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\title{How does vertical integration affect vertical product differentiation? An empirical study of the U.S. airline industry}

\author{Long Shi\footnote{Department of Economics, University of Oklahoma, 308 Cate Center Drive, Norman, OK 73019. E-mail:Long.Shi-1@ou.edu.}}

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\begin{comment}
\begin{abstract}

This paper studies how airlines adjust their quality and price when a competitor integrate / deintegrate with its regional operating partner, and how these adjustments affect the level of vertical product differentiation in the market. I find that as a response to the vertical integration, both major and low cost carriers significantly reduce their departure and arrival delay, and vertical product differentiation increases as the on-time performance of higher-quality major flights gets more improvement. When deintegration takes place, a significant increase of delay is observed for low cost carriers' flights, which again indicates more vertical differentiation. The average fare for both types of carriers rises during vertical differentiation, and it partially proves that quality competition and vertical differentiation mitigates price competition.

\end{abstract}


\textbf{Keywords:} Regional airlines; Vertical integration; Airline industry

\textbf{JEL Classification Codes:} C13, L13, L93

\newpage

\section{Introduction}

As an important sector in U.S. economy, the airline industry has been studied extensively by economists. The rich datasets allow researchers to work on both price (e.g., Borenstein and Rose 1994, Gerardi and Shapiro 2008) and non-price (Kim, Liu and Rupp 2016) strategies of carriers in competition. Among all relevant topics, the development of regional airlines and their role in market competition has drawn more attention in recent years, especially after the enlightening works of Forbes and Lederman.

In 1992, Hanlon pointed out that major carriers could form barriers of entry by outsourcing to regional carriers \footnote{i.e. paying regional carriers to connect passengers under the brand of majors}, and regional carriers would finally "lose their independence". As predicted, regional carriers' reliance on major carriers grew in the following years as major carriers kept expanding their partnership and became the main source of revenue for regional carriers. Data just tell how fast outsourcing to regional carriers expands: In 1998, regional carriers were used on less than 20\% of the routes. 16 years later, regional carriers could be seen on almost 80\% of the routes. At the same time, major carriers also established or acquired more regional airlines as fully-owned subsidiaries. Having noticed the difference in regional carriers' ownership structures, Forbes and Lederman (2009) analyzed the incentive for major airlines to vertically integrate (i.e. using fully-owned regional carriers) on certain routes. The authors argue that fully-owned subsidiaries are more cooperative when unanticipated reconciliation is needed to guarantee mainline flights' access to airport resources, which makes them a reasonable choice at airports with more congested routes and routes under adverse weather conditions.

Following the logic of Forbes and Lederman, a natural question to ask is how integration / deintegration of regional operators affects competition in the market. If major carriers introduce regional subsidiaries mainly to give way to their own fleet, we will expect the on-time performance of the subsidiaries to be worse than independent regional operators, especially during days with heavy air traffic and extremely bad weather. Meanwhile, the operational cost of fully-owned subsidiaries is higher than independent regional carriers, which makes price competition more difficult after vertical integration. If the subsidiary is the only operator for the major on a route, that route would be less competitive and consumer welfare would probably go down. In comparison, the situation is more complicated if majors also operate their own fleet on the route. Theoretically, it can be a situation where product differentiation is enlarged, which possibly mitigates price competition in the market. Looking into different sectors of the market, low cost competitors may be affected more by the change of regional operators as they are arguably more comparable competitors. The other major carriers, on the other hand, may react more to the change of major fleet after the integration / deintegration.This empirical research is to enrich the thin literature regarding the relationship between vertical integration and vertical product differentiation, and hopefully it will be able to support future theoretical exploration.

In this paper, I adopted a similar model as Goolsbee and Syverson (2008) to check how carriers react in quality and price to integration / deintegration of regional operators on the route. Consistent and significant reduction in departure and arrival delay is found when a competing carrier acquires its regional partner. And the comparison across carrier types suggests enlarged vertical product differentiation as the on-time performance of higher-quality major flights gets more improvement. When a competing carrier deintegrates with its regional patner, vertical differentiation will also increase given the significant and positive response in low cost carriers' delay. The average fare for both types of carriers rises during vertical differentiation, suggesting mitigated price competition with larger vertical differentiation.

This paper is organized as follows. The following section is a review of related literature. In section 3, I introduce my data and empirical model. The result is presented and explained in section 4. Section 5 concludes.

\section{Literature Review}

This paper is closely related to the literature about regional carriers. According to the observation of Forbes and Lederman (2007), regional carriers (which operate small regional jets) are mainly used on thin routes. Later empirical works like Pai (2010) also provide consistent evidence. Reiple and Helm (2010) explains that regional carriers can be used to avoid risks under uncertain demand or to lower operational costs.\footnote{Labor costs of regional carriers are lower and the smaller capacity of regional jets better fits thin routes.} With more outsourcing observed on competitive mainline routes in recent years, researchers start to explore the relationship between regional outsourcing and market competition. Tan (2016), for example, finds that independent regional subcontractors are used more on competitive routes, and the use of regional subcontractors lowers the average fare of the major. Forbes and Lederman (2009, 2010) link the ownership structure of regional carriers to the quality of airline service by finding that subsidiaries are used on routes where ex-post reconciliation is more often needed and that the use of subsidiaries improves the on-time performance of majors' own fleet. This paper is actually an extension of their basic findings: Forbes and Lederman find the capability of regional subsidiaries to facilitate their major owners' mainline flight system from the same origin, and my paper focuses on competitors' reaction to it on exactly the same route.

