Household willingness to pay for green energy in Austria : a revealed preference studies

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Household willingness to pay

for green energy in Austria:

A revealed preference studies

Agata Magdalena Furmaniak

Student number: 11373946

Supervisor: Andras Kiss

University of Amsterdam MSc Business Economics Managerial Economics and Strategy

Academic year 2017/2018 15 ECTS

July 2018 Amsterdam

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Statement of Originality

This document is written by Student Agata Magdalena Furmaniak 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.

Statement of Confidentiality

The original version of this document contains confidential information which was excluded from a public view.

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Acknowledgments

This study became a reality thanks to generous support of many people. I would like to express my sincere thanks to all of them.

I would like to thank my dearest partner Stanislaw, for never-ending support and understanding, and who was always by my side with precious advice and valuable remarks. Special thanks to my beloved family and friends, who supported me during this extensive project.

It is a great pleasure to acknowledge my deepest thanks and gratitude to my manager Leon

Makkinga and Pricewise Project team for suggesting the topic of this Master thesis,

professional support during entire project and sharing their extensive knowledge and know-how.

I would like to express my special gratitude to my supervisor Andras Kiss, whose professional guidance and advice helped me to master the research.

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Extract

The paper studied the attitude towards switching process and green energy products. Observed choices from several electricity supply options were the core of the study, which made this paper unique. The methodology allowed to study revealed preferences, unlike modern literature which was based on surveys. Using conditional logit model, the study estimated the willingness-to-pay for green electricity sources and the level of the inertia of switching the electricity supplier. Additionally, based on available individual features, it was possible to detect if any significant differences between personal characteristics exist. (confidential)

Key words:

Willingness to pay, switching costs, household’s inertia, green electricity, renewable, liberalised energy market in Austria,

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Table of Figures

(confidential)

Chart 1. The number of household switched in Austria between 2001 and 2017. Chart 2. The structure of energy mix of the Umweltzeichen-Strom product. (confidential)

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1 Introduction

Thanks to progressing education level people are becoming more aware about their habits. In the XXI century there are expanding groups who run every day to stay fit, eat better quality food and take care of the environment. Or at least that appears to be trendy based on media, Instagram and conversations. It seems easy to fight for the Earth by recycling or avoiding using plastic straws. However, once people are challenged with the option to pay more and become eco-friendly, will they pick up the gauntlet?

The world as we know cannot exist without electricity. Every household in modern societies use it. But do people really care about the source of the electricity in their plugs? When it comes to make a real decision, are they willing to pay extra to increase the usage of renewable energy sources in their town, country or region?

Several papers approached these questions for European countries and suggested positive willingness-to-pay for green products (Zorić 2012, Gossling 2004, Bigerna and Polinori 2014, Bollino 2009). However, despite their valuable insights, the issue needs further development and investigation. This research, using a large sample of households from Austria, adds new arguments to the recent discussion about green electricity. As one of the few modern papers it will estimate switching costs based on observed choices from several electricity supply options. Moreover, using conditional logit model the study will estimate the willingness-to-pay for green electricity sources.

(confidential)

The study highlights differences toward market liberalizations and renewable resources between individual characteristics. It can give valuable insights for creating effective marketing campaigns which will attract more customers to the free choice of green products. It can also start a discussion what can be done to encourage people who are reluctant to the contribution to the development of green energy in Europe.

This thesis is organised as follows. Firstly, I described an energy market overview and collective idea. Furthermore, I discussed modern literature related to switching costs and decision-making literature. Next, I presented the data structure and methodology of getting the data. Then I presented summary statistics, conditional logit model structure and

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estimations with findings and its limitations. At the end I drew conclusions with suggestions for additional extension in further research.

2 Switching the energy supplier

This chapter characterises energy market in Europe and Austria after liberalization movement. It describes switching rates tendencies and introduces the energy collective idea. It also explains the idea of a special green energy product and certification which is the essence of this Master thesis investigation.

