2020 E-Insights

2020

TAMPA BAY E-INSIGHTS REPORT

THE 2020 E-INSIGHTS REPORT IS PRODUCED BY THE MUMA COLLEGE OF BUSINESS AT THE UNIVERSITY OF SOUTH FLORIDA, A PREEMINENT UNIVERSITY, AND IS AFFILIATED WITH THE STATE OF THE REGION INITIATIVE. stateoftheregion.com

An Introduction from the Dean

University of South Florida Muma College of Business

We are excited to join the Tampa Bay Partnership for the 2020 State of the Region Community Report as we unveil new research tracking our regional competitiveness and prosperity. We examine where we stand compared to top markets nationwide and explore the steps policymakers and influencers can take to create a vibrant, inclusive economy.

A companion piece to the research conducted by the Tampa Bay Partnership, the Tampa Bay E-Insights Report is a multi-dimensional quantitative assessment of the performance of the Tampa Bay region along different dimensions of “inclusive economic growth.” By inclusive economic growth, we mean the economic growth that is distributed fairly across society and creates opportunities for all.

This work is transformational. Our scholars use real-time big data to analyze the economic health of the Tampa Bay region using rigorous quantitative methods. The result: data-driven insights that not only inform business and civic leaders about the state of the Tampa Bay region, but also provide policy recommendations to drive the needle in a positive direction.

What Gets Measured is What Gets Done.

One could ask why the USF Muma College of Business has taken up this initiative because, after all, we are a business school and we focus on creating the future talent by providing an outstanding business education.

But we do so much more than simply disseminate information. We equip students with the skills and knowledge needed to take leading positions in business and society. And one of the ways we do that is by focusing on analytics and creativity, using our faculty expertise to contribute to the well-being of the greater community while providing opportunities for our students to conduct relevant business research.

To improve the region’s health, we have to know where we stand relative to similar and aspirant communities. After all, if you cannot measure it, you cannot improve it. However, we do not want to stop there. We want to take a scientific approach and work closely with the business community and policymakers to improve the economic health of the region and to make Tampa Bay a very attractive destination for businesses as well as for the public. Understanding that what gets measured is what gets done, we can then measure our progress and make timely and effective recommendations that can lead to maximum impact. Data is the key.

Real-time Signals, Real-time Findings

One unique feature of this project is that we take a multi-dimensional approach to derive insights about the region. We used real-time big data signals – such as Google Trends and Twitter sentiment – and data about traditional economic indicators to derive a holistic picture of the inclusive economic health of the region.

Real-time signals on job openings suggest the need to nurture young talent in the domains of finance and information technology because these two industries command a major chunk of open, high-paying jobs in the region. Furthermore, we have employed econometric and simulation methods to identify policy initiatives that can boost the inclusive economic growth of the region.

Improving Tampa Bay’s Competitive Position

From our analysis, we have identified two key areas that need attention of the policymakers: higher education and public transit infrastructure. Higher education empowers underprivileged youth with the necessary skill set and opens doors of opportunity that help them improve their economic situation and, hopefully, to come out of poverty. Adequate public transit infrastructure gives the lower income strata section of society easy and affordable access to education, work and health.

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We strongly believe that the best way to predict the future is to shape it. Using the latest tools and research techniques, we can understand where we are and see how policy changes might shape our future.

Enjoy our report,

Moez Limayem, Dean USF Muma College of Business

About the USF Muma College of Business Our mission guides what we do now and our vision guides where we want to go, but it is our strategic priorities that help us focus our actions.

Our first priority is student success. We want students to leave USF with the best possible business education so that they can begin careers in their fields with competitive salaries, using the knowledge gleaned through our programs.

University of South Florida Muma College of Business

The analysis concludes that increased investments in high-skill education (STEM: Science, Technology, Engineering and Mathematics) and public transit infrastructure have the potential to help alleviate income inequality and boost economic mobility in the region leading to inclusive economic growth.

But we are committed to doing more than simply providing the technical training students seek. We are committed to guiding students through their academic journey by providing opportunities for them to develop as professionals from their first moments on campus. • Mission: We emphasize creativity and analytics to promote student success, produce scholarship with impact and engage with all stakeholders in a diverse global environment. • Strategic Vision: We aspire to be internationally recognized for developing business professionals who provide analytical and creative solutions in a global environment.

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TAMPA BAY REGION

2020 Tampa Bay E-Insights Report

THE REGIONAL COMPETITIVENESS REPORT examines the Tampa Bay region’s relative performance across a variety of economic competitiveness and prosperity indicators. What then, exactly, is the Tampa Bay region? The data presented in this report is for the eight counties of Citrus, Hernando, Hillsborough, Manatee, Pasco, Pinellas, Polk, and Sarasota. The region can also be described as the combination of four Metropolitan Statistical Areas (MSAs): Tampa-St. Petersburg-Clearwater (Hernando, Hillsborough, Pasco, Pinellas), Homosassa Springs (Citrus), LakelandWinter Haven (Polk) and North Port-Sarasota-Bradenton (Manatee, Sarasota). In instances where we combine county-level data, or MSA-level data, to create a regional value, we do so by weighting the component values by an appropriate factor – population, number of households, etc. – and it should be noted that, in most instances, the regional value remains close to the “core” value of the Tampa-St. Petersburg-Clearwater MSA.

University of South Florida Muma College of Business

Tampa Bay E-Insights report examines the economic performance of the Tampa Bay region relative to 19 other comparable Metropolitan Statistical Areas (MSAs). These MSAs were selected based on factors TALLAHASSEE JACKSONVILLE such as demography, size of the economy and presence of regional assets such as ports and research universities. The selected MSAs reflect both peer and aspirational relationships with Tampa Bay.

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A data appendix, detailing – as available – the indicator values at the county and MSA level is available at regionalcompetitiveness.org.

This report analyzes the performance of the Tampa Bay region along the dimensions of personal prosperity, income inequality and economic mobility. The analysis consists of four distinct components: examination of real-time signals, study of competitive trends based on traditional economic indicators, identification of drivers of economic outcomes through econometric modeling and policy experiments.

In this report, Tampa Bay is defined as the region consisting of eight counties: Citrus, Hernando, Hillsborough, Manatee, Pasco, Pinellas, Polk and Sarasota. The eight-county area includes four MSAs: Tampa-St. Petersburg-Clearwater, Homosassa Springs, Lakeland-Winter Haven and North PortSarasota-Bradenton. The data presented in the report for Tampa Bay is aggregated for the four MSAs.

Citrus Hernando Pasco Pinellas

Hillsborough

4

Polk

Manatee METROPOLITAN STATISTICAL AREAS (MSAs):  Tampa-St. PetersburgClearwater  Homosassa Springs  Lakeland-Winter Haven  North Port-Sarasota Bradenton

The MSAs studied in the report are shown in the map below.

