AMSTERDAM SCHOOL OF ECONOMICS
MSc ECONOMICS
SPECIALISATION: MARKETS AND REGULATION
DELAY IN THE DELIVERY OF LAND IN CONCESSIONS REGULATED BY RPI-X.
THE CASE OF THE LIMA’S AIRPORT.
Master´s Thesis
María Alejandra Mendez Vega (11650192)
Supervisor: András Kiss
This document is written by Maria Alejandra Mendez Vega who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
TABLE OF CONTENTS
I. INTRODUCTION ... 4
II. PUBLIC-PRIVATE PARTNERSHIPS ... 6
II.1. Problem and research question ... 7
III. CONCESSION OF LIMA´S AIRPORT ... 8
III.1. The concession agreement ... 8
III.2. About the airport ... 8
IV. METHODOLOGY ... 9
IV.1. Estimation of cartel damage ... 9
IV.2. Damage estimation in the delay in the delivery of lands in Lima´s airport ... 12
IV.2.1. Data ... 13
IV.2.2. Estimation of the concession model ... 14
IV.2.2.1. Revenues ... 14
IV.2.2.2. Expenses ... 14
IV.2.2.3. Investments ... 14
IV.2.2.4. Net value of the cash flows ... 15
IV.2.3. Estimation of the counterfactual concession model ... 15
IV.2.3.1. Productivity factor model ... 15
IV.2.3.1.1. Service quantity index ... 17
IV.2.3.1.2. Input quantity index ... 18
IV.2.3.1.3. Total productivity factor of LAP ... 20
IV.2.3.1.4. Input price of LAP (W) ... 21
IV.2.3.1.5. Productivity Factor (X) ... 21
IV.2.3.1.6. Counterfactual productivity factor ... 21
IV.2.3.2. Revenues ... 26
IV.2.3.3. Investments ... 27
V. RESULTS ... 27
V.1. Counterfactual productivity factor ... 27
V.2. Concession model ... 28
V.2.1. Comparison of cases ... 29
VI. CONCLUSION ... 31
VII. ANNEXES ... 34
Annex 1: Weighted Average Capital Cost-WACC ... 34
Annex 2: Real Tariffs in USD ... 36
Annex 3: Weighted Average Capital Cost-WACC in the concession model ... 38
Annex 4: Concession model in thousands of USD ... 39
Annex 5: Counterfactual concession models in thousands of USD ... 41
I. INTRODUCTION
Around the world, the concessions of transport infrastructure, a type of Public-Private Partnerships, are useful tools to develop and improvement of infrastructure. Nevertheless, the benefits of these tools depend on the adequate design and risk assignment in the concession agreement, and on the fulfilling of the agreement obligations on both parts, public and private. The breach of the obligations for one of the parties could affect the economic balance of the concession, resulting in cost overrun, infrastructure deterioration, infrastructure saturation, affectation in the provision of services, among others. These effects have to be quantified so that the responsible party assumes the damages and the economic balance of the concession will be re-established.
In the experience on concessions contracts, one of the recurrent non-fulfillment has been the delay in the delivery of the lands to developing infrastructure. Usually, this obligation is assigned to the government and the private party (Concessionaire) claims for damages. This is because according to the concession agreement, the private party had to develop new infrastructure on these lands. As consequence of the delay in the delivery of the lands, the Concessionaire could not execute the investments, and the company might face different problems, for example, saturation of the existing infrastructure. In the case of concessions in which the tariffs are regulated by RPI-X mechanism, an additional effect arises as a result of the delay. These tariffs depend on the productivity factor (X), which is calculated using information on investment, demand, and others. Then, execution of investments and changes in demand have an impact on the X, and on the tariffs.
The damage quantification is a crucial issue to re-establish the economic balance of the concession. In this sense, this thesis pay attention in the damage quantification answering the following questions: (i) How to determine if there exists the damage to the concessionaire as consequence of the delays in the delivery of lands, in concessions that are regulated by RPI-X; and (ii) how much has to be the compensation to the concessionaire, if the damage exists?
The Lima’s airport case is taken into account to answer the research questions. This airport is a self-financed concession, with a tariff regulation through the RPI-X mechanism for the following services: (i) Single Fee for Airport Use (TUUA), (ii) Landing and Take-off, (iii) Aircraft Parking, (iv) Boarding bridges, and (v) Air cargo facilities use. One of the obligations of the Peruvian government was to deliver the lands to Concessionaire (LAP) for the construction of second runway and a second terminal, but the government did not fulfill the obligation. The economic literature has developed many methods and models for damage quantification. These methods have been used in different fields as environmental, health, personal injuries, antitrust, among others. According to Ashurst (2004), the damage value in an antitrust case is based on comparing a counterfactual situation (when the anticompetitive conduct did not occur) with the real situation to determine the damage value. In the antitrust cases and the non-fulfilment of contract obligations exist a legal framework that supports the compensation of the affected party by the infringement party. Also, in both cases can be identified the action, the part that is responsible for the action, the possible effects of the action, and possibly affected party; which is important for the construction of the counterfactual situation. Due to the similarities in both cases, in this thesis is considered valid to apply the methods of damage estimation in cartels to estimate the possible damage in the delay in the delivery of lands in Lima´s airport case.
Oxera and Komninos (2009) compiled and classified in three approaches the methodologies that are used to estimate damage in antitrust cases: the comparator, the financial and the market structure. They are used according to the quantity and quality of the data. The data available in the Lima´s airport case is mainly from the financial statements of the concession and the operation reports. For this reason, the profitability analysis of the financial approach was chosen to estimate the damage, where the profitability is measured by the net present value (NPV). The NPV is the value of the discounting cash flows resulting from the operation of the airport. The first step in the damage quantification is to determine the NPV of the real situation. The second step is to estimate the NPV of the counterfactual situation. This hypothetical situation considers that the delay in the delivery of the lands did not occur, taking into account the effects of the investment, the changes in the demand as result of the new infrastructure, and the effects on the tariffs. Finally, the NPV of the real and counterfactual situation are compared. If the NPV of the counterfactual situation is higher than the NPV of the real situation, it can be concluded that the delay in the delivery of the lands harmed to the Concessionaire. In that case, the compensation will be the difference between both NPV. It is important to mention that due to the uncertainties that exist in the estimation of the demand, which has a direct effect on income, the counterfactual demand, considers three scenarios: pessimistic, neutral, and optimistic.
My results show that the profitability in the real situation amounted to USD 320 801 thousand. While in the counterfactual situation would amount to USD -18 075 thousand in the pessimistic scenario, to USD 50 589 thousand in the neutral scenario, and to USD 73 660 thousand in the optimistic scenario. It is observed that the net values in the three scenarios of the counterfactual situation are lower than in the real situation. Thus, I conclude that the Concessionaire has not been affected by the delay in delivery of the lands. These results can be explained because the company, as a result of the breach, saves the money of the investments and the financial expenses of the debt. Also, the demand for the services provided by the Concessionaire kept growing despite not having developed new infrastructure.
