Post on 24-Jul-2020
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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