DEMAND ELASTICITY IM PROFITABILITY ON - ROUTE

Faculty of Economics and Business - Zagreb, Trg J.F. Kennedya 6, 10 000 Zagreb, [email protected]

Tomislav HERCEG Faculty of Economics and Business - Zagreb, Trg J.F. Kennedya 6, 10 000 Zagreb, Croatia [email protected]

Abstract Croatia , a national air carrier in Croatia, faces profitability issues for years. It partly relies on Government subsidies for a minimum daily number of flights between the capital and the other Croatian cities. Since Zagreb and Dubrovnik are the most distant and without fast road route (there is no highway in the Dubrovnik-Neretva county), the number of passengers is significant, as well as the turnover. This paper analyses how revenue management on the Zagreb-Dubrovnik-Zagreb air route could impr profitability. The revenue management analysis is based on the determination of demand function dynamics on the mentioned route. Data required for obtaining this research was taken from database, but modified using th preserve corporate secret. Profit management analysis on the above mentioned route is based on the analysis of a revenue function. It is because, due to the agreement Croatia Airlines has with Croatian Government, a fixed number of daily flights to Dubrovnik and back fixes the cost component of a profit function. Therefore only revenue function has a dynamics to be analysed, which in turn depends on demand function. Demand function is estimated as a function where the number of daily passengers (quantity, dependant variable) is affected by a corresponding daily average ticket price, and a moving average of a temperature (10 days average) as a deseasoning tool in the time period 2013-2018. Again, knowing that the daily costs are fixed, a maximum daily revenue can be directly related to a unit demand elasticity. The findings show that, applying the pricing policy here suggested, a 7.5% profit increase can be by a good price management. Furthermore, it was shown that the company sets prices mostly in the inelastic zone of demand, showing that the profit rise could be acquired by a smart, non-linear price increase, depending on the season. Finally, an obvious, but very interesting finding was obtained, confirming that the rise of temperature and the fall in prices cause more passengers to travel on this route.

Keywords: Pricing policy, demand elasticity, temperature, revenue management, seasons

JEL classification: D22, C32, R41

Introduction

In this paper the air transport demand dynamics and its elasticity was analysed on the example -Dubrovnik-Zagreb. Croatia Airlines, a government owned company, struggles in the competing environment, but it does not have only a profit role, but

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also a public service role, helping Croatia to stay both well connected to the world and maintain in-land connection between major Croatian cities. The latter two goals are extremely important for a country as small and as strangely shaped as Croatia. Although the profitability goal is not the only one, still a company should strive to be economically self-supporting and rely less on the Government incentives.

In order to provide evidence whether a company is making a profit maximizing decisions, one agreement between Croatian Government and Croatia Airlines, the latter has to maintain a fixed number of daily flights on the line Zagreb-Dubrovnik-Zagreb in order to obtain subsidies. In this way, an isolated city of Dubrovnik, was provided with a guaranteed connection to the capital. It makes the analysis simpler since a minimum number of daily flights (22 weekly rotations during winter and 28 during line. The other Croatian cities also have minimum rotations set by PSO which makes not only this specific, but the overall costs as well, rather fixed. Therefore, only dynamics of the revenue determines changes in the profitability.

Figure 7 - Table of Croatia Airlines timetable

Winter timetable 2016-2020 Summer timetable 2016-2020

Pula - 6 rotations (via Zadar) - 11 rotations (via Zadar) - 2 rotations Zadar - 6 rotations Osijek - 6 rotations Osijek - 6 rotations Zadar - 11 rotations Split - 22 rotations Split - 26 rotations Dubrovnik - 22 rotations Dubrovnik - 28 rotations

Source: Review of the Air Transport Public Services System within the Republic of Croatia

As seen in the Table 1 there is a difference of 6 flights during two different periods, which prove the seasonality issue in the Croatian market, meaning that the demand is higher in the summer than in the winter.

Demand for the line Zagreb-Dubrovnik-Zagreb, just as any other demand, except the perfectly inelastic one, depends on the own price. However, when it comes to substitutes, there is only car and bus, but since a travel is long, tedious and costly, it does not show any significant impact on the observed demand. Purchasing power can also be a factor, but in the observed period from 2013 2018 a change in the purchasing power has been positive, but not significant, and its further application could be a part of some future studies when a longer trend would be available. However, there is a significant factor that affects demand: time of the year. Since Dubrovnik is a warm sea destination, its visitors tend to visit it more during warm weather, and less when it is cold. However, the limitations in terms of qualitative some important data variability. Therefore a temperature variable is introduced. Its continuous numeric character is beneficial, as opposed to a discrete qualitative categories mentioned

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before, and it has two aspects: deseasoning of the time data and season impact on the pricing policy. However, a daily temperature amplitude might be misleading. Hence a time variable here introduced is a moving average of a temperature (MAT) through +/- 10 days. Using the econometric analysis of a time series a demand function is obtained. The obtained function is then applied to determine daily demand elasticities which, in order to maximize the profit and the revenue, has to be (detailed anylisis in the Section 3: Data and Methodology). That information in turn provides the information on i) whether the actual pricing policy has been maximizing the revenues, ii) what would be the optimal price given the circumstances, and iii) a general guideline for the future pricing policy in order to increase revenues under the fixed costs. On top of the previously mentioned, one will try to determine a seasonality of the route and establish a pure price quantity relation without seasonality impact.

