The Hampton Roads Industry Cluster Mapping Project
Dr. Larry “Chip” Filer Chair and Associate Professor Department of Economics Old Dominion University Norfolk, VA 23529
The Hampton Roads Industry Cluster Mapping Project 2
TABLE OF CONTENTS
EXECUTIVE SUMMARY...... 5
SECTION 1: INTRODUCTION ...... 7
SECTION 2: AN OVERVIEW OF THE HAMPTON ROADS ECONOMY ...... 9
The Decline in Manufacturing ...... 11
The Manufacturing Industry and the Port of Virginia ...... 13
The Damage from Sequestration ...... 13
What Is The Path Forward? ...... 17
SECTION 3: THE CLUSTERS ...... 18
Brief Definition of the Clusters ...... 18
Employment and Wages in the Clusters ...... 20
SECTION 4: ADVANCED MANUFACTURING...... 31
Definition ...... 31
Data Overview ...... 32
Occupational Makeup ...... 34
Cluster Map ...... 40
SWOT ...... 41
SECTION 5: FOOD AND BEVERAGE MANUFACTURING ...... 42
Definition ...... 42
Data Overview ...... 43
Occupational Makeup ...... 44
Cluster Map ...... 49 The Hampton Roads Industry Cluster Mapping Project 3
SWOT ...... 50
SECTION 6: SHIP REPAIR AND SHIP BUILDING ...... 51
Definition ...... 51
Data Overview ...... 52
Occupational Makeup ...... 53
Cluster Map ...... 58
SWOT ...... 59
SECTION 7: PORT OPERATIONS, LOGISTICS AND WAREHOUSING ...... 60
Definition ...... 60
Data Overview ...... 61
Occupational Makeup ...... 62
Cluster Map ...... 66
SWOT ...... 67
SECTION 8: LIFE SCIENCE ...... 68
Definition ...... 68
Data Overview ...... 69
Occupational Makeup ...... 70
Cluster Map ...... 75
SWOT ...... 76
SECTION 9: BUSINESS SERVICES ...... 77
Definition ...... 77
Data Overview ...... 79
Occupational Makeup ...... 80 The Hampton Roads Industry Cluster Mapping Project 4
Cluster Map ...... 85
SWOT ...... 86
SECTION 10: INFORMATION ANALYTICS AND SECURITY ...... 87
Definition ...... 87
Data Overview ...... 88
Occupational Makeup ...... 89
Cluster Map ...... 94
SWOT ...... 95
SECTION 11: TOURISM AND RECREATION ...... 96
Definition ...... 96
Data Overview ...... 96
Occupational Makeup ...... 99
Cluster Map ...... 103
SWOT ...... 104
SECTION 12: CONCLUDING REMARKS ...... 105
The Hampton Roads Industry Cluster Mapping Project
August 29, 2016
Dr. Larry “Chip” Filer Chair and Associate Professor of Economics Old Dominion University 2043 Constant Hall Strome College of Business Norfolk, VA 23529
EXECUTIVE SUMMARY
The Hampton Roads economy has had a tough decade. The national recession combined with declines in federal spending has constrained economic growth and employment growth. The regional economy of the past is not likely to be the regional economy of the future. Exactly what type of economy the region transitions toward is uncertain.
The regional economy has often been referred to as a three-legged stool, with government spending, the port and tourism representing the three legs. Recent and future slowdowns in government spending growth (particularly federal spending) directly threaten one of those legs. However, what the last four years have taught the region is that the other two legs are at risk as well. The private sector of the regional economy is very exposed to contractions in federal spending since a large part of the private sector is focused on government contracting.
Diversifying the economy has long been suggested as necessary. It is now being suggested as mandatory. What sectors will lead the region toward diversification? This report proposes 8 clusters that might lead economic growth over the next decade. Those clusters include: Advanced Manufacturing, Food and Beverage Manufacturing, Ship Repair and Ship Building, Port Operations, Logistics and Warehousing, Life Sciences, Business Services, Information Analytics and Security and Tourism and Recreation.
The Hampton Roads Industry Cluster Mapping Project 6
The report presents data on employment, wages and specialization for each cluster over the last decade. We also present forecasts of employment over the next decade under two scenarios – Status Quo and High Growth.
The fact that these clusters have been successful does not guarantee future success. In fact, if the firms in the clusters continue to rely on federal contracts, we are forecasting contractions in employment over the next decade for some of the clusters and anemic growth, 0.3% annual average, in the others. Alternatively, the high growth scenario forecasts annual average growth in employment of 2.5%. This high growth scenario only exists if a few certain conditions are met.
First, we must not just improve our competiveness, but we must outpace our rivals. The region does not possess a monopoly in any of these clusters, so we are competing against other regions for excellence. We must strive to provide an environment that is relatively better than our competitors. For example, in Port Operations, Logistics and Warehousing that could mean major improvements in our transportation network. In Information Analytics and Security that could mean aggressively pursuing Google Fiber or other high-speed fiber options. Without smart investment, we risk falling behind our competitors.
Second, firms in these clusters must work to wean their revenue from federal contracts. Firms must make efforts to find new lines of business and new customers. This will take time, but the clusters that are being proposed have the potential to do this. In fairness, some of these clusters (Ship Repair and Ship Building in particular) are always going to rely on federal contracts. Therefore, trying to pivot the regional economy away from government contracting entirely is naïve.
Regional economies are consistently struggling to adapt to changing landscapes. What were historic strengths for a region can quickly become weaknesses. This does not always mean that cities must abandon their historic industries. Smart, coordinated investments from businesses and governments can help those industries adapt and grow. This report provides evidence that, with the right strategy, the economy of Hampton Roads’ future may actually originate from some of the region’s traditional stalwarts. Section 1: Introduction
Regional economies are complex. Even regional economies with clear identities (like Silicon Valley) possess some amount of diverse economic activity. This complexity makes it difficult to fully explain the economics of a region without imposing some form of organizing framework. Michael Porter has been the leading proponent of using industrial clusters as such an organizing framework. The concept was designed to move policy-makers away from firms as the unit of interest in regional economic analysis. A reliance on firm-level analysis would create silos of policy that appear to only benefit specific firms. Alternatively, as Porter has suggested, clusters of related firms could all benefit from cluster-focused economic development, regardless of the specific nature of the firm.
Clusters provide a clean, organizing framework for analyzing a complex regional economy. In particular, defining a regional economy by clusters makes it even easier to measure the competitiveness of particular sectors. Clusters are also helpful when it comes to analyzing the workforce needs of a region. An appropriate example would be the difference between analyzing bio-medical firms in Boston versus a bio-medical cluster in Boston. If we were interested in developing a workforce for bio-med firms, we would be most concerned with producing effective scientists, doctors, lab technicians and other health related workers. However, if we think more inclusively about workforce needs for a bio-med cluster, we would want to consider the role that patent attorneys, venture capitalists and engineers play in the success of the cluster. So, by defining bio-med as a cluster, we paint a more accurate picture.
Clusters also provide an identity for regional economies, like the auto industry cluster in Detroit or the music cluster in Nashville. The existence of the cluster reduces the need to use economic development incentives in attracting related firms to strong clusters. Firms want to be geographically near the cluster to exploit the economic benefits, since firms in the cluster benefit from each other. This is often referred to as production spillovers. The presence of a production spillover suggests that firms in the cluster welcome, instead of fear, the growth of related firms. Firms in a cluster feed off all of the other firms in the cluster. In addition, the maturation of the cluster will lead to the creation of new firms in the region and the relocation of related firms to the region with little or no subsidization by development authorities. Ultimately, development strategies will be best served to expand on the region’s strengths (as captured by the clusters) rather than randomly choose firms or industries to try and attract. Cluster analysis will not provide a cure to all that ails a regional economy. However, when performed correctly, cluster analysis can provide important recommendations for the growth of clusters, which translates to regional economic growth. The key to effective cluster analysis is in the definition of the clusters and the ongoing dialogue between policy-makers and cluster
The Hampton Roads Industry Cluster Mapping Project 8
members. Therefore, cluster analysis is at its best when it employs both quantitative (data analysis) and qualitative (interviews) research.
This report provides the quantitative analysis. We use publicly available labor data to confirm the strength of each cluster in terms of employment and wages. We also calculate a metric known as the location quotient (LQ). The LQ is a simple way to measure the specialization of a region in a certain cluster. Generally, an LQ in excess of 1 for a cluster indicates that the region is more specialized in that cluster than the US as a whole. LQs in excess of 2 suggest the region is strongly specialized in the respective cluster. High levels of specialization are suggestive of clusters that act as net exporters of their goods or services. In the Porter vernacular, these clusters are traded and not local serving. Traded clusters bring in new money to a region and, therefore, traded clusters are prized. The calculation of LQs will help to determine the tradability of each cluster.
Another option to measure tradability of the cluster is to simply interview firms in the cluster and ask about their level of business activity outside the region. Therefore, the project also utilizes qualitative interviews to analyze the clusters. The interviews allow us to validate the presence of the cluster, assess the amount of business being done outside the region and provide insights into the needs for cluster growth.
It may seem odd to use interviews to validate the presence of the cluster. After all, we have data at the industry level to measure the size of the clusters. However, the data may not tell the whole story of a cluster. For example, the labor data may put employment in another industry – as is the case with something like unmanned systems companies. These firms should show up in aerospace manufacturing. However, firms in this industry may produce the electronic systems and navigation software and not the actual vehicles or aircraft. Therefore, we would not find these firms in the data for the aerospace industry, but they would be in the data for the electronics manufacturing industry. This type of information can only be obtained from direct interviews.
Additionally, the interviews will shed light on what clusters need to grow. The results from the interviews are contained in a companion report to this report, entitled “Growing and Diversifying the Economic Base of Hampton Roads: Identifying Requirements and Restrictions for Growth from Interviews with Corporate Executives.”
Ultimately, it is imperative that we determine action plans for growing these clusters. Without connecting the clusters to actionable and appropriate development policies, the cluster project simply becomes an exercise in creating a visual image of the regional economy. The Hampton Roads Industry Cluster Mapping Project 9
Section 2: An Overview of the Hampton Roads Economy
The economy of Hampton roads has historically been more stable than the national economy during times of national recession. The 2009 Great Recession was no different. As shown in Figure 2-1, Hampton Roads’ real GDP contracted by less than 1% during the recession, while the nation contracted at nearly 3%.
The real issue for the region has been the recovery out of the 2009 Great Recession. While the nation has averaged 2.05% real GDP growth in the post-recession era, the region has only averaged an anemic 0.3%. Essentially, the Hampton Roads economy has not grown since the end of the recession. That is 5 years of little to no growth! This is a substantial departure from the early 2000’s where real growth averaged around 3% and slightly outpaced the national rate of growth.
Figure 2-1 Real Gross Domestic Product 2002-2014
5.00
4.00
3.00 2.43
2.00
1.00 0.67
0.00
-1.00 -0.14
-2.00
-3.00 -2.78 -4.00 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Hampton Roads United States
Source: Bureau of Economic Analysis
The employment data for the region are equally disturbing. Employment in Hampton Roads has recovered at a much slower pace than either the nation or even the state. Figure 2-2 illustrates the path of employment recovery for the US, the State of Virginia and Hampton Roads. The The Hampton Roads Industry Cluster Mapping Project 10
numbers represent the percentage difference in employment each month relative to peak employment. A value of 0 means that the level of employment in that month matched the level of employment at the pre-recession peak. We are presenting these numbers over the months during the recovery.
The light blue line in Figure 2-2 shows that the United States recovered all the employment lost during the Great Recession in 76 months. The State of Virginia achieved this milestone one month later in 77 months. The pace of recovery in Hampton Roads has been much slower. In fact, the region has yet to recover all of the job losses from the Great Recession, remaining about 1.3% short of pre-recession peak employment.
