Airport Delays and Metropolitan Employment

I-TED 2014 – International Transportation Economic Development Conference

April 10, 2014 | Dallas, Texas

Paulos Ashebir Lakew, University of California, Irvine

Volodymyr Bilotkach, Newcastle University

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Airport Delays and Metropolitan Employment Motivation

Cost of airline delays in 2007 (in $ billion):

• Peterson et al. (2013): a 10% (30%) reduction in delays increases net welfare by $17.6 ($38.5) billion

Relevant Literature Themes1. Airport traffic and urban growth2. Determinants of airline delays 3. Cost of airline delays

Airport traffic and urban growth:

• Airport cities (Berg et al., 1996; Button & Lall, 1999)

• Airport traffic associated with higher service-sector employment and lower manufacturing employment (Brueckner, 2003; Sheard, 2014)

• Effect of air cargo traffic on urban development (Green, 2007; Button & Yuan, 2013)

DIRECTCOSTS

INDIRECTCOSTS

TOTAL COST

Carriers Pass. Economy(Demand)

JEC(2008)

19.1 12.1 9.6 (NA) 40.7

NEXTOR(2010)

8.3 16.7 4.0 (3.9) 32.9

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Airport Delays and Metropolitan Employment Highlights

What we do:

Quantify impact of delays on employment

• Collect data on airline delays, traffic, airports,

and metro-level employment

• Construct quarterly panel

• Airports aggregated to Metropolitan

Statistical Area (MSA) cross-sections

• 40 Periods (2003Q1-2012Q4)

• OLS (2SLS) estimation with MSA fixed-effects

(IVs for endogenous variables)

• Control for exogenous city features

What we find:

Cross-sectional results:

• Frequency and length of delays increase

Total and Service-related Employment

• Extreme-weather delays have a positive

effect on Total Employment

MSA Fixed-effects results:

• Significant downward pressure by delays

on Total, Service, and Goods Employment

• Increase in the share of carrier-controlled

delays reduces Total Employment

• Results hold in both cross-sectional

and fixed-effect specifications3

Airport Delays and Metropolitan Employment Empirical Framework

• Reduced-form relationship invoked between an MSA i’s employment E, outbound (inbound) traffic T, departure (arrival) delays D, and exogenous city features X, in quarter t:

𝐸𝑖𝑡 = 𝛽𝑇𝑖𝑡 + 𝛿𝐷𝑖𝑡 + 𝛾𝑋𝑖𝑡 + 𝜃𝑡𝑄𝑡 + 𝑢𝑖 + 휀𝑖𝑡 , (1)

• where 𝑄𝑡 , 𝑢𝑖, and 휀𝑖𝑡 denote time dummies, MSA-specific intercept, and error term

• 𝑋𝑖𝑡 includes MSA population, young and old population shares, wages, and temperature

• Equation (1) treats relationship between traffic, delays, and economic development as a contemporaneous one (see Brueckner, 2003)

• Potential endogeneity of airline traffic and delays addressed by a 2SLS estimation, using a set of instrumental variables included in 𝑋𝑖𝑡

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Airport Delays and Metropolitan Employment Socioeconomic Variables

Data source: U.S. Bureau of Labor Statistics (QCEW)

2-digit NAICS industry classifications used from BLS data (MSA level):

Dependent Variables (𝐸𝑖𝑡):

• Total employment (TOTEMP)

• Service employment (SERV)• Selected Subsectors

• Leisure & Hospitality employment (LEISHOSP)• Trade, transport and Utilities (TTU)• Professional-Business-Finance-Info. employment (PBIF)• Health, Education and Government (HEG)

• Goods employment (GOODS)• Selected Subsectors

• Manufacturing employment (MANUF)

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Airport Delays and Metropolitan Employment Traffic and Delay Variables

TRAFFIC

• Passengers and freight/mail tons departed (landed) at U.S. airports

• Airport location data from NTAD used to link US airports to corresponding MSAs

• Office of Management and Budget’s (OMB) 2009 CBSA county delineations used to complete crosswalk

DELAYS

• Minute-level schedule delays, gate-to-gate airtime, and flight-level measures for non-stop domestic operations of U.S. major carriers

• >15 minutes departure delays (by origin)

• >15 minutes arrival delays (by destination)

