Power to the People:

An International Analysis of Active Consumer Participation

in Electricity Retail Markets

Hester Huisman

S2718006

15-08-2021

Master’s Thesis

Research Master Economics and Business

Supervisors:

M. Mulder

J.E. Wieringa

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ABSTRACT

The energy transition to renewable energy sources, combined with an increase in electricity demand causes

challenges for regulators to balance the electricity grid. Consumers can play an important role decreasing

the volatility on the grid by becoming active participants. There are three ways in which consumers can

contribute, through self-generation of electricity, participation demand response programs and by active

participation in electricity retail markets. Though the potential of active consumer participation is

promising, the current uptake of this new role remains modest. Hence, policy makers seek how to motivate

consumers take on this new role. Current research is mainly focused on local and national dynamics, which

are hard to generalize. This research takes on an international scope to provide more generalizable insights

in active consumer participation. Through the construction of a unique dataset, I analyze the effects of

various push, pull, and mooring factors on demand response participation and switching behavior in

electricity retail markets. The results highlights the importance to consider mooring factors next to the push

and pull factors and their interactions. Thereby, this research provides a reference analysis for future

research on active consumer participation and with a unique application of the push, pull, mooring

framework for consumer behavior.

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Table of Contents

INTRODUCTION ........................................................................................................................................................ 4

THEORY ....................................................................................................................................................................... 8

LITERATURE REVIEW ............................................................................................................................................... 8 SELF-GENERATION ................................................................................................................................................. 10 DEMAND RESPONSE ................................................................................................................................................ 13 SWITCHING BEHAVIOR ........................................................................................................................................... 18

DATA AND METHODS ........................................................................................................................................... 22

DATA COLLECTION ................................................................................................................................................. 22 VARIABLES .............................................................................................................................................................. 23 METHODS ................................................................................................................................................................ 32

RESULTS .................................................................................................................................................................... 39

DEMAND RESPONSE ................................................................................................................................................ 39 SWITCHING BEHAVIOR ........................................................................................................................................... 46

DISCUSSION ............................................................................................................................................................. 51

THEORETICAL IMPLICATIONS ................................................................................................................................ 53 MANAGERIAL IMPLICATIONS ................................................................................................................................. 54 LIMITATIONS AND FUTURE RESEARCH .................................................................................................................. 54

CONCLUSION ........................................................................................................................................................... 56

REFERENCES ........................................................................................................................................................... 57

APPENDIX ................................................................................................................................................................. 70

APPENDIX I .............................................................................................................................................................. 70 APPENDIX II ............................................................................................................................................................ 78 APPENDIX III ........................................................................................................................................................... 78

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INTRODUCTION

To reduce the emission of greenhouse gases, governments strive to replace the use of fossil fuels by

renewable energy sources. However, generating electricity with these renewable energy sources, such as

wind and solar, is weather dependent and can be difficult to predict in timing and volume. This volatility

of generation causes challenges to manage the balance of the electricity grid. In addition, because of

population growth, electrification of households, and the increased use of electric vehicles, the demand for

electricity is expected to increase by 27.5% from 2015 to 2050 (Mantzos et al., 2019). The increasing

demand requires a more efficient use of energy to reduce greenhouse gas emissions. The volatility of

generating renewable energy combined with high demand result in high costs to manage the electricity grid,

for example because of extensions to prevent congestions. These costs might raise electricity bills for all

consumers. Hence, governments face challenges regarding the energy transition, congestion and balance of

the electricity grid and the growth of electricity demand, while safe-guarding affordability for residential

consumers (hereafter: consumers).

Consumers can play a pivotal role to diminish these barriers and costs of the energy transition (ACER,

2014; Schweiger et al., 2020). Therefore, the European Union aims to empower consumers to actively

participate in electricity markets (ACER & CEER, 2019b). In contrast to the industrial sector, consumers

are able to provide more local solutions for grid management, as many small consumers connected together

to the distribution have a great potential to provide flexibility (ETP SmartGrids, 2010). Active consumer

participation provides therefore a largely untapped potential to make the energy transition more efficient

and to fulfill local-level needs. There are three ways consumers can actively participate in the electricity

market. First, consumers can become prosumers by generating their own electricity, which occurs mainly

through the installation of rooftop solar panels. Second, consumers can participate in demand response

activities, either by shifting the timing of their electricity consumption or by reducing their electricity

demand. Consumers can do so explicitly by adjusting their consumption on request, or implicitly through

automatic systems. Third, consumers can purposely compare electricity retailers and their

contracts/products and switch to offers which better fit their preferences, including preferences of

renewable energy sources. This will enable a competitive market where products better reflect consumers’

desires and product prices are competitive (i.e. related to marginal costs). Through these three activities,

consumers can accelerate an affordable transition to renewable electricity sources.

Active consumer participation is a fairly recent phenomenon. For decades, the electricity system was a

vertically integrated system in most European countries and elsewhere, comparable to other network

industries such as water and telecommunication at that time. This came to an end during the liberalization

restructuring and partly privatization of the sector during the 1990s and 2000s. Currently, competition

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among electricity retailers gives consumers the ability to switch between suppliers while new innovations

such as smart meters, heat pumps, and solar panels provide more opportunities for consumers to actively

participate. Still, customer inertia is one of the four largest market barriers in retail energy markets in Europe

(Lewis et al., 2021). Many consumers do not take on the new participative role to the extent desired by

regulators. Though the number of residential solar panels has increased in the recent years, the potential

could be amplified with 37% annually (IEA, 2020). Furthermore, Europe exploits only 21 gigawatt of

demand response capacity while the theoretical potential comprises 130 gigawatt (COWI, 2016). Moreover,

electricity supplier switching rates differ from 0% to 24% between EU member states, with more than half

below 10% (CEER, 2019). Hence, the desired active consumer participation is modest with a large potential

to increase.

Consumers need to be motivated to become active participants. According to expected-utility theory,

consumers are rational and utility-maximizing. This does not imply that consumers explicitly weight the

benefits and losses of alternative choices, but that consumers behave according to their preferences. Hence,

for consumers to actively participate in the electricity market, the net utility needs to be positive. Electricity

services can be seen as a relatively homogenous good (Bye & Hope, 2005; Joskow & Tirole, 2006).

Therefore, consumers are expected to optimize their utility by seeking low prices (Defeuilley, 2009; Watson

et al., 2002), which would lead to the so-called “law of one price” 1. Clearly, this is not the case for the retail

electricity markets as prices vary between suppliers. This variation in price can suggest heterogeneity

between electricity offers, for example due to services offered by different suppliers. However, this is not

the only reason the law of one price does not hold, as already suggested sixty years ago by Stigler (1961,

p. 214): “... [While] a portion of the observed dispersion is presumably attributable to such difference[s]...it

would be metaphysical, and fruitless, to assert that all dispersion is due to heterogeneity”. Perfectly

homogenous products can differentiate due to costs associated with switching suppliers, creating a lock-in

effect (Klemperer, 1987). The consequence is that consumers do not switch to cheaper alternatives (Farrell

& Klemperer, 2007).

Residential electricity consumers are much more susceptible to cost affecting their net utility than large

industrial electricity consumers. Because of the size and the skills of the large industrial electricity

consumers, they face much lower transaction costs and market barriers. This makes consumer participation

more difficult to motivate and to predict. Various studies are based on pilot projects or small-scale

experiments (A. Palm & Lantz, 2020; Stromback et al., 2011; Yang et al., 2016), which are hard to

generalize to other contexts. Even larger studies are often in the context of only one country (e.g. Bengart

1 “The law of one price is an economic concept that states that the price of an identical asset or commodity will have the same price globally, regardless of location, when certain factors are considered” (Investopedia, 2021).

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& Vogt, 2021; Deller et al., 2021). Even country specific contexts reduce the generalizability of these

studies as consumer behavior in electricity markets can be affected cultural factors and national policies

and infrastructures. Different types of policies may complement each other and are often an element of a

comprehensive policy package (Petrakis et al., 2005; van Houwelingen & van Raaij, 1989). This makes it

difficult to capture and compare the importance of different types of incentives policy makers in various

countries provide to stimulate active consumer participation. This research aims to provide clarity on the

effects of the presence of different types of incentives and policies on active consumer participation. I

empirically compare the relation between incentives and policies in various European countries on the

uptake of the three types of active participation: self-generation of electricity, demand response flexibility

and switch-rates between suppliers. I will separately analyze the impact of these factors for the three types

of active participation. Furthermore, I will pay special attention to interactions between different incentives.

Thereby, I aim to answer the research question how can consumers be motivated engage in active consumer

participation in European countries?

Policy makers can use push, pull and mooring factors to influence the heterogeneity of products and related

switching costs. The push-pull framework is a dominant paradigm in migration research which suggests

that negative forces push people away from a location and positive forces pull people towards places

(Bogue, 1977, 1996). Thereby, these push and pull factors account for the heterogeneity between suppliers.

The push-pull paradigm is extended to include mooring variables, which explain why people stay at or

migrate from their current place (Moon 1995), and thereby account for the switching costs. Bansal et al.

(2005) translate this push-pull-mooring (PPM) paradigm to consumer behavior switching between service

providers. They describe how the PPM factors explain why people switch to certain service providers or

why they remain with their ‘status quo’. This framework is also used in research on switching behavior in

energy markets (e.g. Ek & Söderholm, 2008; He & Reiner, 2017). This research will use the PPM

framework to explain active consumer participation. The push, pull, and mooring can affect consumers’

decisions to shift consumers towards a specific form of active consumer participation or remain their current

status quo. For example, policy makers can pull consumers to the adoption with solar panels through

subsidies. Hence, policy makers can use these factors to increase the expected utility of consumers for

active consumer participation.

This study will add to the current literature of energy consumer behavior as it contributes to the

understanding of consumer response to the presence of different types of incentives. The better

quantification is achieved through the international scope and the interactions with other incentives for

active consumer participation. Thereby, this research does not only show that context matters, but also how

context matters. This will increase scholar’s understanding when policies are most effective and therefore

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help to explain the variation of the effectiveness found by Delmas et al. (2013) and Abrahamse et al. (2005).

This research will do so with the use of a unique dataset. By combining information from different sources,

including different national regulators, governments and institutions, this research is able to investigate

effects of incentives over time and across countries. This kind of panel data is not often used to examine

active consumer participation. Hence, this research can serve as a reference paper for what type of data to

acquire and how to assess different types of consumer participation. Moreover, the research expands the

PPM framework which has been applied to explain the switch from and towards different service providers,

but not yet from and towards different types of behaviors. Using the PPM framework, the research explores

the extent to which behavioral decisions are based on the costs and benefits of certain behaviors versus the

ease or hinder of the change in behavior. Furthermore, this research focusses on three different types of

active consumer participation. Active consumer participation is more complex than energy conservation or

the simple purchase of an energy efficient product. Complex behavior provides higher barriers to

participation through high information costs and high levels of uncertainty in outcomes. Therefore, findings

on energy conservation and energy efficiency cannot be generalized to active consumer participation. This

research helps to better understand how consumers can be motivated to adopt complex consumer behaviors.

This research is as well relevant to policy makers, as it provides important insights on how to facilitate an

effective and affordable energy transition. Consumers play an important role in the realization of this

transition, hence policy makers need insights on how to motivate consumers’ active participation. This

research helps policy makers to understand the role of different types of incentives to increase active

participation. Global warming is an urgent issue which requires a rapid reduction of greenhouse gas

emissions. As global warming may lead to disastrous effects on the ecosystem on which life depends (Davis

et al., 2010), policy makers do not have the time to experiment with various policies. Therefore, it is

important for policy makers to better understand how institutional contexts affect the effectiveness of

information policies for active consumer participation. Moreover, this research focusses on behavioral

change on a country level, instead of an individual level. This provides insights which better fit with policy

implementation and are more scalable, a problem discussed in implementation science (Al-Ubaydli et al.,

2017; List et al., 2019; Supplee & Metz, 2015).

In the following sections, I will provide the theoretical background and formulate my hypotheses. After, I

will describe my choice of sample and methods to analyze the effects of information policies on active

consumer participation. Next, I will present my findings and discuss the implications of the findings and

the limitations of the research.

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THEORY

Literature review

Research about effects of interventions and policies on energy behavior of consumers mainly concerns

energy efficiency and energy conservation. The literature on energy efficiency describes the “energy-

efficiency gap” as a substantial difference between actual and optimal levels of energy use of various

appliances (Jaffe et al., 2004). The primary causes for this gap are high transaction costs and search costs

(Sanstad et al., 2006). Search costs are often related to the acquisition and processing of information to

make a decision. The costs and benefits of energy efficiency are often unobserved. Therefore, sellers of

energy-efficient technologies with ex post benefits might encounter difficulties to transfer this information

(Howarth & Sanstad, 1995). This way, imperfect information can lead to socially inefficient outcomes,

even in competitive markets with rational consumers (Howarth & Andersson, 1993). Consumers sometimes

find it difficult to process all information to make a rational decision. Friedman (2002) found that model

specification based on bounded rationality better predicts residential electricity use than model specification

based on utility maximization. Because the information can be difficult to process, consumers use heuristics

to make decisions. These heuristics can lead to systematic underinvestment in energy efficiency (Kempton

et al., 1992). This biases can occur because consumers disproportionally weight salient factors of products

over less obvious factors (Yates & Aronson, 1983), making consumers overestimate initial investments

over later benefits (C. Wilson & Dowlatabadi, 2007). Moreover, when information is costly to acquire,

consumers may deliberately make decisions based on incomplete information. This form of bounded

rationality is called “rational inattention” and suggests a rational trade-off consumers make between precise

information and the costs of effort to acquire this information (Sallee, 2014). While search costs are related

to making an informed decision, transaction costs are related to the acquisition of the product. These costs

can be financial costs, such as high initial investment. Transaction costs can also be behavioral costs, such

as the effort to install a product. These transaction costs may form barriers for energy efficient technologies.

Hence, in the energy-efficiency gap, search costs influence the information used for consumer’s expected

utility-optimizing decision while transaction costs concern the costs and benefits of the purchase of a

product.

The literature on energy efficiency and energy conservation provides important insights for active consumer

participation. As the search costs influence the expected utility-maximizing decision of the consumer, they

function as push- and pull effect from and towards specific forms of consumer behavior. For example, the

difficulty to estimate the benefits of solar panels may lead be related to the intention to adopt this form of

active consumer participation. The transaction costs influence whether there are barriers to put this intention

into action, and therefore function as mooring factors. Regardless of the similarities, the insights from the

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literature on energy efficiency cannot directly be translated to active consumer participation. Active

participation is often more complex than a one-time purchase of an energy-efficient product. The

complexity of a behavior affects the diffusion of the adoption of a behavior (Centola & Macy, 2007). More

complex behaviors require more information from various sources to acquire accurate and trustworthy

information (Centola, 2010), while the diffusion of simple behaviors requires information of very few

sources (Banerjee et al., 2013). The complexity of active participation also increases uncertainty for

consumers. The costs and benefits are not transparent to the consumer and therefore future costs and

benefits are often unknown. These uncertain outcomes combined with the irreversible nature of many active

participation decisions (even switching supplier is often bound by annual contracts), can increase the

consumers’ discount rates (Howarth & Sanstad, 1995; Jaffe et al., 2004). This may lead to behavioral

failures due to loss aversion, anchoring, or status quo bias (Shogren & Taylor, 2008). Hence the increased

complexity leads to higher barriers for consumers to participate in active consumer participation.

Policy makers aim to motivate active consumer participation with push, pull and mooring factors. Push

factors move consumers from passive behavior to a more active form of participation. For example, if their

current electricity supply becomes too costly, consumers will look for alternative possibilities. Pull factors

are related to the perceived positive effects of active consumer participation. For example, consumers might

want to generate their own electricity to increase their electricity independence. Mooring factors relate to

whether consumers want to switch to a form of active consumer participation or remain their current status

quo. The mooring factors influence both the decision whether to compare the different options and whether

to make the switch. For example, when different electricity suppliers are easy and cheap to compare,

consumers will be more likely to change their electricity suppliers. Therefore, the mooring factors are

dependent on the expected search costs and transaction costs.

This research focusses on the effect of policies on distinct form of active participation. These policies form

the push, pull or mooring factors for consumers’ decision on participation. The three types of active

consumer participation are affected by different push, pull and mooring factors. The push and pull factors

move consumers towards or away from active consumer participation. The mooring factors influence the

ability and willingness to move to the optimal behavior. This leads to the general conceptual framework as

provided in Figure 1, which will be made specific for the three different forms of participation. In the

remainder of this section I elaborate how the different incentives and policies provide push, pull, and

mooring factors influence the different forms of consumer participation.

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Figure 1. General conceptual framework

Self-generation

Push and pull factors

Active consumer participation through self-generation of electricity mainly occurs by installing rooftop

solar panels on residential buildings. The installation of rooftop solar panels can provide benefits to

consumers such as independence, fulfillment of biospheric and altruistic values or financial benefits.

Likewise to energy efficient products, the installation of rooftop solar panels comes with high initial costs

and uncertain future benefits. The uncertain nature of the benefits make it even more difficult for consumers

to make optimal decisions. Consumers do not value the costs and benefits of solar benefits with equal

weights. Implicit discount rates provide higher value for current costs and benefits than future costs and

benefits. Furthermore, consumers often overweight outcomes that are certain over probable outcomes

(Kahneman & Tversky, 1979). This certainty effect implies that consumers overweight the sure initial costs

of the installation of the rooftop solar panels over the uncertain future gains. Prospect theory shows that

this risk aversion is asymmetrical for losses and gains, as consumers are risk averse regarding gains and

risk seeking regarding losses (Kahneman & Tversky, 1979). In the case of rooftop solar panels, the losses

(initial costs) are certain, but the future gains are uncertain. Policy makers can pull consumers towards

active participation through adopting solar panels by providing subsidies for this initial costs. This removes

a major barrier because of the asymmetrical weights of costs and benefits. Next to the high initial costs, the

generation of electricity through rooftop solar panels depends on the weather, which is uncontrollable and

never certain. Moreover, it is hard to estimate future electricity use as this is dependent on a variety of

factors, such as the efficiency of various appliances. Even if the generation the use of electricity are well

predicted, the benefits of the self-generated electricity depend on the electricity prices, which are as well

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hard to forecast. Governments can reduce this uncertainty and pull consumers towards the adoption of solar

panels is through the implementation of feed-in-tariffs (FiTs). FiTs involve the obligation on the part of an

utility to purchase the consumers’ excess generated electricity, which reduces the uncertainty of future

incomes for consumers. Different implementations of the same support policies lead to significantly

different results in solar panel adoption (Campoccia et al., 2009). In most European countries, the

competitiveness of solar panels depends on some form of government subsidy (Campoccia et al., 2014).

