Biography for William Swan
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Transcript of Biography for William Swan
Biography for William Swan
Currently the “Cheap” Economist for Boeing Commercial Aircraft. Previous to Boeing, worked at American Airlines in Operations Research and Strategic Planning and United Airlines in Research and Development. Areas of work included Yield Management, Fleet Planning, Aircraft Routing, and Crew Scheduling. Also worked for Hull Trading, a major market maker in stock index options, and on the staff at MIT’s Flight Transportation Lab. Apparently has a hard time holding a steady job. Education: Master’s, Engineer’s Degree, and Ph. D. at MIT. Bachelor of Science in Aeronautical Engineering at Princeton.
© Scott Adams
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Inte
rna
tio
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ield
, 2
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3$
/Km
Estimated Fares
trend at -2.4%/year
reported yields
Prices, Fares, and Yields
William M SwanChief Economist
Boeing Commercial MarketingJuly 2003
What is to Come
• Yield Changes overstate Fare Changes
• Example Dramatizing Effects
• Hard Work on Vocabulary and Distinctions
• Hard Work on Real Data
• Real Data shows Declining Discount Fares
What is Yield?
• THE common measure of airline fares
• Defined as (Revenues/RPK)
• Data often is international yields– US carriers– Long, consistent source of data
• US dollar base avoids exchange rate issues
• Published government reports date to 1960s
“Econometrics”?
RPK = GDP * Yield-
RPK is air travel in Revenue Passenger Kilometers
GDP is Gross Domestic Product, size of economy
Yield is measure of prices
is income elasticity
is price elasticity
Regress in log-space and publish
Yield is an average: Average yield declines with more long trips Average yield declines with more discount (pleasure) trips
Under half of yield decline is decline in fares: Business fares have gone up? Pleasure fares have gone down, and quality to match
Yield Overstates Fare DeclinesYield is an Imperfect Statistic
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1975 1980 1985 1990 1995 2000 2005Inte
rna
tio
na
l Y
ield
, 2
00
3$
/Km
Estimated Fares
trend at -2.4%/year
reported yields
Concepts by Example
• An example will clarify concepts
• Shows “elasticity” calculated from yield
• Values differ with type of market change
Example Market: Tech Airways
• Long-haul 10,000km– 20 business @ $800 fare ($750 net of tax)– 80 leisure @ $350 fare ($300 net of tax)
• Regional 1600km– 60 business @ $350 ($316 net of tax)– 240 leisure @ $150 ($135 net of tax)
• Yield is $0.061/km (net of tax)
Less Work, More Play
• Leisure demands each up 20%
• No change in fares
• Rise in load factor
• Yield now $0.058, down 5%
• “Elasticity” (%RPK / %yield) = -4.1
• Mix of leisure vs. business trips changed
Globalization Triumphs
• Business and Leisure Long-Haul up 20%
• Growth from base case, not leisure case
• Fares not changed
• Load factor long-haul rises
• Yield now $0.058km (again), down 5%
• “Elasticity” = -2.9
• Mix of Long vs. Short trips has changed
Junk Fares Triumph
• New leisure fares of $300 & $113
• Leisure fare elasticity of -2.0
• 1/3 more long-haul leisure passengers
• 75% more regional leisure passengers
• Yield now $0.050/km (down 18%)
• “Elasticity” = -2.2
• Increased total revenues & load factor
Gouge the Business Market
• Business Fares raised to $850 and $400
• Business fare elasticity = -0.5
• Business passengers down 3% and 7%
• Yield now $0.0623/km (up 2%)
• “Elasticity” = -0.3
• Increased total revenues
General Fare Decrease
• Decrease all fares 5% (this time before-tax)
• Fare elasticities of -0.5 and -2.0, as before
• Yield now $0.058/km (yet again)
• RPKs up 8%
• “Elasticity” = -1.3
• This is a believable value
Review the Bidding
• Base Case Yield = $0.061• Increase Leisure Mix: $0.058, E=-4.1• Increase Long-Haul Mix: $0.058, E=-2.9• Decrease Leisure Fares: $0.050, E=-2.2• Increase Business Fares: $0.062, E=-0.3• Across-the-board Decrease: $0.058, E=-1.3
ALL THESE THINGS HAPPENED
Conclusions
• Yield can decline without fare changes– More long-haul trips– More leisure-fare trips
• “Elasticity” depends on which fares change– Decreasing leisure fares increases revenues– Increasing business fares increases revenues– Across-the-board decrease is what we imagine?
