, Africa, and Asia.

49

But a high share of passengers never leave CLT Nearly 58 percent of passengers never step out of the airport, the highest share of any region, limiting the economic impact of the airport. Share of passengers that pass through airport, Greater Charlotte and peer regions, 2014 Charlotte

57.9%

Atlanta

47.4%

Dallas

36.6%

Frankfurt

29.6%

Minneapolis

26.9%

Phoenix

24.9%

Denver

24.1%

Munich

17.0%

Zurich

16.1%

Copenhagen

7.3%

Cleveland

6.9%

Montreal

6.3%

Kansas City

3.9%

Austin

3.0%

Stockholm

2.8%

San Antonio

2.5%

Melbourne

2.4%

Indianapolis

0.7%

Hamburg

0.6%

Birmingham (UK)

0.4%

“Metro areas that serve as destinations for large numbers of people are, implicitly, points of convergence for new ideas and capital. These places have the right mix of human capital and other resources to incubate new business ventures and to stimulate creativity. The net effect is an employment boost throughout local industries, from high-skill services that rely heavily on air travel to more stationary industries like manufacturing.” - Tomer, Puentes, and Neal, 2012

Source: Brookings analysis of Sabre aviation data.

50

Charlotte trades about $130 billion in goods $130 billion in goods trade (domestic + international) starts or ends in Greater Charlotte, nearly 12 percent of which flows globally. Greater Charlotte firms rely on ports in other U.S. regions to send and receive international goods. Largest port complexes used for Greater Charlotte’s international goods trade, billions USD, 2010

$130 billion Total goods trade value, 33/100 U.S. metros

11.7% International trade share, 67/100 U.S. metros

Port Complex

Total Trade (billions USD)

New York

2.2

Los Angeles

1.7

Miami

1.1

Detroit

1.1

Laredo

0.7

Houston

0.7

Savannah

0.5

Anchorage

0.5

Chicago

0.5

Buffalo

0.4

Source: Brookings analysis of Economic Development Research Group data.

51

Greater Charlotte is not yet a major port complex The region is not a major international port, ranking behind most U.S. peers in terms of freight flow. Of the $2.7 billion in international freight that moved through Greater Charlotte’s ports in 2010, 99.7% travels via air, mainly higher value commodities. Total international freight movement, Greater Charlotte and peer port complexes, millions USD, 2010 Dallas

$33,868

Atlanta

$833.9

$26,736

Cleveland

$19,404

Minneapolis

$535.2 $438.8

$5,591

Denver

$4,474

Indianapolis Charlotte

Top 5 commodities moved by Greater Charlotte’s port complex, millions USD, 2010

$301.8

$293.2

Electronics

Precision Instruments

$3,830 $2,669

Phoenix

$800

Austin

$569

Kansas City

$449

San Antonio

$184

Pharmaceuticals

Chemicals/ Plastics

Machinery

Source: Brookings analysis of Economic Development Research Group data.

52

Broadband speeds lag and access varies by income Quality broadband access allows students, workers, and firms to benefit from the power of the internet. Charlotte’s average download speeds trail most peers; overall broadband access is average and varies significantly by income. Internet download speed, mbps, 2015

Share of households with broadband access, 2014

Broadband access, by income, 2014

Source: Brookings analysis of data from Ookla and U.S. Census Bureau.

53

Commuting times in Charlotte are about average Transportation networks connect firms to global access points like airports and ports, shuttle workers to jobs, and facilitate intra-metro commerce and collaboration. Greater Charlotte’s average commuting times fall in the middle of its peer group, suggesting that the proximity between workers and jobs is not a significant weakness but could still be improved. Distribution of travel time to work for all commuters*, minutes, 2014 Kansas City

13%

Cleveland

11%

Indianapolis

12%

14%

42%

13%

40%

13%

24% 22%

11%

13%

41%

Minneapolis

11%

13%

40%

Charlotte

11%

13%

Phoenix

10%

Austin

11% 9%

Dallas

10%

Atlanta

8%

0 to 19 minutes

13% 11%

23%

38%

12% 10%

25%

39%

26%

36%

25%

33%

0 to 14 minutes

15 to 29 minutes

25%

30 to 44 minutes

5%

7%

6%

8%

5%

8%

6%

9%

6%

8%

7%

9%

7%

10% 12%

45 to 59 minutes

5%

7%

23%

38%

4%

7%

23%

39%

13%

6%

24%

39%

San Antonio

Denver

21%

8% 12%

60+ minutes

Source: Brookings analysis of U.S. Census Bureau data.

