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Evidence from the Airline Industry

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Evidence from the Airline Industry ABSTRACT MUTUAL FORBEARANCE AND PRICE DISPERSION: EVIDENCE FROM THE AIRLINE INDUSTRY by Christopher Alan Granquist We replicate the reduced-form analyses of Evans and Kessides (1994) and Ciliberto and Williams (2014) to find empirical evidence of multimarket contact increasing price levels in a market. We then apply the fixed-effects model to price dispersion but find inconclusive results. We follow in the footsteps of Ciliberto and Williams to use gate ownership at an airport as an instrumental variable. We find evidence of the power of the instruments through first-stage regressions and strength tests. The application of the instrumental variables provides interesting but inconclusive results for both price levels and price dispersion. MUTUAL FORBEARANCE AND PRICE DISPERSION: EVIDENCE FROM THE AIRLINE INDUSTRY Thesis Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Arts in Economics by Christopher Alan Granquist Miami University Oxford, Ohio 2020 Advisor: Dr. Charles Moul Reader: Dr. Jonathan Wolff Reader: Dr. Mark Tremblay c 2020 Christopher Alan Granquist This thesis titled MUTUAL FORBEARANCE AND PRICE DISPERSION: EVIDENCE FROM THE AIRLINE INDUSTRY by Christopher Alan Granquist has been approved for publication by Farmer School of Business and Department of Economics Dr. Charles Moul Dr. Jonathan Wolff Dr. Mark Tremblay Table of Contents 1 Introduction 1 1.1 Introduction . .1 2 Literature Review 1 2.1 Multimarket Contact . .1 2.2 Price Dispersion . .2 2.3 Related Markets . .3 2.4 Contemporary Work . .3 2.5 Contribution . .4 2.6 Data Sources . .4 2.7 Observations . .5 2.8 Dependent Variables . .6 2.9 Control Variables . .7 2.10 Multimarket Contact . .8 2.11 Instrumental Variables . 10 3 Results 11 3.1 Model . 11 3.2 Replication . 12 3.3 Price Dispersion . 12 3.4 First Stage Regressions . 13 3.5 Instrumental Variable Regressions . 14 4 Conclusion 15 4.1 Conclusion . 15 iii List of Tables 1 Summary Statistics ................................. 19 2 Number of Common Markets in 2014 Q1 ..................... 20 3 Average Contact Summary Statistics ........................ 20 4 Log Average Price OLS Regression ........................ 21 5 Gini Coefficient OLS Regression .......................... 22 6 Scaled Standard Deviation OLS Regression .................... 23 7 First-Stage Regression onto Average Contact I .................. 24 8 First-Stage Regression onto Average Contact II .................. 25 9 IV Regression .................................... 26 iv 1 Introduction 1.1 Introduction The reduction in competition due to threat of retaliation across an increasing number of markets by a pair of oligopolistic firms, known as mutual forbearance, is a well-documented economic phe- nomenon. While classic economic theory emphasizes unilateral market power in a single market, firms in the modern world span many markets, and from competition across markets arises a new game for the firm. Microeconomic theory suggests that a pair of firms may more successfully en- gage in tacit collusion when competing across multiple markets, and empirical literature reinforces this idea. The potential effects of collusion are well established. Chiefly, collusion may maintain higher prices than in a competitive market, and likewise collusion may enable a greater degree of price discrimination within a market. If price discrimination lowers the market quantity of goods below the equilibrium without price discrimination, overall market welfare will decrease. Hence, effects on both price and price discrimination from multimarket contact may decrease welfare. The airline industry is a favored industry of empirical industrial organization due to publicly available data and well-defined markets. Evidence for the effect of multimarket contact and its role in facilitating collusion begins with Evans and Kessides (1994),henceforth EK, who use airline data to find evidence of a positive effect of multimarket contact on prices. At the same time, Borenstein and Rose (1994), hereafter BR, use the airline industry to investigate the effects of competition on price dispersion. We believe it is a natural extension to use airline industry data to examine the effect of multimarket contact on price dispersion. We replicate models from the literature, and then apply the models to price dispersion as an independent variable. 