ISSN 1881-6436

Discussion Paper SeriesNo. 18-02

Empirical analysis of Japanese households’ switching behaviour for energy
after the deregulation of electricity and gas retail sales
Shin Kinoshita
September 2018

Faculty of Economics,
Ryukoku University
67 Tsukamoto-cho, Fukakusa, Fushimi-ku,
Kyoto, Japan
612-8577

1

Empirical analysis of Japanese households’ switching behaviour for energy
after the deregulation of electricity and gas retail sales
Shin Kinoshita1
Abstract
The deregulation of electricity retail sales for general households started in April 2016, and thederegulation of gas retail sales started in April 2017. Every household can choose their preferred
electric power and gas company among all suppliers. In the electricity retailsales, urban gas
companies and telecommunication companies start to sell electricity, and in the gas retail sales,
electric power companies start to sell gas. In my questionnaire, in February 2018, the switchingrate of electricity was 19%, and that of gas was only 5.6% out of 1000 households in Kanto andKansai area. New entrants in the gas sales are very few compared with electricity sales.
I analyze households’ switching of a supplier or their choice of an energy source after the
deregulation. Moreover, I analyze the future conditions that households will change a supplieror choose an energy source by a conjoint analysis. As conditions, a monthly total electricity andgas bill, CO2 emissions, the use of nuclear power, the ratio of renewable energy, optionalenergy-saving appliances and additional security services are considered.
After the deregulation, some suppliers provide both electricity and gas. We need to analyze thewhole energy market including both electricity and gas. Under some conditions, somehouseholds might use either of electricity or gas intensively. For example, when some suppliers
provide electricity generated by renewable energy as their main energy source, households who
prefer renewable energy purchase only electricity generated by renewable energy. Other
households who object to nuclear power won’t purchase electricity from suppliers which use
nuclear power and they may purchase gas as a main energy. Through the deregulation, thesubstitution between electricity and gas might be promoted. I estimate the preferences for
suppliers among Japanese households. I used a random parameter logit model for estimation.
From the estimation results, we obtain some policy implications. Firstly, from the negative andsignificant coefficient of a monthly total electricity and gas bill, households choose a supplierand an energy which provides lower bull. Lower bills are the key to be chosen by households.
From the negative and significant coefficient of CO2 emissions, households prefer a supplierand an energy which reduces CO2 emissions. However, energy sources both nuclear power andrenewable energy which suppliers use as energy sources for generation don’t affect households’
switching. The result is different from my hypothesis. Households don’t choose an alternativewith optional appliances because their prices are too expensive. On the other hand, households
need security services.
The notable point of this paper is to evaluate the deregulation in the comprehensive energy
market including electricity and gas. The result proposes the desirable energy supply system
including alliances, elimination and consolidation after the deregulation. The result is relatedwith the energy composition including reoperation of nuclear power plants and prevalence of
renewable energy sources.
Key words: the deregulation of retail electricity and gas sales; renewable energy; thesubstitution between electricity and gas
JEL Classification Code:C25, L51, L94, L95, Q28

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E-mail: skinoshita@econ.ryukoku.ac.jp

Empirical analysis of Japanese households’ switching behaviour for energy
after the deregulation of electricity and gas retail sales2
1. Introduction
The deregulation of electricity retail sales for general households started in April 2016, and thederegulation of gas retail sales for general households started in April 2017. Every household isfree to choose an electric power company and a gas company among all suppliers. In theelectricity retail sales, gas companies and telecommunication companies start to sell electricity,and in the gas retail sales, electric power companies start to sell gas to households. Households
are also free to choose a bill plan except conventional regulated bill plans as well as suppliers.
In March 2017, after the deregulation in electricity retail sales, the number of households who
switched a supplier from an existing major electric power company in their area to a new entrant
was about 2,950,000 in the whole of country, and the switching rate was only 4.7% on averagein the whole of country3. The rate was 7.1% in the Tokyo electric power company area and6.1% in the Kansai electric power company area. In other areas, the rate was only about 1%.
Even though households can choose a new bill plan freely as well as conventionalregulated billplans, the number of household who switched a bill plan in the existing major electric powercompanyin their areawas 2,580,000 (4.1%) in the whole of country in March 2017. On the other
hand, the number of households who switched a gas bill plan was only 270,000 in the whole ofcountry in June 2017. New entrants in gas retail sales are very few compared with electricity
retail sales. In electricity retail sales, an electric power exchange is operated, and new entrants
sell their generated electricity in the exchange and distribute electricity through the power
distribution grid system of existing major electric power companies to households. However, ingas retail sales, new entrances are limited in the area where gas pipes are laid. In gas retail sales,
there is no exchange. New entrants are limited to the companies which treat gas and companies
which can provide gas are limited to the existing major electric power companies which havethermal power plants and generate electricity by natural gas, LP gas companies, oil companies
which import gas. The operation areas of urban gas are limited to urban areas.
In this paper, I analyze that households switch their supplier after the deregulation in electricity
and gas retail sales and the future conditions that households will switch their supplier. Somestudies analyze the preferences for suppliers after the deregulation in electricity retail sales.
However, many studies discuss the deregulation in electricity retail sales limited. After the
deregulation in electricity and gas retail sales, many suppliers provide both electricity and gas.
We need to analyze the whole energy including both electricity and gas. Through thesubstitution of electricity and gas and under some conditions, households may use either ofelectricity or gas intensively. As an example, households who prefer renewable energy purchase
only electricity generated by renewable energy sources. Other households who object to nuclear
power avoid electricity generated by nuclear power and may purchase only gas. We will discuss
the substitution and competition of suppliers and, in addition, of electricity and gas.
I make researches whether households switched an electric power or gas company after the
deregulation or not. I analyze the attributes of households and the reasons to switch their
supplier by a probit model through collected data, and analyze what supplier and energy are
preferred among households in the future through a conjoint analysis which is one of the statedpreference methods. I use a random parameter logit model for estimation.
One of the notable points is to evaluate the deregulation in the comprehensive energy retailsales including both electricity and gas. The results will propose the desirable energy supply
2This study was aided grants-in-aid for scientific research (C) (Kakenhi) of Japan Society for The
Promotion of Science (No. 16K03679).
3This is from the survey of the Agency for Natural Resources and Energy in the Ministry of Economy,
Trade and Industry.

3system including alliances, elimination and consolidation of suppliers after the deregulation.
The results will be related with the energy composition. The discussion of the restart of nuclear
power plants and the prevalence of renewable energy are needed.
This paper consists of the following section. In section 2, the history of the deregulation inelectricity and gas retail sales in Japan is illustrated. In section 3, the related literature isdiscussed. In section 4, I illustrate the survey and a conjoint analysis. In section 5, I illustrate theresults of the survey. In section 6, econometric models are explained. In section 7, theestimation results are discussed. In section 8, subsample analysis is conducted. In section 9, therevealed preference method is conducted. In section 10, we discuss the conclusions and policy
implications.

