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\begin{comment}
\title{How does competition affect product choices? An empirical analysis of the U.S. airline industry\footnote{I would like to thank my co-advisors Qihong Liu and Myongjin Kim for their invaluable insights and guidance. I would also like to thank Kerry Tan, the discussant for this paper at the International Industrial Organization Conference (2017), and Industrial Organization workshop at the University of Oklahoma for helpful comments and suggestions.}}

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

\date{\today}

\begin{document}
\maketitle

\begin{abstract}
This paper studies major airlines' choice of whether or not to outsource operations to regional airlines across routes and over time.  Using panel data of the U.S. airline industry, we find significant differences on the pattern of outsourcing to regional airlines depending on whether the major airlines operate their own major fleets on the route as well. In particular, if $HHI$ increases by $0.1$, the log likelihood of a major airline choosing \textit{complete outsourcing} relative to \textit{no outsourcing}, goes up by 3.3\%. This log likelihood goes down by 5.8\% if the major airline's market share increases by 0.1. In contrast, the log likelihood of \textit{partial outsourcing} relative to \textit{no outsourcing} goes down by 16.7\% if $HHI$ goes by 0.1, and goes up by 17.8\% if the major airline's market share goes up by 0.1. Taking into account the ownership of regional airlines, we find that when facing more LCC competition, major airlines are more likely to rely on wholly owned subsidiaries relative to independent regional airlines. This lends support to the commonly held view that major airlines rely on regional airlines to compete with LCCs. We also investigate how major airlines adjust their prices when facing either LCC entry threat or actual entry. For carrier-routes with no outsourcing, we find that major airlines do not respond in prices to threat of or actual LCC entry. In contrast, on carrier-routes with partial outsourcing, major airlines lower their prices by about 5.2\% one quarter after LCC entry, with no adjustments in other periods.
\end{abstract}

\textbf{Keywords:} Regional airlines; Low-cost carriers; Outsourcing; Airline industry

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

\newpage

\section{Introduction}

Airline industry is probably one of the most studied industries by economists. The literature on the airline industry has considered diverse topics ranging from pricing and price discrimination (e.g., Borenstein and Rose 1994, Gerardi and Shapiro 2008, Dai, Liu and Serfes 2014), hub premium (Borenstein 1989) to airline financial conditions (Borenstein and Rose 1995, Busse 2002), code-sharing (Ito and Lee 2007), fuel cost pass through (Kim, Liu and Shi 2016), product quality (Mazzeo 2003, Prince and Simon (forthcoming), Kim, Liu and Rupp 2016) and low-cost carriers (Goolsbee and Syverson 2008).

Largely missing from this picture are regional airlines and what roles they play in the U.S. airline industry.\footnote{Exceptions include Octer and Pickrell (1988), Forbes and Lederman (2009) and Tan (2016b). See also Forbes and Lederman (2007) for an excellent introduction on regional carriers.} According to a recent Wall Street Journal article, ``Regional carriers are vital to the U.S. travel network, operating 44\% of passenger flights in 2015 and providing the only flights to 65\% of U.S. airports with scheduled service."\footnote{``Pilot Shortage Prompts Regional Airlines to Boost Starting Wages," \textit{Wall Street Journal}, November 7, 2016. This trend can also be seen in Table \ref{table:regional} where I report the number of carrier-route-quarters with vs. without regional outsourcing over time. 1998 and 2014 are the first and last period of my sample. 2002 is around the time where jet technology experienced a breakthrough for small, short-to medium-haul jet to be economically viable.} One may wonder why such an integral component of the industry has been ignored for the most part. One reason may be because even though regional airlines together represent a large part of the industry, there are so many regional airlines which also come with different ownership structures (e.g., they may be owned by major airlines or can be independent). Moreover, since regional airlines are usually operating carriers rather than ticketing carriers, researchers focusing on ticketing carriers would miss them.\footnote{One strand of the literature does scrutinize ticketing-operating carrier combination, in the form of code-sharing. Obviously researchers are mostly interested in code-sharing between major airlines so the operating carriers are still major carriers rather than regional carriers.} Combined, despite the fast growth of regional airlines, they have rarely been analyzed by economists. In contrast, the rapid expansion of low-cost carriers (LCCs, e.g., Southwest) has been well documented.

My interest in regional airlines started with wholly-owned regional airlines (subsidiaries of major airlines), and was inspired by the conjecture that major airlines develop their subsidiary regional airlines to better compete with LCCs (Southwest in particular). Using DB1B data from year 1998 to 2014, I aim to investigate two questions relating to regional airlines and LCC competition. \textit{First}, how do major airlines make their product choices across routes and over time, in terms of whether to fly their own fleets and/or outsource to regional airlines (subsidiaries or subcontractors)? How does this choice depend on market structure (market share, HHI etc.) as well as competition from LCCs? \textit{Second}, how does entry threat and/or actual entry of LCCs affect major airlines' ticket prices? And are there differential impacts on flights operated the major airlines themselves vs. flights operated by regional airlines?

I first analyze major airlines' choice of operating carriers and how that choice is affected by competition. Each major airline can choose a combination among major, subsidiary regional and independent regional airlines. Viewing each as a product choice, we have a total of 7 possible combinations (providing 1, 2 or all 3 products respectively). Having 7 choices is difficult to run estimation but even more tedious to interpret the corresponding results. Therefore, I aggregate subsidiary and independent regional airlines together, and the consider only three choices: (1) major only (no outsourcing); (2) regional only (complete outsourcing) and (3) major and regional (partial outsourcing). A major airline may compete with both major airlines and LCCs.\footnote{Even though Southwest is also a major airline in terms of scale, I code it as an LCC instead, given my focus to identify the impacts of LCC competition.} I apply a mixed effects multinomial logit model to study how major airlines' product choice is impacted by competition. I distinguish among different types of competition, for example, a competing major airline outsourcing to regional airlines (major on regional) vs. an LCC operating its own flights (LCC on LCC). My results suggest that relative to choosing major only, major carriers are more likely to use regional carriers jointly with their own fleets (1) when markets become more competitive, (2) when they have larger market share and (3) when there are more LCCs competing on the same routes. Major airlines competing on the same routes also tend to mimic each other's behavior.\footnote{This is likely triggered by route characteristics which also confirms the importance of using panel data to controll for route fixed effects.} That is, they are more likely to adopt the combination when more competing major carriers use regional airlines as well. In contrast, major airlines are more likely to go from major only to regional only (1) when the market becomes more concentrated and (2) when their market shares go down.

My analysis differs from existing studies on regional airlines in several aspects. \textit{First}, while some papers consider the presence of regional airlines, they do not consider the full combination of operating carriers. For example, as long as regional airlines are used, then the product choices are organized into the same group whether the major airline operates its own flights or not. In contrast, I distinguish between regional only vs. major and regional. As my results show, the rationale of using regional airlines can be quite different between the two product choices of regional only vs. major and regional. \textit{Second}, I use panel data which allow me to control for unobserved route characteristics that are common on the same route over time but are heterogeneous across routes.

I then distinguish between subsidiary and independent regional airlines. Using one quarter of cross section data, I was able to mimic the key results in Forbes and Lederman (2009). That is, major airlines are more likely to choose subsidiaries over independent subcontractors on routes with worse weather conditions, thus requiring more constant re-negotiations. I also take advantage of my panel data and distinguish between complete outsourcing and partial outsourcing in the top nest. The nested logit results are qualitatively similar to the multinomial logits discussed above.

I also analyze how fares vary with competition, in particular, competition generated by LCC entry. For carrier-routes where the major airlines do not outsource to regional airlines (before and after LCC entry), fares do not seem to respond to threat of or actual LCC entry. For carrier-routes where major airlines use regional airlines throughout the sample, major airlines lower their prices by about 5.2\% one quarter after entry, but no further adjustment either before entry or more than one quarter after entry. I also calculate the price gap between major and regional flights on the same carrier-routes, and find that this price gap increases one quarter before LCC entry.

\subsection{Literature Review}

Some of the earlier literature on the airline industry look at the hub-and-spoke system and the related hub premium (Brueckner et. al. 1992, Borenstein 1989). Others look at pricing and price discrimination. For example, Borenstein and Rose (1994) analyze the relationship between price dispersion and market concentration. They find that price dispersion is higher on routes that are more competitive. Gerardi and Shapiro (2008) use panel data and find opposite relationship.\footnote{Dai et. al. (2014) allow and identify a non-monotonic relationship between market concentration and price dispersion.}

This paper is closely related to the literature on product choice involving regional airlines. That is, what conditions would tip a major airline toward using regional airlines as opposed to operate its own major fleet? Rieple and Helm (2008) list a few theories as to why firms may choose to outsource and test these theories using airline industry data. Forbes and Lederman (2009) analyze how major airlines choose between subsidiary and independent regional airlines. The tradeoff is that using fully owned subsidiaries increases operational cost but reduces the cost of making unanticipated schedule adjustments (adaptation). Subsidiaries, being fully owned by the major airlines, will be more cooperative when reconciliation is needed (e.g., due to weather caused delays and cancellations). And ex-ante it is difficult to contract with independent regional airlines to have similar level of cooperation and flexibility. Therefore, the choice of subsidiaries over subcontractors is mainly to reduce the cost of reconciliation. Their results show that subsidiaries are more likely to be used on routes with more adverse weather (more frequent adaption needed) and on routes that are more integrated into the major carriers's network (so the value of adaptation high).\footnote{A follow up paper Forbes and Lederman (2010) analyzes the impacts of vertical integration on efficiency, by comparing the efficiency of routes operated by subsidiaries vs. those operated by independent regional partners. They find that using subsidiaries rather than independent regionals improves the major airlines' efficiency, measured by fewer flight delays and cancelations. Moreover, this efficiency improvement from using subsidiaries is more prominent for airports with more adverse weather and more crowded airports.} Our papers differ in multiple aspects. First, I distinguish between complete outsourcing and partial outsourcing, as the rationale to partner with regional airlines can differ depending on whether the major airlines operate on the routes themselves. Second, instead of cross sectional data, I use panel data which allow me to better control route characteristics which differ across routes but are fixed over time.