The thesis is organized as follows: in section II, it is presented a brief review about the literature about Public-Private Partnerships to understand the problem in the delay of lands in concessions, as well as its effects. Then, section III presents the main characteristics of the concession agreement of the Lima´s airport, as well as the principal characteristic of the airport. Section IV briefly describes the methodologies that are applied in quantifying antitrust damage, and the similitudes between the cartel cases and the Lima’s airport case. Furthermore, there is described the profitability analysis that is used in the damage quantification in the Lima’s airport. In the section V are presented the results of the comparison of the profitability in the counterfactual and real situation.
II. PUBLIC-PRIVATE PARTNERSHIPS
The definition of Public-Private Partnerships (PPPs) is not unique, but rather depends on the normative of each country. According to the World Bank (2011), PPPs are contractual arrangements where the parties share responsibilities and rights for the duration of the contract. PPPs take diverse forms which consist of different mixes of public and private participation.
In the case of Peru, the normative1 defines the PPPs as private investment participation,
whose purpose is to “create, develop, improve, operate and/or maintain public
infrastructure and/or provide public services under the established contractual mechanisms”. This purpose will be achieved incorporating all the experience, knowledge,
equipment and/or technology of the private party and also, allocating the risks and resources adequately between the public and private parties.
Different types of risks exist in PPP which depend on the characteristics of each project. Some examples of risks are about design, construction, operation, environmental, demand, maintenance, and others. In Guasch (2004), it is pointed out that one of the essential elements in the design of PPPs is the identification and the allocation of risks. This is due to the fact that the goal of the design is establishing the financial equilibrium of the contract. An inadequate risk allocation can pass on a major cost of capital and tariff level. For that reason, the risks should be allocated to the party best able to manage them.
The Peruvian normative classifies the PPP according to their financing in self-financed concessions and co-financed concessions. The first group corresponds to projects that can be repaid using the same resources that are generated by the project; in other words, they are projects that do not required co-funding from the government. On the other hand, the co-financed projects need a transfer of public resources and/or the implementation of collaterals in favour of the private party.
The concessions of transport infrastructure, a type of PPP, are useful tools for the governments to develop infrastructure and provide services to the public. In this type of PPP, the concessionaire is the denomination for the private party and, the concession agreement is the contract that is signed by the concessionaire and the public entity that represent to the Government. Typically, a concession is a long-term contract, between 25 and 30 years.
The stability of the revenues is essential to the concessionaire. For that reason, the concession agreement has to specify explicitly the remunerations that will receive the concessionaire, which can be derived from the contractual tariffs and or cofinancing. Also, the agreement has to detail the instructions for reviews and adjustments of the tariffs. Usually, the mechanisms that are used to regulated tariffs are rate-of-return (RoR) or RPI-X (Guasch 2004). Under a RoR, each year the firm´s return can be adjusted to keep constant the rate of return. This type of regulation allocates little risk to the private party about the investment and costs. On the other hand, the RPI-X mechanism specifies maximum prices for a period. These prices are determined as the consumer price index minus the productivity factor of the company. This mechanism generates incentives to save costs, at least between reviews.
It is important to mention that the benefits of the concessions depend on the adequate design and risk assignment in the concession agreement. Also, the fulfilling of the agreement obligations on both parts, public and private is a crucial issue to achieve the benefits. The breach of the obligations for one of the parties could affect the economic balance of the concession, resulting in cost overrun, infrastructure deterioration, infrastructure saturation, affectation in the provision of services, among others. These effects have to be quantified so that the responsible party assumes the damages and the economic balance of the concession will be re-established.
II.1. Problem and research question
In Latin America concessions, one of the most significant problems has been the delay in investment execution as a result of problems in land expropriations (CAF 2015). These situations exist because the projects are awarded before that the lands have been expropriated. Usually, the obligation to land expropriation is assigned to the government because it is the part that is in the best position to assume this risk (World Bank 2015). The delays in land expropriations have repercussions in the investments that the concessionaire has to execute, and in turn, affects the capacity of the infrastructure. The development of infrastructure can increase the demand for products or services provided by the concessionaire because it would have more capacity to provide them. In the particular case of concessions whose tariffs are regulated by RPI-X, the delay in the delivery of the lands has an additional impact in tariffs. Because the tariffs depend on the productivity factor (x), which is calculated using information on investment, demand, and others2.
According to above explanation, delays in land expropriations can affect the financial-economic balance of the concession, and in turns, affects the concessionaire. In that situation, due to the concession agreement allocates the expropriation risk to the government, this has to compensate to the concessionaire if this is the case (World Bank 2015).
Once the effects of the delay in land expropriations in concessions that are regulated by the RPI-X are identified, the research questions that the thesis will be answered are:
• How to determine if there exists or not the damage to the concessionaire as consequence of the delays in the delivery, in concessions that are regulated by RPI-X?
• How much has to be the compensation to the concessionaire, if the damage exists?
In this thesis, the case of Lima´s airport is analysed to answer the research questions, which is a self-financed concession regulated by the RPI-X mechanism. The details of the concession are presented in the next section.
The methodology that is used to estimate the possible damage is based on the antitrust damage estimation literature, which is explained in the section IV.
2 Also, the tariffs depend on the RPI that is the consumer price index, but this is a macroeconomic series which is
III. CONCESSION OF LIMA´S AIRPORT III.1. The concession agreement
In February 2001, the concession agreement for the construction, improvement, conservation, and exploitation of the Lima´s Airport3 was signed by LAP4 (Concessionaire)
and the Peruvian government represented by the Ministry of Transport and Communications (MTC). This is a self-financed concession with a length of 30 years, and an investment commitment of USD 1 062 million.
The concession agreement establishes the tariffs for the first eight years of the concession of the following services: (i) Single Fee for Airport Use (TUUA)5, (ii) Landing and Take-off,
(iii) Aircraft Parking, (iv) Boarding bridges, and (v) Air cargo facilities use. After this period these tariffs will be reviewed by OSITRAN6 every five years. The RPI-X mechanism is used for
this purpose according to the concession agreement.
One of the obligations of the Concessionaire was to build a new passenger terminal and a second runway. The necessary lands for this investment would be delivered by the government. According to the concession agreement, the second runway had to enter into operation in the year 2012, and the second terminal in 2016. The next table presents the schedule of these investments.
Table 1: Schedule of investment of the second runway and second terminal in the Lima´s airport (in thousand USD*)
Project 2007 2008 2009 2010 2011 2012 2013 2014 2015
Second Runway 918 1 836 5 087 38 426 17 042 - - - - Second Terminal - - 5 218 3 597 14 200 5 307 70 899 235 164 70 655
Source: Annex 6 of the concession agreement.
Note: The amounts of investments are in dollars of the year 2000.
III.2. About the airport
The Lima´s airport is the principal airport in Peru. In 2017 more than 20 million passengers used the installations of this airport, which represent 63% of the air traffic in the country. Moreover, since the begging of the concession the number of passengers has been increased approximately by 400%.
The strategic location of the airport, which is the west coast of South America, has made it an important hub in the region. Airlines like Avianca and LATAM have their operation centers in this airport. Also, for the sixth consecutive year, the Lima´s airport was awarded in 2014 as the best Airport in South America according to the Skytrax7.