2. Overview of the deregulation in the electricity and gas retail sales in Japan4
The deregulation of electricity retail sales started in 1995. As the first stage, Independent
Power Producers (IIP) could provide wholesale electricity to electric power companies as new
entrants. In 2000, Power Producer and Suppliers (PPS) as new entrants started to provide
electricity. The targets were limited to large scale consumers such as factories, office buildings,
and commercial facilities. In 2004 and 2005, the target consumers were expanded to smallerscale consumers. High-pressure consumers such as small and medium-sized buildings andfactories became the targets. The deregulation for general households and private shops was tobe discussed in 2007 but it was postponed. Since April 2016 the deregulation for generalhouseholds has started. Now households purchase electricity from all electric power companies
including new entrants and the major electric power companies in other areas.
The deregulation of urban gas started at the same time in electricity retail sales. In 1995 large
scale consumers whose annual contract was over two million m3 became the target of the
deregulation. In 1999 the target is expanded to consumers whose annual contract is over one
million m3. The transportation service was introduced, and the price regulation was changed
from the approval system to the notification system. In 2004 the gas conducting pipe project
was established. The transportation service charge system was enhanced, and the obligation of
the transportation service were expanded to all general gas suppliers and gas conducting pipe
suppliers. The fairness and transparency were aimed in the transportation service. The
transportation service makes any gas suppliers possible to provide gas to large scale consumers
even in areas of other general gas suppliers. Under the transportation service, any large-scale
suppliers provide gas to large scale consumers through the gas conducting pipe of urban gas
companies. In 2007 the target of the deregulation was expanded to consumers whose annual
contract is over 100 thousand m3. The target includes gyms with a hot pool, large scale
restaurants and commercial hotels. Since April 2017, all consumers including generalhouseholds have become the target of the deregulation. They can purchase gas from newentrants as well as existing major urban gas suppliers.

3. Related literature
There are very few related studies about the deregulation of electricity and gas retail sales forgeneral households in Japan because only two years have passed. We discuss the previous
studies about this. My study focuses on the two points. First one is what supplier households
prefer. Second point is what energy households prefer. After the deregulation, various kinds ofsuppliers provide electricity and gas. Some suppliers may provide electricity generated by only
renewable energy. Some suppliers will provide energy-saving appliances with electricity and
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The illustration about the deregulation of electricity and gas retail sales by the Agency for Natural
Resources and Energy in the Ministry of Economy, Trade and Industry is referred.

gas. In addition, I analyze the substitution between electricity and gas. Under some condition,some households use only electricity and some households use only gas. For example,
households who support renewable energy use only electricity generated by only renewableenergy.
Nakajima, Ida and Kinoshita (2005) analyzed the competition between electricity and gas by aconjoint analysis. In 2005 before the deregulation of electricity retail sales for generalhouseholds, the competition between electricity and gas was very heated. The deregulation ofelectricity retail sales for general households was planned to start to be discussed in 2007. An
all-electric service was promoted by electric power companies, and the competition between
electric power companies and gas companies was very heated. They estimated the preferences
for advanced electric and gas appliances. Their aim is like my paper, and I refer their profiles ofa conjoint analysis. In Nakajima, Ida and Kinoshita (2005), households faced the following
three alternatives: 1. They use both electricity and gas 2. They use electricity intensively. 3.
They use gas intensively. In alternative 1, households use electricity and gas in a conventionalway, while in alternative 2 and 3, they use advanced appliances such as a fuel battery. I referthese alternatives. From the results of Nakajima, Ida and Kinoshita (2005), in 2005 thepreferences for an all-electric service and a fuel battery were different across areas and ages.
Advanced appliances were needed among the limited households such as elders. My survey was
conducted in February 2018. Households have enough information about the deregulation
through TV commercial or sales and marketing of electric power and gas companies. We hopeto gain different results from those in 2005.
Ida and Murakami (2016) analyzed households’ choices for suppliers in January 2016 before
the deregulation of electricity retail sales. They estimated the preferences for new coming
electric power companies and new bill plans by a conjoint analysis. The bill plans wereregulated before the deregulation, and households could not choose only conventional electric
light fees. We hope that the time-of-day rate system to reduce electricity consumption at thepeak will be provided after the deregulation. However, sometimes we observe the status-quo
bias that consumers hesitate to switch to a new supplier and a new bill plan due to their
psychological burdens. From the results, though the status-quo bias is observed, households
have their motivation to switch to the time-of-day rate system which is one of the new bill plans.
The attributes of alternatives were bill plans, the ratio of renewable energy, the ratio of nuclear
power and a monthly electricity bill. They analyzed only the deregulation of electricity retailsales, but I analyze and evaluate both the deregulation of electricity and gas.
Goto (2017) examined the switching behavior of households after the deregulation ofelectricity retail sales in Japan. This paper surveyed the switching behavior of households for
suppliers in February 2016, before the deregulation, and December 2016, after the deregulation.
They analyzed the reasons to switch a supplier or to consider switching a supplier by a logit
model. They considered the benefit from switching such as a reduction of bill and the cost fromswitching such as a hesitation of switching from uncertainty of new suppliers, new bill plans,stable electricity supply and the management of new suppliers as the reasons. And the customer
loyalty is the key reason that households don’t switch their supplier because they are notdissatisfied with their current supplier. From the estimation results, the cost of switching is large,
and the customer loyalty is observed. However, they discuss only the deregulation of electricity
retail sales, but the deregulation of gas is outside the scope of the investigation.
There are some studies in foreign countries. Goett, Hudson and Train (2000) analyzed thehouseholds’ choice behavior for electric power companies in U.S by a conjoint analysis. The
attributes are the characteristics of suppliers such as their operation area and their namerecognition, bills and the ratio of renewable energy. Giulietti, Price and Waterson (2005)
analyzed the consumers’ switching behavior for suppliers in a U.K home natural gas market. In
the switching of a supplier, firstly consumers’ mind changes, next they search suppliers and billplans, and lastly, they decide to switch their supplier or not. They use a bivariate probit model

5

for estimation. They observe the search cost and the switching cost. Both intrinsic and external
factors affect their mind and switching. Hortacsu, Madanizadeh and Puller (2016) analyzed theswitching behavior of households for suppliers and bill plans in the electricity market in Texas
U.S. They point out the possibility that households don’t switch their supplier and bill plan due
to the consumer’s habit even if they choose a supplier freely. The reasons are the burdens ofsearch, unconcern, brand superiority. They found that the lower cost information intervention
increased the consumer surplus.