The other relevant strand of literature is about product differentiation in market competition, especially their relationship with integration. There are multiple reasons to believe that passengers think of majors' own flights as higher-quality products comparing to those operated by their regional partners. For example, a passenger would probably think it is safer to take majors' own fleet as they are larger in size and are produced by more famous firms.\footnote{Large aircrafts are more spacious and may also be better equipped for entertainments.} Based on this assumption, we should apply the model for vertically differentiated products to the situation. While no theoretical model has been built to analyze the effect of vertical integration in vertically differentiated markets, the underlying model of Shaked and Sutton (1982) already indicate that firms have the incentive to maximize the quality difference. Given that fully owned subsidiaries face higher operational costs than independent regionals, after integration major carriers should have even stronger motivation to mitigate price competition by differentiating the products. That is to say, vertical integration with regional carriers is supposed to enhance quality competition in markets involved. Matsushima (2009) conducts a theoretical analysis on the effect of vertical integration on horizontal product differentiation, and he concludes that firms will enlarge differences between products after integration. There are also empirical works discussing the effect of ownership changes on product differentiation in the airline industry, but they mostly focus on mergers between competing carriers. Liu et al. (2016) studies the merger between U.S. Airways and America West and finds that they affect both vertical and horizontal differentiation.

\section{Empirical Design}\label{sec:data}

\subsection{Data and Variables}

To measure the level of vertical differentiation, I adopt quality indicators in the on-time performance dataset from Bureau of Transportation Statistics (BTS). Variables used include the monthly average of departure and arrival delays, as well as total number of flight cancellations. All of them are measured at carrier-route level, where route is defined by both origin and destination airports\footnote{That is to say, "Will Rogers World Airport (OKC) to O'Hare International Airport (ORD)" are treated as a different routes from "ORD to OKC".}. Unfortunately, few regional carriers are required to report their on-time performance to BTS and we can only focus on major and low-cost carriers. Similar to regional carriers, low-cost carriers could be viewed as the lower end of quality distribution and the comparison between their on-time performance and that of majors would shed some lights on vertical differentiation in the market. As a supplement, I also check price changes around the time of integration to see if price competition is mitigated by quality differentiation. The average price by carrier-route is constructed using DB1B dataset from BTS, which is a 10\% sample of tickets. This dataset provides clear information on both ticketing and operating carrier and thus allows us to separately test the price reaction of self-operated flights (the higher end of quality distribution) and regional-operated flights (the lower end) when analyzing a major ticketing carrier. To make sure that tickets are comparable, I keep only non-stop, economy-class tickets. To avoid double counting, the return portion of roundtrip tickets is dropped. Also, I drop tickets lower than 10 dollars and the highest 2% in price distribution as these outliers can be special offers and may not reflect the average price level in the market.

The most important explanatory variables are dummy variables indicating when and where vertical integration and deintegration take place. That is to say, the variables take value 1 on a route if a regional operator there is integrated or deintegrated by a competing major ticketing carrier at that specific quarter, otherwise they just take value 0.\footnote{The endogeneity issue is going to be more serious if we check a major's reaction to its own integration / deintegration.} Leads and lags are also added like what Goolsbee and Syverson did in their 2009 paper. Similarly, the operating regional carrier integrated / deintegrated has to be present on the route for these dummies to take value 1. These variables are constructed using DB1B information about ticketing and operating carriers and manually collected timing of vertical integrations. To control for the effect of weather conditions on airline performance, I include precipitation and snowfall at the endpoint as control variables. While a route has two endpoints and thus two values for these weather variables, I pick the higher one for each route. The data source is NOAA report. And to control for the effect of congestion, I include total departures plus arrivals from the origin, which comes from T-100 dataset\footnote{T-100 dataset is another dataset from BTS. I choose it instead of on-time performance data as it reports more carriers than the latter and is supposed to more accurately reflects the level of congestion.} At carrier-route level, I construct the share of passengers transported by regional carriers using DB1B dataset. This variable is to check the size of the route. Finally, I manually collect hub and slot-control status of airports and introduce dummies for routes involving slot-controlled airports, which reflects the access of major carriers to airport resources. When checking the effect of the ownership changes on price, I include HHI, the share of roundtrip tickets \footnote{Both variables are based on DB1B dataset. And to construct HHI, we calculate the market share of ticketing carriers.} and manually collected airline financial conditions (i.e. dummies indicating periods under merger and bankruptcy) as control variables.

Other than these variables, I also construct other variables as intruments. In the first set of models which checks carriers' quality response to ownership changes, I use precipitation and snowfall of the previous year as instruments for the share of passengers carried by owned subsidiaries. Forbes and Lederman (2010) argues that the decision of adopting owned regionals is endogenous, and lagged weather conditions would be perfect instruments for it: On one hand, past weather conditions can affect major carriers' choice of regional partners since they are reliable reference when deciding the necessity of unanticipated reconciliations. On the other hand, these variables do not affect the actual performance in each period and are thus orthogonal to my dependent variables. In the second set of models which checks carriers' price response, I just follow previous literature and use the logarithm of market distance and total enplanements on the route \footnote{These instruments are constructed using DB1B and T-100 dataset respectively.} as the instrument of HHI.

Table 2 is the summary statistics for important variables. As we can see, it is difficult for both major and low cost carriers to depart and arrive exactly at the scheduled time: On average, they leave the origin around 10 minutes later and arrive at the destination 5 minutes later. In terms of total cancellations, each month major carriers cancel about 5 flights on a route while low cost carriers cancel less than 2. But given that major carriers have much larger scale and run more flights than low-cost carriers, this comparison does not necessarily mean that low cost carriers perform better in this aspect. The ownership changes of regional carriers (i.e. integrations and deintegrations) affect about 0.3\% ~ 0.4\% of observations for both groups \footnote{As mentioned earlier, I didn't include  carriers which undertake those integrations / deintegrations so the group of major carriers only contains their competitors on routes involved.}, which suggests that the results for both groups are somewhat comparable. In terms of route characeristics, major carriers' flights depature from busier airports in general, and the weather conditions are quite similar for both major and low-cost carriers' flights. For the major carrier group, both the share of regional operators and that of regional subsidiaries (in all outsourced services) are very low. This is somewhat understandable as we could have included most routes where no regional outsourcing takes place while all completely outsourced routes are definitely excluded\footnote{On those routes we can't observe the performance of major carriers.}.

