ABSTRACT FLYING UNDER THE RADAR: MULTIMARKET CONTACT AND TACIT COLLUSION IN THE U.S. AIRLINE INDUSTRY

by Henry Jameson Shaneyfelt

This paper serves to identify the effects of multimarket contact on tacit collusion through the measures of average price and price dispersion. The assertion of the theoretical literature is that higher levels of multimarket contact are corollary to firms relaxing competitive pricing. It is consequently an expectation that higher prices and more price discrimination will be seen within markets comprising of firms simultaneously competing with rival firms in many markets. Implementing methods used in Evans and Kessides (1994) and Ciliberto and Williams (2014), I employ gate-use data to instrument for the average multimarket contact variable and address any potential bias from endogeneity. Additionally, squared gate-use instruments, which are novel to the existing literature, are included in the analysis. When implementing this new methodology, I find a significant positive relationship between multimarket contact and average price with estimates matching the existing literature. Using the same methodology, a significant positive relationship is found between multimarket contact and price dispersion which has hitherto not been observed. The results of this paper indicate that collusive behavior is indeed present within markets with higher levels of multimarket contact.

FLYING UNDER THE RADAR: MULTIMARKET CONTACT AND TACIT COLLUSION IN THE U.S. AIRLINE INDUSTRY

Thesis

Submitted to the

Faculty of Miami University

in partial fulfillment of

Master of Arts in Economics

by

Henry Jameson Shaneyfelt

Miami University

Oxford, Ohio

2021

Advisor: Dr. Charles Moul

Reader: Dr. Mark Tremblay

Reader: Dr. Peter Nencka

© 2021 Henry Jameson Shaneyfelt This thesis titled

FLYING UNDER THE RADAR: MULTIMARKET CONTACT AND TACIT COLLUSION IN THE U.S. AIRLINE INDUSTRY

by

Henry Jameson Shaneyfelt

has been approved for publication by

Farmer School of Business

and

Department of Economics

______Dr. Charles Moul

______Dr. Mark Tremblay

______Dr. Peter Nencka

Table of Contents 1 Introduction ...... 1 2 Literature Review ...... 2 2.1 Theory ...... 2 2.2 Empirical ...... 3 2.3 Contemporary Work ...... 4 3 Data ...... 4 3.1 Data Sources ...... 5 3.2 Observations ...... 6 3.3 Dependent Variables ...... 7 3.4 Control Variables ...... 7 3.5 Multimarket Contact Variable ...... 8 3.6 Endogeneity of the Multimarket Contact Variable ...... 8 3.7 Instrumental Variables ...... 10 4 Results ...... 11 4.1 Models...... 11 4.2 OLS Regressions ...... 12 4.3 IV First Stage Regressions ...... 13 4.4 IV Second Stage Regressions ...... 14 5 Limitations ...... 15 6 Conclusion ...... 16 Appendix ...... 17 References ...... 30

i List of Tables Table 1: Observed Airports and Airlines ...... 19 Table 2: Comparing Used Gates with ACP Gate Ownership ...... 20 Table 3: Summary Statistics ...... 22 Table 4: Number of Common Markets in 2016-Q2 ...... 23 Table 5: Prices & Multimarket Contact (OLS) ...... 25 Table 6: Price Dispersion & Multimarket Contact (OLS) ...... 26 Table 7: Multimarket Contact & Gates (OLS) ...... 27 Table 8: Prices & Multimarket Contact (2SLS) ...... 28 Table 9: Price Dispersion & Multimarket Contact (2SLS) ...... 29

ii List of Figures Figure 1: Cumulative Market Share of Sampled Airlines Over Time ...... 21 Figure 2: Non-Monotonicity Between Contact and Gates ...... 24

iii Dedication I would like to dedicate this paper to my parents, Paul and Jill, who have never ceased to support me in my academic pursuits.

iv Acknowledgements I would like to thank Dr. Charles Moul for all the help he has provided to me as an advisor. Without his guidance and trust in my ideas, this paper simply could not have happened.

v 1 Introduction Collusion is an intricate game. While it can beget considerable gains for those who execute it well, it can just as easily devastate those who fail to do so. Optimizing firms within a market undoubtedly find the proposition of collusion attractive, but maintaining a cartel through threats of punishment is by no means a simple task. The main reason for this is that historically the threats within a single market are typically not substantial enough to deter defection. However, the modern-day economy is seeing an increasing number of firms competing with rival firms in numerous differing markets at a given time. An intriguing attribute of this newly structured economy is the ability for firms to interact with rivals across markets. Aggressive pricing of a firm in one market may now result in a rival firm responding in a different, mutually served market. This occurrence, known as multimarket contact, has been a topic of interest for many economists who posit that it substantially increases the probability of collusive behavior. While this may seem difficult to follow at first blush, the intuition behind this theory makes it clear why this outcome is a plausible possibility. The general notion driving this theory is that multimarket contact creates the potential for larger-scale retaliations to firms who may try deviating from collusive outcomes. For the sake of example, imagine there are two firms, A and B, that simultaneously compete in markets 1, 2, and 3. If firm A were to attempt to deviate from a collusive outcome in market 1 by cutting prices, firm B could retaliate by not just dropping prices in market 1 but by also dropping prices in markets 2 and 3. Lest this outcome be realized, firm A will find it optimal to price less aggressively (i.e., collude) in all simultaneously served markets. Such is the essence of tacit collusion, where non-competitive outcomes are not explicitly coordinated but mutually understood and maintained. As the multimarket contact between two firms increases, the potential retaliation does the same. Due to this, firms that experience higher levels of multimarket contact may be less likely to behave competitively. As this probability of tacit collusion rises, theory indicates that monopolistic behavior is more likely to be observed. The specific characteristics of this behavior that I consider are higher prices and greater price discrimination. These serve as strong indicators for tacit collusion, as they would be impossible to occur in a competitive setting. Within a market containing competitive firms, the ability for a firm to price highly or price discriminate is ultimately eroded by its rivals’ pricing. Having

1 established this prediction, the challenge then becomes finding an industry that exhibits the needed characteristics. The domestic airline industry is rich with multimarket contact, as there are few routes in which only one carrier serves. It also has considerable variation in multimarket contact, making it a prime target for analysis. In this paper, I measure the effects of increasing multimarket contact on average price and price discrimination. To identify price discrimination, I measure the magnitude of price dispersion in a given market. Initial results replicating Evans and Kessides (1994) yield results that fail to indicate any real collusion. However, I address the potential endogeneity of the multimarket contact variable by instrumenting for it with gate-use data. Ciliberto and Williams (2014) introduced this application of gates as an identification strategy, and I add to it by accounting for non-linearity in the relationship between multimarket contact and gate utilization. When employing this technique, I find that increasing multimarket contact increases both average price and price dispersion. The results from this paper ultimately identify the characteristics of tacit collusion occurring at higher levels of multimarket contact, consistent with the prevailing theory.