Figure 2-2 Job Growth During the Post-Recession Period (Percent Relative to Pre-Recession Peak Employment)
6.00 United States: +3.70% 4.00 Virginia: +3.37% 2.00 Hampton Roads: -1.25% 0.00
-2.00
-4.00
-6.00
-8.00 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 104
US Virginia Hampton Roads
Source: Bureau of Economic Analysis and author calculations
Looking at post-recession employment growth across peer regions presents a clearer view of the uniquely slow pace of economic recovery in Hampton Roads. When compared to 34 peer regions, Hampton Roads ranks last in employment growth at less than 1% annual average growth over the period 2011-2014 (Figure 2-3). The Hampton Roads Industry Cluster Mapping Project 11
Figure 2-3 Average Annual Growth in Employment 2011-2014
4.5%
4.0% U.S. Metro Area Average is 3.5% 1.92% 3.0%
2.5%
2.0%
1.5% 0.46% 1.0%
0.5%
0.0%
Sources: Bureau of Economic Analysis and Hampton Roads Planning District Commission
Figures 2-1 through 2-3 suggest that the region has experienced an economic stagnation that is both strongly persistent (going on 5 years) and unluckily unique (when compared to peer regions).
What is the source of Hampton Roads’ recent economic pain? There seem to be two factors at work. The first, the shrinking of middle class jobs, is a national phenomenon and has been occurring slowly over a decade or more. The second, sequestration, is a more recent, post- recession phenomenon and the Hampton Roads region is particularly exposed.
The Decline in Manufacturing
The shrinking set of jobs for the middle class is a national phenomenon that is affecting all regions. A report published by the Pew Research Center in 2015, showed that the middle class has been shrinking, albeit slowly, for nearly 4 decades. In fact, since 1971, each decade has ended with a The Hampton Roads Industry Cluster Mapping Project 12
smaller share of middle class households than the decade before. The same holds true for our region. In fact, Hampton Roads is slightly more exposed to this effect since it has a long tradition of middle class manufacturing jobs.
Figure 4 illustrates the long, slow decline in jobs in manufacturing over the last decade and a half for the Hampton Roads region. The region has lost 12,000 manufacturing jobs over this period. This is on a backdrop of moderate job expansion in the ship repair industry over the same time period. Those jobs are included in the total manufacturing numbers. So, as shown by the black line in Figure 2-4, the decline in manufacturing jobs (with ship repair excluded) is even more pronounced. It has fallen by 17,700 jobs.
Figure 2-4 Manufacturing Employment in Hampton Roads 2000-2015
70000
60000
50000
40000
30000
20000
10000
0
Manufacturing Ship and Boat Building Manufacturing (excl. Ship Repair)
Source: Bureau of Labor Statistics and author calculations
There has been, however, a recent resurgence in manufacturing in the United States. Many southern states have embraced advanced manufacturing and their respective economic development agencies have employed economic development strategies to identify and recruit The Hampton Roads Industry Cluster Mapping Project 13
advanced manufacturing firms. The State of Virginia appears to be doing the same. Of the whole set of new firms announcing their relocation or creation in Virginia, 41% of them were in manufacturing.1 Virginia Beach and Newport News were the primary regional recipients.
All of this has effort has curbed the loss of jobs in this industry. Manufacturing jobs excluding ship repair (the black line in Figure 2-4), has flattened and even grown recently. This is good news for the region and suggests that this industry, which has had a pronounced loss of jobs over the past decade and a half, could be a source of job creation going forward.
The Manufacturing Industry and the Port of Virginia
The shrinking of manufacturing jobs in the region and US creates an additional headwind. Perhaps the best asset for the region is the Port of Virginia and manufacturing firms are a natural fit in and around port cities.
In fact, ports can serve as a magnate for economic activity miles inland. The BMW decision to establish a production facility in Spartanburg, South Carolina in the early 90’s was driven largely by the proximity to the Port of Charleston.2 BMW’s decision to move to Spartanburg has spawned an automotive cluster that ranks it third in employment among the regions in the United States.
So, there are manufacturing success stories in the United States even against the backdrop of a national decline in manufacturing. Furthermore, a strong, regional distribution network (including rail, road and sea) often produces those success stories. Indeed a regional distribution network currently exists in Hampton Roads. All of the pieces seem to be in place for a robust, manufacturing base for the region. We will have more to say about this later in the report.
The Damage from Sequestration In contrast to the national phenomenon above, sequestration has a more localized impact on our region. Hampton Roads has relied heavily on federal spending, particularly Department of Defense funding, for decades. The region’s economy benefits from both the presence of uniformed personnel at regional installations, as well as the acquisition of federal contracts by firms in the region.
1 Data compiled from new firm announcements in the Virginia Economic Development Partnership Announcement database. 2 See: Michael McDermott, “BMW, Spartanburg, South Carolina: Drivers and Processes in the International Plant Location Decision”, The Southern Business and Economic Journal, January 2011. The Hampton Roads Industry Cluster Mapping Project 14
The second of these is particularly relevant for this project. Over the years, seemingly private- looking firms have relied on federal procurement dollars as a major part of their business model. The constant injection of federal expenditures (particularly those within the Department of Defense) has traditionally sustained Hampton Roads through national contractions. In the new reality of tight federal budgets, that blessing has become a curse.
Table 2-1 shows the direct connection between federal procurement and regional GDP. From 2002 to 2008, Hampton Roads experienced double digit increases in procurement dollars each year. This led to growth rates of 3-4%. From 2009-2015, procurement increased by single digits in most years and also decreased in 3 years. Consequently, rates of regional GDP growth have never exceed 1% in any year since 2008.
The Hampton Roads Industry Cluster Mapping Project 15
Table 2-1 Hampton Roads’ Federal Procurement Spending And Real GRP Growth
Procurement Percent Real GRP Year Dollars a Change Growth b
2002 6.1 3.1% 2003 4.8 -21% 4.3 2004 5.6 17% 3.0 2005 6.5 16% 4.0 2006 8.2 26% 3.1 2007 9.1 11% 2.3 2008 10.3 13% -0.4 2009 9.8 -5% 0.7 2010 10.2 4% -0.1 2011 10.7 5% 0.6 2012 8.9 -17% 1.0 2013 9.5 7% 0.2 2014 10.2 7% -0.1 2015 8.4 -18% Sources: usaspending.gov, Bureau of Economic Analysis and ODU Forecasting Project a measured in billions of current year dollars by place of performance b the gross regional product data series starts in 2001 c estimate for 2015; actual data for 2015 will be released in September 2016
The size, composition and compensation of the military have a more obvious impact on the regional economy. For decades, the region has feared decisions on Base Realignment and Closure (BRAC). We have braced for a base closure or a strike-group relocation, knowing the immense pain the region would feel from that action. Indeed such a decision would have a pronounced and immediate impact. The shrinking of Joint Forces Command (JFCOM) in Northern Suffolk had an immediate and permanent impact on the modeling and simulation efforts of the region, just as the infant industry was beginning to flourish. The industry has never really recovered.
It has been the steady decline in military compensation that has been “boiling the frog slowly” (borrowing an anecdote to describe our slow demise). Figure 2-5 illustrates the dramatic The Hampton Roads Industry Cluster Mapping Project 16
reduction in annual growth rates for 4 different types of compensation across three time periods: 2001-2010, 2010-2014 and 2013-2014. The most dramatic contraction occurred in military employment. From 2001-2010, military personnel in Hampton Roads averaged a 7.2% increase in compensation. Since then, they have barely averaged 1%. Federal civilian and state and local government employees also experienced a pronounced contraction in compensation between the period 2001-2010 and 2010-2014. Contractions in federal, state and local compensation then fuel contractions in private nonfarm compensation. So, while most regions have experienced a small increase in private compensation during the post-recession period, Hampton Roads has experienced a decline of about 1%.
Figure 2-5 Hampton Roads’ Total Compensation Growth Across Employee Type
8.00%
7.00%
6.00%
5.00%
4.00% 2001-2010 2010-2014 3.00% 2013-2014 2.00%
1.00%
0.00% Military Federal State and Private Civilian Govt. Local Govt. Nonfarm
Sources: Bureau of Economic Analysis and author calculations
The Hampton Roads Industry Cluster Mapping Project 17
What Is The Path Forward?
In spite of painful economic realities, Hampton Roads possesses a unique set of assets that can help define and brand the area. The following list represents the most unique and important assets in Hampton Roads:
- Water - East Coast Gateway for People and Products - Location of Strategic Military Importance - Multiple Institutions of Higher Education - Location of Significant American History and Culture - Relatively Affordable Cost of Living - Technically Skilled Workforce
These assets provide a good foundation from which the region can build an economy for the future. Historically, the region has used these assets to become a leader in federal contract acquisition and military power. Now, the region must find a way to utilize these same assets to create a knowledge-based economy. The industries that will comprise the region’s new economy must, simultaneously, (i) capitalize on these existing assets and, (ii) use those assets for expanding opportunities in commercial markets outside of the region. The clusters presented below are well positioned to accomplish these two goals.
The goal of this project is to: 1) identify and clearly define clusters of strength that already exist in the Hampton Roads region 2) validate the clusters 3) establish action items to help grow the clusters
The Hampton Roads Industry Cluster Mapping Project 18
Section 3: The Clusters
Brief Definition of the Clusters
We present eight economic clusters that appear particularly viable for the future economy of HR. These eight clusters were chosen for their historic significance, future growth potential, strong connection to existing regional assets, ability to pivot toward non-federal markets and relatively high average wages. These clusters are already a significant part of the economic fabric of the region and that is an important aspect to this effort. Growing existing clusters is much easier than creating clusters.
Below, we present a brief summary of each cluster along with a detailed analysis of the data for each cluster. The eight clusters are:
1. Advanced Manufacturing (MAN) Hampton Roads has a long history of manufacturing driven by ship repair and ship building. In spite of the downturn nationally in manufacturing, the data over the last 10 years show several companies in the region have flourished. There are, however, noticeable losses in employment associated with the auto industry over the same time period. Manufacturing in Hampton Roads includes both traditional, labor-intensive manufacturing as well as advanced manufacturing. The latter is particularly important for the region going forward.
2. Food and Beverage Manufacturing (BEV) Peanuts, ham and crab are iconic symbols of Virginia. These products also represent important economic activity in the western part of our region and the oceanfront. Recently, this cluster has expanded to include a growing set of new breweries and distilleries to complement the existing cluster. The industry that is surprisingly small is food product manufacturing. This is, perhaps, an opportunity for growth in this cluster.
3. Ship Repair and Ship Building (SRB) Consistently the largest employer in the Hampton Roads region. This cluster has long been fueled by the Department of Defense. However, many shipyards began pivoting toward private sector work within the last five years. Military work will always comprise the majority of the revenue stream for these firms. However, the shift toward commercial work will help the cluster survive tighter federal budgets in the future.
The Hampton Roads Industry Cluster Mapping Project 19
4. Port Operations, Logistics and Warehousing (PLW) The harbor and the Port of Virginia provide the single greatest competitive advantage for Hampton Roads. The port supports a number of related industries such as logistics companies, freight forwarders and warehouses. The firms in this cluster represent the “first mile” and “last mile” in the supply chain for a number of products shipped internationally. However, the port has yet to become the tremendous business attractor that many feel it should. Attracting firms that need the port for distribution is critical. Equally important is attracting firms manufacturing the next generation of port infrastructure and equipment.
5. Life Sciences (LIF) This cluster is more of an aspirational cluster. While the region has a strong medical services presence (with two large, successful health systems) it significantly lags other regions in healthcare research and development. However, there is a handful of successful, though early stage, medical device companies operating in the region. The challenge for LIF is to secure a process for additional growth while being devoid of a major research hospital in the region.
6. Business Services (BUS) BUS is, perhaps, the purest “knowledge based” cluster on the list. A significant amount of the work has, traditionally, been connected to the Department of Defense and that continues to be the trend. However, many regional insurance, engineering and law firms have maritime practices that operate globally and are not dependent on federal dollars. In addition, accounting and law firms are doing an ever- increasing amount of work outside the region. Indeed this cluster is exporting more services today than ever before and we see this cluster as a strong contributor going forward.
7. Information Analytics and Security (IAS) The region has attracted a large number of information technology professionals over the years. Many are former military or government workers with security clearance. As a result, the industry has evolved into one that is heavily dependent on government contracts. Cyber security is emerging as a strong sub-cluster in the area.
