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6FKRRORI(QYLURQPHQWDO6FLHQFH7KH8QLYHUVLW\RI6KLJD3UHIHFWXUH,NXKR .RFKL&ROOHJHRI7UDQVGLVFLSOLQDU\6FLHQFHVIRU,QQRYDWLRQ.DQD]DZD8QLYHUVLW\RU,QQRYDWLRQ.DQD]DZD8QLYHUVLW\Providing electricity price information to households andreducing electricity consumption: Results from a field
experiment in Japan
Kazuma Murakami 1* , Ikuho Kochi 2
1. University of Shiga Prefecture

2. Kanazawa University

September 2023
*Corresponding author. e-mail: murakami.k@ses.usp.ac.jp

Abstract
Electricity accounts for 65.3% of household CO 2 emissions in Japan; therefore, morehousehold energy conservation is needed. This study examines the effects of
information

provisiononvarioushouseholdenergy-saving

behaviorsusing

randomized controlled trials (RCT). For Japanese consumers who have recently
become free to choose their electricity provider, we examine two types of information
provision with the same economic incentives but different framing: information on
the Past - information about historical changes in electricity bills for the average
household of their electricity provider–and information on Others - information about
differences in electricity bills for the average household of different electricity
providers. We collected objective measures of household electricity consumption
levels through meter readings and subjective measures of behavioral changes througha questionnaire. Our results show that information on the Past has more impact on
reducing electricity consumption for households with a higher volume of electricity
consumption than others. The channels for this reduction are the behaviors of “notleaving the air conditioner on,” a constant time-consuming behavior, and “lowering
the refrigerator's internal temperature,” a hassle-free one-time behavior. Informationon the Past can be a low-cost and proactive information-provision measure for local
governments.
Keywords: information provision, social comparison, electricity consumption, energysaving behaviour, randomized controlled trial

11. Introduction
Based on the Paris Agreement, Japan has set a goal of reducing greenhouse gas emissions by 46% in
FY2030 compared to FY2013, with a 66% reduction in the household sector (Headquarters forpromotion of global warming countermeasures in Japan, 2021). In Japan, electricity consumption
accounts for 65.3% of CO 2 emissions from households (Ministry of the Environment, 2022); thus,
households are expected to save even more electricity.
Japan's electricity retail market was fully liberalized in April 2016. This means all households canpurchase electricity from the major electric power companies designated based on their residential
location and other electric power companies. Since electricity prices differ by power companies, 1households can purchase cheaper electricity from power companies outside their residential areas. 2
Many studies have estimated the price elasticity of electricity consumption to understand therelationship between household electricity consumption and electricity prices (Espey and Espey, 2004;
Labandeira et al., 2017; Pellini, 2021). Other studies are based on social experiments that change theunit price of electricity through dynamic pricing and examine its effect on electricity consumption(Borenstein, 2005; Ito et al., 2018; Jessoe and Rapson, 2014; Wolak, 2011). Although these studies
clarify the extent to which changes in electricity prices promote electricity-saving behavior and reduce
electricity consumption, their scope is limited to the effects of past or present electricity prices of thepower companies with which a person contracts. Therefore, they do not provide information on theeffect of other potentially important factor, other households’ behaviors.
Reference Point Dependence in Behavioral Economics (Kahneman and Tverskey, 1979; Tverskey and
Kahneman, 1991) indicates that one's future actions may be influenced not only by their past and present
situation but also by the present situation of others. The studies mentioned above were conducted using
the past or current electricity prices of an electricity company with which one had a contract as thereference point. However, compared with others, many studies have been conducted to verify the effectsof Social Comparison (Festinger, 1954) by using the Home Energy Report (HER) to provide information
about the electricity consumption level of neighboring households with similar attributes (Allcott,2011a; Allcott and Rogers, 2014; Andor et al., 2020; Brandon et al., 2019). They examined whether
information about the difference in electricity consumption levels between oneself and others may
induce more energy-saving behaviors. Household electricity charges are basically determined by the“unit price of electricity × electricity consumption levels”. Therefore, it is assumed that people would
become aware of the difference in electricity charges between themselves and others once they recognize
the differences in "electricity consumption levels". Therefore, it has been verified that energy-saving
behavior is induced by using the same level of electricity charges as others as a benchmark. However,it has not been verified whether energy-saving behavior is induced by making people aware of the

1

In Japan, the Feed-in Tariff (FIT) was introduced in 2012, and the FIT burden is uniformly added to household
electricity bills. The amount of this burden is increasing year by year as the amount of renewable energy introduced
increases. In addition, due to Japan's fuel cost adjustment system, the unit price of electricity changes in tandem withthe price of fossil fuels. As a result, electricity prices for Japanese households differ from those of other households
due to differences in the power supply mix of the electricity companies with which they contract.
2 Before April 2016, 10 major electric companies with defined supply zones provided electricity to households. The
number of registered retail electricity providers was 407 in July 2017 at the time of the survey and 752 as of March2022 (Agency for Natural Resources and Energy, 2022).