Literature Overview

An airline pricing policy is one of the most important factors for a successful airline company. Due to that, demand elasticity plays an important role in determining the fare price, because of the fluctuation and seasonality in flight demand (Mandic et al, 2017). Constant demand fluctuations and market competition forced the airlines to constantly predict demand and match fares with passenger needs while keeping the route efficient (Doganis, 2000). Although some studies have explained air transport elasticity on a particular market (Gallet et al, 2014; Njegovan, 2006), few have shown the individual impact of demand elasticity as a operate in a different market environments, mainly depending on the routes and competition. On some routes airlines have monopoly, while on the others oligopoly. Monopoly on a route means higher tariffs and price discriminations based on the availability of the seats left (G nen et al, 2010).

When a competitor arrives on a market, it would, in majority of the cases, immediately decrease prices until the zero-profit point, except in the case if both airlines have some internal agreements or codeshare. Therefore, airlines will look at competitors pricing and in market share and the amount passengers, just as it is the case in most of the case when monopoly is being transformed into oligopoly (Babic et al, 2018)

Tariff pricing differs by route and type of aircraft used, but in the case of legacy carriers, similar to Croatia Airlines, operating costs can be lower by increasing the network due to economies of scale. On some monopolistic routes, tariffs are higher so as to cover the losses or lower profits on the other routes. In order to keep the legacy carriers, a PSO programmes are introduced, but they change company behaviour. First, a company under PSO has a monopoly on a route for a given period. Secondly, a minimum frequencies or capacity required. By offering and accepting these PSO conditions for a given time-frame air carrier will receive tax money in order to cover these services until zero profit point (G ssling et al, 2017).

Data and Methodology

Dataset is a time series from 26 October 2013 28 February 2018 (1551 observations). It contains the data for the line Zagreb Dubrovnik (number of passengers, Q, and total price of a ticket in EUR, P). Since Dubrovnik is a tourist destination, a deseasoning is required. Hence

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daily temperature was introduced, since one can assume that tourist visit Dubrovnik when the weather is better. However, the weather conditions should not be estimated according to the daily temperature, but according to the average within a few days. Therefore a moving Independent variables were chosen in order to capture only point-to-point passengers with restrictions on domestic tariffs only, passenger adult type and economy class without business class for better statistical data without outliers.

The following models were estimated using a simple OLS regression: (Linear model) (1) (Log model) (2) After a demand function would be obtained, a profit function would be optimized: (3)

Costs ( Croatia Airlines obliged to run 22 weekly flights in the winter season and 28 in the summer season, distributing evenly smaller and larger airplanes (on average 290 seats in the winter season and 580 seats in the summer season daily). Therefore the optimal profit would be obtained depending on the variation of the total profit (R):

(4) Since then which provides: (5) Knowing the own price elasticity and solving for (5) one gets: (unit elastic) (6) Hence a price that satisfies (6) would render: (7) Which in turn provides how a daily air fares should be set depending on the mean average temperature, measuring seasonality, in order to maximize profit: (8)

Findings

Econometric analysis has provided a model where MAT has solved the issue of autocorrelation. All the other required tests have also shown that the model is well defined (P test for the F value is 0.0000), the data is save from heteroscedasticity and both variables are significant (P values are 0.000). The obtained model has provided a following estimate:

(Linear model) (9) (Log model) (9a)

Although both models are well defined, the latter has a lower explanatory power (R2 is smaller) and significance of its coefficients are smaller (2 out of 3 t values are below the values in the linear model). It is the reason why a model (9) will be used in further calculations.

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Solving (8) for the obtained coefficient values, the following temperature price relation is obtained: (10)

Applying (10) on the real data on the entire dataset, a list of optimal prices is obtained, which then produced the quantity as well by stubbing it in (9). These data provide the input for calculation of the optimal total revenue. Having the obtained data, an optimal weighted which is 30.5% higher. In the observed period it would results in the increase of the daily under the same costs. In a year it would have resulted with a both profit and revenue increase line. In the observed period the weighted average price elasticity was 0.648, indicating a price increase would be recommended (in the optimum its value is 1). Demand function can be shown on the Figure 1. Its inverse form is .

Figure 8 - Graph of the estimated demand function

Estimated total revenue function is , where the maximum total revenue is obtained at which gives relation of the optimal quantity and temperature: showing that average 368 passengers are expected. Since the average own price elasticity in the observed period was - passengers travelled, while 302 passengers could have paid 30% more and ensure 7.5% rise in the revenue.

Figure 9 - Graph and a layer curve of the estimated total revenue function

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In the case of the incorporation of the findings of the above presented demand elasticity analysis it is shown that this revenue management technique could bring this company a benefit of almost EUR 0.6 Mill in a year without any additional effort or cost. It shows how important it is to make thorough analysis of pricing policies which might make a difference between a profitable or unprofitable business.

Conclusion

The main purpose of this research is to prove that by a sole application of the revenue management techniques, a company could earn more profits. It is shown that higher price causes quantity demanded to fall, but the mean average temperature (seasonality) is correlated with the fall in the elasticity, thus enabling the price increase. The reach of this analysis, however, can be directly applied to all companies under PSO regulation since their costs are fixed, which transforms profit maximization problem into revenue maximization problem.

Specifically, this research has shown that the optimal average ticket price is 30.5% higher than the current average and therefore, if implemented, it would bring 7.5% higher revenues and profits.

The limitation of this research is the fact that demand is observed as sum of all individual demands, rather than distinguishing between the economy class (price elastic) and business class (price inelastic). Furthermore, future research could try to find how forward bookings forward bookings is not public and which makes it very difficult to obtain that data. Therefore, this research could be a standpoint for further research of demand elasticity on the set of the airlines companies, considering competition market share on specific routes, and profitability.

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