REGIONAL COMPETITIVENESS REPORT

Sarasota

Table of Contents

6. Part 1: Signals from Real-Time Sources 7. Google Trends 10. Real-Time Signals on Job Opportunities 15. Social Media Sentiment 18. Key Insights from Real-Time Signals

19. Part 2: Competitive Trends Based on Traditional Economic Indicators 20. GRP Per Capita 21. Poverty Rate 22. Unemployment Rate 23. Net Migration Rate 24. Income Inequality (Gini Index) 25. Economic Mobility 27. Supplemental Poverty Measure (SPM) 29. Debt-to-Income Ratio 30. House Rents 31. Key Insights from Traditional Economic Indicators

32. Part 3: Identifying Drivers of Economic Outcomes

33. Drivers of Inclusive Economic Growth 34. Educational Attainment Rate: Graduate/Professional 35. Business Establishment Start Rate 36. Mean Household Income Lowest Quintile 37. Transit Availability 38. STEM Degree Per Production Capita 39. Key Insights from Analysis to Identify Drivers of Inclusive Econmic Growth

40. Part 4: Policy Experiments and Looking Ahead 41. Poverty Rate 42. Income Inequality (Gini Index) 43. Economic Mobility 44. Key Insights from Policy Experiments 45. Key Takeaways 46. Next Steps

University of South Florida Muma College of Business

1. An Introduction from the Dean 2. About the USF Muma College of Business 3. 2020 Tampa Bay E-Insights Report 4. Table of Contents 5. Authors and Contact Information

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Authors and Contact Information Faculty

Moez Limayem

Dean, Muma College of Business mlimayem@usf.edu

Balaji Padmanabhan

Director, Center for Analytics & Creativity, Muma College of Business bp@usf.edu

Shivendu Shivendu

Associate Professor, Muma College of Business shivendu@usf.edu Â

Graduate Students

Roohid Syed

Information Systems doctoral student, USF Muma College of Business roohidahmed@mail.usf.edu

Srirag Ramadugu

MS in Business Analytics & Information Systems student, USF Muma College of Business srirag@mail.usf.edu

Uday Karnam

MS in Business Analytics & Information Systems student, USF Muma College of Business udaykarnam@mail.usf.edu

Varsha Sharma

MS in Business Analytics & Information Systems student, USF Muma College of Business varshasharma@mail.usf.edu

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Part 1: Signals from Real-Time Sources One defining feature of this report is that it presents multidimensional view of the economic performance of the Tampa Bay MSA. Traditionally, most of the analyses were focused on traditional economic indicators such as poverty rate, income inequality (the Gini index) and economic mobility. However, such data becomes available with significant time lag and oftentimes provides a macro picture. On the other hand, real-time online data sources such as search engines, social media platforms and job portals provide patterns and signals, which can be used to gain real-time and novel insights about the economic wellbeing of a region.

This section presents insights from four real-time data sources. These data sources are grouped into three categories based on the type of information each presents. Below are the brief descriptions of the three categories of real-time data sources that have been considered for this study.

Google Trends

Google Trends provides insights about online search behavior. This information can be used to assess the state of the inclusive economic growth by analyzing how the search behavior differs across MSAs.

Job Portals and Professional Networks

LinkedIn and Indeed provide data regarding number and types of job openings in different regions. This data can be used to analyze the characteristics of the job market in a region.

Social Media

Twitter data can be used to generate insights about public perception towards different regions. Novel techniques such as sentiment analysis help derive insights about the public opinion on various aspects of the region.

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Google Trends Google Trends is an online tool that analyzes the popularity of search queries on Google across multiple regions. In this report, we aim to capture the level of economic inequality and economic mobility for the Tampa Bay region and 19 other MSAs using the data from this tool.

The Google Trends tool creates a search index for each search unit based on its popularity relative to all other search terms queried in a region. The index value ranges between 0 and 100. The index data has been collected for 20 MSAs of study including the Tampa Bay region between the dates of January 2006 and October 2019.

The analysis for trends of economic inequality and economic mobility includes comparison of search volumes of pairs of related queries. For example, search volume of “cars” versus “used cars” can be used as a proxy for economic inequality. Similarly, by looking at how the search volumes of “manager jobs” and “temporary jobs” change over time, a proxy for economic mobility can be constructed.

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Google Trends

About: Google trends-based proxy for economic inequality.

Source: Google Trends.

Economic Inequality Search Proxy Economic Inequality Ratio

Tampa Bay

St. Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Miami

Houston

Denver

Dallas

Jacksonville

Economic Inequality Search Proxy Economic Inequality

2019

Charlotte

Baltimore

2015

Austin

Atlanta

2011

“

“

SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay region’s competitive position has been declining for the past few Google Trends years (almost all MSAs appear to have increasing inequality).

2.5

2.0

1.5

1.0

0.5 0.0

Economic Inequality SearchRatio Proxy Trend Economic Inequality

Economic Inequality SearchTrend Proxy.Trend Economic Inequality Charlotte

Miami

Minneapolis

.

Tampa Bay

2.5 2.0 1.5 1.0 0.5 0.0 2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2015

2016

2017

2018

2019

Competitive Position Trend 0

Competitive Position

5

10

15

,

20

HTS

2006

2007

2008

2009

2010

2011

2012

2013

2014

-Tampa Bay’s competitive position has been declining for the past few years.

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Google Trends

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SUMMARY OF INSIGHTS AND FINDINGS As per the Google Trends analysis, Charlotte, San Antonio and St. Louis registered highest economic mobility. The Tampa Bay region is in the top half of the group.

Google Trends About: Google Trends-based proxy for economic mobility.

Source: Google Trends.

Tampa Bay

Economic Mobility

Other MSAs

San Antonio Charlotte St. Louis Orlando Houston Dallas Phoenix Tampa Bay Raleigh-Durham Baltimore San Diego Denver Miami Minneapolis Austin Jacksonville Nashville Portland Atlanta Seattle 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Comparative Index

9

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

Real-Time Signals on Job Opportunities The availability of jobs is one of the most important signals of economic strength for any region. Traditionally, such data has been collected with a time lag and reported by federal and state agencies. However, the opportunity to derive real-time insights about labor markets using signals from online platforms such as LinkedIn and Indeed exists. These platforms are increasingly used by both job seekers and employers.

Data on number of full-time, part-time, commission, contract, internship and temporary job openings was collected from Indeed between July 2019 and October 2019, and was then grouped into temporary and permanent jobs. Also, the data on the number of jobs available in different salary ranges was collected from Indeed. The data was grouped into high-paying and low-paying job buckets.

From LinkedIn, data was collected on the number of job openings related to the different job levels such as internship, entry-level, associate, mid-senior, director and executive. Given the importance of the technology industry, data on IT job postings was collected across MSAs from LinkedIn. Using the industry-wise job openings data from LinkedIn, top three job providing industries in the Tampa Bay region were identified.

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Job Opening Signals

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SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay region is near the bottom of the group in terms of IT job openings per capita.

Job Openings

About: Online platforms such as LinkedIn and Indeed provide real-time signals, which can be useful to derive insights about the state of the labor market of a region.

Source: Indeed and LinkedIn.

Permanent Versus Temporary New Job Openings Per Million Individuals

St.Louis Tampa Bay

Tampa Bay

Seattle Houston

San Antonio Phoenix

San Diego

Raleigh-Durham Orlando

Portland

Phoenix

Orlando

Nashville

Minneapolis

Miami

Jacksonville

Houston

Denver

Dallas

Temporary Jobs

Charlotte

Baltimore

Austin

Atlanta

Permanent Jobs

45K

Job openings Job Openings

40K 35K 30K 25K 20K 15K 10K 5K 0K

12K

Tampa Bay

IT Job Openings Per Million Individuals

Other

Average Number ofofJobs Avg number jobs

10K

8K

6K

4K

2K

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SIGHTS

-Tampa Bay is at the near bottom of the group in terms of IT job openings per capita.

Miami

San Antonio

Jacksonville

San Diego

Atlanta

Portland

Charlotte

Nashville

Dallas

St.Louis

Minneapolis

Austin

Raleigh-Durham

Seattle

Denver

Baltimore

0K

Job Opening Signals

“

“

SUMMARY OF INSIGHTS AND FINDINGS Charlotte tops the list in terms of both internship job openings and entry-level job openings. The Tampa Bay region is in the lowest quintile (bottom 20%) of the group.

Job Openings

About: Onlineplatforms platforms such LinkedIn Indeed provide real-time signals, be to useful to About: Online such asas LinkedIn andand Indeed provide real-time signals which which can becan useful derive derive about state of the labor market insightsinsights about the statethe of the labor market of a region.of a region.