2 Literature Review This paper benefits greatly from the existing literature looking at not just multimarket contact in the airline industry but at multimarket contact generally. The theoretical literature on multimarket contact set the foundation for the modern empirical work by tracing out conditions under which multimarket contact has a true impact on collusive behavior. While the empirical literature spans many industries, the work looking at the airline industry has arguably had the most methodological evolution over the past couple decades. Many of these works have yielded important results that have served to better our understanding of multimarket contact’s effects on collusion. To fully appreciate the results and conclusions of this paper, it is necessary to understand the literature that came before it. 2.1 Theory Bernheim and Whinston (1990) was the first paper to address the potential for increased multimarket contact causing non-competitive outcomes. They provide a mathematical framework to produce an irrelevance result. This irrelevance result proved that multimarket contact would not have an effect on the optimal strategy of the firm given identical markets,

2 identical firms, and constant returns to scale in production technology. Building upon this, the authors then prove that removing any of these three criteria results in multimarket contact having nontrivial effects on equilibrium behavior. The authors prove in this paper that the external threat of retaliation across all other simultaneously served markets was enough to alter a firm’s competitive strategy in a single market. Given that in reality, industries typically do not meet these three mentioned criteria simultaneously, this theory has clear applicative power. The task since this paper was published has been finding significant empirical evidence to test this theory. 2.2 Empirical One early attempt at such a test was Evans and Kessides (1994). This was the first paper to assert that the airline industry is a strong candidate for measuring the effects of multimarket contact on average prices. Using data from all domestic airlines between 1984 and 1988, the authors find that multimarket contact does indeed have a positive relationship with average price. The most notable of this paper’s contributions are the construction of the average multimarket contact variable, which depends on the amount of overlap among airlines’ routes, and the use of city-pair controls in OLS analysis. While Evans and Kessides (1994) found evidence that increased multimarket contact has a positive relationship with average price, their methodology did not address any potential endogeneity issues stemming from the multimarket contact variable itself. The first paper to do this was Ciliberto and Williams (2014), using data from all domestic airlines from 2006 to 2008. In this paper, the authors claim that the potential endogeneity of multimarket contact may come from the unobservable heterogeneity that effects the decision to either enter or exit a market. To address this, they instrumented for multimarket contact with gate ownership variables, asserting that the long-term leasing of airport gates makes them plausibly exogenous. This implementation led to substantially larger estimated impacts of contact in analysis, and it laid the foundation for the later structural work that related conduct to contact. Their finding that instrumental variables greatly increased the estimated importance of multimarket contact suggests that endogeneity bias from multimarket contact is driven by cost unobservables. A further explanation of this appears in the next section of this paper. Along with average price, there is an existing parallel literature looking at price dispersion in the domestic airline industry. Borenstein and Rose (1994) looked at the effects of increased competition on price dispersion by comparing measures of market concentration to a

3 pricing Gini coefficient, a well-known measure of statistical dispersion. The main findings of this paper are that price dispersion cannot be explained by cost variation alone, and increased competition is associated with higher levels of price dispersion. The authors contend that a significant portion of the price dispersion was coming from price discrimination arising in a context of monopolistic competition. Not all results in the literature concerning price dispersion were the same, however. In response to Borenstein and Rose (1994), Gerardi and Shapiro (2009) replicate their study with panel data and find contrasting results. Namely, this paper finds that price dispersion actually decreases as competition increases, which is more aligned with standard oligopoly theory. The authors claim that the cause of the different results was primarily that the cross-sectional data of Borenstein and Rose (1994) necessarily omitted distance from their controls. This omission is claimed to have positively biased their results substantially. The goal of this paper is to ultimately unify these two literatures by using what is known about average price and price dispersion to identify collusion. Since success has already been found in finding a significant positive relationship between multimarket contact and average price, this paper aims to replicate the previous results. By doing so, results then found measuring the relationship between multimarket contact and price dispersion have more validity. 2.3 Contemporary Work Kim, Kim, and Tan (2019) attempted a current application of Evans and Kessides (1994) by looking at the effects of multimarket contact on prices and also price dispersion. The authors specifically looked at the effects of on collusive outcomes. The paper found that multimarket contact is positively associated with prices, but the authors fail to find the same positive effect with price dispersion. While this result is discouraging, the authors do not address the potential endogeneity in the multimarket contact variable, which very well could be biasing their results.

3 Data While there is a great deal of replication that occurs within this paper, the method of data collection, the collected data, and the constructed data all make this study unique. The introduction of new instrumental variables to this literature serves as one of its largest