8. Tourism and Recreation (REC) One of the three, traditional pillars of the Hampton Roads economy. REC remains a vital cluster for the economy. Though the wages in this cluster lag those of the other clusters, the sheer size of this cluster makes it worth mention. The water and historical importance of the region makes future growth in the REC cluster possible. Indeed, those two assets are not going away. However, the region needs to focus The Hampton Roads Industry Cluster Mapping Project 20
its efforts on business and conference travelers. Attracting large conferences to the region would help provide higher average wages for the cluster.
Employment and Wages in the Clusters
The eight clusters were responsible for 153,215 private jobs in 2015 (Table 3-1). That represents 20% of the total employment of the region. This share is slightly lower than we would expect and it illustrates the heavy reliance on retail services in the regional economy. In fact, total 2015 employment in retail trade and restaurants was higher than the total employment in the clusters (166,769 versus 153,216).
The REC cluster is the largest employer in the region with 84,413 jobs in 2015. However, a number of those jobs are local in nature. So, utilizing data from the Virginia Tourism Corporation, we estimate an export-oriented employment share for the cluster. Details of the method are presented in Section 11, but we note in the table that after adjusting for this estimate the number of jobs drops to 45,583 or about 54% of the total. A similar procedure was done for the BUS cluster.
The third column of Table 3-1 shows that only 3 of the cluster have gained jobs during the last decade. Ship Repair and Ship Building, Tourism and Recreation and Information Analytics and Security have gained nearly 12,000 jobs over the decade. The other clusters have all lost jobs. However, we must keep in mind that this time period includes one of the largest economic contractions in US history. So, this does not necessarily call into question the viability of these clusters.
The resilience of clusters to both recession shocks and sequestration shocks is important to determine. It is doubtful that Hampton Roads could magically develop a set of clusters that are resilient to national recession. Few industries are. However, it would be valuable to find industries that are resilient to contractions in federal spending.
Figure 3-1 shows the employment growth of each cluster during the recession years (2007-2010) and during sequestration (2011-2014). The results are somewhat surprising. As expected, all of the clusters except SRB lost employment during the recession, with several of the clusters experiencing double digit losses in employment. However, even with Hampton Roads’ tremendous dependence on federal spending, 7 of the 8 clusters performed better during the sequestration period than during the recession period. Only two of the clusters BUS and REC lost The Hampton Roads Industry Cluster Mapping Project 21
jobs during the sequestration period and two clusters (SRB and IAS) experienced double digit employment growth.
This finding is critically important to the project. It seems to support the hypothesis that the recession was a larger factor in the contraction of the Hampton Roads economy than the contraction in federal spending. In fairness, however, it is important to note that during the 2011- 2014 time period, sequestration never really happened. Each time full sequestration was close to becoming the law of the land, a temporary budget agreement was reached and federal spending limits were pushed higher than the sequestration limits. So, the region was spared a great deal of pain. Going forward, we should not expect such agreements to be made every two years.
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Table 3-1 Cluster Employment Overview (All Government and Private Jobs) Change 2015 Average Cluster over Last Employment Wage 10 Years
MAN 10,632 -174 $65,632
BEV 6,056 -688 $43,538
PLW 14,522 -219 $62,508
SRB 36,098 5,642 $69,245
REC 45,583a 2,162 $22,296
LIF 1,767 -617 $62,028
BUS 16,792a -1,945 $80,497
IAS 21,766 4,145 $78,500
Cluster 153,216 8,306 $60,524 Total Region 772,215 -14,561 $43,714 Total a Total employment numbers have been adjusted to capture only the employment that can be considered export oriented. Source: JobsEq and author calculations
Figure 3-1 Cluster Annual Average Employment Growth During Recession (2007-2010) and Sequestration (2011-2014)
Recession Sequestration
20%
14% 15% 13%
10% 7% 5% 4% 5% 3% 1% 0% -1%-1% -5% -3% -5%
-10% -9% -10% -11% -12% -15%
-17% -20% MAN BEV PLW SRB REC LIF BUS IAS
Source: JobsEq and author calculations
The final column of Table 3-1 shows that average wages in the clusters are 38% higher than the average wage for the region ($60,524 compared to $43,714). Six of the eight clusters have average wages that exceed $60,000.
Additionally, Figure 3-2 illustrates the share of jobs in each cluster that have wages classified as high-wage. There is no consensus regarding what constitutes a high-wage job. For the purposes of this study, we define a high-wage job as any job that earns in a wage in the 75th percentile or higher on the regional wage distribution. For 2015, the 75th percentile wage in the regional is $53,700. We round that number down to an even $50,000.3
As far as the share in each industry, IAS leads the way with nearly 80% of the occupations earning $50,000 or more. BUS has the second highest share at 65%. BEV and REC lag the other clusters with only 13% and 12% of the jobs paying over $50,000 respectively. Worth noting is that 6 of the 8 clusters have at least a third of the jobs in the industry classified as high-wage.
This visualization is particularly useful since it looks at occupations within the industry and not just the industry itself. Often times support jobs in high-growth, high-wage clusters could be paid more than support jobs in lower paying clusters. For example, if we look at the Executive Secretary/Executive Administrative Assistant occupation, average wages for this occupation are $50,500 across all industries in Hampton Roads. However, within the IAS cluster, the same occupation has an average wage of $63,000. This is a very beneficial, yet often overlooked, side effect of growing jobs in the eight clusters.
3 High-wage jobs have also been defined as jobs with wages at least 50% higher than the prevailing average regional wage. In the case of Hampton Roads, that would suggest a high-wage job is any job that earns at least $64,200 (= $42,800*1.5). Putting that number into perspective, we would be talking about jobs earning wages in the 88th percentile or higher. This seemed overly aggressive for our region, so the 75th percentile of the wage distribution was used.
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Figure 3-2 Share of Jobs in Cluster with Wages Greater than or Equal to $50,000 (2015 data)
90% 80% 80%
70% 65%
60%
50% 39% 40% 32% 31% 29% 30%
20% 13% 10% 5%
0% IAS BUS LIF MAN SRB PLW BEV REC
Source: JobsEq and author calculations Next, we illustrate employment growth in the clusters against employment growth for the region as a whole and for the United States total. Figure 3-3 shows that employment growth in the 8 clusters strongly outpaced both Hampton Roads and the United States from 2006-2013. Since 2011, employment growth in these 8 clusters has slowed, but remained positive (growing at 5% in 2014) and was still outpacing the region as a whole.
Figure 3-3 Employment Growth for Hampton Roads’ Industrial Clusters Cluster Employment versus total Regional Employment (Index=100 for 2004)
115
110
105
100
95
90 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Regional Clusters Regional Total US total
Source: JobsEq and author calculations
To measure the strength of the clusters, we calculate location quotients. Location quotients (LQ) are a measure of the specialization of a cluster. LQs in excess of 1 suggest that the region has a larger share of employment in the cluster than the US as a whole. Clusters with LQs above 1 are generally going to be export oriented and are going to be at critical mass in the region.
Figure 3-4 presents the LQs for the 8 clusters. As expected, Ship Repair has the highest value at 41.29. In fact, this value is so large that it distorts the graph and had to be moved to a text box. Life Sciences is the only cluster that is significantly below 1, but that is expected. This cluster is a more recent arrival in the region and is earlier in its life cycle.
The Hampton Roads Industry Cluster Mapping Project 27
Even with these very defined clusters, there exist sub-clusters that may have even greater specialization and even higher location quotients. A good example is the Livestock Processing sub-cluster within the Food and Beverage Manufacturing cluster (BEV). BEV includes a wide variety of food manufacturing as well as breweries, has an overall LQ of 1.55. The Livestock Processing sub-cluster has an LQ of 2.03.4
Figure 3-4 Hampton Roads Industrial Clusters: Location Quotients (2015)
LIF 0.33 SRB = 41.29 BUS 0.94
IAS 1
PLW 1.22
REC 1.49
BEV 1.54
MAN 2.55
0 0.5 1 1.5 2 2.5 3
Source: JobsEq and author calculations
Finally, we present two forecast scenarios for each cluster through 2025. The first scenario – “Status Quo” – assumes that the clusters do nothing to reduce their dependence on federal spending. It also assumes that the size of the clusters remain similar to their current size when calculated as a share of regional employment. This forecast uses the historic growth trends in the clusters as a predictor of future growth.
4 The LQ for this sub-cluster was acquired from the US Cluster Mapping Project. The cluster analysis provided by our project does not overlap directly with the cluster analysis from the US Cluster Mapping project. It is comparable in this example as Livestock Processing is a sub-cluster in the BEV cluster. For more information see the US Cluster Mapping Project website – www.clustermapping.us.
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The second scenario – “High Growth” – assumes that the clusters successfully diversify away from federal spending and add meaningful commercial activity to their revenue streams. In this scenario, we use information from interviews and Bureau of Labor Statistics forecasts for cluster industries to forecast employment growth. If the clusters are successful at diversification and achieving critical mass, then employment growth will be substantially different than “Status Quo” predictions.
Table 3-2 presents the forecasts. Columns 2 and 3 show the employment level and employment growth forecast for the “Status Quo” state of nature. Unsurprisingly, the forecasts are not promising. IAS has the highest growth forecast at 14%. Manufacturing and Ship Repair are both forecasted to decline over the next 10 years. Overall, the clusters only grow by 2% while total regional employment only grows by 3%. Both closely match the recent values of employment growth.
Columns 4 and 5 show the same forecasts, but for the “High Growth” scenario. In this scenario, the clusters become more diversified, but also become more concentrated and achieve critical mass. Nearly all of the clusters achieve double-digit percentage growth in this scenario. LIF leads the way with an increase of 57% over the next 10 years. This puts the region more in line with the forecast for Life Sciences nationally. PLW is forecasted to grow at 40% over the forecast period. SRB has the largest difference between the two scenarios (-5% to 31%). Overall, the “High Growth” scenario sees an increase in cluster employment of 22% and an employment increase of 15% for the region overall.
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Table 3-2 Forecasted Employment Growth in the Clusters (2015-2025) 2015-2025 Forecasted Growth
Status % High % Cluster Quo Growth Growth Growth MAN -1,258 -12% 1,596 15% BEV -281 -5% 1,428 24% PLW 628 4% 5,808 40% SRB -1,854 -5% 11,414 32% REC 774 4% 2,735 6% LIF 244 14% 1,008 57% BUS 840 5% 4,869 29% IAS 2,531 12% 5,040 23% Cluster 4,140 3% 33,898 22% Total Region 24,017 3% 115,832 15% Total Source: JobsEq and author calculations
The forecasts in Column 5 might seem well out of line given the recent history of the region. However, it is important to note that these growth rates are not out of line with historical growth in these clusters in the pre-recession period. For example, in just 6 years (2001-2006) the Life Sciences cluster saw employment growth increase by 29%. Over the pre-recession period, SRB experienced a 14% increase in employment, MAN saw a 16% increase and IAS saw a 22% increase in employment. Those historic numbers help put Column 5 into some perspective. The “High Growth” scenario is not asking for anything considerably beyond the potential of the economy. It is assuming successful diversification.
Diversification has been a heavily discussed topic in the region as well as the early part of this report. While this report is intended to provide some hope for diversification in these clusters, it is important to note that our concept of diversification is not “all or nothing.” Indeed, federal work will always be a foundation of the Hampton Roads economy and to think otherwise would be The Hampton Roads Industry Cluster Mapping Project 30
foolish. In the analysis that follows, each cluster is analyzed for its ability to pivot towards commercial work, even if partially, in the economy of the future.
As expected the clusters suggested in this report have become heavily dependent on federal funding over the years. However, many of the firms in these clusters sell goods and services that are marketable to the private sector. For a number of reasons (the profitability of government contracts being the main one) the firms have focused their attention on federal work. Going forward, firms in these clusters will need to develop strategies for “pivoting” away from federal work and toward commercial work. As we have learned during the project, many of them have already begun pivoting with some success. We have also learned that most of the firms have no plans to abandon government procurement entirely.
The following 8 sections present, separately, a detailed analysis of each cluster. The analysis includes an examination of employment and wages for the sub-industries contained within the broader cluster. We also provide the LQ for each sub-industry, a cluster map to highlight the interdependencies among clusters and close with a SWOT analysis of each cluster to outline the opportunities and associated needs for future growth.