Hence, these support schemes form important pull factor towards the adoption of solar panels.

Solar panels differ from regular electricity, as they can be visible to other consumers. Therefore, consumers

are aware of the active participation of peers and might inspire each other. The social norms in a community

can thereby pull consumers to the adoption of solar panels. The influence through social norms can be

through descriptive norms (perceptions of typical behavior) and injunctive norms (perception of approved

behavior) (Cialdini, 2003). Curtius et al. (2018) found that descriptive norms positively affect the intention

of the adoption of PV panels. Exposure of solar panels in a community sends a positive signal to other

consumers, which reduces the perceived uncertainty to invest in solar panels themselves. Moreover, an

increased descriptive norm makes consumers more aware of the benefits of the adoption of solar panels.

The visibility of solar panel therefore creates an advertisement for neighbors. This visibility of solar panels

can set a standard. Consumers might want to improve their social position by conforming to this standard

and adopt solar panels (Fisher & Price, 1992; Veblen, 1899). Moreover, when more people in a community

adopt solar panels, the observed performance increases and the uncertainty decreases. Therefore, only

observing solar panels already increases confidence, motivation, and peer-to-peer communications among

consumers (Rai & Robinson, 2013). Various empirical studies found an increase in adoption of solar panels

due to these peer effects (Graziano & Gillingham, 2015; Rode & Weber, 2016; Schaffer & Brun, 2015).

Next to the descriptive norms Curtius et al. (2018) also found a positive effect of injunctive norms on the

adoption of solar panels, as consumers who perceive that adopting solar panels is approved by their

neighbors are more likely to do so than consumers who do not perceive this. The adoption of solar panels

can be used to signal self-identity, which is an important aspect of making pro-societal decisions (Axsen &

Kurani, 2012). Public visibility of pro-societal behavior creates a preference for green products among

consumers (Griskevicius et al., 2010). The adoption of solar panels makes consumer appear more pro-

societal rather than pro-self, which can strengthen their social status. Consequently, social norms can pull

consumers towards the adoption of solar panels.

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Mooring factors

The total costs of solar panels does not only include the financial costs, but as well non-monetary costs,

including the cost of acquiring information, uncertainty about performance and opportunity costs (Faiers &

Neame, 2006; Zeithaml, 1988). The installation of solar panels is a complex decision. A consumer needs

to take various factors into consideration, such as the type of roof, the amount of direct sunlight, types of

products and different suppliers. The behavioral costs related to acquiring and processing this information

are high because of the capital intensive nature of the technology (Popp et al., 2011). A survey among

Australian consumers indicated that 85% of respondents require educational assistance to understand the

costs and benefits of the adoption of solar panels (Simpson & Clifton, 2017). Interviews with Swedish

consumers show that non-adopters often find the information on the adoption of solar panels too complex,

while more environmentally engaged consumers have access to a lot of information, but find it difficult to

decide when they have enough and the right information. Often, information is too technical or hard to

compare. Even consumers with strong knowledge in related areas find it difficult to compare installers and

are critical towards problems during the installation process (J. Palm & Eriksson, 2018). Therefore,

consumers might encounter problems to make optimal decisions due to bounded rationality or rational

inattention. Policies aimed at transparency can reduce these information processing costs of consumers, and

thereby make the benefits of subsidies more salient. For example, Noll et al. (2014) found that the provision

of complete information through exiting local networks combined with financial tools was successful to

promote the adoption of solar panels. Hence, policy makers can use transparent information provision as

mooring factor to ease the adoption of solar panels.

The adoption of solar often occurs through local community programs in European countries (Creamer et

al., 2019; Walker et al., 2010). These programs stimulate the adoption of solar panels through endorsement

of experienced peers. This reduces perceived risks of the adoption of solar panels, as peers can provide

information that is directly relevant to the deciding consumer (Mills & Schleich, 2009; Stigler, 1961). When

more people in the community adopt solar panels, the observed performance increases and the uncertainty

decreases. Local initiative do not only affect consumers’ intention to adopt solar panels, but as well their

actual decisions (A. Palm, 2016). Local initiatives reduce the behavioral costs and uncertainties related to

the adoption of solar panels, as they help to provide information on the installation of solar panels and

provide testimonials from peers on the costs and benefits. The inclusion of peers in the organization of these

campaigns creates more trust between the energy provider and the consumers, which is crucial for the

outcomes of the initiative (Walker et al., 2010). Case studies in Sweden indicate that personal connects to

persons within the community raised consumers’ interest in the adoption of solar panels (A. Palm, 2016).

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Thereby, the community feeling of the local initiatives can also strengthen the effect of the social norms in

on the adoption of solar panels.

These push, pull and mooring factors lead to the following conceptual model. Testing of the hypothesis

regarding the adoption of solar panels is out of the scope of this thesis, but might inspire future research:

Figure 2. Conceptual framework for self-generation of electricity

Demand response

Demand response programs aim to motivate consumers to change their electricity consumption according

to the local pressure on the electricity grid. Thereby, consumers can help to decrease demand during peak

load. Darby (2013) describes four types of demand response programs, increasing in levels of consumer

involvement. First, demand reduction can decrease peak load. This can be achieved through electricity

conservation or the use of more energy efficient products. For the consumer, this can reduce their electricity

costs, provide more control of usage and improve electricity security at a household level. Second, static

time of use tariffs encourage consumers to change the timing of using electricity-heavy appliances. This

can either be done manually by the consumer, or automatically by electricity utilities. Consumers need to

decide how much external control and interruptions are acceptable in exchange for certain incentives. This

willingness to shift differs much per appliance. For example, the timing of wet appliances such as

dishwashers and washing machines has less impact on the consumer’s life than the timing of cookers or

computer usage (Owen et al., 2011). Through participation in static tariffs, consumers can benefit from the

price differences. Moreover, shifting the timing of electricity usage may contribute to electricity

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conservation for participating consumers (Biggart & Lutzenhiser, 2007; Stromback et al., 2011). Third,

dynamic tariffs charge consumers electricity prices relative to current spot prices. These tariffs are more

complex than static tariffs and include more risks, which requires more effort from consumers. This

drastically changes the traditional customer-utility relationship as consumers are now exposed to hour-level

electricity wholesale markets. The risks for consumers can be reduced through measures as price caps or

alerts. Lastly, dynamic demand requires the highest level of consumer involvement as it includes real-time

load-balancing and the consumer installs smart appliance that respond to the electricity network. Dynamic

demand can especially be beneficial in contexts where balancing the grid is challenging. Through all four

types of demand response programs consumers can help to balance the electricity grid which results in

avoided reinforcement of the grid next to gaining private financial benefits.

Push- and pull factors

Policy makers can affect the push- and pull factors that lead consumers to decide on their utility-maximizing

behavior. Through price differences, consumers can gain financial benefits from demand response

participation. The influences of prices on electricity demand are quantified by their price elasticities.

According to microeconomic theory of utility maximization and consumer rationality, consumers weight

the expected costs and benefits of different actions and behave in a way which maximizes their utility

(Sanstad & Howarth, 1994) . Demand response programs require constant evaluations of these costs and

benefits. The price elasticity of the change in electricity consumption at the same time as the price change

is the commodity’s own price elasticity, defined by:

𝐸𝑃 =%∆𝑄%∆𝑃

,

where 𝐸𝑃 is the own price elasticity, %∆𝑄 is the change in electricity consumption as a consequence of a

price change %∆𝑃. In case of load shifting, the price can as well be defined through the elasticity of

substitution, as consumers can substitute consumption from peak demand to off-peak demand periods. This

is defined by the negative of the relative change in the ratio of peak to off-peak demand, divided by the

percentage change in the ratio of peak to off-peak price (Gyamfi et al., 2013):

𝐸𝑃!"#! =−%∆*

𝑄$𝑄%+

%∆,𝑃&𝑃%-,

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where 𝐸𝑃!"#! refers to the price elasticity of substitution, %∆,'!'"- to the percentage change in peak to off-

peak price ratio, and %∆,&#&"- to the percentage change in electricity consumption. Pilot studies on these

price elasticities indicates that consumers respond to prices (e.g. Faruqui & Sergici, 2010; Ivanov et al.,

2013; Stromback et al., 2011) but with very small elasticities (e.g. Allcott, 2011; Lijesen, 2007; Torriti,

2012). Though larger differences in prices are expected to result in more changes in consumption,

consumers can as well be influenced by the perceived risks associated with time varying pricing. Perceived

risk and complexity can form a barrier for consumers to participate in demand response programs (Allcott,

2011). Therefore, while some consumers are pulled to demand response participation by higher price

differences to acquire more financial benefits, other consumers prefer smaller price differences or caps on

prices to reduce associated risks (Dütschke & Paetz, 2013).

Another important pull factor of demand response participation is the social norm in a community. Allcott

(2011) shows in his research on energy conservation how descriptive social norms (perceptions of typical

behavior) can have substantial influence on consumers behavior. Social norms are expected to be relevant

for participation in demand response programs because the benefits cannot be fully privatized by the

participant. As demand response helps to lower the pressure on the electricity grid, the entire community

benefits from lower costs when other residents participate. When one household in a community

participates, this cannot significantly lower these costs. However, when many participate, this can

substantially reduce congestion and management issues of the electricity grid (Schuitema et al., 2017).

Therefore, consumers encounter a network externality where the value of individual participation partly

depends on the participation of peers. Moreover, demand response is a complex form of active consumer

participation, which therefore requires more referrals to convince consumers of participation than of simple

behaviors (Centola, 2010). This is why many demand response programs currently are organized as local

initiatives (Parrish et al., 2020). Because of the network externalities and the complexity of the behavior,

social norms can pull consumers towards participation in demand response programs.

Another important push factor which affects consumers’ participation in demand response programs is

availability of the required technology, smart meters. Smart meters are an essential necessity to measure

consumers electricity use and link it to the balance of the electricity grid. Therefore, the European Union’s

Third Energy Market Package and in particular Directive 2009/72/EC provide regulatory incentives of

European countries to plan and execute the rollout of smart meters for residents. The progress of the roll

out of smart meters varies among member states and therefore the incentive to participate. Slow outroll of

smart meters is one of the main barriers to demand response participation in residential sectors in Europe

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(Torriti et al., 2010). Consequently, the lack of smart meters pulls consumers towards passive behavior and

the availability of smart meters pulls consumers towards demand response participation.

In summary, the push and pull factors awareness, social norms and availability of technology all lead to

increased consumer participation in demand response programs. This provides the following hypotheses:

Hypothesis 1: An increase in financial incentives positively influences consumers’ participation in

demand response programs.

Hypothesis 2: An increase in social norms indicating demand response programs positively

influences consumers’ participation in demand response programs.

Hypothesis 3: Increased availability of smart meters positively influences consumers’ participation

in demand response programs.

Mooring factors

The benefits for consumers to participate in demand response programs include cost savings, sustainability,

and community benefits, which have to be weighed against the costs including disruption, upfront financial

costs, or management concerns (Downing, 2009). Consumers need to be aware of the costs and benefits of

the demand response program to make their decision regarding participation. Many consumers lack

knowledge on their electricity use and their potential to participate in demand response programs (Kim &

Shcherbakova, 2011). Information helps to increase awareness and improve intention to participate by

making the costs and benefits of participation more salient. However, too complex information can form a

barrier to switching due to high cognitive costs. This can lead to inertia due to rational inattention as

consumers include the behavioral costs of acquiring perfect information in their evaluation (Zhu, 2013).

This may lead to be sub-optimal satisficing behavior, where consumers settle for sufficiently satisfying

options instead the of the utility-maximizing choice (Kim & Shcherbakova, 2011). The behavioral costs of

information are especially high due to the novelty of the demand response programs. Especially in immature

markets, sufficient and transparent information is needed to encourage the adoption of the behavior among

consumers (Thollander et al., 2010). Moreover, the high costs of information may lead to problems related

to information asymmetry, one of the main barriers to demand response programs (Torriti et al., 2010).

When the electricity provider has access to information and cannot successfully communicate these to the

consumer, the two parties can have different incentives which cannot be easily resolved. Principal-agent

problems may arise as the consumer (principal) may not have the required information to decide on the

extent of flexibility to provide to the electricity provider (agent), which can result in opportunistic behavior

of the electricity provider and does not maximize the utility of the consumer (Good et al., 2017). Policy

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makers can curtail these costs of information by making the information more transparent, and therefore

easier to process. This indicates that information provision functions as mooring factor for the financial

incentives to participate in demand response programs.

A second mooring factor is the level of uncertainty involved with participating in demand response

programs. Especially the level of external control and privacy of electricity consumption data are two major

concerns for consumers (Downing, 2009). Trust is often used as a heuristic in assessing risks, especially

for decisions with complex information and uncertain outcomes (Poortinga & Pidgeon, 2003; Siegrist et

al., 2003). In demand response programs, large amounts of communication of information is required, as

they are based on open or untrusted networks and often encompass many physically distributed devices

from various vendors. Therefore, trust in organizing institutions is crucial for acceptance of demand

response programs (Darby & McKenna, 2012; Stenner et al., 2017). The out roll of smart meters

demonstrates the importance of trust in suppliers and government, as this was easily accepted in Sweden,

while providing controversies in the Netherlands (Renner et al., 2011). Recent studies show how trust can

foster participation through customer relations or privacy data management (McKenna et al., 2012;

Stromback et al., 2011). Consumers can lack trust in the perceived motivations of institutions which

organize the demand response programs, which can be enforced by unfamiliarity with demand response

and its benefits (AECOM, 2011). Familiarity of demand response programs through social norms can be a

double edged sword, as it may cause consumers to be concerned about the enabling technology (Hall et al.,

2016). Trust in organizing institutions can help to enforce the positive effect of the familiarity. Therefore,

trust is expected to influence the switching costs to participation in demand response programs. In particular

by enlarging the effects of social norms on demand response participation

In summary, the transparency of information increases the participation in demand response programs and

helps to more easily process the financial benefits or demand response participation. This leads to the

following two hypotheses:

Hypothesis 4: Transparency of information on demand response programs positively influences

consumers’ participation in demand response programs.

Hypothesis 5: Transparency of information on demand response programs positively influences

the relation between financial incentives and consumers’ participation in demand response

programs.

Moreover, trust in organizing institutions reduces the perceived risks of participation in demand response

programs and therefore increases participation. The trust in institutions is also expected to increase the

influence of familiarity through others’ behaviors. This leads to the following two hypotheses:

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Hypothesis 6: Trust in others positively influences consumers’ participation in demand response

programs,

Hypothesis 7: Trust in others positively influences the relation between social norms of consumers

and consumers’ participation in demand response programs.

The hypotheses on participation in demand response programs are visualized in the conceptual model in

Figure 3.

Figure 3. Conceptual framework for demand response

Switching behavior

There are two ways in which switching behavior can foster the energy transition. First, when consumers

actively switch and search out better electricity deals, it fosters retail competition (Waterson, 2003; C. M.

Wilson & Price, 2010). Without competition, utilities perceive little incentives to provide deals according

to consumer’s preferences. Second, when consumers switch, they can switch to electricity of renewable

energy sources. An increase in demand will provide incentives for electricity suppliers to increase

investment and innovation in these electricity resources. Active searching and switching are different but

related activities (Flores & Waddams Price, 2018; C. M. Wilson, 2012). Searching activities often precede

and cause switching activity, but not necessarily. Consumers can as well search and find that their current

offer is their utility-maximizing offer. Moreover, consumers can switch without search costs because of

information effortlessly received by peers or marketing campaigns.

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Push- and pull effects

Consumers can compare offers on various attributes such as pricing schemes, market share and electricity

source. There is heterogeneity of preferences between different consumer segments (Sundt & Rehdanz,

2015a; Yang et al., 2015). Still, price is most often the most important factor (Bird et al., 2002). However,

price differences are not always sufficient to motivate switching behavior. Research on Swedish consumers

shows that price affects switching behavior for bargain hunters, but not for more passive consumers

(Sturluson, 2002). Hence, the price differences need to be large enough to pull consumers to active

comparison of contracts and switching behavior.

Next to pricing, suppliers of electricity want to attract consumers by differentiating their products on various

attributes. An important attribute is the source of the electricity (Vecchiato & Tempesta, 2015a; Yang et

al., 2016). Consumers can be pushed away from their current electricity supply because of their aversion to

fossil fuels and the environmental impact of the generation of electricity through these sources. Prior

research shows that the willingness to pay (WTP) for electricity from a renewable energy source is higher

than the WTP for electricity form a non-renewable energy source (Batley et al., 2001; Roe et al., 2001;

Sundt & Rehdanz, 2015b; Vecchiato & Tempesta, 2015b). Sustainability has become more relevant to

consumers over the previous years. For example, in the Netherlands “environment and sustainability” has

become the main reason to switch suppliers for 16% of the population in 2021, while 13% and 11% in 2020

and 2019 (van der Grient & Kamphuis, 2021). Though many consumers find renewable energy sources

important, they do not always put their intention into action which creates an intention-behavioral gap

(Gupta & Ogden, Denise, 2006; Hobman & Frederiks, 2014). Because of this gap, only extreme attitudes

are found to affect overt behavior (van Doorn et al., 2007). Hence, the effect of this pull factor depends on

mooring factors.

In summary, price differences and renewable electricity sources are both expected to increase active

participation in electricity retail markets. This lead to the following hypotheses:

Hypotheses 8: Price differences of electricity products positively influence consumers’ active

participation in electricity retail markets.

Hypotheses 9: Renewable energy consumption of electricity products positively influences

consumers’ active participation in electricity retail markets.