2difference in "unit price" between themselves and others.
In other words, we do not know how much the electricity consumption changes when one assumes achange in the electricity company and sets a reference point for the electricity prices of other electricity
companies. For example, no studies have examined which group (a) or (b) has a higher energy reduction.
As the reference point, the Past and Others groups have the same $5 monetary incentive, but it has notbeen examined whether one acts more strongly on loss aviation and makes a difference in electricitysaving behavior.
(a) Current energy consumption ( e.g. , 300 kWh/month) is the same as a year ago, but the electricity
bill is $5 higher than the previous year (Past group)(b) Current energy consumption ( e.g. , 300 kWh/month) is the same as others having contracts withother power companies; however, the electricity bill is $5 higher than that of others (Others group).
As shown in (b), the effect of information that makes people aware of differences in electricity prices
based on differences in the unit price of electricity has not been verified, even though they consume thesame amount of electricity as others. In other words, no studies have used the electricity unit price ofothers as a social comparison, but rather the electricity consumption of others.
Another difference between this study and other studies using the HER on social implementation isthe difference in the cost of providing information. The HERs are expensive because they present
customized information for each household. Furthermore, the HER is a power company-driven
information provision measure based on each household's electricity consumption information, available
only to electric power companies. However, information on the unit price of electricity for each electric
power company is generally posted by electric power companies on their websites to provide evidenceof how fossil fuel prices are reflected and to attract customers. Therefore, anyone can obtain thisinformation without any cost. This could lead to low-cost and feasible information provision measuresby local governments rather than relying on electric power companies. It is also an easy measure for
local governments to implement from the standpoint of protecting personal information, as they do notneed to obtain information on the electricity consumption of each household.
In Japan, several municipalities in the Tokyo metropolitan area have implemented information
campaigns to encourage households to save electricity. They presented data on the upward trend inelectricity prices for a household with standard electricity consumption (260 kWh/month) that contracts
with the Tokyo Electric Power Company. However, their electricity-saving effects have not been verified.
It also has not provided comparative information on the low electricity charges of other electric powercompanies. It is necessary to clarify the kind of comparison (reference point) that should be used tomake people aware of their high electricity bills to encourage more energy-saving behavior. This leads
to proposals for more effective information provision measures. This will have implications for
information provision measures by local governments not only in Japan, but also in othercountries and regions where awareness of electricity unit prices is low or is not keeping
pace with the rising trend due to soaring fossil fuel prices and carbon pricing.
This study aims to identify electricity price information based on electricity unit prices that would
promote energy-saving behavior among Japanese consumers, who have recently become free to choose
their electricity provider. We use randomized controlled trials (RCT) to examine whether there are3differences in electricity use between the three groups, considering the different information. The threegroups are Past group, providing information comparing current electricity prices to past electricity
prices (which are lower than current prices); group on Others, providing information comparing current
electricity prices to that of other electric companies (which are lower than their own electricity
companies); and the control group.
In addition, the mechanism of electricity consumption reduction (Allcott and Rogers, 2014; Ito et al.,2018) is clarified. We examined the impact of the information on specific electricity-saving behaviors
in the top four appliances (air conditioners, refrigerators, televisions, and lighting) with the highest
electricity consumption (kWh) in Japanese households (Ministry of the Environment, 2017). This
allowed us to examine the channels through which the intervention affected the changes in electricity
consumption. In doing so, we examine not only time-consuming behaviors that need to be consideredon a daily basis (e.g., not leaving the TV on), but also hassle-free behaviors (e.g., setting the TV to anenergy-saving mode) that have long-term effects. This study also examines the possibility of the longterm power-saving effects of electricity price information that can be collected without cost.
The main results are as follows: First, information in the Past group (information on thePast) leads to reducing electricity consumption (kWh/day) in households with highelectricity consumption. Second, as a mechanism for reducing electricity consumption, past
information and information in the group on Others (information on Others) influence thebehavior of not leaving the air conditioner on, which is a time-consuming, energy-saving
behavior. Third, in hassle-free behavior, information on Others influences the behavior oflowering the refrigerator's internal temperature. Furthermore, past information influences
the behavior of lowering the refrigerator's internal temperature in households with highelectricity consumption. In summary, information on the Past reduced electricity consumption inhouseholds with high electricity consumption and the behaviors of not leaving the air conditioner onand lowering the temperature inside the refrigerator contribute to this reduction. Nonetheless,information on Others influences the behavior of lowering the temperature inside the refrigerator andnot leaving the air conditioner on, but it does not contribute to reducing electricity consumption.
The contributions of this study are as follows: First, studies and social implementation of social
comparison, electricity pricing, and energy-saving behavior. In this study, we set the unit price ofelectricity for other people, rather than their electricity consumption, as the object of social
comparison. In addition, we exogenously provided two reference points, own past electricity prices
and others’ current electricity prices, to examine the differences in their effects on electricity-saving
behavior. To the best of our knowledge, such studies have not been conducted earlier. Regarding social
implementation, the information provided in this study can be implemented by local governments andis expected to contribute to CO 2 reduction in the municipality. Second, the study analyzes the mechanismof electricity consumption reduction due to the intervention. Most intervention studies using RCT havefocused on evaluating macro electricity savings (kWh), and few studies have examined the impact on
specific electricity-saving behavior (McAndrew et al., 2021). Ito et al. (2018) examined the intervention
effects of home appliances on electricity-saving behaviors in Japanese households. However, the study
was limited to analyzing data asking whether they used each of the five targeted appliances efficiently.
4Additionally, some of the five appliances did not consume electricity significantly. In this study, we set
nine specific power-saving behaviors for the top four appliances (air conditioners, refrigerators,
televisions, and lighting) that consumed the most electricity (kWh) in Japanese households andexamined the impact of interventions on them.
This paper is organized as follows. Section 2 presents a literature review, Section 3 presents themethodology and data, Section 4 presents the results and discussion, and Section 5 presents theconclusions and policy implications.
2. Literature review
2.1 Study of electricity prices and electricity consumption
Several studies have examined the price elasticity of electricity consumption. Many early studies used
macro data to estimate short- and long-term price elasticities and made international and domestic
regional comparisons (Espey and Espey, 2004). Subsequently, with the development of individual data,
many studies have used micro-and micro panel data to conduct analyses controlling for household
attributes (Baker and Blundell, 1991; Branch, 1993; Halvorsen and Larsen, 2001; Meier et al., 2013;
Nesbakken, 1999; Schulte and Heindl, 2017). These studies contribute to the recent increase in fossil
fuel prices and the carbon pricing debate. However, it is not always possible to measure consumer
responses to explicitly perceived changes in electricity prices because these studies are not experimental.
Many experimental studies on the relationship between electricity prices and electricity consumption
have been conducted by changing the unit price of electricity through dynamic pricing, making
consumers aware of changes in electricity prices, and analyzing their impact on electricity consumption(Allcott, 2011b; Borenstein, 2005; Fowlie et al., 2021; Herter, 2007; Ito et al., 2018; Jessoe and Rapson,
2014; Wolak, 2011). 3 However, as a matter of course, these studies are designed to examine theresponse to changes in the unit price of electricity of the electricity company with which one contracts.
Therefore, verifying the response to changes in the unit price of electricity when the contract is changed
to another electricity company at cost is beyond the scope of these studies.
Nevertheless, intervention studies have been conducted to verify the impact on electricity
consumption by providing comparative information on the electricity consumption of others and oneself
through the HER and making them aware of the differences (Allcott, 2011a; Allcott and Rogers 2014;
Andor et al., 2020; Ayres et al., 2012; Brandon et al., 2019; Byrne et al., 2018; Crago et al., 2020; Costa
and Kahn, 2013; Myers and Souza, 2020; Ruokamo et al., 2022). These studies provide information on
the electricity consumption of neighboring homes with similar attributes. However, these studies
examined only the effect of social comparison on differences based on electricity consumption.
Therefore, no intervention studies have examined the impact of electricity consumption by making
people aware of the difference in the unit price of electricity from others where electricity consumption
is equal to others. In other words, no studies have used the unit price of other people's electricity forsocial comparison rather than their electricity consumption as the object.

3

Numerous experiments have also been conducted with rebate-type incentives based on electricity reductions, rather
than billing-type incentives that vary the unit price of electricity. Studies targeting Japanese consumers include
Mizobuchi and Takeuchi (2013) and Murakami et al. (2022).

5

Moreover, since the HER also contains information on one's past electricity consumption, it is notpossible to determine whether this information or that on others' electricity consumption influenced
power-saving behavior. Furthermore, regardless of the method of HER provision (e.g., mail, e-mail,Web), the cost of collecting and processing information is high because the information is tailor-madefor each household. Moreover, it is an information provision measure by the electric power company
and cannot be proactively implemented by the local government. The same applies to measures basedon dynamic pricing. This study proposes a low-cost information-provision measure using unit electricity
prices that local governments can implement to encourage households to save energy.
2.2 Study of mechanisms for reducing electricity consumption
Studies have identified mechanisms for reducing electricity consumption, and the effects of energy
conservation behavior and capital investment have been compared. (Allcott and Rogers, 2014; Brandon,
et al., 2017; Ito et al., 2018). Brandon et al. (2017), in a meta-analysis of 38 field experiments with
HERs in the U.S., showed long-term electricity consumption reductions as an effect of capital
investment but not energy conservation behavior. However, Allcott and Rogers (2014) examined theeffect of HER on electricity consumption reduction in a long-term experiment of 4-5 years in the UnitedStates. They found that energy conservation behavior had a large effect, whereas capital investment hada small effect. Ito et al. (2018) examined the effect of dynamic pricing on electricity consumption
reduction for Japanese households in summer and winter over two seasons. They showed that this effectwas due to energy conservation behavior.
Different results have been reported depending on the intervention method, duration, and target region.
We excluded capital investment in this study to clarify the short-term intervention effects of providing
one-time information. We then examined the content of energy conservation behavior in more detail,based on Ito et al. (2018), who targeted Japanese households. Ito et al. (2018) targeted the efficient useof each of the five appliances for energy conservation. The five appliances were air conditioners, electricheaters, personal computers, washers, and vacuum cleaners. However, the study was limited to analyzing
data asking whether they used each appliances efficiently. They did not measure the implementation of
specific power-saving behaviors; therefore, each respondent's judgment of the efficient use of appliances
may differ. In addition, some of these five appliances did not consume electricity significantly.
Therefore, it is insufficient to verify the mechanism of the reduction in macro-electricity consumption(kWh).
This study sets nine specific power-saving behaviors in the top four appliances (air conditioners,
refrigerators, televisions, and lighting) that consume the most electricity (kWh) in Japanese households
and examines the impact of interventions on them. In doing so, we examine not only the six timeconsuming power-saving behaviors that need to be implemented daily but also the three hassle-free
power-saving behaviors that, once implemented, will continue to be effective over the long term. This
allowed us to consider the possibility of long-term power-saving effects of a single simple intervention.
3. Methodology and data
3.1 Research design6

