Entry in Russian market1 By Roman Chustuzian and Alexis Belianin

International College of Economics and Finance Higher School of Economics, ,

First Draft, April 2009

1. Introduction The Russian airline market is unique due to a combination of factors. Geographically, Russia is the largest country in the world, and air routes are by far the quickest (and sometimes unique) mean of transportation. Historically, this country has deeply enrooted tradition in airspace industry and air transport. Economically, this area of the national economy underwent the dramatic period of transition, combining terrific downfall in aircraft production (from over 200 per year in the late 1980s to less than 10 in the 2000s) combined with boost in the business of private carriers. Finally, this industry remains one of the most receptive to income shifts in the Russian economy: In 2000 – 2007, the Russian air passenger transportation industry grew by approximately 10% per year, which made new market entries attractive for . On the other hand, the industry underwent significant consolidation from 393 airlines in 1994 to 198 in 2007, with over 50% of the market held by five largest players. That meant the airlines had to consider seriously the competitive implications of their moves and the moves of their rivals. Hence airlines needed to make sure that their strategic actions were optimal.

Market entry is one of the key strategic actions airlines take. Like other researchers2, we model (airline) market entry with a discrete choice model. But unlike previous works, we focus on the Russian air passenger transportation (RAPT) industry. Since Russia is a developing country, the RAPT industry participants face higher uncertainty as compared to their counterparts in developed countries. To capture this uncertainty, we consider several entry models based on different assumptions. Our favourite among these is the mixed logit model, which allows for flexibility in the covariance matrix, capturing nonlinearity and interdependence of of the regressors’ effects on the probability of entry. Inasmuch as this model allows for airline‐specific regressor coefficients, it should be deemed superior to the other models in terms of both predictive accuracy and empirical validity for business purposes.

In economics literature, there has been quite substantive attention to entry, and the airline industry benefited from a bulk of this attention. Canonical works on entry, beginning from Breshahan and Reiss (1990; 1991) and, in the context of airline industry, Berry (1992), were aimed mostly at the elicitation of the factors affecting decision to enter a particular market, built under various assumptions about market and firm heterogeneity. The focus of our analysis is rather different: we seek for modeling a bunch of entry strategies compared from the business viewpoint. It may be the case that the airline is unable to enter a market prescribed by the ranking for reasons unobserved by the econometrician. Then, it should move one level down in the ranking and consider entering the next‐ranked market. This idea illustrates that our model may be applied in practice. It is so especially because our dataset covers all possible routes (markets) between 34 Russian cities with highest passenger traffic, which routes likely constitute all the markets the airline would consider entering.

1 Preliminary and incomplete (not for circulation). Comments are most welcomed. 2 Examples include Berry (1992), Sinclair (1995) and Oliveira (2008). 1

The rest of the paper is organized as follows. In the next section, we characterize the Russian air passenger transport industry (RAPT). Section 3 briefly reviews the existing literature and sets up our hypotheses. Section 4 describes the formal structural entry model set forth for the estimation strategy. Section 5 contains and discusses the data used. Finally, section 6 reports our results and concludes..

2. The Russian Air Passenger Transportation (RAPT) Industry3

2.1 General outlook

Over the recent years, the Russian air passenger transportation (RAPT) industry has been in contradictory conditions. On the one hand, it was growing by considerable 10% on average in 2000-2007, with unprecedented 18% in 2007 (see figure 1). For comparison, consider the 2007 average global industry growth of only 7.4%. On the other hand, the problems faced by the industry inhibited its development and sometimes were destructive.

Figure 1: Size and Growth of the Russian Air Passenger Transportation Industry

Source: ATO Sourcebook 2008

Besides, the RAPT industry’s size was insignificant internationally, constituting 45.1 million passengers or 110.8 billion passenger-kilometers in 2007, which was only 2% of the global market. By contrast, ’s share was 34%, and Pacific Basin countries’ – 31.8%, and the North America’s – 18.8%.

2.2. Positive industry trends

Clearly, the RAPT industry in its recent development owes much to a boost of consumer demand, which is in turn driven by Russia’s growing income from oil and gas export4. However, it would be a mistake to

3 Written from the standpoint of the 2007 year end. 4 Note, however, that those passengers were mostly either business executives or tourists, whose demand for air transportation was inelastic. 2

attribute the RAPT industry’s development only to Russia’s large income from oil and gas export. In fact, airlines made substantial efforts to improve their businesses. Old-generation aircraft were being replaced with modern aircraft at an increasing rate5, up-to-date safety procedures and e-tickets were being introduced, low-cost and hub airport business models were introduced as well. Further more, airlines were bringing their standards in accordance with international, discussed participation in global airline alliances and showed interest in acquiring foreign assets and in establishing branch offices abroad; which is true not only of the industry leaders but also of those whose market shares ranked 6-15. Nevertheless, the industry remained highly concentrated, with the market leader -RA holding 18% and 5 leading airlines holding 52% of the market.

RAPT industry also saw a great improvement in the regulatory sphere in 2007 – route licensing was abandoned. Until then, an airline was required to receive a license not for air passenger transportation in general, but for each (domestic)6 route it wished to operate on. That rule enabled the Ministry for Transportation to impose quotas on the number of flights, and hence to reduce the total number of carriers by driving the unprofitable ones out of the business. The state of affairs was challenged by the Federal Antitrust Service (FAS) which sought to establish a rule providing enough competition. In the end, the route licensing was abolished in favor of general air passenger transportation licensing. That greatly diminished administrative difficulties faced by the airlines.

Some of other positive trends were the emergence of a group of talented top-managers; little competition with other kinds of transport on routes between the European part of Russia and the Far East, and within the Far East; closeness of the domestic air passenger transportation market for foreign airlines; the possibility of regulation of transitory flights through Russia.