2.1 Push for the green energy

The promotion of renewable resources for energy production became a fundamental factor for the European Union. It wants to decrease CO2 emission and promote the usage of green energy among suppliers and customers. The Directive from 20091_{set the target that in 2020}

20% of total energy consumption in European Union countries will be produced from renewable resources. The question is how official documents and targets influence the behaviour of an individual customer? Any targets cannot be obtained without an active action of European citizens.

2.2 Energy market liberalization

Historically, electricity customers were served by vertically integrated energy companies, which were producing, distributing and supply energy. Households were assigned to specific energy company based on their address which was called an incumbent company. They could not influence charges which they were paying or change the company. Charges were regulated by energy regulators and governments. It was not possible to influence the contract terms, sources of consumed energy, etc.

To change this state European countries were obliged to liberalize energy market in 2007 by a European Union directive2_{, but many of them opened the market already before. Austria}

did it for households in 20013_{. Since then households are operated by two separated energy}

1_{Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the}

use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC (Text with EEA relevance) [2009] OJ L 140/16

2_{Directive 2009/72/EC of the European Parliament and of the Council of 13 July 2009 concerning common}

rules for the internal market in electricity and repealing Directive 2003/54/EC (Text with EEA relevance) OJ L 211/55

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companies – distributors and suppliers. Distributors are responsible for distribution of the energy, managing energy grids and their charges are regulated by regulators. It is also not possible to change the operator. Suppliers offer energy products and compete on the free market. Households are free to choose which supplier and offer do they prefer.4

The openness to choose promotes a competition among companies. They offer more and more different kind of products, with diverse prices and rules. It led to a decrease of the prices and better terms and conditions, which is a huge advantage for customers. On the contrary, the variety of options and possibilities makes it difficult to make decision. It takes time to find the best offer, compare it and decide which one will be the most suitable. Decision makers might be confused. Switching rates show that most of customers tend to avoid any decisions and remain with the current, incumbent supplier. However, the number of switchers vary across Europe and time.

2.3 Switching rates in Austria

Currently there are around 130 different electricity suppliers and around 30 gas suppliers in Austria,5_{which are available both on nationwide or local level. From the start of liberalization}

in 2001 till 2013 we can observe a gradual growth of switching, with an average of 45 000 switchers per year. The outline can be found at the Chart 1. We can recognize a substantial increase in the number of switchers in 2014. It was caused by the start of the collective campaign organised by a Consumer Organisation Konsument’s. A comprehensive explanation will be provided in the next chapter. Additionally, in 2017 more than 200 000 households switched, 23% more than year before and more than any other year before. The total number of switchers exceeded 1.2 million households, which is around 30% of all households6_.

4_{https://www.e-control.at/consumers/electricity/the-electricity-market/players} 5_{https://www.e-control.at/consumers/electricity/comparing-suppliers}

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4 Source: Based on E-Control data.7

2.4 Collective idea

An energy collective campaign has been created to help consumers to receive a cheaper and fair offer for their electricity and gas supply. The idea is based on the group purchasing power which encourages suppliers to offer better energy products. When households are joint in one big group, they can receive lower prices for their energy and save money from the home budget. Collective participants do not negotiate their terms separately with suppliers but by using a market power of the group. Another important advantage is a guarantee to achieve fair terms and conditions without hidden costs or high termination fee. Hidden information is a problem accruing at dozens of free-markets offers. Besides that, it is also a decent way to improve the local energy market by lowering the prices per kWh and boost the competition. The initiative spread around Europe through consumer organisations in last few years. Many successful campaigns have been organised by BEUC (The European Consumer Organisation) and its members such as Consumentenbond in Netherlands, ZPS in Slovenia, Konsument in

7_{https://www.e-control.at/statistik/strom/marktstatistik/verbraucherverhalten_versorgerwechsel} 5,909 25,350 44,232 31,537 22,768 40,756 60,66554,874 48,245 69,781 60,007 40,540 73,525 159,747 102,571 173,981 215,227 0 50,000 100,000 150,000 200,000 250,000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 N UM BE R O F H O US EH O LDS YEAR

Chart 1. The number of household switched in

Austria between 2001 and 2017

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Austria and others.8_{Thanks to all organised campaigns household in Europe till 2017 saved}

over 271.5 million euros.

