The Influences of Economic and Psychological Factors on Energy-Saving
Behavior: A Field Experiment in Matsuyama, Japan

Kenichi Mizobuchi1 : Department of Economics, Matsuyama University, 4-2, Bunkyo, Matsuyama,
Ehime, 790-8578, Japan, E-mail: kmizobuc@cc.matsuyama-u.ac.jp

Kenji Takeuchi : Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada, Kobe, Hyogo
657-8501, Japan, E-mail: takeuchi@econ.kobe-u.ac.jp

Abstract
This study examines the influences of economic and psychological factors on electricity conservation
behavior. A random selection of 236 Japanese households participated in the field experiment, and they
were offered two interventions, such as monetary rewards depending on their reduction in electricity
consumption and comparative feedback. The average saving rates of the (i) economic incentive group(5.9%) and (ii) economic incentive with comparative feedback group (8.2%) are statistically larger thanthose of the (iii) control group (1.6%). Our econometric analysis confirmed that economic andpsychological factors have a positive influence on the decision concerning whether to save electricity, anda reward combined with comparative feedback is most effective. Psychological factors also affect thedecision about how much to save electricity, while economic incentive factors do not influence thisdecision. In particular, social norms, which are psychological factors, have a consistent effect on both thewhether and how decisions. Responses to the questionnaire before and after the experiment suggest thatparticipants may have underestimated the marginal costs of the electricity saving.

Keywords: Comparative feedback; Economic incentive; Electricity saving; Field experiment;
Household energy use; Social norm;

1

Corresponding author. Tel.: +81 89 926 7140.
E-mail address: kmizobuc@cc.matsuyama-u.ac.jp

11. Introduction
The Great East Japan Earthquake in March 2011 and subsequent radiation leak fromthe Fukushima nuclear plant have triggered widespread concerns about the safety of
nuclear plants. Since then, most of the nuclear power reactors in Japan have been shut
down.2 However, replacing nuclear power with an alternative source of electricity inthe short term presents great difficulty. Thus, massive attempts have already been
implemented to cut the peak demand for electricity. The household sector will need toplay a key role in these attempts, but significant energy saving has been less thansuccessful in this sector.3
Many studies have examined effective intervention for encouraging householdenergy saving behavior, such as information provision, public campaigns, goal setting,feedback, comparative feedback, and reward (Winett et al., 1978; Becker, 1978;
McClelland and Cook, 1980; Midden et al., 1983; Brandon and Lewis, 1999;
Abrahamse et al., 2005; Abrahamse et al., 2007; Petersen et al., 2007; Allcott, 2011).
Abrahamse et al. (2005) reviewed thirty-eight field studies in social and environmental
psychology, and evaluated the effectiveness of these interventions. However, it is notclear from these studies which form of intervention is most effective or which has themost enduring effect. One of the main reasons for this fact is that each study focused on
only a few factors. In order to learn about policies that can effectively and efficiently
reduce energy consumption, various data and insights need to be integrated. In
particular, one needs to understand, first, the motivations of consumers in implementing
2

In the interests of safety, as of April 2012, 53 out of Japan’s 54 nuclear power reactors had
been shut down. In fact, by May 2012, the remaining one nuclear power reactor is also expected
to be shut down.
3
In 2011, electricity-saving rates of large and small customers of industrial sector are 29% and
19% higher than the rates of the previous year. However, the rate of electricity-saving amonghouseholds is only 6% (Tokyo Electric Power Company, 2011).
2