4 contributions. In this section, the original way in which the data were collected and the intuition that justifies the newly constructed variables are important to note before getting into the results. 3.1 Data Sources A great majority of the data collected and utilized comes from the Bureau of Transportation Statistics’ DB1B. The DB1B is a 10% random sample of flight itineraries from reporting carriers. The sample used for this analysis is quarterly data spanning from the second quarter of 2014, right after the merger of and U.S. Airways, to the end of 2019. Observations of prices, market distances, and the types of itineraries for the 23 quarters were taken directly from the DB1B. Additionally, several of the variables used in this analysis were constructed using this data. Unlike pricing and market data, airport gates data are not easily accessible. In fact, all data related to airport gates are privately owned by several large companies. One of the largest of these companies is Cirium, owner of the website FlightStats which contains flight records going as far back as 1993. Included in these data are the routes, carriers, and gates that were utilized in the flights of any date. To extract gate data, I developed a sophisticated web scraper. With the web scraper, a cross-section of 33 airports was collected from June 15th of 2016, which falls in the middle of the sample being analyzed. The airports and airlines in the sample can be seen in Table 1. To further justify the utilization of the cross-sectional data with the panel pricing and market data, the gates data were compared to Airport Competition Plans (ACPs). The Wendell H. Ford Aviation Investment and Reform Act for the 21st Century, also known as AIR21, mandated that medium and large hubs, as defined by the Federal Aviation Administration (FAA), are required to submit competition plans. This act was passed in March of 1999 and enacted a year later in April of 2000. It is worth noting that after the initial mandated ACPs are submitted, airports only submit them when they are seeking approval to expand or reconfigure. Also, these plans are not made available by the FAA and must be recovered from individual airports. This explains why ACPs for every airport are not available every year. These plans include information on leasing, airport expansion, and occasionally gate allocation. To ensure the accuracy of the scraped gate data, I compared them to five ACPs that were submitted within a year of the date in 2016 and included gate allocation. The results from this comparison can be seen in Table 2.

5 Looking at the comparisons, the scraped gate data appears to be accurate. The average percent error across airlines is 4%, and the average is being held up by the error in gate estimation for . There are two likely culprits of this slight inaccuracy. The first and most obvious reason is simply the possible changes in gate ownership between the time in which the data was scraped and the time the ACPs were submitted. The second and more subtle reason is the potential for airlines to use common-use gates along with their leased gates. Common-use gates are used by multiple airlines, and the data scraped does not distinguish between them and leased gates. Seeing that the inaccuracies in the Alaska Airline gate measurements were all positive, it is fair to reason that their utilization of common-use gates was the cause. It is also apparent that Allegiant Airlines does not show up in the comparison table. Since this was the smallest airline in the sample, it comes as no surprise that they do not show up on many ACPs. In this particular case, they were not listed as owning any gates in the five ACPs and did not appear when scraping for these five airports. Having acknowledged these points, the results still would suggest that gate utilization is very stable. Berry (1992) also suggests that airports sign long-term leases to aid in capital investment while maintaining low interest on debt issues, an assertion also made by Ciliberto, Murry, and Tamer (2020). This institutional detail and the comparison results allow for cross-sectional data to be used with confidence in the fact that gate-use variation over time is small. 3.2 Observations Observations in this sample are indexed by firm, city-pair, and quarter. A market is defined as a unidirectional route between two airports in a given time period. An example of this would be a flight from Columbus (CMH) to Atlanta (ATL) in the second quarter of 2014. Observations from the data are dropped if there are fewer than 100 passengers flying with a specific firm in a specific market in a given quarter. Since the DB1B is a 10% sample, the threshold becomes 10 sampled passengers, implying 100 total passengers. Observations are also dropped if the price is either lower than $25 or higher than $2,500, as these are likely incorrectly inputted data. These are the same thresholds used in Ciliberto and Williams (2014). Additionally, there are 23 distinct periods in the data that are indexed by t ϵ {1, …, T}, with T = 23. I sample the 10 largest airlines in the data based on passenger volume and they are indexed by j ϵ {1, …, J}, with J = 10. The airlines in this sample are listed in Table 1 along with the sampled airports. Due to consolidation within the airline industry, the cumulative market share of these airlines

6 steadily increases over the observed time period. This positive trend can be seen in Figure 1. By the end of 2019, these ten airlines had a cumulative market share of 75%. This makes this sample adequately representative of the airline industry as a whole. The omissions from this sample include very small-scale airlines such as SkyWest Airlines and which individually do not add substantial observations. With the ten airlines in the sample, there are 1,056 distinct markets in the data that are indexed by m ϵ {1, …, M}, with M = 1,056. The summary statistics for all variables can be seen in Table 3. 3.3 Dependent Variables The dependent variables in this analysis are the Gini coefficient and the natural log of the average fare. As stated previously, the Gini coefficient is a measure of statistical dispersion which is often used to measure income inequality. However, it lends itself well to measuring price dispersion. It ranges from 0, which indicates all consumers pay the same fare, to 1, which indicates one consumer paid a positive fare and all others flew for free. In the analysis, the Gini coefficient is scaled up by 100. The natural log of average price will then be used to measure the percent change in prices. 3.4 Control Variables All of the controls used in this analysis come from Evans and Kessides (1994) and Ciliberto and Williams (2014). The controls are the natural log of distance, the natural log of distance squared, route market share, airport market share, percentage of tickets that are direct, percentage of tickets that are roundtrip, and hub status. Including distance captures the consumer’s decision to travel, as it may change with differing distances. The intuition justifying the inclusion of the natural log of distance squared is that, as the distance of travel increases, flying becomes a much more attractive option relative to driving. That being the case, there is likely going to be some non-linearity associated with the distance variable. The route market share variable is calculated as the percent of tickets sold by a firm in a specific market-period pair. The airport market share variable is the average of the route market share variables at both endpoints in a market. Ciliberto, Murry, and Tamer (2020) also discuss the endogeneity of variables of this nature with the same intuition that was used for claiming the endogeneity of multimarket contact.

7 To account for possible pricing variation across itinerary types, the percentage of tickets which are direct and the percent of tickets which are roundtrip are utilized as controls. Also, the hub variable is the percent of flights in which either the origin or destination of the flight was a hub for the carrier. This control accounts for any cost advantages or market power a firm may have at a hub airport. 3.5 Multimarket Contact Variable For the average multimarket contact variable, we use the construction introduced in Evans and Kessides (1994). To calculate this, a contact matrix must be constructed first. The contact matrix Ct = (cjmt) for every market and period pair, where

c = δδ for j, k ϵ {1, … , J } The δs are binary variables indicating whether or not firm j or firm k is serving that market during that time period. The resulting matrix contains elements with the total amount of simultaneously served markets between two firms. An example of the contact matrix can be seen in Table 4. In this matrix taken from the second quarter of 2016, we see that Delta (DL) is a rival on 97.6% of routes flown by American Airlines (AA) during that quarter. The expectation from this is that prices and price dispersion will be higher in their mutually served markets. The opposite is expected between Alaska Airlines (AS) and (NK), where Spirit is only a rival in 30.3% of Alaska’s routes in that quarter. The contact variable is constructed using the following equation: 1 ������� = �δδ [�(� − 1)]/2

In this equation, � is the number of firms serving market m in quarter t. This variable is then scaled down by one thousand just as it is in Evans and Kessides (1994). 3.6 Endogeneity of the Multimarket Contact Variable As mentioned previously, there is potential for the results to be biased due to multimarket contact being endogenous. While Ciliberto and Williams (2014) identify the existence of this potential, the authors do not provide any explanation for why this may be. In order to fully motivate the employment of instrumental variable analysis, it is necessary to establish and detail a hypothesis for the endogeneity.