Our paper is also closely related to the literature analyzing the impact of LCC competition on prices. Goolsbee and Syverson (2008) is a pioneering study looking at how incumbent airlines respond to the threat of entry. They measure the threat of entry by the situation where Southwest operates at both end airports of a route but do not operate on that route. They document significant fare cuts by incumbent airlines in anticipation of Southwest entry. The fare cuts appear on routes involving Southwest-operating airports, but not on alternative airports that serve the same city. Majority of the fare cuts take place before the actual Southwest entry and are mainly from concentrated routes pre-entry. We consider a variety of LCCs (not just Southwest) and we also allow the LCC entry and entry threat to have differential impacts depending on major airlines' product choice (major vs. regional etc.) Tan (2016a) considers more LCCs and analyzes how incumbent major airlines and LCCs may respond differently to LCC entry. He finds that while incumbent major airlines tend to reduce their fares, incumbent LCCs do not significantly change their pricing strategy. Tan (2016b) considers both regional airlines and LCC competition. He finds that on routes facing either actual or potential competition from LCCs, legacy carriers are more likely to use independent regional partners relative to use their own major fleets or subsidiaries. He also finds that prices are lower on flights operated by independent regional airlines. Similar to Tan (2016b), I consider multiple LCCs but the differences are as follows. I distinguish between whether the major airlines fly their own major fleets when they outsource to regional airlines. I also consider competition from both major airlines and LCCs, and distinguish between whether these competitors operate their own flights or through regional airlines.

The rest of the paper is organized as follows. I discuss the data in Section \ref{sec:data}. Section \ref{sec:prod_choice} analyzes major airlines' choices regarding regional airlines. These choices include no outsourcing, complete outsourcing and partial outsourcing. In Section \ref{sec:price}, I investigate how major airlines adjust their prices when facing LCC entry threat or actual entry, allowing different price adjustments for flights operated by major airlines vs. for flights operated by regional airlines. Section \ref{sec:conclusion} concludes.

\section{Data}\label{sec:data}

My main data set is the DB1B data from Bureau of Transportation Statistics, which contains a 10\% random sample of all tickets. My sample period begins with 1998 when DB1B started providing identifying information for both ticketing and operating carriers so I can see whether the service is outsourced by legacy carriers on each route and to whom if so.\footnote{Data earlier than 1998, while reported, did not reliably identify operating regional carriers (especially in the case of a regional subsidiary of a major airline), a key question for this paper. Currently my sample ends in year 2014 but this can be extended to include more recent data.} I define market as airport pairs, regardless of direction. That is, Chicago O'Hare to New York LaGuardia is treated as the same route as New York LaGuardia to Chicago O'Hare.\footnote{Airlines usually have the same fleet on the two directions, often times the same aircrafts.} To study product choice (whether to operate own fleets or outsource to regional carriers), I use \textit{DB1B Coupon} dataset. Multi-segment itineraries are split into segments, and I include all segments between top 300 airports in the lower 48 states, according to the enplanement data from Federal Aviation Administration (FAA). Regional carriers, likely due to the specific types of aircrafts they use, are thought unrealistic to fly on routes longer than 1,500 miles. I want to consider routes where outsourcing to regional carriers is a realistic option, so I drop routes over 1,500 miles in distance. My focus is on legacy carriers, in particular, whether they outsource part or whole of their operations to regional carriers on a given route. So my eventual data only contain legacy airlines. Though Alaska Airlines and Hawaiian Airlines are sometimes counted as legacy carriers, I drop them in this analysis since their hub-and-spoke systems are based in Alaska and Hawaii.

Since legacy airlines face competition from non-legacy airlines, I calculate all market structure variables at the carrier-route-quarter level before dropping all non-legacy airlines. These market structure variables include market share and Herfindahl Hirschman Index (HHI), which measures the underlying airline's market position and the overall market concentration. HHI does not take into account what type of competition the major airline faces. Also, constructed with the market share of ticketing carriers, it does not reflect the level of competition between operating carriers. For these reasons, I introduce the number of operating competitors based on their types (i.e. major / legacy / regional), and I distinguish regional carriers operating for major carriers and LCCs to test different impacts of major products and LCC products. Together I have 4 ticketing carrier-operating carrier combinations. They include major on major, major on regional\footnote{As mentioned before, here I am counting the number of competing operating carriers. So if two competing major carriers use the same operating carrier on a route, the value of this variable will be 1 rather than 2. The same rule applies to the variable "LCC on regional"}, LCC on LCC and LCC on regional.\footnote{As our summary stats will show later, LCCs occasionally outsource to regional carriers.} The explanatory variables are the numbers of these 4 combinations that the underlying major airline faces among its competitors. I also use DB1B coupon data to construct LCC entry dummies. LCC\_entry marks the first appearance of a low cost carrier on a given route.\footnote{If an LCC reappears after being absent for four consecutive quarters, then the re-appearance is treated as an LCC entry.} I also introduce 4 dummies for the 4 quarters immediately before LCC entry and 4 dummies for the 4 quarters immediately after an LCC entry.

In addition to product choice, I also consider price decisions and the price data come from \textit{DB1B Ticket} data. I obtain ticket prices for the same carrier-routes-quarter which appear in my product choice data. If a major airline flies its own major fleet and also outsources to regional airlines, then I obtain the prices for both types of flights separately (fare\_major vs. fare\_regional). I consider only nonstop, coach-class tickets by legacy carriers. Following what is standard in the literature, I drop prices below \$10 and those above the 98th percentile at the carrier-route-quarter level.

Other variables include population of core-based statistical area (CBSA) at the endpoints, which is from the Census Bureau. There are two airport characteristics variables: slot and hub, indicating whether the route involves a slot-controlled airport or a hub airport respectively. I also track the ownership relationship between regional airlines and major airlines over time, so I can distinguish, for each major airline, whether a regional airline is a subsidiary or an independent subcontractor.\footnote{The airport characteristics and regional airline ownership information are manually collected from the official websites of relevant airlines, Regional Airlines Association (RAA) and Wikipedia.} Following Forbes and Lederman (2009), I include thirty-year average (1980 -2010) weather data when analyzing major airlines' choices between subsidiaries and subcontractors, which is supplemented by quarterly average weather data since the second quarter of 1998. The weather data come from National Climatic Data Center (NCDC).

Summary stats are presented in Table \ref{table:sumstats}. We can immediately see that routes in my sample on average are more concentrated than in many existing studies (the average HHI is almost 0.79). The topic of regional airlines requires me to include many thin routes which in general faces less competition. About 14.5\% and 28\% of the sample involve a slot-controlled airport and hub airport respectively. For each major airline, it faces about 1 major airline operating their own flights, about 0.4 competitors for both major airlines operated by regional airlines and LCCs operating their own flights. We also report summary stats by subsamples: No outsourcing is for carrier-routes where the major airlines operate their own flights only within my sample period; partial outsourcing includes carrier-routes where the major airlines always use a combination of its own fleets and regional airlines'. Comparing the two subsamples, we can see that no outsourcing is more likely to be on longer routes, slightly more competitive markets (lower HHI) with much lower share of round trip itineraries. Average fares differ between the two subsamples but this seems more to be driven by route difference rather than operating carrier difference. In particular, for the partial outsourcing group, average fares are fairly close whether the flights are operated by major or regional airlines (fare\_major seem to have more dispersion with a slightly lower mean relative to fare\_regional).

\section{The choice of operating carriers}\label{sec:prod_choice}

In this section, I analyze major airlines' choice among a combination of major fleet and regional fleet.\footnote{Regional fleets can be through the major airlines' own subsidiaries or independent regional airlines. I will distinguish between these two regional operations in Section \ref{sec:replicate}.} In particular, I group carrier-route-quarter combinations into 3 groups based on project choice: (1) \textit{major only} where the major airlines do not use regional airlines as operating carriers at all; (2) \textit{regional only} where the major airlines use only regional airlines as operating carriers; (3) \textit{major and regional} where the major airlines operate on their own flights but also outsource to regional airlines as operating carriers.

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

\subsection{Substitutes vs. supplements}

Existing literature has investigated how major airlines utilize regional operations. For example, Forbes and Lederman (2009) has looked at when a major airline uses regional airlines and if regional airline is used, whether the regional airline is an independent airline or a subsidiary of the major airline. My paper differs from this literature in several aspects. \textit{First}, I distinguish between regional only and major and regional. In contrast, existing studies do not distinguish whether a major airline operates its own flight or not when a regional airline is used. This distinction is important for the underlying questions in this paper, particularly if one allows the incentives to use regional airlines to differ on regional only routes vs. on major and regional routes.\footnote{Regional only routes are more likely to be thin routes. In contrast, on busy routes it is necessary for major carriers to operate their own flights due to large demand and capacity constraint in airport facilities. In general, there is less competition on thin routes relative to busy routes. If one pools the two groups (regional only vs. major and regional) together, and regress the choice of regional carriers on competition, it will capture variation on competition not only within each group, but also between the two groups which can differ significantly in terms of competition intensity already.} \textit{Second}, I use panel data instead of cross sectional data. It is difficult to control all heterogeneities across carrier-routes. Panel data allows one to better control the heterogeneities (which are fixed over time) and capture more accurate effect of covariates using their variations within the same carrier-route.

The usage of panel data creates its own problems. In particular, the large number of carrier-routes causes incidental parameters problem if one uses carrier-route dummies in the model. Simply speaking, it will be a problem in non-linear estimation if the number of regressors is increasing significantly with observations. Unless there are lots of observations for each group (i.e. carrier-route), adding group dummies will make the estimates biased and inconsistent. To capture the within estimates and at the same time account for the dependence between repeated observations, I adopt the mixed-effects model proposed by Allison (2009) which jointly estimates within- and between- effects with robust standard errors.\footnote{This approach is adapted from Neuhaus and Kalbfleisch (1998).} The underlying idea is that when including both the cluster mean of explanatory variables and the deviation from them as regressors, the coefficient of the deviation terms will capture the effect of within-group variations. In that sense, they are similar to conditional (fixed effects) logit estimates. This approach avoids the incidental parameters problem, is flexible and compatible with different types of discrete choice models, and allows both time-variant and time-invariant variables to be included on the right hand side (see Allison 2009).

I use a set of competition indicators $X_{ijt}$ as explanatory variables. They include HHI, and for each major carrier-route-time combination, the following variables: its market share as well as the number of four basic types of competing products it faces: $major\_on\_major$, $major\_on\_regional$, $LCC\_on\_LCC$ and $LCC\_on\_regional$.\footnote{Note that even though $HHI$ is at the route-time level (i.e., not carrier-specific), I include them in my list of $X_{ijt}$. The choice of this will be more clear after I explain how I treat $X_{ijt}$.} ``Major on major" refers to the number of competing major airlines operating their own flights (with or without outsourcing to regional airlines); ``major on regional" refers to the he number of regional airlines used by competing major airlines; ``LCC on LCC" and ``LCC on regional" are similar except that they measure the number of competing products from LCCs (low-cost carriers). While HHI and market share capture the overall level of competition and the market power of the underlying legacy carrier, the other four variables reflect the competition between different types of ``products". Following previous literature, we isolate LCCs and look at their impact on competition and product choices.

