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Measuring and Understanding the Relationship Between Air Service and Regional Economic Development (2022)

Chapter: Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity

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Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
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Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
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Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
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Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
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Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
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Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
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Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
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Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
×
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Page 63
Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
×
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Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
×
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Page 65
Suggested Citation:"Chapter 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity." National Academies of Sciences, Engineering, and Medicine. 2022. Measuring and Understanding the Relationship Between Air Service and Regional Economic Development. Washington, DC: The National Academies Press. doi: 10.17226/26682.
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55 C H A P T E R 3 Analyzing the Data: Case Studies Illustrate Changes in Air Service and Regional Economic Activity Reflecting the broader objective to help as many airports and communities as possible with understanding the relationship between air service and economic development, the project team examined changes in air service and socioeconomic measures for all U.S. primary service airports and the regions they serve for the period 2008-2019. The team excluded a limited number of airports and regions (e.g., those in Alaska, Hawaii, U.S. territories, and those where air service is supported by the EAS program). This chapter describes the process through which the project team matched airports with corresponding regional economic activity, eventually creating a typology of airports and economic regions from which case studies could be selected for in-depth analyses. Readers interested in more information should refer to the Contractor’s Technical Report, which describes the project methodology in greater detail. The section also includes a summary of observations from the case studies. Because the case studies do not represent a statistically-drawn random sample of airport regions, the conclusions developed from them should not be interpreted as being generalizable to all airports. Generating a Typology of Airport and Economic Regions The project is restricted to the FAA-defined primary service airports – that is, those that receive scheduled commercial passenger service and more than 10,000 passenger boardings each year. For 2019 (the latest data available from the FAA at the time of the analysis, and notably pre-COVID), this included 403 total airports, as summarized in Table 7. Table 7: FAA-Defined Primary Hubs, 2019 Category Number of Airports Large hub 30 Medium hub 32 Small hub 74 Non-hub 267 Total 403 Source: FAA Preliminary 2019 Enplanement Data The project team made two adjustments to the universe of airport regions.

56 First, the team excluded airports where air service was provided only because of the EAS program. By definition, the EAS program subsidizes air service to regions where commercial service would not otherwise be provided. Yet because economy activity in those regions is inadequate to support unsubsidized commercial service and because the carriers providing the EAS service are unable to adjust their routes and capacity without governmental approval, they are not included. As of Feb. 2020, there were a total of 165 airports in the contiguous states, Hawaii, Alaska, and Puerto Rico that were eligible for EAS-subsidized service. The FAA classifies only 63 of them as primary service airports, which the team excluded. The EAS program provides a vital link from these small communities to the national aviation system and economy. Second, the team also excluded airports in the U.S. territories: Puerto Rico, Guam, the U.S. Virgin Islands, American Samoa, Saipan, and the Northern Marianas Islands. These airport regions were considered to be exceptionally unique in terms of the economies that they serve. The project team then matched airports with MSAs, based on the counties where the airports are located and the county-based definitions of the MSAs. Standard federal statistical areas like MSAs were used because socioeconomic data are reported on that basis. In some situations, a commercial service airport may not be located within an MSA but may instead be in an adjoining MSA. Examples are the Durham-Chapel Hill, NC MSA and Boulder, CO MSA. Raleigh– Durham International Airport (RDU) is in Wake County, NC (the Raleigh MSA) which abuts and obviously serves Durham County. Denver International Airport (DEN) is in Denver County, which abuts and obviously serves Boulder County. In such cases, the research tied changes in air service to socioeconomic changes within the broader “CSA,” such as the Raleigh–Durham–Cary CSA, for which changes in economic activity are analyzed in relation to the air service at RDU. In other cases, airports within an MSA may be nearby a separate MSA served by other airports, and the proximity of those airports creates complications for analyzing changes in air service and regional economic activity. For example, Green Bay–Austin Straubel International Airport (GRB) is located within the Green Bay MSA. It is about 30 miles away from Appleton International Airport (ATW) in Appleton, Wisconsin (within its own MSA). Similarly, The Santa Rosa–Petaluma MSA is within the broader San Jose–San Francisco–Oakland, CA CSA. However, analyzing changes in air service at STS in relation to changes within that larger CSA is nonsensical. Its operations are considered only in relation to changes within the immediate MSA. The team then made one final adjustment to the potential universe of commercial service airports and excluded those that were not tied to either an MSA or a CSA. There were 61 airports that were located outside either an MSA or a CSA. Excluding those airports left 279 available for analysis. This includes several airports in remote parts of Alaska and the U.S. Territories. Table 8 summarizes the airports and regions subject to analysis. EAS Air Carriers In the U.S. in 2021, there were 11 air carriers that operated flights to small communities supported by the EAS program. These carriers tend to operate small aircraft. Exactly half of the EAS communities received flights with 50-seat aircraft, and another 44 received flights with 8- or 9-seat aircraft. Most of that service was to large hub airports that also served as major airline hubs (e.g., ATL, LAX, ORD, MSP).

