Dependent Variable

Tactical Cooperation: I define tactical cooperation as the sum of the average fare (ticket price) charged by the members of a given dyad at time t. To calculate this variable, I first calculate the average fare at the firm-market-time level. I do this by multiplying the number of passengers flying market m with carrier i at time t with the market fare they paid and then take the sum of these products to get the total fare charged by carrier i in market m at time t. I then divide the resulting total fare by the number of total passengers served by carrier i in market m at time t to compute the average fare at the firm-market-time level. Following this, I move the average fare calculated at the firm-market-time level up to the firm-time level by calculating its mean across markets at time t. As the final step, I sum the resulting time-specific average ticket prices of members of a given dyad to calculate the dependent variable at the dyad-time level.

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Three characteristics of the empirical context of this study support the use of the sum of the average ticket prices of a pair of rivals to calculate tactical cooperation. First, airlines compete in a Bertrand market where prices are strategic complements and quantities are strategic substitutes. Thus, matching the price moves of a rival is the profit maximizing response and summing the average fares captures that. Second, although price is dictated by the firm that prefers the lowest price, in the empirical context of this study, the firm that prefers the lowest price differs across markets due to the spheres of influence and hub and spoke operations. This in turn enables a pair of rivals to accept low prices in some markets in return for high ticket prices in others, which is the essence of a MF strategy. Since I expect such behavior to lead to an increase in average ticket prices, I sum the average ticket prices of a pair of rivals to capture it. Third, performance failure in line with the theoretical arguments of this dissertation transforms Prisoner’s Dilemma into an assurance game where imitating the pricing behavior of rival is the profit maximizing response (Kollock, 1998). The outcome of such behavior can be captured by tracing the sum of the ticket prices of a pair of rivals over time.

Several factors support the use of ticket prices to calculate the dependent variable. First, pricing as a tactical competitive instrument provides a clear and strong stimulus (Hambrick et al., 1996; Heil and Robertson, 1991). Price moves reveal more information than that provided by other types of tactical moves (Chen and MacMillan, 1992) and the information is easier to interpret (Smith et al., 1991). Hence price increases are more likely than other types of moves to be attributed to the intention of the signaler to cooperate than to other factors such as insufficient capacity or increase in demand.

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Second, pricing as a signaling instrument is highly visible (Chen and Miller, 1994; Chen and Hambrick, 1995; Chen et al., 2002). Consequently, firms are more likely to be aware of and recognize price increases and thus recognize the intention to cooperate and trust.

A third reason to use average fare as a proxy for tactical cooperation is that pricing decisions are frequent but not unique (Leeflang and Wittink, 1992), and so are more likely to quickly reduce the impact of hostile history that exists among rivals on their future interactions. Through frequent price increases, multi-market firms can not only repeatedly demonstrate to their multi-market rivals their intention to cooperate, but they can also reduce the negative impact of their conflict laden history on the effectiveness of their cooperative moves by triggering in their rivals, data-driven, bottom-up information processing, rather than history- driven top-down information processing, in decision making processes. Firms are hostages of their prior beliefs and use reputational beliefs about their rivals to guide their interactions with them (Prabhu and Stewart, 2001). It is therefore difficult for a dyad of rivals to cooperate in the face of historical interaction characterized by hostility. However pricing decisions provide frequent information and feedback and thus reduce the impact of the past on future interactions. Hence faced with frequent price increases, firms are expected to give more weight to the current cooperative moves of their rivals than to their past competitive actions. Thus, when taking actions, they will rely less on prior representations of their rivals (Moore, 1992) as hostile actors and work to update the negative reputational beliefs they held about them (Prabhu and Stewart, 2001).

A fourth reason to use average fares as a proxy for tactical cooperation is that frequency of price changes, coupled with the ability of price moves to affect bottom line results without

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much delay (Chen and MacMillan, 1992; Steenkamp et al., 2005), make pricing an effective tool for developing trust.

Fifth, because they are tactical actions, price increases reduce not only the likelihood, but also the duration of being suckered. A trustee can defect and exploit a trust-giver in order to increase its market share and performance by not reciprocating with an increase in price in the trust-giver’s markets. However price increases can easily be reversed in the face of defection because the implementation requirements of such actions are low (Chen et al., 1992). This in turn can make cheating less effective and reduces the duration of defection.

Sixth, firms that use pricing as a signal of cooperation are better positioned to foresee the likely responses of their rivals than are firms that use other forms of competitive action (Montgomery et al., 2005). Therefore firms that initiate cooperation through price increases can conjecture better about the likely responses of their rivals and can cooperate much more effectively.