Agonizing Detail is Available
• US ticket sample data backs up yield totals
• Represents trip-by-trip ticket lift
• Allows fares by airport pair
• Allows distribution of fares within pair
• Economists “dream data”– Unique transaction level detail
• Domestic data is completely public
A Real Distribution of Fares“Typical” Atlantic Airport-Pair
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Defining Fares
• “Market” is airport-pair: example: SEA-TLS• “Fare” is one-way, net of taxes (unfortunately)• “Discount Fare” is 25%ile fare from distribution• “Business-A Fare” is 90%ile fare • “Business-B Fare” is derived using assumptions
– Discount fare is 67% of tickets
– Average fare is correct
• “Bus-B” approach worked better than “Bus-A”
Fare Distribution: Atlantic
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Quibble #1: Zero Fares
• “Average Fare” data includes frequent flyer trips at zero fare
• Our treatment eliminated these records
• All treatments eliminate unreasonably high fare records—they distort the averages
• Better a small, cleaner, sample
• Used only one-way or clear round-trip tix
US Domestic Fare Distribution
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Quibble # 2: Back-to-back
• Synthetic business round trips made– Purchase 2 round-trips with Saturday stays– Use only one leg of each for 3-day trip– Ticket sample fare is half of round trip– Actual revenue is 2x (a “hidden” business fare)– High no show on non refundables from this
• Distorts fare data, but not total yields
Fare Trend Calculations
• One observation=one airport pair > 24 tix.– Data is average, discount, or business fare
• Cluster all pairs by 250 mile block
• Get median fare and distance– Median avoids outliers
• Regress linear fare formula with distance– Clustering avoids overweighting short-haul
Headline Fare Values• Use fare trend formula
– E.g.: Fare = $110 + $0.05/km
• Exercise at fixed distance– 1600km for US domestic– 7100km for Atlantic
• Data for 10 years: 1991-2001– Third Quarter data– Huge processing, simple “fare” value result
Yield Declines Faster Than Prices
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Yield overstates Fare Declines
• Average fares down 0.4%/year– US domestic case example
• Reported yield decline is 1.4% per year– Many expected 2.1% per year– Missing 0.7% is CPI deflator overstating
• “Elasticities” based on yield understate– By factor of 3 or 4
Yield Decline in Discount Fares
• Discount Fare declined 1.2% per year– Regression slope of 11 years’ data– Fairly robust measurement
• Business-B Fare declined 0.1% per year– Dubious measurement and significance
• Yield decline driven by discount fares only
International Yield, Also
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Average Trend Price
Leisure Trend Price
Trend Price based on Reported Yield
International Case is Worse
• Mix of Atlantic, Pacific, Latin America– 42%, 43%, 15% in 1991 RPKs (US carriers)– 47%, 32%, 21% in 2001 RPKs (US carriers)– Major changes in distance and fare mix
• Yield down 3.5% per year– Average fares down 1.6% per year– Discount fares down 3% per year– Business-B fares down 0.5% per year
Costs Down 0.4% per Year
• Longer hauls mean lower $/ASK
• Smaller airplanes mean higher $/ASK
• Net is 0.4% per year
• Based on constant factor productivities
• Airline costs declined more– Increased productivity– Better airplanes, higher utilization, etc.
Flow-by-Flow Cost SavingsSaving
$/yr1985 Km
2000 Km
1985 Seats
2000 Seats
WORLD -0.4% 1048 1321 192 187
Atlantic 0.3% 6667 6932 320 281
Pacific -0.0% 7772 9150 347 346
Asia-Europe 0.1% 8928 9395 362 336
Other Long 0.2% 5104 5805 302 274
N.America -0.4% 921 1148 155 140
Europe -0.9% 643 821 145 138
Asia -0.3% 843 939 241 220
Other Short -0.3% 1103 1285 190 171
Conclusions
• Yield Declines overstate Fare Declines
• Fare declines driven by discount fares
• Business fares nearly constant
• Intrinsic cost declines less than fare declines– Based on changes in distance and airplane size – Unexplained share implies productivity gains
• Measurement requires excruciating effort