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Airline Code-Sharing and Capacity Utilization: Evidence from the US Airline Industry.

Abstract

Following the passage of the Airline Deregulation Act in 1978, the degree of competition among air carriers, increased significantly. Airlines resorted to many ways--including code-sharing--in response to competitive pressures. This article is a retrospective study on the load factor effects of the largest domestic codeshare partnership established in 2003--between Delta, Northwest, and Continental airlines. After controlling for aircraft configuration, carrier, and market characteristics, we find evidence that code-sharing improves the partners' load factors relative to other carriers in all markets combined. However, we find statistically significant negative (positive) codeshare effects on load factor in markets where the codeshare partners competed (did not compete) prior to code-sharing.

Keywords

Airlines, load factor, codeshare agreement, competition, alliance

JEL classification codes: L93; L13

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Airline alliances are marketing arrangements between two or more air carriers allowing them to cooperate substantially across various levels of their operations. An alliance formation is generally a strategy that air carriers use when outright merger or other means of scaling their operations is not an option. Even when other means of scaling operations are available, alliances still provide quicker access to new markets without incurring entry costs (Iatrou and Alamdari 2005). Alliances among airlines can be as simple as reciprocal frequent-flyer programs where passengers can earn and redeem miles across partner airlines, or more comprehensive as code-sharing where an airline can sell seats on flights operated by its codeshare partner (US General Accounting Office 1999).

Codeshare agreements have a long history in the international market for air travel as the airline industry around the world experienced major shocks due to globalization, (de)regulation--cabotage (Chen and Chen 2003). Conversely, alliance formation among US domestic carriers is relatively recent. Over the last couple of decades, we have seen a proliferation of cooperative initiatives between airlines, perhaps the most prominent being codeshare partnerships. The US domestic airline industry witnessed an uptick in code-sharing in the mid-1990s, as major airlines used it (code-sharing) as a strategy to fend off increased competition from low-cost carriers (Mantovani and Tarola 2007). Consequently, in 1995, Northwest and Hawaiian Airlines submitted a codeshare proposal to the transportation authorities (Ito and Lee 2005). Following suit, the six largest US carriers unveiled their plans to codeshare: Continental/Northwest in January 1998 and Delta/United and American/US Airways in April 1998 (Bamberger, Carlton, and Neumann 2004). In October 2001 Alaska and Hawaiian finalized their codeshare agreement while American West and Hawaiian struck a codeshare arrangement in October 2002. Finally, the implementation of similar partnerships ensued with United/US Airways in January 2003 and Delta/Northwest/Continental in June of the same year.

Codeshare agreements between airlines effectively allow a consumer to purchase a codeshare product from one airline (the ticketing carrier) but actually fly segment(s) of the trip on plane(s) operated by carrier(s) that are partner(s) to the ticketing carrier. To facilitate the offering of such interline codeshare products, partner carriers typically coordinate their operations to achieve a seamless integration of their route networks, which potentially may result in more travel-convenient routing across partner carriers' networks (Yimga 2017). (1)

Airline capacity utilization has received much attention in the literature. (2) Airlines have always been concerned about how optimally they can use their planes in order to maximize passenger traffic density and revenue. Recent data from the US Department of Transportation (DOT) show that in May 2016, aggressive capacity growth has produced adverse effects resulting in falling capacity utilizations for many airlines. For example, United Airlines (UA) witnessed the largest fall in load factor (1.494 year over year) while American Airlines (AA) also saw a drop of 0.9 percent in load factor. Load factor is widely recognized as the main measure of operations performance in the airline industry (Bailey et al. 1985).

From an operations standpoint, load factor is an indication of how capacity, or available seat miles (ASM), responds to air travel demand shocks in a specific market. The ASM adjusts to changes in flight frequency or the use of aircraft of different sizes. In few occasions, the ASM may not respond to changes in demand especially in routes with limited frequency where carriers have very little leverage of canceling flights without damaging the overall routing network (Chen and Chen 2003). Usually, lower capacity or ASM leads to a higher load factor, which means more efficient operations and increased economies of passenger traffic density. On the other hand, given the uncertainties (some caused by code-sharing) associated with air travel demand, a high load factor may indicate low flight frequency, suggesting a greater likelihood of seat shortage and potential loss of revenues. Therefore, finding the optimal seat supply on a particular route means dealing with the delicate tradeoff between the advantages of efficient operations and the downsides of low-grade product convenience. The main purpose of this article is to explore these mechanisms by empirically investigating the load factor impacts of a highly contentious codeshare agreement between three major carriers.

In our empirical exercise, we examine the load factor effects of the tripartite codeshare agreement between Delta, Northwest, and Continental airlines. We choose this codeshare alliance for the following reasons: (a) it involves three major full-service carriers; (b) the alliance was the largest to be ever approved in the history of the US airline industry; and (c) the alliance was one of the most legally contentious. We further explore load factor effects of code-sharing based on the existence of pre-alliance competition between the codeshare firms. This allows us to measure differences in capacity utilization in both markets where the partners competed (we also refer to these markets are overlapping, throughout the article) prior to code-sharing, and markets where they did not compete.

Our analysis delivers a number of compelling results. First, we find asymmetric load factor responses to code-sharing. Overall, code-sharing increases load factor. These load factor effects are robust even after we control for aircraft configuration, carrier, and market characteristics. The increased load factor is possibly owing to higher quality product offerings in terms of better variety of seamless connecting flights, greater opportunities for passengers to earn and redeem frequent-flyer miles, and so on (Gayle 2007). Second, and surprisingly, we find statistically significant negative (positive) codeshare effects on load factor in markets where the codeshare partners competed (did not compete). However, upon further analysis, the evidence does not suggest that the codeshare agreement facilitated collusive behavior on the partners' overlapping routes. Besides the policy implications, these results provide managerial significance in terms of codeshare partner selection, network integration, and flight coordination.

The rest of the article is organized as follows. The next section reviews the literature on the effects of airline alliances, followed by an overview of the Delta, Northwest, Continental codeshare agreement. We then discuss the research methodology and estimation technique. Results are reported before our concluding remarks.

Literature Review

The effects of airline alliances in general and code-sharing in particular have received a lot of attention and have been studied by a number of researchers. Oum, Park, and Zhang (1996) provide an excellent review of the effects of airline strategic alliances. The effects that have been examined include welfare and competition, traffic demand, airfare, partner selection, output and profit, and organizational change (Brewer and Hooper 1998; Deprosse and Franke 1998; Gellman Research Association 1994; Nyathi 1995; Oum and Park 1997; Park 1997; Park and Zhang 1998; US General Accounting Office 1995; Youssef and Hansen 1994; Zea and Feldman 1998).