Mooring effects

Information provision can help consumers to understand the costs and benefits of switching electricity

contract. However, the access to relevant information can pose a barrier to the switching process due to

high searching and switching costs (Ek & Söderholm, 2008). Searching and switching are related to

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different types of behavioral costs. Searching is related to acquiring information to make the right decision,

while switching is related to transaction costs related to the ease and perceived risks to switch between

contracts. Moreover, consumers face decision making costs where they process information to make a

utility maximizing decision. Research indicates a high level of energy aliteracy among consumers (Blasch

et al., 2019; Brounen et al., 2013), which can lead to suboptimal decisions by consumers. When constraints

on factors as resources, time, attention, or ability to process information are too high, consumers might

make faulty decisions due to bounded rationality or make decisions based on heuristics and biases.

Moreover, when these search costs are perceived too high related to the expected benefits, rational

inattention can lead to consumer inertia. This is especially the case when consumers perceive the products

of suppliers to be very similar (Waterson, 2003). These barriers to switching behavior can be reduced with

the provision of transparent information. Gärling et al. (2008) find that higher quality of information

increases switching behavior between suppliers, particularly when the information concerns the economic

benefits for all consumers. Similarly, Ek & Söderholm (2008) found that knowledge on electricity costs

makes consumers more likely to renegotiate their current electricity contracts. Consequently, transparent

information provision reduces barriers of switching to other suppliers and helps to understand the economic

benefits for consumers.

Next to untransparent information, risk aversion can also impede switching behavior. Consumers can stay

with their current supplier to avoid risks related to switching to a less known supplier (Simonson & Tversky,

1992). This “home bias” exists as consumers have most information on their current supplier and therefore,

ceteris paribus, put more confidence in their current supplier over its competitors (Ek & Söderholm, 2008).

Because of this higher confidence level, consumers’ expected value for the current supplier is higher than

that of less known competitors (e.g., Heath et al., 1991). Likewise, the so-called endowment effect makes

consumers value more money for something they possess than they would pay to obtain it (Thaler, 1980),

which makes consumers even more reluctant to switch. This inertia is reinforced by consumers loss aversion

and the overvaluation of losses over gains (Kahneman & Tversky, 1979). This effect is demonstrated in the

research by Juliusson et al. (2007), as they find that this loss aversion makes consumers more reluctant to

choose contracts with variable tariffs. Survey research on residents in Canada found that prices are the most

important factor to consumers who are active in electricity retail markets to switch, while brand-loyalty is

the most important factor to inactive consumers to stay with their current retailer (Rowlands et al., 2004).

Hence, through loss aversion, brand-loyalty can forma a barrier to the decision to switch to another retailer.

This barrier can provide a home-bias which increases the intention-behavior gap of switching to renewable

electricity sources.

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In summary, information provision reduces behavioral costs and therefore increases active participation in

electricity retail markets. Furthermore, information provision makes it easier for consumers to understand

the costs and benefits of switching retailers. This leads to the following hypotheses:

Hypotheses 10: Information provision of electricity products positively influences consumers’

active participation in electricity retail markets.

Hypotheses 11: Information provision of electricity products positively influences the effect of

financial incentives on consumers’ active participation in electricity retail markets.

Brand loyalty provides a barrier to active consumer participation in electricity retail markets as consumers

want to avoid potential losses. This is also expected to hinder consumers to put their intentions of renewable

electricity contracts into action. This leads to the following hypotheses:

Hypotheses 12: Brand loyalty to electricity retailers negatively influences consumers’ active

participation in electricity retail markets.

Hypotheses 13: Brand loyalty to electricity retailers negatively influences the relation between

renewable energy consumption and consumers’ active participation in electricity retail markets.

The hypotheses on consumers’ participation in electricity retail markets can be visualized in the conceptual

framework of Figure 4.

Figure 4. Conceptual framework for participation in retail markets

In the following sections, I will test these hypothesis for the European electricity market. First, I describe

the data collection process and the sample selection. Next, I explain the construction of the different models

22

and describe the results of the analyses. Lastly, I will discuss the theoretical and practical implications of

the results and the limitations of the current research which provide opportunities for future research.

DATA AND METHODS

Data collection

The dataset is contains information of six European countries from 2012 to 2019. The research is focused

on European countries because these countries have collectively and consistently formulated goals for

active consumer participation. The six selected countries are the United Kingdom, Ireland, the Netherlands,

Germany, Norway and Finland. These countries and the timeframe are selected because of data availability.

The data is collected from secondary data sources, including CEER, ACER, Eurostat, national regulator

authorities (NRAs) and websites of national governments.

The intensity of information provision of countries is measured by an index based on expert judgements. I

sent a request to score the six countries for the years 2012, 2015, and 2019 to 52 industry experts including

21 scholars, 16 TSO employees and 10 NRA employees. The request included an overview of relevant

policies per country, so experts could base their answers on similar information. The request included an

overview of relevant policies per country, so experts could base their answers on similar information.

Appendix I provides an example of the request. Not every expert could provide a score for every country.

Table 1 provides an overview of the number of scores per country, the average score and the average

standard deviation of the three years. Based on these scores and the countries’ policies, I provided a score

for each country and each year from 2012-2019.

Table 1. Response and scores for countries.

Country N Mean Average st. dev

UK 5 6.1 1.38 IR 3 4.8 1.47 NL 3 6.4 1.15 DE 6 4.7 1.77 NO 3 6.0 0.91 FI 3 5.3 1.28

Note: Average st. dev is the average of the three standard deviations of the scores for the three years of each country.

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Variables

Dependent variables

The demand response participation can be measured by uptake of consumers of spot price electricity

contracts. Spot price contracts are contracts whose prices are linked to (near) real-time markets and

therefore have highly variable prices during the day. This can provide incentives for consumers regarding

their usage and timing of appliances which use much electricity. However, few European countries offer

these type of contracts to residential consumers. Within our sample, only Norway and Finland show a

reasonable uptake of spot-price contracts among households, with 16% and 8% respectively in 2015.

Therefore, this proxy cannot be used to measure demand response participation.

Though I cannot measure for most countries to which extent consumers choose a spot price contract, I can

see how many consumers remained at their default electricity contract. This provides an indication of the

active participation of consumers in electricity retail markets as it shows consumers’ interest and preference

for other pricing schemes. When consumers are indifferent on the pricing schemes of their contracts, they

will mainly remain with their default contract. When many consumers switch to other types of contracts,

this shows an interest to compare and switch contracts which a necessity for switching to contracts with

spot prices. Therefore, the rate of consumers who remain with the default tariff will serve as a proxy for

demand response participation.

The switching behavior of consumers is measured by the switch rate, which indicates the percentage of

consumers who switched to a different supplier in a year. This captures the external switch rates, which

indicates the percentage of households who switch between different suppliers. Consumers can as well

switch contracts within the same supplier or renegotiate the terms of their current contract with the same

supplier. These are called internal switching, and are not captured by this measure. The data is retrieved

from annual Retail markets reports of CEER and ACER which provide information on switching rates of

member states for the years 2011, 2012, 2013, 2014, 2016, and 2018 (ACER & CEER, 2019a).

Explanatory variables

The active consumer participation of consumers is affected by financial incentives. These financial

incentives are given by electricity retail prices. High household retail prices make electricity a more

important expense for consumers and provide more incentive to optimize these expenses. Moreover,

differences between household retail prices give more incentive to switch contracts as there is more

opportunity to benefit. However, household retail prices in liberalized markets are influenced by demand

factors, such as switching behavior of consumers. Including retail prices in the model to explain active

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consumer participation could lead to endogeneity problems through reversed causality. Therefore,

instrumental variables are required which capture a similar effect of the retail prices, but are not affected

by active consumer behavior. For that reason, the industry retail price and the day-ahead wholesale price

of a country are used as proxies for the household retail price. All three variables are retrieved from the

website of the European Commission, which provides a dashboard for energy prices in the European Union

and main trading partners (EC, 2019). This dashboard provides the data in monthly averages per country. I

estimated the average annual price by averaging these monthly prices. The prices have been adjusted for

inflation and are expressed in Euro 2018 (constant price) terms per MWh. The data misses prices of the

wholesale market for Great Britain from 2011 up until 2014. Therefore, these prices are retrieved from

Great Britain’s NRA Ofgem (Ofgem, 2021). To match the data of Ofgem with those of the EC, the prices

from Ofgem are converted to euros according to the annual conversion rates from the EC (EC, 2021a) and

inflation corrected to express Euro 2018 prices with inflation rates from Great Britain’s Office of National

Statistics (ONS, 2021).

The opportunity for consumers to optimize their financial benefits depends on the differences in prices in

an electricity retail market. The variation of electricity prices is indicated by the household retail price

range. This based on the prices from EC’s dashboard for energy prices in the European Unition and main

trading partners (EC, 2019), just as for the household retail price. The range is estimated by deducting the

lowest monthly average price from the highest monthly average price in a year for each country. Again,

household retail prices in liberalized markets are influenced by demand factors, such as switching behavior

of consumers. Including the household retail price range in the model to explain active consumer

participation could lead to endogeneity problems through reversed causality. Therefore, instrumental

variables are required which capture a similar effect of the retail prices, but are not affected by active

consumer behavior. Two potential qualified instrumental variables are the industry retail price range and

the day-ahead wholesale price range of a country. These ranges are estimated in the same manner as the

household retail price. The data for these variables is retrieved from the same dashboard for energy prices

Regulators take different measures to reduce the costs and barriers of active consumer participation. For

example through market reforms, making relevant information easier to access or process, or reduce risks

of disconnection. The intensity of these measures is captured by the policy index. This is an index which

ranges from 1 (light-handed regulation) to 10 (heavy-handed regulation). The index is based on the

framework of sector-specific regulation of Mulder (2020, p. 79), as depicted in Figure 4. Light-handed

regulation involves little regulatory effort. For example, the threat of regulatory measures when a party

does not behave according to objectives. Therefore, the regulation might be less effective, but this also

softens the effect when the measures are sub-optimal. Heavy-handed regulation involves high regulatory

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transaction costs, making the regulations more likely to be effective. For example, by intervening in the

decisions of the regulated party. However, this also comes with higher risks when the regulations are sub-

optimal. For this reason, many regulators opt for intermediate regulation, which aims to affect the outcome

of the process and not the decisions of regulated parties (Decker, 2015). The index is created by averaging

the scores of expert judgements, as explained in the data collection section.

Figure 4. Framework for intensity of regulatory intervention

Source: (Mulder, 2020)

The availability of technology is mainly enabled through the rollout of smart meters. This rollout varies in

timing and duration between countries. A benchmark report of the EC provides an overview of the rollout

for EU member states (EC, 2020). Therefore, the smart meter rollout indicates the progress of the roll out

in a certain year. This indicates the progress of the roll out of a country in a certain year in percentages. The

data of the progress is retrieved through online official documents. Sources include the website of the UK

government (Department for Business Energy & Industrial Strategy UK, 2020), the NRA of Norway (NVE,

2016), and the European Commission (EC, 2014). For the countries whose process is unknown, a gradual

process with equal smart meter installations each year is assumed.

The social norms of active consumer participation indicate the uptake of the behavior by other consumers

in the country. As it may take some time for consumers to be influenced by another and copy the behavior,

the effect is measured by the measure of active consumer participation in the prior year. This measure shows

the descriptive social norm, what other consumers do, and not the injunctive social norm, what people are

ought to do. Prior research on the distribution of solar panels shows that the descriptive social norms

indicate that descriptive norms have a stronger influence on consumer behavior (Curtius et al., 2018a).

Therefore, prior uptake of the active consumer behavior is expected to be an adequate proxy the social

norms regarding that behavior. Another way in which the social norms can be evoked is through local

community initiatives. The Joint Research Center of the EC provides an overview of all smart gird projects

in EU member states (Gangale et al., 2017). These projects are filtered on demand response projects. The

26

number of active projects in a country in a year, DR projects, is used to capture the effect of local community

initiatives.

The brand loyalty can provide a barrier for consumers to switch to a different electricity retailer. Brand

loyalty is often measured by the duration consumers stay with their retailer. However, the average duration

is also affected by the switching behavior and may lead to endogeneity problems due to reversed causality.

Therefore, I use two instrumental variables as proxy for brand loyalty, individualism and uncertainty

avoidance. These are two of Hofstede’s six dimensions of national culture (Hofstede, 1980). Individualism

describes the collectivism-individualism spectrum of the extent people feel interdependent or independent

of larger wholes. In individualistic countries, more individual choices and decisions are expected.

Uncertainty avoidance concerns the tolerance of people for uncertainty and ambiguity. It does not reflect

risk aversion, but the distrust in the face of the unknown and the preference for habits and rituals. Lam

(2007) found that consumers who score high on individualism and uncertainty avoidance are more likely

to loyal to their current brands. Therefore, I will use these two measures to capture the effect of brand

loyalty. As culture are rather static over time, the variables are country specific but time-invariant. As both

measures represent the construct brand loyalty, I combined individualism and uncertainty avoidance by

taking the mean of the two measures for each observation.

The level of trust consumers have in the providers of electricity depends on consumers’ trust in institutions.

The most important institutions are the regulators and the suppliers. This data is unfortunately only available

on a cross-sectional level, but not over time. Therefore, I will use two proxies trust in government and trust

in EU institutions. The trust in government is retrieved from the Organisation for Economic Cooperation

and Development (OECD) iLibrary (OECD, 2021). The variable indicates the percentage of respondents

who show confidence in their government by answering “yes” to the survey question: “In this country, do

you have confidence in… national government?”. Other possible responses are “no” and “don’t know”.

According to OECD, the sample of respondents beforehand designed to be nationally representative of the

population aged 15 and over. The data is provided on an annual level for all countries. The trust in EU

institutions is retrieved from the database of the EC (EC, 2021e). The measure is based on the

Eurobarometer, which is a biannual survey on the public opinion in EU Member States since 1973. This

measure is the result from the autumn survey. It is provided for all years except for Norway. It measures

the level of confidence among EU citizens in selected EU institutions, the European Parliament, the

European Commission, and the European Central Bank. The variable indicates the percentage of

respondents who answer “tend to trust” about these institutions. Alternative answers are “tend not to trust”,

“don’t know”, and “no answer”. These two variables represent consumers’ general trust in institutions, and

therefore as well in electricity institutions. As the two variables are expected to represent the same construct,

27

trust in institutions, I performed a factor analysis. I tested the internal validity of the construct by estimating

its Cronbach’s alpha. The Cronbach’s alpha has a value of 0.73, which is larger than the threshold value of

0.7 (Malhotra, 2009). This indicates sufficient internal validity. Next, I examined the unidimensionality of

the construct through an explorative factor analysis, which explores the number of dimensions on a scale.

The results show eigenvalues of 1.60 and 0.39. As only one eigenvalue is larger than 1, the construct is

considered unidimensional (Malhotra, 2009). A principle component analysis provides more insights in the

dimensions of the constructs as it shows the degree to which an item contributes to a dimension. The results

show that Trust in government and Trust in EU institutions both sufficiently contribute to the construct, as

the factor loadings of 0.89 and 0.89 surpass the threshold of |0.40| (Malhotra, 2009). Therefore, a new

construct Trust in institutions is created which indicates consumers’ overall trust in institutions. The

construct is created by estimating the mean of trust in government and trust in EU institutions for each

observation. As the construct trust in EU Institutions does not provide data for Norway, the values of the

trust in Government construct are used for these observations.

The extent to which consumers prefer renewable energy contracts can be derived from consumers’ revealed

behavior. In liberalized markets, a higher demand for electricity from renewable energy sources should lead

to a higher uptake of electricity contracts from renewable or “green” electricity contracts. Therefore, green

electricity share is measured by the share of renewable energy sources in the country’s electricity

consumption. The share of renewables in electricity is calculated as the production of electricity from

renewable sources divided by the total production of electricity from all sources in a country plus imports

minus exports. The numerator of this variable is the gross final consumption of electricity from renewable

sources, which includes production by hydropower, wind power, sustainable bioliquids, biogases,

geothermal, solar, tide, municipal waste, or solid biofuels. The denominator includes gross electricity

production from all sources plus total imports of electricity and minus total exports of electricity. Because

of the inclusion of imports and exports, the shares can be larger than 100% when a country produces more

electricity from renewables than total electricity it consumes. The data is retrieved from the EC data base

and includes information of all countries for the years 2011 up until 2018.

Control variables

Next to the explanatory variables of the conceptual models, it is important to control for other factors which

might explain differences in active consumer participation (Bernerth & Aguinis, 2016). This helps to better

capture and distinguish the effects between the explanatory variables and outcome variables.

28

The active participation of consumers might be affected by the Income of the consumers. Financial

incentives can be more lucrative for consumers with lower incomes, but can as well be perceived as higher

risks. Therefore, income is measured by the log of the gross domestic product (GDP) at market prices. The

GDP indicates the total value of all produced goods and services subtracted by the value of goods and

services used for intermediate consumption in their production. As the GDP is expressed in purchasing

power standards (PPS), differences in price levels between countries are eliminated. Moreover, the total

GDP is measured per resident, which enables comparison between countries of different sizes. The data is

aggregated on an annual and country-specific level and retrieved from the database of the EC (EC, 2021c).

The consumption of electricity by households in a country indicates how important electricity is for

consumers. When consumers rely more on other fuels such as gas and petroleum for their, electricity is a

less salient factor in their lives, which may lead to little motivation in active participation in retail electricity

markets. Therefore, consumption is controlled for with the average annual household electricity

consumption in GWh. The measure is retrieved from the database of the EC (EC, 2021b). The measure

includes electricity self-produced electricity and considers it as consumption of electricity. The measure is

converted to KWh and divided by the population size of the country to indicate the average electricity

consumption per consumer.

The size of a country may influence the ability for suppliers to generate electricity or to capture a sufficient

customer base. According to O’Neill et al. (2012), when these indirect effects are presents, population is

more than a scale factor and should be included in the analysis. Therefore, I will as well control for the

population of country, indicated by the country’s population on the 1 January of a year. The data is retrieved

from the database of the EC (EC, 2021d).

Irregularities, outliers, and missing variables

Prior to the analyses, I inspect the data for irregularities, outliers and missing values using descriptive

variables and plots. None of the variables show irregularities. The ranges of all variables make sense with

no percentages over 100% or negative prices. The outliers are inspected in the context of the country using

boxplots. The variance of the household retail prices, the variance of industry retail prices and the rollout

of smart meters show some outliers, but these do not indicate measurement errors are therefore not omitted

from the dataset.