Figure 2: Number of Airlines 2.3. Negative industry features

in Russian Civil Aviation Before discussing the negative features of the RAPT industry, it is essential to understand their fundamental causes. The most important of them was the industry distress in the 1990s, after the USSR collapse. According to the rating agency Expert RA, the distress was caused by the following factors:

• historical: the USSR economy was planned and had guaranteed centralized

Source: Federal Agency for Air Transport 5 This tendency played a key role: Russian old-generation fuel-inefficient aircraft were being replaced with modern efficient aircraft, obtained mostly from foreign suppliers. That was important for cost-cutting (see below). 6 For international flights, airlines need a special approval of the government, which is quite difficult to get. 3

purchases, direct fare regulation, etc; • domestic economy: after 1991, ticket fares increased because of a rise in prices of inputs (fuel); • political: the industry lacked governmental monitoring and support, which were of poor quality; • international economy: Russian airlines faced tough competition on foreign markets, as well as increasing requirements in safety and fuel emission; • Organizational: the aircraft manufacturing industry lacked cooperation, while the RAPT industry was too much segmented. Those were the major causes of the RAPT industry distress in the 1990s, which determined many of the 2000-2007 industry’s problems.

Figure 3: Total Market Shares of 30 Leading

Airlines by Passengers Carried in 2007

1 Aeroflot‐RA 18.1% 2 Sibir 51.6% 3 Russia 4 5 6 VIM‐avia 7 KrasAir 8 Atlant‐Soyuz 68.5% 9 Uralskie Avialinii 10 Aeroflot‐Nord 11 Tatarstan 12 Aeroflot‐Don 13 Domodedovskie 78.2% 14 Avia 15 Orenburgskie Avialinii 16 17 18 KD Avia 85.4% 19 20 Avialinii Kubani 21 Yakutia 22 Kogalymavia 23 Aviaprad 24 25 Yamal 92.9% 26 27 Avialinii Dagestana 28 Alrosa‐Avia 29 Interavia 30 Saratovskie Avialinii Source: ATO Sourcebook

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Next, the fleet of Russian airlines was largely obsolete: old-generation Russian aircraft, which were fuel-inefficient7, constituted 75% of passenger air fleet in 2007. Consequently, the average fuel efficiency in the industry was about 50% less than that of foreign fleet exploited in Russia. Thus, airlines had to spend more on fuel, and on maintenance and repairing too, because most of the fleet were rather old.

Regional airlines could hardly replace old-generation Russian aircraft with new jets. The reason was that customers in regions had too little money to provide the demand generating enough revenues for jet replacement. In general, low income of regional customers, coupled with limited investment by the government, hindered the growth of RAPT industry and especially its domestic and regional segments. In 2007, the domestic and regional shares among total passenger turnover were 43% and 0.8% respectively. In comparison, the share of turnover on domestic routes in the US was 74% in 2006. This means that air transportation is largely unaffordable for Russians. The share of carried passengers to population is only 0.32 in Russia, as compared to 1.1 in Europe, and 2.5 for in the US.

In addition to low income of (potential) customers, the airlines faced a 90% cost increase over 2000- 2006. At the same time, the US saw a 7% cost decrease, which meant that the negative trend was specific to Russia (see figure 4). To some degree, it undermined the Russian airlines’ competitiveness with respect to foreign rivals. Indeed, foreign carriers increased the number of passengers carried from Russian airports abroad by 2.1 times in 2000-2006. This cost increase was determined by a rise in the air fuel price to a large extend, which latter rose from 8,583 rubles in 2003 to 18,166 rubles in 2007 (in nominal terms, see figure 5N. Thus the high air fuel price was another problem affecting the industry. It was magnified by monopolistic positions of fuel suppliers8 that set too high prices.

Figure 4: Approximate Aviation Fuel Price, Figure 5: Air Transportation Nominal Rubles per Ton Cost Dynamics, US cents / pkm 20 000 18166 18 000 16290 16734 16 000 14 000 13330 12 000

10 000 8583 7576 7677 8 000 7087 5437 Nominal Rubles Nominal 6 000 4 000 1505 1668 2 000 1300 1366 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

7 Fuel efficiency is measured in grams of fuel spent on one passenger-kilometer. 8 Airport services and repair and maintenance services also had monopolistic positions. 5

Source: A.B.E. Media, Bank of Moscow

Then, the Russian air transport infrastructure was in poor state. The number of airdromes plunged from 972 in 1992 to about 330 in 2007. To compare, and had territories which were about 3.5% that of Russia, but they had 380 and 350 airdromes respectively. The lack of airdromes (and airports) in Russia was determined by insufficient regional (and domestic) flight demand, resulting from the low income and lack of government subsidies. Furthermore, the remaining airdromes were in poor condition. Only 58% of them had artificial (asphalt) runways, and 48% were equipped with signal lights. That state of affairs resulted from inadequate amount of government investment. Although the latter increased sharply in 2004, only large and most important airports had received funding by 2007. Obviously, many airports could not serve modern foreign jets.

One more issue was the pilot deficit. The number of pilots had been declining at an annual rate of 6% since 1996 and, by 2006, decreased by 57%. The number of pilots leaving Russian airlines was approaching 500 per annum, while the average age of pilots was 50. Although aviation authorities took measures to enroll 500 students for pilot schools annually9, those measures were likely to be insufficient. Hence leading companies arranged their own pilot training programs. Other problems connected with pilots included ineffective legal regulation of work time and medical examination. Finally, some airlines made their pilots work more than permitted by the standards. Those problems were linked to the broad issue of safety. Its level was insufficient, although the number of accidents and catastrophes declined in the last decade.

Next, there were problems in governmental regulation. One of them was an excessive number of regulatory bodies which often addressed issues out of the scope of their responsibilities, while other issues were not addressed at all. Although the Ministry for Transportation commenced the consolidation of regulatory bodies, some regulatory entities were interested in managing airline operations and keeping excessive control capacity. Thus, no one was responsible for achieving specific results, learning from international experience and introducing best global practices.

2.4 Scenarios of development It is not quite Economic Financing of Import Average Hours Passenger Optimistic Innovative Sufficient Abolished 35 2070 305 billion Base-case Commodity- Sufficient Flexible 37 1990 210 billion based Pessimistic Inertial Insufficient Current 40 1840 140 billion Sources: Federal agency for Air Transportation, ATO Sourcebook 2008.