8 There exist idiosyncratic time-varying unobservables by city-pair. The idiosyncrasy explains why the time fixed effects do not capture them, and the variance across time explains why city-pair fixed effects do not capture them either. For conceptual purposes, these unobservables can be classified as either demand or cost. Since the intuition is more straightforward, I will focus on the endogeneity bias for average fare. Suppose that a city-pair is subjected to a positive demand shock. Additional airlines will consequently serve this city-pair, presumably increasing the level of multimarket contact. Even if this increase in multimarket contact had no impact on the average fare, the city-pair can expect a higher average price. This occurs provided that industry costs slope upwards. A simple OLS regression in this scenario will (incorrectly) attribute this price increase to the greater multimarket contact. In the case of demand shocks, the OLS estimate of the multimarket contact variable is positively biased. Now suppose that a city-pair is subjected to a positive cost shock (i.e., costs are higher). This results in airlines leaving the city-pair, thereby decreasing the level of multimarket contact. Even if this decrease in multimarket contact has no impact on the average fare, the city-pair can expect a higher average price. This reflects the higher costs in the market. A simple OLS regression in this scenario will (again incorrectly) attribute this price decrease to the lower multimarket contact. In the case of cost shocks, the OLS estimate of the multimarket contact variable is negatively biased. These examples rely on the notion that entry into city-pair markets is relatively easy. Without evidence for this, the hypotheticals cease to be plausible and the claim of endogeneity bias ceases to be credible. Fortunately, relatively easy entrance into city-pair markets is observed. Berry (1992) notes that market entry is substantially easier for firms with previous presences at both ends of a market. Since incumbent airlines have a fair amount of influence in airport decisions, they can stop entry through the prevention of airport expansion or the construction of new gates. However, since the airlines in this sample have a consistent presence in the sampled airports, the ability to enter new city-pair markets is not hindered. A similar endogeneity argument appears in Manuszak and Moul (2008), which considers the endogeneity bias in a pricing equation based on the number of firms present in a market. While this paper looked at the office supply industry, the principles are the same. The examples

9 and the evidence from the papers suggest that this endogeneity claim is credible. Thus, the instrumental variable analysis approach is warranted. 3.7 Instrumental Variables The instruments used for multimarket contact are similar to those used in Ciliberto and Williams (2014). These include the percentage of gates used by an airline at the origin, the percentage of gates used by an airline at the destination, and the percentage of gates utilized by Southwest at the origin and destination. The intuition behind these is again that the number of gates utilized by a carrier is resistant to shocks due to the long-term nature of airline-airport leasing agreements. It is worth noting that this paper, unlike Ciliberto and Williams (2014), distinguishes between origin gates and destination gates. While Ciliberto and Williams (2014) only looks at the collective share of gates an airline owns at the origin and destination, this paper disaggregates this measure between origin gates and destination gates to observe any potential differences in effects. There are three additional instrumental variables used in this analysis that are new to the literature. These variables are the percentage of gates used at the origin squared, the percentage of gates used at the destination squared, and the percentage of gates utilized by Southwest squared. While Ciliberto and Williams (2014) has a credible identification argument for the usage of gates as an instrument for multimarket contact, the following reasoning suggests that they may not have examined the full relationship between the two variables. The intuition behind these inclusions is that as the number of gates utilized increases, the more likely the airport is going to be regional and one airline will serve it primarily. Following this logic, the highest level of multimarket contact should occur when all carriers are equally competing in the same market. Additionally, the notion of non-linearity is justified by the fact that a firm with a monopoly in a market has an average multimarket contact of 0. An application of Rolle’s theorem proves that there must be at least one turning point in the effect of gates on average multimarket contact. Evidence for this claim comes in the form of Figure 2, where there appears to be a clear turning point in the relationship between contact and gate utilization. The dashed lines in the figure serve to fill what the data misses with the previously stated mathematical intuition. While the figure does not appear to show a purely quadratic relationship, it does warrant the use of squared instruments to account for the non-monotonicity.

10 Additional justification comes from Dieterle and Snell (2016), which looked at the effects of the inclusion of squared instrumental variables in papers which utilized only linear instrumental variable analyses. The authors find that several of the papers that they replicated have significantly different results when the squared instrument is included. The results of this paper coupled with the trend in the data appear to prove that the novel instruments are not only necessary for analysis but an important contribution to the literature.

4 Results 4.1 Models The base models used are reduced form and follow the approach of Ciliberto and Williams (2014). The dependent variables for airline j in city-pair m during quarter t are regressed onto average multimarket contact, the vector of control variables, and the fixed effect variables. The following equations describe the OLS models:

ln(Average Fare) = β ∗ Contact + γ ∗ Controls + ψ + µ + η + ϵ

Gini Coefficient = β ∗ Contact + γ ∗ Controls + ψ + µ + η + ϵ

In these models, β is the coefficient of the average multimarket contact variables, and γ is the vector of coefficients of the vector of controls. The fixed effects for firm, city-pair, and quarter are represented by ψ, µ, and η, respectively. Finally, the idiosyncratic error term is represented by ϵ. Tables 5 and 6 display the OLS estimates for average price and price dispersion. Because a city-pair’s distance is time-invariant, it is impossible to simultaneously include distance and city-pair fixed effects. Similar to Evans and Kessides (1994), each table’s Columns 1-2 employ distance and Columns 3-4 omit distance and include city-pair fixed effects. The changes between these specifications illustrate the potential importance of misspecification of the distance functional form or of other city-pair time-invariant unobservables. Columns 1 and 3 are the replication of Evans and Kessides (1994), as they are distinguishing the differences in estimates when accounting for city-pair fixed effects. Columns 2 and 4 largely mimic Ciliberto and Williams (2014) by omitting the potentially endogenous route market share and airport market

