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October 2, 2017

AEG HQ2 Index

Methodology and Sources

Jason Horwitz, Director of Public Policy &

Economic Analysis

Brian Peterson, Senior Analyst

(c) 2017, Anderson Economic Group LLC

See notice for limited reproduction rights, cautions, and disclaimers.

ABOUT THE AEG HQ2

INDEX

On September 7, 2017, Amazon issued a request for proposals (RFP) as part of

a competitive site selection process for its second corporate headquarters, HQ2.

The response from cities across the U.S. and North America was immediate, as

they recognized the impact that Amazon’s second headquarters could have on

their local economy.

At Anderson Economic Group (AEG), we have extensive experience in site

selection studies, market analyses, and the impact of business incentives and

taxes. Based on that expertise, we have compiled the AEG HQ2 Index, which

captures a city’s measurable advantage in attracting Amazon’s HQ2 based on a

range of metrics.

OVERVIEW OF

APPROACH

The AEG HQ2 index is an average of a city’s relative performance across three

distinct categories that are pivotal in determining the destination for HQ2:

• Access to Quality Labor and Services

• Ease of Transportation

• Cost of Doing Business

In total, the index includes 11 metrics from a range of sources. In each case, we

evaluate the performance of the entire metropolitan area, rather than the city

alone. Each metropolitan area’s performance on each metric is based on a Z-

score, which is defined as the number of standard deviations from the mean of

the values for all metropolitan areas.

AEG HQ2 Index: Methodology and Sources 2

(c) 2017 Anderson Economic Group LLC

We estimate the AEG HQ2 index for 35 cities that meet minimum criteria out-

lined in the Amazon HQ2 RFP.

METHODOLOGY Selecting Metropolitan Areas

In its RFP, among other preferences, Amazon lists the following:

• Metropolitan area with more than one million people

• An international airport with daily direct flights to Seattle, New York, San

Francisco, and Washington, D.C.

• Fiber connectivity

We narrowed our list of cities to the 35 metropolitan areas in the United States

with a population of one million people or more; an airport that has nonstop

flights to Seattle, New York, San Francisco, and Washington, D.C., as well as at

least some international destinations; and some telecommunications services

that provide fiber connectivity.1

Population. For population data, we used numbers from the 2016 American

Community Survey.

Airports. For data on airport flights, we used information from websites for the

Port of Seattle, Reagan National Airport, Dulles International Airport, Balti-

more/Washington International Airport, Google Flight, San Francisco Interna-

tional Airport, and many more.

Fiber connectivity. To determine which cities had fiber technology for telecom-

munications, we performed searches for telecommunications companies by

metropolitan area. We found that all 35 cities that met the previous two stan-

dards had at least some option for fiber communications, and chose not to elim-

inate any cities based on this criteria.

Estimating Each Metric’s Contribution to the Index

For each metric, the following steps result in the metric’s contribution to the

overall AEG HQ2 index.

1. Using a reputable source, collect data for the corresponding metric for each of the

35 metropolitan areas that meet the criteria outlined in the previous section.

2. Estimate each metropolitan area’s Z-score for that metric. This is equal to the

metropolitan area’s value for that metric minus the average value for that metric

across all metropolitan areas, divided by the standard deviation of values for that

metric.

3. Add 3.56 to all Z-scores for all metropolitan areas. The lowest Z-score in any

metric was -3.56, so this allows for all value that contribute to our index to be

positive.

1. At least some cities could add nonstop flights in the interest of attracting HQ2. We did not

speculate as to which cities might do so in determining out list of metropolitan areas.

AEG HQ2 Index: Methodology and Sources 3

(c) 2017 Anderson Economic Group LLC

4. Once the Z-scores had been estimated for all metrics within a given category (i.e.

cost of doing business, ease of transportation, and access to quality labor and

business services), average the value for each metric within that category. Where

data is not available for a given metric for a metropolitan area, average across the

remaining metrics within the category.

5. Once the index value had been calculated for each of the three categories, take the

unweighted average of the values for each category to arrive at the AEG HQ2

index.

See Exhibit 1 on page 9 and Exhibit 2 on page 10 to see the values for each met-

ric and metropolitan area, and how we calculated the index for each category.

Cost of Doing Business Metrics

The “Cost of Doing Business” category includes the following five metrics.