Source: LinkedIn Source: LinkedIn. Tampa Bay

Other

Internship Job Openings Per Million Individuals

1000

JobOpenings openings Job

800

600

400

200

Seattle

Tampa Bay

Atlanta

Miami

Phoenix

Tampa Bay

Seattle

Miami

Atlanta

Phoenix

San Diego

Houston

St.Louis

Dallas

Baltimore

Minneapolis

Raleigh-Durham

Portland

Jacksonville

Austin

Denver

San Antonio

Nashville

Orlando

Charlotte

0

Entry-Level Job Openings Per Million Individuals

Tampa Bay

Other MSAs

45K

40K

35K

Job Job Openings openings

30K

25K

20K

15K

10K

5K

IGHTS

Dallas

Houston

St.Louis

San Diego

Baltimore

Raleigh-Durham

Portland

Minneapolis

Jacksonville

San Antonio

Austin

Denver

Orlando

Nashville

Charlotte

0K

-Charlotte tops the list in terms of both internship job openings and entry-level job openings.

-Tampa Bay is in the lowest quintile (bottom 20%) of the group.

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Job Opening Signals

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SUMMARY OF INSIGHTS AND FINDINGS Charlotte tops the table in terms of mid-senior-level and executive-level job openings by significant amount.

Job Openings

About: Online platforms such as LinkedIn and Indeed provide real-time signals which can be useful to derive insights about the state of the labor market of a region.

Source: LinkedIn.

Tampa Bay

Mid-Senior-LevelJob JobOpenings OpeningsPer PerMillion MillionIndividuals Individuals Mid-Senior-Level

Other

7K

Job Openings Job openings

6K 5K 4K 3K 2K 1K

Houston

Atlanta

Tampa Bay

Miami

Phoenix

Houston

Tampa Bay

Atlanta

Miami

Phoenix

Seattle

San Diego

Dallas

St.Louis

Baltimore

Raleigh-Durham

Portland

Austin

Jacksonville

San Antonio

Nashville

Denver

Orlando

Minneapolis

Charlotte

0K

Executive-Level Job Openings Per Million Individuals Tampa Bay

Other

Executive-Level Job Openings per Million Individuals

1000 900 800

Job Jobopenings Openings

700 600 500 400 300 200 100

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GHTS

San Diego

Seattle

Dallas

Raleigh-Durham

Baltimore

Minneapolis

St.Louis

Denver

San Antonio

Nashville

Portland

Austin

Orlando

Jacksonville

Charlotte

0

-Charlotte tops the table in terms of mid- senior-level and executive-level job openings by significant amount.

Job Opening Signals

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SUMMARY OF INSIGHTS AND FINDINGS IT and financial services are driving the most number of job openings in the Tampa Bay region. This could provide guidance for how to nurture professional talent in the region. Tampa Bay falls near the middle in high paying and low paying job openings.

Job Openings

About: Online platforms such as LinkedIn and Indeed provide real-time signals which can be useful to derive insights about the state of the labor market of a region.

Source: LinkedIn and Indeed.

Services With Most Number Of Job Openings in Tampa Bay Financial Services IT Services Retail

Financial Services 9,535

IT Services 11,249

Retail 3,799 High Paying Versus Low Paying Job Openings Per Million Individuals

Tampa Bay

St.Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Miami

Jacksonville

Houston

Denver

Dallas

Low Paying Jobs

Charlotte

Baltimore

Austin

Atlanta

High Paying Jobs

80K

70K

Job opportunities Job Opportunities

60K

50K

40K

30K

20K

10K

IGHTS

0K

-IT and financial services are driving the most number of job openings in the Tampa Bay region. This could provide guidance for how to nurture professional talent in the region.

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Social Media Sentiment Social media sentiment reflects the positive or negative emotions conveyed through social media posts. In this social media sentiment analysis, Twitter data was used to discover people’s emotions and feelings about each MSA based an analysis of tweets related to each MSA. Tweets were gathered from official Twitter handles belonging to universities, traffic departments, police, news, and local agencies promoting downtown activities for specific cities in each MSA. Tweets for a two-month period (October and November 2019) for all the MSAs were scraped using Twitter’s Python search API. Raw tweets were pre-processed, and an ensemble machine-learning algorithm was used to determine the sentiment of each tweet. The sentiment for all the tweets was then aggregated to derive the overall sentiment of each MSA. After obtaining the overall sentiment, a deep-dive analysis between the most positive MSA and the Tampa Bay region was performed. Also, sentiment analysis was performed for all the cities of the Tampa Bay MSA, including Tampa, Clearwater, St. Petersburg, North Port, Bradenton, Sarasota, Lakeland, Winter Haven and Homosassa Springs.

School/University Downtown

Police Department

City

Traffic

Twitter API

Local News

?

Social Media Sentiment

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Machine Learning Algorithm

Twitter Sentiment Analysis

Twitter Sentiment Analysis

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SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay region is the middle of the pack in terms of overall sentiment. Within Tampa Bay, the positive sentiment is mostly driven by the conversations about downtown, schools and universities. Specifically for schools, positive sentiment stems from conversations about school events and competitions that students appear to be participating in.

About: about a region based on the conversations in twitter aboutabout that About:Twitter Twitter sentiment sentimentanalysis analysisgives givesoverall overallsentiment sentiment about a region based on the conversations on Twitter that region. region.

Source:Twitter TwitterAPI. API Source: Tampa

Twitter Public Sentiment

Other

San Antonio Phoenix Dallas Austin Raleigh Jacksonville San Diego Houston Orlando Nashville St. Louis Tampa Miami Baltimore Denver Seattle Minneapolis Charlotte Portland Atlanta 0

5

10

15

20

25

30

35

40

45

50

55

60

Overall Sentiment

Deeper Look IntoDeeper San Antonio’s Sentiment (Net Sentiment Percentage Positive Tweets) Look Into San Antonio's Downtown 65.22%

Traffic 33.34%

Trinity University 51.36%

School 83.34%

St. Mary’s University 67.38%

City 61.04%

News 58.54%

Police 22.80%

Deeper Look IntoDeeper TampaLook Bay’sInto Sentiment (Net Percentage Tampa Bay's Sentiment Positive Tweets) Downtown 81.16% USF 69.48%

School 88.82%

University of Tampa 87.50%

City 73.28% News 27.76%

S

Traffic 31.34%

- Tampa is the middle of the pack in terms of overall sentiment.

Police 48.58%

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Twitter Sentiment Analysis

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SUMMARY OF INSIGHTS AND FINDINGS St. Petersburg has the overall highest positive sentiment among the cities in the Tampa Bay region.

Twitter Sentiment Analysis About: Twtter sentiment analysis gives overall sentiment about a region based on the conversations in twitter about that region.

Source: Twitter API. Tampa

Other

Tampa Bay Regional Comparison

St. Pete Tampa Sarasota Lakeland Clearwater North Port Bradenton Winter Haven

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

Net Positive Sentiment Overall Sentiment

Tampa Bay Sentiment: Deep Dive City of Lakeland Hillsborough Schools University of Tampa City of North Port Tampa Downtown City of St. Pete City of Tampa Sarasota Police USF St. Pete Police City of Sarasota City of Winter Haven Tampa Police City of Clearwater Bradenton Police Tampa Bay Traffic Clearwater Police Tampa Bay News Winter Haven Police North Port Police Lakeland Police 0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

INSIGH..

Net Positive Sentiment Overall Sentiment

- St. Petersburg has the overall highest positive sentiment among the cities in Tampa Bay.

TAMPA BAY E-INSIGHTS REPORT

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Key Insights from Real-Time Signals • Google Trends indicate that economic inequality is increasing for most of the MSAs compared in this study.

• The Tampa Bay region stands in the middle of the group in terms of economic inequality and economic mobility.

• There is a significant gap between talent supply and talent demand in finance and information technology industries in Tampa Bay. This insight signifies the need to train youth in the domains of finance and IT to bridge the gap.