9 At least in 2008-2009. 6

In order to be successful, Russian airlines need new aircraft10. How many of them will be available for purchase (or leasing) depends on (1) domestic aircraft supply and (2) import tariffs affecting foreign aircraft import. Now, the domestic aircraft supply depends on the mode of Russian economic development11. On the other hand, the amount of imported aircraft depends on the import tariff policy, which we would like to describe in more detail.

The 42% effective import tariff is applicable only to aircraft with capacity between 50 and 300 passengers (as of year 2008). It will remain in the pessimistic scenario. But in the base-case scenario the rate will be flexible. It means that the import rate will be abolished for those types of aircraft which will not be produced in Russia in the foreseeable future. Next, the types of aircraft, which are temporarily not produced in Russia, may be leased for 5-10 years with zero import tariff. If after this period an analogous aircraft is produced in Russia, the import tariff will begin to apply. Finally, in the optimistic scenario the import tariff will be abolished altogether12. To summarize, the import rate policy, overall economic development and financing of airport network development will determine the RAPT industry evolution to a large extend.

2.5 The RAPT industry specifics Industry specifics

The air passenger transportation industry has the following distinctive features:

• The major assets, aircraft, are standardized and not specific to a geographic location. Hence market (route) entry barriers are low and the competition intensity is large. Besides aircraft are liquid assets. • Because air passenger transportation can not be resold, price discrimination is possible13. • The demand is highly dependent on the economy state and hence is cyclical. It is also subject to seasonality: the tourist demand increases in summer and in winter holidays. • Industry entry barriers are large. To illustrate, aircraft prices (in November 2007 USD) range from $50 million for a 100-seat jet to $230 million for a 350-seat jet. Synthesize that with, say, 30-40 jets necessary for a low-cost carrier to be efficient. • Fixed costs are substantial. They include leasing and insurance expenses, fixed expenditures on repair and maintenance, base salary expenses, and general and administrative expenses. • The operational leverage is high. That is, passenger turnover growth causes higher growth of financials and vice versa. • Marginal costs are low. Given available capacity, the cost of carrying an additional passenger is

10 It is especially important in light of competition with foreign competitors which have modern fuel-efficient aircraft. 11 Another way the economic development affects the RAPT industry is due to the economy’s effect on demand. 12 Some airlines do not believe the optimistic scenario will realize. 13 Another form of discrimination implies providing different-quality flight booking opportunities. 7

negligibly small. Since in the short run the capacity is determined by the fleet capacity and the flight schedule, short-run profit maximization is equivalent to revenue maximization. This can be done in three ways: o Increasing fleet utilization, i.e. the number of hours per day the aircraft are in the air; o Increasing the seat load, whose industry average is about 70%; o Revenue management, which includes appropriate incentives for price discrimination, amount and timing of seats available for sale within each class of service, etc. Russia specifics

This sub-section outlines the features specific to the Russian air passenger transportation industry:

• There are no hubs in Russia. Two exceptions are and Moscow. Kaliningrad is a city on a piece of Russian territory adjacent to Poland and Lithuania. Hence it provides a good opportunity for an airline to collect in Kaliningrad all passengers flying from Russia to Europe14. This idea was recently realized by a small but efficient15 Russian airline KD Avia which sought to employ a pure hub model. • Moscow has three large airports in which Russian air transportation is highly concentrated. For example, in 2007, 70% of domestic flight passengers and 77% of international flight passengers traveled through Moscow airports (figure N). The reason for that is most businesses and authorities are located in Moscow. That allows for sufficient seat load on flights between Moscow and Russian regions. Thus, Moscow airports are not hubs in the sense of an operations model, but in the sense that air passenger transportation is concentrated in them16. • There are only two Russian low-cost carriers. The first of them, Sky Express, began its operations in January 2007. By the year end it managed to capture 3% of the domestic market. Another low-cost airline, called Red Wings, provides only charter services but is planning to switch to scheduled flights in future. Finally, a Russian investment company Alfa Group is currently initiating the third low-cost airline.

14 Of course, Kaliningrad can serve as a hub for Europe-Europe flights as well. 15 KD Avia utilized only foreign aircraft, and reached the average aircraft utilization time of 12 hours per day, which was significant for an airline not serving long-haul flights. 16 However, it should be noted that the Russian government is currently working on creating several real hubs across Russia. 8

Figure 6: Share of Passengers Traveling through Moscow Airports, millions of passengers

* ‐ Air Transport Observer estimate

Source: Federal Agency for Air Transportation

• The air fleet of Russian airlines is differentiated, meaning that it has different Russian aircraft and foreign aircraft (Kozlov, 2007). Since short-term flight planning procedures are different for Russian and foreign aircraft, they cannot be easily substituted when necessary. • Airline divisional structures are differentiated (Kozlov, 2007). That is, besides air passenger transportation, some carriers provide repair and maintenance, ground-based and other services. • The Russian airline business is regulated by a large number of laws17 which determine the complexity of the legal space. • Russian airlines are growing extensively, which means that the growth rate depends directly on fleet size. • Substitutes, e.g. train or car trips, play an important role, because they are sometimes cheaper or more convenient. Now, the airline operations determine the cost structure common to many Russian airlines (see figure 7). On the contrary, the revenue structure is specific to an airline business model and strategy and can hardly be generalized.

17 The laws include the Air Code, legislative and sub-legislative acts, administrative orders and constraints, etc., which are sometimes contradictory. 9

Figure 7: Cost Structure of Russian Airlines, 2005

Rent and Air Navigation Lease Services 7.0% 2.7%

Aviation Fuel Other and Lubricants Expenses 39.4% 25.6%

Repair and Airport Maintenance Services 11.7% 13.7%

Source: Ministry for Transportation, RBC

2.6 Major Russian airlines18 Russan domestic market, while being less concentrated than the, for instance, banking sector, still is characterized by a rather large degree of concentration. Aeroflot Group includes the parent company Aeroflot – Russian Airlines, three subsidiaries (Aeroflot Plus, Aeroflot-Don and Aeroflot-Nord) and a cargo airline (Aeroflot-Cargo), is rather closely followed by a few others, and some of them (e.g. that of Sibir/S7) by 2008 actually exceeds Aeroflot’s share in the domestic market. Shares of major companies are illustrated on Figure 8.