Consider a major carrier $i$ operating on route $j$ at time $t$. I first calculate the mean of each variable in $X$ across time,
$$M\_X_{ij}=\frac{1}{N}\sum_t X_{ijt},\quad \forall i, j$$

where $N$ is the number of periods $X_{ijt}$ appears in my sample. Then I calculate the deviation from the mean as
$$D\_X_{ijt}=X_{ijt}- M\_X_{ij},\quad \forall i, j, t.$$

Note that the ``$M\_$" and ``$D\_$" signs in front of the explanatory variables refer to mean and deviation from the mean respectively.

Let $m$ denote the carrier's choice among the three possibilities (or ``product" types) with corresponding log-odds as follows:
\be\label{eqn:MNL1}U_{ijt}^m = \alpha_1^m M\_X_{ijt} + \alpha_2^m D\_X_{ijt}  + \beta^m Z_{jt} + \gamma_{t} +\varepsilon_{ijt},\ee

where $Z_{jt}$ are route characteristics controls which include the following variables. $\ln POP$ is the logarithm of the geometric mean of population (in thousands) at the endpoints. $Disparity$ measures the disparity at the two endpoints on each route, calculated as the ratio of population at the larger endpoint to that at the smaller endpoint. $Hub$ and $Slot$ are dummies indicating whether a slot controlled airport or a hub airport of the ticketing carrier is involved.\footnote{Although the demographic characteristics and airport status also change across periods, I do not divide them into group means and within group deviations. Comparing to the competition variables, the within-group variation of these characteristics takes up a much smaller proportion in total variation: The U.S. population grows very slow in the past decades. Also, the hub status and slot control policy are quite stable in my sample period. It has been argued in the literature that in this situation, within estimators are not very reliable. In addition, these variables are less likely to be correlated with heterogeneities at the carrier-route level.} Lastly, $\gamma_t$ is period dummies to control for common shocks in the industry across time.

We assume that the idiosyncratic error terms $\varepsilon_{ijt}$ follow extreme value distribution. In this case, it can be shown that the probability of airline $i$ choosing option $m$ on route $j$ at time $t$ is
\be\label{eqn:MNL2}Pr_{ijt}^m = \frac{\exp(U_{ijtm})} {\sum\limits_{m}\exp(U_{ijtm})},\ee

where $U_{ijt}^m$ is given in \eqref{eqn:MNL1}.

I estimate the multinomial logit model above with robust standard errors to correct dependence within carrier-routes. The results are presented in Table \ref{table:multinomial}. In all models choice $0$ is the case of no outsourcing (airline chooses major only), $1$ is for complete outsourcing (regional only) while $2$ is partial outsourcing (major and regional).

\subsubsection{Substitutes: major only vs. regional only}

Panel A in Table 4 shows the effect of competition and other route characteristics on the likelihood of complete outsourcing to regional carriers, in comparison to the baseline case of no outsourcing. Under the pooled specification (1) we do not control for any fixed effects. I do this by using the initial variables directly (e.g., HHI), as opposed to their mean and deviation terms (e.g., $M\_HHI$ and $D\_HHI$).\footnote{That is, the explanatory variable is actually $HHI$ rather than $D\_HHI$ in column (1). To ease on notation and save space, I am using the same set of variable name $D\_HHI$ for all columns in Table \ref{table:multinomial}.} As a result, the coefficients capture pooled effects (both within group and between group variations are used in estimation). Relative to no outsourcing, major airlines are more likely to switch to complete outsourcing on more concentrated routes (larger $D\_HHI$). In particular, when D\_HHI increases by $0.1$, the logarithm of their likelihood ratio ($log(\frac{Pr_{ijt}^1}{Pr_{ijt}^0}$)) increases by 0.05. We also see that airlines with higher market share are less likely to go to complete outsourcing. An increase of 0.1 in D\_mktshare would reduce the log likelihood of complete outsourcing ($log(\frac{Pr_{ijt}^1}{Pr_{ijt}^0}$)) by $0.05$. Moving on to the number of competing products, an increase in the number of major or low-cost operating carriers tends to reduce the likelihood of complete outsourcing. In contrast, it is more likely for a major carrier to use regional carriers only when there are more regional carriers flying for its competitors on the specific route. That is to say, complete outsourcing is more favorable when there is more competition between regional operating carriers. We do not find significant impact of the competition from LCCs' operating regional carriers. This seems to go against the view that major airlines rely on regional airlines as a response to increasing competition from LCCs.\footnote{Here we do not distinguish subsidiary vs. independent reginal airlines. We make this distinction in Section \ref{sec:replicate} and find that major airlines are more likely to rely on wholly-owned subsidiaries when facing more LCC competition.}  The route controls ($Z_{jt}$) have the expected signs of impacts. Major carriers are less likely to choose complete outsourcing on routes that have more population or involve hub or slot-controlled airports, but they are more likely to choose complete outsourcing on routes involving more disparity in population between the two end cities.

%Rather there are common factors that affect all major carriers operating on the route help determine their choice of complete outsourcing. For example, if outsourcing to regional carrier is proven to be more cost efficient or reduce uncertainty on a route, major carriers on that route are likely all to take the opportunity to enjoy the advantages of regional carriers.

Next, we control fixed effects by dividing variables into carrier-route means and deviations from them ($M\_X$ and $D\_X$). We do not control time fixed effects ($\gamma_t$) in model (2) but control them in model (3). As the panel structure of our data allows it, adding time fixed effects is supposed to control for common shocks such as adjustments in BTS reporting rules and help us achieve better estimates. Thus we will focus on the result in column (3). Comparing (1) and (3), we now see significant changes for several variables, for example, $D\_HHI$ and $D\_LCC\_on\_LCC$.\footnote{This seems to confirm our earlier assessment on the importance of controlling for route fixed effects which is facilitated by the use of panel data.} To interpret the results, it is important to distinguish within- and between- group effects. Using market share as an example, the coefficient for $M\_mktsh$ is derived using the difference between carrier-routes where complete outsourcing takes place and those where only major fleet are used. It tells us that in general, carriers with higher market share are less likely to choose complete outsourcing. Correspondingly, the coefficient for $D\_mktsh$ shows that if a carrier's market share increases by 10\%, $log(\frac{Pr_{ijt}^1}{Pr_{ijt}^0}$) will drop by 5.8\% on the specific route. While cross-sectional comparison does reveal some information about the impact of market competition, coefficients for $M\_$ terms are subject to the influence of unobserved carrier-route chracteristics and can be biased. So more attention should be paid to the $D\_$ variables. As mentioned above, the pooled estimation makes use of both between- and within- group information. In comparison, coefficients for deviation terms are only explaining within group changes, which doesn't happen so frequently due to the constraint of long-term contracts. This difference explains why most coefficients in (3) (in (2) as well) are significantly smaller than in (1). Despite the difference in magnitude, $M\_$ and $D\_$ coefficients in column (3) are quite consistent in signals, \footnote{The only exception is LCC\_on\_regional, yet neither coefficient is significant.} both indicating qualitatively the same impact of competition factors as in model (1).

Overall, the result above does not support the argument that regional carriers are used for competition: Complete outsourcing is less likely to happen on more competitive routes, and is not associated with boosting market share. Actually, the limited market size decides that there can not be too much competition on those routes even in the predictable future. Instead, outsourcing tends to happen where more regional operating carriers are accomodated. This seems to support the mainstream idea that regional carriers are used on thin routes to avoid risks and save cost.

%Suppose that a carrier has one more $M\_LCC\_on\_LCC$ on route $A$ relative to route $B$, everything else the same. Our results (in column (2)) suggest that this carrier's log likelihood of complete outsourcing goes down by about 0.7. On the other hand, a carrier faces an entry of a single LCC on LCC will raise its log likelihood of complete outsourcing by about 0.4. Similarly for LCC on regional. These results seem to be in line with the common view that major carriers turn to regional airlines (subsidiary or independent) to better cope with the competition from LCCs.

\subsubsection{Supplements: major only vs. major and regional}

Next, we compare the choice of major only vs. major and regional. The results are presented in Table 4, panel B. In model (1) we do not control for any fixed effects. We find that the coefficients for $D\_HHI$ and $D\_mktsh$ have opposite signs as those in panel A model (1). For example, in more competitive markets (lower $D\_HHI$), major carriers are more likely to add regional airlines as supplements relative to choosing their own fleet only. An increase in market share also suggests that the carrier is more likely to add regional operations. In addition, majors are more likely to add regional operations if there are more regional operating carriers flying for their competitors (both for other major carriers and LCCs), but less likely if more major competitors are flying with their own fleet. All these results hold when we include mean variables and time fixed effects in model (3), if we focus on coefficients for deviation terms.\footnote{Actually all significant coefficients for $M\_$ terms are also consistent with $D\_$ terms.} Finally, after distinguishing the within- and between- group effect of LCC\_on\_LCC, we find that a major carrier is more likely to introduce regional supplements to its own fleet if new LCCs enter the market, though the total number of low-cost carriers may not be so large on these routes. Moving onto route characteristics, our results suggest that major carriers are more likely to use regional as supplements on routes involving hub airports and slot-controlled airports, and between endpoints with more comparable population size.

Switching from complete outsourcing to partial outsourcing, we find some support for the "outsourcing for competition" argument. As expected, weak and uncertain demand is not likely to be a serious problem on routes with partial outsourcing, given similar population scale(relative to routes without outsourcing)and more balanced endpoints. On the other hand, the result shows that major carriers choose to partially outsource their service under obviously larger competition pressure. The comparison between Panel A and Panel B suggests that complete outsourcing and partial outsourcing may be driven by different factors, and researchers should pay special attention to the corresponding case when their purpose is to study a specific rationale of outsourcing.

Given results in Table 4\footnote{For reasons explained above, here we adopt the result of model (3)} and average carrier-route characteristics, it is easy to derive the marginal effect of each variable. They are displayed in Table 5. In general, the magnitude of these marginal effects is not large, which is understandable as major carriers' product choice is subject to the constraint of long-term contracts between majors and regional partners as well as caps on regional usage \footnote{These caps, also known as "scope clauses", are required by the union to protect major carriers' own pilots.}. Nevertheless, the signs of marginal effects still lend strong support to our previous conclusions. That is to say, whenever adjustments are possible, major carriers will choose complete outsourcing on thinner routes and choose partial outsourcing on more competitive routes.

\subsection{Subsidiaries vs. subcontractors}\label{sec:replicate}

So far we have only considered whether regional airlines are being used, and have not distinguished between whether the regional airline is an independent airline (subcontractor) or a subsidiary of the major airline. In this section, I expand major airlines' choice sets by distinguishing subsidiaries vs. subcontractors. While partial outsourcing seems to be a favorable choice in competitive environment, it is not yet clear whether it is to facilitate price competition or to improve the quality of major carriers' service. The result in this part is supposed to shed some light on the question.