We set up two reference points–information on the Past and information on Others–and examinedwhich information would encourage more energy-saving behavior among Japanese consumers using an
RCT. Two online questionnaire surveys were conducted in July and September 2017. Information wasprovided in July, and changes in electricity consumption and implementation of energy-saving behaviors
were measured in September.
This survey was conducted among Japanese consumers who could freely choose their electricity
provider since April 2016 and had not changed them as of July 2017, during the survey period. In July
2017, approximately one year after the liberalization of the retail electricity market, the share ofelectricity sold by new entrants to the market was 6.1% (Agency for Natural Resources and Energy,2017). Thus, it is assumed that the awareness of deregulation of the retail electricity market and theelectricity unit prices of other companies was low in this period. Therefore, we believe that presenting
electricity charges based on other companies' electricity unit prices in this survey makes people awareof the differences in electricity prices from other companies. It is assumed that some consumers would
be aware of other companies' electricity unit prices even if they have not changed their electricity
providers. Since changing electricity providers requires switching costs, some consumers may haveabandoned the idea of changing electricity providers by comparing the expected reduction in electricity
prices. Murakami and Ida (2019) analyzed the willingness of Japanese households to switch electricity
providers and found that they would not even if there was a 5% cost reduction. They attribute this to the
influence of status-quo bias. Therefore, even if consumers are aware of other companies' electricity unit
prices if they are presented with the option of saving electricity instead of changing electricity providers,
they are likely to adopt cheaper energy-saving behaviors than changing electricity providers. Based on
the above, the information provided to this survey target using the electricity prices of other companies
may impact electricity consumption.
In the first survey in July 2017, we obtained demographics and other information about the
respondents. Finally, an intervention was conducted to randomly present one of the three types ofelectricity price information.
The second survey, a follow-up to the first one, conducted in September 2017. First, the electricity
consumption (kWh) and duration in August 2016 and August 2017 were ascertained based on meterreadings. 4 This allowed us to calculate the average daily electricity consumption (kWh/day) in August
2016 as a baseline before the intervention, and the average daily electricity consumption (kWh/day) in
August 2017 after the intervention. From these data, we calculated the percentage change (%) in average
daily electricity consumption (kWh/day) due to the intervention. Additionally, the implementation statusof nine specific power-saving behaviors for four home appliances (air conditioners, refrigerators,
televisions, and lighting) and their willingness to switch power companies was ascertained in August
2017. This allowed us to analyze the impact of information on the Past and Others on the rate of change
in electricity consumption, the implementation of specific electricity-saving behaviors, and the4

The monthly meter reading slip of the subscribers to Kansai Electric Power Company (KEPCO), which is the subjectof the study, contains information on electricity consumption and usage periods not only for the year in question but
also for the same period in the previous year. Since the monthly meter reading date for each household is almost thesame (e.g., the 30th of each month), the rate of the average daily electricity consumption (kWh/day) changes can becalculated for each month.

7

willingness to change electricity providers.
The electricity charge information to be provided at the RCT is based on the electricity bill payment(yen/month) for a household with standard Japanese electricity consumption (260 kWh/month). Japanesehouseholds can confirm their electricity consumption (kWh/month) and electricity bill payments(yen/month) using monthly meter readings. Hence, the perceived electricity prices by the household are
not the unit price of electricity (yen/kWh) but the amount paid for electricity (yen/month). Therefore,
the electricity bill payment (yen/month), more highly recognized by people and calculated based on theelectricity unit price, is used as the target of social comparison. In addition, each major electric powercompany publishes the monthly electricity charges (yen/month) for a household with standard electricity
consumption (260 kWh/month) on its website. This amount is calculated based on the electricity unit
price (yen/kWh) of each electric power company × 260 kWh/month, and the transition from the past canalso be viewed. This study aims to contribute to the information provision measures of local
governments based on information that can be collected without cost. Based on the above, the electricity
charge information to be presented is the electricity bill (yen/month) paid by a household with standard
electricity consumption (260 kWh/month) and can be easily collected and understood by consumers.
The specific information provided for the three groups (intervention groups 1 (Past group) and 2(group on Others) and the control group) is shown below and in Fig. 1. As an equivalence frame, thetime periods within the Past group were set such that the amounts for the Past and Others’ groups wereequal to 500 yen (about $4.5). 5
࣭ Intervention group 1 (Past group): Information is presented that the current electricity bill
payment is about 500 yen higher than in the past, although electricity consumption remains thesame. This figure was calculated using the amount in July 2017 at the time of the survey and theamount in December 2016, seven months earlier, for a household with standard electricity
consumption (260 kWh/month) having a contract with Kansai Electric Power Co. (KEPCO). (Topof Fig.1).
࣭ Intervention group 2 (group on Others): Information is presented that their current electricity bill
payment is approximately 500 yen higher than that of the others, although their electricity
consumption is equal. This figure was calculated from the amount in July 2017 for a householdwith standard electricity consumption (260 kWh/month) contracted with KEPCO and the amountin July 2017 for that with standard electricity consumption (260 kWh/month) contracted with
Chubu Electric Power Co., 6 a major power company in the neighborhood. (Middle of Fig.1).
࣭ Control group: Information is presented on the electricity bill payments of a household withstandard electricity consumption (260 kWh/month) having a contract with KEPCO for threemonths ending July 2017. (Bottom of Fig.1).
< Fig.1. >5Calculated at the July 2017 exchange rate: $1 = 110.63 yen.
At the time of the survey, Chubu Electric Power Co. was the third largest electric power company after Tokyo ElectricPower Company and KEPCO in Japan. This company was also a major power company before the full deregulation of
the electricity retail market in April 2016, so it is highly recognized by consumers and assumed to be highly reliable
as an electricity company to switch to. In addition, before the full liberalization of the electricity retail market, thiscompany's electricity supply area was adjacent to that of KEPCO. Therefore, we used data from Chubu Electric PowerCo. rather than that from new entrant electric power companies with cheaper electricity prices.
68

3.2 Setting up the survey subjects
The survey participants were housewives who lived in Osaka Prefecture, who had a contract with
KEPCO, and were home during the daytime on weekdays. The selection process was as follows:
To present information on the electricity bill payment of a household with standard electricity
consumption (260 kWh/month) having a contract with KEPCO, we first defined the scope of the survey
to include subscribers to KEPCO. KEPCO has been a major electric power company before the full
liberalization of the retail electricity market. Therefore, residents who did not change their electric
power companies at the time of the survey were included. Next, we narrowed the sample to residents ofOsaka Prefecture among them. The rate of change in the average daily electricity consumption(kWh/day) was calculated from the electricity consumption (kWh) in August 2016 and August 2017
listed in the meter readings. The rate of change was estimated after adjusting for temperature byincluding cooling-degree days in the model. 7 Osaka Prefecture is the second smallest of Japan's 47
prefectures, with an area of 1,905 km 2 , and the weather conditions do not vary significantly within theprefecture. Therefore, the analysis was limited to residents of Osaka Prefecture to measure the rate ofchange in electricity consumption more accurately. In addition, we included regional fixed effects bydividing Osaka Prefecture into eight districts.
The survey targeted housewives at home during the daytime on weekdays among the residents ofOsaka Prefecture mentioned above, having contracts with KEPCO. Since the accident at the Fukushima
Daiichi Nuclear Power Plant following the Great East Japan Earthquake in 2011, many nuclear power
plants in Japan have not restarted even after periodic inspections, and the power supply is insecure on
weekdays during summer heat waves. Additionally, CO 2 emissions must be reduced to prevent global
warming. That is, reductions in the maximum power consumption (kW) and power consumption (kWh)are required. Therefore, it is important to save electricity for residents who consume large amounts ofelectricity because they are home during the daytime in summer and have a large potential to reduce
their electricity consumption. Housewives who stay at home on weekdays in the summer meet thiscondition.
Based on the above, we first selected those participants who resided in Osaka Prefecture, subscribedto KEPCO, and were housewives from among the monitors of an online survey company. Screening
questions were then used to select respondents who indicated that they were generally at home during
the daytime on weekdays during the summer of 2016 (July–September) and 2017 (July–September).
Finally, a screening question was used to select respondents who indicated no major changes in theexternal environment related to electricity consumption during this period (no relocation, no dwelling
extension or renovation, no change in the number of people living together, or no solar power
installation) as the final target group. The participants were then asked to respond to the questionnaire.
Data were collected from 394 respondents.
3.3 Analytical frames and models

7

Since the monthly meter reading dates differ among households, the cooling degree days vary for each household.
Therefore, the cooling degree days were calculated for each household by adding up the number of eligible days within
the electricity usage period.