54

Bottom Line – Infrastructure The Charlotte-Douglas International Airport is a significant global asset. It is one of the fastest-growing airports among Charlotte’s peer regions. However, a high share of passengers never leave the airport, limiting its impact on the local economy. The airport also anchors freight movement, although Greater Charlotte is not yet a significant port complex. The region tends to rely on the nation’s largest ports to send and receive goods internationally, underscoring the importance of the new intermodal facility at the airport. Digital infrastructure quality and access could be improved. Charlotte’s average internet download speeds trail most peers, and access varies significantly by income. Infrastructure Aviation passenger growth, 2004-2014 Share of passthrough aviation traffic, 2014 Total international freight movement, 2010 Internet download speed, 2015 Share of hosueholds with broadband access, 2014 Share of commuters traveling more than 45 minutes to work, 2014

Peer Rank

Lower

Higher

CLT ATL ATN BIR CLE CPN DAL DEN FRT HAM IND 4 14 6 8 20 2 16 9 12 7 19 20 19 7 1 10 11 18 14 17 2 3 7 2 9 3 1 5 6 18 10 3 8 20 4 13 12 9 6 14 6 4 1 10 7 2 9 6 11 8 2 10 9 3

KC MEL MPLS MON MUN PHX SA 18 1 15 10 11 17 13 8 4 16 9 13 15 5 10 4 8 11 5 19 15 16 11 7 17 5 3 8 11 1 5 7 4

STK ZUR 3 5 6 12 2 1 -

CLT = Charlotte

CPN = Copenhagen

IND = Indianapolis

MUN = Munich

ATL = Atlanta

DAL = Dallas

KC = Kansas City

PHX = Phoenix

ATN = Austin

DEN = Denver

MEL = Melbourne

SA = San Antonio

BIR = Birmingham (UK)

FRT = Frankfurt

MPLS = Minneapolis

STK = Stockholm

CLE = Cleveland

HAM = Hamburg

MON = Montreal

ZUR = Zurich

55

Bottom Line – Charlotte’s competitive position Economic performance: Overall economic growth has been robust over the past decade, but on metrics of inclusion Greater Charlotte has lagged. Trade and Investment: Greater Charlotte is very globally oriented. Exports and foreign direct investment account for a disproportionate share of the regional economy, led by tradable anchors like machinery, transportation equipment, and financial services. But the region is at risk of losing ground to peer metropolitan economies unless it shores up its competitive drivers: •

Innovation: Build up very low levels of research and development, technology commercialization, and venture capital investment;

•

Talent: Help employers overcome challenges in filling job vacancies, especially occupations that require STEM skills; and

•

Infrastructure: Address lagging broadband speeds and disparities in broadband access by income. 56

Conclusion This comparative global benchmarking analysis reveals that Greater Charlotte has significant assets on which to build: globally engaged companies in key advanced industries; a highly educated workforce; and an internationally connected airport. But that same perspective yields other areas that warrant improvement: upgrading the region’s system for innovation and entrepreneurship; addressing employer difficulties in finding STEM workers; and bridging disparities in broadband access. Going forward, whatever their course of action, Charlotte leaders should ensure they are focusing on what Amy Liu of Brookings calls the “markets and civics”: “Markets: Economic development should prioritize building strong business ecosystems for core industries, improving the productivity of firms and people, and facilitating trade—the market foundations from which growth, prosperity, and inclusion emerge. Civics: To get the markets right requires good civics: the work to organize and implement strategies and initiatives that engage stakeholders and partners to achieve long-term goals. A data-driven economic narrative and sense of urgency, networked leadership with high capacity organizations for implementation, and engagement of diverse stakeholders and perspectives to ensure strategies are inclusive are all essential.” - Amy Liu, “Remaking Economic Development,” 2016. 57

