September 2013

Scheduled passenger air transportation in the Producer Price Index: improvements and trends


Another shortcoming of the fare code method is that frequent-flyer mile awards, referred to as zero fares in the industry, were excluded from the PPI because they do not generate revenue. Worse, the problem was exacerbated by rapidly rising numbers of frequent-flyer miles granted through avenues other than air travel, such as credit card spending, hotel stays, rental car bookings, and shopping at preferred vendors.7 The total number of frequent-flyer awards redeemed grew 61.7 percent from 1997 to 2005.8 With the fare code method, this increased granting of frequent-flyer awards was not directly shown as a price decrease in the data for the index.

As airlines changed or removed fare codes that the PPI tracked, the typical procedure was to substitute another fare code with similar characteristics. Base fare codes were frequently taken as substitutes, because they were usually offered each month. These codes, however, did not reflect periodic fare sales and were not always regularly used. As more and more fare codes were replaced over time, the PPI airline index ended up with a large number of base fare codes that tended to increase in a slow and steady manner, irrespective of market conditions.

As figure 2 illustrates, the limitations of the fare code method caused the PPI passenger airline index to fail to capture price declines that were reflected in the A4A Monthly Passenger Yield Index9 in the period from January 2000 to June 2004. During this period, the PPI airline index grew almost 20 percent while the A4A index declined more than 10 percent.

One method that BLS employed to deal with these issues was the introduction of a variation of the fare code method: the “fare code bucket” strategy, which was used from 2000 until 2004. With this approach, a group of fare codes with similar restrictions was presented to respondents on each survey form. When the originally selected fare code was either discontinued or not used in a given period, the respondent was instructed to provide instead the price for a ticket represented by one of the fare codes with similar restrictions. The price for the latter ticket was then directly compared with the price for the ticket covered by the originally selected fare code. The fare code bucket strategy allowed the index to capture some airline price changes that were executed by moving ticket inventory across similar fare codes, but the improvement was only transitory, because it still failed to capture deeply discounted temporary fare sales and zero fares.

Introduction of the new method

To better reflect the increased use of the Internet and discounted and zero fares, BLS analysts developed a new method that uses average prices, calculated by dividing total passenger revenue (excluding taxes and government fees) earned from the sale of all tickets on all of the sampled airline’s flights on a selected O&D by the total number of passengers who traveled on those flights. This price is referred to as the average revenue per passenger, or ARPP, and the associated method is called the ARPP method.


7 “Press room: Top 10 mileage earning methods,” Frequent Flyer Services (Colorado Springs, CO, updated daily),

8 “Press room: Yearly award redemption figures—U.S.,” Frequent Flyer Services (Colorado Springs, CO, updated daily),

9 The yield, or revenue passenger mile, is the average price, excluding taxes, paid to fly 1 mile. (See “A4A monthly passenger and cargo yield (fares per mile).”)

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About the Author

John L. Lucier

John L. Lucier is a supervisory economist in the Division of Industrial Prices and Price Indexes, Office of Prices and Living Conditions, Bureau of Labor Statistics.

William J. Page III

William J. Page III is an economist in the Division of Industrial Prices and Price Indexes, Office of Prices and Living Conditions, Bureau of Labor Statistics.