Evaluating the Impacts of the Swedish Aviation Tax

by

Sofia Fors

Beatrice Ljung

May 2019

Master’s Programme in Economics

Supervisor: Joakim Westerlund

Abstract

The 1st of April 2018 an aviation tax was implemented in Sweden. With an event study approach this paper aims to provide an understanding of the outcome of the new policy. This by investigating if the tax causes significant effects on the stock returns of concerned flight carriers and whether the policy causes a significant change in the number of air passengers travelling from Sweden. By calculating the expected normal performance using the mean model, the market model and the Holt-Winters model, the abnormal behaviour is established and tested for significance. Previous literature provide insights from aviation taxes implemented in other European countries. A negative impact on concerned flight carriers and a decline in the number of air passengers travelling are mainly concluded. The results of this paper indicate no market reaction related to the introduction of the aviation tax. Significant changes are observed when considering the air passenger data. The tax may be an explanation to the results. Yet, there may be other influencing aspects such as economic and environmental considerations.

Keywords: The Swedish aviation tax, Event study, Market model, Mean model, Holt-Winters model, Price elasticity

ii Acknowledgements

First of all, it has been a true pleasure to write this paper together. With this eminent collaboration we have reached far beyond our own expectations. We would further like to show our deep gratitude to Professor Joakim Westerlund for his wise advice, guidance and sense of humour. The time he so generously has given is very much appreciated. We would also like to express our profound gratitude to Christopher Ljung for his constant encouragement and useful critiques of this paper. Finally, we must not forget to thank our fantastic classmates for the support during all the hours spent in the basement of Alpha.

iii Table of Contents

1. Introduction 1 2. Previous literature 2 3. The Swedish aviation tax – from idea to realisation 4 3.1 The design of the aviation tax 6 4. Data 6 5. Method 8 5.1 Determining event dates and estimation periods 8 5.2 Normal and abnormal performance 12 5.3 Significance of results 14 6. Results 15 6.1 Results of the market reaction analysis 15 6.2 Results of the air passenger data 19 7. Discussion 22 8. Conclusion 26 References 27 Appendix 33

iv 1. Introduction

Seeking to reduce environmental impacts of the aviation, the Swedish parliament decided to implement an aviation tax on the 1st of April 2018 (Finansdepartementet, 2017a). The policy was accepted on the 22nd of November 2017 and the period before and after was characterized by inquiries, discussions and publicity in the media. Often, a tax aims to change an existing behaviour apart from bringing in revenues. If there are visible changes related to the policy is therefore interesting to study. Hence, this paper aims to investigate the outcome of the aviation tax. More precisely, what is the market reaction to this policy decision? And further, what is the actual effect on the number of air passengers travelling after the tax being realized?

This paper seeks to answer these questions by: i) Investigating if significant effects are visible on stock returns of concerned flight carriers at different events related to the aviation tax. This in order to find out whether the market is expecting the air operators to be affected by the policy or not. ii) Evaluating if the implementation of the policy causes a significant change in the number of air passengers travelling to domestic and international destinations. This together is believed to provide evidence of the effects related to the introduction of the aviation tax.

To analyse both the market reaction in terms of stock return changes and possible changes in the air passenger data an event study approach is used. Event studies are frequently applied in many areas of research when examining the effect of a certain event (e.g. Schweitzer, 1989; Kothari & Warner, 2007; Kumar, Mahadevan & Gunasekar, 2012; Gilligan & Krehbiel, 1988). The event can be anything from a change within the firm’s control such as earning announcements or stock splits, to something beyond the firm’s control such as a policy change announced by the government (Seiler, 2004). The evaluation of the event is made by comparing the expected normal performance with the actual outcome within a pre-specified event window. For stock returns, the market model and the mean model are used when calculating the expected normal returns based on their common occurrence in the literature (MacKinley, 1997). In contrast to returns, air passenger data is often characterized with seasonal effects (Bermúdez, Segura & Vercher, 2007; Grubb & Mason, 2001).

1 This motivates the choice of the Holt-Winters method when calculating the expected normal performance of the air passenger data. The abnormal behaviour is then calculated and tested for significance using a parametric t-test and a non-parametric Wilcoxon signed rank test.

With the tax being a charge, a hypothesis of a negative market impact is formed. A decline in the number of air passengers is further expected. Yet, the results of this paper indicate no market reaction related to the introduction of the aviation tax. Significance is found at some dates, however most of them are likely to be confounding events. Based on this, the policy is not believed to have an impact on the concerned flight carriers. A weak market response may also indicate the design of the tax not being powerful enough. The findings further provide support for the tax being a mark- up on the price of the flight ticket since a decrease in number of air passengers travelling to Sweden and Europe is observed. The findings are yet ambiguous since an increase in the number of air passengers is found for international non-European destinations. The increase can be explained by economic aspects such as a weak Swedish currency. As a conclusion, the aviation tax may explain the decrease in the number of air passengers travelling to domestic and European destinations. Yet, determining to what extent is more difficult since the decline can be reinforced by other aspects such as environmental considerations.

This paper is organised as follows; section 2 provides insights from previous literature. To understand the process leading to the aviation tax, a short background and the motives behind the policy are presented in section 3. In addition, section 3 gives a description of the tax and whom it concerns. Further, section 4 provides a presentation of the data along with selected delimitations and section 5 describes the method used in this paper. The main results are presented in section 6 and will be more thoroughly discussed in section 7. Finally, a conclusion is provided in section 8.

2. Previous literature

Several countries within the EU have implemented aviation taxes with environmental motives, for instance the Netherlands and Ireland (CE Delft, 2018). Veldhuis and Zuidberg (2009) published a report regarding the implications of the Irish air travel tax introduced in 2008. With a scenario- based study, they conclude the policy to mainly affect the airports, airlines and the tourism industry

2 negatively. The authors claim the revenue loss of the air carriers to originate from two different scenarios. The first scenario is that the flight carriers are able to pass on the full amount of the air travel tax to the air passengers, but the price increase results in a decrease in demand for flight tickets. The second scenario relates to the firms being unable to mark-up the tax amount. In either case, they claim it to have negative economic consequences for the firms, taking the form of lower demand in the primer case and as increased costs in the latter case.

The International Air Transport Association (2019) emphasizes the passengers to be affected negatively by an aviation tax. This in terms of changed travel patterns as a result of a price increase. Gordijn (2010) finds support for this when examining the effects of the Dutch aviation tax. The author claims the tax to result in a decrease in the number of air passengers departing from Amsterdam airport. After the implementation of the policy Gordijn (2010) also observes an increase in the number of Dutch air passengers travelling from airports abroad situated close to the national border.

Moreover, Krenek and Schratzenstaller (2016) examine how an efficient aviation tax is optimally designed. To avoid competition in aviation taxation and to support the nature of the cross-border emissions of international flights, they reach the conclusion of implementing a carbon-based flight ticket tax on an EU-level.

When considering changes in air passenger data related to the Swedish aviation tax Ekeström and Lokrantz (2019) published a report on behalf of the Swedish Transport Agency in January 2019. The authors analyse possible changes in the air travel market six months after the implementation and find the number of passengers travelling within European countries to be less than forecasted. Yet, the number of passengers travelling to international non-European countries is higher than forecasted (Ekeström & Lokrantz, 2019). However, no statistical determination of the change in the number of passengers is made.

Further, Ekeström and Lokrantz (2019) claim the share of the tax paid by the passenger to be determined by the consumer’s price elasticity. An application of price elasticity on air passenger data is made by Brons, Pels, Nijkamp and Rietveld (2002) who examine factors influencing the

3 price elasticity of air travellers. The authors conclude the passenger’s price sensitivity to depend negatively on the flight distance and positively on the number of substitutes available. Thus, the less substitutes and the longer distance, the less price sensitive is the traveller.

With no previous research conducted studying the market reaction caused by the Swedish aviation tax, this analysis seeks to complete the knowledge gap. Moreover, with studies of actual changes in the number of air passengers travelling already been carried out, this analysis contributes with a prolonged period of comparison related to the period after the implementation of the Swedish aviation tax. Another contribution is testing whether the change is statistically significant.