9The analysis model is shown in Equation (1). Ordinary least squares (OLS) was used to examine thedifferences in the impact of different electricity-pricing information on electricity consumption.
ǻY i = Įȕ 1 Past i ȕ 2 Others i ȕ 3 X i ș i ႒ i(1)

Ǽ Y i is the percentage change in average electricity consumption due to the intervention. ǻY i is
calculated as ((Electricity2017 i -Electricity2016 i ) / Electricity2016 i ) × 100. 8 Electricity2016 i is theaverage daily electricity consumption (kWh/day) in August 2016 before the intervention as a baseline
for i. Electricity2017 i is the average daily electricity consumption in August 2017 after the interventions
for i. From this, a negative ǻY i means that electricity consumption was reduced in August 2017
compared to August 2016. Past i is a dummy variable that takes the value of 1 if household i is in the
Past group. Similarly, Others i is a dummy variable that takes the value of 1 if household i is in groupon Others. ȕ 1 and ȕ 2 measure the average treatment effect (ATE) for each information provision on
outcome ǻY. Thus, ȕ 1 and ȕ 2 express the average electricity savings as a percentage of the average
consumption level. X i is the vector of the control variables for the socioeconomic characteristics ofhouseholds and other information for i from the questionnaire survey. ȟ i is a ward dummy, and Osaka
Prefecture is divided into eight wards. ႒ i is an individual-specific error term.
X includes Electricity2016, ǻCDD, awareness of retail electricity market deregulation, annual
household income, 9 age, number of household members, and housing type (dummy variable fordetached houses = 0; apartment buildings = 1). CDD is the number of cooling degree days and is thesum of the difference between the average daily temperature on days when the average daily temperature
exceeds 24 degrees Celsius and the base temperature of 24 degrees Celsius over the period under study.
ǻCDD is the cooling degree days in August 2017 (CDD2017 i ) minus the cooling degree days in August
2016 (CDD2016 i ) for i. From this, if ǻCDD is positive, then for i, August 2017 was a weather condition
with greater cooling demand than August 2016. Awareness of retail electricity market deregulation wasmeasured on a 6-point Likert-type scale in the first survey conducted in July 2017. 10 This item was set
up because intervention group 2 (group on Others) presented information on the electricity prices of
Chubu Electric Power Co., another electricity company different from KEPCO.
Next, as an additional analysis, we set up Model (2), in which the objective variable is the willingness
to change electricity providers after the intervention and examine it using OLS.
Company change i = Įȕ 1 Past i ȕ 2 Others i ȕ 3 X i ș i ႒ i

(2)

Company change i is the willingness of i to change its electricity provider. This was measured in thesecond survey conducted in September 2017 on a 6-point Likert-type scale regarding the willingness tochange electricity companies. 11 This verification was conducted to present group on Others withelectricity rate information based on Chubu Electric Power's electricity unit prices, which are lower thanthose of KEPCO. This verification examines whether information on Others impacts switching
8 Based on Andor et al. (2020) and Mizobuchi and Takeuchi (2013), ǻYi was set as the objective variable. For ease ofinterpretation, ǻYi is multiplied by 100 and expressed as a percentage, as in Andor et al. (2020) and Mizobuchi and
Takeuchi (2013).
9 There were 54 respondents who "don't know/don't want to answer."
10 For the question, "I know in detail about the liberalization of the electricity retail market since last April 2016 (e.g.,
It is now possible to purchase electricity from power companies other than KEPCO)," we measured on a 6-point Likerttype scale from 6 = strongly agree to 1 = not at all agree.
11
The question "I would like to change my electricity purchaser" was measured on a scale of 6 = strongly agree to 1
= not at all agree.

10electricity providers, which would be relatively costlier than hassle-free energy-saving behavior owing
to switching costs. 12 If this hypothesis is confirmed and local governments' information policies
address residents' lack of information about retail electricity market deregulation and consumers switch
to electricity companies with lower electricity prices, this will increase residents' benefits.
Finally, to verify the mechanism of electricity consumption reduction, Model (3) was set up, in which
the objective variable was the implementation of nine specific electricity-saving behaviors for four home
appliances (air conditioners, refrigerators, televisions, and lighting) after the intervention.
Behavior i,j = Įȕ 1 Past i ȕ 2 Others i ȕ 3 X i ș i ႒ i

(3)

Behavior i,j is the implementation status of the power-saving behavior j by i. j consists of nine powersaving behaviors. 13 First, there are six time-consuming energy-saving behaviors that need to be
implemented daily. Next, there are three hassle-free energy-saving behaviors that, once implemented,
remain effective for a long period. These were measured in a second survey conducted in September
2017. Six time-consuming energy-saving behaviors were measured on a 5-point Likert-type scale. 14
Three hassle-free energy-saving behaviors were measured on a 2-point scale: implemented or not
implemented. 15 These nine power-saving behaviors were analyzed by OLS based on Angrist andPischke (2009). 16
3.4 Data
Descriptive statistics are shown in Table 1. A balance test was also performed by testing fordifferences in the means. The results were statistically non-significant except for Kitakawachi in Ward,and the randomization was generally successful. As mentioned above, Ward was included in the model
as a control variable.
< Table 1 >As shown in Table 1, the average daily electricity consumption in August 2016 was 12.271 kWh/day.
The average electricity consumption in August 2017 was calculated 12.536 kWh/day, increasing from
August 2016. One possible reason for this result is that ǻCCD is positive from Table 1, indicating a

12Studies examining savings as a factor in changing electricity providers include Buryk et al. (2015), Daglish (2016),
Ek and Söderholm (2008) and Giulietti et al. (2005). Murakami and Ida (2019) and Shin and Managi (2017) are among
the studies covering the liberalization of the retail electricity market in Japan.
13 As in Ito et al. (2018), we set the implementation of power-saving behavior of appliances after the intervention as
the objective variable.
14
The following six questions were asked, each of which was measured using a five-point scale: "Almost did it (100%)- Generally did it (75%) - Occasionally did it (50%) - Not very much did it (25%) - Almost never did it (0%)".The levelof implementation of each option (100%, 75%, 50%, 25%, 0%) was then taken as an interval scale and scored from 5
to 1. Six questions: "Air conditioner 1: I set the temperature to 28°C" (AC_28°C), "Air conditioner 2: I do not leave
the air conditioner on unnecessarily" (AC_leave), "Refrigerator 1: I do not overfill my fridge with stuff" (Fridge_stuff),"Refrigerator 2: I open and close the door less frequently and shorter" (Fridge_leave), "TV: I turn it off unless I needit" (TV_leave), "Lights: I frequently turn off lights that are not needed" (Light_leave).
15 The following three questions were set, and each was measured on a scale of 1 = implemented and 0 = not
implemented. The three and six power-saving behaviors mentioned above were adopted from the power-saving items
recommended by The Energy Conservation Center, Japan (2012). The hassle-free power-saving behavior related tolighting was not set in this study because it was not indicated in this document. Three questions: "Air conditioner:
avoid placing objects around the outdoor unit to improve ventilation" (AC_outdoor unit), "Refrigerator: set thetemperature inside the refrigerator to "medium" or "low" (Fridge_cooling intensity), and "TV: set the TV to energysaving mode so that the screen is not brighter than necessary (TV_energy-saving mode).
16
Ito et al. (2018) also analyzed the implementation of the energy-saving behaviors measured by binary choice in OLS.
As discussed later, these three energy-saving behaviors measured with binary choice were also analyzed by probit
model to verify their robustness.