A large number of studies have documented the pricing effects of airline alliances (Bamberger, Carlton, and Neumann 2004; Brueckner 2001, 2003; Brueckner and Whalen 2000; Gayle 2008, 2013; Gayle and Brown 2014; Ito and Lee 2007). These studies argue, on the one hand, that airline cooperation from an alliance pushes airfares down in interline markets because of product complementarity and the mitigation of double marginalization. Brueckner (2003) and Brueckner and Whalen (2000) note that code-sharing allows airlines to eliminate a double markup on itineraries with multiple operating carriers, resulting in lower fares. Ito and Lee (2005) also show code-sharing to be associated with lower fares. On the other hand, an alliance can also reduce competition in markets where the partners' route networks overlap (interhub markets, for instance), and this in turn puts pressure on airfares to rise in these markets. Zou, Oum, and Yu (2011) argue that it is possible that an alliance causes fares to rise even in markets where the partners' route segments are complementary rather than overlapping. They make the argument that the improvement in the quality of interline connections that comes with an alliance would subsequently increase demand.

Bilotkach (2005) examines airline alliance formation using transatlantic markets to determine if code-sharing with and without antitrust immunity decreases fares for interline trips equally. The findings suggest that code-sharing and alliance formation both have fare-decreasing effects; however, the code-sharing effect turns out to be more than twice the magnitude of the alliance effect.

Tiernan, Rhoades, and Waguespack (2008) explored the linkage between on-time performance and international airline alliance. Yimga (2017) investigated the on-time performance effects of code-sharing in the US domestic airline industry and found that a codeshare agreement improves the on-time performance of the alliance carriers relative to other airlines. However, this improvement in on-time performance is a function of the degree of competition among the codeshare partners, with the code-share effects on on-time performance being larger in markets where the partners competed in prior to the alliance.

Surprisingly, questions regarding the impact of code-sharing on operations performance measures such as capacity utilization have been left largely unanswered. Are airlines that participate in a codeshare partnership enjoying higher load factors? If so, how large are these load-factor effects? Are these load-factor effects persistent across different types of market? This article attempts to answer these questions by empirically investigating the capacity utilization of the largest domestic alliance--between Delta, Northwest, and Continental airlines--with a particular focus on the codeshare effects in markets where the alliance partners competed and markets where they did not compete prior to code-sharing.

This study contributes to the broad literature of the demand-enhancing potential of airline alliances in general and code-sharing in particular. Inherent features of codeshare arrangements, such as consumers' ability to accumulate and redeem miles across codeshare partners' frequent-flyer programs, build brand loyalty (Evans and Kessides 1993) and increase demand (Chin 2002; Lederman 2008). Lederman (2007) investigated whether enhancements to loyalty programs affect demand by measuring the impact of international frequent-flyer partnerships (FFP) on domestic airline demand. Her findings indicate that enhancements to an airline's FFP are associated with increases in its demand on specifically those routes that depart from airports at which the airline is dominant. Using descriptive analysis of airline operations, Dresner, Flicop, and Windle (1995) evaluate the effects of three trans-Atlantic airline alliances on traffic volume and load factors. Their analysis indicates that in only one of the three agreements have the carriers increased their trans-Atlantic traffic volume and increased their load factors after realigning their route systems to take advantage of the alliance.

Chen and Chen (2003) develop a theoretical model to analyze the impact of different types of global airline alliances on load factor under demand uncertainty. The results derived from their theoretical model were tested using empirical data from the airline industry show that parallel (rather than complimentary) code-sharing results in increased demand.

Background and Definitions

Background of the Delta/Northwest/Continental Codeshare Agreement

On August 23, 2002, Delta, Northwest, and Continental submitted their code-share agreement proposal to the DOT for review. The three legacy carriers request a comprehensive cooperative agreement that involves code-sharing, reciprocal frequent-flyer programs and reciprocal access to airport lounges. The partner carriers claimed, at the time, that the agreement will benefit consumers in the form of seamless service to thousands of new markets, broader availability of low-priced seat, increased frequency of flights, and better time-of-day coverage for travelers, among other benefits. However, the DOT determined that the agreement, if implemented as presented by the three airlines, could result in significant adverse impacts on airline competition unless the airlines agreed to a number of conditions that would limit the likelihood of competitive harm (US Department of Transportation 2003).

Regulators were alarmed by two major issues. First, they expressed concerns about the substantially high combined market share between the three airlines (35%), which was larger than the 23 percent market share of the United/US Airways codeshare agreement that was in operation at the time. Second, they believed that the three carriers had a large number of markets in which their service overlap (3,214 markets) with the potential of affecting nearly 58 million passengers per year (Gayle and Le 2013). These concerns, regulators believed, could potentially facilitate collusion (explicit or tacit) on prices and/or service levels in the partners' overlapping markets.

The codeshare carriers complied with some of the conditions and acknowledged that due to colocation, they will relinquish four gates in Houston (IAH), two gates in Detroit (DTW), five gates in Cincinnati (CVG), and two gates at Dallas Fort Worth (DFW) for common use. Additionally, if the codeshare partners choose to co-locate additional gates at any of the hub airports of any partner or Boston (BOS), the relocating carrier will promptly notify the DOT, and if such co-location creates additional surplus gates (in a manner comparable to the situation at IAH, DTW, CVG or DFW) the partners agree to release any such surplus leased gates.

The Department of Justice's (DOJ) antitrust review concluded that the codeshare agreement would drive down costs for consumers and provide better service, and it approved the alliance on January 17, 2003. It took about six months for the airline partners to meet regulators' conditions, and in June 2003, operating certificates were issued, officially marking the completion of the codeshare partnership.

Definitions

A couple of terminologies are worth noting for future reference throughout the article. These terms follow the definitions from Gayle and Thomas (2015), among others. A market is defined as a directional origin-destination combination. Following Berry, Carnall, and Spiller (2006) and Gayle (2007), markets are defined directionally, meaning that air travel from San Francisco to Boston is a different market than air travel from Boston to San Francisco, for instance. This allows us to separately account for origin and destination city characteristics. Also, we group cities that belong to the same metropolitan areas when an endpoint (origin or destination) is located in a metropolitan area with multiple airports.

A product is defined as a combination of origin, destination, and airline(s). Each flight has a ticketing carrier and an operating carrier. The ticketing carrier is the airline that issues the flight ticket to consumers while the operating carrier is the airline that owns/operates the aircraft that transports passengers. While all products have both an operating carrier and ticketing carrier, these carriers may be different for a given product.