3 The official name is Jorge Chavez International Airport. 4Lima Airport Partners (LAP)
5 TUUA for its acronym in Spanish: Tarifa Unificada de Uso Aeroportuario.
6 The Supervising Agency for Private Investment in Public Transport Infrastructure (Organismo Supervisor de la
Inversión en Infraestructura de Transporte de Uso Público-OSITRAN) is the entity responsible to supervise and regulate the investment in public transport infrastructure.
7 Skytrax is a London-based market research and consulting firm specializing in the air transport industry, and this
At this moment, Lima´s airport has only one terminal and one runway in comparison with other airports in the region with similar traffic.8 The terminal has a total area of 86 600 m2,
while the platform has 360 000 m2.
It is important to mention that the government failed in delivering of lands for the construction of the new runway and terminal. This delay has been for more than ten years, and LAP indicated that this circumstance has been prejudicial to its business.
IV. METHODOLOGY
The damage quantification is a topic that has been widely studied in different fields. For example, in the environmental sector, the damage determination is a main issue to calculate compensations for polluting or declining natural resources (World Bank 2011)
The personal injury cases are another frequent field that has been analysed the damage determination. It is due to the tort law has within its objectives the protection of people and properties from unjust injury, and like so the compensation to the victims in case of harm (see Cathy Okrent 2014). Some examples of personal injury are accidents, defamations, intentional acts, defective products, among others.
Also, the damages analysis also has been studied around the world by the competition authorities. According to van Dijk and Verboven (2008), the economic damage in this field can be caused by various types of anticompetitive behaviours, as price-fixing agreements, exclusionary practices, predatory pricing by a dominant firm, among others. Depending on the behaviour, there are different parties that may suffer damages: customers (direct or indirect), suppliers, and/or competitors. The damage calculation depends on the legal framework because it has to establish “who may bring a claim, the nature of the injury which
must be demonstrated, the provisions surrounding causation, and the policy in relation to passing on”. This case is extended in the next section, which describes characteristics of the
damages estimation and methodologies that are applied in quantifying the antitrust damage.
IV.1. Estimation of cartel damage
In the last years, the damage quantification in cartel cases has been widely studied. For that reason, this particular violation will be explained in this section.
According to Davis and Garcés (2010), the cartels are illegal in the majority of countries. Nevertheless, due to the cartel agreement can be very profitable, when the market conditions are favourable (high market concentration, stability of demand, etc.) there exist a probability that the anticompetitive action occurs.
Friederiszick and Lars-Hendrik Röller (2010) mention that the collusive behaviours have broad effects. In the case of direct customers, three main effects can be identified as result of the anticompetitive agreement: (i) higher prices on sales, (ii) an opposing pass-on effect (higher prices to indirect customers), and (iii) the quantity effect, which is the hypothetical benefit that customers would have if the increase in price did not occur. They, also mentioned, that the cartel may have a positive impact on consumers, for example, as a
8 Some examples are: (i) Santiago-Pudahuel´s Airport in Chile which has two runways and one terminal, and it had
traffic of passenger of 21,4 million in 2017; and, El Dorado airport in Bogota, which has two terminals and two runways. Its traffic was of 30 million of passengers in 2017.
result of a reduction in transport costs or logistics costs. In that case, the positive effects also have to be considered in the analysis to estimate the real damage value.
Ashurst (2004), pointed out that typically the antitrust damage estimation is measured as the difference between the real situation and the counterfactual situation. The real situation is the case where the antitrust violation occurred, while the counterfactual scenario is a hypothetical case where the violation had not taken place. Also, in this counterfactual case is important to identify: which was the action, who did the action, what have been the effects or consequences of the action, and who has been affected by the action.
The economic and finance literature has developed a broad range of methods and model for quantifying damages. According to Ashurts (2004), the methods and models are not mutually exclusive, they can be complemented; also, their application depends on the availability and quality of the data and information.
Oxera and Komninos (2009), in a study prepared for the European Commission, recollected and classified into three categories the different methods and model that are applied in the antitrust damage determination: Comparator-based approaches, Financial-analysis-based approaches, and Market-structure-based approaches. These categories are not mutually exclusive; they can complement each other9.
Below is a brief description of the approaches presented in Oxera and Komninos (2009).
Comparator-based approaches
These approaches use external data to estimate the counterfactual situation, that means that do not consider data related to the infringement. The estimation can be made by comparing different geographic/product markets or consider different periods of time (prices, before, during and/or after an infringement) in the comparison. Also, both ways of estimations can be mixed.
The methods and models of these approaches vary in the degree of complexity, from a comparison of average until application of regressions techniques.
Financial-analysis-based approaches
In this case, internal and external financial information is used to estimate the counterfactual scenario. Oxera and Komninos (2009) pointed out that one advantage of in these approaches is the higher likelihood of obtaining data from company accounts. These approaches can be subdivided into two groups. One is related with the financial performance, which includes the profitability analysis of defendants or claiming and comparing this with a benchmark to estimate the final damage value. The other group considers general financial tools which can be considered with other methods and model, as discounting and multiples10.
9 It is important to mention that Ashurts (2004) established a previous classification: before and after approaches,
yardstick approach, cost-based approach, and simulation. Nevertheless, in this thesis, the classification proposed by Oxera and Komninos (2009) is used because it considered the approaches mentioned by Ashurts (2004) and incorporated various methods based on financial analysis.
Market-structure-based approaches
In this case, these approaches are based on Industrial Organization (IO) theory, which considers a mix of theoretical models, assumptions and empirical estimation to evaluate the counterfactual situation.
The market-structure approaches looking to identify models of IO theory that adjusted to the relevant market, to understand how the competition in this market is, and then estimate the counterfactual price.
It is important to mention that the estimations under these approaches need a lot of data and assumptions to give robust results (European Commission 2013/3440). After the description of the approaches of quantifying antitrust damage, now, it is necessary to analyse the similitudes in a cartel case with the Lima´s Airport case11. This is to determine if the
methods and model of the damage estimation in cartels can be applied in this thesis.
About the legal aspects, the European Commission (EC2013/3440) mentions that “everyone who
has suffered harm because of an infringement of Article 101 or 102 of the Treaty on the Functioning of the European Union (TFEU) has a right to be compensated for that harm”. In the
case of Lima´s airport, as mentioned above, the concession agreement allocated the risk of delivery of lands to the government. For that reason, any damage that is resulting from the breach of that obligation must be compensated by the government. Then, in both cases, there is a legal framework that support that the affected party is compensated by the infringement party.
The next box presents a comparison between the increase of prices in a market A12, as a
consequence of a cartel agreement; and, the case of delay in delivery of lands in the Lima´s airport. There is shown that in both cases can be identified the action, the part that is responsible for the action, the possible effects of the action, and possibly affected party. Also, the counterfactual situations can be identified in the two cases.
11 Lima's airport case refers to the delay in the delivery for the lands to the build the second runway and the second
terminal.