I estimate a nested-logit model similar to that in Forbes and Lederman (2009). Since fully owned subsidiaries and independent regional subcontractors are close substitutes, the availability of one option can affect the probability of choosing the other relative to a third choice. Now that the IIA (Independence of Irrelevant Alternatives) assumption is violated, standard conditional logit model can not be used here. To solve the problem, Forbes and Lederman set two branches, one containing flying with legacy's own fleet only (i.e. no outsourcing) and the other containing alternatives with regional participation (i.e. complete and partial outsourcing). They then check the effect of different carrier-route characteristics separately on the probability of choosing each branch and choosing each alternative ("using their own fleet only" , "using independent regional subcontractors" and "using regional subsidiaries") conditional on the chosen branch.

I first try to replicate the analysis of Forbes and Lederman (2009), using only cross-section data (2nd quarter of year 2000). The results are presented in Table \ref{table:replicate}.  Similar to Forbes and Lederman, I find significant impacts of weather on airlines' choices between subsidiaries and subcontractors. In particular, an increase in precipitation and snowfall raises the probability of using owned subsidiaries relative to subcontracts, while an increase in the number of freezing months reduces that probability.\footnote{Due to our differences in data (public DB1B vs. proprietary data) and the fact that I include many thin routes, our results differ slightly. For example, while Forbes and Lederman (2009) find positive and significant impact of slot controlled airport on using subsidiaries, that impact is insignificant in my sample.} When applying this model to my panel data where cross-sectional weather variables are replaced by quarterly average, I find similar impacts of weather conditions on the likelihood of choosing fully owned regional subsidiaries. Also, I find higher likelihood to use fully-owned subsidiaries on routes involving a slot-controlled or hub airport in my panel replication, which is consistent with Forbes and Lederman (2009) as this type of route is generally more congested and unanticipated reconciliations in schedule are often needed. Different from the sample period in Forbes and Lederman (2009), in recent years major carriers tend to combine subsidiaries and subcontractors on many routes. Rather than dropping such carrier-routes, I adjust my bottom nest alternatives and combine "using both" cases with "using subsidiary only". In other words, major airlines now determine whether to adopt fully owned subsidiaries conditional on the branch choice. As is shown in the Panel C of Table 6, the model of Forbes and Lederman generates quite similar results as before after this bottom adjustment, indicating that the rationale of vertical integration does not change too much with the coexistence of independent subcontractors.

Next, I take advantage of the panel data to conduct similar analysis. The results are presented in Table \ref{table:replicate_panel}. I start with a pooled model where dynamic competition factors are not divided into mean and deviation terms, and the result is shown in column (1). Multiple flaws indicate that the model does not work well: At the bottom nest, we can see that major carriers are more likely to introduce their subsidiaries in less competitive markets (higher $HHI$), which is hard to reconcile with the positive coefficients for number of competing products. \footnote{If major carriers adopt subsidiaries on routes with more of every type of competitor, we should expect a negative relationship between HHI and the likelihood of using subsidiaries.} At the top panel, we find a negative estimate of dissimilarity parameter \footnote{the coefficients for inclusive value}, which often suggests a wrong specification. In comparison, the results become more solid after dividing competition variables into group means and deviation terms. Comparing to model (2) where no time fixed effects are added, model (3) generates very similar results considering the sign of the coefficients. Now, as the market becomes more competitive (lower $D_HHI$), carriers are more likely to use regional subsidiaries. And under both specifications, a larger number of low-cost carriers and regional operating carriers (flying for competing major) increases the likelihood of using fully owned subsidiaries. In terms of the weather controls, the new models suggest similar impact of snowfall and number of freezing months as in Forbes and Lederman (2009), yet the level of precipitation seems to reduce the likelihood of using subsidiaries, which is a contradiction to their findings.

Column (4) and (5) are robustness tests for the result. First, we replace 30-year with quarterly averages\footnote{column (4)}. After this adjustment, precipitation now has a positive effect on the likelihood of vertical integration (i.e. using a regional subsidiary). Meanwhile, it changes the magnitude and significance level of some coefficients. Of these changes, the one in D\_LCC\_on\_LCC coefficient should be a concern as the impact of low-cost competitors is one of my research questions. While the sign does not change, its magnitude and significance level changes significantly when dynamic weather data are used. It shows the necessity of extra work on the choice of weather data. Next, I replace suspicious product choices which appear only once in four consecutive quarters by the normal choice in neighboring periods. \footnote{Outsourcing contracts are generally signed on medium- to long-term rather than quarterly basis.} As column (5) shows, this adjustment in dependent variable does not change the baseline result too much, either in terms of magnitude and significance level.

Forbes and Lederman suggest that having subsidiaries helps major carriers improve the quality (on-time performance) of their own fleets. As a result, major airlines have more incentive to choose subsidiaries (relative to subcontractors) when they face more competition, especially competition from LCCs. Given that regional subsidiaries bear higher labor cost than independent subcontractors, our finding seems to support the quality competition (rather than price competition) story. Yet it is necessary to mention that it applies to routes with less LCCs competing in price, according to the negative coefficient for M\_LCC\_on\_LCC. It will be interesting to see whether things are different with stronger price competition. The top nest analyzes when airlines choose complete outsourcing and partial outsourcing, both relative to no outsourcing. The results are qualitatively similar to the multinomial logit results presented in Table \ref{table:multinomial}.

\section{Regional outsourcing and LCC entry on prices}\label{sec:price}

The previous section is concerned with product competition among major carriers. In this section, I will analyze price competition, with special attention paid to LCC competition measured by entry threat or actual entry of LCCs. To measure LCC competition, I first use a dummy variable to indicate the period of LCC entry -- $LCC\_Entry$ is defined as the first appearance of LCC after at least four periods of absence in my sample. I also include $Pre\_Entry$ and $Post\_Entry$ dummies to denote the immediate 4 quarters before and after entry respectively (8 quarters total). I restrict the sample to be where the major carrier chooses either no outsourcing or partial outsourcing. That is, I remove the observations where the major carrier chooses complete outsourcing (regional only).\footnote{This removal is done at the carrier-route level. For example, suppose that carrier $A$ changed its product choice over time on route $j$, then I would remove on $Ajt$ observations. On the other hand, another major carrier $B$ has the same product choice on route $j$ over time, then I would keep all $Bjt$ observations. This approach gives me a more balanced panel, and allow me to isolate the price response of a major carrier to LLC entry. The downside is that when a major carrier responds to LCC entry on a given route in both price and product choices, then all observations of this carrier-route combinations will be dropped.}

I am interested in the following questions. First, is the price response to LCC entry more significant if the major carrier uses a regional carrier relative to when it does not? Second, when a major carrier chooses major and regional on a route, prices on which portion (major or regional) of its flights are more responsive to LCC entry?

To investigate the first question, I employ the following panel regression model which is similar to that in Goolsbee and Syverson (2008),
\be\label{eqn:price1}\ln P_{ijt}^m = \beta_{1}^m LCC\_Entry_{jt} + \beta_{2}^m Pre\_Entry_{jt} + \beta_{3}^m Post\_Entry_{jt} + \alpha^m X_{ijt} + \gamma_{ij}^m + \theta_{t}^m + \mu_{ijt}^m,\ee

where $\ln P_{ijt}^m$ is the logarithm of average price of the ticketing carrier, $X_{ijt}$ are the commonly used carrier and route characteristics controls which include HHI, route distance, share of round-trip tickets and two dummies reflecting the financial status of the carrier: merger and bankruptcy. It is often argued that HHI may be endogenous in price regression. Following Borenstein and Rose (1994), I adopt 2SLS method and use average endpoint population and the share of average endpoint boarding as instruments.\footnote{$$\frac{\sqrt{ENP_{i,ORI}*ENP_{i,DES}}} {\sum\limits_{i}\sqrt{ENP_{i,ORI}*ENP_{i,DES}}}$$} Finally, I adopt two-way fixed effects to control for unobserved heterogeneities associated with the specific carrier-route and common shocks to the whole industry.

The results are presented in Table \ref{table:price}. Model (1) include only samples ($ijt$) where the major carriers do not outsource to regional airlines (major only). We find that major carriers in general do not adjust their prices significantly for LCC entry.\footnote{None of the LCC entry variables is insignificant.} Even if one ignores statistical significance, the estimates for entry dummies are rather small to be viewed as ``economically significant".\footnote{I use the Heckman two step method and report the mills ratio. It has been argued that Heckman two step method sacrifices efficiency of the estimator.} Our results suggest no more than 2\% adjustment in general, which can be translated to about \$5 for the given average price of $\$245$. This suggests that LCC entry does not seem to directly constraint the prices of major airlines who are not outsourcing to regional airlines. In model (2) we consider the observations ($ijt$) where the major airlines fly their own fleets as well as outsource to regional airlines (major and regional). We find that major carriers do not adjust their prices in anticipation of LCC entry. That is, major carriers do not take preemptive reactions to deter LCC entry. They lower their prices by about 5.2\% in the quarter after entry, but no further adjustment more than one quarter after LCC entry. My results are quite different from those in existing studies. For example, Goolsbee and Syverson (2008) find that incumbent airline respond to threat of entry by Southwest by reducing their prices. Several factors may be responsible for this difference in results. First, I am analyzing entry of not just Southwest, but a total of almost 20 LCCs. It is possible that major airlines take entry by Southwest seriously but not other LCCs. Second, our sample can be quite different, especially on the cross sectional dimension. Because my focus on regional airlines, I include more routes than what is typical. These include many thin routes. Moreover, as explained earlier, if a major airline changes its product choice during my sample periods, then I drop this carrier-route for this part of analysis.

Next, I restrict myself to the subsample where the major carrier partially outsources to regional airlines (major and regional). For this subsample, each carrier-route-quarter gives me two sets of prices, one for flights operated by the major airlines ($\ln\_fare\_major$) and the other for flights operated by regional airlines ($\ln\_fare\_regional$). I also have pooled prices $\ln\_fare$. I then use $\ln_fare$, $\ln\_fare\_major$ and $\ln\_fare\_regional$ as dependent variables respectively to run regressions similar to equation \eqref{eqn:price1}, and the results are presented in columns (2)-(4) respectively. Our results suggest that LCC entry has little impact on prices for the regional flights. Interestingly, we see that entry threat may lead the incumbent major carrier to raise price for its own flights, by about 9.6\% 2 quarters before entry takes place. Moving on to regional flights, our results show that their prices go down by 4.1\% one quarter after LCC entry, but go back up by 8\% 4 quarters after LCC entry. We do not find significant impacts of Pre\_Entry dummies on these prices.