The data shows some missing variables. Table 2 provides an overview of the number of missing variables

per variable. For the switch rate, all variables of the years 2015 and 2017 are missing. For the default tariff

rate, one value is missing for Great Britain, five values are missing for Ireland, one value is missing for

29

Norway and three values are missing for Finland. For trust, two values are missing for Norway. Finally, for

social norms, which is the lagged value of the default tariff rate, the three additional values are missing for

Ireland, Norway and Finland.

Missing values can lead to inefficient or biased estimates. To create a balanced dataset, I impute the missing

values. Though some might argue that imputation leads to so-called “self-hidden Easter eggs”, imputation

of both explanatory and outcome variables leads to unbiased estimates, which is the case for my research.

It is important to consider the reason why variables are missing. All variables have missing values which

are missing at random (MAR). This implies that the probability that a value is missing depends on other

variables available in the data set, which is the variable year for the switch rate and the variable country for

default tariff rate, trust in institutions and social norms. There is no bias for the imputation when the

imputation is based on pure MAR-relationships. I use a data-driven multiple-imputation method to impute

the data. Multiple-imputation methods show to be at least as good as, and commonly outperform, traditional

imputation methods such as pairwise deletion, mean imputation or regression based single imputation

(Schafer & Graham, 2002). I impute the missing values with the function missForest in R, which is a

nonparametric missing value imputation method using random forest decision trees. It imputes the variables

in different iterations and provides the estimation with the lowest normalized root mean squared error

(NRMSE).

To better predict the missing variables of the switch rate, I retrieved the switch rates as published by the

countries’ own NRAs. The values from CEER and ACER are the most appropriate source, as all switch

rates are measured by the same definition, which allows comparability. The switch rates from the NRAs

often differ from these values as the use slightly different definitions for switch rates. For example, they

might include internal switching or renegotiation of current contracts, or they are based on a sample

population instead on the country as a whole. Though the difference of definitions of NRA switch rates

between countries makes the variable unsuitable as outcome variable, the NRA switch rates can be used as

variable for the multiple imputation model to estimate the missing switch rates of 2015 and 2017. In the

multiple imputation model, there is no distinction between predictor and outcome variables. Therefore, it

is no problem to include potentially endogenous variables such as the household retail electricity price.

Other variables included in the model are country, are Country, Year, Demand response projects, Trust in

institutions, Smart meter roll out, Loyalty, Income, Consumption, and Population. The imputation shows a

NRMSE of 0.0002.

Table 2. Missing values per variable per Country Variable Missing values

Switch Rate 10

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Default 10

Trust 2

Social norm 13

Variable overview

Table 3 provides an overview of all predictor names, descriptions and their sources. Table 4 provides an

overview of the descriptive variables of these predictors. The predictors with skew > |0.5| are further

examined on non-normality. The predictors price range, renewable energy consumption, GDP, average

consumption, and population are log transformed to provide a more normal distribution of the variables.

Table 5 provides an overview of the correlations among predictor variables.

Table 3. Predictor names, description and model

Variable Description Default model

Switch model

Default tariff rate % of consumers with a default tariff electricity contract ✓ Switch rate % of consumers who switched to another ✓ Regulation intensity

Index on a scale of 1-10 indicating how light- or heavy handed the regulation of retail electricity market is. ✓ ✓

Price Average household retail electricity price ✓ ✓ Price variation Range of a monthly household retail electricity prices in a year ✓ ✓ DR projects Number of active demand response projects ✓ Brand loyalty Cultural dimensions indicating brand loyalty ✓ Green energy % of household electricity consumption from renewable electricity sources ✓ Technology % of the rollout of smart meters ✓ Social norms Participation in the previous year ✓ Trust Index on a scale of 1-100 indicating consumers’ trust in institutions ✓ GDP Gross domestic product ✓ ✓ Average consumption Average electricity consumption by households ✓ ✓

Population Population size at January 1 ✓ ✓

Table 4. Descriptive variables

Variable Mean St. dev Median Min Max Skew Kurtosis Default tariff rate 42.48 17.970 44.35 6.75 80.00 -0.04 -1.08 Switch rate 12.84 3.551 12.68 5.70 21.40 0.24 -0.47 Price 195.88 54.329 188.54 109.38 311.30 0.79 -0.15 Price range 13.82 12.647 10.23 1.86 57.03 1.43 1.8 Brand loyalty 61.25 4.711 61.50 52.50 66.50 -0.67 -0.58 Regulation intensity 5.25 1.212 5.00 3.00 7.00 -0.26 -0.83 Trust 52.17 10.892 54.3 28.96 71.72 -0.39 -0.78 DR projects 4.88 4.910 3.00 0.00 16.00 0.65 -1.10

31

Technology 0.35 0.371 0.22 0.00 1.00 0.55 -1.25 Renewable cons 0.37 0.323 0.28 0.09 1.10 1.49 0.63 Social norms 43.71 17.605 44.25 8.90 80.00 0.05 -1.10 GDP 45441.04 13661.936 39370 30230 79000 1.20 -0.08 Average consumption 256.38 192.006 145.95 113.75 662.24 1.23 -0.08

Population 296.84 316.071 110.84 45.71 827.92 0.71 -1.37

Table 5. Correlation matrix

32

Methods

For the default tariff rate and the switch rate, it is likely that the household retail electricity price and the

household retail electricity price range are endogenous due to reversed causality. Therefore, I need

instrumental variables which are uncorrelated to the error term (𝐸{𝜖(𝑧(}=0) but sufficiently correlates with

the endogenous variables (𝐶𝑜𝑣{𝑥(𝑧(} ≠ 0). I use the instruments wholesale electricity price and industry

retail electricity price as instruments for household retail electricity price and the instruments wholesale

electricity price range and industry retail electricity price range as instruments for household retail

electricity price range as mentioned before. I expect these instruments to be relevant because they are based

on developments in the wholesale market, just is the household retail electricity price (range). Therefore

they are likely to correlate, as is indicated in Table 5. Furthermore, the instruments are expected to be

exogenous as they are not affected by household consumer behavior. To test these two assumptions, I

perform instrumental variable regressions by two-staged least squares. The first stage tests the relevance of

the instruments by regressing the endogenous variables 𝑋on the set of instruments 𝑍 by OLS, where the

predicted value of 𝑋 will be 𝑋: = 𝑍(𝑍*𝑍)+,𝑍′𝑋. Then, the exogeneity of the instruments is examined by

regressing 𝑦 on 𝑋:. I will test the relevance assumption by a weak instruments test, which provides an F-

test on the first stage regression, with a null-hypothesis of weak instruments. The null hypothesis needs to

be rejected with an F-statistic of at least 10. To assess the endogeneity of the regressors, I will execute a

Wu-Hausman test. This test examines whether the instruments are consistent in the IV model and the OLS

model, with a null-hypothesis of consistency. This is done by adding the residuals of the first regression as

predictors to the second regression, and perform a t-test on these instruments. Finally, a Sargan test will

assess the exogeneity assumption of the instruments. A Sargan test can only be estimated in case of

overidentification, which is the case when the number of instruments exceeds the number of endogenous

variables, with a null hypothesis of exogeneity. This is examined by regressing the residuals of the IV model

on the instruments. A Sargan test indicates whether the instrument set contains at least one endogenous

variable, but does not indicate which. No rejection of the null hypothesis of the Sargan test is necessary to

claim exogeneity of the instruments, but not sufficient. The exogeneity should as well make theoretical

sense.

Default rate model

With the selected variables, I can estimate the following pooled OLS model for the default tariff rate:

Formula 1. Regression equation default rate model

33

log(𝐷𝑒𝑓𝑎𝑢𝑙𝑡(-)

= 𝛽. + 𝛼 log(𝐷𝑒𝑓𝑎𝑢𝑙𝑡(-+,) + 𝛽,𝐻𝐻𝑝𝑟𝑖𝑐𝑒(- +𝛽/log(𝐻𝐻𝑝𝑟𝑖𝑐𝑒𝑟𝑎𝑛𝑔𝑒(-) + 𝛽0𝑃𝑜𝑙𝑖𝑐𝑦(-+ 𝛽1𝑇𝑟𝑢𝑠𝑡(- + 𝛽2𝐷𝑅(- + 𝛽3𝑆𝑚𝑎𝑟𝑡𝑚𝑒𝑡𝑒𝑟(- + 𝛽4 log(𝐺𝐷𝑃(-) + 𝛽5 log(𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛(-)

+ 𝛽6 log(𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛(-) + 𝑌𝑒𝑎𝑟-𝛿- + 𝑐( + 𝑢(-

Where 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 indicates the default tariff rate of country i in year t, 𝐻𝐻𝑝𝑟𝑖𝑐𝑒 indicates the household

retail electricity price, 𝐻𝐻𝑝𝑟𝑖𝑐𝑒𝑟𝑎𝑛𝑔𝑒 indicates the household retail electricity price range, 𝑃𝑜𝑙𝑖𝑐𝑦

indicates the regulation intensity, 𝑇𝑟𝑢𝑠𝑡 indicates the trust in institutions, 𝐷𝑅 indicates the organization of

DR projects, 𝑆𝑚𝑎𝑟𝑡𝑚𝑒𝑡𝑒𝑟 the process of the smart meter rollout, 𝐺𝐷𝑃 indicates the gross domestic

product, 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 indicates the average electricity consumption by households, 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 indicates

the population size, 𝑌𝑒𝑎𝑟- indicates a time fixed effect, 𝑐( indicates the unobserved heterogeneity, and 𝑢(-

is an independent identically distributed error term.

Before I estimate the model, I check for some multicollinearity among the variables. All variables show

VIF-scores higher than 4, indicating multicollinearity problems. Stepwise removal of variables with the

highest VIF-scores leads to removal of the log of 𝐺𝐷𝑃, the log of 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛, and the log of 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛

from the model. Now, all VIF-scores are below the threshold of 4.

The model with the right estimates will be estimated with a pooled-OLS model, a fixed effects model, and

a random effects model, which are all suitable models for panel data. An important assumption for the

models is that the predictors are independent of the error term. A problem is that the unobserved

heterogeneity 𝑐( is often likely to be related to the error term. The three models have different ways to deal

with this.

A pooled-OLS model (Equation 2) would be preferred as this is the most efficient model. However, this

model has strong assumptions as it does not consider any form of individual effects 𝑐(. Therefore, it assumes

𝑐( = 0. This assumption can be tested with the Breusch-Pagan test. If this assumption does not hold, a

random effects (Equation 3) model can be estimated. This model treats 𝑐( as part of the error term.

Therefore, 𝑐( needs to be uncorrelated with all predictors from all periods: 𝐸(𝑐(|𝑥(-) = 0. If this assumption

does not hold, a fixed effects model (Equation 4) can be estimated. This model treats 𝑐( as explanatory

variable and correlation between the unobserved heterogeneity and predictors is allowed: 𝐸(𝑐(|𝑥(-) ≠ 0.

However, the predictors need to be exogenous conditional on the unobserved heterogeneity: 𝐸(𝜀(-|𝑋( , 𝑐() =

0. Through time demeaning, the individual effects are removed from the model (𝑐( −,7∑ 𝑐(7-8, =𝑐( − 𝑐( =

0), and are therefore not endogenous.

Equation 2. Pooled OLS Model

34

𝑦(- =𝛽. + 𝑥(-𝛽, + 𝑢(-

Equation 3. Random effects Model

𝑦(- =𝛽. + 𝑥(-𝛽, + 𝑐( + 𝑢(-

where 𝑐( ~𝑖. 𝑖. 𝑑. (0, 𝜎9/)and 𝑢( ~𝑖. 𝑖. 𝑑. (0, 𝜎"/), independent of all 𝑥(-𝑠.

Equation 4. Random effects Model

�̈�(- = �̈�(-𝛽, + �̈�(-

where�̈�(- = 𝑦(- −1𝑇h𝑦(-

7

-8,

; �̈�(- = 𝑥(- −1𝑇h𝑥(-

7

-8,

; �̈�(- = 𝑢(- −1𝑇h𝑢(-

7

-8,

As the model includes a dynamic element 𝛼𝑦(-+,, there might be an endogeneity problem. The unobserved

heterogeneity 𝑐( is determines 𝑦(- and 𝑦(-+,. Therefore, 𝑐( is correlated to the regressor 𝑦(-+, and pooled-

OLS and random effects models are inconsistent as 𝐸(𝑐(|𝑥(-) = 0 does not hold. The fixed effects model

removes 𝑐( from the equation through within-transformation:

𝑦(- − 𝑦j( = 𝛼(𝑦(,-+, − 𝑦j(,+,) + (𝑥(- − �̅�()′𝛽 + (𝑢(- − 𝑢j()

where 𝑦j(,+, = (𝑇 − 1)+,∑ 𝑦;(7+,;8,

Even though the unobserved heterogeneity 𝑐( is now removed from the equation, 𝑦(,-+, − 𝑦j(,+, is correlated

with 𝑢(- − 𝑢j(, which makes the estimator inconsistent. In a first differenced model, the unobserved

heterogeneity 𝑐( is as well removed:

𝑦(- − 𝑦(-+, = 𝛼(𝑦(,-+, − 𝑦(,-+/) + (𝑥(- − 𝑥(,-+,)′𝛽 + (𝑢(- − 𝑢(,-+,)

However, 𝑦(,-+, − 𝑦(,-+/ is correlated with 𝑢(- − 𝑢(,-+, as 𝑦(,-+, and 𝑢(,-+, are correlated. Therefore, the

first difference estimator is as well inconsistent for dynamic models with one lag. The endogeneity problem

of dynamic models can be solved by transformation of the main equation through first differences

transformation or forward orthogonal transformation and instruments in levels or first differences. These

transformation and instruments are used in the Anderson-Hsiao IV-estimator, Arellano-Bond estimator, and

the Arellano-Bond/Blundell-Bond estimator.

The Anderson-Hsiao IV estimator was proposed in Anderson and Hsiao (1981). It uses a first difference

transformation to remove the unobserved heterogeneity 𝑐( from the model. Then, it provides two

instruments for the endogenous 𝑦(,-+, − 𝑦(,-+/. The first instrument is 𝑦(,-+/. This instrument is relevant as

it is related to 𝑦(,-+, − 𝑦(,-+/: 𝐸(𝑦(,-+/(𝑦(,-+, − 𝑦(,-+/)) ≠ 0, and exogenous as it is not related to 𝑢(- −

𝑢(-+,: E,𝑦(,-+/l𝑢(- − 𝑢(,-+,m- = 0. The second instrument for 𝑦(,-+, − 𝑦(,-+/ is 𝑦(,-+/ − 𝑦(,-+0, which is

35

as well relevant 𝐸l(𝑦(,-+/ − 𝑦(,-+0)(𝑦(,-+, − 𝑦(,-+/)m ≠ 0 and exogenous 𝐸l(𝑦(,-+/ − 𝑦(,-+0)(𝑢(- −

𝑢(,-+,)m = 0. The Anderson and Hsiao IV estimator solves the endogeneity problem, simulation studies

show that it leads to large bias and variances when 𝛼 is close to 1 for instruments in lagged levels and large

variances for many values of 𝛼 for instruments in first differences (Arellano, 1989; Arellano & Bover,

1995). Moreover, the Anderson and Hsiao IV estimator has a moving average error term, for which needs

to be corrected.

A generalized method of moments (GMM) estimation is more efficient than an IV estimation. This

estimation includes more instruments as it includes all possible exogenous lags. Moreover, GMM accounts

for the serial correlation in the first-differenced errors: ∆𝑢(- = 𝑢(- − 𝑢(,-+, and ∆𝑢(,-+, = 𝑢(,-+, − 𝑢(,-+/.

As both contain 𝑢(-+,, the errors are necessarily correlated. The Arellano and Bond, or the dynamic GMM,

estimation, uses instruments in both levels and first differences. Just as the Anderson and Hsiao estimator,

the unobserved heterogeneity 𝑐( is removed through first-difference transformation. Where the Anderson

and Hsiao estimator proposed 𝑦(,-+/ as an estimator for 𝑦(,-+, − 𝑦(,-+/, the Arellano and Bond estimator

proposes to use additional lags, for example 𝑦(,-+/, 𝑦(,-+0, and 𝑦(,-+1 for period 5. Hence, as 𝑡 increases, the

number of available instruments increases as well. If there are endogenous predictor variables, then they

are treated similar to the lagged dependent variables. Predetermined predictor variables have levels lagged

in one or more periods. This leads to the moment condition:

𝐸(𝑍(*∆𝑢() = 𝐸 ,𝑍(*l∆𝑦( − 𝜌∆𝑦(,+, − ∆𝑥(*𝛽m- = 0,

where 𝑍( is a (𝑇 − 2) × 𝐿 matrix of instruments in levels and first differences and ∆𝑢( is a (𝑇 − 2) × 1

vector of (∆𝑢(0, ∆𝑢(1, …, ∆𝑢(7)′. The GMM estimator is then given by

𝛽s<== = l𝑋*𝑍𝑊u𝑍*𝑋m+,𝑋*𝑍𝑊u𝑍*𝑦,

where 𝑊u is a positive definitive weighting matrix. There are two types of 𝑊u to estimate 𝛽s<==. First, the

one-step GMM uses a prespecified 𝑊u = (𝑍*𝑍)+,. Second, the two-step GMM uses an estimate of 𝑊u =

𝑆s+,, where 𝑆s+, = ,>∑ 𝑧(𝑢v(/𝑧(*>(8, , where 𝑢v( are the residuals from a first-step estimation. The inclusion of

many lags in first differences can lead to a lot of data loss, which is especially problematic for small datasets,

like the current dataset. An alternative to the first difference transformation in Arellano-Bond models is

forward orthogonal differencing (Arellano & Bover, 1995). This transformation subtracts the average of all

future observations from the current value:

𝑢(-∗ = w𝑇 − 𝑡

𝑇 − 𝑡 + 1x𝑢(- −

1𝑇 − 𝑡

l𝑢(,-@, + 𝑢(,-@/ +⋯+ 𝑢(7mz.

36

So while first-differencing drops the first observation for each individual, forward orthogonal differencing

drops the last observation.