57 Table 8 : Airport – CSA/MSA Summary: Airports within Major Statistical Areas Total Primary Service Airports 403 EAS Airports 63 Subtotal – Non-EAS Airports 340 Airports within an CSA 193 Airports outside an CSA but inside an MSA 86 Subtotal 279 Airports outside either a CSA or MSA 61 Airports within a MicroSA 26 Airports outside of an MSA or MicroSA 35 Note: MicroSAs or Micropolitan Statistical Areas are similar to MSAs but smaller. MicroSAs have at least one urban cluster of at least 10,000 but less than 50,000 population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties. Several MSAs and CSAs were home to more than one airport. Table 9 summarizes the CSAs/MSAs and shows the number of airports associated. Thirty-two CSAs included more than one airport, and another five MSAs were home to more than one airport. Thus, the analysis focused on those 213 statistical regions served by those 279 primary service airports. Table 9: Airport – CSA/MSA Summary Counts Category Count of CSAs/MSAs Number of Primary Service Airports CSAs with one airport 101 101 CSAs with >1 airport 32 92 MSAs outside of CSAs with one airport 75 75 MSAs outside of CSAs with >1 airport 5 11 Total 213 279 Key Air Service and Economic Data Examined The project team selected a subset of data to consider in analyzing changes in air service and economic activity based on its professional experience and the analysis from the literature review. In general, the key airport and air service variables were: – Airport hub sizes – Whether airports were part of a multi-airport system that served a geography or a smaller airport “in the shadow” (i.e., within a relatively short drive) of a larger facility – Whether airports provided service to international destinations or only domestic locations – Whether the airports’ capacity was principally carried by a ULCC or not – Capacity offered (airline seats available for purchase) – Volume of O&D traffic – Directionality (i.e., whether the airports’ O&D traffic was principally inbound, outbound, or balanced) – Cargo (various metrics)

58 The key socioeconomic data analyzed were: – Total population – Total employment – Employment in selected industry sectors (e.g., manufacturing; IT; finance and real estate; PST services; management of companies) – Regional economic strength, determined by: – Location quotients – Presence of strength in selected economic clusters Time Period of Analysis The project team examined the air service and economic data for three years: 2008, 2015, and 2019. Those dates were selected because they represent the beginning of the Great Recession, the point at which national-level data indicated that air travel had fully recovered from the downturn, and the most recent year for which data was available (and prior to the collapse brought on by the COVID pandemic). With the three years identified, the team analyzed changes in each of the variables for three intervals: – Entire study period 2008-2019 – Recovery from the Great Recession 2008-2015 – Stable growth during 2015-2019 Analysis of Passenger Air Service and Regional Economic Activity The project team focused first on changes in available seat capacity and O&D traffic and changes in regional employment for each of the three time periods. These data were selected because they most immediately reflect the issues that are central to the project. Separate analyses were conducted on the 176 “single airport” CSA/MSA regions versus the 37 “multi- airport” CSA/MSA regions. In general, while recognizing the fundamental underlying differences between single- and multi-airport regions in terms of average population, employment, and key aviation metrics (e.g., capacity offered), the team found little significant difference in the relationship between capacity or passenger traffic and employment between the two groups of regions. That is, for both types of airport regions, the relationship is positive – that as one variable (employment) increases so does the other (capacity). Analyses of changes in economic activity and air service in the multi-airport regions is difficult because changes in economic activity within the broader region cannot readily be associated with or disaggregated from air service at any one airport. As a result, for purposes of creating categories of airport regions, the project team aggregated the air service metrics for all airports serving the region and compared the totals against the economic variables. For all airports, the team analyzed changes in each of the air service variables against the key socioeconomic variables. Changes in Cargo Operations and Regional Economic Activity Similar to the analyses of changes in economic activity and air service metrics, the team analyzed changes in the same economic variables against measures of cargo activity at airports. Those included total cargo