12 For this example, in the market A does not exist intermediaries, and the products are sold directly to the final
Box 1: Comparison of cases
Case Damage in a cartel Damage in Lima´s airport
Action Price agreement Delay in delivery of lands
Who did the action Cartel Government
Effects or consequences of the action
• Price increase in market A. • Reduce the supply
available on the market
There are not investments in the new terminal nor new runway
Possible affected
party Consumers LAP (Concessionaire)
Counterfactual scenario
The price increase had not taken place
The delay in the delivery of the lands had not taken place:
• Execution of investments, which in turns affect:
o Capacity of the infrastructure (more services can be provided). o Tariffs (X is affected by
investments and quantity of services provided)
One difference in both cases is that in the cartel exist a presumption that this is bad for consumers (Davis and Garcés 2010), while in the case of the Lima´s airport per se it is not known if the delay in the delivery of lands harmed to the Concessionaire. On the one hand, the delay might limit the capacity of the infrastructure affecting the long-term incomes of the company; but on the other hand, the company saves the money of the investments and the financial expenses of the debt. For that reason, it is not possible to know if exist damage before the quantification of the effects.
Regardless of this difference, the purpose in both cases is to determine the possible existence of damage and quantifying. Also, it is possible to build a counterfactual situation in the case of Lima´s airport to compare with the factual/real situation. For that reason, it is considered valid to apply the methods of damage estimation in cartels to estimate the possible damage in the delay in the delivery of lands in Lima´s airport.
As mentioned above, the methods and models are not exclusive; they complement each other. Also, the selection of them depends on the availability and quality of the data and information. In the Lima´s airport case, the financial statements of the Concessionaire are available, also information of the quantity of services provided. For that reason, the financial-analysis-based approach is used for the damage determination. Additionally, the comparator-based approaches will used to estimate the counterfactual, using for this purpose a benchmarking (see Table 1)
IV.2. Damage estimation in the delay in the delivery of lands in Lima´s airport
The analysis of profitability is one of the techniques for the determination of damages of a cartel in the financial-analysis-based approaches. According to Oxera and Komninos (2009), the profitability can be measured with the Net Present Value, the Internal Rate of Return, Return on Capital Employed, the return of sales, others.
Oxera and Komninos (2009) pointed out that the in the context of damage estimation, the profitability analysis involves three steps: (i) the estimation of the profitability in the real situation, (ii) the estimation of the profitability in the counterfactual situation, and (iii) the comparison of profitability in both scenarios to obtain the value of the damage. The Figure 1 represents these steps.
Figure 1 Economic framework for calculating the damage
Source: Oxera and Komninos (2009). Page 22, figure 2.2
The Net Present Value (NPV) technique considers discounting the cash flows (inflows and outflows) of the activity of a firm over time. It is important to mention that the regulatory agency uses this technique to evaluate the performance of the concession of the Lima´s airport (see OSITRAN 2004).
In order to maintain concordance with the evaluation of the PPP performance, and because the information to build the cash flows is available (see section IV.2.1 ), the NPV technique is used to determinate the damage in the delay in the delivery of lands in Lima´s airport. In the context of PPP, in this thesis is called concession model to the model that include the cash flows resulting from the operation of the airport, which will be discounted for the calculation of the profitability.
IV.2.1. Data
The information to build the profitability in the real and in the counterfactual scenario is obtained from LAP's financial statements 2001-2015, the Concession reports13 from
2011-2016, and the annual memory of LAP 2015, which are available in the websites of LAP and OSITRAN. Also, information about revenues, retributions for the government, payments for CORPAC, regulation fee, operating and general expenses, investments, and traffic that was directly required to OSITRAN and the Ministry of Transports and Communications (MTC). The benchmarking to estimate the counterfactual demand scenarios used information about air traffic in Latin America which was obtained of the National Airports System Regulatory Body (ORSNA) in Argentina, Chile´s Civil Aviation Board (JAC), Colombia´s Ministry of Transports and, National Statistics and Censuses Institute (INEC) of Panama (see Table 9)
IV.2.2. Estimation of the concession model
As mentioned above, the concession model presents the cash flows resulting from the operation of the airport. These cash flows are the result of subtracting expenses, contractual obligations and investments of the operating revenues of the Concessionaire. The horizon of the model is since the beginning of the concession (2001) until December of 2017, which is the last available information.
IV.2.2.1. Revenues
The operating revenues are the incomes for the provided services by LAP, independently if they are regulated or not. In the case of services whit regulated tariffs by the RPI-X mechanism (TUUA, landing and take-off, aircraft parking, air cargo and boarding bridges), the revenues in the concession model are calculated by multiplying their tariffs (see Annex 1) by the quantity of provided services.
In the case of the other services that are not regulated by RPI-X, their tariffs depend on the commercial policy of the Concessionaire, which are not all known. For that reason, the revenues of these services are collected from the financial statements14.
The net revenues are the revenues after the payment of the contractual obligations as the retribution to the government, payments to CORPAC, and the regulatory fee to OSITRAN. The detail of these obligations is in the section IV.2.3.1.1.
IV.2.2.2. Expenses
The expenses are the necessary outflow cash to the adequate operation in the airport. For example, labor, maintenance, cleaning services, materials, basic services, and general expenses (insurances, outsourcing, consultants, others).
Also, the payment of the income tax has to be considered in the expenses. This payment is calculated by applying the respective rate (25,90%) to the net utility15.
IV.2.2.3. Investments
The concession model considers all the investments that are executed by LAP in compliance with the concession agreement. The investments are considered in the respective years that are executed and not in the last year of their construction.16
The concession model includes the asset recovery value at the end of the horizon (2017). This value is estimated as the sum of the investments and the assets that received at the beginning of the concession minus the accumulated depreciation. The depreciations are calculated using the depreciation rates of Table 3.
14 The information of the years 2016 and 2017 was taken of the LAP´s proposal (C-LAP-GPF/2018/0076) because the
financial statements of these years are not available.
15 The net utility is calculated as revenues minus contractual obligations, expenses, depreciation, and amortizations. 16 The productivity factor model does not consider the investments if they are not finished, because not being in
IV.2.2.4. Net value of the cash flows
Summation of cash flows over time has to consider the time value of money. One dollar today does not have the same value tomorrow. For that reason, a discount rate has to be applied. Oxera and Komninos (2009) point out that in the case of NPV applied as discount rate the cost of capital, which is estimated by the Weighted Average Cost of Capital (WACC). The WACC is chosen because reflects both the cost of debt and the cost of equity. The methodology that is used to calculate the WACC is shown in the Annex 117.
IV.2.3. Estimation of the counterfactual concession model
The counterfactual scenario is the hypothetical situation in which the government delivered the lands. And, in consequence, the Concessionaire would have built the second runway and the second terminal in the Lima´s airport.
As mentioned in the section II.1, the delivery of lands has a direct effect in the investments which represent an outflow in the concession model. Indirectly, the develop of the new infrastructure can increase the demand for the services provided by the concessionaire because it would have more capacity. In concessions whose tariffs are regulated by RPI-X exist an additional effect in tariffs. The tariffs depend on the productivity factor (X), which in turn depends on the investments, demand, among others.
The operation of the new runway and second terminal also affects operating expenses. These counterfactual effects are calculated using the Rule of three18. That means that the
quantity of these inputs will change in the same proportion of the changes in the number of operations.