Finally, the choice of ticket price as a proxy for tactical cooperation is driven by the fact that performance failure, which is one of the independent variables in my model, influences tactical competitive actions, particularly pricing actions, more than it influences strategic competitive actions, especially in the airline industry. More specifically, prior research suggests that initial responses to performance problems are almost exclusively changes in price (Miller and Chen, 1994). Managers of airlines modify and change prices in the face of performance failure because price changes require fewer and more general resources, are easier to reverse and are less challenging to the existing power base and status of decision makers compared to strategic actions (Chen et al., 1992; Connelly et al., 2010; Smith et al., 1991). Hence in this

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context, multi-market firms that suffer from performance problems are expected to carry out tactical cooperation through pricing increases.

Market definition and the trip structure (e.g., route) offered to satisfy demand for a given market is crucial to the calculation of fare. I define a market as a non-directional city-pair. Since there is little or no cross elasticity of demand among city-pair markets (Gimeno, 1999), this demand-based definition ensures clear market delineation required by any study of MF. I also use non-directional rather than directional markets because carrier conduct in a given market (e.g., hub creation) and characteristics of a given market (e.g., market concentration, number of firms, keyness) or network structure of a carrier (e.g., hub economies) do not depend on which city is the origin or destination in a city-pair market.

With respect to trip structure, I look only at direct flights, which include non-stop and on- plane stop trips consistent with the definition of the Department of Transportation. Unlike non- direct flights that escalate rivalry, direct flights facilitate cooperation among carriers (Abramowitz and Brown, 1993; Borenstein, 1989, 1991, 1992). This characteristic of direct flights is essential for this study, which aims to explain the genesis of inter-organizational cooperation among multi-market rivals. In addition, the study of direct flights enables me to match different databases of Department of Transportation such as T-100 Domestic market data and DB1B Market table without losing observations and mixing different market definitions.

The data used to calculate average fare are from the DB1B Market table of the Airline Origin and Destination Survey (DB1B) which is a 10% sample of airline tickets from reporting carriers. The unit of observation of DB1B is at the itinerary level and the periodicity of the data is quarterly. I use the “market fare” variable of DB1B Market table as a basis to calculate average fare. In DB1B Market table, market fare is given for a directional market. However, as

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discussed, I define a market as a non-directional city-pair market and thus I combine flights from point A to point B with flights from B to A before calculating the fare.

To calculate the total fare charged by a carrier in market m at time t reliably and consistently, I merge the DB1B Market table with the DB1B Ticket table by using the itinerary ID that is common to both tables and then filter the merged database in accordance with several data screening criteria as follows. First, I include only those itineraries that have one coupon4 in the market so that market fare is based on direct flights. To this end, I set “MktCoupons” variable equal to one in the DB1B Market. Second, I include only itineraries whose price information is reliable and that are not sold in bulk since unreliable price data and special discounts can confound the calculation of average ticket price (Orlov, 2011). I therefore eliminate tickets purchased by travel agencies for re-sale in packaged tours by using the “bulk fare indicator” of DB1B Market Table. In addition, I eliminate unreliable ticket prices by using the “dollar credibility indicator” variable of DB1B Ticket table. Third, I will include only domestic intra-line tickets (Evans and Kessides, 1994) and flights where the ticketing carrier is also the operating carrier. Without excluding inter-line itineraries and code sharing flights where the operating and ticketing carrier are different, it is difficult to know who is setting the price (Gerardi and Shapiro, 2009) and the manner in which ticket revenues are split through pro-rate agreements. Therefore, I keep only those itineraries that have a single operating carrier and where the reporting and operating carrier is the same airline company. To this end, I set the “Online” variable of DB1B Ticket table to one and equate “TkCarrier” variable to “OpCarrier” variable in DB1B Market table. Fourth, I delete observations from monopolized markets where there is only one incumbent firm because the level of analysis of this study is the inter-

4 _{Number of coupons refers the number of individual flight numbers (coupons) in an itinerary or market. For} example, while a non-stop flight from Atlanta to Chicago requires one coupon, a flight from Atlanta to Chicago through a change-of plane stop at Boston requires two coupons.

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organizational dyad. Fifth, I will delete open-jaw (e.g., ATL-BOS-NYC) and circle round trips (e.g., LAX-MIA-MSP-LAX) (Borenstein, 1989) since I am interested in non-directional city-pair markets. Following such filtering, I calculate the average fare charged by a carrier in market m at time t, and sum the average fares of the members of a given dyad to calculate the dependent variable.

The resulting “filtered DB1B Market table” is used to calculate other independent variables in other models as will be discussed.