3. The Swedish aviation tax – from idea to realisation

The great climate impact of the aviation sector is considered to be the main reason for implementing the aviation tax in 2018. According to the Intergovernmental Panel on Climate Change (IPCC) the human behaviour has a large negative impact on the climate. The transport sector produces approximately 25% of the total global carbon dioxide emissions. These emissions grow together with the demand for goods and the claim for mobility of people. The IPCC states that due to the improved affordability of air travel tickets there will be a large growth in air travel passengers worldwide, which will lead to an even larger increase in the carbon dioxide emissions. The EU emission trading scheme (ETS) is the sole regulation for mitigation of emissions within this sector in the region of Europe. Yet, regulation on a national level has seen to mainly affect the air travel within the country (Intergovernmental Panel on Climate Change, 2014). In Sweden 2014, flights to domestic and international destinations represented 1.2 tonnes of carbon dioxide equivalent per Swede. Of these, 90 % was derived from international flights (Finansdepartementet, 2017a).

This is not the first time an air travel tax has been implemented in Sweden with environmental considerations. A tax was introduced in 1989 targeting domestic flights, but was repealed seven years later since it was conflicted by the EU law. In 2006 another tax was about to be issued, yet rejected by the opposition (Finansdepartementet, 2017a). Regarding the aviation tax in 2018, the process towards a new policy began in 2015 when an investigation was initiated by the Swedish government (Finansdepartementet, 2014; Finansdepartementet, 2015). The results of the

4 investigation were presented in “A Swedish aviation tax” and published in 2016. The report contains a description of the possibilities of a tax aiming to mitigate the carbon footprint of the air transport. Hence, the groundwork of the upcoming aviation tax was in place (Utredningen om Skatt på Flygresor, 2016)

Another motive for the Swedish aviation tax was formed in “A Swedish Aviation Strategy” by the Ministry of Enterprise and Innovation during 2016 and was agreed upon in January 2017. The aim of the strategy is to be indicative for the government by ranking some areas of interest. These areas involve for instance to streamline the sector with new technology and innovation, increase the export of Swedish goods and to mitigate the climate impact of the aviation industry (Näringsdepartementet, 2017). This strategy further motivated the formation of the proposition of an aviation tax.

The Swedish aviation tax was included in the budget proposition of 2018 (Finansdepartementet, 2017b). The proposition was proposed by the government to the Swedish parliament on 20 September 2017. On 22 November 2017, the budget proposition was accepted by the Swedish parliament (Sveriges Riksdag, 2017). Thereby, the Swedish aviation tax was accepted and decided to be implemented on the 1st of April 2018. Figure 3.1 depicts the evolution of the aviation tax, from an idea to realisation.

Investigation of a The referrals from authorities and The Swedish aviation The budget new aviation tax is stakeholders are collected tax is implemented proposition of 2018 is initated The report is sent to put forward autorities and other stakeholders on referral

5 Nov 2015 30 Nov 2016 8 Dec 2016 1 Mar 2017 8 Jun 2017 20 Sep 2017 22 Nov 2017 1 Apr 2018

The Council on Legislation publishes a proposal of the new law which is sent to the Swedish government The budget proposition of 2018 is accepted by the Swedish The report “A Swedish Aviation parliament tax” is published

Figure 3.1 - Timeline of the Swedish aviation tax

5 3.1 The design of the aviation tax

The aviation tax is concerning commercial air travels and is designed as an excise duty. An amount shall be paid for each passenger older than two years travelling in an airplane with more than ten passenger seats. It is not the passenger herself who is obligated to pay the tax, but the operating flight carrier. Further, the tax shall only be paid for travellers and not for flight staff on duty. If the passenger does not arrive at the final destination due to weather, technical issues or other unforeseen events and is instead travelling at a late departure, the tax is not obliged to be paid. The tax is neither obliged to be paid for transit nor transfer passengers, thus if the airplane has transit within Sweden or will have take-off 24 hours after landing in Sweden (SFS 2017:1200).

The flight company becomes liable to pay the tax if a plane departs from a Swedish airport. However, the amount to be paid differs between destinations. The tax amount is 60 SEK per air passenger travelling within Sweden and to Europe (tax level 1). Further, the tax amount per passenger travelling to countries outside Europe is either 250 SEK (tax level 2) or 400 SEK (tax level 3), depending on the travel distance. For a list of countries within each tax level, see SFS 2017:1200. The amount to be paid is adjusted to the annual price level (SFS 2017:1200).

4. Data

When evaluating the market reaction caused by the aviation tax, stock prices of Scandinavian Airlines (SAS) and Norwegian Air Shuttle (Norwegian) are used. The decision to include these companies is based on their superior market share in the Swedish aviation market. Braathens Airlines together with SAS and Norwegian have the greatest market share in the domestic market. Yet, only SAS and Norwegian are listed on the stock market and Braathens Airlines is therefore excluded from the analysis. SAS and Norwegian are further having the greatest market share of the flights to foreign countries consisting of 25% and 18% respectively in 2017 (Konkurrensverket, 2018). Being major players in the Swedish market motivates the choice of including them when analysing the effects on stock returns. Further, a decision of using daily data of returns is made. This in accordance with Seiler (2004) who favours daily data of returns compared to monthly data since the primer gives more precision when trying to isolate the effect of an event.

6 For SAS and Norwegian, daily data of the last price is collected from Bloomberg between the 7th of March 2014 and the 2nd of May 2018. The number of observations in the data differs slightly between the air operators due to aspects such as different public holidays. Using the last price, the returns of the stock 푟푡 is further calculated by taking the log returns using the formula:

푟푡 = 푙푛(푝푡/푝푡−1) (1)

Where 푝푡 is the price at day t and 푝푡−1 is the price one day before. The log returns of SAS and Norwegian are used when performing the event study.

Further, when calculating the expected normal returns using the market model a market index is used. The S&P 500 is commonly applied, but in the context of a Swedish aviation tax an index reflecting the Swedish market is more suitable. This motivates the inclusion of OMXS30 as market index in the model. Yet, Norwegian and SAS also operate outside of Sweden’s borders, therefore an European index such as Euronext is additionally considered. Euronext is motivated by being the largest stock exchange in Europe (Euronext, 2019). For comparative purposes, both OMXS30 and Euronext are used in the event analysis of the stock returns. Data will therefore be collected from Bloomberg for both indices over the period 7 March 2014 to 2 May 2018 and further transformed into log returns.

When analysing changes in air passenger data, monthly data is collected from the Swedish Transport Agency. The decision of monthly data refers to previous literature. Bermúdez, Segura and Vercher (2007) and Grubb and Mason (2001) use monthly data when predicting future air passenger demand. The Swedish Transport Agency provides data of both departures and arrivals at Swedish airports. Since the tax is only levied on airplanes leaving Swedish airports, statistics regarding arrivals will therefore be excluded when evaluating the potential effect of the aviation tax. Monthly data of departures from Swedish airports with commercial traffic is therefore collected from January 2010 to December 2018.

Moreover, in accordance with Grubb and Mason (2001) the aggregated number of passengers at all domestic airports with commercial air travels will be used to dampen individual changes. For a

7 list of included airports, see appendix A1. The departure data is further divided into flights to domestic, European and international non-European destinations. The division is particularly interesting since destinations to Europe and Sweden have the same fixed mark-up whereas international destinations are marked higher. There are two different tax amounts regarding international non-European destinations. Yet, the lack of availability of data divided into these subgroups requires a merge of the two international non-European taxation groups. This merge can potentially affect the result. Another potential limitation refers to the data covering all ages. This in contrast to the aviation tax which applies to people older than 2 years of age. However, with this being a small group unable to travel alone, the overall results is only assumed to be marginally affected.

5. Method

With the aim to study both the market reaction to the aviation tax and its effect on the number of air passengers travelling, an event study approach is used. There are several steps to take into account when designing an event study. One must primarily find an event of interest and clearly define the precise date of its occurrence along with an event window. When calculating abnormal performance, the normal performance must first be determined from an estimation period using the market model, the mean model and the Holt-Winters model in this case. The abnormal performance can then be calculated within the event window period and tested for statistical significance (Seiler, 2004).