4. Outline of the survey
I analyzed whether households switched an electric power or a gas company after the
deregulation in electricity retail sales in April 2016 and in gas retail sales in April 2017. In
addition, I surveyed the reasons they switched or didn’t switch their supplier. I asked the
supplier’s name when they switched. Moreover, I asked that they switched a new bill plan froma regulated one even though they didn’t switch their supplier. As other questions, I asked theelectricity and gas expenditure in January 2018, their interest in energy-efficiency appliancessuch as a storage battery and a private power generation fuel cell (gas cogeneration system),their dwelling type, the number of family members living in the same house, household income,the most preferred energy source etc. I analyzed how these factors cause to switch an electric
power or gas company by a probit model through the collected data.
Only two years have passed since the deregulation in electricity retail sales started, and only
one year has passed since the deregulation in gas retail sales started. Now, the switching amonghouseholds has not prevailed yet. I analyze the future conditions that households switch their
supplier. To analyze the future switching behavior, I use a hypothetical questionnaire. I present
several hypothetical suppliers as alternatives and ask to choose the most desirable supplier forhouseholds. This research has some notable points. First one is what suppliers are preferred byhouseholds. After the deregulation, various suppliers with various services and energy sources
will start their business. I estimate the preferences for various suppliers through a choicequestionnaire. Another point is how the preferences for electricity and gas change after the
deregulation. For example, some suppliers will provide electricity generated by renewableenergy sources to avoid the global heating. Households who prefer renewable energy purchase
only electricity from such a supplier and they don’t purchase gas. Or, other households who
object to nuclear power don’t purchase electricity from suppliers which use nuclear power andmay purchase only gas. I use a conjoint analysis for the hypothetical questionnaire.5
A conjoint analysis is one of the stated preference methods (SPM). Individual preferences canbe estimated for hypothetical goods or services, each of which have several attributes. We canevaluate each attribute by willingness to pay (WTP). Households choose one alternative of the
hypothetical goods or services. A conjoint analysis is used to analyze households’ switching
behavior for suppliers under hypothetical situations wherein a monthly electricity and gas billchanges as an example. A conjoint analysis is one of the choice experiments. In this paper, four
alternatives are presented to households and they choose the most preferred one. Sometimes, thegoods or services have not yet prevailed, and this method is often used in marketing research.
Contingent Valuation Method (CVM) is another popular stated preference method, but it is nota choice experiment. CVM can be used to evaluate users’ valuation of non-marketable targets
such as forests and beaches. CVM evaluates the value of one target and doesn’t evaluate thevalue of each attribute.
In a conjoint analysis, the researcher presents goods or services, each of which has several
attributes to households. The researcher decides the number of attributes. A profile that has few
attributes is not enough to describe a good object of study, but a profile with too many attributes
5

6

I refer to Louviere, Hensher and Swait (2000), Kuriyama and Shoji (2005), Tsuge, Kuriyama and Mitani(2011), and Kuriyama, Tsuge and Shoji (2013) for a conjoint analysis.

makes it difficult for participants to choose among options. In general, five or six attributes issuitable. After attributes and their levels are selected, their profiles are compiled. However, if all
the combinations of attributes and levels are adopted, the patterns are too many and cause strong
correlation between some attributes, i.e., multicollinearity. To avoid these problems, profiles are
created by the orthogonal planning method. We need to select attributes and their levels to avoid
this problem. From various cards that we get through the orthogonal planning method, andselecting cards and their combinations, profiles are made after deleting unrealistic and dominant
cards. I used the Excel conjoint analysis version 2.0 (Esumi) for the orthogonal planning.
I present the following four alternatives and households choose the most desirable one.
Alternative 1: Households purchase electricity from an existing major electric power company
and gas from an existing major gas company. They don’t change both electric power and gas
company after the deregulation. They purchase electricity and gas from the same suppliers
before the deregulation.
Alternative 2: Households switch either electric power and gas company after the deregulation.
They purchase electricity and gas from the same one supplier. They use both electricity and gas
after the deregulation.
Alternative 3: Households switch either electric power and gas company after the deregulation.
They purchase electricity and gas from the same one supplier. They use gas as a main energy.
Alternative 4: Households switch either electric power and gas company after the deregulation.
They purchase electricity and gas from the same one supplier. They use electricity as a mainenergy.
Each alternative represents suppliers which provide electricity and gas. In alternative 1,
households don’t switch both electric power and gas company after the deregulation. Thismeans that they purchase electricity and gas from the existing major electric power and gas
company in their living area like before the deregulation. In alternative 2, 3, and 4, households
switch either electric power and gas company after the deregulation. In alternative 2, households
switch either electric power and gas company after the deregulation and purchase electricity andgas from the same one supplier. However, they use both electricity and gas. In alternative 3,
households switch either electric power and gas company after the deregulation and use gas as amain energy. In alternative 4, households switch either electric power and gas company after the
deregulation and use electricity as a main energy. One of the remarkable points is to find theconditions that households switch suppliers. In alternative 3 and 4, the substitution of electricity
and gas after the deregulation is analyzed. In some conditions, households may use only
electricity or gas.
Each supplier provides electricity and gas with several attributes. In this analysis, a totalmonthly electricity and gas bill, CO2 emissions, use of nuclear power, the share of renewableenergy sources, optional energy-saving appliances, and security services are considered as
attributes. These attributes and their levels will now be discussed in detail.
1. A total monthly electricity and gas bill
Under some conditions, a total monthly electricity and gas bill will change. When households
contract electricity, gas, and telecommunication services with the same supplier, the bill will be
discounted. When households use energy-saving appliances such as a storage battery and a
private power generation fuel cell (gas cogeneration system), the bill will be reduced.
Sometimes, the bill depends on energy sources. Using nuclear power, the bill will be lower.
Using renewable energy sources, the bill will be higher. The levels are -5000 yen, -3000 yen, 2000 yen, -1000 yen, -500 yen, 0 yen (unchangeable), +1000 yen.
2. CO2 emissions
CO2 emissions will change in 2030 compared with 1990 which is the benchmark year of the
Kyoto Protocol. The levels are -30%, -20%, -10%, 0% (unchanged), and +10%. CO2 emissions

7

are related with energy sources. Nuclear power might reduce CO2 emissions. Coal and LNG
might increase CO2 emissions while renewable energy might reduce CO2 emissions.
3. Use of nuclear power
The existing major electric power companies have nuclear power plants and can provide
electricity generated by nuclear power. New coming suppliers don’t have nuclear power plants.
Using nuclear power realizes lower electricity bills and reduces CO2 emissions. In an
econometric analysis, dummy variables are used. For suppliers which use nuclear power, 1 isassigned and for suppliers which don’t use nuclear power, 0 is assigned.
4. The share of renewable energy sources
Some new coming suppliers will provide electricity generated by renewable energy sources
such as solar and wind power as a main energy source. The existing major electric powercompanies also provide electricity generated by renewable energy sources. The levels are 5%,10%, 30% and 50%. In alternative 3, households use natural gas intensively as a main energy
source. The levels are 5% or 10%. In alternative 1, the level is 5% because the share of
renewable energy sources is around 3% before the deregulation for example in 2014.
5. Optional energy-saving appliances
When households choose suppliers to purchase electricity or gas, they can purchase various
kinds of advanced energy-saving appliances. Whether households purchase these appliances or
not is optional, and they don’t need to purchase these appliances if they don’t need them. If theypurchase these appliances, they should pay money as an initial cost. However, purchasing theseappliances reduces their monthly electricity and gas bill and contributes to the global heatingproblem. Households purchase a private power generation fuel cell (a gas cogeneration system)
when they choose gas as a main energy, and they purchase an energy-saving water heater when
they choose electricity as a main energy. The initial cost is assumed one million yen. Whenhouseholds use both electricity and gas, they usually switch only supplier without purchasingenergy-saving appliances. When households don’t switch a supplier, they don’t purchaseenergy-saving appliances6. The utility level, when households choose alternative 1, sets 0.
6. Security services
Households can receive security services and maintenance services of appliances asaccompanying services from electric power and gas companies. The services are assumed to be
free. Households receive services to detect gas leak from gas companies and fix electricity
troubles at outages from electric power companies. When households choose alternative 1, they
can’t receive any services for 0 utility in alternative 1. In an econometric analysis, a dummy
variable is used, assigning 1 with services and 0 without services.
Table 1 is the summary of attributes’ levels.
Table 1 The levels of attributes
Attributes
Levels
A total monthly bill
-5000 JPY, -3000 JPY, -2000 JPY, -1000 JPY, -500 JPY, 0 JPY(unchanged), +1000 JPY
CO2 emissions-30%, -20%, -10%, 0% (unchanged), +10%
Use of nuclear power
Yes (1), No (0)The share of renewable 5%, 10%, 30%, 50%
energy sources
Optional energy-saving Purchase (1million JPY), not purchase (0 JPY)
appliances
Security services
Yes (1), No (0)68The fact was found in the interview for the Osaka gas corporation.