11

greater cooling demand in August 2017 than in August 2016.
Next, KEPCO set the electricity consumption of a standard household at 260 kWh/month; however,
the average electricity consumption of the target of this study, a housewife’s household who was homeduring the daytime on weekdays, in August 2016 was 380 kWh/month (12.271 kWh/day × 31 days).
This is approximately 1.5 times the electricity consumption of a standard household, confirming its
suitability as a policy target.
4. Results and discussion
4.1 Electricity consumption
The analysis results of model (1) with ǻY as the objective variable are presented. The averagetreatment effect is shown in column (2) of Table 2, where the coefficients for Past and Others arenegative, but neither is significant.
< Table 2 >The coefficient of Electricity2016 was negative and significant, with every 1 kWh/day associatedwith a 0.541% reduction in electricity consumption. This implies that households that consumed more
electricity in August 2016 consumed less. This result is similar to those of Allcott (2011b), Andor (2020),
Knitelle and Stlper (2019) and Matsukawa (2018). Households with high electricity consumption can beconsidered as having great potential for electricity consumption reduction. Therefore, even a small
amount of electricity-saving behavior with a low marginal cost is likely to reduce electricity
consumption. The analysis results were considered to have been strongly influenced by the power-saving
effects of households with high electricity consumption.
In contrast, the large cooling demand as the size of ǻCCD and the large number of household members
were factors increasing electricity consumption.
The next step was to examine heterogeneity. From a cost-effectiveness perspective, it is desirable toidentify the information that greatly impacts households with higher electricity consumption. Therefore,
we examined the interaction term between electricity consumption in August 2016 and the information
provided. As shown in column (3) of Table 2, the coefficient of Past×Electricity2016 is significant at 0.713, indicating that information on the Past promotes electricity savings for households that consumed
more electricity in August 2016. Ito et al. (2018) showed that the intervention effect of economic
incentives was greater for households with higher electricity consumption, which is consistent with theresults of this analysis. The average electricity consumption of the participants in August 2016 was 380
kWh/month. This is possibly because households with higher electricity consumption were expected tobear a larger monetary burden than the 500 yen increase in their standard electricity consumption (260kWh/month), as indicated by the past information.
4.2 Willingness to change electric power companies
The following are the results of the Model (2) analysis with Company change as the objective variable.
As shown in Column (5) of Table 2, the coefficient of Others is negative and significant, which meansthat information on Others reduces the willingness to change electricity providers. In addition, thecoefficient of Electricity2016 is negative and significant.
12The interpretation of these results is assumed to have been influenced by the small savings of 500
yen/month, the difference between KEPCO and Chubu Electric Power Co., as indicated in theinformation on Others. It is considered that the decision to switch electric power companies was basedon a comparison of the amount saved and the switching costs of changing electric power companies.
Only small savings of 500 yen were considered to have functioned as decision-making information,
especially for households with high electricity consumption; that is, households with large electricity
payments. In other words, it is possible that households with high electricity consumption assumed thatthe electricity company change would lead to a larger decrease than the 500 yen in savings forhouseholds with standard electricity consumption (260 kWh/month) but believed that the assumed
switching cost would offset this effect. Murakami and Ida (2019) analyzed the willingness of Japanesehouseholds to switch electricity providers and found that they would not even for a 5% cost reduction.
Information on Others in this study showed a cost reduction of 7.3% (500yen/6,890yen), but thewillingness to change electric power companies decreased.
Contrarily, numerous household members and high awareness of retail electricity market deregulation
increase the willingness to change electricity providers. The effect of awareness of the retail electricity
market deregulation in Japan is similar to that of Murakami and Ida (2019) and Shin and Managi (2017),
suggesting a possible lack of information about retail electricity deregulation among consumers. This
result suggests that local governments should inform residents regarding retail electricity deregulation.
Further, as a test of heterogeneity, we examined the interaction term between Electricity2016 and theinformation provided; however, as shown in column (6) of Table 2, no significant results were obtained.
4.3 Power-saving behavior
The following are the results of the Model (3) analysis, with Behavior as the objective variable. The
results for time-consuming power-saving behavior are shown in Section 4.3.1, and the hassle-free
power-saving behavior in Section 4.3.2.
4.3.1 Time-consuming power-saving behavior
Table 3 presents the average treatment effect results of the six time-consuming electricity-saving
behaviors. As shown in Column (2), coefficients of both Past and Others are positive and significant for
AC_leave (not leaving the air conditioner on). This means that Past and Others’ information influenced
the behavior of not leaving the air conditioner on. However, the coefficients of Past and Others were
not significant in column (1) AC_28°C (set the air conditioner to 28°C).
< Table 3 >Given the weather conditions in Japan, many housewives who are home during the daytime in summer
are likely to use air conditioners. As an air conditioner’s energy-saving behavior is based on theintervention of information on the Past or Others, the behavior of not leaving the air conditioner on was
selected instead of setting the air conditioner at 28 degrees Celsius. This result can be considered torepresent a relatively easy-to-implement air conditioner power-saving behavior. Ito et al. (2018) showedthat the intervention promoted the efficient use of air conditioners. This study further clarified specificpower-saving behaviors for efficient use of air conditioners.
13

Next, in column (2) of AC_leave, the coefficient of Electricity2016 is negative and significant. Thismeans households that consumed more electricity in August 2016 did not implement the behavior of notleaving the air conditioner on. The same result holds for the other five behaviors listed in Table 3.
Furthermore, the correlation coefficients between Electricity2017 and these six electricity-saving
behaviors ranged from -0.116 to -0.238, all significant at the 5% level. This suggests that implementing
these six behaviors contributes significantly to electricity consumption.
From the above, it can be stated that the information on the Past and Others influenced the behaviorof not leaving the air conditioner on, contributing to electricity consumption. Even a one-time provisionof information influences time-consuming behaviors that should be implemented daily.
In addition, in Table 4, we test the interaction of Electricity2016 and the information provided as atest of heterogeneity. The results were not significant for any of these behaviors.
< Table 4 >4.3.2 Hassle-free power-saving behaviors
Table 5 shows the results of the average treatment effects of the three hassle-free one-time electricitysaving behaviors. 17 As shown in column (3), the coefficient of Others’ is positive and significant for
Fridge_cooling intensity (lowering the refrigerator's internal temperature). This implies that information
on Others influences the behavior of lowering the refrigerator's internal temperature.
< Table 5 >Additionally, the coefficient of Electricity2016 is negative and significant. This means households
consuming more electricity in August 2016 did not implement measures to lower their refrigerators̓internal temperatures. The same result was observed for the AC_outdoor unit in column (1) (avoid
placing objects around the outdoor unit to improve ventilation). Furthermore, the correlation coefficients
between Electricity2017 and these two power-saving behaviors were -0.096 and -0.092, respectively,
and both were significant at the 10% level. This suggests that the implementation status of these two
behaviors significantly contributes to electricity consumption. From these results, it can be stated that
information on Others influenced the behavior of lowering the temperature inside the refrigerator, which
contributed to electricity consumption.
Based on the above, we summarized the results for information on Others. As shown in column (5)of Table 2, information on Others negatively impacts willingness to change electric power companies.
However, column (2) in Table 3 and column (3) in Table 5 show that information on Others affects the
behavior of not leaving the air conditioner on and lowering the temperature inside the refrigerator.
However, as shown in columns (1)–(3) of Table 2, information on Others does not significantly affectǻY (electricity consumption).
Next, regarding the heterogeneity test results, as shown in Column (4) of Table 5, the coefficient of
Past x Electricity2016 is positive and significant. This means that past information influenced the
behavior of lowering the refrigerator's internal temperature in households with high electricity
consumption in August 2016.
Based on the above, we summarize the results of the past information. Information on the Past reduces
17

Table A1 shows the probit estimation results as a robustness test. Signs and significance are the same as in Table 5
except for Constant.