Since our objective is to study the effects of code-sharing on load factor, we control for virtual codeshare products. A virtual codeshare product has a single ticketing carrier and operating carrier for the itinerary, but the operating and ticketing carriers are different. For example, a flight is ticketed through Delta, but the passenger flies on a Northwest plane. (3)

Research Methodology

To measure the codeshare effects on capacity utilization, we estimate reduced-form regression equations of capacity utilization while controlling for product, carrier, airport, and market-structure characteristics. Possible codeshare effects on capacity utilization are identified using a difference-in-differences identification strategy. This strategy enables us to compare pre-post codeshare periods' changes in capacity utilization of the codeshare firms, relative to changes in capacity utilization of other firms over the same pre-post codeshare periods.

Sample Selection

Data are gathered from the T100 Domestic Segment, published by the DOT. This dataset is a monthly sample of all flights in the United States. Each observation in the dataset represents a flight and includes information on carrier, origin, destination, aircraft type, transported passengers, freight and mail, available capacity, departures performed, aircraft hours, and load factor.

Moreover, because load factor is only measured for individual flights, we restricted our analysis to nonstop service. We collect monthly data for nonstop domestic flights from June through December of 2002 (pre-codeshare) and 2004 (post-codeshare) since the codeshare agreement between Delta, Northwest, and Continental was approved on August 23, 2003. We choose the sample period for three reasons. First, September 2001 substantially changed both the supply-side and the demand-side incentives for air travel (see, for example, Berry and Jia 2010 and Forbes, Lederman and Tombe 2015). We collected data starting from July of 2002 to mitigate some of the post-9/11 effects. For seasonality reasons, we collected data for the same six months in 2004 for post-codeshare period. Second, the time gap between the pre- and post-codeshare periods was included to account for the alliance implementation lag, which can take up to six months before codeshare products may appear in the DOT's database. Third, the six months before and after the codeshare agreement, though short, helps isolate potential confounding factors that are likely to change market structure (hence affect load factor) over a longer time span.

Our analysis focuses on air travel among the cities within the 48 mainland states of the United States. Following Aguirregabiria and Ho (2012) and Berry (1992), products purchased by fewer than nine passengers transported throughout an entire month were omitted. We report the airlines in our sample in table Ai in the appendix.

Route Level Statistics of DL/NW/CO Codeshare Agreement

Table 1 reports code-sharing activity among the three codeshare partners at the route level. Northwest assumed the role of operating carrier for Continental and Delta on 439 and 472 routes, respectively. Continental operated 245 routes for Delta and 269 routes for Northwest. Delta operated 238 and 283 routes on behalf of Continental and Northwest, respectively. Northwest disproportionately operated for Delta and Continental while Delta and Continental ticketed the majority of the codeshare products.

Table 2 shows a more disaggregated analysis of the code-sharing activity among the three partners in routes where their services overlap. The diagonal of table 2 reports the total directional overlapping routes for each pair. Sixty percent (290) of the routes Northwest operated on behalf of Delta and Continental overlap, reinforcing Northwest's dominant operating carrier role. Ninety overlapping routes were operated by Continental on behalf of the other partners while Delta only operated 57 routes.

Measuring Capacity Utilization

We directly measure capacity utilization by computing the load factor for each product in our sample. We define load factor as the ratio of passenger miles to available seat miles. Following Yimga (2017), among others, our analysis is carried out at the carrier-market-month-year level. Over a given month, a carrier may operate a specific origin-destination multiple times, with different load-factor values. We construct our capacity utilization measure by averaging the load factor of products that belong to a given carrier, in a given market, during a specific month and year. We then collapse the data by carrier-origin-destination-month-year combinations and report the group mean of the codeshare partners and other carriers in figure A1 in the appendix. Figure 1 shows that the distribution of load factor is skewed to the left suggesting that the mean load factor (0.613) is less than the median load factor (0.650).

We average explanatory variables and collapse them using the same approach for consistency. Our final working data set has 147,120 usable observations, where an observation is at the level of carrier-origin-destination-month-year combination.

Table 3 reports a preliminary difference-in-differences estimation of load factors before and after the codeshare agreement for codeshare partners versus other carriers. Table 3 indicates that even though the mean load factor improves overall over the pre-post alliance periods for all carriers, the increase in mean load factor is larger for the codeshare partners' products. We test for the difference in mean load factors between codeshare partners and other carriers in column 4 of table 3 and report the difference-in-differences estimate (bold) in column 1. The statistical significance reported in column 4 suggests that the codeshare agreement improved load factor for the codeshare partners relative to other airlines over the pre- and post-codeshare periods.

It should be acknowledged that the difference-in-differences analysis summarized in table 3 has caveats and provides only rough estimates of the effects associated with code-sharing between the three airlines in all markets combined. For instance, these difference-in-differences calculation does not account for persistent shocks that may differ across markets or the differential changes in overlapping versus nonoverlapping markets. In measuring the load-factor effects associated with code-sharing between the three airlines, a formal econometric model presented below, while not perfect, will do a better job at controlling for potential differences in exogenous control variables across markets.

Empirical Model

Our empirical analysis consists of a series of regressions of our dependent variable--capacity utilization (measured by load factor)--on control variables described below.

Our basic reduced-form estimating equation is:

[DepVar.sub.armt] = [alpha] + [beta][X.sub.armt] + [delta][W.sub.armt] + [[lambda].sub.a] + [[mu].sub.m] + [[eta].sub.t] + [origin.sub.r] + [dest.sub.r] + [[epsilon].sub.armt] (1)

where a indexes airlines, r indexes markets, m indexes months and t indexes years. [DepVar.sub.armt] is our dependent variable (load factor), [X.sub.armt] is a vector of control variables, [W.sub.armt] is a vector of dummy variables that captures the codeshare effects. [[lambda].sub.a]'s are airline specific fixed effects, [[mu].sub.m]'s are month fixed effects, [[eta].sub.t]'s are year fixed effects, origin and destination airport specific fixed effects are denoted by [origin.sub.r] and [dest.sub.r], [[epsilon].sub.armt] is the unobserved part of load factor.

The vector [W.sub.armt] includes the following: (1) a dummy variable for codeshare products among Delta, Northwest, and Continental, (2) a post-codeshare time dummy variable, and (3) an interaction term of these dummies. This is equivalent to a difference-in-differences identification strategy where codeshare effects are identified by comparing pre-and post-codeshare periods' changes in load factor of products operated by the codeshare partners, relative to changes in load factor of products operated by other firms over the same pre- and post-alliance periods.