Business tax burden. Each year, AEG publishes its annual state business tax bur-

den rankings, where we rank all 50 states and the District of Columbia on busi-

ness tax burden.2 We define business tax burden as the total state and local taxes

paid as a share of gross operating surplus (a measure of profits) in each state.

For each metro area, we used the business tax burden from our most recent

report for the corresponding state. For metro areas that span multiple states, we

used the tax burden corresponding to the state in which the central city is

located.

Unit cost of labor. The unit cost of labor is total workers’ compensation in a

given industry and metro area divided by the total gross domestic product in that

industry and metro area.3 It provides a measure for the cost of labor that takes

into account the productivity of workers in the region. The data we use is from

the Bureau of Economic Analysis for three different industries that are relevant

to HQ2: information, data processing, and management of businesses. As a

result, there are three total unit cost of labor metrics in the index.

Commercial rent per square foot. For commercial rent per square foot, we used

data from Jones Lang LaSalle’s Office Outlook Q4 2016.

Ease of Transportation Metrics

The “Ease of Transportation” category includes the following two metrics.

Transit trips per capita. We use the number of transit trips from August 2016 to

July 2017 from the Monthly Module in the Federal Transit Authority’s National

2. Jason Horwitz and Brian Peterson, 2017 State Business Tax Burden Rankings: 8th Edition of

the Annual Anderson Economic Group Report on State and Local Business Tax Burden Across

the United States, Anderson Economic Group, April 17, 2017.

3. Compensation is defined as salaries and wages, bonuses, and employer contributions to pen-

sion funds, health insurance, and social insurance programs.

AEG HQ2 Index: Methodology and Sources 4

(c) 2017 Anderson Economic Group LLC

Transit Database. We divide the number of transit trips by population in the

metro area, according to the 2016 1-year American Community Survey.

Congestion. For congestion, we estimate the annual hours of delay due to traffic

per capita. Hours of delay in traffic for each urban area is provided by the Texas

Transportation Institute 2015 Urban Mobility Scorecard. For population, we use

the 2014 population estimate (because that year is the most recent estimate

available for hours of delay).

Access to Labor and Services Metrics

The “Access to Labor and Services” category includes the following four met-

rics.

Degree completions. Using data from the Integrated Postsecondary Education

Data System (IPEDS), we estimated the total number of degrees granted by for-

profit, nonprofit, and public higher education institutions in each of the 35

metro areas. We only included degree fields that were particularly relevant for

potential employees at HQ2. The degrees we included are in the following table,

along with the corresponding Classification of Instructional Programs (CIP)

code.

We include all bachelors and advanced degrees granted in the 2015-16 academic

year. Note that we’ve by no means covered all fields that a qualified employee

at HQ2 may study. There are many history or chemistry majors that could go on

to work in Amazon HQ2. Our goal here is to capture those who have the partic-

ularly specialized knowledge that will be required to fill a significant portion of

the roles at HQ2.

TABLE 1. Degree Fields Included in Our Amazon HQ2 Index

Field of Study CIP Code

Public Relations, Advertising, and Applied Communication 9.09

Computer and Information Sciences and Support Services 11

Computer Engineering 14.09

Electrical, Electronics, and Communications Engineering 14.10

Systems Engineering 14.27

Operations Research 14.37

Legal Professions and Studies 22

Mathematics and Statistics 27

Interdisciplinary Study in Mathematics and Computer Science 30.08

Interdisciplinary Study in Computer Science 30.30

Economics 45.06

Design and Applied Arts 50.04

Business, Management, Marketing, and Related Support Services 52

Source: IPEDS

Analysis: Anderson Economic Group, LLC

AEG HQ2 Index: Methodology and Sources 5

(c) 2017 Anderson Economic Group LLC

Employment in Related Occupations. Using data from the Bureau of Labor Sta-

tistics Occupational Employment Statistics, for each metro area we estimated

the number of people working in occupations that are likely to occur at HQ2.

See Table 2 below for the list of occupations, along with the relevant occupation

code.

Business Services Cluster Employment. We use business services cluster employ-

ment as a proxy for the size and quality of the business services and support for

business services in each metro area. Proximity to world-class business services

are important to headquarters like HQ2. For business services cluster employ-

ment by metro area, we rely on data from the U.S. Cluster Mapping Project

from the Institute for Strategy and Competitiveness at Harvard Business School.