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Section 4: Advanced Manufacturing
Definition
Defining economic activity that relates to advanced manufacturing is challenging. The North American Industry Classification System (NAICS) provides a very clear manufacturing picture by placing those firms into one of three, two-digit codes (31, 32 or 33). Firms within 31 are undertaking activities in food and beverage manufacturing, so they will be included in the Food and Beverage cluster below. That leaves this cluster composed of firms in NAICS codes 32 or 33.
However, the manufacturing activity taking place by firms in one of those two codes can range from low skill, low wage and low tech to advanced and very high tech. For the purposes of this project, we define this cluster as advanced manufacturing and not simply manufacturing. This requires us to go to the six digit NAICS code to uncover just those firms that are truly “advanced.”
To determine what classifies as advanced manufacturing, we follow the definition championed by the Brookings Institute in their recent study on advanced industries.5
The requirement for an industry to make the Brookings’ list was:
• R & D spending in the 80th percentile or $450 per worker • 21% or more of the occupations requiring STEM education
This filtering process leads to 50 possible sub-industries. However, the Brookings study is a national study, so these 50 industries all have critical mass at the national level. For a regional study, 50 industries are too many. To filter down, we eliminate any industry on the Brookings list that does not have an existing critical mass in the region’s 2015 employment data. For example, aluminum production and processing
5 The full report “America’s Advanced Industries: What They Are, Where They Are and Why They Matter” can be found at: http://www.brookings.edu/research/reports2/2015/02/03-advanced- industries#/M47260.
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is one of the industries on the Brookings list, but according to regional employment data, this industry currently has zero employment in the region, so it is discarded.
After both filters are imposed, we are left with 27, 6-digit industries in this cluster. Important to note is that Brookings includes ship repair and ship building as an advanced industry. We would agree. However, the importance of ship repair in the region precipitated its inclusion as a stand-alone cluster instead of part of advanced manufacturing. The fact that ship repair falls on the Brookings list speaks to the tight overlap the region experiences between the MAN and SRB clusters.
Data Overview
As mentioned, there are 27 industries within the MAN cluster. The region is not highly specialized in each and every one of these industries. Figure 4-1 presents the three industries where the region is most specialized. For each of the sectors, there is a fairly obvious, national known firm at the root of the large location quotient. In the case of power-driven handtools it is Stihl, for space research and technology, NASA and for photographic paper it is Cannon. While these three firms are leading their respective sectors, they need not be the only employers in the sector.
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Figure 4-1 Location Quotients for the Strongest Industries in the Advanced Manufacturing Cluster
Power-Driven Handtool 37.65 Manufacturing
Space Research and Technology 20.65
Photographic Film, Paper, Plate, 17.83 and Chemical Manufacturing
0.00 5.00 10.0015.0020.0025.0030.0035.0040.00
Source: JobsEq and author calculations
Figure 4-2 presents the location quotients for the remaining sectors in the MAN cluster. Though none of the sectors match the LQs of the sectors in Figure 3-1, there are many impressive LQs nonetheless. Fifteen of the sectors have LQs exceeding one. Adding those fifteen to the previous three gives the region 18 of the 27 sectors in the MAN cluster with LQs in excess of 1.
In addition, there are industries with LQs below 1, but still well aligned to capitalize on the region’s assets. Each of these has growth potential either through expansion of the existing firms or through attracting firms in those industries to the region.
The Hampton Roads Industry Cluster Mapping Project 34
Figure 4-2 Location Quotients for the Remainder of the Advanced Manufacturing Cluster
Nonferrous Metal Die-Casting… 5.09 Pump and Pumping Equipment… 3.57 Speed Changer, Industrial High-… 3.54 Air and Gas Compressor… 3.45 Printing Machinery and Equipment… 3.29 Motor Vehicle Gasoline Engine and… 2.51 Railroad Rolling Stock Manufacturing 2.12 Noncurrent-Carrying Wiring Device… 1.93 Other Communication and Energy… 1.65 Industrial Truck, Tractor, Trailer,… 1.36 Other Communications Equipment… 1.26 Instruments and Related Products… 1.13 Dental Laboratories 1.08 Sawmill, Woodworking, and Paper… 1.03 Custom Compounding of Purchased… 1.03 Power, Distribution, and Specialty… 0.99 Lawn and Garden Tractor and… 0.86 Sheet Metal Work Manufacturing 0.85 Fertilizer (Mixing Only)… 0.82 Motor Vehicle Electrical and… 0.80 Machine Tool Manufacturing 0.77 Motor Vehicle Seating and Interior… 0.73 Irradiation Apparatus Manufacturing 0.71 Current-Carrying Wiring Device… 0.71
0.00 1.00 2.00 3.00 4.00 5.00 6.00
Source: JobsEq and author calculations
Occupational Makeup
Next, we examine the occupational makeup of the advanced manufacturing cluster. Table 4-1 shows the broad occupational makeup of jobs in the cluster. Unsurprisingly, the cluster is dominated with jobs in the production occupations. The Hampton Roads Industry Cluster Mapping Project 35
These jobs account for 45% of the total employment of the cluster. These jobs include machinists, welders, assemblers, etc. The cluster also employs a large number of architects and engineers, though it is likely more engineers than architects which comprise the 18% share in this category. The remainder of the larger shares falls into management (7%), business and finance (6%), installation and maintenance (4%), transportation (4%), sales (2%) and computer and math (1%).
It is important to note that the data used in this report is typically for private firms only. However, with space research in this cluster, we needed to include government jobs for just that sub-cluster. Why? The occupational data on space research contains no private employment for the space research industry, but the employment data does show over 1,800 government jobs in this industry. Nearly 1,200 of those are in engineering. This is a result of NASA and National Institute of Aerospace’s presence in the region.
Table 4-2 presents detailed data on the top 10 occupations in the cluster. Team assemblers are the largest occupation group in the cluster with 909 jobs (9% share). Aerospace engineers are second with a 4% share. The remainder of the table covers occupations such as production, engineering, management and even sales.
Table 4-2 also presents the average annual wage for the occupation in the region and compares it to the average annual wage for the same occupation at the national level. If the regional occupational wage exceeds the national average it suggests that local firms in the cluster might be able to successfully recruit talent to the area.
A look at columns 3 and 4 in Table 4-2 shows that the region’s pay is comparable to the US in many occupations. The region’s pay significantly exceeds the national average pay for three occupations – welding, front-line supervisors and engineering technicians. One of the more interesting findings in the table is engineering technicians in Hampton Roads get paid over $10,000 more a year than in the US. In fact, according to 2015 data from the Bureau of Labor Statistics, the Virginia Beach- Norfolk-Newport News MSA ranks third in the nation for employment in the engineering technicians field, trailing only Houston and Chicago. Nationally, the federal government employs the majority of the engineering technicians. Such is the case for Hampton Roads as well. The Hampton Roads Industry Cluster Mapping Project 36
The region, however, significantly lags the nation in pay for sales representatives ($89,170 versus $64,200). So, while advanced manufacturing generates a decent share of sales jobs, those jobs pay substantially less in our region than nationally limiting the multiplier effects from expansions in this cluster. The Hampton Roads Industry Cluster Mapping Project 37
Table 4-1 Occupational Breakdown of the Advanced Manufacturing Cluster (Broad Occupation Group) Occupation Employment Share Production 4,738 45% Architecture and Engineering 1,936 18% Management 705 7% Business and Finance 598 6%
Installation and Maintenance 446 4%
Transportation 386 4% Sales 289 2% Computer and Math 184 1% Other 1350 13%
Total 10,632 Source: JobsEq and author calculations
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Table 4-2 Top 10 Detailed Occupations Within Advanced Manufacturing (Private Jobs and Federal Jobs for Space Research Occupations) Regional US Average Average Occupation Employment Share Annual Wage Annual Wage (2015) (2015) Team Assemblers 909 9% $29,000 $31,560 Aerospace Engineers 471 4% $113,500 $110,570 Machinists 369 3% $47,500 $42,120 Welders, Cutters, Solderers, and Brazers 345 3% $47,100 $40,970 First-Line Supervisors of Production and 317 3% $66,100 $59,930 Operating Workers Engineers, all others 272 3% $96,500 $98,150 Inspectors, Testers, Sorters, Samplers, 244 2% $43,200 $39,410 and Weighers Mechanical Engineers 211 2% $87,600 $88,190 Engineering Technicians, Except Drafters, 187 2% $73,800 $62,820 All Other Sales Representatives, Wholesale and Manufacturing, Except Technical and 180 2% $64,200 $89,170 Scientific Products Source: JobsEq and author calculations Finally, we look at the share of employment in the cluster with annual average wages in excess of $50,000. Earlier in the report we explained the rationale behind using wages of $50,000 and up to define high paying jobs.
Table 4-3 shows that 45% of the employment in this cluster receives wages in excess of $50,000. Much of that is being driven by the engineering occupations, but many of the other support occupations (lawyers, human resource managers, sales managers, etc.) all earn high wages. Many of those support occupations earn six figures.
Table 4-3 Share of Employment with Annual Average Wages Exceeding $50,000
Total employment 10,632
Employment with wages over $50k 4,818
Share 45% Source: JobsEq and author calculations
Cluster Map
SWOT
Strengths • Technically skilled workforce • Abundance of regional educational institutions providing quality training programs at all levels (from apprentice programs to four-year engineering degrees) • Several high-profile manufacturers already successfully operating in the region • Port provides a significant piece of infrastructure • Very insulated from contractions in Federal spending
Weaknesses • Workers can’t pass drug tests • Younger laborers are hard to find • Workers are technically skilled but not able to become leaders. • Industry is highly pro-cyclical • Industry has contracted substantially over the last two decades
Opportunities • There is a clear synergy between manufacturing companies and their proximity to efficient distribution networks • Product and process innovation is vital to the industry
Threats • Manufacturing has been a declining industry in the US • Automation will surely reduce the need for labor in the industry going forwar
The Hampton Roads Industry Cluster Mapping Project 42
Section 5: Food and Beverage Manufacturing
Definition
This cluster is essentially a sub-cluster of the advanced manufacturing cluster, though much of the production in this cluster does not meet the requirements for “advanced” as defined previously. This cluster is composed of firms representing old stalwarts of the region (ham, peanuts, seafood and cotton) and new niches (breweries, distilleries and wineries). We broadly define 3 categories for the cluster – food production (including products from both land and sea), farming and farm management and food equipment manufacturing.
Table 5-1 presents an overview of employment in the cluster. Employment is heavily concentrated in food production firms. The last 10 years has seen some moderate growth in both farm management and food equipment manufacturing, but both categories remain very small in employment levels.
Table 5-1 Employment in the Food and Beverage Manufacturing Cluster (Data from 2015) Industry 2005 2015 Growth LQ
Food Production 6,403 5,626 -12% 3.15
Farming and Farm Management 85 115 35% 0.70
Food Equipment Manufacturing 256 315 23% 2.10
BEV Total 6,744 6,056 -10% 1.54 Source: JobsEq and author calculations
The Hampton Roads Industry Cluster Mapping Project 43
Data Overview
BEV has a number of sub-clusters with LQs above 2, lead by coffee and tea manufacturing with an LQ of 5.83. Peanuts and peanut butter, seafood preparation and even breweries all have LQs exceeding 2. The fishing industry – shellfish fishing, finfish fishing and other marine fishing – has LQs exceeding 1. A notable omission on this list is tobacco manufacturing. Indeed, this was a mainstay of the regional economy at one time. However, the industry is all but gone in Hampton Roads and so it was not included in the analysis.
The remaining sub-industries of BEV all have LQs less than 1. With all of this agricultural activity, it is somewhat surprising that food product machinery manufacturing only has an LQ of 0.44 with 2015 employment of only 72. This appears to be an area for potential growth.