Konsument - the Association of Consumer Information (Der Verein für Konsumenteninformation VKI) introduced the collective idea to Austria. They organised four collective energy campaigns between years 2013 and 2017. In 2014, as the result of the campaign, they supported the switching process of over 70 000 households offering a fair offer and high savings. It allowed to save over EUR 12.6 million in total. It substantially influenced the market structure, since it generated over 50% of switches in that year. Following campaigns gathered another couple thousands of switchers, which saved together over EUR 26.6 million. As the result of VKI’s activities, it has been possible to improve Austrian energy market and build a solid group of consumers willing to switch from their old, incumbent energy suppliers.

2.5 Eco-labelled product

Campaigns organised by Konsument focused not only on fair prices and conditions, but also on the source of energy. They promote green electricity among customers. Suppliers which wants to participate in the collective have to agree with a renewable source of energy requirement set by the Organisation. Starting from the collective campaign in 2016, households can choose from two electricity products: green electricity and extra green electricity. The latter one is an offer with a special green Austrian certificate called Umweltzeichen-Strom. It is a bit more expensive offer, yet the supplier must fulfil strict restrictions for creating the electricity mix than in the standard green electricity structure. The requirements are as follow: the energy must be produced of in the maximum of 79% from hydropower, at least 1.5 % from photovoltaics9_{. In addition, at least 10% of the energy has to}

come from a new build plants, which is a contribution into further development of renewable source. The structure of the energy mix has been presented in the Chart 2.

8_{http://www.beuc.eu/publications/beuc-x-2017-074_collective_energy_switch_factsheet_2017.pdf} 9_{https://www.umweltzeichen.at/cms/de/produkte/gruene-energie/idart_2210-content.html}

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Source: Based on https://www.umweltzeichen.at/cms/de/produkte/energie/content.html

2.6 Collective idea evaluation

Collective campaign offers limited choice of products to select from. Customers who participate are fully aware of costs and charges they currently pay and will be charged after the acceptance, based on personal calculations. These features help household to choose a decent product, obtain fair savings and save time while making a decision. They can reduce the time spent on searching and reading complicated terms and conditions. They can be sure that the product offers they got is free of unexpected charges or hidden conditions. On the other hand, customers cannot influence which supplier will win the auction. The limited amount of available offers and retailers can conflict with customers personal preferences, for instance for their local supplier or brand awareness. There are also no bundles available, with telecom or insurances, which might influence the possible savings.

3 Literature review

This section reviews how current research contributed to the analyses of green energy attitudes and studied energy supplier switching behaviour. Several papers estimated the willingness to pay for green energy in different parts of the world. Authors analysed determinants of making a decision to change the supplier, trying to explain switching costs. Furthermore, several papers studied the inertia, which stops customers from changing the supplier and getting cheaper or green energy.

Hydropower, 79% Solar power, 1.5% Other, 19%

Chart 2. The stucture of energy mix of the

Umweltzeichen-Strom product

Hydropower Solar power Other

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3.1 Green electricity literature

The increasing popularity of green electricity and the introduction of law promoting renewable resources have been analysed in several modern papers. The research focused on one hand on the reasons of choosing green products, but also on a willingness to pay (WTP) for green offers of an individual. Several papers described behaviour in few European countries (e.g. Germany, Slovenia, Italy) but also in US.

