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Dr. David Gillen, Steven Landau & Dr. Geoffrey GoslingResearch Objective: Quantify the Economic Benefit from Increased Productivity Due to Improved Airline Connectivity
Sampled Airports/Regions Multi-Airport Regions
San Francisco Bay Area SFO, OAK, SJCChicago metropolitan region ORD, MDWHartsfield-Jackson Atlanta International Airport
Cincinnati/Northern Kentucky International AirportLambert-St. Louis International Airport
Pittsburgh International Airport
Raleigh-Durham International Airport
Denver International AirportPhoenix metropolitan region PHX, AZASalt Lake City International Airport
Boston metropolitan region BOS, MHT, PVDPhiladelphia International Airport
Detroit Metropolitan Wayne County AirportSan Diego International AirportPortland International Airport
Tampa International AirportKansas City International Airport
Tulsa International AirportSan Antonio International AirportNashville International Airport
Connectivity MeasureElasticity (average)
RankRelative Weight
Domestic Destinations with Two or More Daily Nonstop Flights 0.0915 1 1.00
International Nonstop Destinations 0.0375 2 0.41Domestic Nonstop Destinations 0.0284 3 0.31
Percent of the World GDP Served Daily 0.0259 4 0.28
Domestic Destinations with Five or More Daily Nonstop Flights 0.0258 5 0.28
Domestic Airline Hubs Served Nonstop 0.0254 6 0.28
International Flight Departures 0.0182 7 0.20Percent of the World GDP Served Nonstop 0.0169 8 0.18Domestic Flight Departures 0.0164 9 0.18Percent of the World GDP Served Twice or More Daily 0.0161 10 0.18
Number of Airlines 0.0160 11 0.17
Table 1. Sampled Airports: 26 U.S. Airports in 20 Metropolitan Regions & 15 International Hubs
General Approach: Selected a representative sample of 26 airports in 20 metropolitan areas and 15
international hubs. Assembled datasets of air service measures for each sampled U.S. airport, socio-
economic characteristics of each region, and changes in multifactor productivity (MFP) by economic
sector for the years 1995, 2000, 2005 and 2010. Performed an analysis to quantify the effect of changes
in air service on changes in MFP.
Model Specification: The research approach was based on an empirical model that examines how
changes in air service connectivity result in improved productivity. The model specification is:
𝑀𝐹𝑃𝑖 = ∅ 𝐶𝑁′, 𝑍′ (1)
where multifactor productivity in industry sector i is a function of a vector of connectivity measures,
CN', and a vector of other economic factors, Z'.
Regression equations were developed to implement the general model shown in equation (1) for each
industry sector in a region and the set of air service and other economic variables for that region. These
regression models used a log-linear specification, as follows:
𝑙𝑛𝑀𝐹𝑃𝑖 = 𝛼 + 𝛽𝑗 𝑙𝑛𝐶𝑁𝑗 + 𝛾𝑚𝑙𝑛𝑍𝑚 + 𝜖 (2)
where the CNj are the different measures of air service. The variables included in the Zm vector were the
overall labor productivity index for the region, regional population, aggregate output for the region
(measured by GDP), and year dummy variables (which were not logged) for 2000, 2005 and 2010.
These variables were designed to capture factors that can influence the MFP growth in a given industry.
Regions and Industries: Table 1 lists the sample airports and regions used in this analysis, The
selection of metropolitan regions was designed to capture different types of airports including
large hubs, smaller hubs, non-hubs and airports that had been de-hubbed. Table 2 shows the
industry sectors that are based on two-digit NAICS detail.
NAICS Sector31-33 Manufacturing
42 Wholesale Trade
51 Information
52 Finance and Insurance
53 Real Estate and Rental and Leasing
54 Professional, Scientific, & Technical Services
55 Management of Companies & Enterprises
56 Administrative and Support; and Waste Management & Remediation Services
71 Arts, Entertainment, and Recreation
72 Accommodation and Food Services
Other*
International Hubs: Amsterdam, London Heathrow, Frankfurt, Munich, Paris Charles de Gaulle, Madrid, Hong Kong, Singapore, Shanghai, Beijing, Dubai, Seoul Incheon, Tokyo Narita, Copenhagen and Rome
*Agriculture, Forestry, Fishing and Hunting, Retail, Transportation and Warehousing, Educational Services, Health Care and Social Services, arts and Entertainment, Other Services and Public Administration
Table 2. Eleven Economic Sectors
Connectivity Elasticities: Table 3 shows the average elasticity across industries for each
statistically significant connectivity measure included in the model.
