Defining the Region

Regional economies are generally based on a core resident population of some size that conducts economic and social activities. The U.S. federal government (and its statistical agencies like the Bureau of the Census or Bureau of Labor Statistics) use standardized definitions for political-economic regions. Socioeconomic data are collected and reported for these defined statistical areas, although the scope and detail of data vary across each type of statistical area.

Airport staff commonly think about the region served by an airport in terms of its catchment area. However, a region’s economy likely does not correspond with that catchment area, and governments do not measure or count data in a way that corresponds exactly with a catchment area.

IMPORTANT NOTE: Economic data at the regional or sub-state level (as opposed to state or national data) can provide insight that is more directly linked or relevant to an airport’s catchment area. However, regional economies are not the same as an airport’s catchment area, and they do not always entirely overlap one another. For instance, an airport’s catchment area may span multiple regional economies or, conversely, it may only partially serve one regional economy.

Similarly, economic data based on conventional statistical area datasets may not fully capture airport-centered economies because they don’t necessarily match well with the geography of airport-driven economic activity (especially if the airport and related economic activity are located in more suburban or rural areas). In these cases, it may be worthwhile for analysts to consider geographic information system (GIS) software and geodatabase applications that can allow them to assess economic data based on user-defined geographic areas (such as the complete catchment area of the airport).

Other basic measures focus more directly on the scale of economic activity within the region, including the following:

IMPORTANT NOTE: Context is important when assessing any measure of a region. To help contextualize the meaning of a given measure, it can be helpful to:

• Assess how the region has changed over time.
• Compare the region to other geographies (such as similar regional economies or statewide and national averages).

For instance, the unemployment rate for a given region should be compared to statewide and national averages in order to understand whether the regional economy is performing above or below trend, the population should be assessed over time and compared to other geographies in order to understand whether the region is growing or shrinking at a rate above or below trend, and so forth. These kinds of analyses help identify important and exceptional characteristics of a regional economy.

Foreign Direct Investment

Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise resident in another economy. Ownership or control of 10 percent or more of the voting securities of an entity in another economy is the threshold for separating direct investment from other types of investment.

At the end of 2019, foreign investment in the United States totaled $4.4 trillion, with over half coming from countries in Europe. Among individual countries, Japan is the top investor, followed by the United Kingdom and Germany. Among industry sectors, over 40 percent of all foreign investment is in manufacturing, particularly chemicals. The finance and insurance sector is also a major recipient of foreign investment.

Foreign Direct Investment in the United States by Region (in $ millions)

This investment supported 7.9 million jobs in 2019, with employment in every state. Almost 850,000 of those jobs were in California, followed by Texas (680,000), New York (530,000), Illinois (380,000), and Florida (370,000).   

Regional Economic Strengths

Beyond the basic metrics, there are additional measures of a region’s economic structure that could be relevant to air service development. These include approaches that identify the predominant or most impactful industries shaping the regional economy.

Different regions possess different economic attributes or strengths. These differences may arise from a region’s location or geography (e.g., port cities with concentrations in maritime manufacturing, locations near mineral deposits).

Industry Classifications/Sectors

Breakdowns of economic data by industry are made possible through industry classification systems that group businesses and their data (e.g., employees, wages, output) based on similar or related characteristics. In the United States, the most common industrial categorization system is the North American Industry Classification System (NAICS), which uses a six-tier industry grouping structure that becomes more progressively granular in definition at each tier. Under NAICS, businesses are grouped based on their primary economic activity. More information on NAICS can be found on the U.S. Census Bureau website.

The U.S. federal statistical agencies use the NAICS standard for their industry-level data. For sub-state regions, industry-level data are most commonly available at the NAICS two-digit and three-digit levels. Two-digit NAICS codes identify each broad “sector” in the economy (e.g., NAICS Code 48 = Transportation) whereas three-digit NAICS codes pertain to more specific “subsectors” (e.g., NAICS Code 481 = Air Transportation).

Using this information, the leading industries within a region can then be identified through various metrics such as economic clusters and location quotients.

Economic Clusters

Different geographic regions in the United States may be recognized as home to a relatively large amount of expertise in a given industry. Examples of this include finance in New York and entertainment in Los Angeles. Nashville and Austin are similarly known as “music capitals.” Hartford is a known headquarter for insurance companies. At the heart of Silicon Valley, San Jose is among the information technology centers of the country. The concept of “economic clusters” provides a means of quantifying the economic strength of regions. A cluster is a regional concentration of related industries that arise out of the various types of linkages or externalities that span industries in a particular location. Economic clusters have been formally defined by the Harvard University School of Business in concert with the U.S. Economic Development Administration.