Figure 8: Operational Statistics of Major Russian Airlines

Sibir (S7 Aeroflot-RA UTair Transaero STC Russia Airlines) International 5.4 2.1 0.36 2.8 1.6 flights (8.4%) (8%) (47.2%) (54.4%) (11.1%) Passengers carried in 2007 Domestic 2.8 3.6 2.57 0.5 1.7 (growth in parentheses) flights (19.5%) (22%) (18.1%) (37.9%) (4.9%) 8.2 5.7 2.93 3.3 3.3 Total (12%) (16.4%) (21%) (37.9%) (19,9%) Load factor in 2007 (domestic and international) 70.3% 80.9% 71.1% 79.5% 71.6% Moscow (airport Moscow (airport Saint-Petersburg Moscow Moscow (airport Vnukovo) Domodedovo), Moscow Base airports (airport Domodedovo) Tumen Saint-Petersburg (airport Sheremetievo) Hanty-Mansiysk Vnukovo) Source: Transport Clearing House, airline data

18 This sub-section is largely based on company profiles and industry outlooks by Goldman Sachs, Renaissance Capital, and the Bank of Moscow. 10

3. Literature Review Relevant to our research is the flow of literature that focuses on market entry models. It is largely based on classic papers of Bresnahan and Reiss (1987, 1990, 1991b) and Berry (1992). First, Bresnahan and Reiss separate the implications of strategic behavior and minimum efficient scale to entry. For this purpose, they construct an empirical model which can be used to predict the number of firms in the market. Then, Bresnahan and Reiss investigate the minimum market size supporting N firms, and how that market size changes with N. Finally, they show that competition increases at a rate that decreases with the number of incumbents. How fast competition growth falls depends on the industry. Notice, however, that Bresnahan and Reiss model entry into small isolated markets, including retail and professional services. Therefore, their insights cannot be applied directly to modeling entry in the airline industry.

Another influential paper is by Berry (1992) who develops a model of sequential‐move entry in the airline industry. Specifically, he estimates a homogeneous‐product variable profit function for firm j in market m:

mm ),( = XXNV m β −δ N)ln( + ε mo , and a heterogeneous fixed cost

mj = ZF jmα + ε mj , where Xm includes market‐specific regressors, εm0 is a normally distributed shock to all firms’ variable profits, Zjm includes a dummy for serving both endpoints of the focal market, and εmj is an error independent across firms. If the firms are symmetric post‐entry, then only the total number of firms affects profits. Hence the (unique) equilibrium number of firms, N*, must satisfy:

)*,( FNV ε N* ≥+−⋅ 0 , and

),1*( FNV +−⋅+ ε N +1* ≤ 0 .

Berry estimates the model using the simulated method of moments developed in McFadden (1989) and Pakes and Pollard (1989). Berry’s model can be used for estimating not only the equilibrium number of firms in the market, but also the probability of entry by a potential entrant.

One limitation of Berry (1992)’s approach is that firm heterogeneity has a particular form that allows for a unique equilibrium in the number of firms. In case of the general form of firm heterogeneity, equilibria can be multiple in the number of firms. Furthermore, Berry uses a variable profit function that implies symmetric effects of firms’ entries on one another’s profits. What matters is the number of firms, but not their identity.

Ciliberto and Tamer, in the 2007 version of their working paper, construct a model that allows for general forms of heterogeneity across firms without making equilibrium selection assumptions. In fact, it allows for firm‐specific effects of the firm’s presence in the market on competitors’ profits. The identified features of the model “are sets of parameters (partial identification) such that the choice probabilities predicted by the econometric model are consistent with the empirical choice probabilities estimated from the data” (Ciliberto and Tamer, 2007).

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Ciliberto and Tamer apply their methodology to study how different types of firm heterogeneity affect the market structure of the U.S. airline industry. They find that the competitive effects of large network airlines (American, Delta, United) on competitors’ profits differ from those of low‐cost carriers and Southwest. Besides, Ciliberto and Tamer find that the competitive effects rise with an airline’s airport presence.

Our study is different from the previous research in several ways. We concentrate on inferring which markets an airline should enter, rather than analyzing the market structure. In fact, we derive the optimal market entry sequence. Next, we use the mixed logit model, a step hardly taken by other researchers for similar purposes.

4. Theoretical model The variable profit model can be obtained rather naturally in a number of ways – e.g. from the Cournot model with isoelastic demand – see Bresnahan‐Reiss (1990) and Berry (1992). Consider a market with no airline operating, and suppose that potential market demand for this flight is given by

⋅ YSpRGDQ )(),,(= (1)

where D is individual demand of a representative consumer, S is market size (dependent on regressors Y , such as population in the region, income etc., exogenous to the market), R is price of substitutes (say, fuel for rail and bus transportation), p is this market's price and G are market conditions: consumer tastes, expectations of incomes, willingness to travel at all etc. Output of an individual firm is given by qi . Inverting the demand function, ySQpRGD )(/=),,( , so the market price is

SQRGpp )/,,(= (2)

Assume costs of firm i are given by

i i ⋅ + ii ZWFqZWcWC ),(),(=)( (3)

where W is vector of exogenous cost parameters (price of fuel, labor, airport fares), Z is vector of firm‐specific parameters, and Fi is (again firm‐specific) exogenous entry costs, observable to the firm but not to the econometrician.

Firm's profit is

π i ⋅ − ii ⋅ + ii ZWFqZWcqSQRGp ),(),()/,,(= (4)

Assume (c.o. Bresnahan and Reiss, RES 1990) that firm's profits linearly depend on YS ).( This does not affect FOC, so the entry condition can be rewritten as

i ()− i ⋅ ≥ ii ZWFqYSZWcSQRGpv ),()(),()/,,(= (5)

where vi is variable profit of firm i .