5.1 Determining event dates and estimation periods

When the event of interest is decided it is crucial to identify the exact date. This in order to get as precise results as possible when examining the effects of the announcement. The test will naturally be less powerful when lacking precision (Seiler, 2004). Starting out with the market reaction of the aviation tax, the announcement date is related to the day when the budget proposition was accepted by the Swedish parliament, namely on 22 November 2017. When considering the market reaction of governmental decisions one might however have to take into account more than one event date. This is due to the assumption of the event being unforeseen which is central in event studies. Gilligan and Krehbiel (1988) discuss such an assumption to be unrealistic in the case of energy

8 taxation announced by the United States House of Representatives in the 1970’s. The authors find dates related to final vote committee reports and regulatory decisions to be potentially uninformative and thus less probable to provide a market reaction since the decisions may be anticipated. Instead they find it important to distinguish informative dates in the event period where the market’s expectations changed.

Similar to the process of implementing an energy tax, there is a risk of anticipating the outcome of the aviation tax prior to the announcement day. Therefore, several possible event dates are included and evaluated when considering the market reaction to the aviation tax. These are based on information leakages related to the policy that may have affected the market. For that reason, both official dates and more unofficial dates are considered. The primer relates to dates such as the final decision of the budget proposition of 2018 being accepted, whereas the latter involves dates where the media was particularly active in reporting information about the tax (see appendix A2). Table 5.1 presents the included event dates in the market reaction analysis.

Table 5.1 - Event dates included in the event study analysis of the market reaction

Event date Motivation 1 March 2017 Referrals from authorities and stakeholders are collected and an increased news reporting is apparent 8 June 2017 The investigation performed by the Council on Legislation is finished and a proposal of the new law is sent to the Swedish government

20 September 2017 The budget proposition of 2018, where the Swedish aviation tax is included is proposed

22 November 2017 The budget proposition of 2018 is accepted by the Swedish parliament 1 April 2018 The Swedish aviation tax is implemented

The events in the table 5.1 are believed to have an effect on the market. The dates are relevant since new information about the upcoming aviation tax is published. Prior to the acceptance date, there is still uncertainty whether the tax will be accepted or not. After 22 November 2017, the public knows for sure that the policy will be implemented.

9 For the air passenger data instead, the main interest relates to the period after the tax is put into practice since this is the period where the flight carriers are obliged to pay the tax. Therefore, the event study analysis concentrates on the abnormal performance on the date of the tax implementation on the 1st of April 2018 and the preceding months of 2018.

When the event dates are determined, the next step involves designing the event window which is the period where the effects of the announcement is evaluated. There is no consensus of how many days to include but the knowledge of the exact date allows for a shorter event window and a higher precision. A general rule is to cover the entire effect, but to keep the period as short as possible. Suspected leakage has further a crucial role in determining the event window. When being certain of no prior leakage one can use a period of -10 to +10 days where 0 is defined as the date of the event. If there is uncertainty regarding the information being reached out, a wider window is more preferable (Seiler, 2004). Due to the aviation tax being a potentially anticipated governmental decision, the market reaction analysis includes three different lengths of the event window. The event window will thus consist of +/- 5 days to the actual event date as well as +/- 10 days and +/- 20 days. For the air passenger analysis however, the event window is concentrated to the period after the event, including April 2018 to December 2018. Hence, the event window will consist of +8 months to the single event in April.

Moreover, an estimation period must be defined as it will serve as the basis for creating a model of normal returns and a model for a normal pattern of air passenger data. In the case of the aviation tax one wants the estimation period to capture the normal behaviour but still be unaffected by information related to the introduction of the policy. This to avoid an overlap between the estimation period and the event window. When examining the timeline in figure 3.1, 5 November 2015, can be seen as a starting point for the Swedish aviation tax introduced in 2018. On this date the introduction of the investigation was initiated by the Swedish government. This motivates an estimation period prior to this date, thus going from the 7th of March 2014 to the 4th November 2015 when considering the market reaction. This period is referred to as A in the following tables.

After the initiation of the investigation, there is no visible activity in terms of information to the public related to the tax until the report “A Swedish Aviation Tax” is released on 30 November

10 2016. Therefore, another estimation window of the returns is used between the 7th of March 2014 and the 29th November 2016 for the market reaction analysis. This period is referred to as B in the following tables. To also include a prolonged estimation window is motivated by the gains of having more observations when fitting the model. Yet, the shorter estimation period A, going from March 2014 to November 2015, minimizes the risk of including potential leakage related to the aviation tax that may have happened during 2016. Using two different estimation periods is further desirable for comparative purposes.

For the air passenger data instead, the estimation period covers the months going from January 2010 to October 2017 (referred to as C in the following tables). For the number of passengers departing to a domestic, European and international non-European destination, there is one estimation model fitted per subgroup. To account for possible information leakage and price increases before the actual implementation of the tax, the estimation period is limited to October 2017 which is just before the announcement. The belief of no price change happening before the announcement taking place in November 2017, motivates no need for adding a shorter estimation window. The reason for going back to January 2010 and not March 2014 relates to the possibility of capturing a normal pattern of air passenger data when having monthly data. To be able to capture more data points monthly data requires going back longer in time compared to daily data.

Figure 5.1 illustrates the event study process. The interval between T0 and T1 covers the estimation period. The event window is further an interval between T2 and T3 with the event occurring at point 0.

T0 T1 T2 0 T3 Estimation period Event window

Figure 5.1 - Illustrates the estimation period and the event window

Table 5.2 provides a summary of the different event dates, event windows and estimation periods in order to get a clearer picture.

11 Table 5.2 - A summary of the different event dates, event windows and estimation periods

T0 T1 Event date = 0 T2 T3

Event dates and estimation periods of stock returns 4 Nov 2015 (A) -5, -10, -20 +5, +10, +20 7 Mar 2014 1 Mar 2017 29 Nov 2016 (B) -5, -10, -20 +5, +10, +20 4 Nov 2015 (A) -5, -10, -20 +5, +10, +20 7 Mar 2014 8 Jun 2017 29 Nov 2016 (B) -5, -10, -20 +5, +10, +20 4 Nov 2015 (A) -5, -10, -20 +5, +10, +20 7 Mar 2014 20 Sep 2017 29 Nov 2016 (B) -5, -10, -20 +5, +10, +20 4 Nov 2015 (A) -5, -10, -20 +5, +10, +20 7 Mar 2014 22 Nov 2017 29 Nov 2016 (B) -5, -10, -20 +5, +10, +20 4 Nov 2015 (A) -5, -10, -20 +5, +10, +20 7 Mar 2014 1 Apr 2018 29 Nov 2016 (B) -5, -10, -20 +5, +10, +20

Event date and estimation period of air passengers

Jan 2010 Oct 2017 (C) 1 Apr 2018 0 +8

5.2 Normal and abnormal performance

To be able to evaluate the abnormal performance, one must primarily determine the expected normal behaviour. The abnormal behaviour is then calculated as the difference between the expected normal performance and the actual outcome. Having both returns and seasonal count data calls for different procedures when calculating the expected normal returns. Yet, the procedure of determining abnormal returns is similar.

For the market reaction analysis of the stock returns, abnormal returns are calculated as the difference between the realized returns and the expected normal returns. Assuming the efficient market hypothesis to hold, the pattern of the abnormal returns illustrates the impact of the specific event on the stock return. Apart from studying the single abnormal return on the specific event dates, the cumulative abnormal return (CAR) can also be calculated. This is done by simply adding the abnormal returns of the chosen event window (Seiler, 2004). CAR is useful when wanting to make overall conclusions of the effects from the specific event on the event period.

12 The abnormal return is calculated as following:

퐴푅푖푡 = 푅푖푡 − 퐸(푅푖푡|푋푡) (2)

Where 푅푖푡 is the actual observed return on stock 푖 in the event window period 푡 and 퐸(푅푖푡|푋푡) describes the expected normal return calculated by the specified model, conditioning on the information for the expected normal performance model (MacKinley, 1997).

Based on the findings of Holler (2014 cited in Schimmer, Levchenko & Müller, 2015a) the market model is commonly used when calculating the expected normal returns. The author finds the market model to be used in 79.1 % of the cases when analysing a sample of 400 event studies. This motivates the choice of including it in this analysis. The coefficients used in the market model is found by regressing the stock returns on a market index. This is achieved by regressing the returns of SAS and Norwegian on the returns of OMXS30 and Euronext respectively.