Watson (2002) investigated factors which encourage and dispirit people to switch their energy supplier in Germany. Around 70 participants were interviewed in 1998 in the City of Bremen. Green energy was of the 5 factors which boosted people to change their contract, yet they were unwilling to pay a significantly high extra premium for this option. However, similar research about factors has been done using data for US small and medium commercial customers by Goett (2000). It showed that they were willing to pay extra premium for renewable resources. Another research from Germany, conducted by Pichert (2007) tried to answer a question why even though green products are getting more available, customers do not actively choose them. He attributed this tendency to the fact that green products are not a default option, but an extra factor people have to decide on. The hypothesis was that if a green option would be default, it would promote environmental friendly decisions. His natural studies and laboratory experiments, handled in the late 90’s, supported this statement.

3.2 Willingness to pay

Another great interest of the literature focused on the household willingness to pay for green products. Students from Germany, who were studied by Gossiling (2004) were open to green electricity offers and they were willing to pay between 0.02 Euro – 30 Euros per month for them. Bigerna and Polinori (2014) studied WTP for green products in Italy to check if the country will be able to reach the European Union target for the usage of green electricity. Data was obtained in 2007. “Median WTP is between 4.62 EUR and 8.05 EUR every two months per household” (Bigerna and Polinori 2014, p. 110) which confirmed there is an observable WTP for green energy. Another Italian research has been done by Bollino (2009) who interviewed around 1600 people in 2006 in Italy and outlined that female have lower mean WTP for green energy than male.

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Correspondingly, Zorić (2012) investigated Slovenian households and their demographic characteristics using data from a field and Internet survey in 2008. 450 respondents were questioned about their WTP for green products. The research displayed that WTP for renewable depends on the education environmental awareness and income. The decision may be negatively influenced by the age. However, gender, location or household size were found non-significant. Around 77% of the participants were willing to pay the median monthly WTP of EUR 4, while the average monthly WTP was from 4.2% to 8.9% of the amount of money they pay monthly for their bill. Borchers (2007) also studied household characteristics to calculate their WTP but differentiated by green energy sources. He detected a lower WTP for solar than for generic green or wind energy. Speaking about demographic characteristics, customer over 50 and above 30 were more attracted to green alternative. Gender and current electric bill were found non-significant.

3.3 Determinants of switch

The discussion about customer attitude towards energy encroaches on environmental issues attributes. Another excessive discussion is targeted on triggers of the decision to change the energy supplier. Determinants of switch were studied in multiple countries, focusing on the question why people do or do not switch.

He and Reiner (2015) used data from a national wide survey to investigate switching behaviour for gas and electricity of customers in the UK. Based on data from around 2,000 randomly chosen people (from a panel of 185 000 participants), they highlighted the lack of knowledge of energy prices and complexity of household tariffs, the perception of high switching costs and simply lack of experience as issues stopping the process of switch. They stated that higher education supports activity on the energy market, but also questioned a relationship between income and switching movements. Ek and Söderholm (2008) added also that when customers can get only small gains they will not change. Furthermore, if customers expect that search costs will be high they will not decide to switch.

Another important aspect of energy market is a customer inertia. People do not necessary consider any new options for buying energy, since they used to purchase it from specific and predefined supplier. Wieringa (2007) highlights that inertia is a major issue in the liberalizing markets. People are familiar with incumbent suppliers while they are not used to change it

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and they do not have experience of doing it. As a determinates of switch Wieringa (2007) stated the relationship quality, switching costs, number of contracts and usage rate.

Hortaçsu (2017) documented customer inertia in Texas. He stated that customers rarely look for alternative option. Even if they do search and compare their current products with new offers, they perceive their current supplier as a better one. This inertia for switching happens to be larger in neighbourhoods with lower income, older citizens with lower education. On the contrary, he argued that household with bigger bills have higher probability of searching for alternatives. What it is also worth to mention, Hortaçsu (2017) research was one of the few studies based on the real household-level data. Dataset came from an online platform Power to Choose, which helps customers to change their suppliers and promotes the switching process in Texas.