The use of many instruments may lead to efficient estimation, but can as well overfit the endogenous

variables and make the estimation of the optimal weighting matrix 𝑊u imprecise in two-step GMM

estimation (Roodman, 2009). Therefore it can be useful to estimate the model with collapsed instruments,

where 𝐸 ,𝑦(,-+!l𝑢(- − 𝑢(,-+,m- = 0 is only employed for 𝑠 = 2, 3, and thereby reduces the amount of

instruments substantially.

The Arellano-Bover/Blundell-Bond estimator (Arellano & Bover, 1995; Blundell & Bond, 1998), or the

systems GMM, add additional moment conditions to the Arellano-Bond estimator as lagged levels are weak

instruments when the autoregressive process is too persistent. Next to the moment condition 𝐸(𝑍(*∆𝑢() =

0, they use instruments in first differences for equations in levels, which provides the following additional

moment conditions:

𝐸l∆𝑦(,-+!(𝑐( + 𝑢(-)m = 0and 𝐸l∆𝑥(,-+!(𝑐( + 𝑢(-)m = 0

Another way to consider the endogeneity problem caused by the autocorrelation is by estimating a

Generalized least squares (GLS) model. The GLS model is similar to the OLS model (Equation 2), and also

assumes strict exogeneity 𝐸(𝑢|𝑋) = 0, no perfect collinearity rank(𝐸(𝑥*𝑥) = 𝑘), and linearity 𝑦 = 𝑥𝛽 +

𝑢. GLS differs from OLS as OLS assumes 𝑢(- to be a white noise error term (var(𝑢|𝑋) = 𝜎/𝐼A) and GLS

poses the distribution var(𝑢|𝑋) = 𝜎/Ω, where Ω is a positive (semi-) definite matrix which is not

necessarily diagonal and therefore may be autocorrelated. The GLS model rewrites the OLS model in a

way that the OLS assumptions hold:

𝑃𝑦 = 𝑃𝑋𝛽 + 𝑃𝑢,

where the 𝑛 × 𝑛 matrix 𝑃 satisfies Ω+, = 𝑃*𝑃 and var(𝑃𝑢) = 𝜎/𝐼A. When Ω is not known, Ωu can be

estimated using Feasible General Least Squares (FGLS). To estimate Ωu, the residuals 𝑢v = 𝑌 − 𝑋𝛽 is

estimated. The autocorrelation coefficient 𝜌 is estimated as the correlation between 𝑢v(- and 𝑢v(,-+,. Then,

all predictor variables get GLS-transformed until the autocorrelation in the residuals is removed, and the

OLS-assumptions hold.

Switch rate model

The switch rate can be estimated with the following pooled OLS model:

37

log(𝑆𝑤𝑖𝑡𝑐ℎ(-) = 𝛽. + 𝛽,𝐻𝐻𝑝𝑟𝑖𝑐𝑒(- +𝛽/log(𝐻𝐻𝑝𝑟𝑖𝑐𝑒𝑟𝑎𝑛𝑔𝑒(-) + 𝛽0𝑃𝑜𝑙𝑖𝑐𝑦(- + 𝛽1𝐿𝑜𝑦𝑎𝑙𝑡𝑦(+ 𝛽2𝑅𝑒𝑛𝑒𝑤(- + 𝛽3 log(𝐺𝐷𝑃(-) + 𝛽4 log(𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛(-) + 𝛽5 log(𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛(-)

+ 𝑌𝑒𝑎𝑟-𝛿- + 𝑐( + 𝑢(-

Where 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 indicates the default tariff rate of country i in year t, 𝐻𝐻𝑝𝑟𝑖𝑐𝑒 indicates the household

retail electricity price, 𝐻𝐻𝑝𝑟𝑖𝑐𝑒𝑟𝑎𝑛𝑔𝑒 indicates the household retail electricity price range, 𝑃𝑜𝑙𝑖𝑐𝑦

indicates the regulation intensity, 𝐿𝑜𝑦𝑎𝑙𝑡𝑦 indicates the consumers loyalty to brands, 𝑅𝑒𝑛𝑒𝑤 indicates the

consumption of renewable energy by households, 𝐺𝐷𝑃 indicates the gross domestic product,

𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 indicates the average electricity consumption by households, 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 indicates the

population size, 𝑌𝑒𝑎𝑟- indicates a time fixed effect, 𝑐( indicates the unobserved heterogeneity, and 𝑢(- is

an independent identically distributed error term.

Before I estimate the model, I check for some multicollinearity among the variables. All variables show

VIF-scores higher than 4, indicating multicollinearity problems. Stepwise removal of variables with the

highest VIF-scores leads to removal of the log of 𝐺𝐷𝑃, the log of 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛, and the log of 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛

from the model. Now, all VIF-scores are below the threshold of 4.

As the variable loyalty is time-invariant, the model needs to be estimated with a model that can include

both time-variant and time-invariant variables. The model can be estimated with a pooled-OLS model or a

random effects model, but these have strong assumptions regarding the unobserved heterogeneity. Both

models are inconsistent when regressors are correlated with the unobserved heterogeneity 𝑐(, which is often

the case. A fixed-effects model will resolve the correlation with the unobserved heterogeneity 𝑐(, but

remove the time-invariant variables from the model. Therefore, a fixed-effects model will not be

appropriate. Instead, I estimate a correlated random effects model and a Hausman-Taylor model, which

deal with the unobserved heterogeneity while maintaining the time-invariant variable loyalty.

The correlated random effects model, also known as the Mundlak model, allows for correlations between

explanatory variables and individual effects, as the model assumes a specific relation between the them:

𝐸{𝑐(|𝑋(} = �̅�(*𝛾, which can be written as 𝑐( = �̅�(*𝛾 + 𝜔(. Here, 𝜔( is an unobserved individual effect

assumed to be uncorrelated with the explanatory variables: 𝐸(𝜔(|𝑥() = 0. When 𝛾=0, the correlated random

effects model is equal to the random effects model. With this assumption the model can be specified as:

𝑦(- = 𝛽. + 𝑥(-𝛽 + �̅�(𝛾 + 𝜔( + 𝑢(-

For linear models, the estimates of 𝛽 is equal to those estimated by fixed effect models (Mundlak, 1978).

Therefore, the assumption 𝐸(𝑐(|𝑥(-) = 0 is equal to the assumption 𝛾=0, which can be tested by a Hausman

test or a Wald test. However, this is all under the strong assumption that the time-invariant regressors and

38

their coefficients are orthogonal to 𝑐(. It is not certain whether this is the case for the regressor loyalty.

Therefore, I also estimate a Hausman-Taylor model which provides an instrument for the time-invariant

regressors correlated to 𝑐(.

The Hausman-Taylor estimator provides a model to consistently estimate the effect of both time-varying

and time-invariant regressors (Hausman & Taylor, 1981). The regressors are partitioned wither they are

correlated with the unobserved heterogeneity 𝑐(. The model uses exogenous time-varying regressors as

instruments for endogenous time-invariant regressors. The model is specified as:

𝑦(- = 𝑥",(-* 𝛽" + 𝑥9,(-* 𝛽9 + 𝑧",(* 𝛾" + 𝑧9,(* 𝛾9 + 𝑐( + 𝑢(-

where

𝑥$,&' is a 𝑘$ × 1 vector of time-varying regressors which are uncorrelated with 𝑐&

𝑥(,&' is a 𝑘( × 1 vector of time-varying regressors which are correlated with 𝑐&

𝑧$,& is a 𝑔$ × 1 vector of time-invariant regressors which are uncorrelated with 𝑐&

𝑧(,' is a 𝑔( × 1 vector of time- invariant regressors which are correlated with 𝑐&

The model assumes all to be endogenous (𝐸l𝑢(-�𝑥",( , 𝑥9,( , 𝑧",( , 𝑧9,( , 𝑐(m = 0). In the predictor set 𝑤(- =

[𝑥",(-* , 𝑥9,(-* , 𝑧*",( , 𝑧9,(* ], the regressors 𝑥9,(- and 𝑧9,( are correlated to 𝑐( and therefore need instruments. The

instrument for the time-varying correlated regressors 𝑥9,(- can be provided through demeaning, which

provides �̈�9,(-:

�̈�9,(- = 𝑥9,(- − �̅�9,(

The time-invariant correlated regressors 𝑧9,( can be instrumented with the mean of the time-varying

uncorrelated variables �̅�",(, by splitting up the uncorrelated time-varying regressor 𝑥",(-:

�̈�",(- = 𝑥",(- − �̅�",( andthus𝑥",(- = �̈�",(- + �̅�",(

This provides the following Hausman-Taylor instrument set: ℎ(- = [�̈�′",(- , �̈�′9,(- , 𝑧*",( , �̅�",(]. These are all

exogenous regressors which can be used for the estimation of 𝑦(-. For identification, we need at least as

many instruments in ℎ(- as regressors in the predictor set 𝑤(-. Therefore, there need to be at least as many

uncorrelated time-varying regressors as correlated time-invariant regressors 𝑘" ≥ 𝑔9.

Amemiya & MaCurdy (1986) provide an extension of the Hausman-Taylor model, where they make

stronger assumptions which leads to more available instruments and therefore more efficient estimations.

Where the Hausman-Taylor estimator assumes uncorrelatedness between �̅�",( and 𝑐(, the Amemiya and

MaCurdy estimator extends this assumption to uncorrelatedness between 𝑥",(- and 𝑐( at each point in time:

𝐸l𝑥",(-𝑐(m = 0. The new order condition for identification is now 𝑇𝑘" ≥ 𝑔9.

39

The Hausman-Taylor instrument set is expanded to:

ℎ(B= = ��̈�′",(,�̈�′9,(,𝑧′",( 𝑥",(,* …𝑥",(/* ⋯ 𝑥",(7*

⋮⋮⋮⋮ ⋮⋱ ⋮�̈�′",(7�̈�′9,(7𝑧′",( 𝑥",(,* …𝑥",(/* ⋯ 𝑥",(7*

�

RESULTS

This section tests the hypotheses for demand response participation and active participation in retail

electricity markets. I estimate separate models for the two outcome variables, default tariff rate and switch

rate. First, the instrumental variables for both models are selected. Then, different models are estimated and

compared. Next, the results of the best performing model are examined which will show whether the

hypotheses are accepted or rejected.

Demand response

To examine whether the suggested instruments for the endogenous variables household retail electricity

price and the household retail price are relevant and exogenous, I perform an instrumental variable

regression by two-staged least squares using the ivreg function from the AER package in R. The variable

social norms, which is the lag of the default tariff rate, is not included to make sure the analysis is not biased

by autocorrelation in the error term. The results are presented in Table 6. I first assess the instruments

wholesale electricity price, industry retail electricity price, log of wholesale electricity price range, and log

of industry retail electricity price for IV 1. The variables are sufficiently correlated with the endogenous

variables as for both the F-statistic > 10. Moreover, the Wu-Hausman test shows that the estimators of the

IV-regression are not consistent to the OLS estimation, and therefore outperform the OLS estimation. The

Sargan test indicates no endogenous variables among the instrument variable set. This would therefore be

a sufficient instrument set. However, as there are relatively few observations relative to the number of

predictors, fewer instruments would be preferred to create a more parsimonious model. Therefore, more

combinations are explored in the following models. IV 2 uses the instruments range wholesale electricity

price and log of wholesale electricity price range and IV 3 uses the instruments industry retail electricity

price and log of industry retail electricity price. Only IV 3 satisfies the relevance and exogeneity

requirements. I create two new variables: the absolute difference between industry retail and wholesale

prices, and the absolute difference between industry retail price ranges and wholesale price ranges. As they

are skewed, I use the log of these variables. Model IV 4 adds these two variables to the entire instrument

set, which provides relevant and exogenous instruments. IV 5 omits the wholesale related instruments,

40

which leads to an instrument set which satisfies all requirements. IV 6 omits as well the industry retail

market related instruments, which also leads to instruments which satisfy all requirements. The Sargan test

can, however, not examine the overidentifying restrictions as the endogenous variables are just identified.

However, as the Sargan tests IV 4 and IV 5 indicate no endogenous instruments among the instrument set,

I assume that these variables are exogenous. The instruments of IV 6, logs of difference in price and logs

of difference in range are selected as instruments for the endogenous variables as they satisfy the relevance

and exogeneity criteria and lead to a parsimonious model.

Table 6. Results instrumental variable regression IV 1 IV 2 IV 3 IV 4 IV 5 IV 6

Household price -0.008*** (0.002)

-0.003 (0.003)

-0.009*** (0.002)

-0.008*** (0.002)

-0.009*** (0.002)

-0.009*** (0.002)

Log(Household price range) -0.420*** (0.101)

-0.350 . (0.196)

-0.445** (0.124)

-0.425*** (0.097)

-0.440*** (0.116)

-0.507*** (0.113)

Policy index 0.118* (0.053)

0.167 . (0.084)

0.117* (0.057)

0.119* (0.055)

0.119* (0.056)

0.138* (0.057)

Trust -0.032*** (0.004)

-0.029*** (0.006)

-0.033*** (0.005)

-0.032*** (0.004)

-0.033*** (0.005)

-0.035*** (0.004)

DR projects 0.053*** (0.009)

0.036 (0.022)

0.053*** (0.010)

0.052*** (0.009)

0.053*** (0.01)

0.048*** (0.011)

Smart meters 0.563** (0.174)

0.987** (0.333)

0.497* (0.190)

0.556** (0.180)

0.517** (0.171)

0.453* (0.184)

Year2012 -0.010 (0.136)

-0.047 (0.133)

-0.001 (0.149)

-0.008 (0.137)

-0.003 (0.146)

0.012 (0.170)

Year2013 -0.388* (0.143)

-0.441 (0.151)

-0.387* (0.155)

-0.389* (0.145)

-0.389* (0.152)

-0.405* (0.175)

Year2014 -0.400** (0.139)

-0.520* (0.193)

-0.392* (0.148)

-0.401** (0.141)

-0.397* (0.146)

-0.417* (0.167)

Year2015 -0.615** (0.197)

-0.787** (0.233)

-0.601** (0.202)

-0.616** (0.198)

-0.608** (0.201)

-0.633** (0.225)

Year2016 -0.630*** (0.162)

-0.813** (0.261)

-0.622** (0.173)

-0.633*** (0.164)

-0.629*** (0.17)

-0.676** (0.194)

Year2017 -0.692** (0.198)

-0.968** (0.343)

-0.675** (0.213)

-0.695** (0.200)

-0.686** (0.207)

-0.746** (0.225)

Year2018 -0.447 . (0.225)

-0.867* (0.352)

-0.403 . (0.231)

-0.446 . (0.227)

-0.421 . (0.22)

-0.439 .(0.239)

Constant 7.122*** (0.746)

5.609*** (1.278)

7.392*** (0.898)

7.158*** (0.736)

7.316*** (0.856)

7.683*** (0.847)

Instruments Wholesale price ✓ ✓ ✓ Industry price ✓ ✓ ✓ ✓ Log(difference price) ✓ ✓ ✓ Wholesale price range ✓ ✓ ✓ Industry price range ✓ ✓ ✓ ✓ Log(difference price range) ✓ ✓ ✓ R2 0.799 0.818 0.778 0.796 0.784 0.750 Adjusted R2 0.722 0.749 0.694 0.718 0.702 0.654 Weak instruments Price 40.39*** 1.70 44.66*** 30.73*** 33.74*** 50.88*** Weak instruments Range 32.588*** 1.01 62.91*** 21.16*** 30.20*** 23.05***

41

Wu-Hausman test 12.832*** 0.480 4.360* 17.26*** 17.527*** 13.989*** Sargan test 2.391 - - 2.489 1.272 -

Note: endogenous variables in bold, country clustered standard errors in brackets, . indicates p < 0.10 * indicates p < 0.05, **

indicates p < 0.01, *** indicates p < 0.001

I estimate various models with different assumption to test the hypotheses. Table 7 provides an overview

of the results, excluding the effects of the year dummies. An overview of results of the year effects in

Appendix II. First I estimate a Anderson and Hsiao model (AH) with the xtivreg function in Stata. The

model could not be estimated reliably because there are too many regressors relative to the number of

observations. Therefore, the model could not estimate standard errors for the coefficients. As the model

now consists of a rank of 0, no estimate of the F-statistic or the adjusted R2 could be estimated. Therefore,

I do not consider these results as reliable. The Arellano-Bond model can be estimated using forward

orthogonal differencing, which results to less data loss than first-differencing. I estimate the Arellano-Bond

model using the xtabond2 function in Stata. I first estimate a one-step Arellano-Bond model (AB-1).

Because there are too many regressors relative to the number of observations, the Wald 𝜒/ could not be

estimated to evaluate the overall significance of the model. The Arellano-Bond test examines whether there

is serial correlation in the residuals with 𝐻.: 𝐸(∆𝑢(-+/∆𝑢(-) = 0. The test shows a negative significant

correlation of -2.14 with a p-value of 0.032 for the first lag, but an insignificant correlation for the second

lag of -0.49 with a p-value of 0.622. Hence, the endogeneity of the first lag is removed. However, the Sargan

test for overidentifying restrictions is significant with 𝜒/(11) = 25.51 and a p-value of 0.008, which implies

that the instruments are not endogenous. A two-step Arellano-Bond model (AB-2) is neither able to provide

a Wald 𝜒/ for model significance. The model has many omitted variables. The Arellano-Bond tests show

no correlation in the residuals for the first lag with a correlation of -0.94 and a p-value of 0.349 or the

second lag with a correlation of 0.109 and a p-value of 0.278. The Sargan test for overidentifying restrictions

is significant with 𝜒/(5) = 20.96 and a p-value of 0.001. This rejects the null hypothesis of exogenous

instruments. The collapsed Arellano-Bond model (AB-C) neither provides a Wald 𝜒/ for model

significance. The model has many omitted variables. The Arellano-Bond tests show no significant

correlation in the residuals for the first lag with a correlation of -0.99 and a p-value of 0.323 or the second

lag with an insignificant correlation of -0.85 and a p-value of 0.394. The Sargan test for overidentifying

restrictions is significant with 𝜒/(6) = 21.49 and a p-value of 0.002, which indicates endogenous

instruments. This indicates that the Arellano Bond models are not trustworthy to interpret as the overall

significance cannot be estimated and the Sargan tests that the instruments are not exogenous. The systems

dynamic GMM model or Arellano-Bover/Blundell-Bond model (SYS) is significant as a whole a Wald test

𝜒/(19) = 1730.84 and p = 0.000. Many variables are omitted again from the model. The Arellano-Bond

42

tests show no significant correlation in the residuals for the first lag with a correlation of -1.20 and a p-

value of 0.206 or the second lag with an insignificant correlation of -0.66 and a p-value of 0.509. The Sargan

test for overidentifying restrictions is insignificant with 𝜒/(22) = 27.14 and a p-value of 0.206, which

indicates no endogenous instruments in the instrument set. The collapsed systems dynamic model (SYS-C)

is significant as a whole with a Wald test 𝜒/(19) = 204.75 and p = 0.000. Many variables are omitted from

the model. The Arellano-Bond tests show no significant correlation in the residuals for the first lag with a

correlation of -0.70 and a p-value of 0.484 or the second lag with an insignificant correlation of -1.21 and

a p-value of 0.226. The Sargan test for overidentifying restrictions is insignificant with 𝜒/(22) = 27.14 and

a p-value of 0.206, which indicates no endogenous instruments in the instrument set. Though the systems

dynamic GMM models have no autocorrelation in the error terms or endogenous instruments, they have

many omitted variables which makes the models less suitable for testing the hypothesis.