The number of operations is used for this purpose because it reflects the use of the runway better than the passenger or air cargo traffic.
The first step to estimate the cash flows in the counterfactual scenario is estimated the counterfactual productivity factor, which will permit estimate the tariffs that will be considered in the cash flows. In the next section, it is described the model to estimate the productivity factor.
IV.2.3.1. Productivity factor model
As mentioned in the section III.1, the concession agreement established that OSITRAN is in charge of the tariff review using the RPI-X mechanism. For this purpose, OSITRAN developed a methodology, which is based on the formula of Bernstein and Sappington (1999):
17Annex 1 is shown the details of the methodology to calculate the WACC that was developed by OSITRAN in the
context of the productivity factor model. It is important to mention that the WACCs have different purpose in each model. In the productivity factor model, they are used to estimate the capital price, and in the concession model, they are used to take the cash flows to December 2017 in the concession model. Also, they have a difference in their calculation, the productivity factor model does not consider the investments if they are not finished, because not being in operation would not be productive.
18 The Simple Rule of Three or Simple Proportion is, “when from three given quantities, a fourth is required to be found, that shall have the same proportion to the given quantities of the same name, as one of the other quantities has to that of the same name with itself”. According to the book “A new mathematical and philosophical dictionary,
comprising an explanation of the terms and principles of pure and mixed mathematics, and such branches of natural philosophy as are susceptible of mathematical investigation” by Barlow Peter.
Equation 1
X = [(𝑊𝑒− 𝑊) + (𝑇 − 𝑇𝑒)]
Where:
𝑊𝑒: Growth in input prices of the Economy 𝑊: Growth in Input Prices of LAP
𝑇: Growth in the total factor productivity of LAP
𝑇𝑒: Growth in the total factor productivity in the Economy
The RPI-X mechanism is applied considering three baskets of services. The first basket is for passenger which includes the tariffs of the international TUUA and domestic TUUA; the second basket is for airlines considering the tariffs of landing and take-off, aircraft parking, and boarding bridges. Finally, the third basket includes the tariffs of air cargo facilities use (OSITRAN 2013).
Then, weighted average tariffs in a basket could not exceed the annual RPI-X, which is measured by the Consumer Price Index (CPI) of the United States of America minus the productivity factor. The first productivity factor for the period 2009-2013 was -0,61%19, and
the second for the period 2014-2018 is 0,05%20.
The methodology21 uses historical information of LAP to estimate the productivity factor,
since the beginning of the concession (2001). According to OSITRAN, information about the Concessionaire is taken into account because another comparable industry does not exist. Additionally, the approach of single till is used, which means that all the services provided by LAP are considering, independently if they are regulated or not.
In Peru, an economic series that reflect the evolution of the input price in the Economy does not exist. For that reason, in the OSITRAN´s methodology this variable (We) is calculated in
an indirect way using the Christensen’s proposal (2001).
Equation 2
𝑊𝑒= 𝑃𝑒+ 𝑇𝑒
Where Pe represents the variation of the final prices of the Economy (IPC)22. The
Christensen´s proposal is supported with the fact that in a competitive economy the variation of the final prices (Pe) is equal to the variation in the input price in the Economy
(𝑊𝑒) minus the variation in the total factor productivity in the Economy (𝑇𝑒).
In the case of the total factor productivity in the Economy (Te), OSITRAN uses the Te that was
calculated by OSIPTEL23 whose average for the period 2001-2012 was 0,45%.
19 This factor was approved by the Resolution N 047-2009-OSITRAN-CD. It is important to mention that this
productivity factor would not be affected in the counterfactual case because the information necessary for its calculation was 2001-2007. Then, as will explain in the next section, the counterfactual productivity factor only considering the investments of the second runway in the year 2011, year in which the infrastructure had to be finished.
20 This factor was approved by the Resolution N 059-2013-OSITRAN-CD.
21 It is not the purpose of this thesis analyse if the methodology that OSITRAN developed is adequate or not. The real
productivity factor used this methodology, for that reason, a correct comparison needs that the calculation of the counterfactual productivity factor uses the same methodology.
22 IPC is the consumer price index (Índice de Precios al Consumidor de Lima)
23 Supervising Agency for Private Investment in Telecommunications-OSIPTEL (for its acronym in Spanish: Organismo
OSITRAN calculates the variations of the productivity and prices (products and inputs) using the index numbers approach; and, according to the General Regulations of Tariffs (RETA)24,
the Fisher Index is used for this purpose25. The Table 2 presents the formulas to calculate
the respective index numbers.
Table 2: Determination of the Fischer Index
Index Product or service Input o Factor
Fisher 𝑄𝐹= (𝑄𝑝∗ 𝑄𝐿) 1/2 𝑄𝐹∗ = (𝑄𝑃∗∗ 𝑄𝐿∗)1/2 Paasche 𝑄𝑃= ∑𝑀𝑖=1𝑝𝑖𝑡+1𝑦𝑖𝑡+1 ∑𝑀𝑗=1𝑝𝑗𝑡+1𝑦𝑗𝑡 𝑄𝑃∗ = ∑𝑀𝑖=1𝑤𝑖𝑡+1𝑥𝑖𝑡+1 ∑𝑀𝑗=1𝑤𝑗𝑡+1𝑥𝑗𝑡 Laspeyres 𝑄𝐿= ∑𝑀𝑖=1𝑝𝑖𝑡𝑦𝑖𝑡+1 ∑ 𝑝𝑗𝑡𝑦 𝑗𝑡 𝑀 𝑗=1 𝑄𝐿∗= ∑𝑀𝑖=1𝑤𝑖𝑡𝑥𝑖𝑡+1 ∑ 𝑤𝑗𝑡𝑥 𝑗𝑡 𝑀 𝑗=1 Source: RETA
Then, the total factor productivity of LAP is equal to:
Equation 3
𝑇𝑜𝑡𝑎𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑓𝑎𝑐𝑡𝑜𝑟 = 𝑄𝐹(𝑃
1, 𝑃𝑡+1, 𝑦1, 𝑦𝑡+1)
𝑄𝐹∗ (𝑤1, 𝑤𝑡+1, 𝑥1, 𝑥𝑡+1)
Where:
𝑤𝑇: input prices in the period t.
𝑤𝑇+1: input prices in the period t+1.
𝑃𝑇: services prices in the period t
𝑃𝑇+1: services prices in the period t+1
𝑥𝑇: input quantity in the period t
𝑥𝑇+1: input quantity in the period t+1
𝑦𝑇: product quantity in the period t
𝑦𝑇+1: Product quantity in the period t+1
IV.2.3.1.1. Service quantity index
According to the Fischer index formula (see Table 2), the calculation of the service quantity index requires the price and quantities of the services that are provided by LAP.
The Concessionaire gives to OSITRAN the information about the amount of provided services, in other words the demand for the services. The unit of measurement changes depending on the type of the service. For example, in the TUUA the unit is the embarking passenger, while in the landing/take off the unit is the operation.