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In a monopoly market, there is either one or no firm, so = qQ i . Let's specify the model further GR by assuming 1) isoelastic (inverse) demand of a form p = , where α 1> is elasticity of demand; 2) qα no Z variables; 3) F independent of anything (because unobservable anyway). Then

⎛ GR ⎞ v = ⎜ ⎟ = 1−α ScqGRQqSc ⎜ α − ⎟ ()→− max (6) ⎝ Q ⎠ Q The FOC yields

1 α −α α −α)(1 GR ⎛ −α)(1 GR ⎞ −α ⇒ QcGRQ ==)(1 ⇒ Q = ⎜ ⎟ (7) c ⎝ c ⎠ GR GR −α)(1 GR c Recalling QP =)( ⇒ Qα == ⇒ P = ; substituting this back to the Qα P c 1−α variable profit function, 1 1 ⎛ c ⎞⎛ −α)(1 GR ⎞α ⎛ Sα ⎞ 1 +− 1 ()− =)(= SSQcQPv ⎜ − c⎟⎜ ⎟ = ⎜ ⎟()−α)(1 α cGR α (8) ⎝1−α ⎠⎝ c ⎠ ⎝1−α ⎠ ⎛ Sα ⎞ 1 α −1 = ⎜ ⎟()−α)(1 α cGR α (9) ⎝1−α ⎠ In logs, this profit function is

S α 1 1 1 1−α v ln=ln + ln α)(1ln ln ln +++−+ ln cRG (10) 1−α 1− αα αα α 1 ⎛ GR ⎞α 1 because Q = ⎜ ⎟ and must be >1, this may be rewritten as ⎝ p ⎠ α S 1−α 1 1 v β + ln= + ln ln ++ ln RGc (11) 0 1−α ααα where if S ,,G R and c are products of some variables, we can further expand logs and estimate variable profit in the usual way. Entry then occurs whenever v exceeds entry costs, which are assumed to be lognormally distributed (log of it is normal).

If there are several firms instead of one (not assumed symmetric), then qi = 1 ⎛ −α)(1 GR ⎞α ⎜ ⎟ − Q−i , price remains unchanged (!), and variable profit becomes i ()− SqcQPv i =)(= ⎝ c ⎠ 1 ⎡ ⎤ α −1 ⎛ Sα ⎞ ⎛ −α)(1 GR ⎞α ⎛ Sα ⎞⎡ 1 ⎤ ⎢ ⎥ α α ⎜ ⎟ ⎜ ⎟ − Q−i = ⎜ ⎟⎢()−α)(1 − QcGR −i ⎥. In logs, this implies addition of ⎝1−α ⎠⎢⎝ c ⎠ ⎥ ⎝1−α ⎠ ⎣ ⎦ ⎣ ⎦ one more extra term: S 1−α 1 1 v β + ln= + ln ln −++ lnln QRGc (12) 0 1−α ααα −i Model of Berry (1992) is a variant of this; in terms of our empirical strategy, the probability of entry is modeled as logit or probit functions of variable profit consisting of market‐size parameters S, cost parameters, market conditions (G and R, where R is proxied by distance), and market competition Q. This model can be set forth to capture the major factors affecting entry in Russian airline markets. 13

5. Data

5.1 Data sources We define a market as a trip between two Russian cities, irrespective of whether the landing in the destination city is transitory. Terms “market” and “route” are used interchangeably unless stated otherwise. We consider all possible markets between 34 highest‐traffic cities19, which accounted for roughly 77% of the total passenger traffic in 2006 and 2007. Next, we consider 18 largest Russian airlines by passengers carried on domestic and international flights in 2007, which then held 85% of the market.

Our dataset covers years 2002‐2007 that are split into periods as follows. Summer periods, basically, start in March and end in October, whereas winter periods start in October and end in March. Hence the dataset includes 12 periods from “summer 2002” to “winter 2007”, the latter ending in March 2008. The necessity of splitting years into periods comes from the regulatory rule of booking time slots at airports for the period.

We use the following data sources. The Transport Clearing House (TCH) provides data on flight frequencies by period, airline and route. It also specifies aircraft models on which the flights were accomplished. Next, Rosstat provides annual data on the gross regional product, regional and city population, regional and national CPI, and national PPI. Finally, Air Transport Observer Sourcebooks 2006 and 2007 provide data on aircraft fuel efficiency, aviation fuel price and airlines’ base airports.

We convert annual data into per‐period as follows. If the data is as of December of a given year, we treat them as the data of that year’s winter period. The summer period data is the average of the given year and previous year’s observations. Next, if the data is as of January of a given year, we treat them as the data of the previous year’s winter period. The summer period data is the average of the given year and next year’s observations. Finally, we assume that those variables, on which only annual data is available, change linearly from period to period within a year.

4.2 Variable definitions

ENTRYimt is a dummy variable equal to one if an airline chooses to operate on a given route in a given period and zero otherwise. In other words, we assume that each period the airline decides whether to enter (operate on) the route or not.

Market size

POPmt is the arithmetic mean of populations of endpoint cities. It measures market size. Although researchers use other measures like the geometric mean of populations of endpoint cities, or the minimum of the two, there is no consensus which measure performs better.

REALGRPmt is the arithmetic mean of real gross regional products per capita in regions in which endpoint cities are located. It measures the buying power of potential customers, as well as the strength of corresponding economies and the amount of business activity. In constructing this variable, we deflate the nominal gross regional product by regional CPI and then divide it by regional population.

19 The list of 34 highest‐traffic cities includes the cities that enter the rating of top 30 cities by domestic traffic in 2007, or they have airports that enter one of the following ratings: top‐30 Russian airports by aircraft movements in 2006 and 2007; top‐30 Russian airports by international and domestic traffic in 2006 and 2007. All the mentioned ratings are composed by the Transport Clearing House. 14

SUMMERt is a dummy variable being one if the period is summer and zero otherwise. It controls for the demand increase due to more vocation travel in summer.

MOSt is a dummy variable being one if the market involves Moscow, and zero otherwise.

Costs

DISTANCEm is the distance between route endpoints. It is a cost measure for it shows how much fuel the airline spends per flight on a given route. Note that fuel expenses are the major component of variable costs, because they change significantly from route to route. Other cost components change much less. Further more, fuel expenses account for approximately 40% to 60% of Russian airlines’ total costs.

DISTANCEm is calculated using endpoints’ geographic coordinates.