59 tonnage and separate measures of activity and tonnage by integrators (e.g., FedEx), pure or “dedicated” freight operators (e.g., Atlas Air), cargo carried in the belly of passenger airlines, and Amazon Air. A Typology for Selecting Airport Region Case Studies To illustrate how air service has changed in relation to regional economies and select case studies to highlight those changes, the project team developed a categorization of airport regions that reflected broad types of air service and regional economic structures. The categories consider different airport hub sizes (and community populations). – Changes in employment and capacity 2008-2019 and in capacity, by hub size – Sustained growth in traffic and capacity 2008-2015 – Growth in employment and capacity 2015-2019 (capturing an “economic rebound” from the Great Recession) – Multi-airport regions – Includes a focus on non- or small hub “shadow airports” (i.e., airports close to larger airports with which they struggle to retain passenger traffic) – Airport with international service – Regions with cargo airports with different types of operations – Freighter – Amazon Air – Regions with employment strength in particular industry sectors – PST – FIRE – IT – Transportation / Logistics The project team selected airports as case studies that were not necessarily the “typical” airport that met the category and selected instead “exemplars” so that the case studies would more likely clearly illustrate the relationship between the economic activity and air service. Table 10 categorizes the case studies by hub size and different characteristics of the region’s air service and/or regional economy. In some cases, an airport may illustrate more than one aspect of air service or changes in the economy. Figure 27 maps the location of the case study airports, highlighting their geographic dispersion, differences in hub size, and whether they were selected because of their cargo operations. Table 10: Categories of Airport Regions and Airports Selected as Case Studies PST Information Technology Finance and Real Estate Transportation and Logistics Large SAN MIA SAN ATL ATL Medium AUS, RDU RDU AUS Small FAT RNO GSO HSV DSM GSO Non COU COU, GRB, STS ABE Regional Economic Strength Sustained growth Economic rebound Multi-airport region Cargo / freight International serviceHub size Characteristic of airport or region

60 Figure 27: Case Study Airport Locations, Size, and Nature of Operation Examined Observations from the Case Studies For each airport region, the team prepared both a comprehensive case study and an abbreviated version to meet different readers’ levels of interest. On average, the full versions of the case studies are about 15 pages in length while the short versions are 2-3 pages. The collection of comprehensive case studies is offered as a separate document (NOTE link/title). The short versions are included as an appendix to this report. (See Appendix V.) Based on the case studies, the following observations and conclusions are possible. Because they are based only on the 14 case studies, these conclusions should not be considered to be generalizable to other airports. Relationships Between Air Traffic and Regional Economic Activity At all airport regions except one, the amount of O&D traffic and regional employment is highly correlated. Clearly, there is an interrelationship between air traffic and total regional employment: As one increases so does the other. When the relationship is limited to industry sectors that tend to be more highly dependent on aviation (Information; Finance and Insurance; real Estate; PST Services; Management of Companies; and Administrative and support and waste management and remediation services), the correlation remains very high. Table 11 summarizes the correlation coefficients between O&D traffic and total regional employment and employment in “aviation-reliant” sectors for the case study airports. Figures 28 and 29 illustrate the relationship graphically; the upward-sloping trend lines indicates that the relationship if positive: as one variable increases so does the other one. Figure 28 illustrates the relationship