14the electricity consumption (kWh/day) of households with high electricity consumption in August 2016,as shown in column (3) of Table 2. The channels for this are the behavior of not leaving the air
conditioner on, as shown in column (2) of Table 3, and the behavior of lowering the temperature inside
the refrigerator in households with high electricity consumption in August 2016, as shown in column(4) of Table 5.
5. Conclusions and policy implications
This study examines the information provision for reducing household electricity consumption using
electricity charges based on others' electricity unit prices, rather than others' electricity consumption
used in the HER, as the object of social comparison. To clarify the mechanism of electricity consumption
reduction, we examined the effects of nine specific electricity-saving behaviors on the four applianceswith the highest electricity consumption in Japanese households. Through these analyses, we clarify
how presenting electricity charge information, when easily collected, can encourage residents to saveelectricity. The social significance of this study is that it provides suggestions for information provision
measures that can be proactively implemented by local governments. In addition, this study aims tocontribute academically to two research areas: social comparison, electricity prices, electricity-saving
behavior, and mechanisms for reducing electricity consumption.
The analysis results showed that information on the past electricity charges of the electricity company
to which one subscribes reduces electricity consumption in households with high electricity
consumption. However, information on Others–information on electricity charges of other electricity
companies - influenced some electricity-saving behavior but did not contribute to reducing electricity
consumption. As a simple information provision measure, using past information is more effective. Past
information is that municipalities can collect at low cost without violating residents' privacy. Therefore,
this shows the possibility of low-cost information provision by local governments instead of therelatively costly tailor-made HER and dynamic pricing for each household, mainly conducted by electric
power companies.
It would be more efficient and effective if municipalities could provide information targetinghouseholds with high electricity consumption among housewife households, which can be achieved
through cooperation with electric power companies. Nonetheless, although the efficiency of the measure
was reduced, it was also possible to provide information to all households in the municipality or tohousewife households. One problem with using the HER is that providing information on the electricity
consumption of others to households that are making progress in saving electricity may have aboomerang effect, causing an increase in electricity consumption (Knittel and Stolper, 2019). 18 It was
assumed that the information on electricity price increases used in this study was unlikely to increaseelectricity consumption. Therefore, the risk of providing information to all households or housewife
households is relatively small compared to using HER.
However, this study has the following three limitations. Therefore, generalizations of the results ofthis study should be considered. First, the intervention results were based on a monetary level of JPY

18

It has also been shown that posting smiley face illustrations may reduce the boomerang effect (Schultz et al, 2007).

15

500/month (approximately $4.5). This study was not a scenario experiment but a social experiment basedon actual conditions in Japan. Therefore, the results may vary at different monetary levels in different
countries, regions, and periods. In this study, the amount of money was set at 500 yen, as it was necessary
to equalize it in the two intervention groups. Given the current rise in electricity prices in Japan, 500
yen is a relatively small amount. In the intervention experiment, a long-term comparison, such as one
or two years before, would have allowed for a larger price difference. It can be said that the intervention
was effective even at the relatively low level of 500 yen, but further analysis at different monetary levels
is needed.
Second, the study targeted housewives who were home during the daytime on weekdays in summer.
These were chosen for the study because they were assumed to have large electricity reduction potential
and opportunities to save electricity. The analysis showed intervention effects in the high electricity
consumption group. It also affected specific behaviors, such as not leaving the air conditioner on. These
results may also have implications for households that spend more time at home and consume more
electricity because of the increase in telecommuting owing to the coronavirus pandemic. However, these
are the results of an analysis of Japanese housewives who were at home during the daytime on weekdays
in the summer. Therefore, further research is needed to determine the external validity of these results.
Third, the study focused on the short-term effects of summer and did not examine the long-term
effects. Moreover, the short-term effects during winter have not been clarified. Therefore, theverification of these effects is required. However, to verify the mechanism of electricity consumption
reduction, we also verified the effect of electricity-saving behavior, which, once implemented, has along-lasting effect. The results showed that the intervention impacted the behavior of lowering theinternal temperature of refrigerators. However, while information on Others was found to influence thisbehavior for all households, past information tended to influence those with high electricity consumption.
These findings suggest that the information provided to promote electricity-saving behavior may differ
according to electricity-saving behaviors and attributes. Further, each power-saving behavior andattribute needs validation.
In Japan, the price of electricity is expected to rise in the future owing to soaring fossil fuel prices
and the ongoing debate on carbon pricing. Increasing fossil fuel prices is also a global phenomenon.
Therefore, there is a strong need worldwide for measures to provide electricity price information witheasy implementation by local governments, which can promote energy-saving behaviors in households.
This study makes only a minor contribution to solving these problems. In the study of information
provision, the setting of reference points to obtain effective results may vary depending on the field,
target audience, and period. We believe this study will be useful for other research subjects as well, asa study to verify the effectiveness of information provision by devising reference points.
Acknowledgements
This work was supported by JSPS KAKENHI [Grant Numbers JP17K04318, JP20K03296].
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experiment. American Economic Review. 101(3), 83-87. https://doi.org/10.1257/aer.101.3.83

20

Past group㼀㼔㼑㻌㼑㼘㼑㼏㼠㼞㼕㼏㼕㼠㼥㻌㼜㼞㼕㼏㼑㻌㼒㼛㼞㻌㼍㻌㼟㼠㼍㼚㼐㼍㼞㼐㻌㼔㼛㼡㼟㼑㼔㼛㼘㼐㻌㻔㻞㻢㻜㻷㼣㼔㻕㻌㼏㼛㼚㼠㼞㼍㼏㼠㼕㼚㼓㻌㼣㼕㼠㼔㻌㻷㼍㼚㼟㼍㼕㻌㻱㼘㼑㼏㼠㼞㼕㼏㻌㻼㼛㼣㼑㼞㻌㻯㼛㻚㻌㼕㼟㻌㼑㼟㼠㼕㼙㼍㼠㼑㼐㻌㼠㼛㻌㼎㼑㻌㻢㻘㻤㻥㻜㻌㼥㼑㼚㻌㼠㼔㼕㼟㻌㻶㼡㼘㼥㻘㻌㼏㼛㼙㼜㼍㼞㼑㼐㻌㼠㼛㻌㻢㻘㻤㻥㻢㻌㼥㼑㼚㻌㼕㼚㻌㻶㼡㼚㼑㻌㼍㼚㼐㻌㻢㻘㻤㻟㻢㻌㼥㼑㼚㻌㼕㼚㻌㻹㼍㼥㻚㻌㻌㼀㼔㼑㻌㼐㼕㼒㼒㼑㼞㼑㼚㼏㼑㻌㼕㼚㻌㼑㼘㼑㼏㼠㼞㼕㼏㼕㼠㼥㻌㼜㼞㼕㼏㼑㼟㻌㼎㼑㼠㼣㼑㼑㼚㻌㼠㼔㼕㼟㻌㻶㼡㼘㼥㻌㼍㼚㼐㻌㼘㼍㼟㼠㻌㻣㻘㻜㻜㻜㼥㼑㼚 㻰㼑㼏㼑㼙㼎㼑㼞㻌㼣㼕㼘㼘㻌㼎㼑㻌㼍㼎㼛㼡㼠㻌㻡㻜㻜㻌㼥㼑㼚㻚㻢㻘㻤㻥㻢㼥㼑㼚