• The positive Twitter sentiment for the Tampa Bay region is driven by conversations about downtown, universities and schools.

University of South Florida Muma College of Business

• Real-time job market signals indicate that Tampa Bay is falling behind most of the MSAs in terms of job openings.

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University of South Florida Muma College of Business

Part 2: Competitive Trends Based on Traditional Economic Indicators Outcomes of Interest

This report focuses on the following variables that can be viewed as indicators related to the economic prosperity of a region. While some of these are standard measures used in most such reports, a few are specific to the theme of inclusive growth and prosperity for all.

Income inequality refers to the extent to which income is distributed in an uneven manner among the population. Income distribution is measured using the “Gini index.” Economic mobility is measured by the ability of an individual, family or some other groups to move between income strata or quantiles. This report focuses on the following outcome variables: • Gross regional product per capita • Poverty rate • Unemployment rate • Net migration rate • Income inequality (Gini index) • Economic mobility This report also focuses on the following supplemental variables. Though not directly linked to the outcome variables, the supplemental variables help make sense of personal economic prosperity of the region. The supplemental variables that this report focuses on are: • Supplemental poverty measure • Debt-to-income ratio • Mean house rent For all outcome variables except economic mobility, the analysis presented in this report is based on data for a 10-year period (2008-2018). For economic mobility, this report uses data from The Opportunity Atlas (opportunityatlas.org).

GRP Per Capita

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SUMMARY OF INSIGHTS AND FINDINGS Most MSAs from Florida consistently ranked low in this metric. Most MSAs’ GRP per capita dropped in 2009 and Tampa Bay MSA is among few that did not. However, the Tampa Bay MSA has consistently been at the bottom across the years shown.

GRP Per Capita

About: This measurement divides the Gross Regional Product, the value of all goods and services produced in the region, by the population of the region.

Source: Bureau of Economic Analysis, Regional Data, Per Capita Real Gross Domestic Product.

Tampa Bay

St. Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Jacksonville

Houston

Denver

Dallas

Charlotte

Miami

GRP Per Capita

2017

Baltimore

2013

Austin

Atlanta

2008

GRP GRP Per per Capita capita

60K

40K

20K

0K

Atlanta

Seattle

Tampa Bay

GRP Per Capita Trend

GRP GRPPer perCapita capita

60K

40K

20K

0K 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2015

2016

2017

Competitive Position Trend

Competitive Position Competitive Position

0

5

10

15

20

IGHTS

2008

2009

2010

2011

2012

2013

2014

- Most MSAs from Florida consistently ranked low in this metric.

- Most MSAs' GRP per capita dropped in 2009 and Tampa Bay MSA is among few that did not. However, Tampa Bay has

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SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay region’s poverty rate has increased from 2008 to 2011 and has been declining since then. Tampa Bay’s competitive position declined from 10 in 2010 to 16 in 2017. Minneapolis has consistently recorded the lowest poverty rate Poverty Rate among the MSAs in the comparison group.

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Poverty Rate

About: Measures the percentage of population that is living below the federal poverty level as defined by the U.S. Census Bureau (Income thresholds vary by family size).

Source: U.S. Census Bureau, American Community Survey.

Tampa Bay

St. Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Jacksonville

Houston

Denver

Dallas

Miami

Poverty Rate

2017

Charlotte

Baltimore

2013

Austin

Atlanta

2008

Poverty Rate Poverty rate

15

10

5

0

Minneapolis

San Antonio

San Diego

Poverty Rate Trend

Tampa Bay

Poverty Rate Poverty rate

15

10

5

0 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2014

2015

2016

2017

Competitive Position Trend

Competitive Position Competitive Position

0

5

10

15

20

21

GHTS

2008

2009

2010

2011

2012

2013

- Tampa Bay's poverty rate has increased from 2008 to 2011 and has been declining since then.

- Tampa Bay's competitive position declined from 10 in 2010 to 16 in 2017.

Unemployment Rate

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SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay region made a significant progress during 2013-2017 than during 2008-2013. Unemployment rate for Tampa Bay, as with most MSAs compared, has seen consistent decline. The unemployment rate for all the 20 MSAs was higher in 2013 than in 2008 and 2017.

Unemployment Rate

About: Measures the share of the labor force that is jobless. Generally, an individual is considered unemployed if he or she is willing and able to work but unable to find a job.

Source: U.S. Bureau of Labor Statistics, local area unemployment statistics.

Tampa Bay

St. Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Jacksonville

Houston

Denver

Dallas

Charlotte

Miami

Unemployment Rate

2017 Baltimore

2013 Austin

Atlanta

2008

Unemployment Rate

8

6

4

2

0

Denver

Unemployment Rate Trend

Houston

Seattle

Tampa Bay

2009

2010

2011

Unemployment Rate

10 8 6 4 2 0 2008

2012

2013

2014

2015

2016

2017

2014

2015

2016

2017

Competitive Position Trend 0

Competitive Position

5

10

15

20

IGHTS

2008

2009

2010

2011

2012

2013

-Tampa Bay made a significant progress during 2013-2017 than during 2008-2013.

-Unemployment rate for Tampa Bay, as with most MSAs compared, has seen consistent decline.

22

“

SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay MSA has registered a strong growth in net migration rate over the last decade. Tampa Bay has maintained No. 1 position in the recent years. Some MSAs, including Tampa Bay, experienced negative net migration due to the 2008 economic recession.

Net Migration Rate

“

Net Migration Rate

About: Net migration rate is calculated as population change, less the net effect of natural increase (births minus deaths) relative to the population as a whole.

Source: U.S. Census Bureau, Estimates of the Components of Resident Population Change.

Tampa Bay

St. Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Miami

Houston

Denver

Dallas

Jacksonville

Net Migration Rate

2017

Charlotte

Baltimore

2013

Austin

Atlanta

2008

Net Migration Rate Net Migration Rate

2.0 1.5 1.0 0.5 0.0 -0.5 -1.0

Net Migration Rate Net Migration

Denver

San Antonio

Tampa Bay

Net Migration Rate Trend

2.0 1.5 1.0 0.5 0.0

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2014

2015

2016

2017

Competitive Position Trend 0 2

CompetitivePosition Position Competitive

4 6 8 10 12 14 16 18 20

23

IGHTS

2008

2009

2010

2011

2012

2013

-Tampa Bay has registered a strong growth in net migration rate over the last decade.

-Tampa Bay has maintained No.1 position in the recent years.

Income Inequality (Gini Index)

“

“

SUMMARY OF INSIGHTS AND FINDINGS Minneapolis consistently outperformed most of the MSAs. Miami remained at the bottom of the chart in terms of competitive position for all the years. Income inequality has been increasing for most of the MSAs over the years. The competitive position of the Tampa Bay IncomeMSA Inequality (Gini Index) in terms of income inequality in 2016-17 is at all time low in the last decade.

About: Income inequality refers to the extent to which income is disributed in an uneven manner among the population.

Income inequality is measured by gini index, which ranges from 0 to 1. Gini index of 0 implies perfect income equality, i.e., every individual receives equal share. Gini index of value 1 indicates perfect income inequality, which implies that only one individual receives all the income.

Source: U.S. Census Bureau, American Community Survey.

Tampa Bay

St. Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Jacksonville

Houston

Denver

Dallas

Charlotte

Miami

Income Inequality

2017

Baltimore

2013

Austin

Atlanta

2008

Gini Index Giniindex Index Gini

0.50 0.48 0.46 0.44

Income Inequality Trend Miami

Minneapolis

San Diego

2009

2010

Tampa Bay

Gini GiniIndex Index Gini index

0.50

0.48

0.46

0.44 2008

2011

2012

2013

2014

2015

2016

2017

2014

2015

2016

2017

Competitive Position Trend Miami

Minneapolis

Nashville

Tampa Bay

Competitive Position Competitive Position Competitive Position

0

5

10

15

20

GHTS

2008

2009

2010

2011

2012

2013

-Minneapolis consistently outperformed most of the MSAs -Miami remained at the bottom of the chart in terms of competitive position for all the years.