I make profiles, using the levels and combining cards created by the orthogonal planning
method. Profiles are made after deleting unrealistic and dominant cards. For example, the casethat CO2 emissions increase even though nuclear power or renewable energy sources are used,
and the case that monthly bill is higher even though households purchase energy-saving
appliances are deleted. In alternative 4, where electricity is used intensively, it is impossible toprovide cheaper electricity without using nuclear power and with the share of renewable energy
sources 50%7. When the share of renewable energy sources is raised to 30% or 50%, nuclear
power is used, or the billis higher. Table 2 represents an example of profile.
Table 2 An example of profile
Alt. 1:
No switchA
totalmonthlyelectricity and gas bill
CO2 emissions
Use of nuclear power
The share of renewableenergy sources
Optional energy-saving
appliances
Security services

Alt. 3: gas

Alt. 4:
electricity

No change

Alt. 2:electricity
and gas-5000 JPY

-500 JPY-1000 JPY

No change
No
5%

-30%
Yes
5%

No change
No
10%

-30%
Yes
50%

No

No

No

No

No

Yes
(1million JPY)
Yes

No

Households choose the best alternative. They answer with respect to 10 choice questions. Eachquestion has various levels of attributes. The data were collected via a web-based questionnaire,utilizing the services of the Rakuten Research Company. The sample size is 1000 households in
Kanto8 and Kansai9areas. The sample is weighted by each area’s population. The sample size ofKanto area is 667 and that of Kansai area is 333. Before the deregulation, households in Kanto
area purchased electricity from the Tokyo electric power company (TEPCO) and gas from theTokyo gas corporation. TEPCO has some nuclear power plants. Households in Kansai areapurchased electricity from the Kansai electric power company (KEPCO) and gas from the
Osaka gas corporation. KEPCO also has some nuclear power plants. Respondents’ age is under
59 because we assume households use optional energy-saving appliances for a long term. Even
if they purchase these appliances, they need a long term to recover the initial cost by reducing
their monthly bill. Some elders don’t choose the alternative with optional energy-saving
appliances due to their age. Data were collected in February 2018.

5. The results of the questionnaire
In this section, we review the results of the questionnaire. We note the percentage ofhouseholds who switched their electricity or gas supplier after the deregulation. Only 19 %switched their electricity supplier, and only 5.6 % switched their gas supplier. Only 4.2 %switched their electricity bill plan, and only 3.3% switched their gas bill plan.
In the questionnaire, I asked the new supplier which households switched after the
deregulation. In both Kanto and Kansai area, many households choose a major gas company inthe area (the Tokyo gas corporation in Kanto area and the Osaka gas corporation in Kansai area)
for the purchase of electricity and choose a major electric power company in the area (the7The fact was found in the interview for the Osaka gas corporation.
Tokyo, Chiba, Kanagawa and Saitama prefecture
9
Osaka, Kyoto, Hyogo, Shiga and Nara prefecture89Tokyo electric power company in Kanto area and the Kansai electric power company in Kansaiarea) for the purchase of gas.
The primary reason of switching was a cheaper electricity and gas bill. The major second
reason was a stable electricity and gas supply, the stable management of suppliers and the
public notoriety of suppliers. A set discounted bill was also one of the major reasons, but it isincluded in a cheaper bill. Some households switched their supplier by the recommendation ofsales persons especially in gas. The main reasons that households didn’t switch their supplier
were that they were not dissatisfied with their current electric power and gas company and thatthey didn’t trust new suppliers. The fact is related with the customer’s loyalty as Goto (2017)
pointed. Many households feel burdensome in searching bill plans and switching procedures.
This is the switching costs as Goto (2017) pointed.
I asked households that their electricity and gas bill was reduced by switching after the
deregulation as the effects of the deregulation. 61.2 % households reduced their electricity billand 48.7% reduced their gas bill. However, 31% didn’t reduce their electricity bill and 42.3%
didn’t reduce their gas bill.
More than half of households are not interested in advanced appliances such as a fuel battery10.
We expect that to obtain their clients each supplier will develop their own energy services after
the deregulation. Advanced appliances are expected to be developed and promoted, andsuppliers provide long-term energy-saving appliances and comfortable life as value-added
services. However, if households are interested in only reducing their bill and are not interested
in long-term energy savings and value-added services, we couldn’t gain the benefits from thecompetition and innovation.
Table 3 shows the socio-demographic attributes of the sample. Respondents who are
unemployed are more observed than the population.
Table 3 Socio-demographic attributes
Total
Employed
Occupation
UnemployedLess than 2,000
2,000-3,990
Household income 4,000-5,990(thousand JPY)
6,000-7,990
8,000-9,990
More than 10,000
SingleCoupleThree
Familycomposition
Four
Five
More than six
Detached house (including
Dwelling type
two households house)

10

Number
1000
714
286
150
189
251
168
119
123
198
269
256
200
49
28

%
100
71.4
28.6
15
18.9
25.1
16.8
11.9
12.3
19.8
26.9
25.6
20
4.9
2.8

420

42

Other advanced appliances are solar panel, home wind generator, storage battery, energy-saving water
heater (electricity and gas for each), dehumidifier for the bathroom (electricity and gas for each), floor
heating (electricity and gas for each).