11 share, though it is worth noting that their set of controls has key differences to the set used in this paper. In addition to the OLS, there are first and second stage regressions for the instrumental variable analysis. As mentioned earlier, there is potential for the route market share and airport market share variables to be endogenous. As a result of this, these variables are excluded from all of the specifications. Also, the lack of time variation in the gates data results in multicollinearity with the city-pair fixed effects. The city-pair fixed effects are consequently dropped from the instrumental variable analysis and replaced with log-distance and its square. The following equations describe the two-stage least squares model:

First Stage:

Contact = β ∗ Instruments + γ ∗ Controls + ψ + η + ϵ Second Stage:

ln(Average Fare) = β ∗ Contact + γ ∗ Controls + ψ + η + ϵ

Gini Coefficient = β ∗ Contact + γ ∗ Controls + ψ + η + ϵ

In the second stage specifications, average multimarket contact is instrumented with origin gates, origin gates squared, destination gates, destination gates squared, Southwest gates, and Southwest gates squared in multiple combinations. The justification for these specifications appears in the first stage results section. 4.2 OLS Regressions To understand the interpretation of these results, the nature of the unscaled average multimarket contact variable must be understood. To increase this variable by a unit, enough additional markets must become simultaneously served to move the average up by 1. Since this variable is an average, the number of additional markets needed for this one-unit increase is equal to the number of firms competing in the market (e.g., a market with 2 competing carriers only needs 2 additional simultaneously served markets). Having acknowledged this, interpreting these results becomes much less complicated. The results of the six specifications in Table 5 do not tell much of a story. While average multimarket contact has a positive relationship with the log of average fare, the effect is conspicuously small and statistically insignificant in some specifications. For the average price, a

12 new rival firm entering 100 new markets only begets a 1% increase in price at most. This, however, is similar to the initial results found in both Evans and Kessides (1994) and Ciliberto and Williams (2014). As in Ciliberto and Williams (2014) but unlike Evans and Kessides (1994), the inclusion of city-pair fixed effects yields no significant relationship between contact and average fare. The results of Table 6 which had regressions with the Gini coefficient as the dependent variable also tell a counterintuitive story. These results would indicate that price dispersion actually decreases with increased multimarket contact. These results not only contradict theory, but they also are not notable. A new rival firm on 100 markets only decreases price dispersion by 0.25 percentage points at most. Overall, these initial regressions would point to average multimarket contact not having any economically significant or intuitive impact on prices or price dispersion. 4.3 IV First Stage Regressions The most noteworthy result of the first stage regressions in Table 7 is the effect that the squared instrumental variables has on the gates variables. The correction of the attenuation bias on each of the gate variables is clearly substantial. It is also worth noting that this trend persists when looking at different specifications. The statistical significance of the squared instruments in the first stage serves as the ultimate argument for their inclusion. Having not been implemented in this literature before, this is an important finding. It is also evident from these results that there are differing effects on multimarket contact between origin gates and destination gates. This finding appears to be a minor contribution, as Ciliberto and Williams (2014) does not address this possibility. The coefficient on the own-gates variables, when accounting for non-linearity, indicate a significant positive association with the average multimarket contact variable. On the other hand, the Southwest gates variable has a negative association with average multimarket contact. The intuitive response to this result is that the carrier in this variable has, on average, a lower number of gates and presence in the domestic airline industry than the bigger airlines in the sample. The increase in the percentage of Southwest gates means that there are lower percentages of gates held by carriers with larger presences such as American, Delta, and United. It is also worth noting that multimarket contact is decreasing with the distance of the city-pair over all relevant distances.

13 4.4 IV Second Stage Regressions The second stage regressions in Tables 8 and 9 yield significant and meaningful results. In contrast to the OLS results, average multimarket contact has a significantly positive effect on both average price and price dispersion. This appears to confirm the previously stated notion that cost unobservables are driving the endogeneity bias in the multimarket contact variable. Given that this is not a result that has been able to be achieved in contemporary research, the results have significant meaning. Interpreting the results, a new rival in 100 markets is associated with an increase in prices of between 30-38%, ceteris paribus when including the squared gates. This result matches the results of Ciliberto and Williams (2014), which found approximately a 30% increase in average price with comparable increases in multimarket contact. It appears that the inclusion of the squared instruments corrects for positive omission bias as it brings the estimates down and closer to what has been found previously in the literature. These results are much more economically significant than the OLS estimates from Table 5 Column 2 that are also displayed in Table 8. Looking at the effects of distance, average fares appear to increase with distance for all flights over 50 miles. Along with this result, a new rival in 100 markets is associated with an increase in price dispersion between 0.5-1.1 percentage points, ceteris paribus. This result deviates from the contemporary literature, as it reveals a statistically significant and positive relationship between average multimarket contact and price dispersion. It again appears that the squared instruments bring down the estimates, correcting for positive omission bias. These results are of much larger magnitude and have a different sign than the OLS estimates from Table 6 Column 2 that are also displayed in Table 9. Looking at the effects of distance again, price dispersion increases with distance for all short flights (Distance<1,500 miles) and decreases with all long flights (Distance>1,500 miles). This result indicates that price dispersion may not be as relevant in coast-to-coast flights. It is also worth highlighting the other coefficients seen in the second stage results. Direct flights are associated with higher prices and price dispersion. The opposite is seen for roundtrip flights, as they are associated with lower prices and price dispersion. These essentially match the findings of both Evans and Kessides (1994) and Ciliberto and Williams (2014). The hub variable

14 is also associated with higher prices and price dispersion. This is consistent with the literature and the hypothesis of potential market power and cost benefits. Along with these results, there were two tests used to validate the results of the instrumental variable regressions. The first of these two is the Stock-Yogo test, which tests to see if the instruments are strong predictors of the variable that is being instrumented. The threshold for this test is an F-stat of 10, so none of the instruments used were weak predictors. The other test implemented was the Wu-Hausman test, in which the endogeneity of the average multimarket contact variable is measured. If the estimates that came from the base model were similar enough to those of the second stage regressions, then average multimarket contact may not be endogenous at all. The interpretations of this chi-squared test’s results are fairly straightforward, as a p-value less than 0.05 is all that is required for the instrument to be exogenous. The results show that average multimarket contact was indeed endogenous and lends credence to the utilization of this methodology.

