Effect of Merger on Market Price and Product Quality: American and US Airways

https://​www.​justice.​gov/​atr/​horizontal-merger-guidelines-08192010#5c.

https://​www.​dallasnews.​com/​business/​airlines/​2013/​09/​10/​heres-us-airways-defense-of-its-merger-with-american-airlines.

See Robinson (1958) for a more detailed discussion of sources of economies of scale.

However, the same improvement in quality or cost savings can be achieved through the internal growth of an airline, and this improvement is not merger-specific as is described in the Horizontal Merger Guidelines by the DOJ.

Congestion externalities are created when airlines do not consider that adding flights may lead to increased delays for other air carriers. See Mayer and Sinai (2003) for more details.

The route networks of AA, US, and the overlapping markets are shown in “Appendix”.

Farrell and Shapiro (1990) analyzed the general conditions under which horizontal mergers raise prices.

Other studies that analyzed the relationship between concentration and market power in the airline industry include Borenstein (1989) and Abramowitz and Brown (1993).

Borenstein and Rose (2014) gave a very detailed overview of the U.S. passenger airline industry.

https://​www.​faa.​gov/​about/​history/​brief_​history/​.

For a detailed review of deregulation in the U.S. see Winston (1993, 1998).

https://​www.​justice.​gov/​opa/​pr/​justice-department-requires-us-airways-and-american-airlines-divest-facilities-seven-key.

http://​www.​frequentbusiness​traveler.​com/​2013/​12/​supreme-court-declines-to-block-american-us-air-merger/​.

Extremely low fares indicates that those tickets were purchased using frequent flier miles or some kind of promotion by the airlines. I also dropped the itineraries with “not credible” fares.

I have done the analysis with various different pre-merger periods, and the results are qualitatively the same.

Please keep in mind that the total number of observations for passengers, flight frequency, and delay are different because the data sources are different for these three types of data. Price and the number-of-passengers data are from DB1B; and flight frequency and number of seats data are from the T100 Database. The data regarding delay and cancellations come from On Time Performance Database.

See Ashenfelter and Card (1985) and Card and Krueger (2000) for more details about DID analysis.

I have not defined market size in terms of population of the origin and destination because it is well known that people from nearby cities may drive and fly from another city. In that case, population might not be a good indicator to define market size (Li et al. 2018). Nonetheless, I have provided the results in Table 14 in “Appendix” using population as market size, and the results are qualitatively similar. Also, I have not divided the markets by the number of competitors because number of competitors is endogenous; in one of my companion papers, Das (2018b), I show that entry and exit can happen even within a span of 2 years; so even in the short run, number of competitors is endogenous. Again, I have provided the results in Table 15 in “Appendix” using the number of competitors in the pre-merger period as the criteria to divide the markets, and the results are qualitatively similar.

For robustness purpose I have provided the results for non-stop and connecting markets separately in Tables 16, 17, and 18 in “Appendix”. The nature of competition may be different in non-stop markets compared to connecting markets. I find that the results are qualitatively similar to Table 7.

As a robustness check I have also defined divestiture as the number of gates sold to LCCs and I find similar results as shown in Table 19.

I have done several robustness checks such as including the year-quarter fixed effects and the origin and destination fixed effects and clustering with respect to destination to control for hub effects. I have included the results in Tables 20 and 21 in “Appendix”. The results are qualitatively the same and quantitatively very similar to Tables 7 and 8.