For comparison purposes the constant mean return model is additionally used. The normal returns are achieved by calculating the average of the returns over the estimation period which in this case is the average of the stock returns of Norwegian and SAS over the pre-specified estimation windows. The constant mean model is a simplistic model, yet Brown and Warner (1985) highlight the model’s ability to often generate similar results as more advanced models. For more details regarding the market model and mean model see MacKinley (1997).

With the above models being primarily used when considering returns of securities, an alternative model is in place when determining the expected normal number of air passengers travelling. Bermúdez, Segura and Vercher (2007) and Grubb and Mason (2001) use the Holt-Winters model when predicting the normal air passenger data due to its ability to capture seasonality. Holt-Winters is a model of time series behaviour commonly used when the data has a trend and a seasonal component. Having the latter, illustrates the difficulty in using the common stock return models on this type of data. Instead, the normal performance is determined by the Holt-Winters model. The abnormal performance is then evaluated by comparing the expected normal performance with the

13 observed outcome in analogy with the market reaction analysis. For more details regarding the Holt-Winters procedure, see Hyndman and Athanasopoulos (2018).

When calculating the expected normal performance using Holt-Winters model a first step is to make a decision whether an additive or a multiplicative model should be used for the different subgroups related to the aviation tax. The primer is used when the series faces a constant seasonal component and the latter is used when the seasonal component is increasing or decreasing proportional to the level of the series (Hyndman and Athanasopoulos, 2018).

When further selecting the models a choice of smoothing parameters is made. These parameters take on a value between 0 and 1 and can be seen as how much weight to put on the most recent observations. The lower value, the less weight will be put on recent data. For comparative purposes, several models are fitted and evaluated in terms of mean absolute percentage error (MAPE), mean absolute deviation (MAD) and mean square deviation (MSD). Choosing smoothing parameters by minimizing a forecast error measure is in line with the procedure performed by Grubb and Mason (2001). When the lowest value of these accuracy measures is observed three different models, one for each subgroup, are fitted using Minitab. These models are in turn used to determine the expected normal behaviour of April 2018 to December 2018. The choice of not including more than a year when forecasting is due to the method’s ability to provide accurate predictions over a longer time horizon. Da Veiga, Da Veiga, Catapan, Tortato and Da Silva (2014) state that the general advice is to limit the horizon of forecasts to not exceed the seasonal cycle of the series.

5.3 Significance of results

When testing for significance in an event study context one must consider the underlying distribution of the data. A parametric test requires the data to be normally distributed whereas a non-parametric test makes no such assumption and is therefore more broadly applicable (MacKinley, 1997). Yet, parametric tests usually have more power and are therefore more likely to find significant effects when the condition is met (Minitab, 2015).

In the market reaction analysis the abnormal returns are normally distributed under the assumption of the returns being normal and iid. However, Officer (1972) finds the returns to often experience

14 fatter tails. A solution is therefore to complement a parametric test with a non-parametric test, which Schimmer, Levchenko and Müller (2015b) claim to be a common approach in event study contexts. Based on this discussion both a parametric t-test and a non-parametric Wilcoxon signed- rank test are included in the market reaction analysis. Dutta (2014) believes the sign and rank test to be the most prosperous nonparametric tests. This because the test both considers the sign and the magnitude when calculating abnormal returns (Dutta, 2014). Evaluating the presence of normality is equally important when considering the air passenger data since it determines the use of a parametric or a non-parametric test. Yet, based on the above arguments both a t-test and a Wilcoxon signed-rank are included. For illustrative purposes a graph depicting the actual outcome, the expected normal outcome along with a 95% prediction interval is also given for the period April 2018 to December 2018.

In summary, a t-test and a Wilcoxon test statistic are presented for the cumulative abnormal returns of the different event windows in order to draw an overall conclusion of the event. A t-test is also provided for the specific event day in order to see if a significant market reaction is observed on the specific day. This is done for all the selected event dates and all the different estimation periods (for memory refresh see table 5.2). A t-test and a Wilcoxon test statistic are also given for the event window period related to the air passenger data. This along with a 95%-prediction interval using the Holt-Winters model.

6. Results

The event study results of the effects related to the aviation tax are presented below. Firstly, the results of the market reaction analysis are given and summarized. Thereafter, the outcome of the observed air travel patterns is presented and condensed.

6.1 Results of the market reaction analysis

The results of the different event dates related to the aviation tax using the market model with OMXS30 as index are given in table 6.1. Yet, only the significant results are presented. For a complete description, see appendix A3.

15 Table 6.1 - Results of the market model using OMXS30 as market index

AR on Flight Event Estimation event CAR +/-5 CAR +/- 10 CAR +/- 20 carrier date period date Wilcoxon Wilcoxon Wilcoxon T-stat T-stat T-stat T-stat S-R test S-R test S-R test

20 Sep A -0.864 1.03 20 1.603 63 2.14* 228* SAS 2017 B -0.833 1.01 20 1.56 63 2.09* 228*

1 Apr A 0.671 0.370 27 4.28* 101 3.17* 408 Norwegian 2018 B 0.725 0.486 28 4.51* 99 3.33* 413 *Indicates significance on a 5%-level

Starting out with 22 November 2017, when the aviation tax was accepted by the Swedish parliament, no significance is found for the abnormal return on the specific event day taking both SAS and Norwegian and both estimation periods into consideration. Further, the cumulative abnormal returns show no significance when taking the different event windows and different estimation periods into account. Similarly, the test statistic using Wilcoxon signed rank test cannot reject the null hypothesis. Since 22 November is the date of the actual announcement of the aviation tax, this may be seen as the most likely day for a market reaction. When no such result is found, the choice of analysing several event dates in accordance with Gilligan and Krehbiel (1988) is further encouraged.

Moving on to 1 March 2017, no significant results are found for SAS and Norwegian when considering the abnormal returns on the specific event day and the cumulative abnormal returns over the different event windows. The results are the same in terms of significance regardless underlying estimation period. A similar outcome is observed when studying 8 June 2017. 1 March is the date when the referrals regarding “A Swedish Aviation Tax” were submitted and one could observe an increased news reporting. In June instead, the Council on Legislation sent a proposal of an aviation tax law to the Swedish government. With no observed significance, the results suggest that these events had no visible effect on the market.

Regarding 20 September 2017, again no significant results are found for Norwegian. The cumulative abnormal return of SAS does however show significance when considering the +/- 20-

16 day event window solely for both the t-test and the Wilcoxon test statistic. When plotting the cumulative abnormal return (see appendix A4), one can notice a positive growth. This contradicts the belief of a tax affecting the stock price negatively. One could therefore suspect it to be a confounding event. This may also be the case when considering Norwegian on 1 April 2018. Significance is found for both the +/- 10 and the +/- 20-day event window for both estimation periods using the t-test. Again, one can notice a positive growth contradicting the hypothesis of the aviation tax having a negative market impact (see appendix A5). For SAS during the same event date, no significance is found at all.

Table 6.2 shows the significant results from the market model using Euronext as market index. For a complete description of the results see appendix A6.

Table 6.2 - Results of the market model using Euronext as market index

AR on Flight Event Estimation event CAR +/-5 CAR +/- 10 CAR +/- 20 carrier date period date Wilcoxon Wilcoxon Wilcoxon T-stat T-stat T-stat T-stat S-R test S-R test S-R test

20 Sep A -0.823 1.17 18 1.72 61 2.201* 229* SAS 2017 B -0.793 1.13 18 1.66 60 2.14* 227*

22 Nov A 0.843 -0.425 28 -1.73 75 -2.04* 304 2017 B 0.807 -0.321 28 -1.84 70 -2.05* 296 Norwegian 1 Apr A 0.594 0.178 31 4.09* 109 3.11* 405 2018 B 0.615 0.21 30 4.24* 108 3.26* 408 *Indicates significance on a 5%-level

Having the same settings as previously with event dates, estimation periods and event windows only one difference is observed in terms of significance when changing the market index from OMXS30 to Euronext. On 22 November 2017, significance is found for Norwegian using the t-test for the cumulative abnormal return in the +/- 20-day event window when considering both estimation periods. Yet, the Wilcoxon test statistic cannot reject the null. When plotting the cumulative abnormal returns a decreasing pattern is noticed (see appendix A7). This may indicate

17 some sort of reaction related to the introduction of the aviation tax. The result is yet quite ambiguous since the t-test and the Wilcoxon test statistic are not unanimous.