10

Sex

Age (years old)

Collective
housing(condominium,
apartment,
housing complex etc.)
Company housing, dormitory
housing etc.
Male
Female
20-29
30-39
40-49
50-59
Average
Minimum
Maximum

539

53.9

41

4

508
492
209
247
310
234
40.57
20
59

50.8
49.2
20.9
24.7
31
23.4

6. Econometric model
In a choice experiment, the dependent variable is discrete. To estimate a choice model,
therefore, a discrete choice econometric model should be used. A conditional logit model is a
popular model in this context. However, this model assumes an Independent and Identical
Distribution (IID), and this assumption derives from the Independence of Irrelevant Alternatives(IIA). This assumption is restricted and easily violated in many cases. Consequently, a randomparameter logit model (mixed logit model) is used as a general discrete choice econometricmodel 11 . This model allows the random variation of individual preferences, unrestricted
substitution patterns, and correlation among unobserved factors over time.12
A random parameter logit model assumes that each parameter has a specific distribution. The
utility is specified as
U =α x +β z +εThis function specifies that individual n chooses alternative j, where α is a non-randomparameter and β is a random parameter that represents the preference of each individual andvaries among individuals. In this paper, constant terms and the parameter of a monthly bill,which is a price parameter, are non-random parameters. x is a variable vector that includes amonthly bill. On the other hand, the parameters of CO2 emissions, use of nuclear power, theshare of renewable energy sources, optional energy-saving appliances and security services are
random parameters. z is a variable vector that includes CO2 emissions, use of nuclear power,
the share of renewable energy sources, optional energy-saving appliances and security
services. ε is a random error term and has an IID extreme value.
The probability conditional on β is
exp β xL β =Ȃ exp β x
The random parameter logit probability is

11

I tried a nested logit model. But I couldn’t obtain any reasonable results.
The explanation of a random parameter logit model derives from Train (2003) and Louviere et al.
(2000).

12

11

P =

exp β xȂ exp β xf β dβThis probability is the unconditional choice probability calculated as the integral of L βover all β .
The distribution of β must be assumed. Usually, a normal, log-normal, or triangular
distribution, etc., can be assumed. In this paper, normal distribution is assumed.
Simulation methods were used for estimation. The simulated probability is
1
P =
R

"
!#$

L

β!

where R is the number of draws. This simulated probability is an unbiased estimator of P . The
simulated log-likelihood (SLL) is
SLL =

*

)

#$ #$

d lnP (

where dnj is an indicator. It equals 1 if an individual n chooses alternative j, or is 0 otherwise.
The SLL was maximized to capture the maximum simulated likelihood estimator. In addition,
100 Halton draws were used for simulation. For estimation, Limdep NLOGIT 5 was used.

7. Estimation results
In this section, the estimation results of a random parameter logit model are illustrated. Table 4
represents the estimation results.
Table 4 Estimation results
Variables
Random parameter
CO2 emissions
Nuclear power
Renewable energy
Optional appliances
Security services
Non-random parameter
Monthly bill
Constant 1
Constant 2
Constant 3Standard deviation
CO2 emissions
Nuclear power
Renewable energy
Optional appliances
Security services
McFadden R２

12

Coefficient Standard
Error

Z value

P value-0.00608
0.0699-0.01132-0.02644
0.243360.00287
0.09070.002120.001470.05045

-2.12
0.77
-5.34
-18.024.82

0.03430.440900
0-0.00014-0.06440.75973-0.44992

0.00001494
0.113850.06926
0.06409

-9.65
-0.57
10.97
-7.02

0
0.5716000.06757
1.710120.04199
0.03332
0.097920.315645

0.00277
0.069280.001740.00146
0.15937

24.4
24.68
24.16
22.75
0.61

0
0
0
0
0.5389Log likelihood-9487.17

The coefficient of a monthly bill is negative and significant at 1% significance level. A lower
monthly bill increases the probability for the alternative. Households choose a supplier which
provides a lower bill. The coefficient of CO2 emissions is negative and significant at 5%
significance level. If CO2 emissions are reduced, the probability for the alternative increases.
Households prefer a supplier which provides energies to reduce CO2 emissions. The coefficientof use of nuclear power isn’t significant, while the coefficient of the share of renewable energy
sources is negative and significant at 1% significance level. This means that households don’tchoose a supplier which provides electricity generated by renewable energy sources and the useof nuclear power doesn’t affect households’ choice for a supplier. The result is different frommy hypothesis and previous researches13.
It is impossible to realize an all-electric service with higher ratio of renewable energy sources,
without nuclear power and with a lower bill. We assumed that in the profiles of an all-electriccase suppliers use nuclear power when the share of renewable energy sources is 30% or 50%.
Some respondents support renewable energy and don’t support nuclear power. This fact may
cause different results from the hypothesis and previous studies.
The coefficient of optional energy-saving appliances is negative and significant at 1%
significance level. Households don’t choose the alternative with optional energy-saving
appliances. The price of optional energy-saving appliances is 1 million JPY, which is too
expensive. Households don’t purchase optional energy-saving appliances unless the price ormonthly bill becomes much lower. This result means that households are not interested in suchappliances. According to the previous questionnaire, more than half of households are notinterested in such appliances. The estimation result is consistent with the result of questionnaire.
The coefficient of security services is positive and significant at 1% significance level.
Households need the services. After the deregulation, suppliers are expected to acquire theircustomers by providing such free services.
In the previous estimation, we obtain the different results from our hypothesis, which use of
nuclear power isn’t significant and the share of renewable energy sources has a negative impact
on households’ choice and is significant. The results could be caused because we assume that
suppliers use nuclear power when the share of renewable energy sources is higher in making
profiles. That is why a cross term of use of nuclear power and the share of renewable energy
sources is added in the regression equation to analyze households’ preferences for both use of
nuclear power and higher ratio of renewable energy sources. The expected sign is positive.
Table 5 shows the estimation results. The cross term is positive but isn’t significant. Households
don’t prefer electricity which is generated by nuclear power and higher share of renewableenergy sources.
Table 5 Estimation results (with a cross term of use of nuclear power and the share of
renewable energy sources)
Standard
Z value P value
Variables
Coefficient
Error
Random parameter
CO2 emissions-0.00587
0.00276-2.13
0.0335
Nuclear power
0.06692
0.12146
0.55
0.5817
Renewable energy-0.01386
0.00329-4.22
0
Optional appliances-0.03015
0.00153-19.750
Security services
0.24049
0.053394.5
013

Morita and Managi (2013) and Murakami, Ida, Tanaka and Friedman (2015) find that households show
negative evaluation for nuclear power and positive evaluation for renewable energy.