24 RETA for its acronym in Spanish: Reglamento General de Tarifas de Ositran.
In relation to the prices of the services provided by LAP, the methodology considers the real prices that receive LAP, which are calculated as the net revenues over the amount of service provided. The net revenues are the gross revenues minus the retribution to the government, payment to CORPAC26 and the regulatory fee27.
For the period 2001-2012, OSITRAN calculated the average annual variation of the service quantity index, which was 8,71%.
IV.2.3.1.2. Input quantity index
As in the previous case, the quantities and prices of the inputs are necessary to determine the input quantity index. In the case of the Lima´s airport, for its operation, three inputs are considered: labour, materials, and capital.
a. Labour
The labour input in Lima’s airport is classified into two categories: employees and officers. The quantity of labour is determined by the number of man-hours of each category. On the other hand, the price that LAP pays for their labour input is determined as the expenses in labor, which is obtained from the financial statements of LAP, over the quantity of labor.
b. Materials
The materials are goods and services that need LAP for the production of the services, which are not labor or capital. Many products and services belong to this category. For that reason, the OSITRAN´s methodology determines the materials quantity implicitly as the expenses in materials over the prices.
For the prices of the material, the intermediate products price index is used. It is calculated taking as the base of the Lima Consumer Price Index (IPC) and excluding items that do not relate to the expenditure on materials in Lima´s airport28.
c. Capital
The procedure to determine the quantities and prices of the capital input is more complicated than the other inputs. In the case of the quantity, it is measured with the capital stock, which is calculated using the following formula:
Equation 4
𝐾𝑇 = (1 − 𝛿)𝐾𝑇−1+ ∆𝐾
Where:
𝐾𝑇: capital stock in the period T
𝛿: rate of economic depreciation 𝐾𝑇−1: capital stock in the period T-1
∆𝐾:: investment in capital between T and T-1
26 The Peruvian Corporation of Commercial Airports and Aviation - CORPAC by its acronyms in Spanish Corporación
Peruana de Aeropuertos y Aviación Comercial. It is the company responsible for the air navigation services in Peru.
27 According to the concession agreement LAP have to transfer the 46,51% of the gross revenues to the government;
the 50% of the revenues of Landing and Take-off and 20% of international TUUA to CORPAC; and, 1% of gross revenues to OSITRAN as regulatory fee.
In relation to the investments, the methodology does not consider the work in progress as investments because these are not productive. They do not generate value until they are concluded and come into operation.
The capital is classified as intangibles and fixed assets. Also, they are sub-classified in six categories with different useful lives. The next table presents the rates of economic depreciation 𝛿 that OSITRAN considered.
Table 3: Annual rates of economic depreciation
Classification Sub-classification Useful live Depreciation rate
Intangibles Airport improvements 29 3,45%
Intangibles concession costs 30 3,33%
Intangibles Others 10 10%
Fixed assets Search and rescue vehicles 10 10%
Fixed assets Computer equipment 4 25%
Fixed assets Miscellaneous equipment 10 10%
Fixed assets Transport units 5 20%
Fixed assets Furniture and fixtures 10 10%
Source: OSITRAN (2013)
The methodology highlights that the calculation of the input index has to take into account the number of capital unit that was in operation during a specific year. For that reason, if there only is used the KT that was estimated (see Equation 4), probably it is considering the
number of capital unit that was operating on December 31 and not all the year. Then, to obtain the number of units that was working during all the year, the mean capital stock (MK) has to be considered.
Equation 5
𝑀𝐾𝑇 =
𝐾𝑇−1+ 𝐾𝑇
2
Once the mean capital stock is determined; the implicit quantities of capital are determined by dividing the mean capital stock over the adjusted wholesale price index29.
In the case of the series of capital price, this price is defined as the amount of money that LAP will have to pay if it does not have the capital and will have to rent the capital in the market. In other words, this will be the economic cost of hiring capital. OSITRAN uses the following formula for calculating the economic cost of rent the capital.
Equation 6 𝑞𝑖,𝑇 = 𝑟𝑇𝑃𝑖,𝑇−1+ 𝛿𝑖𝑃𝑖,𝑇− (𝑃𝑖,𝑇− 𝑃𝑖,𝑇−1) 1 − 𝑢𝑇 = 𝐶𝐸𝐾𝑖,𝑇 1 − 𝑢𝑇
29 The wholesale price index is calculated by the INEI (National Institute of Statistics and Informatics). OSITRAN made
Where:
qi,t: is the economic cost of capital del asset i
rT: capital cost in the period t (WACC)30
Pi,T(T-1): price of the asset i in the period T(T-1)
δi : rate of economic depreciation of the asset i uT: effective tax rate in period T
𝐶𝐸𝐾𝑖,𝑇: economic cost of the asset i in the period T
Equation 7
𝑢𝑇 =
𝐼𝑅𝑇+ 𝑃𝑇𝑇
𝑉𝐸𝐾𝑇+ 𝐼𝑅𝑇+ 𝑃𝑇𝑇
IRT: Income tax in the period T.
PTT: employee participation in the period T
VEKT: the economic value of capital in period T
Equation 8
𝑉𝐸𝐾𝑇= ∑(𝐶𝐸𝐾𝑖,𝑇∗ 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑄𝑢𝑎𝑛𝑡𝑖𝑡𝑦𝑖,𝑇) 𝐼
𝑖=1
Where 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑄𝑢𝑎𝑛𝑡𝑖𝑡𝑦𝑖,𝑇 is the quantity of the asset i used in the period T.
Then, with the formulas describing above, the price of the capital is estimated.
IV.2.3.1.3. Total productivity factor of LAP
The Fischer index of the service and input of LAP are incorporated in the Equation 2 to estimate the total factor productivity of LAP (component T of the Equation 1). For the period 2001-2012, OSITRAN calculated that the total productivity factor of LAP had an average annual variation of 1,51%.
Table 4: LAP´s Price Index of inputs
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Fisher index of services 1,031 1,030 1,092 1,097 1,059 1,201 1,092 1,072 1,114 1,117 1,105 Fisher index of inputs 1,113 1,045 0,963 1,319 1,267 1,033 0,982 1,061 1,055 1,001 1,037 LAP’s TPF 0,927 0,985 1,135 0,832 0,836 1,163 1,112 1,011 1,056 1,115 1,065 Growth in LAP´s TPF - 0,076 -0,015 0,126 -0,185 - 0,179 0,151 0,106 0,011 0,054 0,109 0,063 Average 2001 - 2012 1,51%
*TPF: Total productivity factor. Source: OSITRAN (2013)
IV.2.3.1.4. Input price of LAP (W)
Taking into account the information about prices and quantities of the input of LAP, OSITRAN calculated the annual average variation of the input price index of LAP, which was 4,02% for the period 2001-2012.
Table 5: LAP´s Price Index of inputs Price Index of inputs 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Laspeyres 1,110 0,852 1,035 0,943 1,113 1,185 0,780 1,712 0,828 1,069 1,095 Paasche 1,130 0,858 1,033 0,877 1,137 1,179 0,790 1,724 0,819 1,074 1,091 Fisher 1,120 0,855 1,034 0,910 1,125 1,182 0,785 1,718 0,823 1,072 1,093 Fisher's index variation 0,113 -0,157 0,033 -0,095 0,118 0,167 -0,242 0,541 -0,194 0,069 0,089 Average 2002 - 2012 4,02% Source: OSITRAN (2013)
IV.2.3.1.5. Productivity Factor (X)
Once all the components of the Equation 1 are determined, the productivity factor can be calculated. For the period 2014-2018, OSITRAN approved the productivity factor amount to 0,05%. The next table presents the results.