The Business Services cluster includes a range of industries, such as corporate

headquarters, consulting services, business support services, computer services,

employment placement services, and more.

Immigrants with Bachelors Degrees or Higher. The talent pool for a city includes

not only those currently working in the city and the number of graduates from

local schools, but also the potential migrants from outside the city. As a proxy

TABLE 2. Occupations Included in Our Amazon HQ2 Index

Field of Study Occ. Code

Chief Executives 11-1010

General and Operations Managers 11-1020

Advertising and Promotions Managers 11-2010

Marketing Managers 11-2021

Sales Managers 11-2022

Public Relations and Fundraising Managers 11-2030

Computer and Information Systems Managers 11-3020

Purchasing Managers 11-3060

Transportation, Storage, and Distribution Managers 11-3070

Compensation and Benefits Managers 11-3110

Human Resources Managers 11-3120

Training and Development Managers 11-3130

Business and Financial Operations Occupations 13-0000

Computer and Mathematical Occupations 15-0000

Lawyers 23-1011

Graphic Designers 27-1024

Public Relations Specialists 27-3030

Editors 27-3041

Technical Writers 27-3042

Advertising Sales Agents 41-3010

Source: Bureau of Labor Statistics Occupational Employment StatisticsAnalysis: Anderson Economic Group, LLC

AEG HQ2 Index: Methodology and Sources 6

(c) 2017 Anderson Economic Group LLC

for a city’s ability to attract professional talent from elsewhere, we use data on

the residents with a bachelors or graduate degree who moved from a different

county, state, or country within the past year. We use data from the American

Community Survey 2016 1-year Estimates.

ABOUT ANDERSON

ECONOMIC GROUP

Anderson Economic Group LLC is a research and consulting firm that special-

izes in economics, public policy, finance, market analysis, and land use eco-

nomics. We have performed studies on site selection, economic impact,

business taxes, and the effectiveness of incentives across the country. AEG has

offices in East Lansing, Chicago, New York, and Istanbul. AEG’s past clients

include:

• Governments, such as the states of Kentucky, Michigan, North Carolina,

Tennessee, and Wisconsin; the cities of Detroit, MI, Cincinnati, OH, Norfolk, VA,

and Fort Wayne, IN; counties such as Oakland County, Michigan, and Collier

County, Florida; and authorities such as the Detroit-Wayne County Port Authority;

• Corporations such as CVS Caremark, GM, Ford, Delphi, Honda, Metaldyne,

Taubman Centers, The Detroit Lions, PG&E Generating; SBC, Gambrinus, Labatt

USA, and InBev USA; automobile dealers and dealership groups representing

Toyota, Honda, Chrysler, Mercedes-Benz, and other brands;

• Nonprofit organizations, such as the University Research Corridor, University of

Chicago, Michigan State University, Van Andel Institute, the Michigan

Manufacturers Association, National Association of Realtors, International Mass

Retailers Association, American Automobile Manufacturers Association,

Automation Alley, and the Illinois Chamber of Commerce.

Visit AEG’s website at: http://www.andersoneconomicgroup.com.

ABOUT THE AUTHORS Jason Horwitz

Mr. Horwitz is a Senior Consultant at Anderson Economic Group, serving as the

Director of the Public Policy and Economic Analysis practice area. Mr. Horwitz

has extensive expertise on state and local economic conditions and on the eco-

nomic and fiscal impacts of public policy. He has provided research, analysis,

and expert testimony on policy in a range of fields, including state and local

taxes, retirement benefits, business incentives, energy policy, and economic

development.

Mr. Horwitz has advised governments, trade organizations, and corporations

across the country on economic issues and the impacts of policy. His work also

includes economic impact studies on universities, hospitals, museums, retailers,

and large-scale events. His work has been featured in Bloomberg Businessweek,

NPR Marketplace, Chicago Sun-Times, Detroit News, Crain's Chicago Busi-

ness, and on WBEZ Radio.

Prior to joining AEG, Mr. Horwitz was the Coordinator of Distribution for the

Community Center of St. Bernard near New Orleans, where he oversaw the dis-

tribution of donated food, clothes, and household supplies to low-income resi-

dents of St. Bernard Parish and New Orleans' Lower Ninth Ward.