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Figure 5-1 Location Quotients for the Food and Beverage Manufacturing Cluster
Coffee and Tea Manufacturing 5.83 Roasted Nuts and Peanut Butter… 4.97 Seafood Product Preparation and… 3.66 Glass Container Manufacturing 3.63 Animal (except Poultry) Slaughtering 3.43 Nonchocolate Confectionery… 2.46 Breweries 2.30 Shellfish Fishing 1.86 Finfish Fishing 1.68 Other Marine Fishing 1.50 Cotton Ginning 1.03 Grain and Field Bean Merchant… 0.93 Food Product Machinery… 0.44 Wineries 0.23 Soil Preparation, Planting, and… 0.15 Farm Management Services 0.08 Postharvest Crop Activities (except… 0.04 Crop Harvesting, Primarily by… 0.04 Farm Labor Contractors and Crew… 0.02
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00
Source: JobsEq and author calculations
Occupational Makeup
Production occupations comprise the largest employment category in the BEV cluster (52% in Table 5-2). This is good news for the region since the food processing occupations tend to pay higher wages and generate larger economic impacts due to the higher value-add. Transportation jobs make up 18% of the cluster. The remaining jobs are evenly spread across office administration, maintenance, farming/fishing, sales and management.
The Hampton Roads Industry Cluster Mapping Project 45
The top 10 occupations are listed in Table 5-3. Unfortunately, column 4 of the table suggests that the top occupations in the cluster do not have high wages. Only one occupation on the list (front-line supervisors) pays above $50,000. Comparing the wages in the region against those in the US shows that these occupations pay right around the national average with one exception. Packaging and filling operators and tenders pay over $40,000 in Hampton Roads while only averaging $29,000 in the US.
Previously it was mentioned that our region might be well positioned to expand more into food product manufacturing and farm equipment manufacturing. Those sub-industries cover firms that produce equipment necessary to process and harvest. For example, firms that manufacture commercial ovens, mixers, pasteurizers, combines, and tractors fall under these two industries. These industries employ large numbers of engineers, production managers and sales representatives all making wages above $60,000. Currently, the region has very little employment in either industry with only 90 in farm machinery manufacturing and, as mentioned before, 72 in food product machinery manufacturing.
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Table 5-2 Occupational Breakdown of Food and Beverage Manufacturing Cluster Occupation Employment Share
Production 2,956 52%
Transportation 1,011 18%
Office Administration 385 7%
Installation/Maintenance 309 5%
Farming/Fishing 200 4%
Sales 192 4%
Management 189 3%
Other 389 7%
Total 5,631 Source: JobsEq and author calculations
The Hampton Roads Industry Cluster Mapping Project 47
Table 5-3 Top 10 Detailed Occupations Within Food and Beverage Manufacturing Regional US Average Average Occupation Employment Share Annual Wage Annual Wage (2015) (2015) Meat, Poultry, and Fish Cutters and 771 14% $22,600 $24,810 Trimmers Slaughterers and Meat Packers 399 7% $27,500 $26,420 Packaging and Filling Machine 362 6% $40,800 $29,770 Operators and Tenders Packers and Packagers, Hand 276 5% $23,000 $23,710 Laborers and Freight, Stock, and 262 5% $26,800 $27,840 Material Movers, Hand Food Batchmakers 204 4% $26,000 $29,210 Helpers--Production Workers 178 3% $26,400 $26,010 First-Line Supervisors of Production 167 3% $66,100 $59,930 and Operating Workers Inspectors, Testers, Sorters, Samplers, 124 2% $43,200 $39,410 and Weighers Industrial Truck and Tractor 123 2% $38,700 $34,090 Operators Source: JobsEq and author calculations The Hampton Roads Industry Cluster Mapping Project 48
The results from table 5-3 suggest that the BEV cluster likely has a small share of total employment with wages exceeding $50,000. Table 5-4 confirms this. Only 15% of the cluster’s employment has wages above $50,000.
Table 5-4 Share of Employment with Annual Average Wages Exceeding $50,000
Total employment 5,631
Employment with wages over $50k 838
Share 15%
Source: JobsEq and author calculations The Hampton Roads Industry Cluster Mapping Project 49
Cluster Map
The Hampton Roads Industry Cluster Mapping Project 50
SWOT
Strengths • Several existing companies have national brand recognition • Western part of the region has land suitable for large production facilities • Coastal areas provide a competitive advantage for fish and shellfish production • Climate provides for a long growing season
Weaknesses • No educational programs in agriculture/agricultural procession (Va Tech has a satellite presence) • Lack of corporate presence along the supply chain (refrigeration, farm equipment, chemicals)
Opportunities • Industry possesses a very long value chain (seeds, machinery, chemicals, robotics) • Strong global growth forecasted • Recent surge in craft beer activity can diversify the cluster
Threats • Competitive threats from many States • Sea level rise may reduce the amount of available land
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Section 6: Ship Repair and Ship Building
Definition
Ship Repair and Ship Building (SRB) is consistently one of the largest industry employers in the Hampton Roads region. The most visible firms in this cluster are the shipyards, both private and government-owned. However, the cluster includes a number of less visible, but equally important firms. From firms that supply products to the industry - electronics, instrumentation, chemicals, metals. To firms that supply services – marine architects, marine attorneys and marine insurance.
Two issues complicate analysis of this cluster. First, many of the intermediate suppliers fall into the advanced manufacturing cluster. So, to prevent double counting, we keep them in the advanced manufacturing cluster. Second, many of those companies produce and sell products and services for industries outside of ship repair and maintenance. That means that even if we wanted to include those firms and their associated jobs in the SRB cluster, it would be nearly impossible to determine exactly how many jobs at those firms are attached directly to the ship repair cluster. As a result, the employment numbers for this cluster do not fully reflect the total employment in the cluster. The numbers reflect a lower estimate since some of the employment related to this cluster is being counted in the Advanced Manufacturing (MAN) cluster.
Unlike many of the other clusters in this report, this SRB cluster contains just two industries. Ship Building and ship repair which is comprised of shipyard doing work on military vessels and large commercial vessels and boat building which contains the firms building smaller personal watercraft.
Table 6-1 shows that regional employment is overwhelming concentrated in ship building and ship repair. This cluster has long been fueled by the Department of Defense. So, the numbers in Table 6-1 include a significant number of government workers along with private workers that are dependent on government work.
However, many shipyards and support firms began pivoting toward private sector work within the last five years. Military work will always comprise the majority of The Hampton Roads Industry Cluster Mapping Project 52
the revenue stream for these firms, but the shift toward commercial work will help the cluster survive tighter federal budgets in the future.
Table 6-1 Employment in the Ship Repair and Ship Building Cluster Industry 2005 2015 Growth LQ
Ship Building and Repairing 30,439 36,802 21% 52.07
Boat Building 17 16 -6% 0.09
SRB Total 30,456 36,098 19% 41.29
Source: JobsEq and author calculations
Data Overview
Figure 6-1 shows that the LQ for Ship Repair and Ship Building is 52.07, while the LQ for Boat Building is only 0.09. This illustrates that the smaller shipyards in the region mostly focus on ship repair and not on building. For example, while Lyon Shipyard, located in Norfolk, has a strong business in the repair and maintenance of tugs and other “brown-water” boats, the shipyard does not build those boats.
This empirical reality is not unique to Hampton Roads. In fact, boat building (with the exception of military vessels) moved overseas to countries like Russia, China, Korea and Japan as the United States became less competitive on a cost basis over the last two decades. Even in areas of the United States that are known for shipbuilding, the dominant business is still ship repair.
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Figure 6-1 Location Quotients for Industries Within the Cluster
Boat Building 0.09
Ship Building and Repairing 52.07
Source: JobsEq and author calculations
Occupational Makeup
The work in the SRB cluster requires diverse educational attainment. Most employees in the industry have technical training from apprentice schools or community colleges. However, the industry also needs highly educated workers particularly in the fields of engineering, architecture and business.
First, we look at an overview of the set of occupations contained in the SRB cluster at a broad level. It is important to note that the data on occupations are for private firms only. This is particularly important for the SRB cluster. While total employment for the cluster is over 36,000 the private firm employment we are examining in the following tables is only 26,514. So, 28% of the employment in the SRB cluster in Hampton Roads is government employment and not private employment.
Table 6-2 provides the occupational breakdown for the cluster. Nearly half of the cluster’s employment is in some form of production occupation. This is not surprising. More interesting is the significant amount of employment in supporting The Hampton Roads Industry Cluster Mapping Project 54
occupations. Construction jobs comprise 15% of the employment or 3,939 total workers. This category includes plumbers, pipefitters, electricians and carpenters. Architecture and engineering makes up 10% of the employment in the cluster. Other important occupations for the SRB cluster include office administrators (8%), installation and maintenance jobs (6%), management jobs (4%) and business and finance jobs (4%).
Digging a bit deeper, Table 6-3 presents information on top 10 detailed occupations in the cluster, along with their employment level, employment share, regional average annual wage and the corresponding national average annual wage as comparison. The highest level of cluster employment is found in the Welders, Cutters, Solderers and Brazers occupation – comprising nearly 12% of the employment in this cluster. The regional average annual wage for this occupation is about $3000 higher in Hampton Roads ($43,900) than it is in the US ($40,970).
Looking at the list of occupations in the cluster, the regional wage is higher than the national wage for three (Welders, Supervisors and Machinists) and significantly lower in three – plumbers and pipefitters, electricians and metal fabricators. Ultimately, the data in the table suggests that the region is paying competitive wages to the most important occupations in the cluster.
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Table 6-2 Occupational Breakdown of the Ship Repair and Ship Building Cluster (Private Jobs) Occupation Employment Share
Production 12,410 47% Construction 3,939 15% Architecture and 2,632 10% Engineering Office Administration 1,992 8%
Installation and 1,624 6% Maintenance
Management 1,104 4% Business and Finance 989 4% Other 1,824 7%
Total 26,514 Source: JobsEq and author calculations
The Hampton Roads Industry Cluster Mapping Project 56
Table 6-3 Top 10 Detailed Occupations Within Ship Repair and Ship Building Cluster (Private Firms) Regional US Average Average Occupation Employment Share Annual Wage Annual Wage (2015) (2015) Welders, Cutters, Solderers, 3,304 12% $43,900 $40,970 and Brazers First-Line Supervisors of Production and Operating 1,418 5% $65,200 $59,930 Workers Team Assemblers 1,335 5% $29,100 $31,560 Layout Workers, Metal and 1,165 4% $45,000 $46,430 Plastic Plumbers, Pipefitters, and 1,029 4% $43,400 $55,100 Steamfitters Electricians 967 4% $46,000 $55,590 Mechanical Engineers 744 3% $85,400 $88,190 Machinists 734 3% $47,200 $42,120 Structural Metal Fabricators 695 3% $33,800 $39,040 and Fitters Mechanical Drafters 644 2% $53,900 $56,610 Source: JobsEq and author calculations
Table 3-3 shows that 31% of the jobs in the SRB cluster qualify as high paying under this criterion. That is a rather high share for a manufacturing based cluster and it illustrates the advanced, technological nature of the production techniques being used in the industry.
Table 5-4 Share of Employment with Annual Average Wages Exceeding $50,000
Total employment 26,514
Employment with wages over $50k 8,333
Share 31.43% Source: JobsEq and author calculations
Cluster Map
The Hampton Roads Industry Cluster Mapping Project 59
SWOT
Strengths • Region recognized as a global leader in ship repair • Specialized workforce exists for the industry • Region possesses firms all along the supply chain (electronics, engines, law, insurance, etc.) • Jones Act provides an advantage to US builders
Weaknesses • Inability to attract younger workers to the industry • Technical training programs are not coordinated across institutions • Aging workforce • Industry is heavily dependent on federal spending, DoD in particular • State personal property taxes are assessed on shipyard infrastructure
Opportunities • Many shipyards are diversifying into more commercial work • The US fleet is aging and requiring more repair and maintenance
Threats • Possibility of shrinking military fleet • Environmental regulations • Sea Level Rise
The Hampton Roads Industry Cluster Mapping Project 60
Section 7: Port Operations, Logistics and Warehousing
Definition
This cluster is one of the three legs of the Hampton Roads economic stool (along with defense and tourism). Its inclusion in this report is not a surprise. It is impossible to miss the various terminals in Hampton Roads when flying into Norfolk International Airport. However, the PLW cluster is far more than just the Port of Virginia. We define the cluster with three categories – material moving, shipping services and warehousing.