㻢㻘㻤㻥㻜㼥㼑㼚

㻶㼡㼚㻚

㻶㼡㼘㻚

㻢㻘㻤㻟㻢㼥㼑㼚

㻭㼎㼛㼡㼠㻌㻡㻜㻜㻌㼥㼑㼚㻢㻘㻢㻠㻢㼥㼑㼚
㻢㻘㻡㻜㻜㼥㼑㼚

㻢㻘㻡㻟㻣㼥㼑㼚
㻢㻘㻠㻜㻡㼥㼑㼚

㻢㻘㻟㻤㻥㼥㼑㼚

㻢㻘㻠㻡㻥㼥㼑㼚

㻢㻘㻜㻜㻜㼥㼑㼚
㻞㻜㻝㻢 㻰㼑㼏㻚㻞㻜㻝㻣 㻶㼍㼚㻚

㻲㼑㼎㻚

㻹㼍㼞㻚

㻭㼜㼞㻚

㻹㼍㼥

Group on Others㼀㼔㼑㻌㼑㼘㼑㼏㼠㼞㼕㼏㼕㼠㼥㻌㼜㼞㼕㼏㼑㻌㼒㼛㼞㻌㼍㻌㼟㼠㼍㼚㼐㼍㼞㼐㻌㼔㼛㼡㼟㼑㼔㼛㼘㼐㻌㻔㻞㻢㻜㻷㼣㼔㻕㻌㼏㼛㼚㼠㼞㼍㼏㼠㼕㼚㼓㻌㼣㼕㼠㼔㻌㻷㼍㼚㼟㼍㼕㻌㻱㼘㼑㼏㼠㼞㼕㼏㻌㻼㼛㼣㼑㼞㻌㻯㼛㻚㻌㼕㼟㻌㼑㼟㼠㼕㼙㼍㼠㼑㼐㻌㼠㼛㻌㼎㼑㻌㻢㻘㻤㻥㻜㻌㼥㼑㼚㻌㼠㼔㼕㼟㻌㻶㼡㼘㼥㻘㻌㼏㼛㼙㼜㼍㼞㼑㼐㻌㼠㼛㻌㻢㻘㻤㻥㻢㻌㼥㼑㼚㻌㼕㼚㻌㻶㼡㼚㼑㻌㼍㼚㼐㻌㻢㻘㻤㻟㻢㻌㼥㼑㼚㻌㼕㼚㻌㻹㼍㼥㻚㻌㻌㼀㼔㼑㻌㼐㼕㼒㼒㼑㼞㼑㼚㼏㼑㻌㼕㼚㻌㼑㼘㼑㼏㼠㼞㼕㼏㼕㼠㼥㻌㼜㼞㼕㼏㼑㼟㻌㼠㼔㼕㼟㻌㻶㼡㼘㼥㻌㼣㼕㼠㼔㻌㼍㻌㼟㼠㼍㼚㼐㼍㼞㼐㻌㻣㻘㻜㻜㻜㼥㼑㼚 㼔㼛㼡㼟㼑㼔㼛㼘㼐㻌㻔㻞㻢㻜㻷㼣㼔㻕㻌㼏㼛㼚㼠㼞㼍㼏㼠㼕㼚㼓㻌㼣㼕㼠㼔㻌㻯㼔㼡㼎㼡㻌㻱㼘㼑㼏㼠㼞㼕㼏㻌㻼㼛㼣㼑㼞㻌㻯㼛㻚㻌㼣㼕㼘㼘㻌㼎㼑㻌㼍㼎㼛㼡㼠㻌㻡㻜㻜㻌㼥㼑㼚㻚㻢㻘㻤㻥㻢㼥㼑㼚

㻢㻘㻤㻥㻜㼥㼑㼚

㻢㻘㻤㻟㻢㼥㼑㼚

Kansai ElectricPower Co.

㻭㼎㼛㼡㼠㻌㻡㻜㻜㻌㼥㼑㼚㻢㻘㻡㻜㻜㼥㼑㼚

㻢㻘㻟㻠㻝㼥㼑㼚

㻢㻘㻟㻥㻜㼥㼑㼚

㻢㻘㻟㻣㻣㼥㼑㼚

Chubu ElectricPower Co.
㻢㻘㻜㻜㻜㼥㼑㼚
㻞㻜㻝㻣 㻹㼍㼥

㻶㼡㼚㻚

㻶㼡㼘㻚

Control group㼀㼔㼑㻌㼑㼘㼑㼏㼠㼞㼕㼏㼕㼠㼥㻌㼜㼞㼕㼏㼑㻌㼒㼛㼞㻌㼍㻌㼟㼠㼍㼚㼐㼍㼞㼐㻌㼔㼛㼡㼟㼑㼔㼛㼘㼐㻌㻔㻞㻢㻜㻷㼣㼔㻕㻌㼏㼛㼚㼠㼞㼍㼏㼠㼕㼚㼓㻌㼣㼕㼠㼔㻌㻷㼍㼚㼟㼍㼕㻌㻱㼘㼑㼏㼠㼞㼕㼏㻌㻼㼛㼣㼑㼞㻌㻯㼛㻚㻌㼕㼟㻌㼑㼟㼠㼕㼙㼍㼠㼑㼐㻌㼠㼛㻌㼎㼑㻌㻢㻘㻤㻥㻜㻌㼥㼑㼚㻌㼠㼔㼕㼟㻌㻶㼡㼘㼥㻘㻌㼏㼛㼙㼜㼍㼞㼑㼐㻌㼠㼛㻌㻢㻘㻤㻥㻢㻌㼥㼑㼚㻌㼕㼚㻌㻶㼡㼚㼑㻌㼍㼚㼐㻌㻢㻘㻤㻟㻢㻌㼥㼑㼚㻌㼕㼚㻌㻹㼍㼥㻚㻣㻘㻜㻜㻜㼥㼑㼚
㻢㻘㻤㻥㻢㼥㼑㼚

㻢㻘㻤㻥㻜㼥㼑㼚

㻶㼡㼚㻚

㻶㼡㼘㻚

㻢㻘㻤㻟㻢㼥㼑㼚

㻢㻘㻡㻜㻜㼥㼑㼚

㻢㻘㻜㻜㻜㼥㼑㼚
㻞㻜㻝㻣 㻹㼍㼥

Fig. 1. The specific information provided for the three groups.

21

Table 1
Descriptive statistics and balance tests.
Total
(1)
0.551
(0.498)3.086(1.005)Age44.713(8.062)
Annual household income (10,000 yen)614.559(336.367)
Awareness of retail electricity market deregulation
3.602(1 to 6)(1.089)䂴CCD (CCD2017 minus CCD2016)
2.753(21.603)
Electricity2016 (kWh/day)
12.271(6.675)Ward
䚷Osaka0.269(0.444)
䚷Mishima
0.160(0.367)
䚷Toyono0.114(0.318)
䚷Kitakawachi
0.122(0.328)
䚷Nakakawachi
0.094(0.292)
䚷Minamikawachi
0.051(0.220)䚷Senboku
0.127(0.333)
䚷Sennan
0.063(0.244)
N394
Housing type(detached houses=0, apartment buildings=1)
Number of household members

control
group
(2)
0.547
(0.500)3.058(0.991)44.590(7.634)621.488(397.011)
3.662(1.053)
1.584(21.207)12.685(7.594)0.266(0.444)0.158(0.366)0.115(0.320)0.165(0.373)0.101(0.302)0.036(0.187)0.101(0.302)0.058(0.234)139

Pastgroup onP-value
group
Others(control-Past)
(3)
(4)
(5)
0.489
0.629
0.339
(0.502)(0.485)
3.129
3.069
0.547
(0.999)(1.036)44.34545.3020.799(8.341)(8.256)648.333
565.1520.555(302.228) (289.039)
3.576
3.560
0.497
(1.063)(1.167)
5.106
1.334
0.170
(21.447)(22.198)
11.92312.191
0.335(5.375)(6.930)0.273(0.447)0.180(0.385)0.122(0.329)0.086(0.282)0.101(0.302)0.043(0.204)0.144(0.352)0.050(0.219)139

0.267(0.444)0.138(0.346)0.103(0.306)0.112(0.317)0.078(0.269)0.078(0.269)0.138(0.346)0.086(0.282)
116

P-value(control-Others)
(6)
0.183
0.929
0.479
0.225
0.470
0.927
0.588

0.893

0.985

0.633

0.650

0.854

0.767

0.047 **

0.218

1.000

0.518

0.759

0.161

0.274

0.366

0.792

0.384

Standard deviations in parentheses. Annual household income was 340 samples (control group: 121, Past group: 120,
group on Others: 99), because 54 respondents don't know/don't want to answer the question about household income.
The number of non-respondents is balanced across the three groups (control group: 18, Past group: 19, group on Others:
17). ***, ** and * indicate statistical significance at the 1%, 5%, 10% level, respectively.