13Cross term (nuclear power andrenewable energy sources)
Non-random parameter
Monthly bill
Constant 1
Constant 2
Constant 3Standard deviation
CO2 emissions
Nuclear power
Renewable energy
Optional appliances
Security services
Cross term (nuclear power andrenewable energy sources)
McFadden R２
Log likelihood

0.00047

0.00303

0.16

0.8757-0.00014-0.04367
0.75504-0.50228

0.00001491
0.11510.070420.08553

-9.36
-0.38
10.72
-5.87

0
0.7044000.07103
1.73663
0.04302
0.033120.258430.00575

0.00288
0.07401
0.001860.001370.20197
0.0022

24.64
23.46
23.09
24.11
1.28
2.62

0
0
0
0
0.20070.00890.314399-9504.45

The monthly bill of an all-electric service with higher share of renewable energy sources andwithout nuclear power would become higher. To analyze that households prefer electricity
generated with higher ratio of renewable energy sources even if the bill is higher, a cross termwith a monthly bill and the ratio of renewable energy sources is added to the regression
equation. Table 6 shows the estimation results. The coefficient of the cross term is positive andsignificant at 1% significance level. Households tend to choose a supplier which provides
electricity generated with higher ratio of renewable energy sources even if the bill is higher.
Table 6 Estimation results (with a cross term of a monthly bill and the share of renewableenergy sources)
Variables
CoefficientStandard ErrorZ
P valuevalue
Random parameter
CO2 emissions-0.00632
0.00276-2.29
0.0219
Nuclear power
0.10032
0.091161.1
0.2711
Renewable energy-0.00913
0.0025-3.65
0.0003
Optional appliances-0.02979
0.00161-18.510
Security services
0.29176
0.05288
5.52
0
Non-random parameter
Monthly bill
-0.00019
0.00002119-8.92
0Cross term (monthly bill 0.0000026436
0.0000008101
3.26
0.0011and renewable energy)
Constant 1
0.0376
0.114610.33
0.7429Constant 20.733440.07029
10.43
0Constant 3-0.50658
0.06538-7.75
0Standard deviation
CO2 emissions
0.07186
0.0029724.2
0Nuclear power
1.74169
0.07677
22.69
0Renewable energy
0.043770.00185
23.64
0Optional appliances
0.032450.0013124.79
0Security services
0.13292
0.219820.6
0.545414

McFadden R２
Log likelihood

0.314552-9502.33

8. Subsample analysis
In the questionnaire, I investigate households’ socio-demographic attributes such as householdincome and the number of family members and their consciousness about energy problems suchas electricity and gas bills and renewable energy. I divide into some subsamples for example
higher and lower income group. The differences of preferences for suppliers between
subsamples are analyzed. I use a random parameter logit model for estimation.
Table 7 shows the estimation results of lower income (less than 6 million JPY) and higher
income group (more than 6 million JPY). In the higher income group, the coefficient of nuclear
power is not significant, but in the lower income group, it is positive and significant at 10%
significant level. Lower income group accepts nuclear power compared with higher income
group.
Table 7 Lower income vs higher income
Lower income
Variables
Coefficient
Random parameter
CO2 emissions-0.00814
Nuclear power
0.20332
Renewable energy-0.01216
Optional appliances-0.0279
Security services
0.24424
Non-random parameter
Monthly bill
-0.00012
Constant 1-0.13316
Constant 2
0.72134
Constant 3-0.50758
２0.316435
McFadden R
Log likelihood-5590.97
Sample size
5900Note) *** 1%, ** 5%, *10% significant level

Higher income
Coefficient
**
*
***
***
***

-0.01515-0.11532-0.01088-0.02449
0.24411***-0.00017
0.038130.84996-0.42612
0.318131-3875.61
4100

***
***

***
***
***
***
***
***
***

Table 8 shows the estimation results of younger (less than 39 years old) and elder (more than
40 years old) group. In the elder group, the coefficient of CO2 emissions is not significant, but inthe younger group, it is negative and significant at 10% level. Younger group chooses a supplier
which provides energy that contributes to the global heating problem.
Table 8 Younger vs elder households
Younger
Variables
Coefficient
Random parameter
CO2 emissions-0.00675
Nuclear power
0.08913
Renewable energy-0.00984
Optional appliances-0.03107
Security services
0.18869
Non-random parameter

Elder
Coefficient
**
***
***
**

-0.000096197
0.0115-0.01656-0.02553
0.32288

***
***
***

15

Monthly bill
-0.00017
Constant 1-0.34684
Constant 2
0.43765
Constant 3-0.66169
0.300639
McFadden R２
Log likelihood-4421.01
Sample size
4560Note) *** 1%, ** 5%, *10% significant level

***
**
***
***

-0.00011
0.15141
1.08602-0.26493
0.333196-5028.66
5440

***
***
***

Table 9 shows the estimation results of households who live in a detached house andhouseholds who live in a collective housing. Collective housing includes company anddormitory housing. But we don’t find notable differences between two subsamples.
Table 9 Detached house vs collective housing
Detached house
Variables
Coefficient
Random parameter
CO2 emissions
0.00091
Nuclear power
0.11015
Renewable energy-0.00875
Optional appliances-0.02334
Security services
0.21803
Non-random parameter
Monthly bill
-0.00016
Constant 1-0.00032
Constant 20.61075
Constant 3-0.68418
２0.295938
McFadden R
Log likelihood-4099.36
Sample size
4200Note) *** 1%, ** 5%, *10% significant level

Collective housing
Coefficient

***
***
***
***
***
***

-0.00208
0.07472-0.01666-0.03014
0.31103-0.00012-0.17851
0.83571-0.33661
0.330521-5382.95
5800

***
***
***
***
***
***

Table 10 shows the estimation results of small (1-3 members) and big (4-6 more overmembers) family. In the small family group, the coefficient of CO2 emissions is not significant,but in the big family group, it is negative and significant at 10% level. If the family size isbigger, households choose a supplier which provides energy that reduces CO2 emissions.
Table 10 Small vs big family
Variables
Random parameter
CO2emissions
Nuclear power
Renewable energy
Optional appliances
Security services
Non-random parameter
Monthly bill
Constant 1
Constant 2

16

Small family
Coefficient

Big family
Coefficient-0.00222
0.15251-0.01889-0.02955
0.2953-0.00857-0.08606-0.0092-0.02884
0.18499*-0.00018
0.00560.63004***-0.00012-0.15319
0.7996

***
***
***
***
***

*
***
*

***

Constant 3-0.38619
0.324823
McFadden R２
Log likelihood-6767.24
Sample size
7230Note) *** 1%, ** 5%, *10% significant level***-0.56785
0.290112-2725.99
2770***

Table 11 shows the estimation results of households who live in Kanto and Kansai area. But
we don’t observe any notable differences between Kanto and Kansai area. In Kanto area, thecoefficient of CO2 emissions is significant, but in Kansai area, it is not significant.
Table 11 Kanto vs Kansai area
Kanto
Variables
Coefficient
Random parameter
CO2 emissions-0.00994
Nuclear power
0.03053
Renewable energy-0.01397
Optional appliances-0.03344
Security services
0.29178
Non-random parameter
Monthly bill
-0.00014
Constant 1-0.06716
Constant 2
0.76262
Constant 3-0.42877
２0.322166
McFadden R
Log likelihood-6267.65
Sample size
6670Note) *** 1%, ** 5%, *10% significant level

KansaiCoefficient
***
***
***
***
***
***
***

-0.00606
0.17715-0.0102-0.02523
0.19727-0.00015-0.01524
0.74095-0.51175
0.30095-3227.07
3330