Benchmarking results Trade and Investment Export share of GDP, 2014 % change in export share of GDP, 2003-2014 Annualized export growth, 2003-2014 Share of jobs in foreign-owned establishments (FOEs), 2011 Change in share of jobs in FOEs, 1991-2011 Greenfield FDI per capita, 2009-2015 Innovation R&D conducted at universities per $1,000 of GDP, 2013 Business-funded R&D conducted at universities per $1,000 GDP, 2013 Percentage of university scientific publications cited in top 10 percent, 2010-2013 Percentage of university scientific publications conducted with industry, 2010-2013 Patents per 10,000 inhabitants, 2008-2012 Startup activity rank, Kauffman Foundation, 2015 Venture capital investments per capita, 2005-2014 Share of jobs in advanced industries, 2014 % change in advanced industries employment, 2008-2014 Talent Share of 15+ population with tertiary education, 2013 Median duration of job openings, 2013 Share of workers in STEM occupations, 2013 Median duration of STEM job openings, 2013 Labor force participation rate, 2014 Change in labor force participation rate, 2000-2014 Foreign-born share of total population, 2013 Share of foreign-born population with at least some college or associate's degree, 2013 H-1B guest worker visias per 1,000 workers, 2010-2011 F1 student visas approved per 1,000 higher education students Infrastructure Aviation passenger growth, 2004-2014 Share of passthrough aviation traffic, 2014 Total international freight movement, 2010 Internet download speed, 2015 Share of hosueholds with broadband access, 2014 Share of commuters traveling more than 45 minutes to work, 2014

Peer Rank

Lower

Higher

CLT 1 1 4 1 10 10 CLT 18 10 20 20 18 7 16 8 5 CLT 10 11 6 9 16 18 17 8 4 7 CLT 4 20 7 18 6 6

ATL 8 7 7 2 4 8 ATL 6 3 7 5 14 4 5 9 7 ATL 8 4 9 4 17 20 13 7 3 4 ATL 14 19 2 10 4 11

ATN 7 10 3 8 3 1 ATN 4 1 4 6 7 1 1 1 1 ATN 5 9 2 11 12 15 8 4 1 3 ATN 6 7 9 3 1 8

BIR 2 BIR 12 9 16 16 20 BIR 16 19 9 12 BIR 8 1 8 -

CLE 4 6 10 9 11 20 CLE 8 7 5 7 9 10 10 7 11 CLE 15 3 5 3 13 11 20 6 8 6 CLE 20 10 3 20 10 2

CPN 9 CPN 1 11 1 3 7 CPN 6 3 6 9 CPN 2 11 4 -

DAL 3 2 2 5 7 15 DAL 17 8 8 10 12 6 6 2 6 DAL 12 8 8 6 14 14 7 10 2 1 DAL 16 18 1 13 7 10

DEN 11 8 5 7 8 18 DEN 13 2 2 2 11 2 2 3 3 DEN 2 2 1 2 7 10 14 9 7 2 DEN 9 14 5 12 2 9

FRT 6 FRT 11 15 8 5 17 FRT 17 5 3 6 FRT 12 17 9 -

HAM 14 HAM 7 10 12 10 19 HAM 20 4 2 11 HAM 7 2 6 -

IND 2 4 8 3 1 13 IND 14 4 13 3 8 8 9 6 8 IND 14 10 3 5 15 16 19 2 10 8 IND 19 3 6 14 9 3

KC 5 5 6 4 2 16 KC 19 11 19 18 19 9 12 4 4 KC 7 6 7 8 8 13 18 3 9 9 KC 18 8 10 5 5 1

MEL 3 MEL 5 12 19 17 18 MEL 9 10 5 1 MEL 1 4 19 -

MPLS 6 9 9 6 5 19 MPLS 9 5 3 14 2 11 3 5 9 MPLS 1 1 4 1 6 17 16 5 5 5 MPLS 15 16 4 15 3 5

MON MUN PHX 10 11 11 10 9 5 11 17 MON MUN PHX 2 10 16 9 18 6 16 17 9 15 15 4 13 5 8 14 13 10 10 MON MUN PHX 18 11 13 7 10 7 11 9 18 8 4 20 3 4 10 11 6 10 MON MUN PHX 10 11 17 9 13 15 8 16 11 7 8 7

SA 9 3 1 11 6 12 SA 15 6 17 11 20 3 15 11 2 SA 19 5 11 10 20 12 15 1 11 11 SA 13 5 11 17 11 4

STK 7 STK 3 14 4 1 4 STK 4 1 1 5 STK 3 6 2 -

ZUR 4 ZUR 1 13 6 11 ZUR 3 2 7 2 ZUR 5 12 1 -

CLT = Charlotte

CPN = Copenhagen

IND = Indianapolis

MUN = Munich

ATL = Atlanta

DAL = Dallas

KC = Kansas City

PHX = Phoenix

ATN = Austin

DEN = Denver

MEL = Melbourne

SA = San Antonio

BIR = Birmingham (UK)