The array of various businesses related to the port is important for amplifying its economic impacts. Economic value add is not generated by simply moving containers from ships to rail or truck and sending them on their way which is largely what this cluster is measuring. Attracting firms to the region that rely on the port and/or strong logistics networks will prove much more impactful.
Table 7-1 shows an overview of employment in the three categories of the cluster. Overall, the cluster has lost jobs the last decade, but most of those losses were in the material moving category. Employment in firms providing shipping services grew by 35%.
Table 7-1 Employment Overview of Port Operations, Logistics and Warehousing Cluster Industry 2005 2015 Growth LQ
Material Moving 6,495 5,487 -16% 0.90
Shipping Services 3,421 4,629 35% 2.50
Warehousing 4,825 4,406 -9% 1.03
PLW Total 14,741 14,522 -1% 1.22
Source: JobsEq and author calculations The Hampton Roads Industry Cluster Mapping Project 61
Data Overview
Figure 7-1 shows the location quotients for each of the sub-industries in the cluster. There are several with LQs in excess of 1 and 6 with LQs above 2. Clearly, the region is highly specialized in this cluster.
Figure 7-1 Location Quotients for the PLW Cluster
Other Support Activities for… 19.51 Deep Sea Freight Transportation 16.33 Port and Harbor Operations 7.29 Marine Cargo Handling 6.25 Navigational Services to Shipping 3.88 Process, Physical Distribution,… 2.39 Coastal and Great Lakes Freight… 1.79 Freight Transportation… 1.61 Nonscheduled Chartered Freight… 1.48 Farm Product Warehousing and… 1.36 Inland Water Passenger… 1.13 General Warehousing and Storage 1.05 Other Warehousing and Storage 0.97 Refrigerated Warehousing and… 0.95 General Freight Trucking, Long-… 0.46 Inland Water Freight… 0.40 Specialized Freight (except Used… 0.32 General Freight Trucking, Long-… 0.29
0.00 3.00 6.00 9.00 12.00 15.00 18.00
Source: JobsEq and author calculations
The Hampton Roads Industry Cluster Mapping Project 62
Occupational Makeup
Table 7-2 shows the occupation breakdown of the PLW cluster. Transportation related occupations comprise over half of the cluster illustrating the strong focus the region has developed in moving materials. The second largest occupational group is office administration (22% share). This includes freight agents, shipping clerks and customer service representatives. Business and finance jobs are 5% of the cluster. These jobs include purchasing agents, logisticians and business support services like lawyers and accountants. Wages in the business and finance occupational category average over $60,000 and this category is expected to grow over the near term even in the status quo scenario.
Two clear findings emerge when looking at specific occupation codes within the PLW cluster (Table 7-3). First, the top three employment levels fall under transportation related categories. Given that over half of the employment in the cluster falls under transportation, this is not unexpected. The remainder of the top 10 is as diverse as the cluster, however, with captains, pilots, freight agents, customer service representatives, shipping clerks and operations managers rounding out the list.
Second, the wages in the region are very comparable to the US average. Only captains, mates and pilots appear to make significantly less than the US average - $83,150 in the US compared to $75,100 in the reigon.
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Table 7-2 Occupational Breakdown of Port Operations, Logistics and Warehousing Occupation Employment Share
Transportation 6,969 53%
Office Administration 2,908 22%
Business and Finance 722 5%
Management 662 5% Installation and 599 5% Maintenance Sales 372 3%
Production 275 2%
Computer and Math 227 1%
Other 470 4%
Total 13,204 Source: JobsEq and author calculations
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Table 7-3 Top 10 Detailed Occupations within the Port Operations, Logistics and Warehousing Cluster Regional US Average Average Occupation Employment Share Annual Wage Annual Wage (2015) (2015) Laborers and Freight, Stock, and 2,121 16% $27,600 $27,840 Material Movers, Hand Heavy and Tractor-Trailer Truck 1,189 9% $38,700 $42,500 Drivers Industrial Truck and Tractor 906 7% $38,700 $34,090 Operators Cargo and Freight Agents 513 4% $46,900 $44,470 Captains, Mates, and Pilots of Water 458 3% $75,100 $83,150 Vessels Sailors and Marine Oilers 440 3% $40,200 $42,910
Stock Clerks and Order Fillers 304 2% $25,100 $25,940
Customer Service Representatives 293 2% $30,200 $34,560
Shipping, Receiving, and Traffic Clerks 292 2% $32,400 $32,350
General and Operations Managers 287 2% $119,400 $119,460 Source: JobsEq and author calculations Table 7-4 provides the share of employment in the PLW cluster that has wages exceeding $50,000. In 2015, the cluster had 29% of its employment in occupations with wages higher than $50,000. The cluster is dominated by jobs related to moving cargo and wages for those jobs are generally below $50,000, though highly experienced truck drivers can make over $50,000.6
Ultimately, job growth in the cluster is expected to occur in occupations such as data analysts, customs brokers and operations managers. These occupations can pay six figures. So, there is reason to believe that this share will be much higher than 29% in the future.
Table 7-4 Share of Employment with Annual Average Wages Exceeding $50,000
Total employment 13,204
Employment with wages over $50k 3,792
Share 29% Source: JobsEq and author calculations
6 Wages for heavy and tractor-trailer drivers in the 90th percentile was $57,600 in 2015.
Cluster Map
The Hampton Roads Industry Cluster Mapping Project 67
SWOT
Strengths • Well established, historic cluster • Deep-water access to Port of Virginia terminals • Region is surrounded by navigable rivers • Port is a major asset for economic development initiatives
Weaknesses • Existing transportation network may not support port expansion • Port activity is not very diverse o Container traffic dominates • Hampton Roads is not a primary market • Lack of regional training programs for maritime, logistics and supply chain jobs
Opportunities • Increase the number of port-related support firms in Hampton Roads o Currently less than half of the cluster’s legal services spending is spent on firms in HR • Inland water-based freight movement • Rivers provide opportunities for differentiated, private terminals o Kinder Morgan has exploited this, but other opportunities remain • Channel depth provides ability to handle Post-Panamax ships
Threats • Port of Virginia is not very cost competitive • Other East Coast ports are aggressively pursuing expansion strategies
The Hampton Roads Industry Cluster Mapping Project 68
Section 8: Life Science
Definition
The LIF cluster is clearly aspirational. The region is only starting to develop some capabilities in this area. The region possesses assets that can help develop this cluster, but at the moment the region is in the early stages of this cluster’s life cycle.
It is important to note that this cluster does not include health services and health administration, commonly referred to as healthcare. Healthcare is easily the fastest growing sector in the regional economy adding nearly 17,000 since 2007. But, healthcare is a local industry and, thus, not traded.
Instead, two types of firms define the LIF cluster –firms that manufacture health related products (Medical Manufacturing) and labs that engage in biotech related research and development (Medical R&D). The firms that manufacture biotech products could just as easily fall into the advanced manufacturing cluster. Indeed, some of them may already be showing up in those numbers.
Table 8-1 presents an overview of employment in the LIF cluster. Overall the cluster has contracted since 2005, but that is due to the massive contraction in medical manufacturing. The source was a dramatic reduction in ophthalmic goods manufacturing (contact lenses and prescription eyeglasses) in 2010. Research and development grew slowly, but steadily, through the last decade.
Table 8-1 Employment in the Life Sciences Cluster Industry 2005 2015 Growth LQ Medical 1,097 381 -65% 0.12 Manufacturing Research and 1,287 1,386 8% 0.67 Development
LIF Total 2,384 1,767 -26% 0.33
Source: JobsEq and author calculations The Hampton Roads Industry Cluster Mapping Project 69
Analysis of strong biotech clusters in Boston and Washington, DC/Maryland suggest that the private sector tends to dominate the manufacturing activity while large federal labs (National Institutes of Health, Food and Drug Administration and Centers for Disease Control) dominate the employment in R&D. The region is at a distinct disadvantage on both fronts. First, our private sector has not embraced manufacturing in the life sciences, with a few notable exceptions. Second, while our region has many federal facilities, their focus is not in health.
Data Overview
Figure 8-1 presents the LQs for each of the sub-industries. Unfortunately, the region does not have specialization in any of these sub-industries. Dental labs and medical labs have the two highest LQs, but we must be cautious in interpreting those results. Much of the economic activity in those two industries is local. It is difficult, with the data we have, to separate the export-oriented R&D labs from the local-serving health labs.
These findings are disappointing, but reflect a cluster in its infancy. To that extent, the region will see periods of progress and regress. One of the most important industries for this cluster is R&D in biotechnology. Currently, it has a 0.15 LQ, and employment of only 128. However, that level of employment is actually down from a high in 2007 of 779. After significant research on firm closings, we were unable to determine the reason for the significant loss of these jobs during 2007.
The Hampton Roads Industry Cluster Mapping Project 70
Figure 8-1 Location Quotients for the LIF cluster
Dental Laboratories 1.08
Medical Laboratories 0.99
Irradiation Apparatus Manufacturing 0.71 All Other Basic Organic Chemical 0.55 Manufacturing Surgical Appliance and Supplies 0.24 Manufacturing Ophthalmic Goods Manufacturing 0.23 Biological Product (except Diagnostic) 0.16 Manufacturing Research and Development in 0.15 Biotechnology Dental Equipment and Supplies 0.03 Manufacturing Pharmaceutical Preparation 0.03 Manufacturing Electromedical and Electrotherapeutic 0.02 Apparatus Manufacturing Surgical and Medical Instrument 0.01 Manufacturing Medicinal and Botanical Manufacturing 0.01
0.00 0.20 0.40 0.60 0.80 1.00 1.20
Source: JobsEq and author calculations
Occupational Makeup
Tables 8-2 and 8-3 provide a more detailed look at the occupations that are being employed by the LIF cluster. The infancy of the cluster in our region is apparent. Column 2 of Table 8-2 illustrates that healthcare and office administration dominate employment. Ideally, production, manufacturing or life, physical and social science research would dominate employment. By comparison, a similar cluster defined for the Northern Virginia/DC/Maryland region found that 21% of the employment was in a science occupation and only 4% was in healthcare.
The Hampton Roads Industry Cluster Mapping Project 71
Table 8-3 illustrates the same pattern. The cluster’s employment in concentrated in low paying service oriented occupations. Instead, we would like to see this list contain biomedical engineers, chemical engineers and medical scientists to name a few. The Hampton Roads Industry Cluster Mapping Project 72
Table 8-2 Occupational Breakdown of Life Sciences (Private Jobs) Occupation Employment Share
Healthcare 575 34%
Office Administration 331 20%
Production 278 17%
Management 108 6% Life, Physical and Social 80 5% Science Other 303 18%
Total 1,675 Source: JobsEq and author calculations
The Hampton Roads Industry Cluster Mapping Project 73
Table 8-3 Top 10 Detailed Occupations within the Life Sciences Cluster (Private Jobs) Regional Average US Average Occupation Employment Share Annual Wage (2015) Annual Wage (2015)
Phlebotomists 124 7% $31,300 $32,770 Medical and Clinical Laboratory 121 7% $39,000 $41,420 Technicians Medical and Clinical Laboratory 113 7% $47,800 $61,860 Technologists Customer Service Representatives 53 3% $30,200 $34,560
Team Assemblers 48 3% $29,100 $31,560
Dental Laboratory Technicians 39 2% $48,800 $40,520
Couriers and Messengers 38 2% $32,000 $29,130
Office Clerks, General 29 2% $29,300 $31,890
Medical Secretaries 29 2% $30,700 $34,330
General and Operations Managers 25 2% $119,400 $119,460
Source: JobsEq and author calculations Finally, we present the wages for the cluster. Only 40% of the cluster currently makes above $50,000. That is quite low, but it is reflective of a cluster that is in its early stages. Should the region be able to find a way to grow this cluster, this share along with total employment will surely rise.