24

University of South Florida Muma College of Business

Economic Mobility Economic mobility broadly captures the ability of people to move from a lower income stratum to higher income strata. This is measured using two variables: Absolute mobility and relative mobility.

Absolute Economic Mobility

One way of determining absolute economic mobility is to examine the average income percentile of children whose parents were at the 25th percentile of the national income distribution. If the children are at a higher income percentile than 25, then it indicates that there has been positive economic mobility. The higher the income percentile of the children, the higher the absolute economic mobility.

For example, if, in region A, the children of parents who were at 25th percentile are at the 40th percentile, and in region B, the children of parents who were at 25th percentile are at the 32nd percentile, then one can conclude that region A has experienced higher economic mobility than region B. “The Opportunity Atlas” is an interactive database with a user-friendly tool to assess the economic mobility across various regions in the United States. This tool provides an in-depth understanding of how the average outcomes (for example, household income) of children varied by demographic subgroups.

“Which neighborhoods in America offer children the best chance to rise out of poverty?” is the kind of question can be answered by using data from the Opportunity Atlas.

The research paper “The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility” by Raj Chetty, John N. Friedman, Nathaniel Hendren, Maggie R. Jones and Sonya R. Porter provides detail on how this publicly available dataset was constructed to examine children’s outcomes in adulthood using anonymized longitudinal data covering nearly the entire U.S. population. The sources of data used in this process were (1) the U.S. Census 2000 and 2010 short forms; (2) federal income tax returns from 1989, 1994, 1995 and 1998-2015; and (3) the U.S. Census 2000 long form and the 2005-2015 American Community Surveys.

Relative Economic Mobility

Relative mobility refers to the expected difference in the income percentile rank of children belonging to two parents whose income percentile rank differs by 1 unit.

For example, consider region A, with relative economic mobility of 0.2. This implies that the gap in income percentile of children of parents at the 10th percentile of income distribution and those of the parents at the 20th percentile is going to be, on average, 2.

Hence, the children of these parents are closer to each other in their (percentile) ranks within their cohort, compared to how close the parents were to each other in their (percentile) ranks within their cohort. Relative economic mobility is often used to check how much individuals “move” across income percentile ranks as compared to their parents.

(Source: Table 4 in the ‘Geography of Mobility: Metropolitan Statistical Area Intergenerational Mobility Statistics’ dataset from https://opportunityinsights.org/data/ )

Economic Mobility

“

“

SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay MSA is at No. 13 in terms of absolute economic mobility.

Minneapolis achieved the highest absolute economic mobility in the MSAs considered for this study.

Economic Mobility

About: The absolute mobility is the average rank of the children whose parents are at 25 percentile in the income ranking (measured on a scale of 0-100). Relative mobility is the increment in the rank of the child with an increment in the rank of the parent in terms of income.

Source: Opportunity Insights.

Tampa Bay 45

Absolute Economic Mobility

Other MSAs

Absolute Economic Mobility

44 43 42 41 40 39 38 37

Baltimore

Nashville

Raleigh-Durham

Jacksonville

Atlanta

Charlotte

Miami

Phoenix

Portland

Seattle

San Diego

St. Louis

Tampa Bay

Orlando

Miami

Austin

Phoenix

Dallas

San Antonio

Portland

Denver

Houston

Denver

Tampa Bay 0.42

Seattle

San Diego

Minneapolis

36

Relative Economic Mobility

Other MSAs

Relative Economic Mobility

0.40 0.38 0.36 0.34 0.32 0.30 0.28 0.26

IGHTS

San Antonio

Houston

Orlando

Austin

Minneapolis

Tampa Bay

Dallas

Nashville

Jacksonville

Atlanta

Raleigh-Durham

Charlotte

St. Louis

Baltimore

0.24

- Tampa bay is at No. 13 in terms of absolute economic mobility.

- Minneapolis achieved the highest absolute economic mobility in the MSAs considered for this study.

26

Supplemental Poverty Measure (SPM)

University of South Florida Muma College of Business

The official poverty measure (poverty rate) was developed in the early 1960s and only a few minor changes have been made since then. According to this measure, a family’s poverty status is determined by comparing its before-tax income to that of the poverty threshold value. This threshold value represents the cost of a minimal diet multiplied by three (in the 1960s the average family spent about a third of their income on food).

27

After many years of research, analysis and debate, the Interagency Technical Working Group suggested a new measure to supplement the current measure of poverty, which is the “Supplemental Poverty Measure.�

While the official poverty measure is based on cash resources, the SPM, in addition to cash resources, considers non-cash benefits and subtracts necessary expenses (such as taxes and medical expenses). Though the SPM is a more nuanced indicator that captures aspects that traditional poverty indicators do not, it should not be viewed as a substitute for the currently used poverty rate measure.

Supplemental Poverty Measure

“

“

SUMMARY OF INSIGHTS AND FINDINGS Charlotte holds the first rank for having lowest value of SPM for all the years (2015 to 2018). There is significant gap in the values of SPM for Seattle and San Diego, both holding the No. 19 and No. 20 positions, respectively. The Tampa Bay MSA has been improving slightly over the years.

Supplemental Poverty Measure

About: A measure measure of considers non-cash benefits in in addition to cash resources and and subtracts necessary expenses About:A ofpoverty povertythat that considersnoncash benefits addition to cash resources subtracts necessary such as taxes and expenses. expensessuch as medical taxes and medical expenses.

Source: U.S. American Community Survey.

Source: U.S. Census CensusBureau, Bureau, American Community Survey.

Tampa Bay

St. Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Miami

Jacksonville

Denver

Houston

Supplemental Poverty Measure

2018

Dallas

Charlotte

2017

Baltimore

Austin

2016

Atlanta

2015

Supplemental Poverty Measure

1.5 1.4 1.3 1.2 1.1 1.0 0.9

Competitive Position Trend Charlotte

Dallas

San Diego

Tampa Bay

0 2 4

Competitive Position

6 8 10 12 14 16 18 20 22

TS

2015

2016

2017

2018

-Charlotte holds the first rank for having lowest value of SPM forall the years (2015 to 2018).

-There is significant gapin the values of SPM for Seattle and San Diego, both holding 19 and 20 positions respectively.

28

Debt-to-Income Ratio

“

“

SUMMARY OF INSIGHTS AND FINDINGS Houston maintained No. 1 position for all years consistently. In 2018, San Diego was the only MSA with household debt-to-income ratio greater than 2.

Debt-to-Income Ratio About: Measures the MSAs household debt relative to the household income for each MSA. Source: Board of Governers of the Federal Reserve System.

Tampa Bay

St. Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Miami

Jacksonville

Houston

Dallas

Charlotte

Denver

Debt-to-Income Ratio:Highest Ratio: Highest Quantile Quantile Debt-to-Income

2018

Baltimore

Austin

2013

Atlanta

2008

Debt-to-Income Ratio

2.5 2.0 1.5 1.0 0.5 0.0

Debt-to-Income Ratio: Ratio:High Highest Quantile Trend Debt-to-Income Quantile Denver

Houston

Phoenix

San Antonio

Tampa Bay

Debt-to-Income Ratio

2.5

2.0

1.5

1.0

0.5 0.0 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2015

2016

2017

2018

Competitive Position Trend 0

Competitive Position

5

10

15

20

29

GHTS

2008

2009

2010

2011

2012

2013

2014

- Houston maintained No. 1 position for all years consistently.

- In 2018, San Diego was the only MSA with household debt-to-income ratio greater than 2.

