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References Bailey, Elizabeth E. "Deregulation and Regulatory Reform of U.S. Air Transportation Policy," in Bridger M. Mitchell and Paul R. Kleindorfer, eds., Regulated Industries and Public Enterprise: European and United States Perspectives, Lexington, Mass.: Lexington Books, 1980. Borenstein, Severin, "The Dominant-Firm Advantage in Multiproduct Industries: Evidence from the U.S. Airlines," Quarterly Journal of Economics, 1991, pp. 1237-1266. Borenstein, Severin, "The Evolution of U.S. Airline Competition," Journal of Economic Perspectives, 6, Spring 1992, pp. 45-73. Bryant, Adam, "From Frequent-Flier Miles, Currency for the Earthbound,” New York Times, August 21, 1994. Kleit, Andrew N., "Computer Reservations Systems: Competition Misunderstood," Antitrust Bulletin, Winter 1992, pp. 833-861. Michael E. Levine, "Is Regulation Necessary? California Air Transportation and National Regulatory Policy," Yale Law Journal, 74, July 1965, pp. 1416-1447. Michael E. Levine, "Airline Competition in Deregulated Markets: Theory, Firm Strategy, and Public Policy," Yale Journal on Regulation, 4, 1987, pp. 393-494. Steven A. Morrison and Clifford Winston, The Economic Effects of Airline Deregulation, Washington, D.C.: The Brookings Institution, 1986. Steven A. Morrison and Clifford Winston, The Evolution of the Airline Industry, Washington, D.C.: The Brookings Institution, forthcoming. Stephenson, Frederick J., and Fox, Richard J., "Corporate Attitudes Towards Frequent-Flier Programs," Transportation Journal, 27, Fall 1987, pp. 10-22. U.S. Department of Transportation, Office of the Secretary of Transportation, Study of Airline Computer Reservation Systems, DOT-P-37-88-2, Washington, D.C., May 1988.U.S. Department of Transportation, Office of the Secretary of Transportation, Airline Marketing Practices: Travel Agencies, Frequent-Flier Programs, and Computer Reservation Systems, Secretary’s Task Force on Competition in the U.S. Domestic Airline Industry, Washington, D.C., February 1990. U.S. Department of Transportation, Office of the Secretary, Secretary’s Task Force on Competition in the U.S. Domestic Airline Industry: Industry and Route Structure, Volume 1, Washington, D.C., February 1990.

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U.S. General Accounting Office, Airline Competition: Higher Fares and Reduced Competition at Concentrated Airports, GAO/RCED-90-102, Washington, D.C., July 1990. U.S. General Accounting Office, Airline Competition: Industry Operating and Marketing Practices Limit Market Entry, GAO/RCED-90-147, Washington, D.C., August 1990. U.S. National Research Council, Transportation Research Board, Winds of Change: Domestic Air Transport since Deregulation, Special Report 230, Washington, D.C., 1991.

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Figure 1

Data Sources and Variable Construction: Calculated by the author from the U.S. Department of Transportation’s Data Bank 1A (DB1A), a ten percent sample of airline tickets.

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Figure 2

Data Sources and Variable Construction: The number of effective competitors at the route level is calculated from the Department of Transportation’s ten percent sample of airline tickets (Data Bank 1A). Each airline’s share of passengers on each domestic route was calculated from a subsample of Data Bank 1A consisting of one-way tickets with two or fewer segments and roundtrip tickets with two or fewer segments on the outbound and return legs. These route-level measures were aggregated across routes based on the fraction of sampled passengers and passenger miles on each route. (See text for explanation of effective competitors.)

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Figure 3

Data Source and Variable Construction: Calculated by the author from the U.S. Department of Transportation’s Data Bank 1A (DB1A), a ten percent sample of airline tickets.

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Figure 4

Data Sources and Variable Construction: Calculated by the author from the U.S. Department of Transportation’s Data Bank 1A (DB1A), a ten percent sample of airline tickets.

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Figure 5

Data Sources and Variable Construction: Domestic yield is from Air Transport Association, Air Transport 19xx: The Annual Report of the U.S. Scheduled Airline Industry, various issues. Real yield was calculated by adjusting nominal yield using the Consumer Price Index.

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Figure 6

Data Sources and Variable Construction: Yield in each period (for 100 mile bands) was calculated from a subsample of the U.S. Department of Transportation’s ten percent sample of airline tickets (Data Bank 1A). This subsample was all domestic round trip ticket with two or fewer segments outbound and two or fewer segments return. (To correct for possible coding errors in the data airlines submitted, a fare screen was used to screen out tickets with fares that seemed unreasonably high or low.) Fares were adjusted for inflation using the Consumer Price Index.

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Figure 7

Data Sources and Variable Construction: See text. Figures in the graph are nominal, i.e., not adjusted for inflation.

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Figure 8

Data Sources and Variable Construction: The distribution of air fares was calculated from a subsample of the U.S. Department of Transportation’s ten percent sample of airline tickets (Data Bank 1A). This subsample was all domestic round trip tickets with two or fewer segments outbound and two or fewer segments return. (To correct for possible coding errors in the data airlines submitted, a fare screen was used to screen out tickets with fares that seemed unreasonably high or low. Thus, frequent flier tickets are excluded.)

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Figure 9

Data Sources and Variable Construction: Fares at each of the fifteen GAO concentrated airports are from a subsample of the U.S. Department of Transportation’s ten percent sample of airline tickets (Data Bank 1A). This subsample was all domestic round trip tickets with two or fewer segments outbound and two or fewer segments return. See text for more details.

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Figure 10

Data Sources and Variable Construction: Fares actually paid in 1993:4 are from a subsample of the U.S. Department of Transportation’s ten percent sample of airline tickets (Data Bank 1A). This subsample was all domestic round trip tickets with two or fewer segments outbound and two or fewer segments return. To correct for possible coding errors in the data the airlines submitted, a fare screen was used to eliminate tickets with fares that seemed unreasonably high. Tickets with low fares were not removed in order not to remove valid frequent flier tickets. However, the percentage of zero fare tickets was reduced by 1.7 percent of total tickets (2.5 percent of total passenger miles) to correct for coding errors. (See footnote 6 in this chapter for the origin of these correction factors.) For each ticket in the subsample, the fare actually paid was compared with an estimate of the "regulated" fare that would have been charged if fares were based on our adjusted Standard Industry Fare Level (SIFL) fare formula. The reason the passenger mile figure is more favorable than the passenger figure is because, on average, long-haul fares have fallen and short-haul fares have risen.

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Figure 11

Data Sources and Variable Construction: Load factor is the percentage of seats filled with revenue passengers. It is calculated by dividing revenue passenger miles by available seat miles (for both international and domestic service). The source of the data is Air Transport Association, Air Transport 19xx: The Annual Report of the U.S. Scheduled Airline Industry, various issues.

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Figure 12

Data Sources and Variable Construction: Calculated by the author based on data in the U.S. Department of Transportation’s Service Segment Data base, a (complete) census of airline flights.

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Figure 13

Data Sources and Variable Construction: The percentage and composition of connecting passengers was calculated from a subsample of domestic trips in the U.S. Department of Transportation’s ten percent sample of airline tickets (Data Bank 1A). This subsample consisted of one-way tickets with two or fewer segments and roundtrip tickets with two or fewer segments on the outbound and return legs.