The generalized least squares model (GLS) is estimated with the plm function in R with robust country

clustered standard errors. First, a fixed effects model is estimated. The Breusch-Godfrey/Wooldrigde test

for serial correlation in panel models and Wooldridge’s test for serial correlation in FE models both do not

indicate autocorrelation in the error term with 𝜒/(8) = 9.27 and p = 0.001, and F(1, 40) = 0.145 and p =

0.705 respectively. However, I do not trust these tests as they are built for larger sample sizes . I therefore

assume there is serial autocorrelation in the error term as 𝑦(,-+, − 𝑦j(,-+/ is correlated with 𝑢(- − 𝑢j(,-+,.

Therefore, the variables are GLS transformed. The GLS model is as a whole significant with F(13, 23) =

7.57 and p = 0.000. Unlike the GMM models, all variables can be estimated in the model. As the GLS

model resolves the problem with autocorrelation in the error term and does not omit any predictors, this

model is the most appropriate model to estimate the interaction effects. The GLS model including the

interaction effects (GLS int) is as a whole significant with F(17, 19) = 7.98 and p = 0.000. The adjusted R2

of the model including interactions is higher than the GLS model without interactions, which indicates that

the GLS model including interactions explains more of the variance in the log of the default tariff rate than

the GLS model without interactions, while controlling for additional predictors. Hence, the additional

predictors, the interaction effects, improve the model performance. To test whether the GLS int model

outperforms the GLS model, I conduct a likelihood ratio test. This test indicates that the model with extra

regressors significantly outperforms the restricted GLS model, with 𝜒/(4) = 18.19 and p = 0.001. As the

dataset contains only few observations, it is important to have a parsimonious model for the interpretation

of the estimates. I therefore compare different versions of the GLS interaction model by iteratively omitting

insignificant predictors and comparing models with a likelihood ratio test, until I find the optimal model

(Final GSL). I use this model test the hypotheses.

43

Table 7. Results Dynamic models

AH AB-1 AB-2 AB-C SYS SYS-C GLS GLS int Final GSL

Log (Default tariff rate)

Lag Dif -3.329 (.)

2 Lag dif -1.713 (.)

Lag 0.135 (0.171)

0.000 (.)

0.000 (.)

0.000 (.)

0.000 (.)

0.102 (0.243)

-0.706 (1.155)

2 Lag 0.626*** (0.216)

0.000 (.)

Price (IV) -0.184 (0.380)

0.000 (.)

0.000 (.)

0.000 (.)

0.000 (.)

-0.223 (0.385)

0.964 (0.727)

0.913 . (0.510)

Dif 5.222 (.)

Lag dif 6.715 (.)

2 lag dif 5.004 (.)

Lag dif 1.136*** (0.267)

0.000 (.)

0.000 (.)

0.000 (.)

0.000 (.)

2 Lag dif 0.000 (.)

0.000 (.)

Price range (IV) 0.052

(0.049) 0.000

(.) 0.000

(.) 0.000

(.) 0.000

(.) 0.022

(0.045) 0.014

(0.174)

Dif 0.013 (.)

Lag dif 0.066 (.)

2 lag dif 0.643 (.)

Lag dif 0.012 (0.025)

0.000 (.)

0.000 (.)

0.000 (.)

0.000 (.)

2 Lag dif 0.000 (.)

0.000 (.)

Policy -0.017 (0.046)

0.000 (.)

0.000 (.)

0.000 (.)

0.000 (.)

-0.03 (0.051)

0.867 . (0.471)

0.829** (0.244)

Dif 0.021 (.)

Lag dif -0.750 (.)

2 lag dif -0.056 (.)

Lag dif -0.131** (0.056)

0.000 (.)

0.000 (.)

0.000 (.)

0.000 (.)

2 Lag dif 0.000 (.)

0.000 (.)

DR projects 0.009 (0.007)

0.000 (.)

-0.037 (0.043)

0.048 (0.043)

-0.003 (0.031)

-0.015 (0.015)

-0.048 (0.042)

-0.020 . (0.011)

Dif -0.086 (.)

Lag dif 0.149 (.)

2 lag dif -0.201 (.)

Lag dif -0.003 (0.009)

0.032 (0.024)

0.000 (.)

-0.193 (0.197)

-0.103* (0.058)

2 Lag dif -0.011 (0.013)

-0.076*** (0.025)

Trust -0.034*** (0.009)

-0.012*** (0.004)

-0.032** (0.014)

-0.037 (0.030)

-0.039*** (0.008)

-0.027* (0.01)

-0.068 (0.07)

-0.027*** (0.007)

Dif -0.206 (.)

Lag dif -0.149

44

(.)

2 lag dif -0.113 (.)

Lag dif 0.008 (0.008)

-0.005 (0.003)

-0.041** (0.018)

-0.088 (0.088)

-0.053*** (0.016)

2 Lag dif -0.001 (0.002)

-0.011 (0.007)

Smart meter 0.327 (0.514)

0.000 (.)

0.000 (.)

0.000 (.)

0.000 (.)

0.442 (0.300)

-0.047 (0.445)

Dif 18.293 (.)

Lag dif -15.439 (.)

2 lag dif 1.584 (.)

Lag dif 0.028 (0.579)

0.000 (.)

0.000 (.)

0.000 (.)

0.000 (.)

2 Lag dif 0.000 (.)

0.000 (.)

Log(Price IV) × Policy -0.197 .

(0.101) -0.182** (0.053)

Log(Price range IV) × Policy

0.007 (0.032)

Trust × DR projects 0.001

(0.001) Trust × lag log(default tariff)

0.011 (0.018)

Constant 0.660 (.) 0.000

(.) 0.000

(.)

N 24 30 30 30 42 42 42 42 42

R2 0.018 0.811 0.877 0.854

Adjusted R2 - 0.662 0.735 0.760

F-statistic / Wald test - - - - 1730.84

*** 204.75

*** 7.57*** 7.98*** 13.25***

AB test for AR(1): -2.14* -0.94 -0.99 -1.20 -0.70

AB test for AR(2): -0.49 1.09 0.85 -0.66 -1.21

Sargan 25.51** 20.96*** 21.49 27.14 27.14

Note: . indicates p < 0.10 * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, dif = first difference, lag = first lag, 2 lag = second order lag

The Final model is a whole significant with F(11, 25) = 13.25 and p = 0.000. The model has an R2 of 0.854

which implies that the model explains 85.4% of the variance of the log of the switch rate. The adjusted R2

is 0.980 and is higher than the adjusted R2 of the GLS model without interaction effects of 0.662, and the

adjusted R2 of the complete model with all interactions of 0.735. This indicates that the increase in variance

explained compared to the GLS model is not only because of the increase of predictors, but because of the

addition of meaningful predictors. The log likelihood of the model is 38.386, which is significantly higher

than those of the GLS model (32.983) and not significantly different from the GLS int model (42.077), as

demonstrated by likelihood ratio tests with 𝜒/(2) = 10.81 and p = 0.005, and 𝜒/(6) = 7.38 and p = 0.287

respectively.

45

The residuals are examined on normality. The Shapiro-Wilk test (W = 0.991, p = 0.983), Kolmogorov-

Smirnov test (D = 0.068 and p =0.891) and the Jarque-Bera test (J =0.070 and p = 0.968) are all three

insignificant, which indicates normality of the residuals.

Regarding the pull/push effects, the results of the final model do not indicate that the financial incentives

significantly improve participation in demand response programs. The instrument for electricity price,

log(dif price), is not significant under 𝛼 < 0.05 with β = 0.913 and p = 0.086. However, as the sample size

is only 36 observations of 6 countries, I accept the hypotheses with 𝛼 < 0.10. This implies that the total

effect of log(dif price) is 0.913 – 0.182 = 0.731. Hence, when the difference between the wholesale and the

industry retail price increases with one percent, the default tariff rate increases with 73.1% for an average

policy index, ceteris paribus. The positive sign indicates a negative relation to demand response

participation, as a higher default tariff rate indicates less active participation in the market. Hence, the sign

of log(dif price) is opposite of the expectation in Hypothesis 1. The instrument for electricity price range,

log(dif price range), does not significantly affect log(default tariff rate) as the variable is not included in

the optimal model. Therefore, Hypothesis 1 is rejected. The final model neither indicates that social norms

partly improve participation in demand response programs, as the first lag of the log of default tariff rate is

not included in the optimal model, and DR-programs is significantly related to log(default tariff rate) under

𝛼 < 0.10 with β = -0.020 and p = 0.076. This implies that for one additional DR program in a year, the

default tariff rate decreases with 2%, ceteris paribus. Therefore, Hypothesis 2 is partly accepted. The model

indicates that the availability of technology does not significantly reduce the demand response participation,

as smart meter rollout is not included in the optimal model predicting log(default tariff rate). Therefore,

Hypothesis 3 is rejected.

For the mooring effects, the model indicates that information policies are negatively, significantly related

to demand response participation, as regulation intensity is positively related to log(default tariff rate) with

β = 0.829 and p = 0.002. The effect of regulation intensity also needs to consider the significant interaction

with the electricity price. The total effect of the regulation intensity is 0.829-0.182 = 0.647 for an average

electricity price, ceteris paribus. The positive sign indicates a negative relation to demand response

participation, as a higher default tariff rate indicates less active participation in the market. This implies that

the direction of the coefficient is opposite from the expectation, and Hypothesis 4 is rejected. The interaction

between the electricity price and the regulation intensity is negatively, significantly related to demand

response participation with β = -0.182 and p = 0.002. As the main effect of the electricity price is not

significant, the significant interaction effect implies a cross-over interaction. This implies that the effect of

the electricity price becomes negative, dependent on the value of the information policy. Hence, when the

regulation intensity increases with one, the effect of the price on log(default tariff rate) decreases with

46

0.182, ceteris paribus. As an increase the policy index makes the effect of the electricity price coefficient

more negative, this supports Hypothesis 5. The interaction between the electricity price range and regulation

intensity is insignificant as it is not included in the optimal model. Therefore Hypothesis 5 is partly

accepted. The model indicates that the demand response participation increases when the level of trust in

institutions increases, as trust is negatively, significantly related to log(default tariff rate) with β = -0.027

and p = 0.001. This implies that the default tariff rate decreases with 2.7% when trust increases with one

point on the scale, ceteris paribus. Therefore, Hypothesis 6 is accepted. The interactions between trust and

the social norm variables are both not included in the model, and therefore insignificant. Hence, Hypothesis

7 is rejected.

Switching behavior

Just as for the default tariff model, I examine the relevance and exogeneity of different instrumental

variables using the ivreg function from the AER package in R. The results are presented in Table 8. I first

assessed the instruments wholesale electricity price, industry retail electricity price, log of wholesale

electricity price range, and log of industry retail electricity price for IV 7. The variables are sufficiently

correlated with the endogenous variables as for both the F-statistic > 10. Moreover, the Wu-Hausman test

shows that the estimators of the IV-regression are not consistent to the OLS estimation, and therefore

outperform the OLS estimation. The Sargan test indicates no endogenous variables among the instrument

variable set. Hence, this instrument does not satisfy all criteria. IV 8 uses the instruments range wholesale

electricity price and log of wholesale electricity price range and IV 9 uses the instruments industry retail

electricity price and log of industry retail electricity price. These two models neither satisfy both the

relevance and exogeneity requirements. I include again the instruments the absolute difference between

industry retail and wholesale prices, and the absolute difference between industry retail price ranges and

wholesale price ranges in logs. Model IV 10 adds these two variables to the entire instrument set, which

provides relevant and exogenous instruments. IV 11 includes the wholesale price and the difference

instruments, which provides a set satisfying all requirements. IV 12 omits as well the wholesale price

instrument, and satisfies all requirements. The Sargan test can, however, not examine the overidentifying

restrictions as the endogenous variables are just identified. However, as the Sargan test of IV 11 indicates

no endogenous instruments among the instrument set, I assume that these variables are exogenous. The

instruments of IV 12, the logs difference in price and logs of difference in range are selected as instruments

for the endogenous variables as they satisfy the relevance and exogeneity criteria and lead to a parsimonious

model.

47

Table 8. Results instrumental variable regressions switch IV 7 IV 8 IV 9 IV 10 IV 11 IV 12

Household price -0.002***

(0.000)

-0.002

(0.003)

-0.002***

(0.001)

-0.001*

(0.001)

-0.001 .

(0.001)

-0.002 .

(0.001)

Log (Household price

range)

0.17 0**

(0.056)

0.712

(0.516)

0.132*

(0.057)

0.227***

(0.063)

0.314**

(0.104)

0.267 *

(0.128)

Policy index 0.075*

(0.032)

-0.084

(0.159)

0.081*

(0.033)

0.068*

(0.033)

0.045

(0.04)

0.051

(0.034)

Loyalty 0.002

(0.007)

0.066

(0.063)

-0.001

(0.006)

0.007

(0.008)

0.017

(0.012)

0.013

(0.013)

Log(Renewable energy

consumption)

-0.183***

(0.037)

-0.291

(0.174)

-0.176***

(0.036)

-0.191***

(0.041)

-0.208***

(0.05)

-0.201***

(0.047)

Year2012 -0.035

(0.067)

-0.036

(0.217)

-0.034

(0.073)

-0.038

(0.066)

-0.039

(0.076)

-0.037

(0.067)

Year2013 0.039

(0.095)

0.351

(0.383)

0.021

(0.100)

0.062

(0.096)

0.110

(0.115)

0.090

(0.122)

Year2014 0.151*

(0.065)

0.601

(0.475)

0.126 .

(0.067)

0.184*

(0.068)

0.253*

(0.098)

0.225*

(0.100)

Year2015 0.195**

(0.066)

0.696

(0.498)

0.169*

(0.070)

0.229**

(0.070)

0.305**

(0.103)

0.276*

(0.102)

Year2016 0.332***

(0.076)

0.956

(0.614)

0.296***

(0.081)

0.383***

(0.081)

0.479***

(0.118)

0.437**

(0.126)

Year2017 0.308**

(0.091)

1.096

(0.765)

0.263**

(0.096)

0.369***

(0.100)

0.490**

(0.145)

0.438**

(0.147)

Year2018 0.270*

(0.131)

0.768

(0.515)

0.248 .

(0.135)

0.294*

(0.134)

0.367*

(0.148)

0.345*

(0.144)

Constant 1.576**

(0.554)

-3.195

(4.824)

1.926***

(0.509)

1.048

(0.653)

0.277

(1.061)

0.709

(1.253)

Instruments

Wholesale price ✓ ✓ ✓ ✓

Industry price ✓ ✓ ✓

Log(difference price) ✓ ✓ ✓

Wholesale price range ✓ ✓ ✓

Industry price range ✓ ✓ ✓

Log(dif price range) ✓ ✓ ✓

R2 0.772 -0.744 0.771 0.753 0.67 0.718

Adjusted R2 0.694 -1.341 0.692 0.669 0.557 0.622

Weak instruments Price 358.47*** 6.12** 523.49*** 206.71*** 128.71*** 188.98***

48

Weak instruments Range 5.49** 0.43 9.76*** 3.73** 5.77** 7.31**

Wu-Hausman test 2.069 3.300* 0.985 1.332 14.443*** 14.007***

Sargan test 5.318 - - 17.758** 0.191 -

Note: endogenous variables in bold, country clustered standard errors in brackets, . indicates p < 0.10 * indicates p < 0.05, **

indicates p < 0.01, *** indicates p < 0.001

I estimate various models with different assumption to test the hypotheses. Table 9 provides an overview

of the results excluding the effects of the year dummies and the between effects of the CRE model. A

complete overview of results of the year effects in Appendix III and the between effects in Appendix IIII.

First I estimate a pooled-OLS model (P-OLS) with the reg function in Stata. The fixed effects (FE), the

random effects (RE) and correlated random effects (CRE) models are estimated using the xtreg function in

Stata. I test the assumption of 𝑐( = 0 with a Breusch and Pagan Lagrangian multiplier test for random

effects. The test rejects the null hypothesis of unobserved individual country effects with 𝜒/(1) = 0.000 and

a p-value of 1.000. This implies that a pooled OLS model is preferred over a random effects model. A

Hausman-test tests whether the coefficients of the random effects model and the fixed effects model are

consistent. The test is significant with 𝜒/(4) = 19.05 and a p-value of 0.001. This is not surprising, as the

unobserved heterogeneity is 0. The correlated random effects (CRE) model is estimated with country

clustered robust standard errors. There are too many predictors relative to the observations to estimate the

Wald-statistic. The Hausman test examines the null-hypothesis 𝐸(𝑐(|𝑥(-) = 0 (or 𝛾 = 0). This hypothesis

is rejected with 𝜒/(4) = 135.69 and a p-value of 0.000. This is as well unsurprising as the Lagrangian

multiplier test already indicated that 𝑐( = 0. As previous tests indicate 𝑐( = 0, all time-varying variables

are classified as 𝑥",(- and used as instruments for the time-invariant variable loyalty. I estimated the

Hausman-Taylor model (HT) and the Amemiya and MaCurdy model (AM) with robust clustered standard

errors using the plm package in R. The Hausman-Taylor test examines the null-hypothesis 𝐸l𝑥",(𝑐(m = 0

and 𝐸l𝑧",(𝑐(m = 0. The test rejects the null hypothesis with 𝜒/(1)= 0.738 and a p-value of 0.390. This

implies that the instruments are valid and therefore the Hausman-Taylor estimator is preferred over the

fixed effects model. To test if the additional restrictions of the Amemiya and MaCurdy estimator hold, a

Hausman test can be executed to test if the coefficients are consistent. When the coefficients are consistent,

the additional restrictions hold. The Hausman test does not reject the null hypothesis of consistency with

𝜒/(1)= 0.000 and a p-value of 1.000. These extreme values are likely to because the estimates are exactly

similar to each other.