AEG HQ2 Index: Methodology and Sources 7

(c) 2017 Anderson Economic Group LLC

Mr. Horwitz holds a Master of Public Policy from the Harris School of Public

Policy at the University of Chicago and a Bachelor of Arts in Physics and Phi-

losophy from Swarthmore College. He is a board member at the Civic Federa-

tion, and is the cco-chair their Task Force on Chicago’s Regional

Competitiveness.

Brian Peterson

Brian Peterson is a Senior Analyst with Anderson Economic Group, working in

the Public Policy and Economic Analysis practice area. His work focuses on

economic and fiscal impact analysis. Mr. Peterson has worked with public and

private clients on issues such as pension reform, property tax impact analysis,

and environmental economics. He also has previous experience in regional eco-

nomic development and transportation planning.

Mr. Peterson holds a Master’s degree in Urban Planning from the University of

Wisconsin Milwaukee and Bachelor’s degrees in Economics and Urban Studies

from the University of Minnesota Twin Cities.

CONTRIBUTORS Jonathan Waldron

Mr. Waldron is a Senior Analyst with Anderson Economic Group, working in

the Public Policy and Economic Analysis practice area. While at AEG, Jonathan

has performed research and analysis for a wide range of clients, including uni-

versities, trade associations, and businesses. His recent work includes analysis

of business tax incentives; analyses of economic determinants of migration

trends; benchmarking studies; and assessments of tax reform proposals.

Judy Zhang

Judy Zhang is an Analyst with Anderson Economic Group, working in the Pub-

lic Policy and Economic Analysis Practice Area. She has a background in eco-

nomic and public policy analysis. While at AEG, Ms. Zhang has contributed to a

number of projects including an impact study of state business tax incentives, a

survey analysis related to real estate closing costs, and an assessment of pension

reform and alternative investment.

COPYRIGHT NOTICE &

DISCLAIMERS

This entire report and associated materials, including tables, is copyright (c)

2017 by Anderson Economic Group LLC. Permission granted to reproduce in

its entirety, including this notice, for news media and research purposes. All

other rights reserved. Resale without permission, and use in derivative works, is

expressly prohibited. “Fair use” excerpts may be included in news or research

reports provided a complete citation is given to the author, title, and publisher.

This report is based on publicly available information; and regional, industry,

and other information known to us that we deem, in our professional judgement,

to be reliable or indicative at the current time.

AEG HQ2 Index: Methodology and Sources 8

(c) 2017 Anderson Economic Group LLC

This report does not constitute investment or tax advice. Readers are advised

that this report, like all reports analyzing the likely course of future events, con-

tains analyses, projections, and conjectures based on limited and imperfect

information. Therefore, the actual future course of events are certain to deviate

in some manner from those anticipated in this report. We may revise this report

without notice to past readers.

Exhibit 1. Values for Each Metric for Each Metropolitan Area

Metropolitan AreaBusiness Tax

Burden

Unit cost of

Labor

(Information)

Unit Cost of

Labor (Data

Processing)

Unit Cost of

Labor

(Management

of Businesses)

Commercial

Rent per

Square Foot

Transit Trips

per Capita

Congestion

(Annual Hours

of Delay per

Capita)

Completion of

Related

Degrees

Employment in

Related

Occupations

Business

Services

Employment

Immigrants

with Bachelors

or Higher

New York-Newark-Jersey City, NY-NJ-PA 11.3% $0.41 N/A $0.84 $73.01 211.7 33.0 130,864 1,010,910 671,478 307,260

Los Angeles-Long Beach-Anaheim, CA 7.7% $0.39 $0.35 $0.88 $38.27 42.5 49.3 88,853 535,460 519,049 154,677

Chicago-Naperville-Elgin, IL-IN-WI 9.4% N/A $0.34 $0.84 $30.00 62.7 34.8 66,749 539,040 389,939 123,952

Dallas-Fort Worth-Arlington, TX 7.3% $0.37 $0.71 $0.87 $25.94 10.2 34.0 34,119 352,910 293,225 134,857

Houston-The Woodlands-Sugar Land, TX 7.3% N/A $0.63 N/A $30.78 13.3 40.6 21,265 334,470 285,612 98,117