House Rents

“

“

SUMMARY OF INSIGHTS AND FINDINGS Mean house rents in Seattle, San Diego and Denver increased at higher rates compared to other MSAs. Unlike zero bedroom, four bedroom mean rents never decreased over the years for all the MSAs. House Rents About: Rent estimates at the 50th percentile (or median).

Source: Office Of Policy Development And Research.

Raleigh-Durham

San Antonio

San Diego

Seattle

St. Louis

Tampa Bay

Raleigh-Durham

San Antonio

San Diego

Seattle

St. Louis

Tampa Bay

Portland

Phoenix

Orlando

Nashville

Minneapolis

Miami

Jacksonville

Houston

Dallas

Charlotte

Denver

Zero-Bedroom RentEstimates Estimates Zero Bedroom Mean Rent

2018

Baltimore

Austin

2013

Atlanta

2008

1400 1200

Mean Rent $

1000 800 600 400 200 0

Portland

Phoenix

Orlando

Nashville

Minneapolis

Miami

Jacksonville

Houston

Denver

Dallas

Charlotte

Baltimore

Austin

Atlanta

Four Bedroom Mean Rent Estimates Four-Bedroom Mean Rent Estimates

3500

3000

Mean Rent $

2500

2000

1500

1000

500

IGHTS

0

- Mean house rents in Seattle, San Diego and Denver increased at higher phase compared to other MSAs. - Unlike zero bedroom, four bedroom mean rents never decreased over the years for all the MSAs.

30

University of South Florida Muma College of Business

Key Insights from Traditional Economic Indicators • The Tampa Bay region has consistently ranked lowest in the group in terms gross regional per capita.

• The Tampa Bay region’s poverty rate has been declining for the past few years.

• Income inequality for the Tampa Bay region is increasing, as it is with most other MSAs. • The Tampa Bay region is in the lower half of the comparison group in terms of absolute economic mobility.

• Debt-to-income ratio has been declining over the years for most of the MSAs, including Tampa Bay.

In the previous section, we looked at the performance of Tampa Bay region in comparison to other MSAs on various indicators of inclusive economic growth. The trend graphs enable us to see the direction in which the region is moving in terms of competitive position as well as actual values across the outcomes. At this juncture, a couple of questions arise.

1. Can we do something to perform better?

2. Are there any policy initiatives that might be taken to improve the competitive position of the Tampa Bay region in coming years?

To answer these questions, econometric models were built to identify the drivers of inclusive economic growth.

The independent variables used for the analysis are possible drivers of the economic growth.

These variables fall into five different categories: economic vitality, talent, infrastructure, civic quality and innovation. These possible driver variables have been identified after many interviews with the business leaders from the greater Tampa Bay region. A total of 19 variables were considered for the analysis. The annual data for these variables for the MSAs from 2008 through 2017 was collected from federal sources such as the U.S. Census Bureau, U.S. Bureau of Labor Statistics and the U.S. Bureau of Economic Analysis.

The data for the four Tampa Bay MSAs (Tampa-St. Petersburg-Clearwater, Homosassa Springs, Lakeland-Winter Haven and North Port-Sarasota-Bradenton) was aggregated to derive the values for the Tampa Bay region and the data for Raleigh and Durham was aggregated to derive the values for Raleigh-Durham region. In summary, this report uses data for six outcome variables (for economic mobility outcome variable different strategy was used as described below) and 22 possible economic drivers for 20 regions for 10 years. The data was adjusted for the cost of living and inflation.

Panel data methods were used to create models for each outcome. For each of the outcomes, multiple drivers were identified. One prime driver for each outcome was identified based on the strength of potential causal explanation.

The outcome variable of economic mobility has data that compares the outcome over two time intervals. Thus, this data cannot be included in the panel data model as there is only one number for each MSA which reflects opportunities for economic mobility over a long time period (around 40 years). In order to identify drivers for economic mobility, this report adopts an innovative approach. The driver for economic mobility is identified by using a regression analysis where the outcome variable (or y variable) value for each MSA is derived from the Opportunity Atlas, and the values of each of potential drivers (or x variables) are derived by taking the average values of each variable over the time period.

University of South Florida Muma College of Business

Part 3: Identifying Drivers of Economic Outcomes

Drivers of Inclusive Economic Growth The significant drivers for each of the indicators of inclusive economic growth are given in the tables below. The sign indicates the direction of impact. The plus (+) sign indicates the impact in the positive direction which implies that as the value of the driver variable increases, the value of the outcome variable of interest increases. Similarly, the minus (-) sign indicates the impact in negative direction. The report identifies one primary driver for each of the outcome economic indicator as an actionable driver. The choice was made based on the strength of causal explanation. The primary drivers, which are highlighted in yellow, can be used for policy initiatives.

GRP Per Capita

Unemployment Rate Mean Household Income Lowest Quintile

-

Mean Household Income Lowest Quintile

+

Educational Attainment (Graduates/Professional)

-

Average Wage Rate

+

Mean Commute Time

-

Mean Commute Time

-

Net Business Establishment Entry Rate

-

Transit Availability

+

Labor Force Participation Rate (25-64)

+

Share of Commuters with 1+ Hour of Commute Time

+

Mean House Value Per Square Feet

-

University R&D Expenditures

+

Business Establishment Start Rate

+

Net Migration Rate

Poverty Rate

Mean Household Income Lowest Quintile

+

Mean Household Income Lowest Quintile

-

Air Traffic Growth Rate

+

Transit Availability

-

Mean Commute Time

+

Share of Commuters with 1+ Hour of Commute Time

+

Merchandise Exports Growth Rate

-

Mean House Value Per Square Feet

-

Labor Force Participation Rate (ages 25-64)

-

Labor Force Participation Rate (ages 25-64)

-

Income Inequality

33

Economic Mobility

Transit Availability

-

Stem Degree Production Per Capita

-

Transit Availability

+

Educational Attainment Rate: Graduate/Professional

“

“

SUMMARY OF INSIGHTS AND FINDINGS Tampa Bay’s educational attainment (graduate/professional) has consistently increased over the years. Raleigh-Durham has the highest graduate attainment rate for 8 of the 10 yearsRate: and has been maintaining No. 1 position for all the years.

Educational Attainment Rate: Graduate/Professional Educational Attainment Graduate/Professional

About: Measures the the percentage percentage of of the thepopulation, population,25 25years yearsor orolder, older,who whohave haveattained attainedaagraduate graduateororprofessional professionaldegree. degree.

About: Measures Source: U.S. Census Census Bureau, Bureau, American AmericanCommunity CommunitySurvey.

Survey.

Source: U.S.

EducationalAttainment AttainmentRate: Rate:Grad/Prof Grad/Prof Educational

Tampa Bay Tampa Bay

St. Louis St. Louis

Seattle Seattle

San Diego San Diego

San Antonio San Antonio

Portland Portland

Phoenix Phoenix

Orlando Orlando

Nashville Nashville

Minneapolis Minneapolis

Miami Miami

Jacksonville Jacksonville

Houston Houston

Denver Denver

Dallas Dallas

Raleigh-Durham Raleigh-Durham

Educational Attainment AttainmentRate: Rate:Graduate/Professional Graduate/Professional Educational

2018 2018

Charlotte Charlotte

Baltimore Baltimore

2013 2013

Austin Austin

Atlanta Atlanta

2008 2008

20 20

15 15

10 10

5 5

0

Educational Attainment AttainmentRate: Rate:Graduate/Professional Graduate/ProfessionalTrend Trend Educational

Educational Attainment Rate: Grad/Prof Grad/Prof

Raleigh-Dur..

