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Network Structure and Consolidation in the U.S. Airline Industry, 1990–2015

Abstract

We study the effect of consolidation on airline network connectivity using three measures of centrality from graph theory: Degree; Closeness; and Betweenness. Changes in these measures from 1990 to 2015 imply: (i) the average airport services a greater proportion of possible routes; (ii) the average origin airport is fewer stops away from any given destination; and (iii) the average hub is less often along the shortest route between two other airports. Yet, we find the trend toward greater connectivity in the national network structure is largely unaffected by consolidation—in the form of mergers and codeshare agreements—during this period.

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Notes

  1. For example, US Airways had a strong presence in the East coast, while United had a strong presence in the West coast.

  2. America West and Continental had a code-sharing agreement from 1994 to 2002. The agreement was signed as part of the restructuring plan for America West, while it was under Chapter 11 protection from 1991 to 1994. Northwest and Alaska had a codesharing agreement in the early 1990s as well. Both agreements were limited in scope. For research on these early agreements, see Bamberger et al. (2004) and Ito and Lee (2007).

  3. These are the mergers for which each merging party made up more than 1% domestic scheduled-service passenger revenues prior to merging.

  4. Alaska completed its acquisition of Virgin American at the end of 2016.

  5. For more a detailed theoretical treatment of competition and network effects in the airline industry, see Breuckner and Spiller (1991) and Hendricks et al. (1997, 1999).

  6. A related literature is largely concerned with characterizing properties of equilibrium networks either when a single agent (usually a firm) controls an entire network, or when individual nodes choose when to form links with other nodes (see Jackson and Wolinsky 1996; Bala and Goyal 2000; Hendricks et al. 1999). Another set of literature studies the effects of an existing network structure on agent decision-making. For example, Mossel et al. (2015) and citations within their paper study how network structure affects information flow.

  7. In our analysis we will have a time dimension, which is here omitted for sake of simplicity.

  8. We repeated our analysis treating MSAs as network nodes, rather than airports, by aggregating the network across airports within a metropolitan area. We treat the airports in Washington DC, New York City, Chicago, Dallas, and Houston each as a single node. We find that this has no qualitative effect and a negligible quantitative effect on our results and conclusions. However, we expect if the analysis went beyond just the network structure and examined consumer welfare more directly, there could be interesting differences depending on the substitutability of airports within a MSA.

  9. The definition counts each pair of airports twice to account for paths in both directions. If we re-write the formula to count each airport pair once in both the numerator and denominator, we get the same result.

  10. We use the U.S. Department of Commerce’s 2012 data to identify Metropolitan Statistical Areas in the U.S.

  11. We use the network analysis program that has been prepared by Grund (2015).

  12. See Borenstein (1992) for a comparison of the hubs of the airline industry in the early 1990s, and the ones that the network measures here presented identify.

  13. In the Betweenness regression we have included only airports for which Betweenness is non-zero.

  14. In our regression analysis of the effect of mergers, we leave out the merger of Trans World and American, because it will be impossible to distinguish the effect of this merger from the effect of the September 11th attacks, as the two events occurred within the same year.

  15. Per Table 1, we include approximately 42% of the airports when we analyze the Betweenness measure.

  16. Note that we exclude the merger of Transworld and American in 2001, because the merger and the terrorist attacks on September 11, 2001 both affected the industry in that year.

  17. We drop the month of the merger, as it is not clear whether this month belongs to the pre- or post- period.

  18. U.S. Bureau of Economic Analysis, Gross Domestic Product [GDP] , retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/GDP, June 11, 2017. \(JetFuelPrice_t\) is the U.S. Gulf Coast Kerosene-Type Jet Fuel price in dollars per gallon from the U.S. Energy Information Administration, retrieved from https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=pet&s=eer_epjk_pf4_rgc_dpg&f=m.

  19. This is in the same spirit as Morrison (1996), who looks at the effects of mergers 8 and 9 years after they were completed.

  20. So far in our analysis, we have seen Degree and Closeness trend together. This is intuitive, but is not necessarily always the case because—given a fixed number of nodes—average Closeness will reflect both the number and layout of links in a network, while average Degree is affected only by the number of links.

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Acknowledgements

Federico Ciliberto acknowledges the Quantitative Collaborative at the University of Virginia for financial support.

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Correspondence to Federico Ciliberto.

Appendix

Appendix

See Tables 12, 13, 14, 15, 16 and 17.

Table 12 Top 20 airports by standardized Closeness, by year
Table 13 Average merger effects: Degree
Table 14 Average merger effects: Closeness
Table 15 Average merger effects: Betweenness * 1[AlwaysBetween = 1]
Table 16 Average merger effects: IsBetween
Table 17 Average codeshare effects: all outcomes

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Ciliberto, F., Cook, E.E. & Williams, J.W. Network Structure and Consolidation in the U.S. Airline Industry, 1990–2015. Rev Ind Organ 54, 3–36 (2019). https://doi.org/10.1007/s11151-018-9635-y

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Keywords

  • Network
  • Airlines
  • Consolidation
  • Mergers
  • Codeshare agreements
  • Airports