Control Variables

To isolate the effects of code-sharing on load factor, we construct a number of variables to control for factors that may influence flight load factor. First, we control for the level of congestion at an airport by including a dummy variable (Slot Control) in the analysis to control for slot-controlled airports (Yimga 2017). This is a dummy variable coded 1 if the origin and/or destination airports of a given product are slot-controlled. The slot-controlled airports in our data sample are New York LaGuardia, New York Kennedy, Washington National, and Chicago O'Hare.

Second, we control for market distance by including the nonstop flight distance between market city-pairs.

Third, we include a measure of an airline's presence at the market origin city. This variable allows us to draw inference on how economies of passenger traffic density--measured indirectly by the number of departures performed in a market by a carrier during a given month--might affect flight load factor (Chen and Chen 2003).

Fourth, since load factor is also a function of available seats in an aircraft, we construct controls for aircraft types since different types of planes have different sizes and capacities (Chen and Chen 2003). We obtained aircraft type information from the T-100 database of the DOT and report the aircraft types in our working sample in table A2 (see appendix).

Fifth, to control for market structure, we include a dummy variable that equals 1 if the market share of any given carrier in a market is 0.5 or less. This variable captures the competitiveness of a market and was modeled in the spirit of Forbes (2008). This dummy variable is determined by using the market share based on the number of passengers transported in a given directional origin and destination city pair.

Sixth, another measure of competitive conditions we include in our analysis is the Herfindahl-Hirschman Index (HHI). Unlike the competitiveness variable, which does not capture the heterogeneity in market structures since it is a dichotomous variable, the HHI is a continuous measure of market concentration (Gayle 2007). The HHI of each market is calculated by taking the sum of the squared market shares (of passengers transported) of all carriers in a market.

Seventh, we include a series of dummy variables and interaction variables from these dummies to capture the codeshare effects on load factor. We create the dummy variable [T.sup.dnc] to indicate the post-codeshare period and the dummy variable DNC to identify products that are code-shared by Delta, Northwest, or Continental. We also create the dummy [MKT.sup.dnc], which is a zero-one market-specific dummy variable that takes the value of one for origin-destination markets in which any two of the three codeshare partners competed prior to code-sharing.

Finally, we include a series of monthly dummy variables that controls for seasonality in air travel. We report the summary statistics in table 4.

Empirical Results and Discussion

Impact of Measured Determinants of Capacity Utilization

In this section, we report empirical analyses of the impact of some measured determinants and code-sharing on load factor. The results are presented in table 5.

The coefficient estimate on Departures Performed is negative and statistically significant suggesting that higher flight frequency results in lower load factor. Higher flight frequency provides an array of options to consumers which in turn may drive down the average load factor per flight.

The level of congestion at the airport is probably one of the most important factors that may explain variations in capacity utilization. The coefficient estimate on Slot Control captures the amount of congestion at slot-controlled airports. The coefficient is negative and statistically significant. This finding is consistent with our expectations since Slot Control captures possible travel inconveniences for passengers due to airport traffic congestion at slot-controlled airports.

Building on existing work examining the relationship between air travel service and market structures, we investigate how market competitive conditions affect carriers' load factor. We control for market-level competition by including the dummy variable Competitive Market. The coefficient estimate on Competitive Market is negative and statistically significant suggesting that carriers that operate in competitive markets experience negative demand shocks.

The coefficient estimate on the HHI is positive and statistically significant, reinforcing the fact that carriers that operate in highly concentrated (less competitive) markets enjoy higher load factors.

The positive and statistically significant coefficient estimate on Distance indicates that market distance is a predictor of load factor, suggesting that carriers that operate longer market distances tend to have fuller flights.

Seasonality is another important factor that explains changes in load factor. To control for seasonality, we included month dummy variables (omitting July for singularity issues). The month dummy variables are statistically significant suggesting that seasonality play a major role in determining load factor.

Overall Capacity Utilization Effects of the Codeshare Agreement

We now turn to the remaining rows of table 5 that contain key variables in examining the effects of the codeshare agreement on load factor. To achieve our ultimate objective of correctly identifying the codeshare effects on load factor, it is imperative to control for the determinants of load factor discussed above. Furthermore, since we are using a difference-in-differences identification strategy, it is also important to control for persistent differences in load factor across carriers (Chen and Gayle 2018). Such controls are especially crucial if the load factor of products offered by the code-share firms are persistently different from load factor of products offered by other firms in our sample. If we fail to control for persistent load factor differences, we may erroneously assign measured differences in load factor to the codeshare agreement.

To measure persistent differences in load factor of codeshare products operated by the partners and other products, we include a dummy variable DNC that identifies products that are code-shared by Delta, Northwest, or Continental. The positive and statistically significant coefficient estimate on DNC suggests that the mean load factor of products associated with Delta, Northwest, or Continental is greater than the mean load factor of products offered by other carriers in the sample.

The coefficient estimate on [T.sup.dnc] measures, on average, how load factor changes over the pre-post codeshare agreement period for products that are not associated with Delta, Northwest, or Continental airlines. The positive coefficient estimate on [T.sup.dnc] indicates that the mean load factor of products operated by airlines other than Delta, Northwest, and Continental airlines increased over the pre--and post-codeshare periods.

Finally, the interaction term [T.sup.dnc] x DNC identifies the difference-indifferences estimate that measures whether load factor of products ticketed/operated by the codeshare carriers changed differently relative to load factor of products ticketed/operated by other airlines. [T.sup.dnc] x DNC captures changes in load factor in Delta/Northwest/Continental products (relative to other products) due to the code-sharing agreement. The coefficient estimate on [T.sup.dnc] x DNC is positive and statistically significant, suggesting that the codeshare agreement caused the mean load factor of Delta/Northwest/ Continental products to rise compared to the mean load factor of other carriers over the pre- and post-codeshare periods. In other words, the codeshare agreement is associated with higher load factor. This result is supported by the preliminary results reported in table 3 and figure At in the appendix, and strengthens the case for economies of passenger traffic density spurred by code-sharing.

Codeshare Effects Accounting for Pre-Codeshare Competition

To measure the load-factor effects of the codeshare agreement in specific markets (markets where the partners competed versus markets where they did not compete), we re-estimate our model with the inclusion of a market dummy variable [MKT.sup.dnc] and present the results in table 6. [MKT.sup.dnc] is a zero-one market-specific dummy variable that takes a value of 1 only for origin-destination markets in which any two of the three codeshare partners competed prior to the alliance.

The effects of the codeshare agreement on load factor in markets where the alliance firms competed before code-sharing is determined by the sum of the coefficients on [T.sup.dnc] and interaction variables [T.sup.dnc] x DNC and [T.sup.dnc] x DNC x [MKT.sup.dnc]. Given that the coefficient estimate on [T.sup.dnc] is not statistically different from zero, the codeshare effects in markets where the partners competed is -0.0106 [o + 0.0185 + (-0.0291)] in column 1 of table 6. Similar negative load factor effects are obtained in columns 2 and 3 of table 6. These negative effects suggest that the Delta/Northwest/Continental codeshare agreement is associated with decreases in load factor in overlapping markets.