• Starting June 2003, cause-of-delay data: • Carrier controlled: Carrier and Late Aircraft

• Exogenous to carrier: Extreme Weather, National Air System (NAS), and Security

• Cancellations (by origin) and Diversions (by destination)

Data source: U.S. Department of Transportation (Bureau of Transportation Statistics) and NTAD (2012)Form 41 Traffic (T-100 Segment Tables) and On-time Performance databanks

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Airport Delays and Metropolitan Employment Instruments

• HUB indicates hub cities for passenger (cargo) carriers• HUB = 1 if a carrier at an airport serves at least 25 (20)

destinations/quarter (focus cities dropped), HUB = 0 otherwise• If hub city has multiple airports, HUB is equal to fraction of city’s

airports (to discount the hub airport’s share of traffic)

• Example: For Los Angeles-Long Beach-Santa Ana MSA, HUB = ¼

• SLOT denotes slot-controlled airports operating at capacity• DCA, EWR, JFK, LGA, and ORD

• LEISURE dummy for Las Vegas, NV and Orlando, FL• Hub cities for FedEx and UPS central-sorting airports

(Memphis, TN and Louisville, KY) captured by a SORT indicator

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Airport Delays and Metropolitan Employment Instruments

• PROXIMITY dummy• Captures traffic-diversion from small-to-large cities (for better services, network

connection, lower fares, facilities, etc.)

• Indicator of a small passenger (cargo) MSA that is within 150 miles of a large one• Small and Large MSAs identified based on their annual traffic output (k-means clustering)

• Smallest and largest airports in the airport identified within the small and large MSAs, respectively

• Dummy constructed to equal 1 if distance between these airports is <= 150 miles

• Airport-to-airport Great Circle distances calculated using NTAD airport coordinates

Small PASS. (CARGO) MSA: < 300K pass. (15K US tons of freight) per year

Large PASS. (CARGO) MSA: > 5 million pass. (175K US tons of freight) per year

Smallest airport in MSA

Largest airport in MSA

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Airport Delays and Metropolitan Employment Instruments

WEATHER

Data source: National Oceanic and Atmospheric Administration’s (NOAA) Global Historical Climatology Network (GHCN) stations

From selected weather stations located at (in vicinity of) airports in our sample –

• PRCP: Precipitation (rain and melted snow in mm), MSA average

• SNOW: and Snowfall (in mm), MSA average

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Airport Delays and Metropolitan Employment Controls

WEATHER

• Maximum January Temperature (TMAXJAN)• MSA averages of highest January temperatures recorded at the corresponding

airport GHCN stations (converted to degrees Celsius)

DEMOGRAPHIC

Data source: U.S. Census Bureau’s Intercensal Estimates

• Population (POP)

• YOUNG POP share: 14 and younger

• OLD POP share: 65 and older

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Airport Delays and Metropolitan Employment Preliminary results (by origin MSA)

• Impact of traffic on employment:• Cross-sectional results: consistent with Brueckner (2003) and Sheard (2014)

• Fixed-effects results: coefficient on TRAFFIC higher for Total and Goods Employment (0.11 and 0.42, respectively), lower for Service-sector Employment (0.06)

• Impact of departure delays and traffic on employment:• Cross-sectional results:

• Frequency (count of delays), length of delays (mean and median), and Cancellations all associated with higher levels of Total Employment and Service Employment

• Results hold for OLS and 2SLS estimations

• Goods employment unaffected by delays

• Fixed-effects results:

• Frequency and length of delays (and total sum of delayed minutes) put significant downward pressure on Total, Service, and Goods Employment

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Airport Delays and Metropolitan Employment Preliminary results (by destination MSA)

• Impact of arrival delays and traffic on employment:• Follow patterns similar to departure delays (by origin MSA)

• Cross-sectional results: • Frequency and length of delays increase Total Employment and Service Employment

• Fixed-effects results:• Delays put significant downward pressure on Total, Service, and Goods Employment

• Extreme Weather delays consistently have a positive effect on Total Employment (as their share increases, while reducing share of carrier-controlled delays)

• Results mostly hold in cross-sectional analysis

• Increase in share of carrier-controlled delays reduces Total Employment• Results hold in both cross-sectional and fixed-effect specifications

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