\subsection{Empirical Models}

The description of variables already explains my model setttings partially, here I just formalize them through equations below:

\begin{equation}
\begin{split}
Performance_{ijt}= \alpha_{1}integration_{jt} +\alpha_{2}deintegration_{jt} +\sum_{k=1}^4 \beta1_k \left( lag_k integration_{jt} \right)\\ + \sum_{k=1}^4 \beta2_k \left( lag_k deintegration_{jt} \right) + \sum_{k=1}^4 \phi1_k \left( lead_k integration_{it} \right) \\+ \sum_{k=1}^4 \phi2_k \left( lead_k deintegration_{it} \right) + \left[\gamma X_{ijt} +\lambda Y_{jt} \right]+ \theta_{ij} + \sigma_t
\end{split}
\end{equation}

\begin{equation}
\begin{split}
\ln fare_{ijt}=\alpha_{1} integration_{jt} +\alpha_{2} deintegration_{jt} +\sum_{k=1}^4 \beta1_k \left( lag_k integration_{jt} \right) \\ + \sum_{k=1}^4 \beta2_k \left( lag_k deintegration_{jt} \right) + \sum_{k=1}^4 \phi1_k \left( lead_k integration_{it} \right) \\ + \sum_{k=1}^4 \phi2_k \left( lead_k deintegration_{it} \right) + \left[\gamma X_{ijt} + \tau Z_{it} \right]+ \theta_{ij} + \sigma_t
\end{split}
\end{equation}

As is shown in the equations, two separate sets of coefficients are assigned to integration and deintegration dummies, as well as corresponding lead and lag dummies. The panel structure of my data allows me to add two-way fixed effects to control for idiosyncrasies by carrier-route and common shocks to the whole industry. Two-stage least squares are used to solve endogeneity issues discussed above. More details will be covered in the following part.

% There is no outside option of not operating on the route at all. This should be something to think about later on.

\section{Results}

\subsection{Quality Adjustments}

\subsubsection{Major Carriers}

Table 3 shows the reaction of major carriers to competitors' integrations and deintegrations with regional partners. From the first column, we can see that major carriers' departure delay is going down around the period of integration, though for most periods the coefficients are insignificant. To be more specific, the departure delay is significant reduced by over two minutes two and four quarters before integrations. And the same change takes place at the quarter of integration and two quarters after it. The result suggests that major carriers are trying to improve the service quality when a competitor tries to do so through vertical integration. As the previous discussion goes, the vertical integration with regional operator is a signal of switch from price competition to quality competition, and our finding here lends support to the argument. From another angle, if there is no similar improvement for lower-quality carriers, we can argue that vertical integration leads to further vertical differentiation. Another point that deserves more explanation is the timing of significant adjustments. Obviously, they take place not only at the quarter of integration. Actually there can be more than one reason behind it. First, the periods of integration is manually collected and they could mark different stages (e.g. announced, approved, completed, etc.) for different integration events. The actual acquisition process could have started earlier or later than the marked period. What's more, it is very common for competing carriers to take preemptive actions or to react later since it takes time to make arrangements. In comparison, the situation is more complicated around the time of deintegration. There are both positive and negative coefficients and both of them are significant. While the aggregate effect seems to be strictly positive, the inconsistency in their signs makes it difficult to draw really reliable conclusion from these coefficients. It somewhat makes sense since deintegration, as the firms stated in their official announcement, is for the regional carrier to find more competitive cost structure. As there is no clear relationship between worse on-time performance and lower operational costs, major competitors here do not have to respond in quality.

The second column of the table shows major carriers' adjustments in arrival delay when integration happens. Comparing it to column (1), we can see that carriers' reaction in arrival delay is very similar to that in departure delay: Integration leads to significant reduction in arrival delay at the quarter of integration. Quantitatively, it means by the period of integration, competitors' flights will arrive over five minutes earlier than before. Two and four quarters after the integration, competitors' arrival delay will further drop by more than nine minutes. Also similar to column (1), there is no clear pattern for arrival changes around the time of a deintegration. All explanations above apply to adjustments in arrival delay as well, and undoubtedly arrival delay partially depends on departure delay. That is why their trends are so similar.

Following the same logic, it is easy to understand the first part of column (3): Total cancellations decrease around the time of competitors' integration. The interesting part of this column is the effect of deintegration. Different from the previous two columns, the coefficients for deintegration dummies are quite consistent and indicate a reduction in cancellation when deintegration happens. It is difficult to find a conclusive story for our finding because "total cancellations" itself is a more complicated indicator, which does not necessarily reflect the quality of service. To be clearer, we can possibly attribute fewer cancellations after deintegration to more outsourcing of major carriers: Facing stronger price competition\footnote{I have explained above that a strong incentive for deintegration is the cost advantage of independent regional carriers.}, a major carrier may want to withdraw from the route and leave more service to its regional partner. When fewer flights are offered, total cancellations will probably go down as well. If that was the case, we can not simply argue that the quality of service is improved.