3.4 Research gap

The literature about customer’s willingness to pay for a new energy product and switching behaviour in general is very broad. Multiple researches approached this subject for European countries, US and Asia. However, the majority of these studies was based on surveys, where people were facing decisions about purchases, but only theoretically. Respondents could share their preferences, but their decisions were not binding. It gives more insights about their characteristics yet reduces the validity of the outcome accuracy. Pichert (2007) highlighted that 50-90% people are interested in green options, but actual switching behaviour does not follow this preference (Pichert, 2007, p. 64). It might influence the authentication of studies based on survey by increasing willingness to pay for green products and decrease inertia to switching process in general.

There are only a few publications known to the author which used real customer data. However, examinations were focused on switching behaviour, not on renewable energy. Hortaçsu (2017) with his research about Texas, used data from a compare engine for years 2002 - 2006. He was able to calculate electric bills with high precision, due to extensive knowledge about household consumption, retailer and tariffs. Lanot and Vesterberg (2017) got data about real customers with their decisions, yet only from one retailer, which might influence the results.

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Furthermore, the access to the recent databases is highly limited. Most of the studies obtained data from the early years of energy market liberalization. Shortly after the deregulation customers were less aware about switching procedures than now, which is visible in the law number of switchers. Lanot and Vesterberg (2017) used data from years 2010-2012 in Sweden, which is one of the latest datasets on the literature market. In fact, the number of switchers significantly grew within last few years which illustrates that the customer’s attitude towards the switching process developed. Numerous new suppliers entered the market offering different kind of products, bundles and offers. The increasing competition decreased supply charges. Considering all these features, current situation deserves a broader investigation and analyses.

3.5 Contribution

(confidential)

4 Research question

This section will consist of the research question I investigate and hypotheses I intend to verify in my research thesis.

4.1 Research question

The aim of this study is to test how much consumers are willing to pay for environment friendly energy product and how much this willingness varies between different factors. (confidential)

5 Model

In this chapter I will briefly discuss a theoretical foundation of the statistical modelling of theory of choice. I discuss the utility function structure and probabilities formulas. Moreover, I highlight main assumptions and a guidance how to interpret calculated coefficients.

5.1 Theoretical background

To model consumer switching decisions, I used a random utility model developed by McFadden (1973). He stated that a study of choice behaviour can be described by 3 parts: “the objects of choice and sets of alternatives available to decision model, the observed attributes of decision-makers the model of individual choice and behaviour and distribution of behaviour patterns in the population” (McFadden 1973, p. 106).

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11 5.1.1 Utility

The theory says that the individual must choose from one available alternatives, which are different from each other and mutually exclusive. It is possible to derive a level of utility for each alternative. The individual is supposed to choose an option with the highest level of the utility. The utility consists of two parts:

• 𝑉_𝑗 – a systematic component, which is a function of a different observed variables 𝑥𝑗

• 𝜀𝑗 – an unobserved component, which includes the impact of unobserved variables,

which influence the utility level of a specific alternative. The utility of each alternative can be expressed by:

𝑈₁ = 𝛽₁𝑇∗ 𝑥₁+ 𝜀₁ = 𝑉₁+ 𝜀₁ 𝑈₂ = 𝛽₂𝑇∗ 𝑥₂+ 𝜀₂ = 𝑉₂ + 𝜀₂ ⋮ ⋮

𝑈_𝐽 = 𝛽_𝐽𝑇∗ 𝑥_𝐽 + 𝜀_𝐽 = 𝑉_𝐽+ 𝜀_𝐽 5.1.2 Probabilities

For the decision maker utility and choices are deterministic, yet random for the researcher, since some parts of the decision determinates are unobserved (included in the error term). This is the reason why the choice can be only modelled by using probabilities.

The probabilities can be derived from:

𝑃𝑙 =

𝑒𝑉𝑙

∑ 𝑒𝑗 𝑉𝑗

5.1.3 Hypotheses and assumptions

For this research I will use a conditional logit models with alternative specific variables. It is based on 3 hypotheses:

• H1: independence of errors, • H2: Gumbel distribution of errors, • H3: identically distributed errors.