Washington-Arlington-Alexandria, DC-VA-MD-WV 11.7% $0.45 N/A N/A $37.25 66.9 41.5 66,914 624,470 465,501 182,926

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 8.8% $0.51 N/A N/A $26.25 53.9 28.3 45,700 114,810 219,222 94,736

Miami-Fort Lauderdale-West Palm Beach, FL 9.6% $0.44 $0.52 $0.81 $36.94 22.9 33.4 32,700 120,190 146,117 89,422

Atlanta-Sandy Springs-Roswell, GA 7.1% $0.44 $0.49 $0.82 $23.91 23.0 33.0 28,237 414,370 275,421 132,292

Boston-Cambridge-Newton, MA-NH 9.1% $0.48 N/A $0.81 $33.99 82.5 34.7 60,001 342,470 243,494 119,552

San Francisco-Oakland-Hayward, CA 7.7% $0.56 N/A $0.84 $73.65 95.7 42.0 27,035 264,820 232,966 137,302

Phoenix-Mesa-Scottsdale, AZ 8.3% $0.48 $0.62 $0.84 $24.48 14.6 39.7 53,382 264,480 143,470 63,228

Detroit-Warren-Dearborn, MI 8.0% $0.44 $0.49 N/A $19.88 8.6 40.6 13,360 80,190 190,024 54,153

Minneapolis-St. Paul-Bloomington, MN-WI 9.7% $0.44 $0.56 N/A $25.87 27.2 35.4 26,816 308,580 207,480 69,606

San Diego-Carlsbad, CA 7.7% $0.47 $0.49 $0.83 $31.44 30.3 25.7 31,081 189,960 95,386 47,831

Tampa-St. Petersburg-Clearwater, FL 9.6% $0.45 N/A $0.86 $23.01 9.0 28.2 15,013 159,510 98,122 46,772

Denver-Aurora-Lakewood, CO 9.0% $0.41 N/A $0.87 $26.99 35.3 35.0 17,332 235,180 141,830 91,407

St. Louis, MO-IL 7.0% $0.48 $0.76 $0.85 $19.05 15.4 31.5 22,142 168,050 115,419 41,720

Baltimore-Columbia-Towson, MD 8.2% $0.45 N/A N/A $23.34 38.1 33.7 16,665 195,510 128,739 53,290

Charlotte-Concord-Gastonia, NC-SC 7.0% $0.51 $0.70 N/A $24.47 9.2 28.5 9,181 166,410 81,914 52,706

Orlando-Kissimmee-Sanford, FL 9.6% $0.46 N/A $0.87 $21.01 11.2 32.6 17,100 126,630 90,823 46,992

San Antonio-New Braunfels, TX 7.3% $0.43 $0.75 $0.44 $23.68 15.8 33.2 10,150 109,940 65,801 32,409

Portland-Vancouver-Hillsboro, OR-WA 6.8% $0.49 $0.61 N/A $27.56 46.0 36.4 8,131 158,480 87,042 58,159

Sacramento--Roseville--Arden-Arcade, CA 7.7% $0.47 N/A $0.91 $23.76 11.1 33.3 9,579 144,210 48,853 37,777

Cincinnati, OH-KY-IN 7.3% $0.37 $0.54 N/A $19.35 9.0 29.6 13,046 128,320 78,977 31,505

Las Vegas-Henderson-Paradise, NV 10.7% $0.41 $0.24 $0.83 N/A 32.6 32.2 5,276 72,040 60,949 20,330

Kansas City, MO-KS 10.7% N/A N/A $0.85 N/A 7.8 28.5 10,282 151,060 85,666 37,142

Austin-Round Rock, TX 7.3% $0.42 $0.55 N/A $34.08 13.1 34.1 17,529 156,230 90,610 52,930

Cleveland-Elyria, OH 7.3% $0.39 $0.43 $0.83 $18.98 20.6 25.5 8,156 116,390 73,388 20,689

Indianapolis-Carmel-Anderson, IN 7.0% N/A N/A $0.87 $19.78 4.6 29.9 8,708 128,330 66,258 31,131

San Jose-Sunnyvale-Santa Clara, CA 7.7% $0.68 $0.09 N/A $58.08 19.7 53.6 16,977 268,300 157,937 58,101

Nashville-Davidson--Murfreesboro--Franklin, TN 7.8% $0.39 $0.66 $0.59 $23.59 5.6 32.5 8,132 106,760 70,319 35,273