FRT = Frankfurt

MPLS = Minneapolis

STK = Stockholm

CLE = Cleveland

HAM = Hamburg

MON = Montreal

ZUR = Zurich

58

Peer Methodology Global peer cities were selected based on economic characteristics and competitiveness factors. Classifying and identifying peers allows policymakers and stakeholders to better understand the position of their economies in a globalized context as well as to conduct constructive benchmarking. To select peers we utilized a combination of principal components analysis (PCA), k-means clustering, and agglomerative hierarchical clustering. These commonly used data science techniques allowed us to group metro areas with their closest peers given a set of economic and competitiveness indicators. For this report we selected 14 economic variables: population, nominal GDP, real GDP per capita, productivity (defined as output per worker), total employment, share of the population in the labor force, and industry share of total GDP (8 sectors). We included seven additional variables that measure one of the four quantitative dimensions of the competitiveness analysis framework used in this report. The variables included are: share of the population with tertiary education (talent), stock of greenfield foreign direct investment (FDI) (trade), number of international passengers in 2014 (infrastructure), number of highly cited papers between 2010 and 2013 (innovation), mean citation score between 2010 and 2013 (innovation), and average internet download speed in 2014 (infrastructure). Our analysis proceeded in three steps. First, we applied PCA to reduce the number of dimensions of our data by filtering variables that are highly interrelated while retaining as much variance as possible. PCA generates “components” by applying a linear transformation to all the variables. To successfully perform our clustering algorithm we selected the number of components that explain 80 to 90 percent of the variance of a dataset. For this report we selected the first seven components, which accounted for 84 percent of the total variation of the data. The second stage applied a k-means algorithm to the seven components, a process which calculates the distance of every observation in our dataset to each other, then generates a cluster centroid and assigns each data point to the closest cluster. K-means repeats this procedure until a local solution is found. This algorithm provides a good segmentation of our data and under most circumstances it is a sufficient method for partitioning data. However k-means sometimes generates clusters with multiple observations, thus obscuring some of the closest economic relationships between metro areas. To improve the results of k-means we implemented a third step, hierarchical clustering, which follows a similar approach to k-means. Hierarchical clustering calculates Euclidean distances to all other observations, but generates a more granular clustering that permits clearer peer-to-peer comparison.

59

Data Sources Oxford Economics: Economic indicators as well as selected indicators corresponding to talent for non-U.S. metropolitan areas were provided by Oxford Economics (OE). Economic variable such as GDP, Gross Value Added (GVA), employment, unemployment rates, educational attainment, and industry-level employment and output were collected by OE from national statistics bureaus in each country or from providers such as Haver, ISI Emerging Markets, and Eurostat. Population estimates and the share of the foreign-born population were based on official population projections produced by national statistical agencies and or organizations such as Eurostat, adjusting migration assumptions on a case-by case basis. The study uses gross value added (GVA) and Gross Domestic Product (GDP) in nominal terms at purchasing power parity rates, and in real terms at 2009 prices and expressed in U.S. dollars. All the indicators were provided at the metropolitan level. Moody’s Analytics: Economic indicators for U.S. metro areas were provided by Moody’s Analytics. Moody’s uses data published by the Bureau of Labor Statistics (BLS) and by the Bureau of Economic Analysis (BEA) to generate their estimates of employment and GDP at the county level. We aggregated those estimates to metropolitan areas using the current Census Bureau definition. For real GDP, both total and at the industry level, Moody’s provides 2009 chained dollars. For nominal analysis they report their estimates in current dollar.

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Data Sources Advanced Industries: Brookings identifies 50 four-digit NAICS industries as “advanced” in the U.S. economy. For more information: Mark Muro and others, “America’s Advanced Industries: What they are, where they are, and why they matter” (Washington: Brookings Institution, 2015). Advanced Industries NAICS Code 2111 2122 2211 3241 3251 3252 3253 3254 3259 3271 3279 3311 3313 3315 3331 3332 3333 3336 3339 3341 3342 3343 3344 3345 3346

Industry Oil & Gas Extraction Metal Ore Mining Power Generation & Supply Petroleum & Coal Products Basic Chemicals Resins & Synthetic Rubbers Pesticides & Fertilizers Pharmaceuticals Misc. Chemicals Clay & Refractory Products Stone & Mineral Products Iron & Steel Products Aluminum Products Foundries Agri., Constr., Mining Machinery Industrial Machinery Commercial & Service Machinery Engine & Power Equipment General Purpose Machinery Computer Equipment Communications Equipment Audio & Video Equipment Semiconductors Precision Instruments Magnetic & Optical Media