Plenty of coefficients for control variables are in accordance with our expectation, at least in their signs. For example, we expect worse weather conditions to increase the length of delay and the number of cancellations. In contrast, the share of subsidiaries is expected to reduce them. The total number of flights at the origin, however, has a negative coefficient while congestion is supposed to cause longer delay and more cancellations. One possible reason for this contradiction is the way I construct this variable: Large airports are always connected to more endpoints and they always have longer hours of operation. Carriers' smooth operation on a larger number thinner routes and longer off-peak hours possibly disguises the delay and cancellation problem in busy hours. Setting more restrictions on the sample will probably change the result and that is what I am going to do next.

\subsubsection{Low Cost Carriers}

The quality reaction of low cost carriers is quite different from major carriers, as we can see from Table 4. Before integration, both departure and arrival delay fluctuates without any pattern. After integration, however, the changes become quite consistent: In all four consecutive quarters following the integration, there is a non-trivial and significant reduction in delay. Comparing the aggregate change with major carriers, it seems that vertical integration enlarges the quality gap between major and low cost carriers. And just like the case for major carriers, it is difficult to capture the effect of integration on low cost carriers' flight cancellations. Overall, products become more differentiated with vertical integration as low cost carriers do not improve their performance as much as major carriers.

The difference between major and low cost carriers is more significant in their reactions to deintegration. While major carriers do not really react to deintegration in their on-time performance, low cost carriers remarkably "increases" the delay when deintegration takes place. For almost every quarter of the two-year period, we can find a positive and significant coefficient. There is one possible explanation for this trend: Given their limited access to airport resources and limited power on regional partners, it possibly costs much more for low cost carriers to compete in quality with major carriers. When the regional carrier gets more advantageous cost structure, low cost carriers may have to lower their quality to prepare for the price competition.

The coefficients for control variables here are all in accordance with the intuition. Variables related to regional operators are not included in regression as low cost mainly operates on their own. Their occasional code-share relationship with regional carriers is quite different from the solid partnership between major and regional.

\subsection{Price Adjustments}

Table 5 shows the result of price regression, which suggests competitors' response in price when the ownership of a regional operator is changing. The first column shows the price change for major ticketing carriers and the second column shows the price change of low cost carriers. As the dependent variable is a logarithm, the coefficients of the dummies could be interpreted as the increasing rate of average fare when they take value 1. For example, the coefficient for "LAG4\_Integration" in column (1) indicates that four quarters before an integration is marked on the route, the average price for a major competitor will increase by 3.6\%. The coefficients for integration dummies are always positive for both types of carriers, which from another angle proves that improved quality mitigates price competition in the market. Overall, there are more significant changes in the price of major carriers than low cost carriers, and the magnitude of changes is also larger for major carriers. These facts are consistent with our impression that price is still the major attraction of low cost carriers.

According to the table, both major or low cost carrier raises their price when deintegration happens in the market. Whereas relatively stable quality\footnote{Table 3 shows that major competitors do not change their quality significantly when there is deintegration.} could give major competitors an advantage over the carrier involved in the deintegration, there seems to be no reason for low cost carriers to charge higher prices when the quality of their service also drops. More information like the deintegrating carrier's rearrangement of operators and the change in its performance is needed to better understand its low cost competitors' response. In a word, only the price adjustment to integrations supports the quality competition analysis in the previous section.

\section{Concluding remarks}\label{sec:conclusion}

This is an empirical study of competing airlines' response to the vertical integration or deintegration between a major carrier and its regional partner. It is meant to enrich the thin literature that relates vertical integration to vertical product differentiation. I find that both major and low cost carriers improve their quality when vertical integration takes place, yet the quality gap is still enlarged as major carriers seem to have stronger reaction. When deintegration takes place, the quality gap also goes up with major carriers keeping about the same quality and low cost carriers perform significantly worse. The complexity of the result calls for some adjustment of the basic Shaked and Sutton (1982) model which takes factors like different cost structures of competitors into consideration.

More work could be done to to further improve this paper. First, more restrictions can be set on the sample to make the major carrier group even more comparable to the low cost counterpart. For instance, the comparison is more convincing if the integrated / deintegrated regional carrier takes similar and significant shares for in both subsamples. It would also be meaningful to take advantage of the detailed information from on-time performance data and focus on busy hours of a day and on days with extreme weather conditions, since carriers' performance means much more to passengers in those situations. Last but not least, the result will be improved if more control variables can be found and appropriate methodologies can be used to resolve the concern of endogeneity.

\bigskip

\newpage

\begin{thebibliography}{99}

\bibitem{} Borenstein, Severin, (1989). ``Hubs and High Fares: Dominance and Market Power in the U.S. Airline Industry," \textit{RAND Journal of Economics}, 20, 344-365.

\bibitem{} Borenstein, S. and N. Rose (1994). ``Competition and Price Dispersion in the U.S. Airline Industry," \textit{Journal of Political Economy} 102(4), 653-683.

\bibitem{} Borenstein, S. and N. Rose (1995) ``Bankruptcy and Pricing Behavior in U.S. Airline Markets," \textit{American Economic Review} 85(2), 397-402.

\bibitem{} Brueckner, J., N. Dyer, and P. Spiller, (1992). ``Fare Determination in Airline Hub and Spoke Networks," \textit{RAND Journal of Economics}, 23, 309-333.

\bibitem{} Busse, M. (2002) ``Firm financial condition and airline price wars," \textit{RAND Journal of Economics} 33 (2), 298-318.

\bibitem{} Dai, M, Q. Liu and K. Serfes, (2014), ``Is the effect of competition on price dispersion non-monotonic? Evidence from the U.S. airline industry,'' \textit{Review of Economics and Statistics} 96(1), 161-170 (2014).

%\bibitem{} Fageda, X. and R. Flores-Fillol (2012). ``Air services on thin routes\: Regional versus low-cost airlines," \textit{Transportation Research Part A\: General} 42, 702-714.