I also explore how the price gaps between major flights and regional flights on the same routes vary with LCC entry. Let $Gap_{ijt}$ denote the gap between the price for flights operated by regional airlines and by major airlines directly (Gap=major price-regional price). The econometric model is given by
\be\label{eqn:gap}Gap_{ijt} = \beta_{1} LCC\_Entry_{jt} + \beta_{2}* {\sum\limits_{k=1}^4 Pre\_Entry_{j,t-k}} + \beta_{3}* {\sum\limits_{k=1}^4 Post\_Entry_{j,t+k}} + \alpha X_{ijt} + \gamma_{ij} + \theta_{t} + \mu_{ijt},\ee

where $X_{ijt}$ are commonly used carrier and route control variables as in equation \eqref{eqn:price1}. The results are presented in column (5). The results suggest that price gap increases one quarter before entry, but other LCC entry dummies do not seem to have significant impacts on the price gap.

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

This paper looks at major airlines' product choices in terms of whether or not to outsource to regional airlines. Different from many existing studies, I take into account whether the major airlines fly their own fleets as well when they outsource. My results show that this distinction is important. The log likelihood of using a regional airline change in opposite directions with market share and HHI, depending on whether major airlines operate their own flights on the route as well. I then take into account the ownership of regional airlines by distinguishing between subsidiaries and independent subcontractors. My results lend support to the commonly held view that major airlines are more likely to rely on subsidiaries relative to subcontractors when facing more LCC competition. I also analyze how major airlines adjust their prices when facing threat of or actual entry by LCCs. My results document little price adjustments, except that on partially outsourced routes, major airlines lower their prices one quarter after LCC entry with no significant price adjustments in other periods. This price analysis is based on balanced sample, i.e., major carriers have the same product choice (no, complete or partial outsourcing) on the route throughout my sample period. It is plausible that major airlines may adjust their product choices when facing LCC entry or entry threat. Restricting my sample to carrier-routes with no product choice change over time would eliminate such observations. I will explore this in future research.
\bigskip

\newpage

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\end{thebibliography}
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\end{comment}
\begin{document}
\setcounter{page}{36}

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

\begin{table}[htbp!]\centering \caption{Presence of regional airlines over time \label{table:regional}}
\medskip

\resizebox*{!}{.4\textwidth}{
\begin{tabular}{l c c  }\hline\hline
\multicolumn{1}{c}{\textbf{Case}} & \textbf{Number of Observations}
 & \textbf{Percentage} \\ \hline
\textbf{1998} \\
No Regional Operating Carrier Involved & 20937 & 82.85  \\
Regional Operating Carrier Involved & 4335 & 17.15  \\
\hline
\textbf{2002} \\
No Regional Operating Carrier Involved & 12862 & 67.77  \\
Regional Operating Carrier Involved & 6117 & 32.23  \\
\hline
\textbf{2014} \\
No Regional Operating Carrier Involved & 2408 & 20.98  \\
Regional Operating Carrier Involved & 9071 & 79.02  \\ \hline
\hline
\end{tabular}}
\end{table}

\begin{table}[htbp!]\centering \caption{Major Airlines' Fully Owned Subsidiaries \label{table:regional}}
\medskip

\resizebox*{!}{.6\paperwidth}{
\begin{tabular}{l*{2}{c}}
            &\multicolumn{1}{c}{}&\multicolumn{1}{c}{}\\
\hline
Major Carriers    &   Subsidiaries   \\\hline

American Airlines (AA)     &                                 American Eagle Airlines     \\
                                                      &      Executive Airlines     \\
                                                      &      Flagship Airlines     \\
                                                      &      Business Express     \\
                                                      &      Envoy Airlines      \\
                                                      &      PSA Airlines         \\
                                                      &      Piedmont Airlines    \\
Continental Airlines (CO)   &    Continental Express / Expressjet (full ownership until 2002)     \\
Delta Airlines (DL)                                   &      Comair Airlines (full ownership since 1999) \\
                                                      &      Atlantic Southeast Airlines (1999 - 2005)        \\
                                                      &      Compass Airlines (sold in 2010)     \\
                                                      &      Mesaba Airlines (sold in 2010)     \\
                                                      &      Pinnacle Airlines (full ownership since 2013)     \\
                                                      &      Endeavor Air     \\
Northwest Airlines (NW)    &     Express Alines I / Pinnacle Airlines (full ownership until 2002)     \\
                                                      &      Compass Airlines     \\
                                                      &      Mesaba Airlines     \\
                                                      &      US Airways Shuttle (full ownership since 1997)     \\
US Airways (US)              &     PSA Airlines     \\
                                                      &      Piedmont Airlines     \\
                                                      &      Allegheny Airlines     \\
                                                      &      US Airways Shuttle (full ownership since 1997)     \\
\hline
\end{tabular}}
\end{table}

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

\begin{tabular}{l c c  }\hline\hline
\multicolumn{1}{c}{\textbf{Variable}} & \textbf{Mean}
 & \textbf{Std. Dev.} \\ \hline
 \textbf{Whole sample (No/complete/partial outsourcing)}\\
mktshare & 0.486 & 0.436 \\
HHI & 0.789 & 0.231 \\
slot & 0.145 & 0.352 \\
hub & 0.278 & 0.448 \\
major\_on\_major & 0.983 & 1.272 \\
LCC\_on\_LCC & 0.39 & 0.645 \\
major\_on\_regional & 0.409 & 0.765 \\
LCC\_on\_regional & 0.006 & 0.078 \\
Distance & 994.284 & 653.659 \\
freezing\_months & 3.262 & 1.733 \\
precipitation & 44.917 & 10.879 \\
snowfall & 33.735 & 26.866 \\
\multicolumn{1}{c}{Obs}	&	\multicolumn{2}{c}{378364}\\ \hline

\textbf{Major only (No outsourcing)} \\
avg\_fare & 245.454 & 184.461  \\
LCC\_entry & 0.028 & 0.164  \\
HHI & 0.822 & 0.222  \\
mktdistance & 945.829 & 341.969  \\
roundtrip & 0.327 & 0.416  \\
merger & 0.064 & 0.245  \\
Bankruptcy & 0.114 & 0.317  \\
\multicolumn{1}{c}{Obs}	&	\multicolumn{2}{c}{27026}\\	\hline

\textbf{Major and regional (Partial outsourcing)} \\
%avg\_fare & 192.187 & 74.404 \\
Fare\_major & 192.798 & 106.049 \\
Fare\_regional & 194.918 & 73.258 \\
LCC\_entry & 0.018 & 0.131  \\
HHI & 0.838 & 0.219  \\
mktdistance & 318.207 & 207.967 \\
roundtrip & 0.816 & 0.198 \\
merger & 0.088 & 0.283  \\
Bankruptcy & 0.137 & 0.344  \\
\multicolumn{1}{c}{Obs}	&	\multicolumn{2}{c}{8964}\\ \hline
\end{tabular}
\end{table}


\begin{table}[htb!]
\begin{center}
\footnotesize
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{Multinomial Logit Results (Panel A)\label{table:multinomial}}
\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}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}\\
\hline
\textbf{0}           &       \textbf{Major only}         &      &          \\
\hline
\textbf{1}              &      \textbf{Regional only}            &                     &                        \\
(D\_)HHI         &       0.518\sym{***}&           0.220\sym{***}&       0.325\sym{***}       \\
            &    (0.0686)          &            (0.0615)           &       (0.0675)                    \\

(D\_)mktshare    &      -0.543\sym{***}&        -0.489\sym{***}&      -0.583\sym{***}                       \\
            &      (0.0443)          &                  (0.0448)         &    (0.0477)                   \\

(D\_)major\_on\_major     &      -0.843\sym{***}&                -0.679\sym{***}&     -0.0594\sym{***}   \\
            &     (0.0213)          &               (0.0166)         &    (0.0171)                          \\

(D\_)major\_on\_regional     &       0.561\sym{***}&            0.221\sym{***}&      0.0390\sym{**}                  \\
            &       (0.0229)         &                    (0.0163)         &    (0.0172)                   \\

(D\_)LCC\_on\_LCC     &      -0.411\sym{***}&        0.403\sym{***}&      -0.126\sym{***}                         \\
            &    (0.0303)         &              (0.0363)         &     (0.0271)                           \\

(D\_)LCC\_on\_regional     &     -0.0150         &            0.372\sym{***}&     -0.0803                \\
            &     (0.132)         &                (0.0896)            &      (0.0892)                       \\

M\_HHI        &                     &       0.537\sym{***}&       0.798\sym{***} \\
            &                     &       (0.133)         &     (0.156)               \\

M\_mktshare   &                     &      -0.452\sym{***}&      -0.802\sym{***} \\
            &                     &     (0.0774)         &    (0.0908)        \\

M\_major\_on\_major   &                     &      -1.186\sym{***}&      -0.838\sym{***} \\
            &                     &    (0.0419)         &    (0.0465)      \\

M\_major\_on\_regional   &                     &       1.060\sym{***}&       1.129\sym{***} \\
            &                     &     (0.0509)         &    (0.0579)           \\

M\_LCC\_on\_LCC   &                     &      -0.719\sym{***}&      -1.149\sym{***} \\
            &                     &     (0.0581)         &    (0.0704)      \\

M\_LCC\_on\_regional  &                     &       0.343         &       0.128       \\
            &                     &        (0.531)         &     (0.560)           \\

ln\_POP      &      -0.561\sym{***}&      -0.367\sym{***}&      -0.729\sym{***}\\
            &     (0.0258)         &     (0.0287)         &    (0.0373)             \\

disparity   &     0.00163\sym{**} &     0.00117\sym{**} &   0.0000485           \\
           &  (0.000658)         &      (0.000557)         &  (0.000705)                \\

hub         &      -1.598\sym{***}&      -1.719\sym{***}&      -1.693\sym{***}\\
            &    (0.0598)         &     (0.0638)         &    (0.0740)          \\

slot        &      -0.206\sym{***}&      -0.436\sym{***}&      -0.124\sym{*}        \\
            &    (0.0601)         &    (0.0620)         &    (0.0718)              \\
\hline
\end{tabular}}
\end{center}
\end{table}


\begin{table}[htb!]
\begin{center}
\footnotesize
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
{\large{Table 4 continued: Multinomial Logit Results (Panel B)}}
\medskip

\resizebox*{!}{.9\textheight}{
\begin{tabular}{l*{3}{c}}
\hline\hline
   &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}\\
            &\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}\\
\hline
\textbf{0}           &       \textbf{Major only}         &      &          \\
\hline
\textbf{2}               &          \textbf{Major and regional}            &                     &                        \\
(D\_)HHI         &      -0.906\sym{***}&             -1.877\sym{***}&      -1.672\sym{***}             \\
            &      (0.104)           &               (0.103)         &     (0.102)                \\