Overall, the pooled-OLS outperforms the other models as this is the least restricted model and all effects

can be directly measured without the use of instruments. The strong assumptions of the pooled-OLS model

hold and therefore allow this efficient estimation. Therefore, the interaction effects are estimated using the

49

OLS model (P-OLS int). This model is significant as a whole with F(12, 35) = 10.61 and p = 0.000.

Moreover, the adjusted R2 outperforms the adjusted R2 of the pooled OLS model without interactions.

However, only one interaction effect is significant under 𝛼 < 0.10. To test whether the P-OLS int model

outperforms the P-OLS model, I conduct a likelihood ratio test with 𝜒/(3)= 6.77 and p = 0.080. This test

indicates that the model with extra regressors does not significantly outperform the restricted P-OLS model.

As the dataset contains only few observations, it is important to have a parsimonious model for the

interpretation of the estimates. I therefore compare different versions of the model by iteratively deleting

insignificant predictors and comparing models with a likelihood ratio test, until I find the optimal model

(Final). I use this model to test the hypothesis.

Table 9. Results Switch rate P-OLS FE RE CRE HT AM P-OLS int Final

Log(Difference

price)

-0.339***

(0.077)

-0.599***

(0.209)

-0.339***

(0.077)

-0.599**

(0.285)

-0.473***

(0.130)

-0.473***

(0.130

0.220

(0.343)

Log(Difference

price range)

-0.056

(0.037)

-0.027

(0.032)

-0.056

(0.037)

-0.027

(0.037)

-0.020

(0.029)

-0.020

(0.029)

-0.195

(0.146)

-0.059 .

(0.035)

Policy 0.104**

(0.041)

0.040

(0.043)

0.104**

(0.041)

0.040

(0.052)

0.046

(0.038)

0.046

(0.038)

0.561*

(0.230)

0.352***

(0.045)

Log(Renewable

energy

consumption)

-0.256***

(0.048)

-0.093

(0.200)

-0.256***

(0.048)

-0.093

(0.108)

-0.209 .

(0.110)

-0.209 .

(0.110)

-0.015

(0.035)

-0.276***

(0.046)

Loyalty -0.035***

(0.006)

0.000

(.)

-0.035***

(0.006)

-0.011***

(0.000)

-0.038*

(0.019)

-0.038*

(0.019)

-1.118

(1.604)

-0.034***

(0.006)

Log(Difference

price) × Policy

-0.005

(0.095) .

-0.059***

(0.012)

Log(Difference

price range) ×

Policy

-0.004

(0.091)

Log(Renewable

energy

consumption) ×

Loyalty

0.096

(0.097)

Constant 4.987***

(0.692)

4.625***

(0.945)

5.179***

(0.749)

-1.753***

(0.087)

6.032***

(1.348)

6.032***

(1.348)

1.222

(2.276)

3.511***

(0.405)

Country fixed

effect 0 0.813 0.813

N 48 48 48 48 48 48 48 48

F-test / Wald

test 10.61*** 8.83 *** 127.27*** - 102.87*** 107.61*** 9.25*** 11.74***

50

R2 0.784 0.758 0.784 0.902 0.754 0.755 0.813 0.801

Adjusted R2 0.710 0.670 0.670 0.725 0.733

Note: . indicates p < 0.10 * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001

The Final model is a whole significant with F(12, 35) = 11.74 and p = 0.000. The model has an R2 of 0.801

which implies that the model explains 80.1% of the variance of the log of the switch rate. The adjusted R2

is 0.733 and is higher than the R2 of the GLS model without interaction effects of 0.670 and the adjusted

R2 of complete model with all interactions of 0.733. This indicates that the increase in variance explained

compared to the GLS model is not only because of the increase of predictors, but because of the addition

of meaningful predictors. The log likelihood of the model is 30.479, which is significantly higher than the

GLS model (28.552) and not significantly different from the GLS int model (31.935), as demonstrated by

likelihood ratio tests with 𝜒/(0) = 3.86 and p = 0.000 and 𝜒/(3) = 2.91 and p = 0.405.

The residuals are examined on normality. The Shapiro-Wilk test (W = 0. 0.972, p = 0.312), Kolmogorov-

Smirnov test (D = 0.095 and p =0.335) and the Jarque-Bera test (J =0.610 and p = 0.679) are all three

insignificant, which indicates normality of the residuals.

Looking at the push and pull factors, the financial incentive electricity price does not directly significantly

affect supplier switching, as the instrument log(dif price) is not included in the optimal model. The

instrument for the electricity price range, log(dif price range) is not significant under 𝛼 < 0.05 with β =

-0.059 and p = 0.096, but as the sample size of 42 observation among 6 countries is rater small, I accept a

significance level of 𝛼 < 0.10. This implies that the if the difference in price range increases with 1%, the

switch rate decreases with 5.9%, ceteris paribus. This is the opposite sign of the prediction. Therefore,

Hypothesis 8 is rejected. The log of renewable electricity consumption is negatively related with the log

of supplier switching with β = -0.276 and p = 0.000. This implies that 1% increase in the percentage of

renewable electricity consumption by households leads to 27.6% in the switch rate, ceteris paribus. This is

the opposite direction as predicted in Hypothesis 9, which is therefore rejected.

For the mooring factors, the regulation intensity is positively related to the log of switching behavior with

β = 0.352 and p = 0.000. The total effect of regulation intensity should as well include the interaction effect,

which is 0.352 – 0.059 = 0.293. This implies that when the policy index increases with one point, and the

difference in the electricity price is one euro, the switch rate increases with 35.2%, ceteris paribus.

However, this effect decreases with 5.9 percentage points for every 1% increase in the electricity price

range, ceteris paribus. Therefore, Hypothesis 10 is accepted. The interaction between the electricity price

and regulation intensity is significantly negative with β = -0.059 and p = 0.000. The electricity price does

not directly significantly affect the switch rate, which indicates a cross-interaction where the sign and

significance of electricity price depend on the regulation intensity. This implies that one percent increase

51

the electricity price under a policy index of 1 decreases the switch rate with 5.9%, which becomes more

negative with 5.9 percentage points for every point increase in policy index, ceteris paribus. This is the

opposite sign from Hypothesis 11. The interaction between the electricity price range and the regulation

intensity is not included in the optimal model, and therefore not significant. Hypothesis 11 is therefore

rejected. Brand loyalty is negatively significantly related to the log of the switching rate with β = -0.034

and p = 0.000. This implies that when the brand loyalty changes with one point on the scale, the switch rate

decreases with 3.4%, ceteris paribus. Therefore, Hypothesis 12 is accepted. The interaction between brand

loyalty and renewable energy consumption is not included in the model and therefore not significant. Hence,

Hypothesis 13 is rejected.

DISCUSSION

Governments in Europe face challenges in balancing the electricity grid due to increases in volatile

renewable electricity generation and electricity demand. Active consumer participation is crucial to ensure

a safe and affordable electricity supply. However, policy makers struggle to motivate consumers to engage

in active participation in electricity retail markets as active participation creates high financial and

behavioral costs for consumers. Policy makers can use various pull, push, and mooring factors to motivate

consumers to switch from a passive to a more active role. This research evaluates the effectiveness of these

factors on three types of consumer participation and thereby aims to answer the research question how can

consumers be motivated engage in active consumer participation in European countries?

To answer this research questions, push, pull and mooring factors are identified for all three types of active

consumer participation, and empirically tested for demand response participation and participation in retail

electricity markets. The demand response is proxied by the percentage of consumers with a default tariff

contract. The results of the final GLS model should be interpreted with care, as the model is estimated with

many regressors for few observations. The results give an indication of the relations, but the true hypotheses

need a larger to dataset to draw meaningful interpretations out of the correlations. The results indicate a

negative correlation between the increase in the electricity price and demand response participation. This

contrasts the expectation of Hypothesis 1. An explanation could be that higher electricity prices create more

uncertainty in the market, which makes consumers reluctant to engage in dynamic tariffs (Allcott, 2011).

Moreover, the social norms partly influence the demand response participation through the demand

response projects, but not through prior behavior of others. The insignificant of prior behavior by others

might be because default response participation is invisible to others. This can reduce the perceived social

descriptive norm and decrease peer effects. Moreover, the rollout of smart meters does not seem to

52

significantly affect the default response participation. This is surprising, as smart meters are a prerequisite

for many forms of demand response management. The insignificant relation might be caused by the

imperfect proxy of the default tariff rate for demand participation.

The mooring factor of the information provision regulation indicates that a tighter regulation is correlated

to a decrease in demand response participation. The opposite correlation is expected, as tighter regulation

is expected lead to more standardization and clear provision of information. A possible explanation for this

negative correlation is that tighter regulation might lead to more rules and information, which may create

an information overload. This could increase the behavioral costs of processing this information to the

extent that it outweighs the benefits. Moreover, the interaction effect between the regulation intensity and

the electricity price indicates that policies do not function independently from each other. Interestingly, the

direct correlations of electricity price and regulation intensity are both negative, while the interaction is

positive towards demand response participation. This can indicate co-dependence of financial incentives

and information provision to foster demand response participation. Without sufficient high values of the

other predictor, the individual predictor can lead to a decrease in demand response participation because of

increased uncertainty of higher prices without clear information, or much information without clear

financial benefits. The mooring factor trust is positively correlated with demand response participation.

This is as expected as the external control and the provision of personal data require high levels of trust in

institutions. These levels of trust do not significantly affect the correlations with social norms, which might

indicate that the effect of demand response projects is not dependent on the level of trust in institutions.

In general, the analysis of participation shows push/pull and mooring factors are both correlated with the

demand response participation and can be useful for policy makers to encourage active consumer

participation.

Active participation in electricity retail markets is measured by the external switch rate. The results of the

final pooled OLS model should just like the GLS model be carefully interpreted, as the model is estimated

with many regressors for few observations. The results give an indication of the relations, but the true

hypotheses need a larger to dataset to draw meaningful interpretations out of the correlations. The financial

incentive of the electricity price range is negatively correlated with the switch rate. This contrasts with the

hypothesized effect, as price difference are expected to provide more opportunity for financial benefits.

However, price fluctuations might as well cause perceived uncertainty and unpredictability for consumers.

This may lead to a status quo effect where consumers stay with their current supplier as they want to avoid

future losses. Moreover, the renewable energy consumption is negatively correlated to the switch rate,

which is also opposed the hypothesized effect. The negative correlation might be because consumers who

53

care for renewable energy sources might be less sensitive to price differences. Hence, when these consumers

switched to renewable energy contracts, they might be less likely to switch again.

The mooring factor regulation intensity is positively correlated with the switch rate. This is as hypothesized,

as standardized transparent information is expected to make it easier for consumers to switch suppliers. The

interaction of the electricity price and regulation intensity is negatively correlated with the switch rate. This

contrasts with the hypothesized effect. Tight regulation is expected to make the financial incentives more

salient and transparent to consumers. The results indicate that electricity prices are only negatively

correlated with the switch rate when the regulation is tight. A possible explanation is that high prices can

result in perceived uncertainty among consumers, but only when consumers are well aware of these prices.

Hence, lower prices among tight regulation correlate with higher switching behavior. As hypothesized, the

mooring factor brand loyalty is negatively correlated with the switching rate. The brand loyalty does not

significantly affect the correlation between renewable energy consumption and the switch rate. This might

indicate that consumers of renewable energy sources are more interested in the contract and the related

electricity source than in the green image of the retailer’s brand.

Overall, the analysis of participation the retail electricity markets indicates that push/pull factors as well as

mooring factors can be important instruments for policy makers to encourage active consumer participation.

Theoretical implications

This research adds to the existing body of research on active consumer participation. Most studies are

focused on local pilot initiatives or national contexts. These effects cannot always be generalized to other

contexts because of local policies, infrastructures and cultural differences. Cross-country analyses of active

consumer participation are often conceptual papers, or focused on only one factor of consumer

participation. This research adds to this literature as it analyzes these relations over a larger scope, and

therefore examines which effects hold over a variety of contexts. Including more countries in a panel dataset

helps to identify important factors which influence consumer participation and thereby help to understand

which factors are most important to encourage certain consumer behavior. By retrieving and combining

data from different sources, this research provides a reference to future research on where to acquire data

and which proxies to use to estimate the underlying mechanisms.

The research is based on the PPM framework, which is originally created to explain migration. The

framework has been used before to investigate the “migration” of consumers between electricity suppliers,

but not to analyze the “migration” between two different types of roles in the electricity market. Thereby,

the PPM factors explain how a consumer shifts from one type of behavior to another. This framework can

54

well be applied to this contexts as the two types of behaviors are rather constant behaviors, and therefore

can be compared to two different locations. The PPM framework provides a clear classification to

understand the different factors affecting the behavioral change and inertia and fits well with the micro-

economic cost-benefit analysis. This research provides scholars a new, useful application of the PPM

framework for rather constant behaviors/roles.

Managerial implications

The insights from this research are as well valuable for marketing managers and policy makers. This

research helps policy makers to understand which factors are important to motivate consumers to actively

participate in retail electricity markets. As this research focusses on active consumer participation in

different countries, the result show more general relations than research focused on specific locations or

countries. This implies that the insights are easier to translate to the policy maker’s local or national context.

This can especially be helpful for countries with low levels of active consumer participation, as this implies

that they require less own local pilot projects but can already learn from the experience of other countries.

This research also helps policy makers and marketing mangers through the use of the easy to understand

PPM framework. This framework highlights that consumer behavior is not only affected by costs and

benefits of two options, but as well by the mooring factors which determine the ease to which a consumer

can switch between two types of behavior. Consumers are hard-wired to follow the path of least resistance

and this should be taken into consideration for policies and strategies aimed to influence consumer behavior.

The PPM framework is easy to translate to policies and regulations and therefore can help to understand

the effects of various interventions.

Limitations and future research

Though the research provides interesting and important insights for scholars and practitioners, it is

important to point out its limitations which open avenues for future research.

The hypothesis are tested on a dataset which consists of data from 6 countries. A larger sample containing

more countries would provide more consistent predictions as this would provide better ratio of predictors

relative to observations. This would lead to more robust estimates. The countries are selected based on their

availability of relevant data. It is possible that countries with more data available on consumer participation

are more focused on consumer participation and have higher participation rats. A larger sample would be

more random and contain more diverse countries. This provide better generalizable results. The lack of data

55

in most countries also provides an opportunity for future research. As many countries lack insights on the

behavior of consumers in the electricity markets, there is a lot to explore for scholars. Future research can

create their own measures through surveys, experiments and field research to acquire better insights in

active consumer participation in electricity markets.

The lack of data leads to another limitation in my research, the use of proxies. As demand response

participation is only implemented in a few countries for consumers, it is hard to measure what causes

variation in participation. This research uses the default tariff rate as proxy for demand response

participation. However, future research could improve the validity of the research by directly measuring

participation, such as consumer contracts in dynamic tariffs. Future research can then as well measure

different types of demand response participation and compare the effects on the different types. For

example, the effect of the PPM factors on participation in time of use tariffs and real time tariffs.

To measure of the regulation intensity is currently developed through the response of an expert judgment

panel on a scale of 1 to 10. However, different experts can have different meanings to the various scale

points, and differently interpret the severity of certain measures. Future research might update this index

with a more objective index based on factors as existence of centralized switching procedures, availability

of comparison websites, unbundling of retailers, entry barriers for retailers, standardized contracts, speedy

complaints procedure, price regulation (e.g. price caps), and the role of default suppliers. The information

on these factors can be acquired through interviews with NRAs or industry experts.

This research analyses the different types of consumer participation separately, but these are not necessarily

independent of each other. The different forms of participation share similar and sometimes even the same

PPM factors. Combining the different forms of consumer participation in an integrated model could provide

interesting insights on whether the forms of active consumer participation serve as complements to each

other, or substitutes. This can provide important insights on whether the types of active participation should

be promoted together or separately.

These models serve as reference points for future research which can further explore the dynamics of active

consumer participation. Future research can include regional policies next to national policies in the model

as subsidies and flexibility programs are often on local scale. Moreover, this research focusses on the

consumer participation, but not on the intensity or length of participation. For example, future research

could focus on the frequency of switching behavior or the length of participation in demand response

programs. Finally, future research may as well extent this research to participation of retailers next to

consumers. For example the extent to which retailers provide feed in tariffs for consumers with solar panels

or whether they provide time of use tariffs. Researchers can investigate which policies provide the best

environment for retailers and consumers to increase demand response practices.

56

CONCLUSION

To conclude, this research demonstrates the importance of mooring factors to encourage active consumer

participation. While prior research mainly assessed active consumer participation on a local or national

level, this research takes an international scope to provide more generalizable insights. The results of the

different forms of active consumer participation show the importance of various pull and push factors, and

how these factors are affected by mooring factors. The small sample size limits the validity of the results.

Hence, future research can expand the dataset and build on the data to provide more useful insights in active

consumer participation. Policy makers can use the PPM framework for the design and assessment of their

incentives for active consumer participation.