The coefficient estimates on [T.sup.dnc] and the interaction variable [T.sup.dnc] x DNC in table 6 have a different interpretation than in table 5. In fact, the sum of these coefficient estimates measures changes in load factor in markets where the codeshare partners did not compete prior to their agreement. The sum of these coefficient estimates suggests the load factor increased in these nonoverlapping markets. Overall, what this suggests is that we find negative (positive) codeshare effects on load factor in markets where the codeshare partners competed (did not compete) prior to code-sharing.

To uncover what is driving this decrease in load factor in competing markets for codeshare partners, we examine and present the percentage changes in load factor, passengers transported and seat supply in table 7. Table 7 shows that load factor increased for both groups of carriers but for different reasons. The codeshare partners witnessed a 4.5 percent increase in load factor in overlapping markets due to relatively larger change in passengers transported versus the change in seats. For other carriers, load factor increased by 5.1 percent because the rate of decrease in available seats (7.7%) outweighed the drop in passengers transported (1.6%). Therefore, the negative load factor effects of code-sharing we observe in these overlapping markets come from the difference in load factor percentage changes between the two groups in column 3 of table 7, despite the fact that passenger volume and seat supply increase (decrease) for codeshare partners (other carriers).

Correcting for Endogeneity

Every empirical study has some endogeneity issues that should at least be discussed and possibly addressed. The fact that carriers decide on the number of departures to operate in a specific market raises selectivity issues. Passengers tend to prefer to fly with a carrier that offers more frequent service and at the same time it is more likely that carriers offer more frequent service on routes with higher passenger volumes. This feedback loop poses a potential endogeneity problem.

To address this concern, we construct instruments for Departures Performed. The instrument variables we use for the two-stage least squares (2SLS) estimation are: (1) available passenger miles and (2) the flight's itinerary distance flown. We estimate the load factor equation using 2SLS and present the results in tables 8 and 9. A Hausman test and Durbin chi-square test both confirm endogeneity by rejecting the exogeneity of Departures Performed at conventional levels of statistical significance. Due to the economics of space, we only report the Hausman test results in tables 8 and 9.

To confirm the validity of instruments used in the 2SLS regression, we estimate first-stage reduced-form regressions of the endogenous variable. First-stage reduced-form regression where we regress Departures Performed against the instruments yields statistically significant coefficients, suggesting that the instruments explain variations in the endogenous variable. [R.sup.2] measure for the regression of Departures Performed against the instruments is 0.6356. We also performed Stock and Yogo's (2005) test for weak instrument. The test rejects the null hypothesis that the instruments are weak, because the test statistic exceeds the critical values of conventional rejection rates of a nominal 5% Wald test. This test indicates that we do not have a weak-instrument problem. The 2SLS estimation results are qualitatively similar to the OLS results.

Discussion on Managerial and Policy Implications

From a managerial perspective, entering into a codeshare agreement is also a risk-pooling endeavor as the partner airlines use their collective capabilities to deal with uncertainty in demand. Demand uncertainty may increase (or decrease) load factor depending on the likelihood that the potential seat shortage of one codeshare partner can be offset by another partner's excess capacity. Therefore, accurately forecasting demand patterns and flight load factor is crucial for managers because doing so has serious implications on codeshare partner selection, network integration, and how partner airlines coordinate their flight schedules. These implications also call for determining the optimal seat supply on a particular route, as code-share partners deal with the seemingly delicate tradeoff between the advantages of efficient operations and the shortcomings of lower service convenience (Brenner 1982).

From a policy perspective, the negative load factor effects we found in overlapping markets (markets in which the potential partners' own flights were already competing between the origin and destination cities before the codeshare agreement) may be suggestive of potential collusive behavior on service levels, as the DOT feared, among the codeshare partners. However, upon further investigation, we found that passenger traffic for the codeshare partners actually increased in these overlapping city pairs, indicating no evidence that the codeshare partners engaged in collusive behavior.

Conclusion

The main objective of this article is to empirically investigate the load factor effects of the Delta/Northwest/Continental codeshare agreement, with a particular focus on differences in load-factor changes in the partners' overlapping and nonoverlapping markets.

We find that the codeshare agreement between Delta, Northwest, and Continental is associated with improved load factor for the alliance firms relative to other carriers. We also find asymmetric load-factor responses to code-sharing in markets where the partners competed versus markets where they did not compete. We find statistically significant negative (positive) codeshare effects on load factor in markets where the codeshare partners competed (did not compete). Furthermore, we find no evidence that these codeshare partners facilitated collusion on service. The findings of this study provide new insights into managerial and policy issues surrounding airline alliances.

Though our results are compelling, there are other important market effects that we did not explore that may be fertile topics for future research. Another interesting extension would be to analyze possible entry deterrence effects of this codeshare agreement. Also, since we found negative load factor effects in overlapping markets, can we quantify the consumer welfare implications? Exploring these questions may provide a better understanding of the distributional effects of code-sharing in particular and alliances in general.

Jules Yimga

Corresponding Author

Embry-Riddle Aeronautical University

jules.yimga@erau.edu

Javad Gorjidooz

Embry-Riddle Aeronautical University

DOI: 10.5325/transportationj.58.4.0280

Notes

This research is funded in part by a grant from the Embry-Riddle URI (Undergraduate Research Institute) Ignite and Faculty Research Development Program. For very helpful comments and suggestions, we thank co-editor Yoshinori Suzuki, two anonymous referees, Adin Herzog, Anne Boettcher, and Robin Sobotta. Any remaining errors are our own.

(1.) Interline indicates that at some point in the trip, when passengers change planes they also change carriers.

(2.) An airline's capacity utilization measures how efficiently an airline fills seats on its planes. We use airline's load factor (passenger-miles as a proportion of available seat-miles) to proxy for capacity utilization.

(3.) Gayle (2008) and Ito and Lee (2007) provide a more comprehensive analysis of the code-sharing practice.

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Caption: Figure 1 Load factor distribution.
Table 1/Number of Code-Sharing Routes

                     Ticketing Carrier

Operating Carrier    CO     DL    NW

CO                          245   269
DL                   238          283
NW                   439    472

Source: Adapted from Shen 2015.