Table 8-4 Share of Employment with Annual Average Wages Exceeding $50,000
Total employment 1,675
Employment with wages over $50k 656
Share 40%
Source: JobsEq and author calculations
Cluster Map
The Hampton Roads Industry Cluster Mapping Project 76
SWOT
Strengths • Large health services industry in the region
Weaknesses • Lack of federal labs • Lack of large, university research hospital • Regional focus for the industry is on treatment not research and development
Opportunities • Create an innovative culture within the physician community • New technologies (3D printing, computing speed) are reducing the barriers to entry in this industry
Threats • Region has a very small cluster when compared to other metro areas (Baltimore, Boston, Research Triangle)
The Hampton Roads Industry Cluster Mapping Project 77
Section 9: Business Services
Definition
Business Services (BUS) represents the largest employment of the 8 clusters in the report. The cluster is composed of financial service companies (mostly private banks) and business service companies, such as law offices and engineering services, but excluding consulting services, as they were included in the IAS cluster.
However, not all of the employment is occurring in traded services. Teasing out traded employment and earnings from local serving is tricky. Regional employment data shows over 4,000 lawyers in the metro area. How many of those 4,000 are doing export-oriented work? Without surveying every firm, we could never know for sure.
We know through interviews that many of the regional firms are selling as much as 50% of their services to firms and agencies outside the region. That is a start for narrowing down export-oriented employment. In addition to that anecdotal evidence, we have data on the dollar value of purchases of the regional firms services. So, suppose that local hospitals purchase $21 million of legal services from the regional law firms. This value would be considered local and not counted towards the share of traded revenue in the industry. However, if the ship repair industry purchased $21 million in legal services from regional firms, that amount would be included. This methodology does NOT produce an estimate of how much business regional firms are conducting with firms outside the region, but an estimate of the amount of revenue that is related to export-oriented firms within the region.
Table 9-1 presents data on the dollar value of purchases from the industries in the BUS cluster. We present total dollar value estimates for each industry and an estimate of the dollar value associated with export-based industries. Column 4 of Table 9-1 presents the share of each industry’s purchases that we estimate as export oriented. The shares range from 7% for financial services to 52% for engineering services. Overall, the cluster is estimated to provide about 35% of its services to export-oriented firms within the region.
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Table 9-1 Value of Cluster Services Purchased Industry-Level (2015 Estimates) Total Export- Export
Purchased Oriented Share
Financial Services $238,499,000 $15,837,000 7%
Business Services
Offices of Lawyers $345,281,000 $86,157,000 25%
Architectural Services $45,245,000 $16,492,000 36%
Engineering Services $615,691,000 $317,556,000 52%
Total $1,244,716,000 $436,042,000 35%
Source: JobsEq and author calculations
We adjust the employment in the cluster by the shares presented in Table 9-1. Table 9-2 presents the employment numbers for the cluster. 2015 employment was 16,792, down from 18,737 in 2005. Overall, the cluster has an LQ of 1.03, so slightly above average employment concentration in the US as a whole. By way of comparison, the unadjusted employment in the BUS cluster was 36,350 in 2015. So, after filtering to remove local traded employment, the BUS cluster lost 54% of its total employment.
The Hampton Roads Industry Cluster Mapping Project 79
Table 9-2 Employment for the Business Services Cluster Industry 2005 2015 Growth LQ
Financial Services 118 140 19% 0.35
Business Services
Legal Services 1,258 1,134 -10% 0.79
Architectural Services 303 228 -25% 0.63
Engineering Services 7,387 6,265 -15% 2.28
Management of Companies 9,671 10,025 4% 0.89
BUS Total 18,737 16,792 -10% 1.03
Source: JobsEq and author calculations
Data Overview
Figure 9-1 illustrates the LQs for the cluster. Only engineering services exceeds one and it exceeds two, at 2.28. The LQ for engineering services parallels the finding in Table 9-2 showing that 52% of the services of that industry are export-oriented. We would expect an industry to be more traded as the LQ increases.
The Hampton Roads Industry Cluster Mapping Project 80
Figure 9-1 Location Quotients for the Business Services Cluster
Engineering Services 2.28
Management of Companies 0.89
Legal Services 0.79
Architechtural Services 0.63
Financial Services 0.35
0 0.5 1 1.5 2 2.5
Source: JobsEq and author calculations
Occupational Makeup
Occupations in the BUS cluster are concentrated in the services. Office administration has the highest employment share at 22% (Table 9-3). Architecture and engineering, business and finance and management all have double-digit shares in the cluster. These jobs tend to be highly technical and well paying. Indeed, the “knowledge based” jobs that economists predict will be the jobs of the future.
Looking at specific occupations in the cluster does not provide any great insights (Table 9-4). The shares for even the top ten occupations are still quite small, suggesting that employment in BUS is equally distributed across a number of occupations. The good news is most of those occupations are well paying.
Looking at columns 4 and 5, many of the occupations listed earn over $50,000 and 3 of them earn over $100,000. However, all of the regional wages shown in column 4 lag the national wages provided in column 5. This could simply be reflecting the The Hampton Roads Industry Cluster Mapping Project 81
large, urban nature of the type of employment contained in the cluster. It is important to keep in mind that the wage data presented does not take into account costs of living. So, while Hampton Roads’ wages lag the national average for these occupations, the region may also lag the cost of living of the metros where these occupations are concentrated.
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Table 9-3 Occupational Breakdown of Business Services (Private Jobs) Occupation Employment Share
Office Administration 3,533 22%
Architecture and Engineering 2,744 17%
Business and Finance 2,590 16%
Management 2,514 16%
Computer and Math 1,390 9%
Legal 712 5%
Sales 556 3%
Other 1,959 12%
Total 15,998 Source: JobsEq and author calculations
The Hampton Roads Industry Cluster Mapping Project 83
Table 9-4 Top 10 Detailed Occupations within Business Services (Private Jobs) Regional Average US Average Occupation Employment Share Annual Wage (2015) Annual Wage (2015)
Civil Engineers 667 4% $78,600 $87,940
General and Operations Managers 518 3% $119,400 $119,460
Accountants and Auditors 486 3% $71,200 $75,280
Lawyers 443 3% $122,100 $136,260
Bookkeeping, Accounting and Auditing Clerks 432 3% $37,600 $38,990
Customer Service Representatives 418 3% $30,200 $34,560
Office Clerks, General 408 3% $29,300 $31,890
Secretaries and Administrative Assistants 392 2% $33,200 $35,200
Business Operations Specialists 351 2% $72,500 $73,480
Financial Managers 324 2% $125,900 $134,330
Source: JobsEq and author calculations Table 9-5 shows that 70% of the employment in the cluster earns a wage in excess of $50,000. This is second only to the Information Analytics and Security cluster.
Table 9-5 Share of Employment with Annual Average Wages Exceeding $50,000 (Private Jobs)
Total employment 15,998
Employment with wages over $50k 11,214
Share 70% Source: JobsEq and author calculations
Cluster Map
The Hampton Roads Industry Cluster Mapping Project 86
SWOT
Strengths • Globally branded firms with niches in maritime law and maritime insurance • Firms are exporting more of their services than ever before • Strong capabilities in engineering services
Weaknesses • Region has a low share of the population with Bachelors or high education levels
Opportunities • Growing employment in firms that are tightly aligned with the clusters (i.e. Maritime Law, Maritime Insurance) • Increase engineering activity related to flood mitigation and resiliency
Threats • Region is not uniquely known for business and finance • Region does not possess a clearly defined “financial district”
The Hampton Roads Industry Cluster Mapping Project 87
Section 10: Information Analytics and Security
Definition
The Information Analytics and Security cluster (IAS) is a promising cluster for the region. Traditionally, the cluster has been heavily connected to federal spending. As a result, Hampton Roads has seen no employment growth in this cluster over the last 10 years, while the US has seen employment growth of 3% on average. Pivoting the cluster toward more commercial sector work and building capability and mass in the cluster are the most pressing issues.
Traditional computer programming and coding is a part of the IAS cluster, but we recognize that the region has no comparative advantage in that area. So, the components of this cluster were expanded to include data analytics and network security. This is very important since nearly 2,000 regional jobs are classified as computer systems analysts. Narrowly defining the cluster as just computer programming and/or software development would ignore those jobs. In addition, the region has recently been developing an interest in network security, commonly known as cyber-security. We try to include data on those jobs as well, to our best ability.
The sub-industries in the IAS cluster are broadly categorized into three areas: software publishing, computer related services and data analytics and consulting services. Employment data is provided in Table 10-1. The data show that the region is slightly more specialized than the nation as a whole in data analytics, but the cluster as a whole has about the same proportion of employment in the region as the nation. One of the more surprising results from the table is the rapid growth in data analytics jobs over the last decade. The growth is mostly due to growth in engineering, physical and life sciences analysis positions.
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Table 10-1 Employment for the Information Analytics and Security Cluster Industry 2005 2015 Growth LQ
Software Publishers 144 386 10% 0.22
Computer Related Services 12,156 12,123 0% 1.00
Data Analytics and Consulting 5,321 9,257 74% 1.03
IAS Total 17,621 21,766 2% 0.93 Source: JobsEq and author calculations
One of the difficulties with the data in this cluster, much like the BUS cluster, is that many of these jobs are support jobs within firms not identified as IAS firms. For example, occupational data show that there are 386 information security analysts in the region. However, when we just look at firms within the IAS cluster, there are 165 network security analysts employed. Therefore, 221 cyber-security analysts are working in a support role for firms in the region that are not identified as in the information technology industry. Those firms include banks, law firms and hospitals, among a host of others. Since the work from those jobs is not being exported, those jobs are not counted, though important for the regional economy.
Data Overview Figure 10-1 illustrates the LQs for the sub-industries in the cluster. The region is more specialized than the nation in R&D in engineering, physical and life sciences owed to the many research universities and labs in the area. The region also has LQs above one in systems design and R&D in social sciences and humanities.
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Figure 10-1 Location Quotients for the Information Analytics and Security Cluster
Research and Development in the 1.73 Physical, Engineering, and Life… Computer Systems Design Services 1.57 Research and Development in the 1.04 Social Sciences and Humanities Other Computer Related Services 0.94 Data Processing, Hosting, and 0.91 Related Services Administrative Management and 0.78 General Management Consulting… Human Resources Consulting 0.73 Services Other Management Consulting 0.65 Services Other Scientific and Technical 0.64 Consulting Services Computer Facilities Management 0.64 Services Marketing Consulting Services 0.59 Custom Computer Programming 0.47 Services Software Publishers 0.22
0.00 0.50 1.00 1.50 2.00
Source: JobsEq and author calculations
Occupational Makeup
Table 10-2 provides the breakdown of occupations in the IAS cluster. Computer and math jobs dominate the cluster, as one would expect, with software developers and systems analysts the majority of those jobs. Office administration jobs are second (14% share) with business and finance jobs third. The total number of private jobs in the cluster are 20,349. Comparing that number to the total jobs number from column 3 of Table 9-1, suggests that approximately 1,418 jobs in the cluster are government jobs.
The Hampton Roads Industry Cluster Mapping Project 90
Table 10-3 shows the top 10 specific occupations within the IAS cluster. Software developers hold the largest share of jobs (9%) and systems analysts are second (6%). Somewhat troubling are the large differences in US average pay for the occupations on the list when compared to the regional average pay. For the top 2 occupations, regional average pay trails the US average pay by over $10,000. That could be an issue when trying to attract workers to the region. Though not shown in the table, information security analysts in the region earn $6,800 less than the national average.
A word of caution on the wage data presented. Many of the jobs in this cluster are located in major metropolitan areas where costs of living may exceed costs of living in Hampton Roads. So, while the numbers suggest the region is lagging the nation in compensation to this cluster, it is doing so in nominal and not real terms. The Hampton Roads Industry Cluster Mapping Project 91
Table 10-2 Occupational Breakdown of Information Analytics and Security (Private Jobs) Occupation Employment Share
Computer and Math 7,609 37%
Office Administration 2,818 14%
Business and Finance 2,710 13%
Management 2,172 11%
Life, Physical and Social Science 1,408 7%
Architecture and Engineering 1,133 6%
Sales 1,084 5%
Other 1,415 7%
Total 20,349 Source: JobsEq and author calculations
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Table 10-3 Top 10 Detailed Occupations within Information Analytics and Security (Private Jobs) Regional Average US Average Occupation Employment Share Annual Wage (2015) Annual Wage (2015) Software Developers, Applications 1,752 9% $91,500 $102,160
Computer Systems Analysts 1,138 6% $80,500 $90,180
Software Developers, Systems Software 976 5% $96,300 $108,760
Computer User Support Specialists 865 4% $48,400 $52,430
Computer Programmers 844 4% $65,700 $84,360
Management Analysts 810 4% $90,200 $91,770
General and Operations Managers 580 3% $119,400 $119,460
Customer Service Representatives 558 3% $30,200 $34,560
Sales Representatives, Services, All Other 539 3% $58,300 $62,360
Computer and Information Systems Managers 537 3% $135,400 $141,000 Source: JobsEq and author calculations The Hampton Roads Industry Cluster Mapping Project 93
Pay in the IAS cluster is quite high. 82% of the jobs in the cluster earn $50,000 or more (Table 10-4). Even more telling, 14% of the jobs in the cluster earn 6 figures.