Certain economic clusters are particularly relevant for airports to understand because the industry sectors that comprise these economic clusters are known to have a high propensity to travel. These sectors include Business Services, Transportation and Logistics, Finance, and Information Technology:

• Business Services include employment and economic activity related to corporate headquarters, consulting services, business support services, computer services, employment placement, engineering, architectural and drafting services, and similar fields.
• Transportation and Logistics include air transportation, specialty air transportation, ground transportation support activities, trucking, and bus transportation.
• Financial Services include financial investment activities, credit intermediation, credit bureaus, monetary authorities such as central banks, securities brokers, dealers, and exchanges.
• Information Technology includes electronic components, computers and peripherals, semiconductors, software publishers, software reproducing, process and laboratory instruments, medical apparatus, and audio and video equipment.

Other tradeable sectors that have a high propensity to fly include biopharma, insurance, and communications.

Airports can access publicly available cluster data for their relevant regional economy (where applicable) using the U.S. Cluster Mapping Project.

Location Quotients

A Location Quotient (LQ) is a mathematical ratio that compares the concentration of a specific industry in a specific area to the concentration of that industry nationwide. Location quotients can be calculated using different variables and geographies. The example shown uses employment, which is commonly used for calculating LQs. Comparing regional data to national data is also common practice. The calculation itself is relatively straightforward:

An LQ of 1.0 indicates that the share of employment in a given industry in the local region is the same as it is nationally. An LQ greater than 1.0 indicates a higher proportion of regional industry employment relative to the national average.

For airport policymakers, such data can help demonstrate the linkage between local economic activity and air service based on employment and wages. The basic uses of a location quotient include the following:

• Determining local or regional specialization by quickly and effectively identifying those industries or occupations that stand out because of their higher-than-average per capita employment.
• Identifying the local economy’s export industries or “traded” clusters. Industries with a high location quotient tend to export lots of goods and services out of the community, and therefore are most likely to need air transport services.
• Identifying industries and occupations that are under-performing in the local economy at least in terms of the relative level of specialization or uniqueness of the economic activity.

When considering an industry’s LQ, analysts should also consider the number of absolute jobs involved in the industry and the percent change over time. A high LQ signals a unique specialization, but the actual impact on the local economy largely depends on the number of jobs present in the area. A positive or negative change in an industry’s LQ will be much more impactful if the industry also employs a lot of people.

Other Measures of Regional Competitiveness

Many other metrics can influence perceptions of a community or region, thus affecting decisions about whether to move, relocate, or stay in an area. As a result, these metrics may be significant to an airport’s stakeholders, especially those directly working in local economic development. Some of these include a variety of metrics related to measuring Quality of Life (QOL) or understanding the relative appeal of a community.

QOL measures can include, but are not limited to, the following metrics:

• Educational levels
• Crime rates
• International connectedness (e.g., air service connectivity)
• Cost of living and housing affordability
• Strength and diversity of tax base (diversity affects the sustainability of the tax base if one sector takes a downturn)
• Considerations of mobility (e.g., congestion)

A lot of data related to these kinds of variables is publicly available (including from the same federal statistics agencies, like the Bureau of Economic Analysis and Census Bureau, that compile more basic socioeconomic measures) and can be analyzed, indexed, and compared across various geographies. At the same time, some metrics may not be widely available at the regional level or may require more involved analytical capabilities such as GIS software.

Further, QOL indicators are often quite complex multidimensional assessments that can be challenging to interpret or communicate to non-economists and a lay audience. Richard Young has noted in “Quality of Life Indicator Systems—Definitions, Methodologies, Uses, and Public Policy Decision Making” (citing Diener and Suh 1997) that at their core:

QOL indicators explore and identify what factors are important to the good life, which do not rest solely on wealth or gross domestic product (GDP).

However, there is no uniformly accepted way of “quantifying a quality” or measuring something like QOL. Analysts should be open to considering a range of variables and approaches to develop a holistic understanding of the QOL in their region of interest.

Some institutions have sought to enhance this process by consolidating or ranking several variables to produce a more comprehensive QOL assessment. For example, analysts with access to ESRI Business Analyst can refer to its Socio-Economic Index, which measures overall QOL by airport region and is defined uniformly across the country and regularly updated.

The ESRI Socio-Economic Index is a composite of select characteristics covering measures of financial well-being, educational attainment, and labor force characteristics such as employment status, occupation, and type of employment. The index itself is a ratio of the local area relative to the national average. Values above 100 indicate an above-average status.