5 Limitations There are several limitations in this paper to be acknowledged. While the sample generated from the ten carriers and 1,056 unique city-pair markets is substantial, the addition of other airlines and city-pairs can only improve the robustness of the results. The inclusion of these observations could further account for lower levels of multimarket contact, giving a more accurate picture of the effect of multimarket contact across all levels. Given the accessibility of the data, the effort required to make these addenda would be trivial. While the limitation in data from the DB1B is noteworthy, the most glaring data deficit lies with the gates. Subject to financial and temporal constraints, cross-sectional data was the most that could have been collected. The improvements that can come from panel gate data are by no means negligible. Being able to account for changes in gate utilization over the observed time periods would add an entirely new dimension to the analysis. Consequently, the changes in gate use would undoubtedly correct the slight inaccuracies that the instruments in this paper have. The correction for this inaccuracy can also come in the form of gate ownership data. The inaccuracies that came from counting common-use gates would be remedied by using data reflecting true gate ownership. While these improvements may not yield large differences in the results, they would certainly further solidify the findings this paper has on the effects of contact on conduct.

15

6 Conclusion As I mentioned before, collusion is difficult to maintain. However, it is clear that it becomes far less tricky to maintain when threats can be made across several mutually served markets. As a result, firms are expected to respond to this changing set of incentives and engage in more collusive outcomes. It is now just a matter of detecting it. The goal since the advent of Bernheim and Whinston (1990) has been to prove that the theory occurs in the real world. The empirical work in this literature has been using several unique methods in attempts to align with theory. This paper not only is consistent with theory, but it also implements a novel methodology that significantly augments the accuracy of the empirical side of this literature.

16 Appendix Airport Competition Plans (ACPs) and Airport Websites: Atlanta International Airport (ATL) 2016: https://www.atl.com/wp-content/uploads/2016/10/ATL-Competition-Plan.pdf Buffalo Niagara International Airport (BUF) 2017: https://elements.nfta.com/media/2590/buf-fy2017airline-competition-plan.pdf Nashville International Airport (BNA) 2016: https://flynashville.com/wp-content/uploads/2020/02/FAACompetitionPlanUpdate.pdf Charlotte-Douglas International Airport (CLT) 2016: https://assets.ctfassets.net/jaw4bomip9l3/1I8wCWpSVUUNO83NMFL22j/6c9007a4fb502c3ebf5478 c21a686a2a/Competition_Plan.pdf Columbus International Airport (CMH) 2018: https://columbusairports.com/storage/production/20190726082711-cmh-airport-competition-plan- 07102019-signed.pdf Dallas Love Field International Airport (DAL) 2005: https://www.dallas-lovefield.com/home/showpublisheddocument?id=1824 Denver International Airport (DEN) 2020: https://www.flydenver.com/sites/default/files/pdf/DEN_InfoMap.pdf Dallas Fort Worth International Airport (DFW) 2004: https://webfids.dfwairport.com/cs/groups/webcontent/documents/webasset/p1_008171.pdf Detroit Metropolitan Wayne County Airport (DTW) 2008: https://www.metroairport.com/sites/default/files/business_documents/comp_plan/dtw_2008_competi tion_plan_update.pdf Newark Liberty International Airport (EWR) 2013: https://www.panynj.gov/content/dam/airports/pdfs/2013-Airline-Competition-Plan.pdf Honolulu International Airport (HNL) 2013: https://hidot.hawaii.gov/airports/files/2012/12/HNL- Competition-Plan-Update_FY-2013-20130125.pdf Washington Dulles International Airport (IAD) 2015: https://www.mwaa.com/sites/default/files/iad_competition_plan_update_2015_2.pdf

Chicago Midway International Airport (MDW) 2013:

17 https://www.flychicago.com/SiteCollectionDocuments/Business/AboutCDA/CompetitionPlan_MD W.pdf Memphis International Airport (MEM) 2003: https://flymemphis.com/wp-content/uploads/2019/11/Competition-Plan.pdf Milwaukee International Airport (MKE) 2007: https://www.mitchellairport.com/application/files/8814/9784/3507/FAA_Approval_of_2007_Compe tition_Plan_Update.pdf Minneapolis-St. Paul International Airport (MSP) 2016: https://www.mspairport.com/sites/default/files/2017-06/2016-Competition-Plan-Update.pdf Oakland International Airport (OAK) 2003: https://www.oaklandairport.com/wp-content/uploads/2016/05/competitionplan_fy_03_04.pdf Chicago O’Hare International Airport (ORD) 2020: https://www.flychicago.com/business/CDA/factsfigures/Pages/facility.aspx Philadelphia International Airport (PHL) 2015: https://www.phl.org/drupalbin/media/PHL15comppplan.pdf Phoenix Sky Harbor International Airport (PHX) 2002: https://www.skyharbor.com/docs/default-source/default-document-library/airline-competition- plan.pdf?sfvrsn=82689a88_2 San Antonio International Airport (SAT) 2012: https://flysanantonio.com/wp-content/uploads/2020/06/final-signed-Competition-Plan-Jan-2012- 1.pdf Seattle-Tacoma International Airport (SEA) 2014: https://www.portseattle.org/sites/default/files/2018-09/Airport%20Competition%20Plan%20- %202014.pdf Sacramento International Airport (SMF) 2004: https://sacramento.aero/download.php?f=/2004_Competition_Plan_Update.pdf Lambert-St. Louis International Airport (STL) 2016: https://www.flystl.com/uploads/documents/aeronautical-services-3-4-other/3.4.1.3_Competition- Plan_STL_2016.11.10.pdf Tampa International Airport (TPA) 2018: https://www.tampaairport.com/sites/default/master/files/FAA%20Competition%20Plan.pdf