The mean model provides similar results as the market model with the OMXS30 and Euronext as market indices with the same settings regarding event dates, estimation periods and event windows (see table 6.3). For a complete description of the results see appendix A8.

Table 6.3 - Results of the mean model

AR on Flight Event Estimation event CAR +/-5 CAR +/- 10 CAR +/- 20 carrier date period date Wilcoxon Wilcoxon Wilcoxon T-stat T-stat T-stat T-stat S-R test S-R test S-R test

20 Sep A -0.826 1.18 17 1.87 55 2.26* 216* SAS 2017 B -0.784 1.16 16 1.83 54 2.23* 211*

22 Nov A 0.737 -0.374 27 -1.82 69 -2.07* 290 2017 Norwegian 1 Apr A 0.601 0.237 28 4.03* 99 3.07* 423 2018 B 0.619 0.300 32 4.12* 95 3.19* 429 *Indicates significance on a 5%-level

The previous results in terms of significance are equal to the results of SAS using the mean model. Thus, significance is found solely on 20 September 2017, when considering both the t-statistic and the Wilcoxon test statistic. Yet, the results for Norwegian differs slightly. Instead of the +/- 20-day event window being significant using both estimation periods, only the estimation period going from the 7th of March 2014 to the 4th of November 2015 (A) show significance and further a negative cumulative abnormal return (see appendix A7). Apart from this, the results are in line with previous findings.

To summarize, few significant results can be concluded. In all settings no significance is found for the abnormal returns on the specific event date. Yet, to make overall conclusions of the effects from the specific event one should also include the tests of the cumulative abnormal returns referring to MacKinley (1997). Albeit few, there are significant cumulative abnormal returns.

18 However, some of these show a positive graph when plotted and are therefore assumed to be confounding events.

There is one event date when the cumulative abnormal return is significant and display a negative graph when plotted. This applies to Norwegian regarding 22 November 2017 when the implementation of the tax is announced, for the +/- 20-day event window. The result is found using the constant mean model and the market model with Euronext as market index. Yet, the t-test shows significance solely and not the Wilcoxon test statistic. Since the market reaction is negative this may be a result of the policy implementation. There is thus a possibility that the market believes Norwegian to be more affected by the policy compared to SAS. This may explain the significant cumulative abnormal return of the +/- 20 window. However, the market reaction is visible as early as the 5th of November which indicates either information leakage or a confounding event (see appendix A7). The non-significant abnormal return on the specific day further supports this. With no significant results found for SAS on this event date, there is a risk that the results of Norwegian is a consequence of a firm-specific event rather than the aviation tax.

Apart from this, one can conclude the two indices to yield similar results. The same applies to the choice of estimation period. Thus, the choice of market index and estimation period seem to have a marginal effect on the results. There are further no results supporting a market reaction due to the aviation tax when considering both SAS and Norwegian. This applies when using all models, the two estimation periods and the both indices on all chosen event dates1. Veldhuis and Zuidberg (2009) claim the flight carriers to be negatively affected by an aviation tax. Yet, this belief is not reflected in the Swedish market since no reaction is visible.

6.2 Results of the air passenger data

Figure 6.1 presents the results of the air passengers travelling to domestic destinations. The actual values, the predicted values and the prediction intervals are plotted over the period from April 2018 to December 2018. It is found that the actual numbers of the domestic air passengers is lower than

1 Apart from the market model and the mean model, analyses of the market reaction have been performed using CAPM and Holt-Winters model. The CAPM shows results in line with the above models. The results of the Holt-Winters model further confirms the inadequacy of using a model correcting for trend and seasonality when having non-seasonal and mean-reverting returns.

19 the predicted numbers throughout the period and instead the actual values closely follow the lower bound of the prediction interval. Significance is found in June, September, November and December using a t-test (see appendix A9). Significance is also found when considering the Wilcoxon signed rank test (see appendix A10). The results suggest the number of air passengers travelling to domestic destinations to be lower than predicted throughout the period.

900 000

800 000

700 000

600 000

500 000

400 000

300 000

200 000 NUMBER OF AIR AIR PASSENGERS NUMBER OF

100 000

0 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Nov-18 Dec-18

Actual value Predicted value Prediction interval

Figure 6.1 - The actual and predicted values together with a 95% prediction interval of the air passengers travelling to domestic destinations

When plotting the results of the predictions and the prediction intervals with the actual values of the European air passengers one can see a similar pattern as the domestic air passengers (figure 6.2). Thus, the actual values of the European air passengers is lower than predicted over the whole period and closely follow the lower bound of the prediction interval. The results display a significant decrease in the number of passengers in seven months including; May, June, August, September, October, November and December using a t-test (see appendix A9). A significant decrease is further confirmed by Wilcoxon signed rank test (see appendix A10). Hence, the results are similar to the previous and suggest a decrease in the number of air passengers travelling to Europe over the specified period.

20 1 800 000

1 600 000

1 400 000

1 200 000

1 000 000

NUMBER OF AIR AIR PASSENGERS NUMBER OF 800 000

600 000 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Nov-18 Dec-18 Actual value Prediction Prediction interval

Figure 6.2 - The actual and predicted values together with a 95% prediction interval of the air passengers travelling to European destinations

Lastly, the results of the Holt-Winters method based on air passengers travelling to international non-European destinations are illustrated in figure 6.3. The graph suggests an opposite outcome to the above results. Here, the actual value exceeds the predicted value in every observed month. The real value of passengers and the upper bound of the prediction interval follow each other over the period illustrated. The results show a significant increase in passengers travelling during June, July, October, November and December using a t-test (see appendix A9). Significance is also found using the Wilcoxon signed rank test (see appendix A10). Thus, the results present an increase in travellers from Sweden to international non-European destinations, with a significant amount over specific months.

21 250 000

200 000

150 000

100 000

NUMBER OF AIR AIR PASSENGERS NUMBER OF 50 000

0 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Nov-18 Dec-18

Actual value Predicted value Prediction interval

Figure 6.3 - The actual and predicted values together with a 95% prediction interval of the air passengers travelling to international non-European destinations

By summarizing, after the implementation of the aviation tax the above results suggest an overall decrease in the number of air passengers travelling to domestic destinations and within Europe and an overall increase in the number of passengers travelling to international destinations outside of Europe. This in line with the results of the report conducted by Ekeström and Lokrantz (2019).

7. Discussion

Aiming to answer if the aviation tax causes significant effects on the stock returns of Norwegian and SAS and whether the policy causes a significant change in the number of air passengers travelling, a discussion of the results is presented.

When considering the market’s response to the aviation tax regarding both SAS and Norwegian, no reaction from the market is seen. The few events that are significant are likely to be confounding or firm-specific. The lack of reaction suggests the market to not expect the flight carriers to be affected by the aviation tax. A reason for such result may be the fact that similar policies have been

22 introduced in the past but abolished a few years later. Hence, the tax policy is not something completely new to the market and possibly resulting in a smaller reaction. Another aspect related to the absence of response is the time inconsistency of previous policies, causing the market to expect the tax to be present in a finite number of years and then be repealed.

Further, the design of the aviation tax may be another reason for the observed outcome. There is a risk that the policy is not considered effective enough. This argument is related to Krenek and Schratzenstaller (2016) who conclude the carbon-based flight ticket tax on an EU-level to be the most efficient. The Swedish aviation tax is not carbon-based and is implemented on a national level, thus there are reasons to believe the tax not being powerful enough in order to cause a market reaction.

Gilligan and Krehbiel (1988) raise concern towards the tax events to possibly be foreseen. When market actors have updated information about the directions of the policy, one can suspect significant abnormal returns to be less likely. With the direction of the aviation tax being possibly foreseen, this is another aspect probable to affect the weak outcome.

Moreover, Veldhuis and Zuidberg (2009) claim the flight carriers to be negatively affected by an aviation tax due to it being taken up as an increased cost or resulting in a demand decline. The lack of reaction may therefore indicate the market to not expect a revenue loss in the future due to these aspects. Despite the market’s belief, the actual outcome turned out differently in terms of the number of air passengers travelling. The results show a decrease in domestic and European air travellers but an increase in international non-European air passengers.