(D\_)mktshare    &       1.456\sym{***}&            2.002\sym{***}&       1.782\sym{***}                    \\
            &      (0.0657)           &               (0.0864)         &    (0.0833)                     \\

(D\_)major\_on\_major     &      -0.472\sym{***}&              -0.422\sym{***}&     -0.0410\sym{***}            \\
            &     (0.0186)          &               (0.0160)         &    (0.0154)                    \\

(D\_)major\_on\_regional     &       0.945\sym{***}&     0.527\sym{***}&       0.405\sym{***}             \\
            &     (0.0258)        &             (0.0212)         &    (0.0205)                \\

(D\_)LCC\_on\_LCC     &      0.0360         &           0.393\sym{***}&      0.0647\sym{**}            \\
            &      (0.0375)          &              (0.0294)         &    (0.0298)                                \\

(D\_)LCC\_on\_regional     &       0.483\sym{***}&      0.591\sym{***}&       0.169\sym{*}      \\
            &      (0.131)         &               (0.0959)         &    (0.0940)                      \\

M\_HHI        &                     &      -0.112         &      0.0355        \\
            &                     &     (0.184)         &     (0.195)               \\

M\_mktshare   &                     &       1.526\sym{***}&       1.411\sym{***}\\
            &                     &    (0.0996)         &     (0.107)         \\

M\_major\_on\_major   &                     &      -0.726\sym{***}&      -0.593\sym{***}\\
            &                     &    (0.0454)         &    (0.0486)             \\

M\_major\_on\_regional   &                     &       1.617\sym{***}&       1.678\sym{***}\\
            &                     &      (0.0565)         &    (0.0591)       \\

M\_LCC\_on\_LCC   &                     &      0.0707         &     -0.0848       \\
            &                     &      (0.0722)         &    (0.0757)              \\

M\_LCC\_on\_regional   &                     &       1.402\sym{***}&       0.994\sym{*}    \\
            &                     &      (2.77)         &      (1.91)         \\

ln\_POP      &      0.0977\sym{**} &       0.246\sym{***}&    -0.00658      \\
            &       (0.0380)         &       (0.0410)         &    (0.0433)       \\

disparity   &    -0.00146         &    -0.00133         &    -0.00282\sym{**}  \\
            &    (0.00102)          &      (0.000925)         &   (0.00117)          \\

hub         &       2.128\sym{***}&       2.010\sym{***}&       2.059\sym{***}\\
            &      (0.0510)         &     (0.0583)         &    (0.0632)          \\

slot        &       0.870\sym{***}&       0.700\sym{***}&       0.964\sym{***}\\
            &     (0.0806)         &      (0.0826)         &    (0.0845)     \\

Time Dummy       &          No         &          No         &         Yes           \\
\hline
\(N\)       &      291262         &      291262         &      291262           \\
\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!]
\medskip

\begin{center}
\caption{Multinomial logit -- Marginal effects}\label{table:multinomial_ME}
\medskip

\resizebox*{!}{.9\textheight}{
\begin{tabular}{l c c  }\hline\hline
\multicolumn{1}{c}{\textbf{Variable}} & \textbf{Marginal Effect} \\ \hline
\textbf{Complete Outsourcing\_Regional Only} \\
D\_HHI &  0.079   \\
D\_mktshare &  -0.108  \\
D\_major\_on\_major  &  -0.005  \\
D\_major\_on\_regional  & -0.007   \\
D\_LCC\_on\_LCC &  -0.014   \\
D\_LCC\_on\_regional & -0.013   \\
M\_HHI & 0.079  \\
M\_mktshare & -0.12   \\
M\_major\_on\_major  & -0.067   \\
M\_major\_on\_regional  & 0.066  \\
M\_LCC\_on\_LCC & -0.112   \\
M\_LCC\_on\_regional & -0.015   \\
ln\_pop  & -0.073   \\
disparity  & 0.000   \\
hub & -0.227   \\
slot & -0.039   \\ \hline
\textbf{Partial Outsourcing\_Legacy and Regional} \\
D\_HHI &  -.0187  \\
D\_mktshare & 0.206 \\
D\_major\_on\_major  & -0.003  \\
D\_major\_on\_regional  & 0.042 \\
D\_LCC\_on\_LCC & 0.01  \\
D\_LCC\_on\_regional & 0.02  \\
M\_HHI & -0.019  \\
M\_mktshare & 0.173  \\
M\_major\_on\_legacy  & -0.04  \\
M\_major\_on\_regional  & 0.147  \\
M\_LCC\_on\_LCC & 0.023  \\
M\_LCC\_on\_regional & 0.102  \\
ln\_pop  & 0.02  \\
disparity  & 0.000  \\
hub & 0.267  \\
slot & 0.106  \\ \hline
\hline
\end{tabular}}
\end{center}
\end{table}

\newpage

\begin{table}[htb!]
\medskip

\begin{center}
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{Cross section: Subsidiary vs. subcontract regional airlines}\label{table:replicate}
\medskip

\begin{tabular}{l*{1}{c}}
\hline\hline
            &\multicolumn{1}{c}{(1)}\\
            &\multicolumn{1}{c}{chosen}\\
\hline
\textbf{Top Nest}        &   \textbf{Whether to outsource}    \\
hub         &       2.040\sym{***}\\
            &     (24.49)         \\

ln\_(Population at the larger endpoint)     &       0.137\sym{**} \\
            &      (2.48)         \\

ln\_(Population at the smaller endpoint)     &      -0.611\sym{***}\\
            &    (-14.91)         \\

distance    &      -0.350\sym{***}\\
            &    (-24.79)         \\

slot        &       0.292\sym{**} \\
            &      (2.34)         \\

Inclusive Value    &       0.142\sym{**} \\
            &      (2.22)         \\
\hline
\(N\)       &       20937         \\
\hline
\textbf{Bottom Nest}    &    \textbf{Subsidiary or subcontractor}       \\
hub    &      0.0721\sym{**} \\
            &      (1.96)         \\

precipitation &      0.0114\sym{**} \\
            &      (2.29)         \\

snowfall &    0.000940         \\
            &      (1.51)         \\

freezing months &     -0.0422\sym{**} \\
            &     (-2.09)         \\

distance &     -0.0118\sym{*}  \\
            &     (-1.84)         \\

slot  &    -0.00336         \\
            &     (-0.15)         \\
\hline\hline
\multicolumn{2}{l}{\footnotesize \textit{t} statistics in parentheses}\\
\multicolumn{2}{l}{\footnotesize \sym{*} \(p<0.1\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}
\end{center}
\end{table}

\newpage

\begin{table}[htb!]
\medskip

\begin{center}
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\textbf{Table 6 continued: Panel replication with dynamic weather data: Subsidiary vs. subcontract regional airlines (Panel B)}
\medskip

\begin{tabular}{l*{1}{c}}
\hline\hline
            &\multicolumn{1}{c}{(1)}\\
            &\multicolumn{1}{c}{chosen}\\
\hline
\textbf{Top Nest}        &   \textbf{Whether to outsource}    \\
hub         &       0.255\sym{***}\\
            &    (0.0326)         \\
ln\_LPOP     &       0.111\sym{***}\\
            &   (0.00752)         \\
ln\_SPOP     &      -0.467\sym{***}\\
            &   (0.00608)         \\
distance    &      -0.141\sym{***}\\
            &   (0.00250)         \\
slot        &      -0.503\sym{***}\\
            &    (0.0275)         \\
Inclusive Value  &    2.405\sym{***}\\
            &     (0.107)         \\
\hline
\(N\)       &      608742         \\
\hline
\textbf{Bottom Nest}    &    \textbf{Subsidiary or subcontractor}       \\
hub   &       2.749\sym{***}\\
            &     (0.118)         \\
precipitation &      0.0482\sym{***}\\
            &   (0.00162)         \\
snowfall &     0.00809\sym{***}\\
            &  (0.000606)         \\
freezing\_months &     -0.0901\sym{***}\\
            &    (0.0107)         \\
distance &      -0.179\sym{***}\\
            &   (0.01000)         \\
slot  &       1.431\sym{***}\\
            &    (0.0690)         \\
\hline\hline
\multicolumn{2}{l}{\footnotesize Standard errors in parentheses}\\
\multicolumn{2}{l}{\footnotesize \sym{*} \(p<0.1\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}
\end{center}
\end{table}

\newpage

\begin{table}[htb!]
\medskip

\begin{center}
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\textbf{Table 6 continued: Panel "replication" with dynamic weather data: Whether to use a Subsidiary (including the mixture)(Panel C)}
\medskip

\begin{tabular}{l*{1}{c}}
\hline\hline
            &\multicolumn{1}{c}{(1)}\\
            &\multicolumn{1}{c}{chosen}\\
\hline
\textbf{Top Nest}        &   \textbf{Whether to outsource}    \\
hub         &       0.273\sym{***}\\
            &    (0.0287)         \\
ln\_LPOP     &      0.0950\sym{***}\\
            &   (0.00741)         \\
ln\_SPOP     &      -0.449\sym{***}\\
            &   (0.00598)         \\
distance    &      -0.139\sym{***}\\
            &   (0.00241)         \\
slot        &      -0.429\sym{***}\\
            &    (0.0243)         \\
Inclusive Value      &       1.780\sym{***}\\
            &    (0.0561)         \\
\hline
\(N\)       &      653769         \\
\hline
\textbf{Bottom Nest}    &    \textbf{Subsidiary or subcontractor}       \\
hub   &       2.606\sym{***}\\
            &    (0.0786)         \\
precipitation &      0.0436\sym{***}\\
            &   (0.00115)         \\
snowfall &     0.00646\sym{***}\\
            &  (0.000402)         \\
freezing\_months &     -0.0473\sym{***}\\
            &   (0.00759)         \\
distance &      -0.207\sym{***}\\
            &   (0.00749)         \\
slot &       1.315\sym{***}\\
            &    (0.0448)         \\
\hline\hline
\multicolumn{2}{l}{\footnotesize Standard errors in parentheses}\\
\multicolumn{2}{l}{\footnotesize \sym{*} \(p<0.1\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}
\end{center}
\end{table}

\newpage

\begin{sidewaystable}[htb!]
\medskip

\begin{center}
\footnotesize
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{Panel: Subsidiary vs. subcontract regional airlines}\label{table:replicate_panel}