Table 10-4 Share of Employment with Annual Average Wages Exceeding $50,000
Total employment 20,349
Employment with wages over $50k 16,767
Share 82% Source: JobsEq and author calculations The Hampton Roads Industry Cluster Mapping Project 94
Cluster Map
SWOT
Strengths • Sequestration proof – 13% employment growth during sequestration period • Significant demand exists from other regional clusters (BUS, SRB, LIF) • Exiting military provide a unique source of talent • Region is home to DOD cybersecurity commands (i.e. NAVIFOR) • Regional educational institutions are providing graduate training in cybersecurity
Weaknesses • Wages for many of the occupations significantly lags US averages • Immature telecommunications infrastructure • Private companies struggle to compete with the government for talent • Lack of critical mass of firms • Recent base realignment has moved some talent out of the region
Opportunities • Federal spending on cybersecurity is forecasted to grow rapidly • Atlantic high speed cable (MAREA) being laid by Microsoft and Facebook from Virginia Beach to Spain • Talent can be imported from competitor areas that have higher costs of living
Threats • Washington, DC/Northern Virginia is a major competitor for talent with far more job openings than Hampton Roads • Cluster will always be vulnerable to federal spending cuts
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Section 11: Tourism and Recreation
Definition
Tourism is always considered one of the three legs of the Hampton Roads economic stool, along with the port and the military. The nature of this cluster’s trade is rather unique. The goods and services the cluster provides never leave the region, but clearly the cluster brings in revenue from external sources.
The REC cluster in Hampton Roads is composed of service establishments such as hotels, motels, restaurants and bars. The area is also rich with tourist and recreation destinations, such as historic sites, museums, amusements parks and golf courses. Of course, this definition of the cluster means that it is also very local-serving, particularly in the tourist offseason, making the calculation of employment for the cluster difficult.
Data Overview Determining export-focused employment levels for the hotel and restaurant industry present the biggest challenge. Even at the height of the tourist season, local consumers will eat at restaurants and stay at hotels at the oceanfront or in Williamsburg. One option would be to drop the employment at restaurants entirely and assume that it is mostly local serving. This seems an overly restrictive assumption particularly in places like Williamsburg and the Virginia Beach Oceanfront. Instead, we address this complication by using tourism data to estimate a traded share percentage for the cluster.
The Virginia Tourism Corporation (VATC) breaks the state up into regions with the Hampton Roads area falling into their Coastal Virginia region. In 2014, the Virginia Beach Convention and Visitors Bureau estimated that 44,521 direct jobs were created in the Coastal Region as a direct result of tourism (2nd row of Table 11-1). Alternatively, if we look at total 2014 employment data from all of the tourism related NAICS codes, we find that employment totaled 82,251 (3rd row of Table 11-1). These two numbers suggest that the traded employment share is 51%. About half of the full-time annual employment in the cluster is traded.
The Hampton Roads Industry Cluster Mapping Project 97
Table 11-1 Traded Share for REC Cluster Employment 2014
Total Direct Employment 44,521
Total Employment in Cluster 82,251
Traded Share 54% Source: JobsEq, VB Convention and Visitors Bureau and author calculations
The 51% share estimate seems reasonable. During peak season, the share of export-oriented activity is likely much higher, but the share is substantially lower during the off-season. In the cluster analysis that follows we will report total employment numbers for the cluster, but keep in mind that only about half of the cluster is direct employment from non-local tourism.
Table 11-2 presents the employment data for the three sub-clusters within REC. As with the other clusters, the data covers that last decade. The three sub-clusters within REC are Accommodations and Food (including hotels and restaurants), Destinations and Entertainment (including historic sites, museums, amusement parks, and performing arts companies) and Tourist Services (including sightseeing transportation, travel guides, and convention and visitor bureaus). Not surprisingly, the largest employment is in the Accommodations and Food sub-cluster. However, at least half of that industry grouping is catering to just a local clientele.
The region is also specialized in this industry with an LQ of 1.21. Destinations and Entertainment employment grew a modest 1.2% over the last decade. The region is even more specialized with an LQ of 1.49. This is in large part to the 19.72 LQ for Historical Sites (See Figure 11-1). Finally, Tourist Services contracted by 24% over the last decade and also has an LQ less than 1. Employment in the cluster, as a whole, grew by 6% over the last decade (an average annual growth rate of 0.6%.) This actually lagged the nation and the state, however. National employment in this cluster grew at an average annual rate of 1.9% over the decade and Virginia employment grew at 1.5% on an annual average. This is surprising, yet a stark reminder that even our largest industries have been struggling to keep pace since the end of the recession. The Hampton Roads Industry Cluster Mapping Project 98
Table 11-2 Employment for the Tourism and Recreation Cluster Industry 2005 2015 Growth LQ
Accommodations and Food 69,934 74,919 7% 1.21
Destinations and Entertainment 8,490 8,796 4% 1.49
Tourist Services 984 698 -34% 0.61
REC Total 79,408 84,413 6% 1.22 Source: JobsEq and author calculations
Figure 11-1 provides a more detailed look at some of the location quotients in the REC cluster. There are a number of industries that have LQs in excess of 2. As mentioned previously, historical sites leads the way at 19.72. Amusement parks and marinas also exceed 2 and other traveler accommodation is close at 1.99. This category includes lodging that does not fall under hotels, motels, bed-and-breakfast inns, etc. Housing for seasonal and international workers during the summer peak season at the oceanfront and in Colonial Williamsburg is likely driving the LQ for this category. A number of the other industries exceed 1. Clearly, the region has a specialization in tourism.
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Figure 11-1 Location Quotients for the Tourism and Recreation Cluster
Historical Sites 19.72
Amusement and Theme Parks 3.05
Marinas 2.01
All Other Traveler Accommodation 1.99
All Other Amusement and 1.96 Recreation Industries
Scenic and Sightseeing 1.90 Transportation, Water
Museums 1.53
Recreational Goods Rental 1.01
Convention and Visitors Bureaus 1.01
Tour Operators 0.50
0.00 5.00 10.00 15.00 20.00 25.00
Source: JobsEq and author calculations
Occupational Makeup The primary concern with the tourism industry is the wages paid to workers in the industry. The industry is consistently generating large economic impacts for the region, but it is more from quantity than quality. Tables 11-3 and 11-4 reflect this reality. The addition of restaurants to the cluster makes food service occupations the overwhelming majority of the employment in the cluster. Without food service, hotel workers dominate the cluster. As a result, the cluster appears to be a low-pay, low-skill cluster.
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Table 11-3 Occupational Breakdown of the Tourism and Recreation Cluster Occupation Employment Share
Food Preparation 62,266
Building and Grounds Maintenance 4,553
Office Administration 3,536
Sales 3,336
Personal Care 2,706
Management 2,367
Transportation 1,703
Other 3,946
Total 84,413 Source: JobsEq and author calculations
Table 11-4 shows the top ten detailed occupations within the cluster. Again, food preparation dominates the list. Average wages are quite low for all of the occupations listed, but in-line with national averages for the respective occupations.
Table 11-4 Top 10 Detailed Occupations within Tourism and Recreation Cluster Regional Average US Average Occupation Employment Share Annual Wage (2015) Annual Wage (2015) Combined Food Preparation and Serving 16,708 19% $19,400 $19,710 Workers, Including Fast Food
Waiters and Waitresses 14,788 17% $24,100 $23,020
Cooks, Restaurant 6,899 8% $24,400 $24,430
First-Line Supervisors of Food Preparation and 4,399 5% $35,200 $33,330 Serving Workers
Cooks, Fast Food 3,220 4% $18,200 $19,610
Food Preparation Workers 3,165 4% $21,500 $22,050
Maids and Housekeeping Cleaners 3,036 4% $20,500 $22,990
Dishwashers 2,823 3% $18,600 $20,360
Cashiers 2,423 3% $19,100 $21,010
Hosts and Hostesses, Restaurant, Lounge, and 2,333 3% $18,600 $20,530 Coffee Shop Source: JobsEq and author calculations
The REC cluster is not just a cluster of low-pay jobs. The number of jobs paying above $50,000 in the cluster is 4,147 (Table 11-5). This generates a 5% share for the cluster. This is the lowest share of the 8 clusters. However, digging a bit deeper reveals that of those 4,147 jobs earning in excess of $50k, a quarter of those (1,070) earn in excess of $100,000.
Indeed, the cluster is home to some very high earning occupations. Hotels and restaurants need general managers. On average, they earn $120,800. The industry also needs IT managers, financial managers and sales managers. All of those occupations earn in excess of $100,000 as well.
Table 11-5 Share of Employment with Annual Average Wages Exceeding $50,000
Total employment 84,413
Employment with wages over $50k 4,147
Share 5% Source: JobsEq and author calculations
Overall, the REC cluster is not likely to be the main source of regional wage growth in the future. Then again, it is not designed to be. The REC cluster is, and always will be one of the major employment clusters for Hampton Roads. The REC cluster will, also, always be the most visible exporter. Maintaining the clusters strength is vital to the region’s economy. Understanding why the region has recently lost jobs in tourist services is important. In addition, it is important to try and generate high paying jobs in the industry, though previous research from other tourist destinations has suggested that goal will be elusive. 7
7 R. Geoffrey Lacher and Chi-Ok Oh, “Is Tourism a Low-Income Industry? Evidence from Three Coastal Regions,” Journal of Travel Research 51, no. 4 (2012): 464.
Cluster Map
SWOT
Strengths • Numerous historical sites • Opportunities for outdoor recreation • Abundant supply of various lodging options • Major east coast oceanfront destination
Weaknesses • Most jobs are relatively low paying • Regional debates on name hinder marketing efforts and lead to city level instead of regional level marketing • Airport access to the region is limited • Existing transportation network limits intra-regional tourism
Opportunities • Widening of I-64 between Williamsburg and Newport News will help intra-region travel between tourist destinations •
Threats • Many high-value tourism assets will require large investments to protect them from sea- level rise and recurrent flooding. • Changing tastes by consumers, particularly millennial travelers, could hurt tourism in certain parts of the region.
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Section 12: Concluding Remarks
The Hampton Roads region has had a difficult decade. The national recession and declining federal spending has resulted in stagnant economic growth. How does the region regain its economic footing and begin growing. This report presents 8 clusters that seem the most likely candidates for growth. These clusters are well aligned to the region’s assets, are relatively immune from federal spending cuts and pay above average wages. Growing these sectors is key to the growth of the region.
Each of the clusters has inherent strengths and some clear weaknesses. Many of the weaknesses are systemic across all clusters. So, while there is a level of nuance in how each cluster is impacted by poor transportation, it is a consistent weakness for each cluster. Many of these cross-cutting issues need to be addressed sooner rather than later.
Each cluster also has some opportunities that could accelerate growth. Some of those opportunities are cross-cutting as well. The region needs to find ways to capitalize on those opportunities right away. Some of the opportunities presented are unique to certain clusters. These opportunities should be confirmed and further explored by groups of experts in the respective industries.
The economic reality of the region over the past decade is unpleasant. However, in the midst of that pain, there are success stories. The clusters presented in this report have withstood the last decade better than others in the region. They are also forecasted to grow over the next decade. However, the report does not intend for these clusters to become the sole focus of the region. The requirements for growth of these clusters are requirements for growth of the region as whole. Ultimately, growing these clusters to compete nationally and globally will generate spillovers to other sectors, providing additional growth opportunities for the region and generating new sectors of excellence over the coming decade.