Table 2/Number of Overlapping Code-Sharing Routes

Ticketing/Operating   CO/DL   CO/NW   DL/CO   DL/NW   NW/CO   NW/DL

CO/DL                  238
CO/NW                          439
DL/CO                  39              245
DL/NW                          290             472
NW/CO                          11      90              269
NW/DL                  57                      72              283

Source: Adapted from Shen 2015.

Table 3/Difference-In-Differences Estimation Results,
Pre- and Post-Codeshare

Outcome        Mean Load    S. Err.     [absolute     P > [absolute
Variable       Factor (1)     (2)     value of] (3)   value of] (4)

Before
  Control        0.504
  Treated        0.608
  Diff(T-C)      0.104       0.003        38.36         0.000 ***
After
  Control        0.539
  Treated        0.662
  Diff(T-C)      0.124       0.003        44.01         0.000 ***

Diff-in-Diff     0.019       0.004        4.97          0.000 ***

Notes: Treated (T) denotes the codeshare partners DL, NW and CO.
Control (C) denotes other carriers.

*** p < 0.01; ** p < 0.05; * p < 0.10

Table 4/Summary Statistics

Continuous Variables         Mean      Std. Dev.   Min     Max

Load factor (a)              .613        .186      .002     1

No. of departures             37        51.572      0      1424
performed
(per product per month)

Nonstop flight distance     587308      580.515     0      4962
(miles)

HHI (Herfindahl-             2.351       1.915     .171   19.718
Hirschman Index)

Categorical Variables      Frequency

Slot-controlled               483
markets (b)

Non-slot-controlled         14,831
markets

Competitive markets          5,048

Noncompetitive markets      10,266

Codeshare products           4,084
by DL, NW and CO (DNC)

Overlapping markets          1,946
between DL/NW/CO
(MKT (DNC))

Number of Products          35,225

Number of Markets (c)       15,314

(a) Ratio of passenger miles to available seat miles.

(b) Dummy variable coded 1 if origin and destination
airports are slot-controlled.

(c) A market is a directional
origin-destination combination.

Table 5/OLS Regression Results for Effect
of Code-Sharing on Load Factor

                                  (1)           (2)           (3)

Departures Performed (log)    -0.0058 ***   -0.0057 ***   -0.0172 ***
                               (0.0003)      (0.0003)      (0.0003)
Slot Control                  -0.0343 ***   -0.0343 ***     -0.0078
                               (0.0067)      (0.0067)      (0.0061)
Competitive Market            -0.0182 ***   -0.0182 ***   -0.0450 ***
                               (0.0008)      (0.0008)      (0.0008)
HHI                           0.0027 ***    0.0026 ***      -0.0003
                               (0.0002)      (0.0002)      (0.0003)
Distance (log)                0.0471 ***    0.0470 ***    0.0481 ***
                               (0.0006)      (0.0006)      (0.0007)
DNC                           0.0206 ***    0.0202 ***    0.0183 ***
                               (0.0021)      (0.0021)      (0.0019)
[T.sup.dnc]                     0.0008        0.0003        0.0007
                               (0.0008)      (0.0008)      (0.0007)
[T.sup.dnc] X DNC             0.0095 ***    0.0101 ***    0.0103 ***
                               (0.0024)      (0.0024)      (0.0020)
August                                      -0.0056 ***   -0.0051 ***
                                             (0.0013)      (0.0011)
September                                   -0.0099 ***   -0.0088 ***
                                             (0.0013)      (0.0011)
October                                     -0.0097 ***   -0.0093 ***
                                             (0.0013)      (0.0011)
November                                    -0.0083 ***   -0.0089 ***
                                             (0.0013)      (0.0011)
December                                    -0.0114 ***   -0.0116 ***
                                             (0.0013)      (0.0011)
Constant                      0.1736 ***    0.1801 ***    0.1950 ***
                               (0.0178)      (0.0178)      (0.0312)
Aircraft Type Fixed Effects        x             x             x
Market Orig. Fixed Effects                                     x
Market Dest. Fixed Effects                                     x
Carrier Fixed Effects                                          x
Number of Observations          147120        147120        147120
[R.sup.2]                        0.46          0.46          0.62

The equations are estimated using ordinary least squares (OLS).
Fixed effects are incrementally included in each
specification but were not reported for brevity.

Note: Standard errors are in parentheses.
*** p < 0.01; ** p < 0.05; * p < 0.10

Table 6/OLS Regression Results for Effect of
Code-Sharing on Load Factor in Overlapping Markets

                                  (1)           (2)           (3)

Departures Performed          -0.0059 ***   -0.0058 ***   -0.0173 ***
  (log)                        (0.0003)      (0.0003)      (0.0003)
Slot Control                  -0.0413 ***   -0.0414 ***     -0.0066
                               (0.0068)      (0.0068)      (0.0061)
Competitive Market            -0.0199 ***   -0.0199 ***   -0.0450 ***
                               (0.0008)      (0.0008)      (0.0008)
HHI                           0.0024 ***    0.0024 ***      -0.0002
                               (0.0002)      (0.0003)      (0.0003)
Distance (log)                0.0474 ***    0.0473 ***    0.0479 ***
                               (0.0006)      (0.0006)      (0.0007)
DNC                           0.0186 ***    0.0182 ***    0.0191 ***
                               (0.0021)      (0.0021)      (0.0020)
[T.sup.dnc]                     0.0008        0.0003        0.0007
                               (0.0008)      (0.0008)      (0.0007)
[T.sup.dnc] x DNC             0.0185 ***    0.0191 ***    0.0162 ***
                               (0.0026)      (0.0026)      (0.0023)
[MKT.sup.dnc]                 0.0155 ***    0.0155 ***      -0.0022
                               (0.0014)      (0.0014)      (0.0015)
[T.sup.dnc] x DNC x           -0.0291 ***   -0.0290 ***   -0.0189 ***
  [MKT.sup.dnc]                (0.0038)      (0.0038)      (0.0032)
August                                      -0.0056 ***   -0.0051 ***
                                             (0.0013)      (0.0011)
September                                   -0.0099 ***   -0.0088 ***
                                             (0.0013)      (0.0011)
October                                     -0.0096 ***   -0.0093 ***
                                             (0.0013)      (0.0011)
November                                    -0.0083 ***   -0.0088 ***
                                             (0.0013)      (0.0011)
December                                    -0.0115 ***   -0.0116 ***
                                             (0.0013)      (0.0011)
Constant                      0.1734 ***    0.1798 ***    0.1952 ***
                               (0.0178)      (0.0178)      (0.0312)
Aircraft Type Fixed Effects        x             x             x
Market Orig. Fixed Effects                                     x
Market Dest. Fixed Effects                                     x
Carrier Fixed Effects                                          x
Number of Observations          147120        147120        147120
[R.sup.2]                        0.46          0.46          0.62