San Antonio Raleigh-Durham San Antonio

St. Louis St. Louis

Tampa Bay Tampa Bay

20

15

10

5

0 2008

2009 2009

2010 2010

2011 2011

2012 2012

2013 2013

2014 2014

2015 2015

2016 2016

2017 2017

2018 2018

2015 2015

2016 2016

2017 2017

2018 2018

Competitive CompetitivePosition PositionTrend Trend

Competitive CompetitivePosition Position

0 5 10 10 15 15 20 20

IGHTS SIGHTS

2008 2008

2009 2009

2010 2010

2011 2011

2012 2012

2013 2013

2014 2014

-- Tampa Tampa Bay's Bay's educational educational attainment attainment(graduate/professional) (graduate/professional)has hasbeen beenconsistently consistentlyincreasing increasingover overthe theyears. years.

-- Raleigh-Durham Raleigh-Durham has has the the highest highest graduate graduateattainment attainmentrate ratefor for88of ofthe the10 10years yearsand andhas hasbeen beenmaintaining maintainingNo. No.11position position

34

“

SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay region started at No. 16 in 2008 and has made significant progress to stand currently at the 4th position. Tampa Bay and St.Louis are the only MSAs where the most recent net business establishment rate is higher than it was in Business Establishment Start Rate 2008 just prior to the recession.

“

Business Establishment Start Rate

About: Measures the number of new businesses (with employees) started in a year, divided by the number of businesses (with employees) in the previous year.

Source: U.S. Census Bureau, Business Dynamic Statistics, Establishment Characteristics Data Tables.

Business Establishment Start Rate

Tampa Bay

St. Louis

Seattle

San Diego

San Antonio

Raleigh-Durham

Portland

Phoenix

Orlando

Nashville

Minneapolis

Miami

Houston

Denver

Dallas

Charlotte

Jacksonville

Business Establishment Start Rate

2017

Baltimore

2013

Austin

Atlanta

2008

15

10

5

0

Business Establishment Start Rate Trend

Business Establishment Start Rate

Baltimore

Jacksonville

Miami

Tampa Bay

15

10

5

0 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2014

2015

2016

2017

Competitive Position Trend

Competitive Position

0

5

10

15

20

35

GHTS

2008

2009

2010

2011

2012

2013

-Tampa Bay started at No. 16 in 2008 and has made significant progress to stand currently at the 4th position.

-Tampa Bay and St.Louis are the only MSAs where the most recent net business establishment rate is higher than it was

Mean Household Income Lowest Quintile

“

“

SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay region saw a slight decline in median household income from 2008 to 2011 but has gradually improved since then. The Tampa Bay region has been fluctuating between position 17 and 19 in the competitive positions since 2008.

Miami has Income been consistently held bottom spot in the competitive positions.

Mean Household Lowest Quintile Mean Household Income Lowest Quintile

Mean Income Lowest Quintile Meanincomelowestquant Meanincomelowestquant

Tampa Bay Tampa Bay

St. Louis St. Louis

Seattle Seattle

San Diego San Diego

San Antonio San Antonio

Portland Portland

Phoenix Phoenix

Orlando Orlando

Nashville Nashville

Minneapolis Minneapolis

Miami Miami

Jacksonville Jacksonville

Houston Houston

Mean Household Income Lowest Quintile Mean Household Income Lowest Quintile Denver Denver

Dallas Dallas

2017 2017

Charlotte Charlotte

Baltimore Baltimore

2013 2013

Austin Austin

Atlanta Atlanta

2008 2008

Raleigh-Durham Raleigh-Durham

About: Measures the average household income for the households that have income in the lowest 25% of all households.

About: U.S. Measures average household income for the households that have income in the lowest 25% of all households.

Source: Censusthe Bureau, American Community Survey.

Source: U.S. Census Bureau, American Community Survey.

15K 15K

10K 10K

5K 5K 0K 0K

Mean Lowestquintile Quintile Mean Income income lowest Mean income lowest quintile

Miami Miami

Mean Household Income Lowest Quintile Trend St. Louis Tampa Bay Mean Household Income Lowest Quintile Trend

Minneapolis Minneapolis

St. Louis

Tampa Bay

15K 15K 10K 10K 5K 5K 0K 0K

2008 2008

2009 2009

2010 2010

2011 2011

2012 2012

2013 2013

2014 2014

2015 2015

2016 2016

2017 2017

2014 2014

2015 2015

2016 2016

2017 2017

Competitive positionTrend trend Competitive Position Competitive position trend

Competitive position Competitive Position Competitive position

0

5

0

5

10 10

15 15

20 20

IGHTS HTS

2008 2008

2009 2009

2010 2010

2011 2011

2012 2012

2013 2013

- Tampa Bay saw a slight decline in median household income from 2008 to 2011 but has gradually improved since then.

TampaBay Bayhas saw a slight decline between in medianposition household income from to 2011positions but has gradually improved since then.

- -Tampa been fluctuating 17 and 19 in the 2008 competitive since 2008.

- Tampa Bay has been fluctuating between position 17 and 19 in the competitive positions since 2008.

36

Transit Availability

“

“

SUMMARY OF INSIGHTS AND FINDINGS The Tampa Bay region ranked dead last for almost a decade. Seattle has the highest revenue miles per capita and is consistently ranked No. 1. RaleighTransit availability Durham’s rank has risen from No. 18 to No. 10 over the last decade.

Transit availability

About: Measures, on a per capita basis, the number of miles traveled by public transit vehicles during revenue service, About: Measures, on a per capita basis, the number of miles traveled by public transit vehicles during revenue service, meaning that the vehicle is transporting passengers while on the road. meaning that the vehicle is transporting passengers while on the road.

Source: Federal Transit Administration, National Transit Database, Annual UZA Sums.

Source: Federal Transit Administration, National Transit Database, Annual UZA Sums.

Revenue Miles Per Capita Revenue Revenue miles milesper percapita capita

Tampa Bay

St. Louis

Tampa Bay

Seattle

St. Louis

San Diego

Seattle

San Antonio

San Diego

Raleigh-Durham

San Antonio

Portland

Raleigh-Durham

Portland

Phoenix Phoenix

Orlando Orlando

Nashville Nashville

Minneapolis Minneapolis

Miami Miami

Houston Houston

Denver Denver

Dallas Dallas

Jacksonville Jacksonville

TransitAvailability Availability Transit

2017 2017

Charlotte Charlotte

Baltimore Baltimore

2013 2013

Austin Austin

Atlanta Atlanta

2008 2008

30 30

20 20

10 10

00

Revenuemiles Milesper Percapita Capita Revenue Revenue miles per capita

Raleigh-Dur..

Raleigh-DurhamSeattleSeattle

Tampa Bay

Transit Trend TransitAvailability Availability Trend Tampa Bay

30 30

20 20

10 10

0 0

2008 2008

2009 2009

2010 2010

2011 2011

2012 2012

2013 2013

2014 2014

2015 2015

2016 2016

2017 2017

2014 2014

2015 2015

2016 2016

2017 2017

Competitive Position Trend Competitive Position Trend

CompetitivePosition Position Competitive Competitive Position

0 0 5 5 10 10 15 15 20 20

IGHTS TS

37

2008 2008

2009 2009

2010 2010

2011 2011

2012 2012

2013 2013

- Tampa Bay ranked dead last for almost a decade.

Tampa has Baythe ranked dead last formiles almost decade.

--Seattle highest revenue peracapita and is consistently ranked No. 1.

STEM Degree Per Production Capita

“

“

SUMMARY OF INSIGHTS AND FINDINGS Tampa Bay and Minneapolis MSAs have improved in STEM degree production per capita at higher rate than other MSAs. The Tampa Bay MSA has steadily improved its ranking since 2009 but dropped in 2017.

STEM Degree Per Production Capita

About: Measure number of individualsCapita graduating with degrees in science, technology, engineering and mathematics STEM DegreeofPer Production (STEM degrees) per thousand individuals.

About: Measure of number of individuals graduating with degrees in science, technology, engineering and mathematics Source: Higher Education Information System.