NAICS code 3351 3352 3353 3359 3361 3362 3363 3364 3365 3366 3369 3391 3399 5112 5152 5172 5174 5179 5182 5191 5413 5415 5416 5417 6215

Industry Electrical Lighting Equipment Household Appliances Electrical Equipment Misc. Electrical Equipment Motor Vehicles Motor Vehicle Body & Trailers Motor Vehicle Parts Aircraft Products & Parts Railroad Rolling Stock Ships & Boats Misc. Transportation Equipment Medical Equipment & Supplies Jewelry, Sporting Goods Software Products Cable & Other Programming Wireless Telecom Carriers Satellite Telecommunications Other Telecommunications Data Processing & Hosting News & Media Architecture & Engineering Computer Systems Design Management Consulting R&D Services Medical & Diagnostic Laboratories

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Data Sources Exports: Export data are derived from a number of sources including: Census, BEA, Moody’s analytics, BLS, NAFSA, IRS, EIA, and Sabre. The estimates include both goods and services and are adjusted to reflect the export value-add at the point of production using the local share of national output to allocate national exports for each industry and county. For more information: Nick Marchio, “Brookings export database methodology” (Washington: Brookings Institution, 2015). www.brookings.edu/~/media/research/files/interactives/2015/exportmonitor/brookingsexport-series-methodology-nm-5715.pdf Foreign Direct Investment: Jobs in foreign-owned establishments are derived from data from Dun and Bradstreet (D&B), the National Establishment Time Series (NETS), and the Bureau of Economic Analysis (BEA). The estimates include all foreign investment activity into the United States between 1991 and 2011, excluding real estate and EB-5 investment. Brookings utilized Moody’s private-sector employment totals to calculate the shares of domestic jobs in foreign-owned establishments. For more information: Nick Marchio, “Methodological Appendix for FDI in U.S. Metro Areas: The Geography of Jobs in Foreign-Owned Establishments” (Washington: Brookings Institution, 2014). www.brookings.edu/~/media/research/files/reports/2014/06/20-fdi-us-metro-areas/method-appendix.pdf The source of the greenfield FDI data is the Financial Times’ fDi Markets database. This database tracks all crossborder investment into new physical projects or expansions of an existing investment, otherwise known as “greenfield” investment. Company announcements form the basis for the database and each submission is manually verified before being published. In cases when the capital investment and job counts are not publicly released, analysts impute the value invested and jobs created using an econometric model. The primary sources of the data are newswires, internal sources, top business journals, industry organizations, investment agencies, and data purchased from private vendors. Brookings’ analysts assigned metro areas to the city-level information available in the database and processed the flows between different investor and recipient geographies and industry levels. The preferred metric is the cumulative stock of FDI invested and jobs created over the reference period from 2009 to 2015. All value measures were inflation-adjusted to 2014 dollars. For more information see http://www.fdimarkets.com/faqs/ 62

Data Sources Patents: Patents data are derived from the OECD’s REGPAT database. The OECD manages this database as part of the Patent Cooperation Treaty, which offers patent protection to organizations and individuals planning to do business in multiple countries. A number of research decisions went into the construction of the patent estimates. Patent locations correspond to the inventor’s place of residence or workplace. In cases when there are multiple inventors, the patent was apportioned in equal shares to each co-inventor. Patents that fall under multiple International Patent Classification (IPC) technology codes were also apportioned in equal shares to each technology class in order to account for the cross-cutting nature of technological development. To mitigate year-to-year fluctuations in invention activity, patents were summed in five-year intervals. The time dimensions represent the “priority year” when the patent was first filed. This year is closest to the actual date of invention and is the most relevant reference date when assessing an area’s technological activity at a specific point in time. Since patent filing is a costly and administratively burdensome process the analysis excludes patents submitted in 2013 and 2014 since patents filed in these years only account for a portion of patents actually invented and may bias places and organizations with better systems for shortening lag time between the date of invention and the application year. For more information: Stephane Maraut and others, “The OECD REGPAT Database: A Presentation” (Paris: OECD, 2008). www.oecd.org/sti/inno/40794372.pdf