Notes: The equations are estimated using Ordinary
Least Squares (OLS). Fixed effects are incrementally
included in each specification but were not reported
for brevity. Standard errors are in parentheses.
*** p < 0.01; ** p < 0.05; * p < 0.10

Table 7/Percentage Changes in Overlapping
Markets across Carrier Groups

                            Average Percentage Change

                       Number of      Number of      Load
                     passengers (1)    Seats W    Factor (3)

Codeshare Partners        8.3             2          4.5
Other Carriers            -1.6          -7.7         5.1

Table 8/2SLS Regression Results for Effect
of Code-Sharing on Load Factor

                                    (1)             (2)

Departures Performed (log)      0.0319 ***       0.0318 ***
                                 (0.0005)         (0.0005)
Slot Control                    -0.0381 ***     -0.0379 ***
                                 (0.0023)         (0.0023)
Competitive Market              -0.0048 ***     -0.0049 ***
                                 (0.0008)         (0.0008)
HHI                             -0.0017 ***     -0.0018 ***
                                 (0.0003)         (0.0003)
Distance (log)                  0.0465 ***       0.0464 ***
                                 (0.0006)         (0.0006)
DNC                             0.0133 ***       0.0129 ***
                                 (0.0022)         (0.0022)
[T.sup.dnc]                     0.0023 ***        0.0016 *
                                 (0.0008)         (0.0008)
[T.sup.dnc] x DNC               0.0120 ***       0.0127 ***
                                 (0.0025)         (0.0025)
August                                          -0.0066 ***
                                                  (0.0013)
September                                       -0.0121 ***
                                                  (0.0013)
October                                         -0.0120 ***
                                                  (0.0013)
November                                        -0.0113 ***
                                                  (0.0013)
December                                        -0.0142 ***
                                                  (0.0013)
Constant                        0.1430 ***       0.1510 ***
                                 (0.0186)         (0.0186)
Aircraft Type Fixed Effects          X               X
Market Orig. Fixed Effects
Market Dest. Fixed Effects
Carrier Fixed Effects
Endogeneity Test:              F(1,147000) =   F(1,146995) =
[H.sub.0]: Departures Perf.     10194.5 ***     10148.2 ***
is exogenous                   (p = 0.0000)     (p = 0.0000)
Number of Observations            147120           147120
[R.sup.2]                          0.41             0.41

                                    (3)

Departures Performed (log)      0.0216 ***
                                 (0.0006)
Slot Control                    -0.0557 ***
                                 (0.0046)
Competitive Market              -0.0162 ***
                                 (0.0009)
HHI                             -0.0017 ***
                                 (0.0003)
Distance (log)                  0.0559 ***
                                 (0.0007)
DNC                             0.0216 ***
                                 (0.0020)
[T.sup.dnc]                       0.0011
                                 (0.0007)
[T.sup.dnc] x DNC               0.0134 ***
                                 (0.0021)
August                          -0.0061 ***
                                 (0.0011)
September                       -0.0111 ***
                                 (0.0011)
October                         -0.0120 ***
                                 (0.0011)
November                        -0.0115 ***
                                 (0.0011)
December                        -0.0135 ***
                                 (0.0011)
Constant                        0.2046 ***
                                 (0.0325)
Aircraft Type Fixed Effects          X
Market Orig. Fixed Effects           X
Market Dest. Fixed Effects           X
Carrier Fixed Effects                X
Endogeneity Test:              F(1,145194) =
[H.sub.0]: Departures Perf.     6671.01 ***
is exogenous                   (p = 0.0000)
Number of Observations            147120
[R.sup.2]                          0.58

Notes: The equations are estimated using two stage
least squares (2SLS). Fixed effects are incrementally
included in each specification but were not reported for brevity.
Standard errors are in parentheses.
*** p < 0.01; ** p < 0.05; * p < 0.10

Table 9/2SLS Regression Results for Effect of Code-Sharing
on Load Factor in Overlapping Markets

                                    (1)              (2)

Departures Performed (log)       0.0317 ***      0.0316 ***
                                  (0.0005)        (0.0005)
Slot Control                    -0.0409 ***      -0.0407 ***
                                  (0.0024)        (0.0024)
Competitive Market              -0.0059 ***      -0.0060 ***
                                  (0.0009)        (0.0009)
HHI                             -0.0018 ***      -0.0019 ***
                                  (0.0003)        (0.0003)
Distance (log)                   0.0467 ***      0.0466 ***
                                  (0.0006)        (0.0006)
DNC                              0.0121 ***      0.0116 ***
                                  (0.0022)        (0.0022)
[T.sup.dnc]                      0.0023 ***       0.0017 **
                                  (0.0008)        (0.0008)
[T.sup.dnc] x DNC                0.0188 ***      0.0195 ***
                                  (0.0028)        (0.0028)
[MKT.sup.dnc]                    0.0109 ***      0.0108 ***
                                  (0.0015)        (0.0015)
[T.sup.dnc] x DNC x             -0.0220 ***      -0.0220 ***
  [MKT.sup.dnc]                   (0.0039)        (0.0039)

August                                           -0.0066 ***
                                                  (0.0013)
September                                        -0.0120 ***
                                                  (0.0013)
October                                          -0.0120 ***
                                                  (0.0013)
November                                         -0.0112 ***
                                                  (0.0013)
December                                         -0.0142 ***
                                                  (0.0013)
Constant                         0.1427 ***      0.1506 ***
                                  (0.0186)        (0.0186)
Carrier Fixed Effects                x                x
Aircraft Type Fixed Effects          x                x
Market Orig. Fixed Effects
Market Dest. Fixed Effects
EndogeneityTest:               F(l, 146998) =   F(l,146993) =
[H.sub.0]: departures perf.     10165.3 ***      10119.6 ***
is exogenous                    (p = 0.0000)    (p = 0.0000)
Number of Observations             147120          147120
[R.sup.2]                           0.41            0.41

                                    (3)

Departures Performed (log)      0.0217 ***
                                 (0.0006)
Slot Control                    -0.0558 ***
                                 (0.0046)
Competitive Market              -0.0164 ***
                                 (0.0009)
HHI                             -0.0016 ***
                                 (0.0003)
Distance (log)                  0.0564 ***
                                 (0.0007)
DNC                             0.0217 ***
                                 (0.0020)
[T.sup.dnc]                       0.0011
                                 (0.0007)
[T.sup.dnc] x DNC               0.0197 ***
                                 (0.0024)
[MKT.sup.dnc]                   0.0113 ***
                                 (0.0016)
[T.sup.dnc] x DNC x             -0.0202 ***
  [MKT.sup.dnc]                  (0.0034)

