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.