61 between total O&D traffic and regional employment in the greater Fresno, California region. Figure 29 focuses the analysis onto changes in total O&D traffic and employment in “aviation-dependent” industry sectors in the Greater Des Moines region. Table 11: Summary of Correlation Coefficients from Case Study Airports ATL 0.9307 0.9833 AUS 0.9872 N/A COU 0.8885 0.9563 DSM 0.9721 0.9662 FAT 0.9564 0.9797 GRB 0.0504 N/A GSO * 0.8078 0.9913 MIA 0.9820 0.9786 RDU 0.9524 0.9851 RNO 0.8369 0.9617 SAN 0.9669 0.9715 STS ** 0.9702 0.9423 * excludes 2008 ** excludes 2008-13 before runway lengthened N/A = not available Total O&D and Total EmploymentAirport Total O&D and Aviation-Reliant Sector Employment Caveat: Correlation does not establish causation. That is, it is not evident whether rising total employment levels leads to more air traffic, or whether more air traffic leads to more total employment. However, as discussed in Chapter 1, academic research completed in the last two decades on air service and regional economic activity has clearly found that air service leads to increased economic activity, especially in industry sectors with a relatively high dependence on aviation as an intermediate factor of production. Further, several of the case studies revealed anecdotal evidence where airport or local economic stakeholders had first-hand knowledge of how enhancements in air service directly and immediately led to expansions in economic activity and employment. The correlation coefficient is a statistical measure of the strength of the relationship between changes in two variables. Positive numbers indicate that as one variable changes, the second also changes in the same direction. A value of exactly 1.0 means there is a perfect relationship between the two variables; a change of one unit in one variable is tied to a change of one unit in the other variable. A correlation coefficient with a value of 0.9 or greater represents a very strong relationship.

62 Figure 28 : Scatterplot of Total O&D Traffic and Total Regional Employment, Fresno Area Figure 29: Scatterplot of Total O&D Traffic and Employment in Aviation-Reliant Industry Sectors, Greater Des Moines Regions served by airports with international air service had evidence of increasing foreign investment and job growth. Increases in those types of economic activity were especially clear in the regions served by

63 AUS, SAN, and RDU, where new nonstop service to Europe contributed to new foreign investment by European-based firms. Engagement with Regional Stakeholder Organizations All the case study airports engaged with one or more regional economic stakeholders for purposes of air service development. Stakeholder organizations can include those that represent local or regional business interests (such as the chamber of commerce), regional economic development councils or authorities, other public organizations such as local public universities, and other groups. Those stakeholders can be knowledgeable about emerging business activities and needs, which is important for the aviation community to understand and support. The extent of the engagement varied significantly. In several case studies, regional stakeholder organizations were active members of committees with airport officials working to enhance air service. This helped ensure that the interests of the business community were clearly communicated to airport staff. The airports also participate with stakeholder committees to convey a consistent message to the organizations’ memberships. In some airports, the need for air service to new markets originates with the business community, which pushes the airport to pursue those opportunities. Some airports reach directly to major employers in the region to better understand how they travel and what their air service needs might be. This can include private businesses and public organizations, such as universities. Regional economic development groups may target selected industry sectors that have been identified as “high wage” and “high growth” industries for the area. Because these often include sectors with a high reliance on aviation, there is a natural connection between the stakeholders and airports. Some airports’ strategic goals incorporate regional economic development goals, “mindfully developing the airport in ways that support its role as an economic driver for the region.” However, among the case study regions, most stakeholder organizations did not have standing committees or structures that addressed commercial air service issues. Many organizations supported efforts to improve transportation matters generally, although those tended to focus on highway considerations or deficiencies. Several case study airports – especially smaller facilities that are owned and operated by municipal governments – are connected to the municipal economic development authorities. In cases like these, the stakeholder organizations’ interests may be focused on “Main Street” issues of filling vacant downtown storefronts with retail, highway improvements, and business retention rather than air service and business activities that are dependent on aviation. These can be challenging situations, but airports and the business community can make a compelling story that better clarifies the connection between air service and employment, especially in those industry sectors that are reliant on aviation. The case studies include examples of airports and regional stakeholders working toward economic development and air service goals that tie the two together. There are examples where air service improvement is specifically listed as a key strategy for community development. Beyond “big business” development, the airport’s priorities around balanced air service options and comprehensive connectivity are designed to support and attract small businesses and to serve the community at large. In this manner,