Table 6: Productivity factor developed by OSITRAN for the period 2014-2018
Component Value
Growth in input price of the Economy 𝑊̇ 𝑒 3,02%
Growth in Input Prices of LAP 𝑊̇ 4,02%
𝑊̇ − 𝑊̇ 𝑒 1,01%
Growth in the total factor productivity of LAP 𝑇̇ 1,51% Growth in the total factor productivity in the Economy 𝑇𝑒̇ 0,45%
𝑇𝑒̇ − 𝑇̇ 1,06
Productivity factor (X) 0,05%
Source: OSITRAN (2013).
IV.2.3.1.6. Counterfactual productivity factor
In the counterfactual case some information of the model of the productivity factor changes, related with the inputs and the quantity of the service. These changes are described below.
A. Counterfactual capital input
As mentioned in the previous section, the methodology of the productivity factor does not take into account the work in progress. Due to the fact that the information used to calculate the productivity factor is of 2001-2012, the counterfactual productivity factor considers the investments in the second runway because it had to go into operation in 2012. The investments in the second terminal are not considered because its construction had to be finished in 2015 (see Table 1).
Also, all the investments of the second runway are considered in the year 2011, independently of their execution date. For keeping the approach that the investments are considered only when the infrastructure is ready to be operated.
The investments in Table 1 are in dollars of 2000, which are part of the technical proposal of the Concessionaire. These amounts have to be adjusted by prices to incorporate the investment of the second runway in the productivity factor model. For this purpose, the construction materials price index of Lima31 is used. The next table shows the adjusted value
of the investments.
Table 7: Adjusted investment of the second runway and second terminal in the Lima´s airport (in thousand USD)
Project 2007 2008 2009 2010 2011 2012 2013 2014 2015 Total
Second
Runway 1 326 3 043 7 013 60 352 26 162 - - - - 97 896 Second
Terminal - - 7 194 5 649 21 799 8 106 103 322 344 066 96 781 586 917 In the counterfactual case, the investments in the year 2011 amount to USD 100 933 thousand, which is higher than in the real case because of the investments in the second terminal, which amounts to USD 97 896 thousand.
This counterfactual investment also affects the components of the WACCs that are used to calculate the price of the capital, as the financial structure and the cost of debt (see Equation 6). According to the Concessionaire, it will finance the 55,6% of the investments with debt and the rest with equity32. For that reason, this thesis considers that the Concessionaire
would have financed the counterfactual investments in the same way.
This counterfactual investment also affects the financial structure and the cost of debt, which are components of the WACCs (see Equation 6). According to the Concessionaire, it will finance 55,6% of the investments with debt and the rest with equity33. For that reason,
this thesis considers that the Concessionaire would have financed the counterfactual investments in the same way. On the other hand, it is assumed that the same conditions of the real debt will apply to the new debt, these conditions are the payment period (10 years), and the interest rate (6,88%)34.
The next table shows the values of the counterfactual WACCs, which takes into account the changes in the financial structure and the cost of debt. These WACCs differ of the real values only in the years 2011 and 2012 as result of the investments in the second runway.
31 The construction materials price index of Lima is published monthly by INEI.
32 In the Peruvian newspaper “El Comercio”, the Chief Financial Officer of LAP announced that the 55,6% of the
investments will be financed with debt (February 2018). (https://elcomercio.pe/economia/peru/lap-alista-financiamiento-expansion-jorge-chavez-noticia-500521).
33 In the Peruvian newspaper “El Comercio”, the Chief Financial Officer of LAP announced that the 55,6% of the
investments will be financed with debt (February 2018). (https://elcomercio.pe/economia/peru/lap-alista-financiamiento-expansion-jorge-chavez-noticia-500521).
Table 8: Counterfactual WACC Components 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 E/(D+E) 100,0% 100,0% 82,5% 41,3% 40,9% 41,2% 40,5% 42,7% 50,8% 41,9% 49,5% 51,8% D/(D+E) 0,0% 0,0% 17,5% 58,7% 59,1% 58,8% 59,5% 57,3% 49,2% 58,1% 50,5% 48,2% Capital cost 15,3% 15,3% 13,4% 16,3% 14,7% 14,6% 14,7% 14,4% 14,5% 14,8% 12,4% 11,4% Debt cost 5,4% 6,7% 7,5% 3,7% 5,2% 6,7% 4,8% 5,0% 5,3% 5,5% 5,8% 5,6% WACC 15,3% 15,3% 12,4% 8,9% 9,1% 10,0% 8,8% 9,0% 10,0% 9,4% 9,1% 8,6%
B. Counterfactual quantity of the service
As previously mentioned, the second runway had to enter into operation in the year 2012. In consequence, if the demand would have experienced some changes as result of the operation of the new runway, only the demand in the year 2012 would show some changes in the productivity factor model.
Now, when the demand for air transport services is analysed, many factors have to be taken into account, not only the improvements in the infrastructure. According to ICAO (2016), the air traffic demand is in function of macroeconomic, microeconomic and random non-economic factors.
For example, the international trade and GDP per capita are positively related to the air cargo. When disposable income increase, there is higher demand of leisure travel. In the case of the microeconomics factor, the specific regulations can be mentioned, which affect the market access and the ticket prices. Other microeconomic factors are infrastructure, input costs, and market structure. Finally, natural phenomena like Tsunamis, earthquakes, volcanic eruptions are factors that can affect the air traffic, which are not predictable. The connectivity is one of the principal characteristics in the aircraft industry, the development of an airport does not only depend on its own potential, but also it is influenced by the development of other airports in the region. An airport does not develop as a unit but rather a block.
According to the characteristics described above, the air development of the region is considered as a proxy for the performance of the demand in the Lima´s airport in the counterfactual situation.
The technique of comparison of averages of the comparator-based approach is used to calculate the air development of the region. In this case, a benchmarking of the evolution of the growth rates of passengers, aircraft movements, and air cargo was built. The benchmarking considers four Latin America countries with similar features of Peru35. Some
countries were not taking into account because information about air traffic is not available36 (see Table 9).
35 The countries of the sample, as well as Peru, have upper middle incomes. (According to the World Bank national
accounts data, and OECD National Accounts data files. https://data.worldbank.org/indicator /NY.GDP.PCAP.CD?locations=ZJ-CL.)
36Dominica, Dominican Republic, Grenada, Guyana, Saint Lucia, San Vicente, the Grenadines. Also, were excluded
from the sample Cuba and Venezuela because their results are influenced by their political situation. In the case of Brazil and Mexico, they are not considered because the traffic is too high, not comparable with Peru. And, Paraguay and Costa Rica were excluded because their traffic is too low, not similar to Peru.