57

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APPENDIX

Appendix I Example requestion regulation intensity index. European electricity retail market regulations: classification of intensity Dear …, I am a starting PhD student at the University of Groningen. My research is focused on consumer participation in electricity retail markets. In my first project, I focus on the effect of countries’ policies on consumer switching behavior. I will take a special focus on policies aimed to ease the process of active participation. To capture the effect of these policies, I will create an index based on experts’ judgements which indicates the intensity of the regulation. Therefore, I want to ask you if you would be willing to take five minutes to fill in your scores on the intensity of the policies of these countries. The results are handled discreetly, without mentioning your name, function or employer. Your expert judgement would mean a lot to my research and thereby help to find important insights on incentives for active consumer participation in electricity markets. The next page provides a form in which you can indicate your scores for the six selected countries (United Kingdom, Ireland, the Netherlands, Germany, Norway, and Finland) for three years (2012, 2015, and 2019). The level of intensity can be rated on a scale of 1 (very light-handed regulation) to 10 (very heavy handed

regulation), based on the framework of Mulder (2020, p.79) as explained in the Appendix. Please rate the intensity of the retail market regulation on a scale of 1 to 10 of the countries by checking the boxes

with the right scores. The Appendix provides an overview of relevant policies per country. After, you can reply the answered file to h.m.huisman@rug.nl . In case you have any questions or other remarks regarding the research do not hesitate to contact me via email (h.m.huisman@rug.nl) or phone +316 ** ** ** ** Thank you in advance for your help! Kind regards, Hester Huisman

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Please indicate here your expert judgement of the intensity of the regulation of retail electricity markets for the years 2012, 2015 and 2019 (see appendix for description of regulation).

United Kingdom

1 2 3 4 5 6 7 8 9 10

2012

2015

2019

Ireland

1 2 3 4 5 6 7 8 9 10

2012

2015

2019

The Netherlands

1 2 3 4 5 6 7 8 9 10

2012

2015

2019

Germany

1 2 3 4 5 6 7 8 9 10

2012

2015

2019

Norway

1 2 3 4 5 6 7 8 9 10

2012

2015

2019

Finland

1 2 3 4 5 6 7 8 9 10

2012

2015

2019

72

Appendix Appendix I. Overview of framework Mulder (2020, p.79) provides the following framework to classify the intensity of a regulation:

This framework looks at sector-specific regulation. Light-handed regulation involves little regulatory effort. For example, the threat of regulatory measures when a party does not behave according to objectives. Therefore, the regulation might be less effective, but this also softens the effect when the measures are sub-optimal. Heavy-handed regulation involves high regulatory transaction costs, making the regulations more likely to be effective. For example, by intervening in the decisions of the regulated party. However, this also comes with higher risks when the regulations are sub-optimal. For this reason, many regulators opt for intermediate regulation, which aims to affect the outcome of the process and not the decisions of regulated parties (Decker, 2015). Appendix II. Overview of policies per country An overview of relevant policies per countries can be found in the tables below. The regulations are aimed to increase or ease consumer participation in electricity retail markets. Some descriptions provide links to relevant websites case you wish to acquire more knowledge on the policy. Moreover, the policies are classified into four categories of policies, based on Mulder & Willems (2019): Structural measures, Contracting restrictions, Information provision, and Monitoring.

73

United Kingdom

Year Name Description Category

1 2004 Energy UK Safety Net

The Safety Net is a pledge by the larger six suppliers to never knowingly disconnect a vulnerable customer. A customer is considered vulnerable if for reasons of age, health, disability or severe financial insecurity, they are unable to safeguard their personal welfare or the personal welfare of other members of the household. Link

Structural regulation

2 2005

British Electricity Trading and Transmission Arrangement (BETTA)

Single Great Britain electricity market. Link Structural regulation

3 2008 Complaint handling regulations

Complaints handling standards Regulations that are to be prescribed for handling complaints in the energy sector under section 43 of the Consumers, Estate Agents and Redress Act 2007. Link

Contracting restrictions

4 2008- 2012

Non-discrimination condition

To address “unfair” price differentials between areas, differences in charges by suppliers for different payment types must be cost-reflective. Link

Contracting restrictions

5 2009 Energy Act 2009

An Act to define the term “green energy”; to promote its development, installation and usage; and for connected purposes. Link

Structural regulation

6 2009

Energy companies' Consolidated Segmental Statements

It aims to increase transparency of energy companies’ revenues, costs and profits by requiring the large vertically integrated energy companies to produce and publish Consolidated Segmental Statements (CSS). Link

Information provision

7 2010

Regulation of marketing to domestic customers

Guidance on domestic marketing discouraging doorstep selling and direct marketing by retailers. Link

Contracting restrictions

8 2011 Energy Act 2011

An Act to make provision for the arrangement and financing of energy efficiency improvements to be made to properties by owners and occupiers; about the energy efficiency of properties in the private rented sector; about the promotion by energy companies of reductions in carbon emissions and home-heating costs; about information relating to energy consumption, efficiency and tariffs; for increasing the security of energy supplies; about access to upstream petroleum infrastructure and downstream gas processing facilities; about a special administration regime for energy supply companies; about designations under the Continental Shelf Act 1964; about licence modifications relating to offshore transmission and distribution of electricity; about the security of nuclear construction sites; about the decommissioning of nuclear sites and offshore infrastructure; for the use of pipelines for carbon capture and storage; for an annual report on contribution to carbon emissions reduction targets; for action relating to the energy efficiency of residential accommodation in England; for the generation of electricity from renewable sources; about renewable heat incentives in Northern Ireland; about the powers of the Coal Authority; for an amendment of section 137 of the Energy Act 2004; for the amendment and repeal of measures relating to home energy efficiency; and for connected purposes. Link

Structural regulation

9 2013 The Retail Market Review

Promoting transparency of energy company profitability is an important aspect of Ofgem’s efforts to rebuild consumer confidence in the energy market and improve competition, implementation of Simpler Tariff Choices and Clearer Information. Link

Information provision

10 2014

Improve the transparency of energy company profits

Promoting transparency of energy company profitability is an important aspect of Ofgem’s efforts to rebuild consumer confidence in the energy market and improve competition. Link

Information provision

11 2015 Debt Assignment Protocol

The process known as the Debt Assignment Protocol (DAP) is designed to help prepayment customers who are in debt to switch suppliers. Link

Contracting restrictions

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12 2015 Energy Café

An Energy Café is a local community initiative providing energy advice and advocacy in a welcoming setting - often with tea and cake! People who attend Energy Cafés will receive tailored advice, information and support on a range of issues, including: how to effectively engage in the energy market to reduce energy bills, how to deal with fuel debt, and how to reduce energy consumption and energy costs by cutting unnecessary energy use and energy loss from their properties. Energy Cafés provide an opportunity to identify vulnerable members of the community and refer them to other services such as housing and health authorities. They can also act as a triage service providing appropriate referrals to other services such as a home visit service. Link

Energy audits Energy bill support Information and awareness Social support

13 2016

Helping consumers make informed choices – proposed changes to rules around tariff comparability and marketing

We're consulting on the principles that we might use to require suppliers to help ensure that domestic consumers are able to make informed choices about their energy supply. This includes choices made by consumers in response to a marketing approach from suppliers or their representatives. It will also set out the consequential amendments we propose to make to the Retail Market Review (RMR) Clearer Information tools, in light of the Competition and Markets Authority's (CMA) recommendation to remove the majority of the RMR Simpler Tariff Choices rules. Link

Information provision

14 2018 Smart Meters Act 2018

An Act to extend the period for the Secretary of State to exercise powers relating to smart metering; to provide for a special administration regime for a smart meter communication licensee; and to make provision enabling half-hourly electricity imbalances to be calculated using information obtained from smart meters. Link

Structural regulation

15 2018 Tariff Cap Act

This legislation requires Ofgem to design and implement a temporary cap on standard variable tariffs and fixed term default tariffs (‘default tariffs’). Link

Contracting restrictions

Ireland

Year Name Description Category

1 1990

Code of Practice for Energy Suppliers

The Code of Practice for Energy Suppliers strengthens the rights of electricty and gas customers. Several rules on disconnecting customers' energy supply are included. For example, a supplier must contact the customer multiple times before they are allowed to disconnect the customer's supply and vulnerable customers cannot be disconnected between the months of November and March. Link

Disconnection protection

2 2005 Full market opening Structural

regulation

3 2007 Single electricity market (SEM)

Legal Framework for SEM goes active. Link Structural regulation

4 2010

Business market segements deregulated

Retail Market Opening Business Process Approval. Link Structural regulation

5 2011 Domestic market deregulated

CER Announces Price Deregulation. Link Structural regulation

6 2013

Price comparison website uswitch

CER Accredits a Price Comparison Website. Link Contracting restrictions

7 2014 The EAI Energy Engage Code

Energy suppliers signed up to the EAI Energy Engage Code commit to never disconnect an engaging customer. An engaging customer is a customer who is communicating with the supplier and genuinely working to clear arrears on their account. Link

Disconnection protection

8 2014 Voluntary code on disconnections

Most suppliers committing to never disconnecting an engaging customer. Link Contracting restrictions

9 2015 Comparison website Bonkers

CER Accredits a Price Comparison Website. Link Information provision

75

10 2017 Estimated Annual Bill

Requirements for suppliers to display an Estimated Annual Bill (EAB) in their marketing and advertising, to give customers 30 Days’ Notice prior to the end of the customer’s fixed-term contract, and to issue an Annual Prompt to customers who have been on the same tariff for 3 years or more (highlighting the availability of alternative tariffs). Link

Contracting restrictions

11

2018

I-SEM

The Single Electricity Market (SEM) on the island of Ireland is undergoing a radical transformation arising from changes to European legislation. These changes are designed to create a single wholesale market across the European Union (EU). The new market design will result in the Integrated Single Electricity Market (I-SEM). Link

Structural regulation

12 2018 Customer engagement campaign Switch on

Campaign to inform energy consumers of their savings, their rights and their safety. Link

Information provision

13 2020 Comparison website Powertoswitch

CER Accredits a Price Comparison Website. Link` Information provision

Netherlands

Year Name Description Category

1 2004 Opening retail market

Structural regulation

2 2004 Tariff surveilance (Safety net)

Four weeks before offering a new contract to consumers or four weeks before a price change, retailers are required to submit the contract to the reg- ulator, which will check whether the contracting conditions and in particular the price, are not unreasonable.

Contracting restrictions

3 2004

Assessment of price comparison websites

Requirements for price comparison websites. Link Monitoring

4 2004

Sampling experience customer experience

Monitor experience of consumers. Monitoring

5 2006 Code of conduct retailers I

Codes of conduct are meant to improve market transparency (by making information more comparable), but also to prevent abuses (telemarketing, door-to-door selling).

Information provision

6 2008 Full unbundling retailers

Energy-distribution companies were forced by the government to unbundle ownership of commercial activities. Network operation and ownership, however, could not be privatized and remained in hands of local and national governments. Link

Structural regulation

7 2008

New market model: reatilers single point of contact

This measure gives additional safeguards against disconnection for vulnerable households, and prohibits disconnection of all households during the winter (October 1 - April 1).

Structural regulation

8 2008

Maximum penalty contract breach

Maximum on the penalty that consumers pay in case of early contract breach. Link Contracting Restrictions

9 2009 Capacity tariff for distribution

Fixed tariff of distribution costs for households. Link Structural regulation

10 2009 Guidelines on information provision

Inclusion of guidelines in Electricity law. Link Information provision

11 2009

Voluntary guidelines composition of energy bill

Non-binding agreements. Link Information provision

12 2009 Code of conduct retailers II

Update of first code of conduct retailers. Link Information provision

13 2012

Prohibition for automatic renewal of contracts

After termination of a fixed contract, a consumer has the opportunity cancel the contract every month. Link

Contracting restrictions

76

14 2012 Obligation to offer a model contract

Each energy retailer should offer a standardized product that is identical across retailers on all aspects except price. Link

Contracting Restrictions

15 2013 Code of conduct smart meters

Guidelines on individual meter data. Link Information provision

16 2015 Code of conduct retailers III

Includes the provision that consumers need to be precisely informed about the total annual costs of a specific offer. Link

Information provision

17 2018 Disconnection protection households

This measure gives additional safeguards against disconnection for vulnerable households, and prohibits disconnection of all households during the winter (October 1 - April 1). A household consumer is considered vulnerable if the termination of the transport or the supply of electricity or gas would result in very serious health risks to the consumer or a member of the same household. Link

Contracting restrictions

Germany

Year Name Description Category

1 2008 Caritas Electricity saving check

This measure provides certain households with an energy audit to advise on energy efficiency improvements, as well as some basic energy saving equipment (for example LED lights). Long-time unemployed persons are trained to give the advice. Households were also given the option to buy a new energy efficient fridge against a lower price. Link

Information provision

2 2009 Liberalization Metering in the electricity and gas sector was liberalized, allowing a free market for metering service suppliers.

Structural regulation

3 2011

Tightening of unbundling requirements for TSOs.

Structural regulation

4 2014

Launch Market Transparency Unit for Electricity and Gas Wholesale Trading

Monitor electricity and gas wholesale trading in order to detect any irregularities in price developments at the wholesale level which could be attributed to abusive practices. Link

Information provision

5 2017

Sector inquiry on comparison websites

A large number of website operators were questioned on topics such as rankings, financing, corporate links, reviews and market coverage, in order to uncover and specify possible violations of consumer law provisions. Link

Monitoring

Norway

Year Name Description Category

1 1990 Liberalisation retail market consumers

Structural regulation

2 1997 Abolishment switching fees

Consumers do not incur any direct pecuniary costs of switching retailer. Contracting restrictions

3 1998

Consumers could switch retailer whenever they wanted.

Initially, consumers could switch retailer at the end of each quarter only. Now at any time.

Contracting restrictions

4 1998 Price comparison tool

The Norwegian Competition Authority ran a price comparison tool for electricity from 1998, to enable consumers to compare electricity contracts. Link

Information provision

77

5 2015 New price comparison tool

The old tool was replaced by The Consumer Council of Norway’s new price comparison tool strompris.no. Link

Information provision

I 2016

Requirements for the design of invoices for consumers

Make invoices clear and easy for the consumer to understand, include information on how to compare suppliers. Link

Information provision

7 2017 Launch Elhub New IT solution for information exchange between actors in the power market. Link Information provision

Finland

Year Name Description Category

1 2004 Electricity market act

Act to ensure preconditions for an efficiently functioning electricity market so as to secure the sufficient supply of high-standard electricity at reasonable prices, including requirements on invoices. Link

Structural Regulation

2 2006 Web-based comparison tool

Tool to facilitate supplier switching and, in general, to increase customers’ awareness on electricity prices.

Information provision

3 2013 Disconnection prohibition in winter

Households that rely on electricity or natural gas to heat their homes cannot be disconnected during the winter months (October 1 - April 30). Link

Contracting restrictions

4 2013 Unbundling Structural Regulation

5 2013 Amendments Electricity market act

Including conditions of sale addressed to consumers must be communicated in clear and comprehensible language and must not include obstacles to the exercise of consumers' rights which are not directly provided for in contracts. Link

Information provision

6 2015 Fingrid datahub

A shared system to clarify and speed up this exchange of information. Datahub will improve the operation of all parties – the electricity consumers, electricity suppliers and the parties responsible for electricity transmission – since all data and transactions associated with the consumption of electricity are located in a single system, are up-to-date and equally available for all eligible parties. Link

Information provision

7 2018

Renewed price comparison tool

The Energy Authority has also addressed issues concerning different forms of abuse of the price comparison website by suppliers aiming to appear as one of the suppliers with the cheapest products. Link

Information provision

78

Appendix II Year effects of dynamic models:

AH AB-1 AB-2 AB-C SYS SYS-C GLS GLS int GLS Final

2011 Omitted

2012 Omitted -0.088 (0.151)

-0.002 (0.069)

2013 Omitted -0.177 (0.155) Omitted Omitted Omitted Omitted -0.077

(0.101) -0.005 (0.094)

-0.038 (0.083)

2014 Omitted 0.016 (0.199) Omitted Omitted Omitted -0.022

(0.112) 0.058

(0.109) 0.014

(0.083)

2015 -1.224 (.)

-0.277** (0.144) Omitted Omitted Omitted -0.003

(0.136) -0.182 (0.130)

-0.112 (0.135))

-0.150 (0.094)

2016 -2.348 (.)

-0.108*** (0.056) Omitted Omitted -0.183

(0.142) -0.100 (0.134)

-0.186 . (0.096)

2017 -1.824 (.) 0.017

(0.053) -0.084 (0.151) Omitted -0.269

(0.185) -0.113 (0.188)

-0.260 . (0.129)

2018 Omitted 0.164 (0.112)

0.017 (0.076) Omitted Omitted Omitted -0.381

(0.252) -0.252 (0.267)

-0.429* (0.173)

Appendix III

Year effects dynamic models.

P-OLS FE RE CRE HT AM P-OLS int P-OLS

Final 2011 0.000

(.) -0.252* (0.126)

-0.193 (0.127)

-0.252*** (0.072)

2012 0.032 (0.091)

-0.194 (0.144)

-0.161 (0.125)

-0.194 (0.132)

0.044 (0.067)

0.044 (0.067)

0.013 (0.087)

-1.118 (1.604)

2013 0.011 (0.092)

-0.218 (0.131)

-0.181 (0.120)

-0.218* (0.129)

0.028 (0.066)

0.028 (0.066)

-0.003 (0.088)

-0.005 (0.095)

2014 0.131 (0.098)

-0.050 (0.131)

-0.062 (0.106)

-0.050 (0.136)

0.190** (0.073)

0.190** (0.073)

0.114 (0.093)

-0.004 (0.091)

2015 0.215** (0.106)

0.051 (0.127)

0.022 (0.101)

0.051 (0.147)

0.290*** (0.078)

0.290*** (0.078)

0.197 (0.100)

0.096 . (0.097)

2016 0.334*** (0.109)

0.173 (0.118)

0.141 (0.098)

0.173 (0.115)

0.419*** (0.081)

0.419*** (0.081)

0.309* (0.102)

0.158** (0.107)

2017 0.208* (0.123)

0.049 (0.080)

0.016 (0.091)

0.049 (0.083)

0.322*** (0.090)

0.322*** (0.090)

0.191 (0.115)

0.260 (0.112)

2018 0.193 (0.127)

0.000 (.)

0.000 (.)

0.000 (.)

0.303** (0.100)

0.303** (0.100)

0.159 (0.118)

0.116 (0.131)

79

Appendix IIII

Between effects CRE model

CRE

Mean price 0.889*** (0.299)

Mean price range -0.257*** (0.041)

Mean regulation 0.690*** (0.026)

Mean renew energy -0.307 (0.190)

Mean price × regulation

0.000 (.)

Mean price range × regulation

0.000 (.)

Mean renew energy × brand loyalty

0.000 (.)