The reduction in domestic and European air travellers can possibly be explained by a price increase2 followed by the aviation tax. When considering domestic travels there are other means of transport accessible. The same applies to European destinations, although the distance is longer. It is thus easier for people to change to other options when going to domestic or European locations explaining the decrease to these destinations. Yet, the longer the distance, the harder it is to

2 When comparing price indices of air travel tickets conducted by Statistics Sweden month by month between 2017 and 2018 (April to December) there is also reason to believe a price increase to have taken place. The price indices consist of one domestic index and one international index.

23 exchange the aviation to other means of transport. This can thus explain the absence of decrease in the number of air travellers to international destinations. However, the suggested increase is harder to motivate as a consequence of the aviation tax.

When explaining the results in terms of price elasticity, it is possible to suspect the domestic and European travellers to be more price sensitive than travellers to international non-European destinations. In accordance with Brons et al. (2002), this is due to the flight distance being shorter and the existence of several substitutes such as other means of transports for the domestic and European routes. More price sensitive passenger groups along with a price increase are likely to explain the decrease in air passengers to destinations within Sweden and Europe, as the results suggest. Routes outside of Europe on the contrary involve a longer distance and have less substitutes. This means, as Brons et al. (2002) state, that the price sensitivity of the travellers is lower than for shorter distances. Hence, the flight carrier is able to mark-up the price of the flight ticket with demand being less affected. To not observe a decrease in air passengers travelling to international non-European destinations is therefore not as startling as compared to domestic and European destinations. However, an increase is still not expected. A possible explanation to the increase may be the exchange rate of the Swedish krona. Until now, solely the price of the flight ticket has been considered. Travellers may however base their price sensitivity on accounting for the costs of the whole trip, including accommodation and food expenses. During 2018, the value of the Swedish krona weakened more than previous years and hence the travel expenses in the euro area became higher (Ohlin, 2018). This may have created greater incentive for the leisure passengers to travel to countries outside of Europe with a currency being even weaker.

Relevant when discussing the concept of price elasticity is the tax amount being paid in the different tax regions. The amount per passenger is less in absolute value for domestic and European destinations compared to international non-European locations. For the latter, the tax amount is more than four times greater. A decrease in the number of passengers is only visible for the domestic and European destinations, which is expected for the short distance flights due to the higher price sensitivity of these travellers. Due to the high tax amount a decrease is also expected for the international non-European travellers despite them having a lower price sensitivity. Yet, this is not the case and this illustrates the power of a relatively small tax amount to change the

24 behaviour when considering domestic and European passengers. On the contrary, an even larger amount is required to observe a decrease in the number of international non-European travellers.

There may however be other factors causing the changed behaviour in travel patterns. Such aspects are important to consider in order to determine whether the outcome is due to an increase in the flight ticket or due to other factors. The concept of “flight shame” where a person is ashamed to fly due to the great climatic impact of the aviation may be such example. Instead of an increased ticket price being the reason why consumers select other means of travels, environmental considerations may be the main cause. This can possibly explain the decline in domestic and European air travellers. Again, an exchange of the aviation to more environmental means of transport is more easily performed when considering closer destinations such as locations within Sweden or possibly within Europe rather than to international locations. Yet, the increase in air travellers to international destinations is still questionable in a “flight shame” context. For a person who is ashamed to fly, it is reasonable to believe that she rather reduces her flying than the opposite, despite having less transport means available.

So far, the focus of the discussion has been directed to explain the observed results. Nevertheless, it is also important to highlight aspects that may have affected the magnitude of the outcome. One such circumstance stems from the possibility of people flying from airports outside of Sweden mentioned by Gordijn (2010). This is likely to have caused less travellers flying from Sweden, but not necessarily less Swedish air passengers flying in total. For instance, this happened when the Dutch aviation tax was put into practice. Dutch air passengers chose airports located abroad but near the national border (Gordijn, 2010). There is thus reason to believe that the aviation tax is not as powerful when including the results of the Swedish air passengers travelling from abroad. Yet, to fully confirm this one would have to investigate whether an increase in the number of air passengers travelling from airports located near Swedish borders can be noticed. With this not being the aim, one can still highlight the potential effect on the above presented results.

25 8. Conclusion

This paper finds no significant market reaction related to the implementation of the aviation tax, indicating the policy to not be expected to affect the concerned flight carriers. The method for evaluating this is extensive with several event dates, models, indices and estimation periods and should provide good sensitivity in detecting a potential reaction. Further, significant changes in the number of air passengers after implementation is found in this analysis, indicating a changed transport pattern. The aviation tax can possibly explain the decrease in air passengers travelling to domestic and European destinations. The result can further be reinforced by other aspects such as environmental considerations. Yet, the increase in air passengers travelling to international non- European destinations is more difficult to explain in a tax policy context but can be understood when considering a weak currency.

Finally, the lack of market reaction and the increase in air passengers travelling to international destinations may indicate an insufficiently designed tax. This provides incentive for the policymakers to improve the formation. For instance, raising the amount directed at international non-European air passengers may be favourable to see a decline and possibly a market reaction. Problematizing around the optimal design of the tax is advantageous to achieve the best possible outcome, yet this is beyond the scope of this analysis.

26 References

Bermúdez, J., Segura, J. & Vercher, E. (2007). Holt–Winters Forecasting: An alternative formulation applied to UK air passenger data. Journal of Applied Statistics, vol. 34, no. 9, pp. 1075-1090, Available Online: https://www.researchgate.net/publication/24083728_Holt- Winters_Forecasting_An_Alternative_Formulation_Applied_to_UK_Air_Passenger_Data [Accessed 15 April 2019]

Brons, M., Pels, E., Nijkamp, P., & Rietveld, P. (2002). Price Elasticities of Demand for Passenger Air Travel: A meta-analysis. Journal of Air Transport Management, vol. 8, no. 3, pp.165-175, Available Online: https://www.sciencedirect.com/science/article/pii/S0969699701000503 [Accessed 15 April 2019]

Brown, S.J. & Warner, J.B. (1985). The Case of Event Studies, Journal of Financial Economics, vol. 14, no. 1, pp. 3-31, Available Online: https://ac-els-cdn-com.ludwig.lub.lu.se/0304405X8590042X/1-s2.0-0304405X8590042X- main.pdf?_tid=fa21eadc-d03b-4aa8-ac6d- 9fd9f6213185&acdnat=1549187008_9dfbbbf8d1ab0daaf4a8ea0ea95eae32 [Accessed 26 March 2019]

CE Delft. (2018). A Study on Aviation Ticket Taxes [pdf] Available Online: https://www.transportenvironment.org/sites/te/files/publications/2018_12_CE_Delft_7L14_A_st udy_on_aviation_ticket_taxes_DEF.pdf [Accessed 29 March 2019]

Da Veiga, C.P., Da Veiga, C.R.P., Catapan, A., Tortato, U., & Da Silva, W. (2014). Demand Forecasting in Food Retail: A comparison between the Holt Winters and ARIMA models, Wseas Transactions on Business and Economics, vol. 11, pp. 608-613, Available Online: http://www.wseas.org/multimedia/journals/economics/2014/a085707-276.pdf [Accessed 1 April 2019]

27 Dutta, A. (2014). Parametric and Nonparametric Event Study Tests: A review, International Business Research, vol. 7, no. 12, pp. 136-141, Available Online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.654.8572&rep=rep1&type=pdf [Accessed 9 April 2019]

Ekeström, M. & Lokrantz, M. (2019). Första Halvåret med Flygskatt [pdf] Transportstyrelsen, Available Online: https://www.transportstyrelsen.se/globalassets/global/publikationer/luftfart/forsta-halvaret-med- flygskatt.pdf?fbclid=IwAR1rOjOn2qG_9qQhvtXRXl83K6ZVXY1eUg8KotCd8JcjpnP3_dBEDu qJTUw [Accessed 1 April 2019]

Euronext. (2019). We are Euronext, Available Online: https://www.euronext.com/en/we-are- euronext [Accessed 15 April 2019]

Finansdepartementet. (2014). Budgetpropositionen för 2015 (Prop. 2014/15:1), Stockholm: Government of Sweden, Available Online: https://data.riksdagen.se/fil/097F06EC-4082-48C5- 8DFB-182ADB442FB9 [Accessed 28 March 2019]