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Data Sources University Research Impact: University scientific impact data come from the Centre for Science and Technology Studies (CWTS) at Leiden University. This publicly available database tracks bibliometric performance data for 750 universities with the largest publication output in internationally recognized journals. The database relies on the Thomson Reuters Web of Science citations indices which researchers cleansed, geocoded, and classified into fields of study. CWTS reports publications based on full-counting methods which gives equal weight to all publications from a university and fractional counting methods which apportion shares to each collaborator. For more information: L. Waltman and others, “The Leiden Ranking 2011/2012: Data collection, indicators, and interpretation.” Journal of the American Society for Information Science and Technology 63(12), 2419–32. www.leidenranking.com/methodology Venture Capital: Venture capital data are derived from PitchBook, a private financial research firm that collects and tracks global private equity activity. PitchBook analysts deploy web crawlers to perform a daily systematic scan of media reports and public filing information on deals which they then record and validate through a manual review process. In assembling their database they include address-level data for both investors and recipient companies, industry, investor details along with the deal value. Brookings took the data and then assigned the investors and recipients to metropolitan geographies. The primary statistic in the analysis is the cumulative stock of venture capital which is the sum total of year-to-year investment flows. Secondary statistics examine the number of investors and companies along with data between different geographies, deal categories, and industries. The advanced industries classification is an approximate grouping based of detailed industry categories matched to Brookings’ NAICS-based definition. All value measures were inflation-adjusted to 2014 dollars. For more information: http://blog.pitchbook.com/wp-content/uploads/2014/06/3Q-2014-PEBreakdownMethodology.pdf

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Data Sources Aviation: Aviation data are derived from Sabre Aviation Solutions’ global demand dataset (GDD). The dataset includes a record for every international itinerary entering and leaving the United States or any large global metro area with output of at least $100 billion in 2014. Each record includes the origin and destination airports, plus up to three connecting airports with the number of passengers and total revenue generated from that specific itinerary for that year. The GDD is based on a variety of sources including information developed from direct business relations between Sabre and over 400 global airlines. For international itineraries not reflected in their database, Sabre imputes missing flights and passenger levels based on additional market data. The result is a complete dataset of travel into and out of major global aviation centers. Brookings assigned all airports to global metropolitan areas, obtained latitude and longitude coordinates to derive distance measures, cleaned anomalous records, and aggregated the passenger and revenue flows to better facilitate regional analysis. All value measures were inflationadjusted to 2014 dollars. For more information: Adie Tomer, Robert Puentes, and Zachary Neal, “Global Gateways: International Aviation in Metropolitan America” (Washington: Brookings Institution, 2012). www.brookings.edu/~/media/research/files/reports/2012/10/25-global-aviation/25-globalaviation.pdf Freight: This analysis uses a unique database measuring goods traded among U.S. metropolitan areas, nonmetropolitan regions, and international geographies. We used the data foundation and design scheme of the publicly available Freight Analysis Framework (FAF). The database provides a comprehensive view of freight movement to, from, and within the United States. Originally based on calendar year 2007, Version 3.2 has been provisionally updated to estimate 2010 total freight volumes, or flows, by annual tonnage, value, and ton-mileage. With an interest in showing domestic and international freight flows in, out, and among all of the country’s metropolitan areas, Brookings worked with the EDR Group to estimate freight movement across combined statistical areas (CBSAs). For more information: Adie Tomer, Robert Puentes, and Joseph Kane, “Metro-to-Metro: Global and Domestic Goods Trade in Metropolitan America” (Washington: Brookings Institution, 2013). www.brookings.edu/~/media/Research/Files/Reports/2013/10/21-metro-freight/SrvyMetroToMetro.pdf?la=en 65

Acknowledgments This report is made possible by funding from the Central Piedmont Community College. Special thanks go to Dr. Tony Zeiss and Mary Vickers-Koch for their support and guidance. This presentation was prepared by Joseph Parilla, Senior Research Associate, Brookings Metropolitan Policy Program. For their research support and advice, he would like to thank Alan Berube, Jesus Leal Trujillo, and Nick Marchio. ABOUT THE METROPOLITAN POLICY PROGRAM AT BROOKINGS The Metropolitan Policy Program at Brookings delivers research and solutions to help metropolitan leaders build an advanced economy that works for all. To learn more visit www.brookings.edu/metro. FOR MORE INFORMATION Metropolitan Policy Program at Brookings 1775 Massachusetts Avenue, NW Washington, D.C. 20036-2188 Telephone: 202.797.6000 Fax: 202.797.6004 Website: www.brookings.edu Joseph Parilla Senior Research Associate Metropolitan Policy Program at Brookings [email protected]

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