\bibitem{} Forbes, S. and M. Lederman (2007). ``The Role of Regional Airlines in the U.S. Airline Industry,"  Working paper.

\bibitem{} Forbes, S. and M. Lederman(2009). ``Adaptation and Vertical Integration in the Airline Industry," \textit{The American Economic Review} 99(5), 1831-1849.

\bibitem{} Forbes, S. and M. Lederman(2010). ``Does vertical integration affect firm performance? Evidence from the airline industry," \textit{RAND Journal of Economics} 41(4), 765-790.

\bibitem{} Gerardi, K. and A. Shapiro (2008). ``Does Competition Reduce Price Dispersion? New Evidence from the Airline Industry," \textit{Journal of Political Economy} 117(1), 1-37.

\bibitem{} Goolsbee, A. and C. Syverson (2008). ``How Do Incumbents Respond to the Threat of Entry? Evidence from the Major Airlines," \textit{Quarterly Journal of Economics} 123(4), 1611-1633.

%\bibitem{} Graham, B. and T. Vowles (2006). ``Carriers within Carriers\: A Strategic Response to Low-cost Airline Competition," \textit{Transport Reviews} 26(1), 105-126.

%\bibitem{} Hanlon, J.P. (1992). ``Regional air services and airline competition," \textit{Tourism Management} 13(2), 181-195.

\bibitem{} Heiss, F. (2002). ``Structural choice analysis with nested logit models," \textit{The Stata Journal} 2 （3）, 227-252.

\bibitem{} Ito, H. and D. Lee (2007). ``Code Sharing, Alliances, and and Airfares in the U.S. Airline Industry," \textit{The Journal of Law $\&$ Economics} 50(2), 355-380.

\bibitem{} Kaemmerle, K. (1991). ``Estimating the Demand for Small Community Air Service," \textit{Regional Science and Urban Economics} 25(2-3), 101-112.

\bibitem{} Kim, M, Q. Liu and L. Shi (2016). ``Airline fuel costs: Hedging and pass-through," working paper.

\bibitem{} Kim, M, Q. Liu and N. Rupp (2016). ``When do Firms Offer Higher Product Quality? Evidence from the Allocation of Inflight Amenities," working paper.

%\bibitem{} Lin, M. (2012). ``Airlines-within-airlines strategies and existence of low-cost carriers," \textit{Transportation Research Part E} 48, 637-651.

\bibitem{} Mazzeo, M. (2003). ``Competition and Service Quality in the U.S. Airline Industry," \textit{Review of Industrial Organization} 22: 275-296.

\bibitem{} Neuhaus, J.M. and J.D. Kalbfleisch (1998). ``Between- and Within-Cluster Covariate Effects in the Analysis of Clustered Data," \textit{Biometrics} 54, 638-645.

\bibitem{} Oster, C. and D. Pickrell (1988). ``Code Sharing, Joint Fares, and Competition in the Regional Airline Industry," \textit{Transportation Research Part A\: General} 22(6), 405-417.

%\bibitem{} Pai, V. (2010). ``On the factors that affect airline flight frequency and aircraftt size," \textit{Journal of Air Transportation Management} 16, 169-177.

\bibitem{} Prince, J. and D. Simon. ``The Impact of Mergers on Quality Provision: Evidence from the Airline Industry," forthcoming, \textit{Journal of Industrial Economics}.

\bibitem{} Rieple, A. and C. Helm (2008). ``Outsourcing for competitive advantage\: An examination of seven legacy airlines," \textit{Journal of Air Transportation Management} 14, 280-285.

\bibitem{} Tan, K. (2016a). ``Incumbent Response to Entry by Low-Cost Carriers in the U.S. Airline Industry," \textit{Southern Economic Journal} 82(3), 874-892.

\bibitem{} Tan, K. (2016b). ``Pro-Competitive Vertical Integration: The Relationship between Legacy Carriers and Regional Airlines," working paper
\end{thebibliography}
\newpage
\end{comment}

\begin{center}
\textbf{\large{Chapter Three Appendix - Tables}}
\end{center}

\begin{table}[htbp!]\centering \caption{List of Integrations and Deintegrations}
\medskip

\begin{tabular}{l*{3}{c}}
            &\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}\\


\textbf{Major Carrier}    &   \textbf{Regional Carrier}  &    \textbf{Time} \\\hline
\textbf{Integrations}    &    &    \\\hline
American Airlines (AA)                     &      Business Express   &  12/1998  \\
Delta Airlines (DL)                            &      Atlantic Southeast Airlines      &   03/1999  \\
                                                      &      Comair Airlines     &  10/1999  \\
                                                      &      Pinnacle Airlines                    &  05/2013    \\
Northwest Airlines (NW)                    &      Mesaba Airlines                     &  04/2007    \\\hline
\textbf{Deintegrations}    &    &    \\\hline
Continental Airlines (CO)                  &      Continental Express / Expressjet &   04/2002     \\
Delta Airlines (DL)                            &      Atlantic Southeast Airlines         &    09/2005    \\
                                                      &      Compass Airlines                      &    07/2010     \\
                                                      &      Mesaba Airlines                         &    07/2010    \\
Northwest Airlines (NW)                    &     Express Alines I / Pinnacle Airlines  &  05/2002     \\
\hline
\end{tabular}
\end{table}

\begin{table}[htbp!]\centering \caption{Summary stats \label{table:sumstats}}
\medskip

