Biography for William Swan Chief Economist, Seabury-Airline Planning Group. AGIFORS Senior Fellow. ATRG Senior Fellow. Retired Chief 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. 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

Boeing 11-Year World Airline Traffic Outlook Short Form of what we do for a living in my shop 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: Growth is an Average Over Cycles

Underlying Theme A TREND is a projection of past growth A FORECAST includes reasons why The FUTURE includes acts of will

My Forecast Could Have been a “Trend”

Three Forecasting Mistakes  We now have a new forecasting method  We still have similar results William M. Swan Chief Economist Boeing Marketing

Three Lessons (our mistakes) 1.Do not let the model’s form determine the answer -- algebra can force interpretation 2.Statistics do not preclude the use of reason --logical demonstrations are also valid 3.Do not give up --statistics can see through scatter Plus our Bonus lesson: An example of business sleaze (“Occam’s Toothbrush”)

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:  GDP did 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

Error #1: Travel Does Not Grow as GDP 1.5 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?”

Air Travel Share of GDP Is Independent of Income

Independent Data Source Same Pattern

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

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

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 = GDP 0 * 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

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

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

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 Yield Overstates Fare Declines Yield is an Imperfect Statistic

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

Hard Lesson: Work the Data It is easy to grab data a run with it It takes work to muck about in the data Learn what is being measured Play with the data, examine outliers You will gain more from good data than good modeling Professors only publish modeling

Error #2: Value of Service Can be Measured It 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

Value of Better Service Approximated by Cost Growth 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

Humility: Service got Worse Half 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.

Error #3: Scatter is Not Noise Do not give up the statistics

Cheating on Statistics what explains scatter? Quality of cuisine hypothesis -- untasted A “Statistically Significant” result is at 95% –Means 5% chance of being random coincidence Ran 40 regressions, found 2 “Significant” results –% women in the workforce Politically incorrect, do not pursue –International trade as % of GDP Tells a good story, makes sense, follow this up Moral of the story: statistics can find new ideas If in “publish or perish” world –Write two papers, get two brownie points

 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 Travel Grows With Trade International Trade drives some Air Travel Growth

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

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

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

Business Share of Traffic Declines Slowly What set of assumptions fits all the available data?

Conclusion: Data is a Nuisance Continually 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.”

William Swan: Data Troll Story Teller Economist