2 Literature Review 2.1 Multimarket Contact The microeconomic theory of multimarket contacted was formalized by Bernheim and Whinston (1990), hereafter BW. BW produce an irrelevance result for multimarket contact; competition across markets would not affect firm strategy under assumptions of identical markets, identical firms, and constant returns to scale. In the absence of these assumptions, competition across mar- kets may have an effect on firm strategies. EK argue that the airline industry is an ideal industry to test for multimarket contact. The hub system for airlines encourages airport dominance, which differentiates production costs across markets. Firms also sort into national, regional, or low-cost carriers, with varying access to capital and production costs across markets. Finally, there are sig- nificant returns to scale since the cost of flying only marginally increases between flying a single passenger to flying at full capacity. 1 EK then test the hypothesis that prices in the airline industry are affected by multimarket com- petition using panel data from 1984 to 1988. EK use an OLS regression on log prices on a suite of controls and a constructed variable to measure multimarket contact, controlling for fixed effects. The multimarket contact variable constructed in EK, labeled AverageContact, is an important con- struction in future works addressing multimarket contact in the airline industry. The results con- clude that empirical evidence upholds the BW theory of multimarket contact and tacit collusion, such that markets whose firms compete in a number of other markets find significantly higher prices overall as compared to markets whose firms face a low amount of multimarket contact. EK also find that the inclusion of market fixed-effects have a significant negative effect on the magnitude of multimarket contact. Ciliberto and Williams (2014), henceforth CW, reexamine multimarket contact in the airline industry by revisiting EK. Using updated data from 2004 to 2007 combined with survey data concerning gate leases, CW explore structural parameters of tacit collusion. The chief contribution of this paper provides a structural analysis of the effect of multimarket contact using evidence from the airline industry. Relevant to our paper, they replicate EK’s reduced-form analysis and CW but note that the AverageContact variable as constructed by EK is itself endogenous. CW then explore a wider variety of control specifications as well as using number of gates leased by a firm and their competition at airports in the market as an instrumental variable for the AverageContact regressor, finding that the IV method is needed to discern a significant effect of multimarket contact on average prices. 2.2 Price Dispersion Concurrently to EK in 1994, BR investigate price dispersion in the airline industry. BR uses the Gini coefficient (defined in Section 3.4) as a measure of price dispersion. The results find that price dispersion cannot be entirely explained by cost variation and instead that price discrimina- tion is driving a significant effect of price variation. BR further explain possible sources of price dispersion, emphasizing that price discrimination may increase in the face of increasing compe- tition under a model of monopolistic competition. BR also use an OLS regression of Gini on a suite of controls as well as several variables to measure competition and then instrument the com- petition variable. The result of BR is a finding of a positive association of competition within a market to price dispersion, of which a significant portion of the price dispersion is attributed to price discrimination. Gerardi and Shapiro (2009), henceforth GS, revisit BR, using panel data and replicated cross- sectional data to find contrasting results to BR. Similar to BR, GS use an OLS regression of Gini on controls and various measures of competition, and then instrument the competition variable. GS’s results suggest that the actual effect of competition on price dispersion is negative, in line with standard oligopoly theory, and that the difference between BR and GS may be reconciled by 2 omitted variable bias in the cross-sectional work of BR. Specifically the effect of omitting distance may cause a significant positive bias to the effect of competition on price dispersion. This error is roughly caused by weak instruments, which GS are able to handle with a more appropriate set of instruments alongside panel data methods. 2.3 Related Markets Empirical evidence of the effect of multimarket contact extends beyond the airline industry. Jans and Rosenbaum (1997), for example, investigate the effect of multimarket contact on pricing in the cement industry. Jans and Rosenbaum examine panel data over 16 years for 25 points in the cement industry to find a significant effect of multimarket contact, specifically finding an increase in multimarket contact relating to an increase in prices above marginal cost. Parker and Roeller (1997) examine the mobile telephone industry, in part considering the effect of multimarket contact. Parker and Roeller are able to explain part of noncompetitive prices in the mobile telephone duopoly
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