\begin{tabular}{l c c c c c}\hline\hline
\multicolumn{1}{c}{\textbf{Variable}} & \textbf{Mean}
 & \textbf{Std. Dev.}& \textbf{Min.} &  \textbf{Max.} & \textbf{N}\\ \hline
\textbf{Major Carriers} \\ \hline
Departure delay & 7.919 & 9.992 & -46 & 1170 & 261480\\
Arrival delay & 5.475 & 11.874 & -60 & 1182 & 261456\\
Cancellation & 4.832 & 13.149 & 0 & 578 & 261578\\
Integration  & 0.001 & 0.022 & 0 & 1 & 261578\\
Deintegration & 0.002 & 0.046 & 0 & 1 & 261578\\
Total flights & 28054.711 & 17723.789 & 176 & 80565 & 261578\\
Precipitation & 4.258 & 2.844 & 0 & 29.4 & 261578\\
Snowfall & 2.654 & 6.305 & 0 & 82.709 & 261578\\
Slot & 0.185 & 0.388 & 0 & 1 & 261578\\
Regional share & 0.095 & 0.208 & 0 & 1 & 261578\\
Subsidiary share & 0.069 & 0.238 & 0 & 1 & 261578 \\\hline

\textbf{Low Cost Carriers} \\ \hline
Departure delay & 9.677 & 9.557 & -53 & 349 & 147346\\
Arrival delay & 4.501 & 11.634 & -82 & 770 & 147318\\
Cancellations & 1.624 & 5.182 & 0 & 194 & 147380\\
Integration  & 0.001 & 0.034 & 0 & 1 & 147380\\
Deintegration  & 0.003 & 0.056 & 0 & 1 & 147380\\
Total flights & 20357.43 & 16154.248 & 39 & 80565 & 147380\\
Precipitation & 4.062 & 2.896 & 0 & 29.53 & 147380\\
Snowfall & 2.485 & 6.191 & 0 & 82.709 & 147380\\
Slot & 0.082 & 0.274 & 0 & 1 & 147380\\\hline

\end{tabular}
\end{table}


\begin{table}[htb!]
\begin{center}
\footnotesize
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{Quality Regression for Major Carriers}
\medskip

\resizebox*{!}{.75\paperheight}{
\begin{tabular}{l*{3}{c}}
\hline
            &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}\\
            &\multicolumn{1}{c}{Departure delay}&\multicolumn{1}{c}{Arrival delay}&\multicolumn{1}{c}{Cancellations}\\
\hline
LAG4\_Integration    &      -2.224\sym{*}  &      -4.079         &      -0.834         \\
            &     (1.277)         &     (2.496)         &     (0.779)         \\
LAG3\_Integration     &      -0.483         &      -1.332         &      -1.544\sym{**} \\
            &     (1.353)         &     (2.234)         &     (0.653)         \\
LAG2\_Integration     &      -2.521\sym{**} &      -3.533         &      -2.548\sym{**} \\
            &     (1.174)         &     (2.151)         &     (1.042)         \\
LAG1\_Integration     &      -1.705         &      -3.512         &      -2.757\sym{***}\\
            &     (1.596)         &     (2.415)         &     (0.782)         \\
Integration        &      -2.512\sym{*}  &      -5.176\sym{**} &      -1.549         \\
            &     (1.381)         &     (2.205)         &     (0.963)         \\
LEAD1\_Integration   &      0.0861         &      -0.386         &     -0.0631         \\
            &     (1.240)         &     (2.547)         &     (1.579)         \\
LEAD2\_Integration   &      -3.331\sym{***}&      -5.557\sym{***}&      -1.271         \\
            &     (1.105)         &     (2.030)         &     (1.184)         \\
LEAD3\_Integration   &      -2.179         &      -3.285         &      -3.423\sym{***}\\
            &     (1.392)         &     (2.750)         &     (1.074)         \\
LEAD4\_Integration   &      -1.734\sym{*}  &      -3.455\sym{**} &      -2.682\sym{***}\\
            &     (0.888)         &     (1.681)         &     (0.879)         \\
LAG4\_Deintegration  &       9.233\sym{***}&       18.11\sym{***}&       2.905\sym{*}  \\
            &     (1.606)         &     (2.724)         &     (1.693)         \\
LAG3\_Deintegration  &       8.219\sym{***}&       15.03\sym{***}&       2.234         \\
            &     (1.416)         &     (2.386)         &     (1.439)         \\
LAG2\_Deintegration  &      -0.432         &      -0.857         &      -1.355\sym{**} \\
            &     (0.727)         &     (1.321)         &     (0.601)         \\
LAG1\_Deintegration  &      -1.223\sym{*}  &      -2.010\sym{*}  &      -1.268\sym{***}\\
            &     (0.632)         &     (1.165)         &     (0.491)         \\
Deintegration      &       0.274         &       0.119         &      -1.500\sym{***}\\
            &     (0.579)         &     (0.942)         &     (0.504)         \\
LEAD1\_Deintegration &      -0.484         &      -0.350         &      -1.038\sym{**} \\
            &     (0.693)         &     (1.260)         &     (0.498)         \\
LEAD2\_Deintegration &      -1.175\sym{*}  &      -2.463\sym{**} &      -2.892\sym{***}\\
            &     (0.655)         &     (1.159)         &     (0.542)         \\
LEAD3\_Deintegration &       0.459         &      -0.403         &      -0.597         \\
            &     (0.698)         &     (1.168)         &     (0.484)         \\
LEAD4\_Deintegration &       0.897         &       1.113         &      -0.283         \\
            &     (0.736)         &     (1.215)         &     (0.543)         \\
Total\_flights&  -0.0000571\sym{***}&   -0.000124\sym{***}&   -0.000140\sym{***}\\
            & (0.0000185)         & (0.0000328)         & (0.0000237)         \\
Regional\_share&       5.577\sym{***}&       9.322\sym{***}&      -1.525         \\
            &     (1.074)         &     (1.860)         &     (1.060)         \\
Subsidiary\_share  &      -24.55\sym{***}&      -50.17\sym{***}&      -7.817         \\
            &     (4.548)         &     (7.763)         &     (5.160)         \\
Snowfall    &      0.0961\sym{***}&       0.160\sym{***}&       0.239\sym{***}\\
            &   (0.00558)         &   (0.00814)         &    (0.0123)         \\
Precipitation&       0.268\sym{***}&       0.409\sym{***}&       0.218\sym{***}\\
            &   (0.00966)         &    (0.0137)         &    (0.0108)         \\
Hub         &       3.959\sym{**} &       0.717         &       0.263         \\
            &     (1.765)         &     (2.438)         &     (0.489)         \\
Slot        &      -2.091\sym{***}&      -1.879\sym{**} &       4.628\sym{***}\\
            &     (0.499)         &     (0.903)         &     (1.202)         \\
Fixed Effects       &         Yes         &         Yes         &         Yes         \\
Period Dummies   &         Yes         &         Yes         &         Yes         \\
\hline
\(N\)       &      261159         &      261151         &      261246         \\
\hline\hline
\multicolumn{4}{l}{\footnotesize Standard errors in parentheses}\\
\multicolumn{4}{l}{\footnotesize \sym{*} \(p<0.1\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}}
\end{center}
\end{table}