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Moreover, an important assumption is that a probability ratio for two alternatives depends only on their own characteristics and do not depend on any other alternative. This assumption is called a IIA hypothesis – the independence of irrelevant alternatives. This assumption comes from a Hypothesis 1 about independence of the errors.

The probabilities of two alternatives 𝑙 and 𝑚 can be presented as:

𝑃𝑙 = 𝑒𝑉𝑙 ∑ 𝑒_𝑗 𝑉𝑗 𝑃_𝑚 = 𝑒 𝑉𝑚 ∑ 𝑒𝑗 𝑉𝑗

From where we can derive the ratio: 𝑃𝑙

𝑃_𝑚 = 𝑒𝑉𝑙

𝑒𝑉𝑚

5.1.4 Coefficient interpretations

Conditional logit model estimates cannot be directly interpreted. Calculated coefficients have to be transformed. Using a marginal rate of substitution, it is possible to calculate how much money individual are willing to pay (Willingness to Pay – WTP) for the examined goods or services. WTP can be calculated by dividing coefficients of each attributes (for example (𝛽𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒)) by a price coefficient (𝛽𝑝𝑟𝑖𝑐𝑒). To be able to interpret WTP in money terms, price

variable must be in money terms (for instance in euros).

𝑊𝑇𝑃 = 𝛽𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒 𝛽_{𝑝𝑟𝑖𝑐𝑒}

Another transformation is a calculation of marginal effects. One has to derive probabilities with respect to the explanatory variables which are investigated. Individual-specific variables 𝑧_𝑖 can be calculated using following equation:

𝜕𝑃_𝑖𝑗

𝜕𝑧_𝑖 = 𝑃𝑖𝑗 (𝛽𝑗 − ∑ 𝑃𝑖𝑙 𝛽𝑙

𝑙

)

Based on formula we can conclude that the sign of expression in the brackets depends on the average of betas weighted with their probabilities. However, it means that for the greatest

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and the smallest beta the sign of the beta is directly interpretable. For values in between, it is not deductible without additional calculations.

To interpret correctly an alternative-specific variable (𝑥𝑖𝑗) estimates, marginal effect has to

be derive as follows:

𝜕𝑃_𝑖𝑗

𝜕𝑥_𝑖𝑗 = 𝛾𝑃𝑖𝑗

(

1 − 𝑃𝑖𝑗

)

𝜕𝑃_𝑖𝑗

𝜕𝑥_𝑖𝑙 = − 𝛾𝑃𝑖𝑗𝑃𝑖𝑙

The sign of the coefficient can be directly interpretable. To be able to find an upper bound the calculated marginal effect, we can use the rule of thumb and divide the coefficient by 4 (since we multiply the investigated coefficient by the product of two variables probabilities, which is 0.25 at maximum).

6 Data

In this section I will discuss the methodology and dataset. (confidential)

7 Empirical results

This section consists of empirical findings. (confidential)

8 Discussion and conclusion

The paper studied the behaviour of participants of energy collective in Austria in 2016/2017. Subscribers faced the decision if to accept one of two available electricity offers: green product with standard energy mix and extra green mix. The aim of the research was to calculate their willingness to pay for the extra green option and switching costs. Additionally, based on available individual features, it was possible to detect if there are any significant differences between personal characteristics.

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8.1 Future research suggestions

The studied question needs further investigation. However, it is difficult to obtain data about customer choices. Regulators across Europe announce the number of switchers and household quarterly and yearly, yet only in total numbers. They do not announce information about the popularity and accessibility of green products. More detailed and updated information would help to develop modern research. Survey based papers do not cover real decisions.

(confidential)

Due to increasing necessity of using renewable resources, more careful research is needed. On one hand to support energy producers and suppliers so they will understand decision maker behaviour better. But also, for governments and energy authorities to attract customers and encourage them to choose more green products.

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