Finansdepartementet. (2015). Kommittédirektiv: Skatt på flygresor (Dir. 2015:106), Stockholm: Government of Sweden, Available Online: https://www.regeringen.se/4aafa4/contentassets/98b8f05099b04c58935dc3ac52c223bd/skatt-pa- flygresor-dir.-2015106 [Accessed 8 April 2019]

Finansdepartementet. (2017a). Lagådsremiss: Skatt på flygresor, Stockholm: Government of Sweden, Available Online: https://www.regeringen.se/49c8c5/contentassets/1e6ea0359b8b4fa8a265ff4387d8eb57/skatt-pa- flygresor.pdf [Accessed 8 April 2019]

Finansdepartementet. (2017b). Budgetpropositionen för 2018 (Prop. 2017/18:1), Stockholm: Government of Sweden, Available Online:

28 https://www.regeringen.se/4a6e13/contentassets/79f6d27416794f0bb146c792e02b65fc/budgetpr opositionen-for-2018-hela-dokumentet-prop.-2017181.pdf [Accessed 28 March 2019]

Gilligan, T.W. & Krehbiel, K. (1988). Complex Rules and Congressional Outcomes: An event study of energy tax legislation, The Journal of Politics, vol. 50, no. 3, pp. 625-654, Available Online: https://www.jstor.org/stable/pdf/2131461.pdf?casa_token=eK2c9kk_Mf4AAAAA:aYwDLRdur A0pdkp_bEWxsuCN5YNmwfhilPm04B7BEjfzb7fOCQUt8xTchTQqD9LhM- 2p3zyQJzlrRRHh4OFuNbi8rywdfVA2NM3X7e6J3Ua3AuWkJfnY [Accessed 1 April 2019]

Gordijn, H. (2010). The Dutch Aviation Tax: Lessons for Germany?, Netherlands Institute for Transport Policy Analysis [pdf] Available Online: https://www.infraday.tu- berlin.de/fileadmin/fg280/veranstaltungen/infraday/conference_2010/papers_presentations/paper- --gordijn.pdf [Accessed 2 April 2019]

Grubb, H. & Mason, A. (2001). Long Lead-time Forecasting of UK Air Passengers by Holt– Winters Methods with Damped Trend. International Journal of Forecasting, vol. 17, no. 1, pp. 71-82, Available Online: https://www.sciencedirect.com/science/article/pii/S0169207000000534 [Accessed 15 April 2019]

Hyndman, R.J. & Athanasopoulos, G. (2018). Forecasting: Principles and practice, [e-book] 2nd edn, OTexts: Melbourne, Australia. Available Online: https://otexts.com/fpp2/holt-winters.html [Accessed 10 April 2019]

Intergovernmental Panel on Climate Change. (2014). Climate Change 2014 Mitigation of Climate Change: Contribution of working group III to the fifth assessment report of the Intergovernmental Panel on Climate Change [pdf] Available Online: https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_full.pdf [Accessed 16 April 2019]

29 International Air Transport Association. (2019). “Green” Taxes [pdf] Available Online: https://www.iata.org/policy/Documents/environmental_tax_update.pdf [Accessed 3 May 2019]

Konkurrensverket. (2018). Konkurrensen i Sverige 2018 [pdf], Available Online: http://www.konkurrensverket.se/globalassets/publikationer/rapporter/rapport_2018-1_kap25- langvaga-persontransporter-med-flyg-och-tag.pdf [Accessed 8 April 2019]

Kothari, S.P. & Warner, J.B. (2007). Econometrics of Event Studies, Handbook of Corporate Finance, vol. 1, chap. 1, pp. 4-32, Available Online: http://www.jufinance.com/dba/event_study_chapter_1_2008_vol_1.pdf [Accessed 16 April 2019]

Krenek, A. & Schratzenstaller, M. (2016). Sustainability-Oriented EU Taxes: The example of a European carbon-based flight ticket tax, working paper, no.1, Available Online: http://ec.europa.eu/budget/mff/Library/hlgor/selected-readings/41-Carbon-basedFlightTicketTax- Schratzenstalle.pdf [Accessed 1 April 2019]

Kumar, S., Mahadevan, A. & Gunasekar, S. (2012). Market Reaction to Dividend Announcement: An empirical study using event study technique, Prestige International Journal of Management & IT- Sanchayan, vol. 1, no. 1, pp. 141-153, Available Online: http://pjitm.com/doc/papers/paper-9.pdf [Accessed 16 April 2019]

MacKinley, C. (1997). Event Studies in Economics and Finance, Journal of Economic Literature, vol. 35, pp. 13–39, Available Online: https://pdfs.semanticscholar.org/aac6/83a678a12a3dcd73389aac7289868847ea73.pdf [Accessed 15 April 2019]

Minitab. (2015). Choosing Between a Nonparametric Test and a Parametric Test, web blog post, Available Online: https://blog.minitab.com/blog/adventures-in-statistics-2/choosing-between-a- nonparametric-test-and-a-parametric-test [Accessed 27 April 2019]

30 Näringsdepartementet. (2017). En Svensk Flygstrategi - för flygets roll i framtidens transportsystem (N2017.2). Stockholm: Ministry of Enterprise and Innovation, Government of Sweden, Available Online: https://www.regeringen.se/49005b/contentassets/661596dee93f47f397f45dfb59bb9171/en- svensk-flygstrategi---for-flygets-roll-i-framtidens-transportsystem.pdf [Accessed 27 March 2019]

Officer, R.R. (1972). The Distribution of Stock Returns, Journal of the American Statistical Association, vol. 67, no. 340, pp. 807-812, Available Online: https://www.jstor.org/stable/pdf/2284641.pdf?refreqid=excelsior%3Aee4f7c1130686524304a370 2c4f4ab6e [Accessed 5 May 2019]

Ohlin, J. (2018). Därför är den Svenska Kronan så Svag, Available Online: https://www.svt.se/nyheter/ekonomi/darfor-ar-den-svenska-kronan-sa-svag [Accessed 26 April 2019]

Schimmer, M., Levchenko, A., & Müller, S. (2015a). Expected Return Models, Available Online: https://www.eventstudytools.com/expected-return-models [Accessed 14 April 2019]

Schimmer, M., Levchenko, A., & Müller, S. (2015b). Significance Tests for Event Studies, Available Online: https://www.eventstudytools.com/significance-tests#t-test [Accessed 3 March 2019]

Schweitzer, R. (1989). How Do Stock Returns React to Special Events?, Business Review, Issue Jul, pp. 17-29, Available Online: https://www.phil.frb.org/-/media/research-and-data/publications/business- review/1989/brja89rs.pdf [Accessed 5 May 2019]

Seiler, M.J. (2004). Performing Financial Studies: A methodological cookbook, Upper Saddle River: Prentice Hall Regents

31 SFS 2017:1200, Lag om skatt på flygresor, Stockholm: Ministry of Finance, Government of Sweden

Sveriges Riksdag. (2017). Statens Budget 2018 Rambeslutet, Available Online: https://www.riksdagen.se/sv/dokument-lagar/arende/betankande/statens-budget-2018- rambeslutet_H501FiU1 [Accessed 1 April 2019]

Utredningen om Skatt på Flygresor. (2016). En Svensk Flygskatt (SOU 2016:83), Stockholm: Ministry of Finance, Government of Sweden, Available Online: https://www.regeringen.se/4ae35b/contentassets/34d1f308247b4718b85ee4cb9ec49e4a/2016en- svensk-flygskatt-sou-201683 [Accessed 28 March 2019]

Veldhuis, J. & Zuidberg, J. (2009). The Implications of the Irish Air Travel Tax [pdf] Available Online: http://www.seo.nl/uploads/media/2009-77_The_Implications_of_the_Irish_Air_Travel_Tax.pdf [Accessed 5 May 2019]