18 Table 1: Observed Airports and Airlines Airports: Airlines: Atlanta (ATL) American Airlines (AA) Nashville (BNA) Alaska Airlines (AS) Buffalo (BUF) JetBlue Airlines (B6) Baltimore (BWI) Delta Airlines (DL) Cleveland (CLE) (F9) Charlotte (CLT) Allegiant Airlines (G4) Columbus (CMH) (HA) Cincinnati (CVG) Spirit Airlines (NK) Dallas Love Field (DAL) (UA) Denver (DEN) Southwest Airlines (WN) Dallas Fort Worth (DFW) Detroit (DTW) Newark (EWR) Honolulu (HNL) Washington D.C. (IAD) Indianapolis (IND) Las Vegas (LAS) New York City (LGA) Chicago Midway (MDW) Memphis (MEM) Milwaukee (MKE) Minneapolis-St. Paul (MSP) Oakland (OAK) Chicago O’Hare (ORD) Philadelphia (PHL) Phoenix (PHX) San Diego (SAN) San Antonio (SAT) Seattle (SEA) San Francisco (SFO) Sacramento (SMF) St. Louis (STL) Tampa (TPA)

19 Table 2: Comparing Used Gates with ACP Gate Ownership AA AS B6 DL F9 G4 HA NK UA WN BUF 2017 +14% +8% 0% +4% - - +4% - +4% +8% BNA 2016 +10% +23% - 0% 0% - - - 0% -3% CLT 2016 +3% +32% 0% +1% +1% - - - +2% +2% IAD 2015 +1% +1% 0% +1% 0% - +1% - -14% 0% PHL 2015 -5% +27% +1% +2% +2% - - +2% +2% 0% Average Error 7% 18% 0% 2% 1% - 3% 2% 4% 3%

Note: Element values contain the difference between the percentage of gate ownership estimated with the scraped data and the percentage of gate ownership estimated with the ACPs. Airports listed: Buffalo (BUF), Nashville (BNA), Charlotte (CLT), Washington D.C. (IAD), Philadelphia (PHL). Airlines listed: American Airlines (AA), Alaska Airlines (AS), JetBlue Airlines (B6), Delta Airlines (DL), Frontier Airlines (F9), Allegiant Airlines (G4), Hawaiian Airlines (HA), Spirit Airlines (NK), United Airlines (UA), and Southwest Airlines (WN).

20 Figure 1: Cumulative Market Share of Sampled Airlines Over Time

80

75

70

65

Cumulative Market Share (%) Market Cumulative 60

55 2014 2015 2016 2017 2018 2019 2020 Time

21

Table 3: Summary Statistics (N=98,193) Statistic Mean St.Dev. Min. Max. Gini Coefficient (x100) 23.59 6.355 5.00 64.09 Average Fare ($) 254 104 30 1,573 Contact 0.55 0.19 0 1.46 Distance 1,448 971 12 6,041 Route Mkt Share 0.25 0.28 0 1 Airport Mkt Share 0.21 0.15 0 0.93 Direct 0.30 0.40 0 1 Roundtrip 0.69 0.20 0 1 Hub 0.38 0.49 0 1 Origin Gates (%) 0.06 0.08 0 0.7 Dest. Gates (%) 0.06 0.08 0 0.7 WNGates (%) 0.03 0.04 0 0.61

22 Table 4: Number of Common Markets in 2016-Q2 AA AS B6 DL F9 G4 HA NK UA WN AA 862 249 180 841 309 12 55 209 773 609 AS 249 251 82 249 104 3 51 76 225 179 B6 180 82 184 180 66 1 12 58 170 144 DL 841 249 180 973 309 12 55 210 782 711 F9 309 104 66 309 309 8 4 114 302 247 G4 12 3 1 12 8 12 2 0 12 6 HA 55 51 12 55 4 2 57 3 54 4 NK 209 76 58 210 114 0 3 210 181 156 UA 773 225 170 782 302 12 54 181 806 566 WN 609 179 144 711 247 6 4 156 566 734

Note: The off-diagonal numbers represent the number of markets served simultaneously by the carrier in the row and the carrier in the column. The numbers on the diagonal are the total number of markets served by a carrier. Airlines listed: American Airlines (AA), Alaska Airlines (AS), JetBlue Airlines (B6), Delta Airlines (DL), Frontier Airlines (F9), Allegiant Airlines (G4), Hawaiian Airlines (HA), Spirit Airlines (NK), United Airlines (UA), and Southwest Airlines (WN).

23 Figure 2: Non-Monotonicity Between Contact and Gates

Note: The solid line represents the relationship found between multimarket contact and gates in the data. The dashed lines represent the theoretical continuations of the relationship based on the intuition outlined in section 3.7.

24 Table 5: Prices & Multimarket Contact (OLS, N=98,193) Dependent variable: ln(Average Fare) (1) (2) (3) (4) Contact 0.115*** 0.150*** 0.023* 0.020 (0.006) (0.006) (0.013) (0.014) ln(Distance) -1.017*** -1.213*** (0.029) (0.030) ln(Distance)2 0.095*** 0.110*** (0.002) (0.002) Route Mkt Sh. 0.040*** 0.033** (0.006) (0.015) Airport Mkt Sh. 0.655*** 0.691*** (0.013) (0.030) Direct -0.119*** -0.029*** -0.104*** 0.014** (0.003) (0.003) (0.008) (0.007) Roundtrip -0.097*** -0.048*** -0.133*** -0.107*** (0.006) (0.006) (0.009) (0.010) Hub -0.015*** 0.018*** 0.008* 0.054*** (0.002) (0.002) (0.005) (0.005) City-Pair FE No No Yes Yes R2 0.737 0.715 0.785 0.768 Note: All regressions account for airline and year-quarter fixed effects. Heteroskedastic robust standard errors are in parentheses. *p<0.10, **p<0.05, ***p<0.01