August                          -0.0061 ***
                                 (0.0011)
September                       -0.0111 ***
                                 (0.0011)
October                         -0.0120 ***
                                 (0.0011)
November                        -0.0115 ***
                                 (0.0011)
December                        -0.0135 ***
                                 (0.0011)
Constant                        0.2030 ***
                                 (0.0325)
Carrier Fixed Effects                x
Aircraft Type Fixed Effects          x
Market Orig. Fixed Effects           x
Market Dest. Fixed Effects           x
EndogeneityTest:               F(l,145192) =
[H.sub.0]: departures perf.     6661.55 ***
is exogenous                   (p = 0.0000)
Number of Observations            147120
[R.sup.2]                          0.58

Notes: The equations are estimated using two stage least
squares (2SLS). Fixed effects are incrementally included
in each specification but were not reported for brevity.
Standard errors are in parentheses.
*** p < 0.01; ** p < 0.05; * p < 0.10

Table A1/Airlines in Sample

Code               Airlines

AA          American Airlines (a)
AS           Alaska Airlines (a)
B6             JetBlue Airways
CO        Continental Air Lines (a)
DL           Delta Air Lines (a)
EV            Atlantic Southeast
HP          America West Airlines
MQ              American Eagle
F9          Frontier Airlines (a)
FL           AirTran Airways (a)
G4              Allegiant Air
HA            Hawaiian Airlines
HP          America West Airlines
N7            National Airlines
NJ            Vanguard Airlines
NK             Spirit Air Lines
NW          Northwest Airlines (a)
OO               SkyWest (a)
QX               Horizon Air
RP           Chautauqua Airlines
SM     Sunworld International Airlines
SY           Sun Country Airlines
TZ             ATA Airlines (a)
UA           United Air Lines (a)
US              US Airways (a)
WN            Southwest Airlines
YX             Midwest Airlines

* Carrier is involved in virtual codeshare product.

Table A2/Aircraft Types in Sample

Code   Aircraft

030    Cessna 180
033    Cessna 185A/B/C Skywagon
034    Helio H-250/295/395
035    Cessna C206/207/209/210 Stationair
036    Cessna 172 Skyhawk
040    De Havilland DHC2 Beaver
042    De Havilland DHC3 Otter
079    Piper PA-32 (Cherokee 6)
080    Piper PA-18 (Super-Cub)
084    Piper PA-28 (Cherokee)
091    Float/Amphib Turbine
094    Land-Turbine
111    Beech King Air 90
117    Beech Baron (55 Series)
122    Cessna C-310 Series
125    Cessna C-402/402A/402B
131    Pilatus Britten-Norman BN2/A Islander
133    Beech 65/65A-8O/65B-8O (Oueen Air)
160    McDonnell Douglas DC-3/A/C.C-47/B
170    Grumman C-21A (Goose)
190    Piper PA-23-250 (Aztec/ Apache)
194    Piper PA-31 (Navajo)/T-1020
195    Piper PA-34/39 (Twin Commanche)
201    Pilatus Britten-Norman BN2ATrislander
315    Bell B-206A
360    Robinson R44
404    Beech C99
405    Beech 1900 A/B/C/D
406    Beech 200 Super Kingair
412    Casa/Nurtanio C212 Aviocar
416    Cessna 208 Caravan
417    Cessna 406 Caravan II
430    Convair CV-580
441    Aerospatiale/Aeritalia ATR-42
442    Aerospatiale/Aeritalia ATR-72
449    Dornier 328
450    Fokker Friendship F-27/ Fairchild F-27/A/B/F/J
456    Saab-Fairchild 340/B
461    Embraer EMB-120 Brasilia
467    Swearingen Metro III
469    British Aerospace Jetstream 31
471    British Aerospace Jetstream 41
478    Piper T-1040
479    Pilatus PC-12
482    De Havilland DHC8-400 Dash-8
483    De Havilland DHC8-100 Dash-8
484    De Havilland DHC8-300 Dash 8
485    De Havilland Twin Otter DHC-6
486    Shorts Harland SC-7 Skyvan
489    Shorts 360
491    De Havilland DHC8-2000 Dash-8
602    FokkerF28-4000/6000 Fellowship
612    Boeing 737-700/700LR/ Max 7
614    Boeing 737-800
616    Boeing 737-500
617    Boeing 737-400
619    Boeing 737-300
620    Boeing 737-100/200
621    Boeing 737-200C
622    Boeing 757-200
623    Boeing 757-300
624    Boeing 767-400/ER
625    Boeing 767-200/ER/EM
626    Boeing 767-300/300ER
627    Boeing 777-200ER/200LR/233LR
628    Canadair RJ-100/RJ-100ER
629    Canadair RJ-200ER /RJ-440
630    McDonnell Douglas DC-9-10
631    Canadair RJ-700
632    Domier328Jet
634    Boeing 737-900
635    McDonnell Douglas DC-9-15F
640    McDonnell Douglas DC-9-30
645    McDonnell Douglas DC-9-40
650    McDonnell Douglas DC-9-50
654    McDonnell Douglas DC9 Super 87
655    McDonnell Douglas DC9 Super 80/MD81/82/83/88
656    McDonnell Douglas MD-90
676    Embraer-140
690    Airbus Industrie A300B/ C/F-100/200
691    Airbus Industrie A300-600/R/ CF/RCF
692    Airbus Industrie A310-200C/F
694    Airbus Industrie A320-100/200
696    Airbus Industrie A330-200
698    Airbus Industrie A319
699    Airbus Industrie A321
710    Boeing 727-100
715    Boeing 727-200/231A
730    McDonnell Douglas DC-10-10
732    McDonnell Douglas DC-10-30
733    McDonnell Douglas DC-10-40
735    McDonnell Douglas DC-10-30CF
740    McDonnell Douglas MD-11
760    Lockheed L-1011-1/100/200
765    Lockheed L-1011-500 Tristar
816    Boeing 747-100
817    Boeing 747-200/300
819    Boeing 747-400
835    Avroliner RJ85
868    British Aerospace BAe-146-300
676    Embraer-140
690    Airbus Industrie A300B/ C/F-100/200
691    Airbus Industrie A300-600/R/ CF/RCF
692    Airbus Industrie A310-200C/F

Figure A1 Mean group load factor.
Pre vs. Post Code-sharing

Other Carriers      Load Factor

Before               0.50
After                0.54

Codeshare Partners   Load Factor

Before               0.61
After                0.66

Note: Table made from bar graph.
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Author:Yimga, Jules; Gorjidooz, Javad
Publication:Transportation Journal
Date:Sep 22, 2019
Words:9716
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