***
***
**
***
***
***

In the questionnaire, I asked households the most important energy problem. Some households
answered that the realization of lower electricity and gas bill is the most important. I compare
their behavior between the households who answered the realization of lower bill is the mostimportant (lower bill group) and the households who didn’t answer it (not lower bill group). In
the lower bill group, the coefficient of nuclear power is positive and significant at 5% level. We
didn’t obtain such a result in other analysis. They are permissive for nuclear power and they
think nuclear power is reasonable if their bill is reduced.
Table 12 Low bill vs not low bill
Variables
Random parameter
CO2 emissions
Nuclear power
Renewable energy
Optional appliances
Security services
Non-random parameter
Monthly bill
Constant 1
Constant 2

Lower bill
Coefficient
0.00085
0.26317-0.0155-0.02603
0.21622-0.00019
0.224340.71066

Not lower bill
Coefficient

**
***
***
***
***
***

-0.01615-0.00339-0.00858-0.02742
0.24618***-0.0001-0.42367
0.75739

***
**
***

***
***
***

17

Constant 3-0.63315
0.31541
McFadden R２
Log likelihood-5039.42
Sample size
5310Note) *** 1%, ** 5%, *10% significant level***-0.37559
0.327675-4371.274690***

9. Analysis by a revealed preference
In the questionnaire, I asked households whether they switched an electric power company andurban gas company after the deregulation. I analyze the factors that households switch their
supplier. A dummy variable is used as a dependent variable where 1 is assigned whenhouseholds switched their supplier and 0 is assigned when households didn’t switch their
supplier. Binary probit model is used for estimation because the dependent variable is discreteand has two values.
As independent variables, a monthly electricity and gas expenditure in January 2018, adetached house dummy where 1 is assigned to households who live in a detached house and 0 is
assigned to households who live in a collective housing, the number of family members wholive together, annual household income, male dummy, age, Kanto dummy where 1 is assigned
to households who live in Kanto area and 0 is assigned to households who live in Kansai area
are included. In addition, independent variables about households’ preferences and perceptions
are included. The preferences for renewable energy, a lower bill and a stable supply areincluded. I use dummy variables for the independent variables. In the preferences for renewableenergy, I assign 1 for households who support renewable energy as the most desirable futureenergy source, and 0 for households who support non-renewable energy. Renewable energy
includes solar, wind, geothermal, biomass and hydraulic power. Non-renewable energy includes
nuclear, coal, oil and natural gas. In the preferences for a lower bill, I assign 1 for households
who think that the realization of a lower electricity and gas bill is the most important energy
problem, and 0 for households who think that other energy problems are the most important. In
the preferences for a stable supply, I assign 1 for households who think a stable supply as themost important energy problem, and 0 otherwise.
The dependent variable is whether households switched their supplier or not, and whether theyswitched their bill plan or not in both electricity and gas.
Now, I illustrate the estimation results about the switching of an electric power company intable 13.
Table 13 Estimation results (switching of an electric power company)
Z value
Variables
CoefficientStandard Error
Electricity expenditure
0.049358
0.028067
1.76
Housing-0.09306
0.108801-0.86
Family members
0.068525
0.044119
1.55
Household income
0.108891
0.03155
3.45
Male-0.05485
0.0965-0.57
Age0.000847
0.004735
0.18
Kanto-0.05719
0.099975-0.57
Renewable energy
0.016893
0.102686
0.16
Lower bill0.273539
0.119906
2.28
Stable supply-0.21797
0.14729-1.48
Constant-1.76967
0.283153-6.25
２0.0444
McFadden R
Log likelihood-464.647

18

P value
0.079
0.392
0.12
0.001
0.57
0.858
0.567
0.869
0.023
0.139
0

The coefficient of household income is positive and significant at 1% level. Households withhigher income tend to switch their supplier after the deregulation. The coefficient of lower priceis positive and significant at 5% level. Households who think that the realization of a lower billis the most important tend to switch their supplier. The coefficient of electricity expenditure ispositive and significant at 10% level. Households who spend more electricity expenditure tend
to switch their supplier.
Table 14 shows the estimation results about the switching of an electricity bill plan.
Table 14 Estimation results (switching of an electricity bill plan)
Variables
CoefficientStandard Error
Z value

P valueElectricity expenditure-0.00296

0.042377

-0.07

0.944

Housing
Family members
Household income
Male
Age
Kanto
Renewable energy
Lower bill
Stable supply
Constant
McFadden R２
Log likelihood-0.25515
0.134122-0.03553
0.186584-0.00307-0.17977-0.16209-0.24629-0.20265-1.45801
0.0275-169.454

0.174489
0.065657
0.049539
0.15107
0.0071720.151233
0.156668
0.178697
0.207082
0.41134

-1.46
2.04
-0.72
1.24
-0.43
-1.19
-1.03
-1.38
-0.98
-3.54

0.144
0.041
0.473
0.217
0.669
0.235
0.301
0.168
0.328
0

Only the coefficient of the number of family members is positive and significant at 5% level.
Households who have more family members tend to switch their electricity bill plan.
Next, I illustrate the estimation results of the switching of a gas company in Table 15.
Table 15 Estimation results (switching of a gas company)
Variables
CoefficientStandard Error
Gas expenditure
Housing
Family members
Household income
Male
Age
Kanto
Renewable energy
Lower bill
Stable supply
Constant
McFadden R２
Log likelihood

0.015162
0.327962-0.08464
0.090536
0.22272-0.01709-0.52584-0.0567
0.269177-0.58477-1.0571
0.1088-168.661

0.048477
0.1771410.074088
0.049659
0.154171
0.007408
0.152026
0.158219
0.187797
0.286853
0.430373

Z value

P value

0.31
1.85
-1.14
1.82
1.44
-2.31
-3.46
-0.36
1.43
-2.04
-2.46

0.754
0.064
0.253
0.068
0.149
0.021
0.001
0.72
0.152
0.041
0.014

The coefficient of age is negative and significant at 5% level. Younger households tend toswitch their supplier after the deregulation. The coefficient of Kanto dummy is negative andsignificant at 1% level. Households who live in Kanto area don’t switch their supplier. The

19

coefficient of a stable supply is negative and significant at 5% level. Households who think astable supply is the most important don’t switch their supplier. The coefficient of a detachedhouse dummy is positive and significant at 10% level. Households who live in a detached houseswitch their supplier.
Table 16 shows the estimation results about the switching of a gas bill plan.
Table 16 Estimation results (switching of a gas bill plan)
Variables
CoefficientStandard Error
Gas expenditure
Housing
Family members
Household income
Male
Age
Kanto
Renewable energy
Lower bill
Stable supply
Constant
McFadden R２
Log likelihood

0.076469-0.57491
0.088077
0.114518
0.168455-0.01647-0.33695-0.02011
0.002312
0.021274-1.89651
0.0735-118.1780.059759
0.225081
0.086725
0.060198
0.180816
0.008965
0.179036
0.187392
0.2261120.252075
0.485565

Z value

P value

1.28
-2.55
1.02
1.9
0.93
-1.84
-1.88
-0.11
0.01
0.08
-3.91

0.201
0.011
0.31
0.057
0.352
0.066
0.06
0.915
0.992
0.933
0

The coefficient of a detached house dummy is negative and significant at 5% level.
Households who live in a detached house don’t switch their gas bill plan. The coefficient ofhousehold income, age and Kanto dummy is positive and significant at 10% level. Younger
households, households with higher income and households who live in Kanto area tend toswitch their gas bill plan after the deregulation.

