APPENDIX A

Economic Impacts: Details and Methodology

This appendix provides details on how the three types of economic impacts described in Chapter 4 were estimated.

Regional Economic Impacts

The values were derived from regressions that explain the variation in economic impacts from hundreds of airport economic impact studies collected in Appendix 3a of ACRP Report 132: The Role of U.S. Airports in the National Economy (55). The ACRP Report 132 appendix contains complete data on economic impacts, various activity levels, and other characteristics for 182 commercial service airports and 806 general aviation (GA) airports. The income and output values were updated to 2022 using the same consumer price index applied in the original study (79). Regressions were then run to find the best relationship between activity (and other factors) and the economic impacts.

Commercial Service Airports: The FAA defines commercial airports as ones with more than 2,500 annual enplanements (80). Using this definition to segregate the database, the following regression results were found to provide relatively tight relationships between enplanements and the three economic impacts: regional jobs, regional income, and regional economic activity. All three regressions report relatively high adjusted R-squared values and the coefficients for enplanements are highly significant and explain most of the variation in economic impacts.

The main findings are that on average,

• One hundred additional enplanements produce one regional job,
• One added enplanement adds $437 to regional income, and
• One added enplanement produces $1,299 in regional economic activity.

For relatively small changes in aviation activity, these values provide insights into the jobs, regional income, and regional activity that are affected if an airport continues to grow or if growth slows down.

Updating Dollar Figures

To update the impacts, calculate the expected change in enplanements (future-year enplanements minus current-year enplanements) and use the factors shown in the bullets above to estimate the change in regional jobs, regional income, and regional economic activity. For example, to estimate future regional jobs, divide the change in the number of enplanements by 100. To estimate changes in regional income or regional economic activity, multiply the change in the number of enplanements by $437 or $1,299. For GA airport, operations and based aircraft drive

economic activity. Using a future TAF forecast of operations, to estimate the change in jobs, divide by 183; to estimate the change in regional income and regional economic activity, multiply by $231 and $657 respectively. To estimate the effects of expected changes in based jets shown in TAF, multiply the change in based jets by 13.9, $666,126, and $2,188,262 to obtain estimates of the change in jobs, regional income, and regional activity. Then add the impacts from changes in operations and changes in based jets to get totals.

Regional income and regional economic activity are expressed in 2022 dollars. To derive future-year impacts, multiply the estimated impacts by the ratio of the future-year’s consumer price index divided by the 2022 value of 1.32 (Figures A-1 and A-2).

GA Airports: For GA airports, analysis suggested that GA operations and the number of jet aircraft based at an airport explained a good deal of the variation in economic impacts. The coefficients for both operations and based aircraft were highly significant. The main findings for GA airports are that on average:

• 183 additional operations produce one job; one additional based jet aircraft produces 13.9 jobs.
• 1 operation produces $231 in regional income; one based jet produces $666,126 in regional income.
• 1 operation produces $657 in regional output; one based jet produces $2.19 million in regional output.

For relatively small changes in aviation activity, these values provide insights into the jobs, regional income, and regional activity that are affected if an airport continues to grow or if growth slows down (Figure A-3).

Cost of Using Another Transportation Mode

For each commercial service airport, DB1B data were assembled for 2019. The data report:

• Origin
• Destination
• Fare
• Number of passengers
• Number of stops

Passengers connecting at the airport were stripped out so that the analysis could focus on the cost to local consumers if they had to make the same trips via other modes of transportation. Two additional variables were added to the database:

• Estimated aviation block time (meaning the time from gate departure to gate arrival).
• Flight distance.

In the base case, the full price of travel (FPTb) was calculated for each aviation trip in the database for each airport: fare + (block time x the value of time of $47.10). The average FPTb for domestic and seven international regions was then calculated.

If the same trips had to be taken by alternative mode(s), only surface modes would be available, as described in the following:

• For domestic trips and trips to Canada and Mexico, the consumer was assumed to travel by automobile at an average speed of 74 miles per hour (the high average speed was assumed to assure that the cost of the alternative mode was minimized). The out-of-pocket costs to operate the automobile were charged at the 2022 (second half) IRS rate of 62.5 cents per mile with an average of two passengers per car (81). Distance versus commercial aviation was increased by 20% to account for circuity.

• For all other trips, the consumer would drive to the nearest port with service to the destination and then take a cruise ship at the lowest fare. A cruise ship’s average speed was assumed to be 20 knots (23 mph).
• Because many trips via surface modes would take several days, some assumptions had to be made about how to value time on a 24-hour clock. The study team sought to minimize the cost of the other mode to make the conclusions conservative.
  • For automobile portions of the trip, the assumed travel time was multiplied by the value of time; any stopover time was ignored under the assumption that consumers would productively use the remaining time to sleep and/or undertake other activities. No hotel or other stopover costs were assumed.
  • For cruise ship portions of a trip, consumers would be underway for entire days; but the value of time is assessed against only 8 hours of each day, with the remainder taken up by sleep and other activities that consumers would undertake whether they were traveling or not.
• The trips were segregated into domestic trips and international trips in seven regions. The average FPT for the alternative mode (FPTm) was calculated for each region.
• The percentage increase was then calculated as (FPTm/FPTb)-1.

