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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l Y

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