32 Appendix

Table A1 - List of included airports

Arvidsjaur airport Linköping airport Sundsvall Timrå airport Borlänge airport Luleå airport Härjedalen Sveg airport Gällivare airport Lycksele airport Torsby airport Göteborg Landvetter airport Malmö Sturup airport Trollhättan Vänersborg airport Hagfors airport Mora-Siljan airport Umeå airport Halmstad airport Norrköping airport Vilhelmina airport Hemavan Tärnaby airport Pajala airport Visby airport Jönköping airport Ronneby airport Växjö Småland airport Kalmar airport Skellefteå airport Åre Östersund airport Karlstad airport Stockholm Arlanda airport Ängelholm airport Kiruna airport Stockholm Bromma airport Örebro airport Kramfors Sollefteå airport Stockholm Skavsta airport Örnsköldsvik airport Kristianstad Österlen airport Stockholm Västerås airport

Table A2 – News reporting during 2017- the number of articles in the Swedish Daily News when using “Aviation tax” as search word

Month Number of articles in the Swedish Daily News January 4 February 7 March 19 April 7 May 2 June 13 July 8 August 18 September 15 October 8 November 1 December 1

33 Table A3 - Complete results of the market model using OMXS30 as market index Flight Event Est. AR on CAR +/-5 CAR +/- 10 CAR +/- 20 carrier date period event date Wilcoxon Wilcoxon Wilcoxon T-stat T-stat T-stat T-stat S-R test S-R test S-R test 1 Mar A 0.364 -0.390 24 -0.347 96 -0.285 425 2017 B 0.341 -0.357 24 -0.316 97 -0.001 428 8 Jun A 1.69 1.19 22 1.43 84 1.45 323 2017 B 1.64 1.17 22 1.40 83 1.44 320 20 A -0.864 1.03 20 1.60 63 2.14* 228* SAS Sep 2017 B -0.833 1.01 20 1.56 63 2.09* 228* 22 A 0.630 0.878 16 -0.103 89 -0.716 424 Nov B 0.631 0.872 16 -0.632 88 -0.654 425 2017 1 Apr A -0.726 -0.0320 33 0.412 110 0.254 418 2018 B -0.676 -0.0162 32 0.432 108 0.275 422 1 Mar A -0.888 -0.502 18 -0.571 112 -0.863 367 2017 B -1.04 -0.0127 22 -0.536 115 -0.828 370 8 Jun A -0.888 -0.502 32 -0.571 110 -0.249 387 2017 B -0.946 0.142 32 0.262 111 -0.126 390 20 A -1.07 1.05 27 1.43 93 0.670 409 Norwegian Sep 2017 B -1.08 1.04 27 1.37 94 0.679 408 22 A 1.02 -0.295 29 -1.60 93 -1.81 307 Nov 2017 B 0.792 -0.311 28 -1.80 70 -1.85 296 1 Apr A 0.671 0.370 27 4.28* 101 3.17* 408 2018 B 0.725 0.486 28 4.51* 99 3.33* 413 *Indicates significance on a 5%-level

34 40%

35%

30%

25%

20% Market model using OMXS30 15% estimation period A

10% Market model using OMXS30 estimation period B 5%

0%

Figure A4 - Cumulative abnormal returns of SAS, 20 September 2017, using the market model with OMXS30 as market index 60% 55% 50% Market model using OMXS30 45% estimation period A, (+/- 20) 40% Market model using OMXS30 35% estimation period B, (+/-20) 30% Market model using OMXS30 25% estimation period A (+/-10) 20% Market model using OMXS30 estimation period B (+/-10) 15% 10% 5% 0% -5% -10%

Figure A5 - Cumulative abnormal returns of Norwegian, 1 April 2018, using the market model with OMXS30 as market index

35 Table A6 - Complete results of the market model using Euronext as market index Flight Event Est. AR on CAR +/-5 CAR +/- 10 CAR +/- 20 carrier date period event date Wilcoxon Wilcoxon Wilcoxon T-stat T-stat T-stat T-stat S-R test S-R test S-R test 1 Mar A 0.323 -0.504 24 -0.364 98 -0.121 416 2017 B 0.294 -0.476 24 -0.338 98 -0.104 417 8 Jun A 1.777 1.20 24 1.51 84 1.56 314 2017 B 1.72 1.17 24 1.48 84 1.54 313 20 A -0.827 1.17 18 1.72 61 2.20* 229* SAS Sep 2017 B -0.793 1.13 18 1.66 60 2.14* 227* 22 A 0.401 0.741 20 -0.244 95 -0.793 410 Nov 2017 B 0.394 0.725 19 -0.210 92 -0.737 416 1 Apr A -1.09 -0.154 28 0.159 107 0.186 415 2018 B -1.04 -0.145 28 0.167 107 0.196 414 1 Mar A -0.995 -0.618 19 -0.603 110 -0.985 351 2017 B -1.12 -0.604 19 -0.581 112 -0.986 349 8 Jun A -0.841 -0.156 30 0.274 108 -0.338 415 2017 B -0.838 -0.865 32 0.356 105 -0.205 429 20 A -1.05 1.19 26 1.52 91 0.724 403 Norwegian Sep 2017 B -1.06 1.25 26 1.56 92 0.781 401 22 A 0.843 -0.425 28 -1.73 75 -2.04* 304 Nov 2017 B 0.807 -0.321 28 -1.84 70 -2.05* 296 1 Apr A 0.594 0.178 31 4.09* 109 3.11* 405 2018 B 0.615 0.205 30 4.24* 108 3.26* 408 *Indicates significance on a 5%-level

36 0% Mean model estimation -5% period A

-10% Market model using Euronext estimation -15% period A

-20% Market model using Euronext estimation period B -25%

-30%

-35%

-40% Figure A7 - Cumulative abnormal returns of Norwegian, 22 November 2017, using the mean model and the market model with Euronext as market index

37 Table A8 - Complete results of the mean model Flight Event Est. AR on CAR +/-5 CAR +/- 10 CAR +/- 20 carrier date period event date Wilcoxon Wilcoxon Wilcoxon T-stat T-stat T-stat T-stat S-R test S-R test S-R test 1 Mar A 0.669 -0.458 27 -0.302 99 0.045 427 2017 B 0.652 -0.409 27 -0.249 106 0.996 410 8 Jun A 1.68 1.05 23 1.40 80 1.31 324 2017 B 1.62 1.04 22 1.38 77 1.31 313 20 A -0.826 1.18 17 1.87 55 2.26* 216* SAS Sep 2017 B -0.784 1.16 16 1.83 54 2.23* 211* 22 A 0.317 0.753 10 -0.380 98 -0.853 406 Nov 2017 B 0.313 0.754 20 -0.324 95 -0.763 409 1 Apr A -1.13 0.000 33 0.202 110 0.274 415 2018 B -1.07 0.0303 33 0.235 112 0.320 423 1 Mar A -0.580 -0.564 17 -0.527 111 -0.128 370 2017 B -0.562 -0.501 21 -0.443 113 -0.660 382 8 Jun A -0.864 -0.271 30 0.192 106 -0.562 401 2017 B -0.846 -0.208 31 0.277 103 -0.445 409 20 A -1.04 1.20 26 1.69 81 0.836 388 Norwegian Sep 2017 B -1.02 1.26 26 1.77 77 0.953 373 22 A 0.737 -0.374 27 -1.82 69 -2.07* 290 Nov 2017 B 0.756 -0.311 21 -1.74 70 -1.95 395 1 Apr A 0.601 0.237 28 4.03* 99 3.07* 423 2018 B 0.619 0.300 32 4.12* 95 3.19* 429 *Indicates significance on a 5%-level

38 Table A9 - Results of the air passenger data using parametric test

European International Month in Domestic air Event date Estimation period air non-European 2018 passengers passengers air passengers T-stat T-stat T-stat April -0.792 -1.26 1.93 May -1.24 -2.01* 0.079 June -2.23* -2.00* 2.04* July -0.124 -1.47 3.05* 1 Apr 2018 C August -0.851 -2.18* 1.81 September -3.11* -3.05* 1.34 October -1.90 -3.58* 2.17* November -2.61* -3.28* 3.23* December -2.80* -2.77* 4.81* *Indicates significance on a 5%-level

Table A10 - Results of the air passenger data using non-parametric test

International non- Domestic air European air European air passengers passengers passengers Event Estimation Wilcoxon S-R Wilcoxon S-R Month Wilcoxon S-R test date period test test 1 Apr Apr to C 0* 0* 0* 2018 Dec *Indicates significance on a 5%-level

39