Biography for William Swan

1. 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. Likes dogs and dark beer. • Scott Adams

2. Three Forecasting Mistakes • We now have a new forecasting method • We still have similar results William M. SwanChief EconomistBoeing Marketing

3. Boeing 11-Year World Airline Traffic OutlookShort Form of what we do for a living in my shop Growth is an Average Over Cycles GDP growth for world: 2.8% RPK growth for the world: 4.4% We predict these averages Tables, regional growth, charts, lots of stuff on resultant aircraft fleets: http://www.boeing.com/commercial/cmo

4. Underlying Theme • ATREND is a projection of past growth • A FORECAST includes reasons why • The FUTURE includes acts of will

5. My Forecast Could Have been a “Trend”

6. Three Lessons (our mistakes) • Do not let the model’s form determine the answer -- algebra can force interpretation • Statistics do not preclude the use of reason --logical demonstrations are also valid • Do not give up --statistics can see through scatter Plus our Bonus lesson: An example of business sleaze(“Occam’s Toothbrush”)

7. Do Not Let the Model’s Form Determine the Answer Our traditional formula: RPKs ~ GDP* Yield- is the “GDP elasticity”  is the “price elasticity” • Problems with algebra: • GDPdid not distinguish between sources of growth • -- per-capita wealth increase GDP or population growth GDP? • Yield is a poor surrogate for ticket prices • All growth MUST be attributed to GDP or Yield

8. Error #1:Travel Does Not Grow as GDP1.5 Cross-sectional data: Travel Share not rising with incomes: • Travel Share of GDP measured as ASK/GDP ratio • Data shows small negative correlation with per-capita income • No acceleration of travel share after joining middle class Time-series data confirmed pattern: • Growth of Travel Share was independent of growth of GDP • Based on Country-by-Country data Conclusion: • Travel grows linearly with GDP growth • Remaining 1/3 of travel growth is “something else” Useful question: “What Else?”

9. Air Travel Share of GDP Is Independent of Income

10. Independent Data Source Same Pattern

11. Same Data, Different Display: No “S” in Travel Per Person

12. Same Data, Different Display: Say Good-Bye to the S-Curve

13. GDP Elasticity Absorbed All Growth Lousy data on fares: price elasticity understated. Therefore, GDP picked up price effect: RPK(t) = GDP(t)* Yield(t)- became RPK(t) = GDP(t) but should have been ASK/GDP = GDP0 * F(t) Travel Share (ASK/GDP) grows with time (t) Time trend F(t) confounded earlier analyses because there wasn’t one in the algebra

14. Another Reason GDP Was Over-Emphasized Beware Heteroscedasticity • Traffic in short term responds to consumer confidence • Consumer confidence moves when GDP moves are large • Therefore large GDP moves show large traffic responses • Least-squared calibration weights large moves more Result: • Calibrations over-stated GDP elasticity of travel Long-term growth must expect average consumer confidence

15. The “What Else?” question Time trend is “other effects” • Price effects • More nonstops stimulate travel • Global trade stimulates travel Secular Trend in Travel Share of GDP

16. 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 2.2% yield decline is decline in fares: • Business fares have gone up • Pleasure fares have gone down, and quality to match

17. 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 2.2% yield decline is decline in fares: • Business fares have gone up • Pleasure fares have gone down, and quality to match

18. Humility: Measurement Biases Half the time an Economist talks, he talks about measures, not answers Taxes and airport fees seem to keep going up: • Added 0.4% /year to ticket price in US last decade • Greater increases for international flying • Reported “yields” are net to the airline • Actual fare changes had tax and fee increases The other messy nit – inflation: • In the US, CPI inflation is 0.3% higher than GDP inflation • CPI overstates inflation • 0.3% growth is not negligible • In the UK, similar answer but smaller difference • Elsewhere, not so bad Please do not compare GDP growth using GDP deflator with airline revenue growth without taxes deflated by CPI

19. Error #2:Value of Service Can be MeasuredIt has proven almost impossible to calibrate service elasticities • But that does not preclude the use of reasoning • Here is the reasoning: ASKs doubled with almost no growth in aircraft size: • Lots of new flights, new times, new places It could have gone the other way: • Just bigger airplanes would have saved 1.5%/year Cost Savings foregone represent value added by new services: • Market produced high-service result • Implication is that value exceeds cost savings Surprise!– Value growth exceeds fare reductions in size

20. Value of Better Service Approximated by Cost Growth 1985-1995 Schedules • Growth absorbed almost entirely with frequencies. • Foregone cost savings approximately 15% in 10 years. • Value created approximately 15% in 10 years • This should stimulate as much travel as a fare decrease

21. Humility: Service got WorseHalf the time an Economist talks, he talks about measures, not answers We estimated cost=value of more direct services and frequencies We did not estimate the loss of value associated with: • Lower reliability • More delays • Smaller personal space on airplane • Higher load factors • Worse food • Busier flight attendants • Worse airport access • Longer airport processing times • More trouble finding the best fare  So we may have overstated the net quality effect.

22. Error #3:Scatter is Not NoiseDo not give up the stats

23. Travel Grows With TradeInternational Trade drives some Air Travel Growth • Travel Share (ASK/GDP Ratio) grows with increased Trade: “Trade” measured as Imports+Exports as % of GDP Trade growing nearly twice as fast as GDPs •  Cross-sectional data significant: “Trade” explains some of the scatter in Travel Share • Time-series data also significant: Change in Trade creates change in Travel Service\ • Same ratios, either way

24. Bonus Lesson: An example of business sleaze Proof by Assumptions “Test”: (Occam’s Toothbrush) Introducing a technique often used in business

25. Occam’s Toothbrush Is there a reasonable set of assumptions that fit all known data AND Allow my answer to be “right”?

26. Final Surprise: Business Demand is growing almost as fast as Pleasure Demand (“Demand” is the demand curve, not the traffic count) Proof by Occam’s Toothbrush: “What is the most reasonable set of assumptions that allow data?” Business travel share (survey data) declines only 3% in 10 years • We expect trade growth to drive business travel • We expect service quality <=> business travel Overall traffic has been growing 5% annually Fares are declining only for pleasure demands What is minimum business demand growth beyond the 3% from GDP? -- it turns out pretty high

27. Business Share of Traffic Declines SlowlyWhat set of assumptions fits all the available data?

28. Conclusion: Data is a NuisanceContinually upsetting well-established platitudes Nobody can tell if a forecast is right Everybody can tell if a forecast has changed We have not changed total trends of growth We have changed: • Where in the world the growth may be • What we look for if trends are to change • How we explain it “It is better to light one poor candle than to curse the darkness.”

29. William Swan: Data Troll Story Teller Economist