22

Table 2
Analysis results of electricity consumption and willingness to change electricity companies.

Past
Others(1)-0.256(1.753)-0.620(1.650)

Housing type
Number of household members
Age
Annual household income
Awareness of retail electricity market deregulation
ǻ&&'Electricity2016
Past × Electricity2016
Others × Electricity2016
Constant
Ward dummy
R2
N

2.253
(2.674)
yes
0.011
394

ǻ<
(2)
(3)
-1.0517.473(1.822)(4.578)-1.060-2.707(1.724)(3.445)-2.458-2.793 *(1.606)(1.572)2.144 **
2.276 **
(0.864)(0.829)-0.018-0.044(0.108)(0.102)0.000
0.000(0.002)(0.002)-0.074-0.075(0.695)(0.664)0.147 *** 0.140 ***(0.035)(0.035)-0.541 *** -0.433 **(0.154)(0.154)-0.713 **(0.321)0.140(0.233)
4.392
4.469(6.098)(6.032)yes
yes
0.14470.1686340
340

Company change
(5)
(6)
0.020
0.001
(0.143)(0.317)-0.271 *-0.617 **(0.153)(0.281)-0.082-0.089(0.135)(0.134)0.260 *** 0.258 ***(0.066)(0.065)0.000-0.001(0.008)(0.008)0.000
0.000(0.000)(0.000)0.288 *** 0.293 ***(0.069)(0.069)0.004
0.003(0.003)(0.003)-0.016 *-0.026(0.009)(0.016)0.001(0.023)0.028(0.019)2.803 *** 1.113 **1.272 **
(0.207)(0.518)(0.525)yes
yes
yes
0.04410.16770.1726
394
340
340

(4)
0.103
(0.144)-0.280 *(0.147)

Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, 10% level,
respectively.

23

Table 3
Analysis results of time-consuming power-saving behavior.

Past
OthersElectricity2016
Constant
Controls
Ward dummy
R2
N

AC_28䉝 AC_leave Fridge_stuff Fridge_leave TV_leave Light_leave
(1)
(2)
(3)
(4)
(5)
(6)
0.036
0.480 *** 0.173
0.061
0.083
0.211
(0.186)(0.164)(0.154)(0.150)(0.158)(0.146)0.145
0.314 *
-0.0020.112-0.129-0.049(0.185)(0.167)(0.158)(0.157)(0.171)(0.155)-0.040 *** -0.044 *** -0.019 *-0.020 **-0.028 *** -0.022 **(0.010)(0.009)(0.010)(0.009)(0.010)(0.009)4.004 *** 2.917 *** 3.730 *** 2.942 *** 2.858 *** 2.638 ***(0.681)(0.603)(0.564)(0.550)(0.576)(0.547)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
0.091
0.118
0.053
0.035
0.051
0.067
327
327
340
340
340
340

Robust standard errors in parentheses. The sample size for (1) and (2) is 327 because 13 respondents indicated thatthey "do not own/use air conditioners." Controls include Housing type, Number of household members, Age, Annual
household income, Awareness of retail electricity market deregulation, and ǻCCD. ***, ** and * indicate statisticalsignificance at the 1%, 5%, 10% level, respectively.

Table 4
Analysis results of time-consuming power-saving behavior (verification of heterogeneity).

AC_28䉝 AC_leave Fridge_stuff Fridge_leave TV_leave Light_leave
(1)
(2)
(3)
(4)
(5)
(6)
Past
-0.2700.136-0.121-0.125-0.1240.212(0.409)(0.313)(0.321)(0.306)(0.324)(0.313)
Others-0.0110.268-0.0730.130-0.2770.206(0.324)(0.301)(0.311)(0.287)(0.309)(0.268)
Electricity2016-0.049 **-0.051 *** -0.027 *-0.023 *-0.036 **-0.015(0.016)(0.014)(0.015)(0.012)(0.015)(0.014)
Past × Electricity2016
0.025
0.028
0.024
0.015
0.017
0.000
(0.030)(0.021)(0.023)(0.021)(0.025)(0.023)
Others × Electricity2016 0.012
0.003
0.005
-0.0020.012-0.020(0.021)(0.020)(0.022)(0.019)(0.021)(0.017)
Constant
4.092 *** 2.964 *** 3.786 *** 2.949 *** 2.942 *** 2.522 ***(0.692)(0.608)(0.585)(0.560)(0.607)(0.569)Controls
yes
yes
yes
yes
yes
yes
Ward dummy
yes
yes
yes
yes
yes
yes
2
0.093
0.122
0.055
0.037
0.052
0.070
R
N
327
327
340
340
340
340
Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, 10% level,
respectively.

24

Table 5
Analysis results of hassle-free power-saving behavior.

AC_outdoor unit
(1)
(2)
Past
0.013
-0.133(0.057)(0.130)
Others0.002-0.137(0.058)(0.124)
Electricity2016-0.008 **-0.014 **(0.004)(0.006)
Past × Electricity2016
0.012(0.010)
Others × Electricity2016
0.011(0.009)Constant
0.666 *** 0.735 ***(0.198)(0.207)
Controls
yes
yes
Ward dummy
yes
yes
2
0.049
0.056
R
N
327
327

Fridge_cooling intensity
(3)
(4)
0.081
-0.108(0.063)(0.131)0.106 *
0.084
(0.063)(0.118)-0.006 *-0.010 *(0.004)(0.005)0.016 *
(0.009)0.002(0.007)0.353
0.377(0.224)(0.230)
yes
yes
yes
yes
0.058
0.066
340
340

TV_energy-saving mode
(5)
(6)
0.024
0.113
(0.063)(0.140)-0.067-0.066(0.067)(0.131)-0.004-0.002(0.004)(0.006)-0.007(0.010)0.000(0.009)0.229
0.222(0.218)(0.227)
yes
yes
yes
yes
0.086
0.088
340
340

Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, 10% level,
respectively.

Table A1
Analysis results of hassle-free power-saving behavior (probid analysis).

AC_outdoor unit
(1)
(2)
Past
0.043
-0.438(0.191)(0.443)
Others0.013-0.435(0.192)(0.401)
Electricity2016-0.023 *-0.043 **(0.012)(0.017)
Past × Electricity2016
0.038(0.031)Others × Electricity2016
0.034(0.025)Constant
0.367
0.571
(0.655)(0.684)Controls
yes
yes
Ward dummy
yes
yes
2
0.044
0.050
Pseudo RN
327
327

Fridge_cooling intensity
(3)
(4)
0.209
-0.320(0.175)(0.370)0.298 *
0.239
(0.176)(0.344)-0.017 *-0.029 *(0.010)(0.015)0.044 *
(0.026)0.004(0.022)-0.459-0.395(0.625)(0.643)
yes
yes
yes
yes
0.046
0.052
340
340

TV_energy-saving mode
(5)
(6)
0.064
0.305
(0.171)(0.387)-0.180-0.186(0.185)(0.359)-0.010-0.005(0.011)(0.017)-0.020(0.029)0.001(0.025)-0.711-0.730(0.593)(0.617)
yes
yes
yes
yes
0.065
0.066
340
340

Coefficient is reported. Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%,5%, 10% level, respectively.

25