FUELEFFimt (in tons of fuel per passenger‐kilometer) is the average fuel efficiency of aircraft used by the airline on a given route, weighted by the number of flights performed on a given aircraft model.

FUELEFFimt is a major determinant of the fuel expenses.

Competition

SUBSm is a dummy variable being one if the route has distance of less than 1500 kilometers and zero otherwise. It indicates whether the airline faces significant competition with substitutes like railway or automobile transportation.

TOTFREQmt is the total number of flights on a route in a given period. It is negatively related to the availability of time slots at endpoint city airports. Hence TOTFREQmt is inversely related to the availability of free space in the market.

HHImt is the Herfindahl index based on the flight frequency shares of the 30 airlines in the dataset. It measures the market power of airlines on a route. Since these airlines held 93% of the (international and domestic) market in 2007, we consider it safe to ignore the airlines which are not in the dataset, when constructing HHImt.

4.3. Descriptive statistics Table 1 summarizes entry in the Russian markets over the sample grouped in the following ways. A firm is classified as entered (present) on a market if it serves a particular itinerary in a particular period; otherwise it is classified as not present (although it might have been present there at some period over the time span). New entry denotes instances in which the firm was not in the market in period t‐1 and entered it in period t within the sample; exit is defined analogously. Table 1 shows the average number of markets operated by each of the 18 companies, new entries and exits across all 12 periods.

As the table shows, of 1122 possible markets (33*34 cities in the sample), on average the companies operated on its small subset of 346. Firm presence in the market is quite uneven: some firms operate small number of markets, whereas other are quite diverse in terms of total number of markets, as well entries, implying substantial heterogeneity. Finally, it is worth stressing that exit is much more frequent than entry, reflecting seasonality of the markets, as well as intensive reallocation of firms across markets, but most of all, shrinking number of aircrafts available to the companies.

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Table 1. Presence and entry.

new % to % to present entry present new exit present 1 Аэрофлот‐Дон 10.92 2.08 19% 5.00 46% 2 Авиалинии Кубани 15.08 1.92 13% 4.83 32% 3 Аэрофлот‐Норд 15.17 3.50 23% 6.92 46% Аэрофлот‐Российские 4 авиалинии 21.92 0.75 3% 4.92 22% 5 ВИМ‐авиа 2.17 0.75 35% 2.17 100% 6 Владивосток Авиа 16.33 2.67 16% 6.83 42% 7 ГТК Россия/Пулково 49.75 6.50 13% 16.25 33% 8 Дальавиа 42.17 5.17 12% 13.42 32% 9 Домодедовские авиалинии 7.67 0.42 5% 1.75 23% 10 КД авиа 4.42 1.25 28% 3.25 74% 11 Красноярские авиалинии 29.58 5.67 19% 11.17 38% 12 Небесный Экспресс 1.58 0.67 42% 1.67 105% 13 Сибирь 37.42 8.17 22% 16.17 43% 14 Татарстан 9.83 2.67 27% 4.58 47% 15 Трансаэро 6.42 0.58 9% 2.33 36% 16 Уральские Авиалинии 21.58 5.92 27% 9.75 45% 17 Кавминводыавиа 13.75 1.25 9% 4.75 35% 18 ЮТэйр 40.33 10.00 25% 18.25 45% Total 346.08 59.92 17% 134.00 39% Source: Transport Clearing House, authors’ calculations

Table 2 presents similar indicators per periods. Although the market for air transportation has been gradually expanding in terms of volumes, there has been much less expansion to the new markets. Also, all three indicators exhibit substantial seasonality: in summer periods companies fly about 1/3 more than in winter ones.

Table 2. Presence, entry and exit by periods. Source: Transport Clearing House, authors’ calculations.

Presence Entry Exit 1 760 ‐ ‐ 2 542 27 335 3 803 274 228 4 571 18 271 5 805 250 203 6 571 25 288 7 783 237 213 8 595 36 328 9 804 238 215 10 610 46 300 11 802 231 254 12 628 54 342 Total 8,274 1,436 3,228

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Table 3. Entry and exit for the Moscow vs provincial markets.

presence entry exit Outside In Outside In Outside In Moscow Moscow Moscow Moscow Moscow Moscow 1 Аэрофлот‐Дон 79 52 24 1 41 19 2 Авиалинии Кубани 146 35 18 5 46 12 3 Аэрофлот‐Норд 2 48 1 12 3 36 Аэрофлот‐Российские 4 авиалинии 4 259 1 8 5 54 5 ВИМ‐авиа 2 24 1 8 3 23 6 Владивосток Авиа 163 33 24 8 60 22 7 ГТК Россия/Пулково 400 62 28 3 68 18 8 Дальавиа 492 14 62 0 156 5 9 КД авиа 42 92 14 5 37 21 Красноярские 10 авиалинии 316 11 60 1 114 2 11 Сибирь 254 19 79 8 137 20 12 Татарстан 94 2 27 0 45 4 Уральские 13 Авиалинии 236 195 65 19 101 57 14 Кавминводыавиа 144 24 10 5 45 10 15 ЮТэйр 385 100 107 13 184 28 Total 2,974 99 591 13 1,183 35 Source: Transport Clearing House, authors’ calculations

Table 3 shows entry and exit in the major market of Moscow. As can be seen, most companies (with the exception of three) fly to and from Moscow, but most new entries are concentrated outside of Moscow area. This is because the Moscow market is the most concentrated, and entry in here is highly competitive.

Table 4 lists entry behavior by cities of Russia. The most intensive markets, in addition to Moscow, are summer destinations of Anapa and . Among the markets to enter Moscow remains the most attractive but, as we have already said, it is also the most ‘populated’ one. Moscow in fact, occupies a unique position inside of Russia: effectively it is the only hub over the country, and an overwhelming majority of transit air traffic goes through it. A final destination of new entry includes distant cities (mostly Siberian), which constitutes another peculiarity of Russia: as long as people from these regions can afford flying, they would (and are) gladly switch to air transport from the rail, which is very slow and inefficient. Exit behavior is also not homogeneous: from some markets there are almost no leaves, which is of course attributable to the fact that these markets (northern ones) are being served by air companies only.

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Table 4. Presence, entry and exit by cities.