\resizebox*{!}{.6\paperwidth}{
\begin{tabular}{l*{5}{c}}
\hline\hline
            &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}&\multicolumn{1}{c}{(5)}\\
            &\multicolumn{1}{c}{Normal Conditional Logit}&\multicolumn{1}{c}{Hybrid Model w.o. time dummies}&\multicolumn{1}{c}{Hybrid Model with time dummies}&\multicolumn{1}{c}{Contemporary Data}&\multicolumn{1}{c}{Robust Product Choice}\\
\hline
\textbf{Bottom Nest}    &    \textbf{Subsidiary vs. subcontractor}  &  &   & & \\
(D\_)HHI &       0.728\sym{***}  &      -3.617\sym{***}  &    -2.095\sym{***} & -1.493\sym{***}&      -2.361\sym{***}\\
            &    (0.0586)    &     (0.294)     &      (0.223)    &     (0.179)    &     (0.216)    \\
(D\_)mktshare &      -0.734\sym{***}  &      4.435\sym{***}  &       2.240\sym{***}&       1.516\sym{***}&       2.635\sym{***}\\
            &    (0.0421)   &     (0.302)     &      (0.179)    &     (0.137)   &     (0.177)    \\
(D\_)major\_on\_major &       0.129\sym{***}  &       0.157\sym{***} &     -0.0605  &      0.0188   &     -0.0828\sym{**}    \\
            &    (0.0101)    &    (0.0456) &    (0.0396)    &    (0.0301)    &    (0.0375)  \\
(D\_)major\_on\_regional &      0.0900\sym{***} &       1.170\sym{***} &       0.692\sym{***}&       0.378\sym{***}&       0.647\sym{***}\\
             &    (0.0112)  &     (0.0633)     &    (0.0531)     &    (0.0404) &    (0.0505) \\
(D\_)LCC\_on\_LCC &       0.297\sym{***} &       0.358\sym{***} &       0.234\sym{***} &      0.0836&       0.253\sym{***}\\
           &    (0.0181)  &    (0.0839) &      (0.0715)     &    (0.0531)   &    (0.0677)   \\
(D\_)LCC\_on\_regional  &       1.155\sym{***} &      -0.688\sym{*} &      -0.158    &       0.121 &       0.173    \\
            &     (0.109)  &     (0.396)  &    (0.346)     &     (0.265)   &     (0.322)  \\
MHHI &    &       3.100\sym{***} &   0.632\sym{**} &    -0.348\sym{**}    &       0.734\sym{***}\\
            &   &     (0.355) &   (0.254)    &     (0.162)    &     (0.242)   \\
Mmktshare &    &       4.442\sym{***} &   2.676\sym{***}&       1.004\sym{***}&       2.611\sym{***}\\
            &   &     (0.246)  &  (0.179)     &     (0.115)   &     (0.173)   \\
M\_major\_on\_major & &      -2.615\sym{***}   &  -2.283\sym{***}&      -1.486\sym{***}&      -2.130\sym{***}\\
          &  &    (0.0674) &    (0.0705)   &    (0.0726)   &    (0.0677)     \\
M\_major\_on\_regional &  &       1.548\sym{***} &    0.582\sym{***}&     -0.0353 &       0.633\sym{***} \\
            &  &    (0.0937) &   (0.0707)      &    (0.0465)   &    (0.0692)  \\
M\_LCC\_on\_LCC &  &      -2.428\sym{***} &   -2.171\sym{***}&      -1.444\sym{***}&      -1.982\sym{***}\\
            &   &    (0.0927)  &   (0.0821)   &    (0.0742)  &    (0.0776)       \\
M\_LCC\_on\_regional &  &      -10.56\sym{***} &   -11.68\sym{***}  & -11.94\sym{***}&      -11.92\sym{***}\\
            &        &   (0.793)    &    (0.0742)     &     (0.770)    &     (0.763)     \\
freezing\_months &     -0.0928\sym{***} &      -0.261\sym{***} &      -0.290\sym{***}&      -0.255\sym{***} &      -0.280\sym{***}\\
           &   (0.00809)  &    (0.0102)  &    (0.0105)     &    (0.0106)    &    (0.0104)      \\
precipitation  & -0.0381\sym{***}&    -0.00993\sym{***} &    -0.00510\sym{***}&      0.0125\sym{***}&    -0.00288\sym{**}\\
           &   (0.00141)   &   (0.00125) &     (0.00136)       &   (0.00109)   &   (0.00135)     \\
snowfall  & 0.00461\sym{***} &     0.00475\sym{***} &    0.00514\sym{***}&     0.00642\sym{***}&     0.00487\sym{***}\\
           &  (0.000371) &  (0.000611)  &      (0.000630)   &  (0.000489)    &  (0.000625)     \\
hub  &      -0.845\sym{***} &       4.078\sym{***} &       3.545\sym{***}&       2.128\sym{***}&       3.427\sym{***}\\
             &    (0.0437)   &     (0.125)  &     (0.128)     &     (0.113)   &     (0.125)      \\
slot   &      -0.845\sym{***} &       2.544\sym{***} &       2.376\sym{***}&       1.547\sym{***}&       2.409\sym{***}\\
          &    (0.0437)   &    (0.0906)  &     (0.0822)   &    (0.0793)    &    (0.0822)    \\
(Continued on Next Table)   &  & & & & \\
\hline
\multicolumn{6}{l}{\footnotesize Standard errors in parentheses; \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\);   Column (4) - Dynamic Weather Data; Column (5) - Robustness Operating Carrier}\\
\end{tabular}}
\end{center}
\end{sidewaystable}

\newpage

\begin{sidewaystable}
\begin{center}
\footnotesize
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
Table 7 continued: Top nest
\medskip

\resizebox*{!}{.6\paperwidth}{
\begin{tabular}{l*{10}{c}}
\hline
            &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}&\multicolumn{1}{c}{(4)}&\multicolumn{1}{c}{(5)}&\multicolumn{1}{c}{(5)}\\
            &\multicolumn{1}{c}{chosen}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{chosen}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}\\
\hline
\textbf{Top Nest}    &   \\textbf{No / Partial / Complete Outsourcing} & & & & &   \\\hline
& \textbf{Complete}     &      \textbf{Partial}   & \textbf{Complete}     &      \textbf{Partial}  & \textbf{ Complete}     &      \textbf{Partial} & \textbf{Complete}     &      \textbf{Partial} & \textbf{Complete}     &      \textbf{Partial} \\
(D\_)HHI       &       0.432\sym{***}  &      -1.135\sym{***} &       0.694\sym{***} &      -0.702\sym{***} &       0.597\sym{***} &  -0.987\sym{***}         &       0.534\sym{***}&      -1.242\sym{***} &       0.608\sym{***}        &      -0.890\sym{***}\\
          &    (0.0338)    &    (0.0409)  &    (0.0621)  &     (0.124) &      (0.0604)       &     (0.102)     &    (0.0641)  &    (0.0939)  &    (0.0600)   &    (0.0990)  \\
(D\_)mktshare   &      -0.399\sym{***} &       1.779\sym{***} &      -1.121\sym{***} &       0.494\sym{***} &      -0.882\sym{***}&       1.088\sym{***}&      -0.856\sym{***}&       1.401\sym{***}  &      -0.943\sym{***}&       0.848\sym{***}\\
          &    (0.0194)   &    (0.0272) &    (0.0536) &     (0.125) &    (0.0425)      &     (0.0781)       &    (0.0436)  &    (0.0677)  &    (0.0424)   &    (0.0771) \\
(D\_)major\_on\_major &      -0.862\sym{***}  &      -0.538\sym{***} &      -0.697\sym{***} &      -0.482\sym{***} &     -0.0391\sym{***}&      0.0238  &     -0.0186  &     -0.0234 &      -0.0423\sym{***}&  0.0208 \\
         &   (0.00955)  &   (0.00828) &    (0.0134) &    (0.0202) &     (0.0143)        &      (0.0190)     &    (0.0153) &    (0.0167)&    (0.0142)&    (0.0182)\\
(D\_)major\_on\_regional &       0.538\sym{***} &       0.886\sym{***} &      0.0521\sym{***} &      0.0549\sym{*} &     -0.0599\sym{***}  &  0.127\sym{***}&     -0.0414\sym{**}&       0.197\sym{***}&     -0.0616\sym{***}&       0.110\sym{***}\\
          &   (0.00913)  &   (0.00981)  &    (0.0156) &    (0.0285) &     (0.0158)        &      (0.0248)   &    (0.0163)     &    (0.0208)   &    (0.0157) &    (0.0239)    \\
(D\_)LCC\_on\_LCC  &      -0.459\sym{***} &      -0.109\sym{***} &       0.354\sym{***}  &       0.241\sym{***} &      -0.158\sym{***} &     -0.0538\sym{*} &     -0.0969\sym{***}&     0.00496 &      -0.161\sym{***}&     -0.0575\sym{**}\\
         &    (0.0134)   &    (0.0136) &    (0.0222) &    (0.0331) &     (0.0230)        &     (0.0302)   &    (0.0247)   &    (0.0265) &    (0.0229) &    (0.0290)   \\
(D\_)LCC\_on\_regional &      -0.159\sym{**}  &      0.0513  &       0.436\sym{***}&       0.687\sym{***} &      -0.112     &      0.0568  &     -0.0875 &     -0.0154 &      -0.136  &     -0.0176   \\
            &    (0.0769)  &    (0.0716) &    (0.0970) &     (0.123) &     (0.0981)         &      (0.110)  &     (0.102)  &    (0.0897)   &    (0.0981)   &     (0.106)\\
MHHI   & &     &      -0.136\sym{*} &      -1.965\sym{***} &       0.462\sym{***} &      -0.766\sym{***}&       0.539\sym{***}&      -0.290\sym{***}&       0.462\sym{***}&      -0.783\sym{***}\\
          & & &    (0.0701)   &     (0.152) &      (0.0656)       &     (0.117)    &    (0.0635)   &    (0.0893)   &    (0.0648) &     (0.114)    \\
Mmktshare  & & &      -1.208\sym{***}  &      -0.303\sym{***} &      -1.283\sym{***}   &       0.304\sym{***} &      -1.211\sym{***}&       1.041\sym{***}&      -1.237\sym{***}&       0.374\sym{***}\\
          & & &    (0.0474)  &     (0.109) &    (0.0415)   &      (0.0810)    &    (0.0389)   &    (0.0588)  &    (0.0407)  &    (0.0786) \\
M\_major\_on\_major  & & &      -0.878\sym{***} &       0.206\sym{***} &      -0.573\sym{***} &       0.271\sym{***}&      -0.443\sym{***}&      0.0599\sym{**} &      -0.581\sym{***} &       0.254\sym{***}\\
          & &  &    (0.0161)  &    (0.0266) &    (0.0168)   &      (0.0280)  &    (0.0188)   &    (0.0285)  &    (0.0167)  &    (0.0269) \\
M\_major\_on\_regional   & & &       0.818\sym{***} &       0.976\sym{***} &       1.036\sym{***}  &       1.430\sym{***}&       0.955\sym{***}&       1.576\sym{***}&       1.047\sym{***}&       1.416\sym{***}\\
            & & &    (0.0198) &    (0.0403)  &     (0.0192)        &     (0.0319)   &    (0.0195)    &    (0.0247)   &    (0.0192)  &    (0.0316)  \\
M\_LCC\_on\_LCC   & & &       0.354\sym{***} &       0.778\sym{***} &      -0.955\sym{***} &       0.554\sym{***}&      -1.049\sym{***}&       0.387\sym{***}&      -0.955\sym{***}&       0.554\sym{***}\\
          & & &    (0.0222)   &    (0.0340) &    (0.0235)       &     (0.0311)       &    (0.0256)   &    (0.0290)  &    (0.0234) &    (0.0297)\\
M\_LCC\_on\_regional   & & &       0.436\sym{***} &       4.668\sym{***} &       1.442\sym{***}  &       4.503\sym{***}&       1.082\sym{***}&       3.778\sym{***} &       1.564\sym{***}&       4.628\sym{***}\\
       & & &    (0.0970)    &     (0.249)  &      (0.200)     &     (0.222)     &     (0.202)  &     (0.204) &     (0.200) &     (0.215) \\
ln\_POP      &      -0.543\sym{***} &       0.142\sym{***} &      -0.360\sym{***}  &       0.278\sym{***} &      -0.731\sym{***} &   -0.0262\sym{**} &      -1.058\sym{***} &     -0.0787\sym{***} &      -0.728\sym{***}&     -0.0136 \\
          &   (0.00902)   &   (0.00971) &   (0.00973) &    (0.0113) &   (0.0114)       &     (0.0118)   &    (0.0158)  &    (0.0154) &    (0.0114) &    (0.0118)    \\
disparity  &     0.00138\sym{***} &    -0.00222\sym{***} &    0.000865\sym{***} &    -0.00255\sym{***} &   -0.000209   &   -0.00367\sym{***}  &     0.00185\sym{***} &     0.00302\sym{***} &   -0.000256&    -0.00374\sym{***}\\
        &  (0.000193)   &  (0.000221)  &  (0.000190) &  (0.000243) &     (0.000211)        &    (0.000259)  &  (0.000673)  &  (0.000657)  &  (0.000212)&  (0.000260)  \\
hub     &      -1.398\sym{***} &       2.508\sym{***}  &      -2.389\sym{***} &       0.280\sym{***} &      -2.336\sym{***}   &       0.540\sym{***}&      -1.910\sym{***}&       1.055\sym{***}&      -2.272\sym{***}  &       0.593\sym{***}\\
         &    (0.0276)   &    (0.0246) &    (0.0377) &    (0.0594) &    (0.0406)      &      (0.0604)    &    (0.0390)  &    (0.0510)  &    (0.0398)  &    (0.0591)  \\
slot    &     -0.0291    &       1.266\sym{***} &      -0.852\sym{***} &      -0.315\sym{***} &      -0.523\sym{***}&      0.0118 &     -0.0380 &       0.297\sym{***} &      -0.525\sym{***} &     -0.0271\\
         &    (0.0245)  &    (0.0295) &    (0.0274) &    (0.0441) &    (0.0288)       &     (0.0413)   &    (0.0322) &    (0.0412)  &    (0.0289)&    (0.0413) \\
Inlusive Value   &      -1.036\sym{***}  &      -1.093\sym{***}  & 8.601\sym{***} &       6.705\sym{***} &       5.029\sym{***} & 4.830\sym{***} &       3.203\sym{***} &       2.616\sym{***}&       4.904\sym{***}&       4.489\sym{***}\\
           &    (0.0455) &    (0.0492) &     (0.327) &     (0.259) &     (0.198)          &    (0.210)    &     (0.175)    &     (0.142)  &     (0.202)    &     (0.196)   \\
\hline
Time Dummy   &        No        &         No   &         No        &         No      &         Yes        &         Yes     &         Yes        &         Yes   &         Yes        &         Yes  \\
\hline
\(N\)       &     1455030     &        1455030   &     1455030     &        1455030    &     1455030     &        1455030   &     1071280  &     1071280 &     1455030     &        1455030 \\
\hline\hline
\multicolumn{11}{l}{\footnotesize Standard errors in parentheses;  \sym{*}\(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\);   Column (4) - Dynamic Weather Data; Column (5) - Robustness Operating Carrier}\\
\end{tabular}}
\end{center}
\end{sidewaystable}