Updating: To update this analysis without changing the underlying trips, multiply dollar values by the ratio of the future-year’s consumer price index divided by the 2022 value of 1.32. To update the trips, use a new DB1B and repeat the analysis with then-year’s costs.

Cost of Using an Alternative Airport

Using the same DB1B database, and the base case FPTb from the previous analysis of other modes, the incremental cost to consumers of using a nearby commercial airport with approximately equivalent service was derived as follows:

• The alternative airport was identified as being the one closest with the same or better FAA hub classification. This meant that for large hubs the alternative was a large hub (medium hubs were assumed to be the alternate for truly isolated large hubs like HNL); in some cases, this meant lengthy drives to distant alternatives. For all other airports, the closest airport with at least the same level of service was adopted; often, this meant that the consumer would access a more distant airport but with better service (more destinations and more nonstop services).
• The distance to the alternative airport was estimated.
• The average speed to drive to the airport was assumed to be 55 mph. This assumption is less generous to the alternative airport than the one made for the alternative mode. In this instance, the study team wanted to recognize that often, consumers have a realistic choice whether to drive to a more distant airport with better service; also, many of these trips would be within congested metropolitan areas, and the average speed would be lower than via interstates between cities.
• The automobile time to travel to the alternative airport was valued as time to travel multiplied by the U.S. DOT value of time of $47.10 per hour.
• Out-of-pocket costs to operate the vehicle used the same IRS rate of 62.5 cents per mile as in the alternative mode analysis, and it was assumed that two passengers were in the car for each trip.
• To account for differences in the quality of service between the two airports, the percentage of originating travelers connecting on trips to and from the subject airport was compared to the percentage connecting to and from the alternative airport. The difference was then calculated as PCTm − PCTb and then multiplied by the U.S. DOT value of time ($47.10 per hour) and by the average connect time (1 hour) to derive the Connection adjustment. Note: The difference in elapsed time for a connection versus a nonstop may be closer to 2 hours once approach, taxi, and takeoff/climb increments are accounted for. But offsetting this in many cases, the

• alternative for a smaller airport is a larger hub where access time and out-of-pocket costs are likely much higher.
• The data were separated into domestic and international regions, and the percentage increase in the FPT due to using an alternative airport was calculated as (travel time x $47.10) + (distance x IRS rate) + or − Connection adjustment)/ FPTb.

Updating: To update this analysis without changing the underlying trips, multiply dollar values by the ratio of the future-year’s consumer price index divided by the 2022 value of 1.32. To update the trips, use a new DB1B and repeat the analysis with then-year’s costs.

Connectivity Benefits

The connectivity benefits to a community of increased air service (of different types) are derived using a model developed in ACRP Report 132 (55) and updated information drawn from the OAG. The methodology is summarized in Figure A-4 (taken from Chapter 4), which reports in the last column the gain to Austin’s regional GDP from different potential improvements in air service reported in the first column.

The data required to run the model were assembled from the OAG for a single week in January 2023. They are summarized in Figure A-5. These data were supplemented with data on city Metropolitan Statistical Area (MSA) GDP from the U.S. Bureau of Economic Analysis and the GDP of individual countries from the World Bank World Development Indicators (12/22/2022).

• The MSA GDP for Austin in 2021 was $198.3 billion. This is used in the last column of Table A-4 to derive the incremental change in GDP due to a change in air service.
• The international daily nonstops from Austin served countries producing 6.6% of the world’s GDP. For the analysis, it was assumed that Austin added daily nonstop service to Panama (which was not in the January OAG schedule although less than daily service to Panama City started later in 2023), which would increase its GDP served by about 1%.

The data are then input into column b in the top table. Column c of that table shows the assumed changes in air service. For example, at Austin, adding one domestic city with at least one nonstop service per week results in a 1.5% increase in that air service metric.

To derive the estimated changes in GDP due to a change in air service, the method reported at the top of column e in the top table was applied. For each type of change in air service, the coefficient in column a was multiplied by the percentage change in column d divided by 1% and by Austin’s GDP in millions of dollars. So, adding one domestic city with at least one nonstop service per week would add $72.7 million to Austin’s metro GDP: 0.000241973 x (1.5%/1.0%) x $198,300 million.

These data may be useful in describing the value of different types of air service and the consequences of continued growth or reduced growth in the future.

Updating: To create an analysis for your airport, run a current OAG and count up the number of flights in each of the categories shown in the above table. Look up the GDP of your city and the GDP of other foreign countries served from the sources cited below. Then repeat the analysis shown in the table on page 86. Specify the added service you want to analyze in column c. Then make the calculations as illustrated in the table.

To update this analysis without changing the underlying trips, multiply dollar values by the ratio of the future-year’s consumer price index divided by the 2022 value of 1.32. To update the trips, use a new OAG and repeat the analysis with the then-year’s GDP values for an individual city from the U.S. Bureau of Economic Analysis and the GDP of individual countries from the World Bank World Development Indicators.

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