Biography for William Swan

1. Biography for William Swan Chief Economist, Seabury-Airline Planning Group. AGIFORS Senior Fellow. ATRG Senior Fellow. Retired Chief Economist for Boeing Commercial Aircraft 1996-2005 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. Education: Master’s, Engineer’s Degree, and Ph. D. at MIT. Bachelor of Science in Aeronautical Engineering at Princeton. Likes dogs and dark beer. (bill.swan@cyberswans.com) • Scott Adams

2. Prices, Fares, and Yields William M Swan Chief Economist Boeing Commercial Marketing July 2003

3. 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

4. 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

5. “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 Regressin log-space and publish

6. Yield Overstates Fare Declines Yield is an Imperfect Statistic 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

7. Concepts by Example • An example will clarify concepts • Shows “elasticity” calculated from yield • Values differ with type of market change

8. 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)

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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?

16. 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

17. A Real Distribution of Fares“Typical” Atlantic Airport-Pair

18. 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”

19. Fare Distribution: Atlantic

20. 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

21. US Domestic Fare Distribution

22. 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

23. 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

24. 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

25. Yield Declines Faster Than Prices

26. 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

27. 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

28. International Yield, Also

29. 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

30. 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.

31. Flow-by-Flow Cost Savings

32. 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