\begin{table}[htb!]
\begin{center}
\footnotesize
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{Quality Regression for Low Cost Carriers}
\medskip

\resizebox*{!}{.75\paperheight}{
\begin{tabular}{l*{3}{c}}
\hline
            &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}\\
            &\multicolumn{1}{c}{Departure delay}&\multicolumn{1}{c}{Arrival delay}&\multicolumn{1}{c}{Cancellations}\\
\hline
LAG4\_Integration    &       0.129         &      -1.812\sym{**} &      -1.377\sym{***}\\
            &     (0.724)         &     (0.899)         &     (0.345)         \\
LAG3\_Integration    &       2.707\sym{***}&       1.030         &      -0.859\sym{*}  \\
            &     (0.805)         &     (0.971)         &     (0.479)         \\
LAG2\_Integration    &     -0.0216         &      -0.675         &       2.299\sym{***}\\
            &     (0.530)         &     (0.699)         &     (0.631)         \\
LAG1\_Integration    &       1.508\sym{**} &       1.688\sym{*}  &      -0.890\sym{**} \\
            &     (0.736)         &     (0.917)         &     (0.378)         \\
Integration         &       1.645\sym{**} &       0.411         &       0.243         \\
            &     (0.776)         &     (0.822)         &     (0.344)         \\
LEAD1\_Integration   &      -1.322\sym{**} &      -2.313\sym{***}&      -1.222\sym{***}\\
            &     (0.628)         &     (0.681)         &     (0.332)         \\
LEAD2\_Integration   &      -2.272\sym{**} &      -2.337\sym{**} &      -1.541\sym{***}\\
            &     (0.932)         &     (0.959)         &     (0.360)         \\
LEAD3\_Integration   &      -1.873\sym{**} &      -1.567\sym{*}  &       3.316\sym{***}\\
            &     (0.852)         &     (0.949)         &     (0.850)         \\
LEAD4\_Integration   &      -1.675\sym{**} &      -2.737\sym{***}&       0.999\sym{**} \\
            &     (0.832)         &     (0.877)         &     (0.423)         \\
LAG4\_Deintegration  &       1.197\sym{**} &       0.312         &       0.978         \\
            &     (0.493)         &     (0.625)         &     (0.641)         \\
LAG3\_Deintegration  &       1.971\sym{***}&       2.213\sym{***}&       0.999\sym{***}\\
            &     (0.479)         &     (0.662)         &     (0.317)         \\
LAG2\_Deintegration  &       2.008\sym{***}&       2.229\sym{***}&      0.0671         \\
            &     (0.551)         &     (0.657)         &     (0.244)         \\
LAG1\_Deintegration  &       1.985\sym{***}&       1.409\sym{**} &       0.374\sym{**} \\
            &     (0.496)         &     (0.593)         &     (0.167)         \\
Deintegration       &       3.410\sym{***}&       3.496\sym{***}&      0.0512         \\
            &     (0.513)         &     (0.573)         &     (0.167)         \\
LEAD1\_Deintegration &       2.801\sym{***}&       2.911\sym{***}&       0.181         \\
            &     (0.495)         &     (0.586)         &     (0.241)         \\
LEAD2\_Deintegration &       2.543\sym{***}&       2.438\sym{***}&      -0.641\sym{***}\\
            &     (0.547)         &     (0.663)         &     (0.199)         \\
LEAD3\_Deintegration &       2.039\sym{***}&       1.640\sym{***}&     0.00924         \\
            &     (0.456)         &     (0.541)         &     (0.190)         \\
LEAD4\_Deintegration &       2.771\sym{***}&       3.312\sym{***}&       0.517\sym{**} \\
            &     (0.447)         &     (0.569)         &     (0.245)         \\
Total\_flights&    0.000105\sym{***}&    0.000122\sym{***}&  -0.0000105         \\
            & (0.0000221)         & (0.0000282)         & (0.0000132)         \\
Snowfall    &       0.102\sym{***}&       0.172\sym{***}&       0.184\sym{***}\\
            &   (0.00649)         &   (0.00838)         &   (0.00968)         \\
Precipitation&       0.247\sym{***}&       0.321\sym{***}&       0.120\sym{***}\\
            &    (0.0118)         &    (0.0144)         &   (0.00669)         \\
Fixed Effects       &         Yes         &         Yes         &         Yes         \\
Period Dummies   &         Yes         &         Yes         &         Yes         \\
\hline
\(N\)       &      261159         &      261151         &      261246         \\
\hline\hline
\multicolumn{4}{l}{\footnotesize Standard errors in parentheses}\\
\multicolumn{4}{l}{\footnotesize \sym{*} \(p<0.1\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}}
\end{center}
\end{table}