25 Table 6: Price Dispersion & Multimarket Contact (OLS, N=98,193) Dependent variable: Gini Coefficient (1) (2) (3) (4) Contact -2.469*** -2.182*** -0.700*** -0.722*** (0.132) (0.132) (0.254) (0.257) ln(Distance) 19.384*** 19.047*** (0.628) (0.613) ln(Distance)2 -1.381*** -1.352*** (0.045) (0.043) Route Mkt Sh. -1.415*** -0.494* (0.141) (0.300) Airport Mkt Sh. 7.273*** 10.361*** (0.297) (0.636) Direct 5.011*** 5.445*** 3.287*** 4.652*** (0.068) (0.062) (0.153) (0.132) Roundtrip -4.775*** -4.572*** -3.105*** -2.839*** (0.152) (0.150) (0.218) (0.229) Hub 0.959*** 1.216*** 0.113 0.722*** (0.044) (0.043) (0.098) (0.101) City-Pair FE No No Yes Yes R2 0.277 0.272 0.364 0.351 Note: All regressions account for airline and year-quarter fixed effects. Heteroskedastic robust standard errors are in parentheses. *p<0.10, **p<0.05, ***p<0.01

26 Table 7: Multimarket Contact & Gates (OLS, N=98,193)

Dependent variable: Contact (1) (2) (3) (4) (5) (6)

Origin Gates 0.054*** 0.270*** 0.041*** 0.223*** (0.011) (0.019) (0.011) (0.019)

(Origin Gates)2 -0.386*** -0.336*** (0.039) (0.039)

Dest. Gates 0.087*** 0.257*** 0.079*** 0.214*** (0.010) (0.019) (0.010) (0.019)

(Dest. Gates)2 -0.294*** -0.244*** (0.036) (0.036)

WN Gates -0.071*** -0.390*** -0.058*** -0.341*** -0.063*** -0.347*** (0.012) (0.024) (0.012) (0.024) (0.012) (0.024)

(WN Gates)2 1.258*** 1.168*** 1.180*** (0.096) (0.094) (0.094) ln(Distance) -0.174*** -0.133*** -0.185*** -0.157*** -0.181*** -0.155*** (0.028) (0.028) (0.028) (0.028) (0.028) (0.028) ln(Distance)2 0.007*** 0.004** 0.008*** 0.006*** 0.008*** 0.006*** (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Direct -0.071*** -0.080*** -0.068*** -0.072*** -0.069*** -0.073*** (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Roundtrip 0.096*** 0.091*** 0.098*** 0.096*** 0.096*** 0.094*** (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Hub -0.026*** -0.028*** -0.023*** -0.024*** -0.024*** -0.025*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

R2 0.321 0.325 0.319 0.322 0.320 0.322

Note: All regressions account for airline and year-quarter fixed effects. Heteroskedastic robust standard errors are in parentheses. *p<0.10, **p<0.05, ***p<0.01

27 Table 8: Prices & Multimarket Contact (2SLS, N=98,193) Dependent variable: ln(Average Fares) OLS Estimates (1) (2) (3) (4) Contact 0.150*** 4.545*** 3.780*** 3.284*** 2.911*** (0.006) (0.432) (0.162) (0.178) (0.156) ln(Distance) -1.213*** -0.353** -0.503*** -0.600*** -0.673*** (0.030) (0.147) (0.106) (0.096) (0.085) ln(Distance)2 0.110*** 0.071*** 0.078*** 0.082*** 0.086*** (0.002) (0.009) (0.007) (0.006) (0.006)

Direct -0.029*** 0.257*** 0.207*** 0.175*** 0.151*** (0.003) (0.030) (0.013) (0.013) (0.011)

Roundtrip -0.048*** -0.479*** -0.404*** -0.355*** -0.319*** (0.006) (0.044) (0.020) (0.020) (0.018)

Hub 0.018*** 0.112*** 0.096*** 0.085*** 0.077*** (0.002) (0.011) (0.006) (0.005) (0.005) Origin Gates - Yes Yes Yes No Dest. Gates - Yes Yes No Yes WN Gates - Yes Yes Yes Yes (Origin Gates)2 - No Yes Yes No (Dest. Gates)2 - No Yes No Yes (WN Gates)2 - No Yes Yes Yes Stock-Yogo - 73*** 134*** 117*** 125*** Wu-Hausman - 2,090*** 5,382*** 2,267*** 1,864*** Note: All regressions account for airline and year-quarter fixed effects. Heteroskedastic robust standard errors are in parentheses. *p<0.10, **p<0.05, ***p<0.01 Leftmost column shows estimates from Table 5 Column 2.

28 Table 9: Price Dispersion & Multimarket Contact (2SLS, N=98,193) Dependent variable: Gini Coefficient OLS Estimates (1) (2) (3) (4) Contact -2.182*** 17.624*** 5.341*** 10.878*** 6.277*** (0.132) (2.573) (1.338) (1.916) (1.774) ln(Distance) 19.047*** 22.923*** 20.519*** 21.603*** 20.702*** (0.613) (1.129) (0.784) (0.934) (0.836) ln(Distance)2 -1.352*** -1.528*** -1.419*** -1.468*** -1.427*** (0.043) (0.074) (0.053) (0.062) (0.055)

Direct 5.445*** 6.733*** 5.934*** 6.294*** 5.995*** (0.062) (0.181) (0.107) (0.139) (0.131)

Roundtrip -4.572*** -6.515*** -5.310*** -5.853*** -5.402*** (0.150) (0.301) (0.208) (0.248) (0.236)

Hub 1.216*** 1.641*** 1.377*** 1.496*** 1.397*** (0.043) (0.071) (0.051) (0.061) (0.057) Origin Gates - Yes Yes Yes No Dest. Gates - Yes Yes No Yes WN Gates - Yes Yes Yes Yes (Origin Gates)2 - No Yes Yes No (Dest. Gates)2 - No Yes No Yes (WN Gates)2 - No Yes Yes Yes Stock-Yogo - 73*** 134*** 117*** 124*** Wu-Hausman - 71*** 37*** 65*** 29*** Note: All regressions account for airline and year-quarter fixed effects. Heteroskedastic robust standard errors are in parentheses. *p<0.10, **p<0.05, ***p<0.01 Leftmost column shows estimates from Table 6 Column 2.

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30