Raleigh, NC 7.0% $0.45 N/A N/A $22.27 6.7 24.0 7,507 96,000 50,103 30,962

New Orleans-Metairie, LA 7.6% $0.29 N/A $0.88 N/A 18.1 40.2 6,315 48,840 29,168 16,474

Salt Lake City, UT 7.3% $0.47 N/A N/A $22.99 38.3 24.5 21,323 102,710 71,147 20,936

Notes: Immigrants include both domestic and foreign migrants. Unit cost of labor is defined as total compensation divided by total gross domestic product within the industry and region. N/A means "not available."

Source: AEG analysis using base data from the Bureau of Economic Analysis, American Community Survey, Department of Labor, Integrated Postsecondary Education System, Jones Long LaSalle, Department of Transportation, U.S.

Census Bureau, U.S. Cluster Mapping Project, AEG Annual Business Tax Burden Report

Exhibit 2. Adjusted Z-Score for Each Metric by Category

Metropolitan AreaBusiness Tax

Burden

Unit cost of

Labor

(Information)

Unit Cost of

Labor (Data

Processing)

Unit Cost of

Labor

(Management

of Businesses)

Commercial

Rent per

Square Foot

Cost of Doing

Business

Transit Trips

per Capita

Congestion

(Annual Hours

of Delay per

Capita)

Ease of

Transportation

Completion of

Related

Degrees

Employment in

Related

Occupations

Business

Services

Employment

Immigrants

with Bachelors

or Higher

Labor and

Business

Services

New York-Newark-Jersey City, NY-NJ-PA 1.32 4.11 N/A 3.37 0.40 2.30 8.29 3.73 6.01 7.42 8.27 7.00 7.55 7.56

Los Angeles-Long Beach-Anaheim, CA 4.04 4.38 4.61 2.99 2.96 3.79 3.83 1.18 2.50 5.85 5.56 5.95 4.93 5.57

Chicago-Naperville-Elgin, IL-IN-WI 2.73 N/A 4.70 3.32 3.57 3.58 4.36 3.45 3.91 5.02 5.58 5.06 4.40 5.02

Dallas-Fort Worth-Arlington, TX 4.32 4.71 2.47 3.08 3.87 3.69 2.98 3.57 3.28 3.80 4.52 4.39 4.59 4.32

Houston-The Woodlands-Sugar Land, TX 4.32 N/A 2.97 N/A 3.51 3.60 3.06 2.53 2.80 3.32 4.41 4.34 3.96 4.01

Washington-Arlington-Alexandria, DC-VA-MD-WV 0.99 3.49 N/A N/A 3.04 2.50 4.47 2.39 3.43 5.03 6.07 5.58 5.42 5.52

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 3.21 2.55 N/A N/A 3.85 3.20 4.13 4.48 4.30 4.23 3.16 3.88 3.90 3.79

Miami-Fort Lauderdale-West Palm Beach, FL 2.59 3.76 3.59 3.61 3.06 3.32 3.31 3.66 3.49 3.74 3.19 3.37 3.81 3.53

Atlanta-Sandy Springs-Roswell, GA 4.46 3.72 3.80 3.59 4.02 3.92 3.32 3.73 3.52 3.58 4.87 4.27 4.55 4.31

Boston-Cambridge-Newton, MA-NH 2.97 3.08 N/A 3.68 3.28 3.25 4.89 3.47 4.18 4.77 4.46 4.05 4.33 4.40

San Francisco-Oakland-Hayward, CA 4.04 1.88 N/A 3.35 0.35 2.40 5.23 2.33 3.78 3.53 4.02 3.97 4.63 4.04

Phoenix-Mesa-Scottsdale, AZ 3.54 3.04 3.01 3.37 3.98 3.39 3.10 2.68 2.89 4.52 4.01 3.36 3.36 3.81

Detroit-Warren-Dearborn, MI 3.77 3.66 3.78 N/A 4.32 3.88 2.94 2.54 2.74 3.02 2.96 3.68 3.20 3.22

Minneapolis-St. Paul-Bloomington, MN-WI 2.53 3.72 3.38 N/A 3.88 3.38 3.43 3.35 3.39 3.52 4.27 3.80 3.47 3.76