Name presence entry exit number % number % number % 1 Анапа 287 7.16 99 13.77 107 6.65 2 Архангельск 98 2.45 13 1.81 38 2.36 3 Владивосток 161 4.02 25 3.48 74 4.6 4 Волгоград 117 2.92 28 3.89 48 2.99 5 Екатеринбург 265 6.61 38 5.29 115 7.15 6 Иркутск 302 7.54 59 8.21 114 7.09 7 Казань 224 5.59 42 5.84 84 5.22 8 Калининград 266 6.64 33 4.59 97 6.03 9 Краснодар 225 5.62 45 6.26 89 5.53 10 Красноярск 174 4.34 27 3.76 74 4.6 11 Минеральные Воды 60 1.5 7 0.97 39 2.43 12 Москва 673 16.8 81 11.27 283 17.6 13 Мурманск 43 1.07 8 1.11 23 1.43 14 Нарьян‐Мар 29 0.72 2 0.28 8 0.5 15 Нижневартовск 98 2.45 17 2.36 34 2.11 16 Нижний Новгород 91 2.27 17 2.36 26 1.62 17 Новосибирск 227 5.67 34 4.73 84 5.22 18 Новый Уренгой 43 1.07 10 1.39 17 1.06 19 Норильск 89 2.22 28 3.89 38 2.36 20 Омск 81 2.02 20 2.78 31 1.93 21 Пермь 35 0.87 7 0.97 11 0.68 22 Петропавловск‐ Камчатский 39 0.97 6 0.83 16 1 23 Ростов‐на‐Дону 62 1.55 12 1.67 33 2.05 24 Самара 52 1.3 11 1.53 26 1.62 25 Санкт‐Петербург 96 2.4 9 1.25 41 2.55 26 Сочи 105 2.62 28 3.89 32 1.99 27 Сургут 26 0.65 6 0.83 11 0.68 28 Тюмень 11 0.27 1 0.14 2 0.12 29 Уфа 21 0.52 3 0.42 3 0.19 30 Хабаровск 4 0.1 2 0.28 6 0.37 31 Челябинск 3 0.07 1 0.14 4 0.25 Total 4,007 100 719 100 1,608 100 Source: Transport Clearing House, authors’ calculations

Next Table 5 summarises mean market shares of the carriers (s) and Herfindahl‐Hirschmann indices (hhi) calculated over the sample in each market. As can be seen, concentration in the markets varies quite substantially from market to market, which again reveals substantial heterogeneity of the markets and firms which operate on them.

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Table 5. Concentration indices by markets.

Obs mean std min max 1 Анапа s 444 0.191 0.334 0 1 hhi 444 0.297 0.369 0 1 2 Архангельск s 192 0.332 0.409 0 1 hhi 192 0.666 0.377 0 1 3 Владивосток s 480 0.322 0.337 0 1 hhi 480 0.520 0.307 0 1 4 Волгоград s 180 0.233 0.378 0 1 hhi 180 0.394 0.400 0 1 5 Екатеринбург s 648 0.321 0.360 0 1 hhi 648 0.621 0.314 0 1 6 Иркутск s 528 0.231 0.350 0 1 hhi 528 0.537 0.357 0 1 7 Казань s 372 0.308 0.430 0 1 hhi 372 0.576 0.435 0 1 8 Калининград s 432 0.231 0.362 0 1 hhi 432 0.434 0.399 0 1 9 Краснодар s 480 0.310 0.375 0 1 hhi 480 0.531 0.364 0 1 10 Красноярск s 480 0.366 0.378 0 1 hhi 480 0.650 0.308 0 1 11 Минеральные Воды s 264 0.492 0.386 0 1 hhi 264 0.633 0.295 0 1 12 Москва s 1440 0.168 0.243 0 1 hhi 1440 0.454 0.222 0.117 1 13 Мурманск s 96 0.414 0.441 0 1 hhi 96 0.742 0.340 0 1 14 Нарьян‐Мар s 48 0.375 0.479 0 1 hhi 48 0.605 0.483 0 1 15 Нижневартовск s 180 0.329 0.424 0 1 hhi 180 0.685 0.373 0 1 16 Нижний Новгород s 120 0.201 0.381 0 1 hhi 120 0.315 0.436 0 1 17 Новосибирск s 444 0.265 0.351 0 1 hhi 444 0.527 0.335 0 1 18 Новый Уренгой s 84 0.310 0.407 0 1 hhi 84 0.503 0.416 0 1 19 Норильск s 144 0.248 0.378 0 1 hhi 144 0.469 0.428 0 1 20 Омск s 156 0.397 0.461 0 1 hhi 156 0.542 0.447 0 1 21 Пермь s 48 0.271 0.449 0 1 hhi 48 0.542 0.504 0 1 22 Петропавловск‐Камчатский s 96 0.417 0.432 0 1 hhi 96 0.491 0.401 0 1 23 Ростов‐на‐ s 168 0.410 0.399 0 1 Дону hhi 168 0.572 0.364 0 1 24 Самара s 108 0.379 0.425 0 1 hhi 108 0.555 0.400 0 1 25 Санкт‐Петербург s 228 0.372 0.388 0 1 hhi 228 0.749 0.289 0 1 26 Сочи s 144 0.199 0.364 0 1 hhi 144 0.410 0.421 0 1 27 Сургут s 60 0.489 0.484 0 1 hhi 60 0.643 0.436 0 1 28 Тюмень s 12 0.081 0.279 0 0.968 hhi 12 0.411 0.509 0 1 29 Уфа s 24 0.125 0.338 0 1 hhi 24 0.125 0.338 0 1 30 Хабаровск s 48 0.479 0.421 0 1 hhi 48 0.750 0.190 0 1 31 Челябинск s 12 0.750 0.452 0 1 hhi 12 0.750 0.452 0 1 Source: Transport Clearing House, authors’ calculations

This evidence suggests the following estimation strategy. We will explore the factors determining entry to specific markets using the specification of the model in Section 5, paying specific attention to firm heterogeneity, particular markets (such as Moscow), and firm interaction. Several models can be 19

estimated using our data; summary statistics of the variables used in our calculations are provided in Table 6.