Table 9: Growth rates of air traffic in four Latin America countries a) Growth rates of passengers
Country 2012 2013 2014 2015 2016 2017
Argentina 9,2% 4,4% 6,0% 8,8% 7,3% 10,9%
Chile 17,3% 8,2% 4,6% 5,3% 10,7% 11,1%
Colombia 14,5% 15,8% 10,2% 10,5% 3,9% 1,0%
Panamá 17,0% 8,9% 9,8% 4,5% 8,5% 5,9%
b) Growth rate of aircraft movements*
Country 2012 2013 2014 2015 2016 2017
Argentina 11,2% -0,3% -2,0% 3,0% -1,5% -2,4%
Chile 6,3% 5,5% -4,6% -0,5% -15,1% 4,0%
Colombia 0,8% 4,6% 1,8% 2,4% -15,5% -2,6%
Panamá 9,0% 6,2% 5,5% 9,5% -0,6% 5,9%
*Aircraft movement also known as operations
c) Growth rate of air cargo
Country 2012 2013 2014 2015 2016 2017
Argentina 1,0% 0,9% -10,4% -4,3% 1,8% 5,4% Chile 6,6% -6,0% -1,3% 4,0% 12,8% -5,4% Colombia 2,8% 1,0% 39,2% -10,8% -3,2% 11,8% Panamá -31,0% 12,1% 3,5% -6,0% -18,3% 3,2%
Sources: ORSNA, JAC, Aerocivil, and INEC.
The estimations of a counterfactual situation always have a degree of uncertainty. For that reason, the analysis considers three demand scenarios to mitigate the uncertainty: pessimistic, neutral and optimistic. These scenarios are based on the benchmarking.
• Pessimistic
In the hypothetical case that in 2012 there would have been an additional runway, the demand would not have dramatic increases because there exists a limit capacity in a complementary infrastructure, that is the terminal.
Additionally, it is unlikely that the development of the second runway would have affected negatively the demand for the services provided by LAP. According to this, the pessimistic scenario considers that the demand does not change and takes the values of the real demand.
• Optimistic
The optimistic scenario assumes that the development of the second runway helps to increase the demand of the airport services in the same proportion as the country whose traffic grew the most in the region. The traffic growth in the region was presented in Table 9. This assumption is taking into account the importance of the connectivity in an airport and its relation to the development of the region.
In that case, the optimistic scenario considers that the passenger traffic grew 17,3%, the air cargo traffic by 6,6% and the aircraft movements by 11,2% in the year 2012 (see Table 10).
• Neutral
The neutral scenario is an intermediate case between the optimistic and pessimistic scenarios. Then, this scenario is calculated as the average growth rate of the optimistic and pessimistic scenarios. The Table 10 shows that in the year 2012, in the pessimistic scenario, the passenger traffic grew 15,1%, the air cargo traffic by 4,5% and the aircraft movements by 10,5%.
Table 10: Scenarios of the growth rate of the quantity of services provided by LAP in the counterfactual case
Unit 2012 2013 2014 2015 2016 2017 Passenger Optimistic 17,3% 15,8% 10,2% 10,5% 10,7% 11,1% Neutral 15,1% 13,8% 7,6% 9,9% 10,4% 10,2% Pessimistic 13,0% 11,9% 5,0% 9,3% 10,1% 9,4% Operation Optimistic 11,2% 6,2% 5,5% 9,5% 6,3% 5,9% Neutral 10,5% 4,7% 3,4% 8,4% 6,3% 5,8% Pessimistic 9,8% 3,2% 1,3% 7,3% 6,3% 5,7% Air cargo Optimistic 6,6% 1,7% 6,5% 4,0% -2,4% 2,9% Neutral 4,5% 1,3% 4,3% 1,7% -3,3% 0,7% Pessimistic 2,5% 1,0% 2,0% -0,6% -4,3% -1,4% The growth rate of passengers is used to estimate the counterfactual quantities of the TUUA services, while the growth rate of aircraft movements is used to estimate the counterfactual quantities of Landing and Take-off, boarding bridges and Aircraft Parking37. Finally, to
estimate the counterfactual quantity of air cargo the growth rate of air cargo is used. The other services provided by LAP, which are not regulated, can be divided into two groups. In the first group, the services deepened on the aircraft movements and not on the spaces in the terminal, these services are Ground Handling, catering and fuel. To estimate the counterfactual quantity of these services the growth rate of aircraft movements is used because the demand of this services is complementary with the operations.
In the second group are the services that directly depend on the spaces in the terminal, as rent of counters, rent of offices, commercial areas, duty-free, and others. As mentioned above, this model only takes into account the operation of the second runway in 2012, so the capacity of the terminal would not change in this year. For that reason, in the counterfactual case, the demand for these services has not been affected.
37 In 2016, in all the countries of the sample, the operations decreased compared to 2015, but the operation in Lima´s
airport increased by 6,3%. For that reason, only in this year, in the three scenarios, the growth rate that is considered is 6,3%, that is the real development of LAP.
C. Counterfactual labor and material input
The operation of the new runway also affects labor and materials inputs. These counterfactual effects are calculated using the Rule of three38. That means that the quantity
of these inputs will change in the same proportion of the changes in the number of operations. The number of operations is used for this purpose because it reflects the use of the runway better than the passenger or air cargo traffic.
IV.2.3.2. Revenues
The revenues in the counterfactual concession model are affected by changes in the demand (quantity of services) and tariffs, as result of the investments in the second runway and second terminal.
As mentioned above, the development of infrastructure can affect the demand. For that reason, the same three scenarios that were considered in the counterfactual productivity model (optimistic, neutral and pessimistic)39 are considered to incorporate the possible
effects of the demand.
In the case of the regulated tariffs, the concession agreement establishes that LAP can charge as maximum the RPI-X. Then, the RPI is measured by the Consumer Price Index (CPI), whose value does not depend on the counterfactual scenario because it is a macroeconomic series. But, in this case, the tariffs have to be calculated using the counterfactual productivity factor (2014-2018), which takes into account the new investment (see the methodology of the productivity factor in the section IV.2.3.1.6.)
The revenues of the other services provided by LAP40 that depend on the operations but not
directly of the space in the terminal (as ground handling, catering, and fuel) have been considered counterfactual since 2012, the year in which it goes into operation the second runway (2012).
Meanwhile, the services that directly depend on the spaces in the terminal (as rent of counters, rent of offices, commercial spaces, duty-free, and others) are affected by the changes in demand since 2016, the year in which it enters in operation the second terminal. The counterfactual revenues of these services are estimated taking into account the growth rate of the aircraft movements. This is because the number of operation reflect the demand of the airlines (rent of offices, counters, etc.) and the demand of passengers (duty-free, commercial spaces, others).
38 The Simple Rule of Three or Simple Proportion is, “when from three given quantities, a fourth is required to be found, that shall have the same proportion to the given quantities of the same name, as one of the other quantities has to that of the same name with itself”. According to the book “A new mathematical and philosophical dictionary,
comprising an explanation of the terms and principles of pure and mixed mathematics, and such branches of natural philosophy as are susceptible of mathematical investigation” by Barlow Peter.
39 See Table 9.