\newpage

\begin{table}[htb!]
\begin{center}
\footnotesize
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{Price Regression}
\medskip

\resizebox*{!}{.75\paperheight}{
\begin{tabular}{l*{2}{c}}
\hline
            &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}\\
            &\multicolumn{1}{c}{lnfare\_Major}&\multicolumn{1}{c}{lnfare\_LCC}\\
\hline
LAG4\_Integration    &        0.0361\sym{**}         &         0.0341\sym{*} \\
            &       (0.0179)         &     (0.0200)                   \\
LAG3\_Integration    &        0.0273 &        0.0169        \\
            &     (0.0194)          &      (0.0188)                \\
LAG2\_Integration    &     0.0132           &       0.0223      \\
            &      (0.0198)            &       (0.0232)               \\
LAG1\_Integration    &        0.0271 &         0.0131    \\
            &     (0.0187)          &      (0.0210)               \\
Integration        &        0.0292 &         0.0539\sym{***}              \\
            &     (0.0192)          &     (0.0191)                  \\
LEAD1\_Integration   &       0.0270 &         0.0172  \\
            &      (0.0189)          &      (0.0228)            \\
LEAD2\_Integration   &      0.0525\sym{***} &      0.0500\sym{**} \\
            &       (0.0198)          &     (0.0232)                 \\
LEAD3\_Integration   &      0.0711\sym{***} &       0.0310  \\
            &     (0.0218)         &      (0.0223)               \\
LEAD4\_Integration   &      0.0914\sym{***} &     0.0556\sym{***} \\
            &      (0.0188)          &     (0.0202)              \\
LAG4\_Deintegration  &       -0.00249   &       0.0605\sym{***}              \\
            &      (0.0143)        &      (0.0185)             \\
LAG3\_Deintegration  &      0.0525\sym{***}&         0.0204  \\
            &     (0.0168)          &      (0.0175)                 \\
LAG2\_Deintegration  &        0.110\sym{***}&        0.0741\sym{***}    \\
            &     (0.0225)         &     (0.0188)           \\
LAG1\_Deintegration  &       -0.0251&       0.0399\sym{***}  \\
            &     (0.0169)         &     (0.0142)                  \\
Deintegration      &         -0.0153    &       0.0346\sym{***}      \\
            &       (0.0127)           &     (0.0140)                 \\
LEAD1\_Deintegration &      -0.0152 &       0.0238\sym{*}       \\
            &     (0.0145)         &     (0.586)              \\
LEAD2\_Deintegration &       0.0443\sym{**}&        0.0188 \\
            &       (0.0183)          &      (0.0159)                 \\
LEAD3\_Deintegration &        0.00764  &         0.0216       \\
            &      (0.0174)         &     (0.0166)             \\
LEAD4\_Deintegration &       0.0414\sym{**}&       0.0431\sym{***} \\
            &        (0.0183)        &      (0.0135)              \\
HHI &    0.945\sym{***}&    1.122\sym{***}         \\
            &     (0.113)         & (0.138)               \\
Roundtrip\_share    &       -0.234\sym{***}&       -0.0641\sym{***}\\
            &    (0.00773)        &       (0.0113)                       \\
Merger &       0.0246\sym{***}&      0.0753\sym{***}\\
            &    (0.00398)        &   (0.00677)                \\
Bankruptcy &        -0.0128\sym{***}&        -0.0687\sym{***}\\
            &    (0.00380)        &     (0.0118)                   \\
Fixed Effects       &         Yes         &         Yes             \\
Period Dummies   &         Yes         &         Yes             \\
\hline
\(N\)       &      245471         &       98150                 \\
\hline\hline
\multicolumn{3}{l}{\footnotesize Standard errors in parentheses}\\
\multicolumn{3}{l}{\footnotesize \sym{*} \(p<0.1\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}}
\end{center}
\end{table}

\begin{comment}
\textbf{(QL: I removed the following from the main text. Later on, when there is more time, you will need to add them back in Appendix here.)}

According to the rule of BTS, multi-segment itineraries should be reported by the operating carrier of the first segment. And if the reporting carrier does not have information regarding the following operating carriers\footnote{For example, if the reporting carrier is a code-share partner of the ticketing carrier and only takes charge of the first segment, it may not know the operating carriers on the following segments.}, it is allowed to report the ticketing carrier as the operating carrier on all the other segments. This rule undermines the accuracy of DB1B observations and will wrongly increase “no outsourcing” cases.To control its impact on my results I drop suspicious carrier-route combinations: If all "no outsourcing” records on a route are reported by a different carrier and if none of them is reported as the first segment of an itinerary, I drop all tickets on that route and by that ticketing carrier. As mentioned before, my work differs from the previous literature by distinguishing “partial outsourcing” from “complete outsourcing”, so it is as important to make sure of the presence of both legacy and regional carriers before adopting the carrier-route observation. But there are exceptions: I will take suspicious "no outsourcing" observations as true if I can find consistent records in T-100 dataset from BTS. Different from DB1B, T-100 observations are based on segments so there is no inconsistency between reporting and operating carriers.

Since I am checking price adjustments under different operating carrier combinations, I drop carrier-routes if there is uncertainty with the "no outsourcing" observations. If outsourcing happens following the entry of some low cost carrier,  whether it is triggered by that entry or not, it can be difficult to disentangle the price effect of LCC entry from that of outsourcing. To make the explanation more straightforward, I only keep carrier-routes with consistent operating carrier combination throughout the sample period.


\end{comment}

\end{document}