10. Conclusions and policy implications
In this paper, I analyzed households’ choice behavior for suppliers and energy sources in Japanafter the deregulation of electricity and gas retail sales in April 2016 and 2017 respectively.
After the deregulation, suppliers which provide various bill plans and services will be expected
to start their business. Now, very few households switch their supplier. I examined the reasonsthat households switched their supplier by a probit model and the future conditions thathouseholds will switch their supplier by a conjoint analysis and estimate the preferences for theattributes such as bill plans, services and energy sources which suppliers provide. On the other
hand, the substitution between electricity and gas will be promoted, and as a result, somehouseholds use only electricity or gas intensively.
From the estimation results, the coefficient of a monthly bill was negative and significant.
Households prefer suppliers which provide lower bill plans. The coefficient of CO2 emissions
was negative and significant. They prefer suppliers which provide eco-friendly energy to reduce
CO2 emissions. However, I couldn’t obtain expected results in energy sources. The coefficientof nuclear power was not significant, and that of renewable energy sources was negative andsignificant. We expected that the coefficient of nuclear power was negative and significant andthat of renewable energy sources was positive and significant. It is impossible to provide an allelectric service with higher ratio of renewable energy sources, without nuclear power and with alower bill. In an all-electric service, a higher bill or use of nuclear power should be accepted.
The estimation results would be caused because I make profiles considering these facts. I

20

estimated the regression with a cross term of the ratio of renewable energy sources ratio and amonthly bill. I obtained the positive and significant coefficient. From the results, households
would accept a higher bill for the higher ratio of renewable energy sources. Households needfree security services. But they don’t need optional advanced energy-saving appliances such as afuel battery. This result is consistent with their respond in the questionnaire. In the questionnaire,they are not interested in such advanced appliances.
We discussed the substitution between electricity and gas. Some households may use a cheaperenergy either of electricity or gas intensively. Some households who support renewable energy
may use electricity generated by renewable energy intensively. However, it is impossible toprovide electricity generated by renewable energy as a main energy source with a lower bill andwithout nuclear power. An all-electric service needs nuclear power if the bill is reduced. It isdifficult to realize an all-electric service unless we resume nuclear power plants and raise theratio of nuclear power. From the reason, the substitution between electricity and gas will not be
prevailed. After the regulation, we will use both electricity and gas and purchase them from theonly one supplier as the most possible scenario. On the other hand, households who object tonuclear power and support renewable energy may choose a supplier which uses renewableenergy as a main energy source and may accept a higher bill.
In this paper, I analyzed the households’ switching behavior for electricity and gas suppliers byboth stated and revealed preference method. Now, only two years have passed since thederegulation of electricity retail sales started, and only one year has passed since thederegulation of gas retail sales started. Now, it is difficult to evaluate the deregulation including
both electricity and gas. We need to evaluate the whole energy market including both electricity
and gas by using real consumers’ switching data.
Acknowledgements
This study was aided grants-in-aid for scientific research (C) (Kakenhi) of Japan Society for
The Promotion of Science. I was supported by the Osaka Gas corporation about the current
survey of the deregulation. I thank Mr. Makoto Hiranuma, Mr. Takahiro Noguchi and Mr.
Tomo Takayama in the interview in Feb. 2018. All remaining errors are mine.
References
Giulietti, Monica, Catherine Waddams Price and Michael Waterson (2005), “Consumer choiceand competition policy: a study of UK energy markets”, The Economic Journal, 115, 949-968
Goett, Andrew, Kathleen Hudson and Kenneth Train (2000), "Customers' choice among retailenergy suppliers: The willingness-to-pay for service attributes", The Energy Journal,21, 1-28
Goto Hisanori (2017) “Review and analysis of competition in retail electricity market forresidential customers - Retail price development and residential customers' behavior after full
liberalization – “ (in Japanese), CRIEPI Research Report, Y16005, the Central ResearchInstitute of Electric Power Industry, 1-56
Hensher, David A., John M.Rose and William H.Greene (2005), Applied Choice Analysis A
primer, Cambridge
Hortacsu, Ali, Seyed Ali Madanizadeh and Steven L. Puller (2016) “Power to choose? Ananalysis of consumer intertia in the residential electricity market”, NBER Working Paper 20988,
1-44
Ida Takanori and Kayo Murakami (2016) “Investigation of consciousness of consumers' choiceof electric power before total liberalization of electric power”, Journal of the Society of
Instrument and Control Engineers (in Japanese), Vol.55, No.7, The Society of Instrument andControl Engineers, 598－603

21

Kuriyama Koichi, Yasushi Shoji etc. (2005) Economic evaluation of environment and tourism,(in Japanese), Keisoushobou
Kuriyama Koichi, Takahiro Tsuge and Yasushi Shoji (2012) Introduction of economic
evaluation of environment for beginners, (in Japanese), Keisoushobou
Louviere, Jordan J., David A.Hensher and Joffre D.Swait (2000), Stated Choice Methods
Analysis and Application, Cambridge
McFadden (1974),"Conditional logit analysis of qualitative choice behavior", in P.
Zarembka,ed., Frontiers in Econometrics, Academic Press, New York, 105-142
Morita, Tamaki and Shunsuke Managi (2015) "Consumers’ willingness to pay for electricity
after the Great East Japan Earthquake”, Economic Analysis and Policy, 48, p82-105
Murakami, Kayo, Takanori Ida, Makoto Tanaka and Lee Friedman (2015), "Consumers'
willingness to pay for renewable and nuclear energy: A comparative analysis between the US
and Japan", Energy Economics, 50, 178-189
Nakajima, Miki, Takanori Ida and Shin Kinoshita (2006) "The conjoint analysis for energy
demand of households" Journal of Public Utility Economics (in Japanese) 58.2, 23-33
Revelt, David and Kenneth Train (1998) "Mixed logit with repeated choices: Households'
choices of appliance efficiency level", The review of economics and statistics, Vol.80, No.4,
647-657
The Agency for Natural Resources and Energy in the Ministry of Economy, Trade andIndustry “The deregulation of electricity retail sales”
http://www.enecho.meti.go.jp/category/electricity_and_gas/electric/electricity_liberalizatlib/(access date Feb 2018)
The Agency for Natural Resources and Energy in the Ministry of Economy, Trade andIndustry “The deregulation of gas retail sales”
http://www.enecho.meti.go.jp/category/electricity_and_gas/gas/liberalization/(access date Feb 2018)
Train, Kenneth E. (2002), Discrete Choice Methods with Simulation, Cambridge
Tsuge, Takahiro, Koichi Kuriyama and Yohei Mitani (2011), New technique of environmental
evaluation, (in Japanese), Keisoushobou

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