\newpage

\begin{table}[!htbp]
\medskip
\begin{center}
\footnotesize
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{Regional carriers and prices\label{table:price}}
\medskip

\resizebox*{!}{.75\paperwidth}{
\begin{tabular}{l*{5}{c}}
\hline
            &\multicolumn{1}{c}{(1) Ln\_fare}&\multicolumn{1}{c}{(2) Ln\_fare}&\multicolumn{1}{c}{(3) Ln\_fare\_major}&\multicolumn{1}{c}{(4) Ln\_fare\_regional}&\multicolumn{1}{c}{(5) Gap}\\
            &\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}\\
\hline
HHI         &      0.171\sym{**}   &      0.193\sym{***}    &      -0.115      &       0.363\sym{*}   &     -62.88\sym{***}  \\
            &    (0.0783)     &      (0.0327)         &     (-0.61)      &      (1.71)  &     (-2.60)       \\
roundtrip\_share   &     -0.312\sym{***} &        -0.118\sym{***} &      0.0372   &      -0.172\sym{***}  &      -7.589        \\
            &    (0.00838)     &       (0.00696)    &      (0.65)      &     (-3.21)       &     (-0.85)           \\
merger      &      -0.0546\sym{***}   &    0.00604\sym{*}    &       0.117\sym{***}    &  0.0625\sym{**}  &       2.626       \\
            &     (0.00966)       &      (0.00322)      &      (3.22)         &      (2.49)     &      (0.58)       \\

Bankruptcy  &     -0.00693\sym{*}  &      0.0255\sym{***}  &        0.0979\sym{***}    &      0.0396  &       13.33\sym{**}  \\
            &      (0.00394)       &       (0.00240)      &      (2.92)       &      (1.34)    &      (2.52)     \\

Pre\_Entry4  &      0.0198\sym{***}    &     0.0181\sym{***}     &       -0.0215    &      0.0141         &      -8.215      \\
            &      (0.00524)       &      (0.00412)        &       (-0.47)     &      (0.14)      &     (-0.87)      \\

Pre\_Entry3  &     0.0148\sym{***}    &     0.0175\sym{***}  &        -0.108   &      -0.308          &       13.93    \\
            &     (0.00568)     &       (0.00414)       &      (-0.77)      &     (-1.29)         &      (0.69)         \\

Pre\_Entry2  &     0.00723      &       0.0130\sym{***}   &         0.0963\sym{*}    &     -0.0381     &       3.773    \\
            &     (0.00563)        &      (0.00414)      &      (1.66)              &     (-0.34)      &      (0.29)    \\

Pre\_Entry1  &     -0.00578   &     0.00231  &      0.0358    &     -0.0128   &       9.489\sym{**}      \\
            &     (0.00598)    &     (0.00412)        &      (1.19)      &     (-0.45)         &      (2.17)       \\

LCC\_entry   &   -0.0140\sym{***}  &      -0.0225\sym{***} &    -0.0225    &    -0.00571            &      -0.625         \\
            &     (0.00527)      &     (0.00406)       &    (-1.09)       &     (-0.24)         &     (-0.14)        \\

Post\_Entry1 &     -0.0351\sym{***}    &     -0.0526\sym{***}  &      -0.0387  &     -0.0413\sym{*}   &       3.300       \\
            &      (0.00532)      &   (0.00411)       &       (-1.06)       &     (-1.69)     &      (0.75)       \\

Post\_Entry2 &    -0.0538\sym{***} &      -0.0647\sym{***}   &        0.0733   &      0.0208      &       21.91       \\
            &       (0.00597)        &      (0.00413)       &        (0.37)      &      (0.12)   &      (0.73)        \\

Post\_Entry3 &     -0.0532\sym{***}     &       -0.0539\sym{***}    &      0.0765  &       0.149       &      -8.107      \\
            &     (0.00630)        &       (0.00417)        &      (0.60)     &      (1.01)     &     (-0.79)    \\

Post\_Entry4 &    -0.0505\sym{***}      &       -0.0413\sym{***}  &       0.0585   &      0.0803\sym{**}   &       5.570        \\
            &     (0.00623)        &      (0.00421)         &    (1.48)         &      (2.00)           &      (0.78)  \\

mk\_lee       &        -0.209\sym{***}    &      0.00645  &      -0.120\sym{*}    &      0.0510      &      -27.69\sym{**}  \\
            &     (0.0371)    &    (0.0101)        &     (-1.82)       &      (0.99)     &     (-2.35)      \\

Carrier-Route F.E.       &      Yes        &         Yes   &     Yes  &   Yes    &         Yes   \\

Time F.E.       &      Yes        &         Yes      &    Yes        &         Yes      &         Yes  \\
\hline
\(N\)       &       87468   &       102328     &   5014    &    5014     &    5014    \\
\hline
\end{tabular}}
\medskip

(1): major only carrier-routes; (2)-(5): major and regional carrier-routes

standard error in parentheses for columns (1) - (2); $t$ statistics in parentheses for columns (3) - (5)

\footnotesize \sym{*} \(p<0.1\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)


\end{center}
\end{table}

\clearpage

\begin{comment}
\begin{center}
\textbf{A1: Data cleaning instructions}
\end{center}

The Bureau of Transportation Statistics requires the operating carrier of the first segment to report a multi-segment itinerary. If that reporting carrier doesn't have information on the other operating carriers, the ticketing carrier can be reported as operating carrier on the following segments. Given this reporting rule, observations of "major operating by itself" become suspicious if none of them appears as the first (or the only) segment of an itinerary and none of them is reported by the ticketing carrier throughout a quarter. So I drop those observations if I don't find the ticketing carrier on that specific route in T-100 dataset.

\begin{center}
\textbf{A2: Robustness Check: Product Choice}
\end{center}

DB1B dataset is only a 10\% sample of tickets. If a major carrier uses its own fleet or a regional partner regularly but not frequently on a route, that operating carrier might be omitted and the DB1B record will be misleading. Given that common outsourcing contracts are signed at least on annual basis, for robustness check I only take "regional (subsidiary / subcontractor) only" or "major only" observations as what they are if the record is consistent in at least 4 consequent quarters. Otherwise, I will treat them as the mix case in that 4-quarter period.


\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}