64 the value of air service is not simply about making it easier for corporate executives to visit the region but also contributing toward a standard of living that will attract the businesses and residents who want to see the community thrive. Measuring Changes in Connectivity For most case study region airports, connectivity declined following the Great Recession but then improved as the airline industry and national economy recovered. Figure 30 summarizes how connectivity (as measured using the IATA connectivity index) changed from 2008 to 2019 at Raleigh–Durham International Airport (RDU). Connectivity there declined after 2008 and did not recover until 2015. It then proceeded to grow by a total of +36 percent between 2015 and 2019. This growth was driven by expanded service to Denver, Los Angeles, San Francisco, and Seattle, as well as the introduction of service to Paris and Montreal in 2016 and 2019, respectively. Figure 30: Changes in Connectivity at RDU Source: InterVISTAS analysis of schedule data using the IATA methodology. There is appreciation among airports and stakeholders of the value of improving connectivity, especially where new service to a hub would reduce circuity and improve accessibility to international markets. Many stakeholders would like to see increases in the number of markets served, often for improving business accessibility. A stronger story can be told when linkages and insights from the business community are uncovered. Even a single business that gained access to new markets for its products or improved access for intermediate products can make a powerful anecdote. Accessibility can attract new and more diverse business into a region, particularly from industry sectors that are more reliant on air service which tend to support higher paying job opportunities. In this manner,

65 air service facilitates not only economic growth but a high quality of growth that supports a higher standard of living and a more resilient economy. Conveying Economic Impact and How Air Service Supports Regional Economic Activity Airports and stakeholders tended to focus mostly on airport economic impact assessments rather than evaluations of how changes in air service contributes to broader regional economic development.  Most of the case study airports offer little if any information on their websites on either economic impact or how air service supports regional economic activity. Those that do only include summary information from an economic impact study of the airport.  Some regions recognize that the airport plays a pivotal role contributing to regional economic development. Commercial air service is an important consideration for site selectors and businesses decisions on where to expand or locate.  Regional stakeholder organizations use their own metrics to gauge performance. These do not generally tie to measures which airports typically report, although some are often used in airport economic impacts, such as jobs supported.  The result is that the metrics used by airports to tout their economic contributions do not connect or resonate with regional stakeholder groups. Several stakeholders noted challenges of conveying economic concepts to the public.  Some suggested using individual stories to personalize how an individual’s employment or business is tied to the airport or airlines.  Concise messaging and context play a role in delivering research findings to key stakeholders, including local elected officials and the general public. Large impact numbers cannot necessarily convey a message on their own and should be accompanied with benchmarks, comparisons, graphics, or some additional context that helps an audience quickly and accurately interpret the results.  Standard marketing strategies can have a role in generating awareness for the airport as a driver of economic growth. Local advertisements that showcase key air service development successes or at the airport can help generate support from the community for future initiatives.

Next: Chapter 4 Engaging with Regional Stakeholders on the Airport s Contributions to Economic Activity »
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Airport economic impact studies may accurately measure the activity that occurs on airport properties or is tied directly to airport operations (such as off-site parking and hotels that accommodate airline crew who overnight in a location), but they do not capture how air service supports business and employment throughout the region.

The TRB Airport Cooperative Research Program's ACRP Web-Only Document 53: Measuring and Understanding the Relationship Between Air Service and Regional Economic Development provides airports and major regional stakeholders concerned with economic development with the information and tools necessary to understand and communicate the nexus between air service and regional employment.

The Web-Only Document is supplemental to ACRP WebResource 12: Air Service Development and Regional Economic Activity. Supplemental to the Web-Only Document is a Case Study Compilation with the full versions of the 14 case studies performed as part of the project.

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