San Diego-Carlsbad, CA 4.04 3.20 3.76 3.43 3.47 3.58 3.51 4.88 4.19 3.68 3.59 3.02 3.10 3.35

Tampa-St. Petersburg-Clearwater, FL 2.59 3.52 N/A 3.19 4.09 3.35 2.95 4.49 3.72 3.08 3.41 3.04 3.08 3.15

Denver-Aurora-Lakewood, CO 3.06 4.17 N/A 3.09 3.79 3.53 3.64 3.42 3.53 3.17 3.85 3.34 3.84 3.55

St. Louis, MO-IL 4.50 3.11 2.17 3.26 4.38 3.49 3.11 3.97 3.54 3.35 3.46 3.16 2.99 3.24

Baltimore-Columbia-Towson, MD 3.65 3.61 N/A N/A 4.06 3.77 3.72 3.62 3.67 3.14 3.62 3.25 3.19 3.30

Charlotte-Concord-Gastonia, NC-SC 4.56 2.66 2.55 N/A 3.98 3.44 2.95 4.45 3.70 2.86 3.45 2.93 3.18 3.11

Orlando-Kissimmee-Sanford, FL 2.59 3.46 N/A 3.08 4.24 3.34 3.00 3.79 3.40 3.16 3.23 2.99 3.08 3.12

San Antonio-New Braunfels, TX 4.32 3.88 2.21 7.30 4.04 4.35 3.13 3.69 3.41 2.90 3.13 2.82 2.83 2.92

Portland-Vancouver-Hillsboro, OR-WA 4.71 2.93 3.06 N/A 3.75 3.61 3.92 3.21 3.56 2.82 3.41 2.97 3.27 3.12

Sacramento--Roseville--Arden-Arcade, CA 4.04 3.24 N/A 2.70 4.03 3.50 3.00 3.69 3.35 2.88 3.33 2.70 2.92 2.96

Cincinnati, OH-KY-IN 4.33 4.77 3.50 N/A 4.36 4.24 2.95 4.27 3.61 3.01 3.24 2.91 2.82 2.99

Las Vegas-Henderson-Paradise, NV 1.74 4.20 5.27 3.49 N/A 3.68 3.57 3.85 3.71 2.72 2.92 2.79 2.62 2.76

Kansas City, MO-KS 1.75 N/A N/A 3.26 N/A 2.51 2.91 4.44 3.68 2.90 3.37 2.96 2.91 3.04

Austin-Round Rock, TX 4.32 3.97 3.44 N/A 3.27 3.75 3.05 3.56 3.31 3.18 3.40 2.99 3.18 3.19

Cleveland-Elyria, OH 4.33 4.51 4.12 3.41 4.39 4.15 3.25 4.91 4.08 2.82 3.17 2.87 2.63 2.87

Indianapolis-Carmel-Anderson, IN 4.50 N/A N/A 3.05 4.33 3.96 2.83 4.23 3.53 2.85 3.24 2.82 2.81 2.93

San Jose-Sunnyvale-Santa Clara, CA 4.04 N/A 6.14 N/A 1.50 2.92 3.23 0.49 1.86 3.16 4.04 3.46 3.27 3.48

Nashville-Davidson--Murfreesboro--Franklin, TN 3.90 4.45 2.74 5.82 4.04 4.19 2.86 3.81 3.34 2.82 3.11 2.85 2.88 2.92

Raleigh, NC 4.56 3.53 N/A N/A 4.14 4.08 2.89 5.15 4.02 2.80 3.05 2.71 2.81 2.84

New Orleans-Metairie, LA 4.07 5.92 N/A 2.95 N/A 4.32 3.19 2.61 2.90 2.76 2.78 2.57 2.56 2.67

Salt Lake City, UT 4.30 3.22 N/A N/A 4.09 3.87 3.72 5.07 4.40 3.32 3.09 2.86 2.63 2.97

Notes: Immigrants include both domestic and foreign migrants. Unit cost of labor is defined as total compensation divided by total gross domestic product within the industry and region. N/A means "not available."

Source: AEG analysis using base data from the Bureau of Economic Analysis, American Community Survey, Department of Labor, Integrated Postsecondary Education System, Jones Long LaSalle, Department of Transportation, U.S. Census Bureau, U.S. Cluster Mapping Project, AEG