(STEM degrees) per thousand individuals.

Source: Higher Education Information System.

STEM Degree Production Per Capita

Tampa Bay

St. Louis

Seattle

Tampa Bay

San Diego

St. Louis

San Antonio

Seattle

San Diego

Raleigh-Durham

San Antonio

Portland

Raleigh-Durham

Phoenix

Portland

Phoenix Orlando

Nashville Orlando

Minneapolis Nashville

Miami

Minneapolis

Jacksonville

Miami

Houston

Jacksonville

Denver

Dallas

Denver

Charlotte

Baltimore

STEM Degree Production Per Capita

2017

Charlotte

Austin

Baltimore

2013

Austin

Atlanta

STEM Degree Production Per Capita

Atlanta

2008

STEM Degree Production Per Capita

2017

Houston

2013

Dallas

2008

6 6

4

2

4

2

00

STEM Degree Production Per Capita STEM Degree Production Per Capita TrendTrend STEM Degree Production Per Capita

STEM Degree Production Per Capita

Charlotte Charlotte

Raleigh-Durham Raleigh-Dur..

TampaBay Bay Tampa

66

4

2

0

4

2

0

2008

2008

2009

2009

2010

2011

2010

2012

2011

2013

2012

2014

2013

2015

2014

2016

2015

2017

2016

2017

Competitive Position Trend

Competitive Position Trend

0

0 Competitive Position Competitive Position

5

5 10

10 15

15 20

INSIGHTS IGHTS

20

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

- Tampa Bay and Minneapolis have improved in STEM degree production per capita at higher rate than other MSAs. 2009 improved 2010 2012 2014 2015 2016 - Tampa2008 Bay has steadily its ranking 2011 since 2009 but dropped in2013 2017.

2017

- Tampa Bay and Minneapolis have improved in STEM degree production per capita at higher rate than other MSAs. - Tampa Bay has steadily improved its ranking since 2009 but dropped in 2017.

38

Key Insights from Analysis to Identify Drivers of Inclusive Economic Growth

University of South Florida Muma College of Business

Econometric models were built for each of the outcome variables to identify the driver variables.

Below were the key insights derived from the econometric analysis and trend analysis of the driver variables.

39

• Transit availability is the primary driver for three variable indicating inclusive economic growth: poverty rate, income inequality and economic mobility. Higher public transit availability leads to lower poverty rate and income inequality and higher economic mobility.

• STEM education (science, technology, engineering and mathematics) is negatively related to income inequality.

• The Tampa Bay region has consistently maintained last position in terms of public transit availability.

• STEM degree production per capita for the Tampa Bay region has seen a consistent rise both in terms of absolute value and competitive position last decade.

• The results signify the key role played by public transit infrastructure and STEM education in achieving higher inclusive economic growth.

Part 4: Policy Experiments and Looking Ahead

The policy experiments were performed using technique called “sensitivity analysis,� which is described below.

The competitive positions of the MSAs for three years (2018, 2019 and 2020) on the outcomes related to inclusive economic growth were forecast. If all remains the same and all the MSAs follow the trends that they have been following for the recent years on the outcomes, the MSAs will achieve the forecasted competitive positions. The econometric models were used to forecast the values of outcomes for Tampa Bay when some of the values of the driver variables for Tampa Bay are changed (keeping all else constant for other MSAs). This method allows researchers to forecast the Tampa Bay region’s competitive positions for the next three years should certain policy initiatives be implemented.

For this study, the policy experiment of increasing public transit availability by 20 revenue miles per capita every year was selected. This policy initiative was chosen because public transit availability was found to be a common driver for all the three variables that indicate inclusive economic growth.

This section presents the results of policy experiments on the outcomes of poverty rate, income inequality and economic mobility to assess the impact of policy initiatives on the inclusive economic prosperity of the Tampa Bay region.

University of South Florida Muma College of Business

In the previous section, drivers for the outcome variables were identified. This section presents policy experiments, which can inform business and civic leaders about the impact of certain policy initiatives on the inclusive economic growth of the Tampa Bay region0.

Poverty Rate Poverty Rate Policy initiative: Increase in transit availability by 20 revenue miles per capita for 2018, 2019 and 2020.

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Income Inequality (Gini Index) Income Inequality (Gini Index) Policy initiative: Increase in transit availability by 20 revenue miles per capita for 2018, 2019 and 2020.

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Economic Mobility Economic Mobility Policy initiative: Increase in average transit availability by 20 revenue miles per capita..

Economic Mobility -- Competitive CompetitivePosition PositionTrend TrendWith Without PolicyInitiative Initiative Economic Mobility Out Policy

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Key Insights from Policy Experiments Policy experiments were performed for Tampa Bay on three economic outcome variables related to inclusive economic growth: poverty rate, income inequality and economic mobility. The policy initiative of increasing public transit infrastructure availability by 20 revenue miles per capita every year was considered, as transit availability was found to be common primary driver for all the three variables. The following insights were gleaned from the experiments.

• With the policy initiative, the Tampa Bay region’s competitive positioning improved moderately in terms of poverty rate.

• The policy experiments confirm that public transit infrastructure plays a crucial role in the inclusive economic growth of a region by providing the impoverished strata of the society easy and affordable access to education, work and health.

University of South Florida Muma College of Business

• The policy initiative had significant effect on the competitive positioning of the Tampa Bay region in terms of income inequality and economic mobility.

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Key Takeaways

University of South Florida Muma College of Business

This report presents multidimensional analysis of the Tampa Bay region’s performance in terms of inclusive economic growth relative to 19 other MSAs. The three primary variables considered for the study are poverty rate, income inequality and economic mobility.

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• Data from real-time sources such as Google Trends, LinkedIn and Twitter was considered along with the traditional U.S Census data to obtain a holistic view of inclusive economic growth of the Tampa Bay region.

• Analysis of data from real-time sources revealed a significant gap in the talent supply relative to demand for finance and information technology industries in Tampa Bay. • Trend analysis of traditional indicators related to inclusive growth suggests that the Tampa Bay region is performing poorly among the comparison group in terms of poverty rate and income inequality. However, in terms of economic mobility, the Tampa Bay region is performing a little better, landing in the middle of the pack.

• Econometric models were built to identify the primary drivers of inclusive economic growth of the Tampa Bay region.

• Econometric analysis concluded that public transit infrastructure availability and STEM education are the key drivers for inclusive economic growth of a region.

Next Steps • USF researchers plan to continue expanding the kinds of real-time big data signals for this type of study. For example, scholars are exploring real-time sources on traffic patterns and retail purchase behavior across MSAs.

• USF researchers will consider deeper study of whether any of the big-data signal can serve as reliable proxy of traditional economic indicators. This can be valuable because the proxy can be used to assess the economic health of the region on a real-time basis and can help in faster policy decision making.

• USF Muma College of Business faculty are building an index of real-time big data signals which can gauge economic prosperity in real time. Such an index would be a single number that can provide a composite measure of economic health of a region.

• Faculty and student researchers look forward to directly partnering with the business community to help potentially leverage real-time big data signals for long-term growth.

Community ideas and feedback are welcome!

University of South Florida Muma College of Business

• Most of the real-time big data signals obtained were through APIs and scraping. USF faculty and student researchers will explore the possibility of building direct and deeper partnership with the source companies. They encourage the community to reach out should there be opportunities to connect with local or regional providers of such real-time big data signals.

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TAMPA BAY E-INSIGHTS REPORT

THE 2020 E-INSIGHTS REPORT IS PRODUCED BY THE MUMA COLLEGE OF BUSINESS AT THE UNIVERSITY OF SOUTH FLORIDA, A PREEMINENT UNIVERSITY, AND IS AFFILIATED WITH THE STATE OF THE REGION INITIATIVE. stateoftheregion.com