Note that aviation networks connect different industries in different ways and the relative
effect of improvements in connectivity on MFP varies across industries as well. For example,
increasing the number of domestic nonstop destinations has more than twice the effect on MFP for
manufacturing as for wholesale trade, with a model coefficient (elasticity) of 0.034 versus 0.015.
On average, considering only values that were statistically significant, the number of domestic
destinations served by two or more daily nonstop flights is the most important connectivity
measure affecting productivity.
The measure with the second highest elasticity (the number of international nonstop destinations)
would have to increase from its current value by 2.5 times the increase in the number of
domestic destinations served by two or more daily nonstop flights to have the same impact on
multi-factor productivity.
The variable Percent of the World GDP Served Daily points out that while adding flights or
destinations is important, these flights should be to important destinations in terms of the
overall level of economic activity (as measured by GDP) in the regions or countries served by
those destinations.
Table 3. Elasticity Per Connectivity Measure (Averaged Across All Sectors)
Overview Elasticities
The relative impact of each connectivity measure is illustrated in the fourth column of Table 3,
which compares the elasticity of each measure to the measure with the greatest impact on
MFP, namely the number of domestic destinations served by two or more daily nonstop flights.
The number of international nonstop destinations, the measure ranked with the second highest
elasticity, would have to increase 2.5 times of an increase in the number of domestic
destinations served by two or more daily nonstop flights to have the same impact on multifactor
productivity.
Below the top three measures, the connectivity variables fall into two groups with similar
elasticity values, those with elasticities in the range 0.025 to 0.028 and those in the range of 0.016
to 0.018.
Outcomes
Industry
Domestic Domestic Destinations
Number of Airlines
Nonstop Departures
Airline Hubs Served
Nonstop
Nonstop Destinations
Two or More Daily Nonstop
Flights
Five or More Daily Nonstop
Flights
Manufacturing $158 $85 $123 $356
Wholesale Trade $43 $51 $30 $64
Information $24 $19
Finance & Insurance $151 $226 $99
Real Estate & Rental & Leasing $95 $176 $180 $49
Prof., Scientific, & Tech Services $57 $112
Management of Companies & Enterprises $8 $26
Admin. & Support & Waste Management Services $11 $33
Arts, Entertainment, & Recreation $3 $4 $7
Accommodation & Food Services $0 $20
Other* $3 $272
Total $201 $453 $374 $686 $654 $119
Industry
International Percent of World GDP
Nonstop Departures
Nonstop Destinations
Served Nonstop
Served Daily
Served Twice or More Daily
Manufacturing $172 $56
Wholesale Trade $38 $6
Information $39 $23 $41
Finance & Insurance $42 $34
Real Estate & Rental & Leasing $236
Prof., Scientific, & Tech Services $82 $153
Management of Companies & Enterprises $7 $18 $16 $14
Admin. & Support & Waste Management Services $23 $95 $51
Arts, Entertainment, & Recreation $14
Accommodation & Food Services $19
Other* $100 $95
Total $192 $683 $68 $361 $71
Table 4 Impact of Changes in Different Connectivity Measures on Industry Value Added Assuming a 1% Increase in Connectivity (2010 $Ms)
Results shown in Table 4 identify which connectivity measures appear to have the strongest effect
on value added for different industries. The projected impacts in Table 4 represent the total impacts
across the 20 regions, and are based on an assumed 1% increase in each variable over the 20 regions.
Each connectivity measure shows different effects by economic sector. These impacts vary by the
elasticity value per connectivity variable of each sector and the relative size of value added in each
sector in the 20 regions. For example:
In Table 3, the number of airlines is ranked 11th in terms of the effect of this connectivity measure
on productivity based on the average elasticity values, but this measure has a fairly strong effect on
the manufacturing sector, which forms a large proportion of total GDP. Thus, the number of
airlines accounts for the third largest increase in value added for the manufacturing sector
($158 million) of all the air service measures.
The number of domestic airline hubs served Nonstop strongly effects the finance and insurance
sector, accounting for the highest amount of value added for this sector ($226 million) of all the air
service measures.
The highest overall impacts (all sectors) are increases in: (1) the number domestic nonstop
destinations; (2) international nonstop destinations; and (3) the number of domestic destinations
served by two or more daily nonstop flights.
TRB Paper # 15-5034
This research was part of
ACRP 03-28: The Role of
U.S. Airports in the
National Economy
Assumed nonstop connectivity changes for 26 airports plus improved connections between the 20 U.S. regions and 15 international hubs.
DAVID GILLEN
TRB 94th Annual Meeting | 2015
MEASURING THE RELATIONSHIP BETWEEN AIRLINE NETWORK CONNECTIVITY AND PRODUCTIVITY
Thank you to Mead & Hunt Inc. for the airport photographs.
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