Table 6. Summary statistics.

Variable Obs Mean Std. Dev. Min Max freq 8160 63 171 0 3771 seats 8160 7062 22364 0 529047 fueleff 8160 20 20 0 70 pop, '000 8160 2211 2160 84825 7522 realgrp, mln RuR 8160 46100 36300 12100 274000 nomgrp, mln RuR 8160 87500 79200 16000 703000 nomfuelprice 8160 13022 3996 7627 18166 realfuelprice 8160 6878 654 6045 7916 distance 8160 2449 1626 136 7793 totfreq 8160 330 677 0 7166 hhi 8160 1 0 0 1 s 8160 0 0 0 1 fueleff1 4153 39 6 19 70 avfueleff 8028 39 4 19 44 efftype 8160 0 0 0 1 Source: Transport Clearing House, authors’ calculations

6. Model and estimates There may be several estimation strategies. First, conventional logit and probit can be used on pooled cross‐section data, which are useful anyway as a benchmark case. Second, we can exploit the advantage of panel data to capture individual fixed or random effects. Also, one may follow the steps by Breshanan‐Reiss to use ordered probit model for the number of firms presented in the market. Finally, one may seek for more flexible forms, such as mixed logit models, which may also be used for predictive purposes.

Table 7 shows results of standard probit regression, used as a benchmark. Variables included are logs of population, real GRP and distance, dummies for summer season and Moscow, fuel efficiency of the companies calculated as described in section 4, fuel price (as a constant proxy for the cost of air transportation), and total frequency of flights and Herfindahl‐Hirschmann index as measures of market concentration. Most coefficients are significant and have predicted signs, although coefficients of fuel and GRP are not significant.

Table 8 repeats this sort of analysis for the random effects probit model. This time the coefficients of all variables (including fuel ones) become significant, reflecting the fact that variance of fuel cost across time, disregarded in pooled cross‐section model, has been accounted for in the panel regression. Random effects model allows to capture firm hererogeneity, although in a restrictive (additive) way – in the random effects term. Table 8 shows that panel‐level variation is responsible for less than 15% of total variation, which finding goes at odd with explicitly demonstrated heterogeneity across firms. This warrants the usage of more flexible model which would allow heterogeneity at the level of both slopes and intercepts, which may be justified as follows.

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Table 7. Probit regression, pooled cross section.

Probit regression Number of obs = 8160 LR chi2(9) = 1269.62 Prob > chi2 = 0.0000 Log likelihood = -5019.9656 Pseudo R2 = 0.1123

------entry | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------lpop | .0923815 .0264361 3.49 0.000 .0405676 .1441954 lrgrp | .0338043 .0270912 1.25 0.212 -.0192935 .086902 ldist | .1751959 .0233184 7.51 0.000 .1294928 .2208991 summer | .2407106 .030283 7.95 0.000 .1813571 .3000641 mos | -.1772242 .0675244 -2.62 0.009 -.3095695 -.0448789 classfueleff | -.0045407 .0041095 -1.10 0.269 -.0125952 .0035137 lrealfuelpri | -.0621802 .1609163 -0.39 0.699 -.3775704 .2532101 totfreq | .000285 .0000316 9.02 0.000 .000223 .0003469 hhi | 1.244558 .0421981 29.49 0.000 1.161851 1.327265 _cons | -3.564682 1.596442 -2.23 0.026 -6.693651 -.4357131

Table 8. Random effects probit

Random-effects probit regression Number of obs = 8160 Group variable: carriernum Number of groups = 18

Random effects u_i ~ Gaussian Obs per group: min = 96 avg = 453.3 max = 1116

Wald chi2(9) = 1140.13 Log likelihood = -4909.3224 Prob > chi2 = 0.0000

------entry | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------lpop | .0765399 .0271057 2.82 0.005 .0234137 .1296661 lrgrp | .0299726 .0301273 0.99 0.320 -.0290759 .089021 summer | .2606858 .0308663 8.45 0.000 .2001891 .3211825 mos | -.1736045 .0734737 -2.36 0.018 -.3176102 -.0295988 ldist | .103307 .0262805 3.93 0.000 .0517982 .1548158 classfueleff | -.0291884 .0058918 -4.95 0.000 -.040736 -.0176407 lrealfuelpri | -.2774453 .1655284 -1.68 0.094 -.601875 .0469844 totfreq | .0003157 .0000332 9.51 0.000 .0002507 .0003808 hhi | 1.265377 .0430738 29.38 0.000 1.180953 1.3498 _cons | .0248013 1.714023 0.01 0.988 -3.334622 3.384225 ------+------/lnsig2u | -1.750619 .3713694 -2.47849 -1.022748 ------+------sigma_u | .416733 .0773809 .2896028 .5996709 rho | .1479691 .0468201 .07738 .2644924 ------Likelihood-ratio test of rho=0: chibar2(01) = 221.29 Prob >= chibar2 = 0.000

Firm‐level heterogeneity should be reflected not only in their market sizes, but also in their behavior, as captured by interactions between them, and their reactions on actions of each other. For instance, if in a growing market one firm starts entering new markets, another firm should react on its behavior by entering as well, which means that the (heterogeneous) coefficients of the market characteristic variables will be correlated. This sort of dependence can be captured by the mixed logit models with random slopes (rather than intercepts, as in random effects probit), and nondegenerate covariance matrix of these slopes. Estimation of this model is presented in Table 9; it not only reveals the highest log‐likelihood and exhibits the best fit of all, with all coefficients being significant and having predicted signs, but also shows a substantial and negative correlation between random parts of concentration

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coefficients. This means that, inasmuch as most markets over the country are ‘free’, companies ceteris paribus prefer coming to the less populated markets, in order not to engage in costly competition prone to price wars.

Table 9. Mixed logit model

Altogether, this suggests that mixed logit model is probably the most suitable model for descriptive analysis of all the models considered. This is clearly attributable to its great flexibility, which comes of course at a cost of high computational intensity, and requires careful interpretation. However, given the potential of this model and its suitability to capture substantially nonlinear patterns of preferences and agents’ heterogeneity, it is also the most suitable for forecasting and policy‐relevant analysis.

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