certain types of energy-saving behavior. Second, a careful analysis is required of thevarious factors that influence household behavior, for leaving out crucial factors willlead to biases in the assessment of a particular factor’s contribution to householdbehavior (van den Bergh, 2008). Therefore, an extension to the broadest possible rangeof policy instruments would require considering a great many factors, such as economic,
psychological, and demographic.
Traditionally, economic and psychological approaches differ, so research efforts
tend to be confined only to each academic area. Economists tend to examine theinfluence of external conditions—such as price, income, and other socio-economic
characteristics—upon pro-environmental behavior. Thus, econometric studies ofpro-environmental household behavior that include psychological variables relating toattitudes, knowledge, perceptions, and values are rare. On the other hand, studies bypsychologists that include these variables exclude economic variables. A few studies
have considered both economic and psychological factors in analyzing households’
pro-environmental behavior (Clark et al., 2003; Kotchen and Moore, 2007).
Within traditional economic theory, individuals maximize their own utility, subject
to budget constraints, and they have little incentive to strive toward a public good—i.e.,
pro-environmental behavior—so they will choose a “free ride” instead. However, thistheoretical prediction is rarely shown empirically. Thus, economists have started toconsider the role of psychological factors, such as warm-glow altruism (Andreoni,1990) and paternalistic altruism (McConnell, 1997), in motivating individuals to makecontributions to public goods. In the psychological discipline, Fransson and Garling(1999) have reviewed many studies examining the relationship between psychologicalvariables and pro-environmental behavior. The influence of some psychological factors,
3such as attitudes, knowledge, perceptions, and values, has been demonstrated in terms
of pro-environmental behavior. By means of a natural experiment, Guagnano et al.
(1995) proved the hypothesis that attitudinal factors and external conditions act in
combination to influence pro-environmental behavior. They also showed, specifically,that external conditions affect the strength of attitude-behavior relationships. Thus,
strong positive external conditions increase the likelihood of attitudes that give rise to
pro-environmental behaviors.
As a result, recent developments in economics (psychology) emphasize the need toconsider internal (external) influences. However, as we mention above, what is lacking
is a systematic integrated analysis of interventions or instruments: psychological(internal) factors—such as attitude, knowledge, motivation, and perception—and
economic (external) factors, such as prices (subsidy or tax) and incomes.
This study aims to evaluate the influences of both economic and psychologicalfactors on encouraging electricity conservation behavior. We conducted a field
experiment based on an intervention study from October to November of 2011 in
randomly selected Japanese households. As far as we know, this is the first study thatexamines the influence of both factors on electricity-saving behavior based on
behavioral data (van den Bergh, 2008). Moreover, we divide the decision of electricity
saving behavior into whether to save and how much to save, and reveal the main
contributing factors to each type of decision-making.
This paper is organized as follows: section 2 discusses previous studies onhousehold energy-saving behavior. In section 3, we describe our field experiment, inwhich we used subsidies as an economic incentive for electricity conservation behavior
within households, and section 4 discusses the empirical analysis. Section 5 analyzes
4the marginal costs of electricity conservation behavior and discusses some limitations of
our study, and section 6 is the conclusion.

2. Factors of influence on household energy saving behavior
Over the past several decades, many scholars have emphasized the importance ofeconomic or psychological factors for encouraging household energy conservation
behavior (Stern, 1992; Dwyer et al., 1993; Abrahamse et al., 2005; van den Bergh,2008). This section gives a brief overview of relevant studies that have used economic
and psychological factors—such as reward, comparative feedback, social norms,
environmental concerns, goal setting, and some socio-economic variables—with a
specific focus on these factors’ influence on households’ energy-saving behavior.
To evaluate the effects of taxes and subsidies for household energy usage, many
previous studies have focused on the behavioral impacts of energy prices by a degree of
price elasticity with time-series, cross-section, and panel data (Silk and Joutz, 1997;
Vaage, 2000; Halvorsen and Larsen, 2001). Most of these studies showed statisticallysignificant price elasticities. However, the degree of elasticity fluctuates, depending on
the data and estimation method (Espey and Espey, 2004). In addition, some field studies
are based on an intervention through economic rewards (e.g., money or prizes) to
examine household energy-saving behavior (Winett et al., 1978; Midden et al., 1983;
McClelland and Cook, 1980; Petersen et al., 2007). These studies indicate that
economic rewards have been successful in reducing household energy consumption. For
example, Winett et al. (1978) studied the effect of certain energy-saving interventions,
including an economic incentive (i.e., a reward for reducing electricity consumption),
involving 107 single-family households in Texas. The electricity conservation by the5group with an economic incentive was significantly higher than that by otherintervention groups and the control group. Moreover, if households were in competition
with each other, the influence of the rewards was more effective (McClelland and Cook,
1980; Petersen et al., 2007). These results support the use of an economic incentive;
however, two problems remain in these previous studies. One is the low statisticalreliability because of the small number of households involved in the experimentalgroups (fewer than 20). Another is an identification problem—that is, the effects ofrewards could not be isolated since a combination of other interventions, such as
information, and individual and comparative feedback, was used in the study. Our field
experiment provides a solution for these problems.
Feedback is widely used to encourage energy conservation (Abrahamse et al., 2005).
In general, feedback is given in terms of a household’s own energy savings in order toconfirm the effectiveness of the effort to save energy; feedback becomes more effective,in particular, if its frequency is increased. Another type of feedback, which gives
participants information about the energy savings of the other people in the study, isreferred to as comparative feedback (Midden et al., 1983; Siero et al., 1996). This typeof feedback has been found to be more effective than individual feedback in promoting
energy conservation (Brandon and Lewis, 1999). Comparative feedback may be more
effective because of the sense of competition or because people derive satisfaction from
conforming to social norms. In the former case, when participants can compare theirsavings levels among themselves, they may try to reduce energy more than otherparticipants. Such competition may be especially effective when the contest is also
accompanied by a reward. The latter condition is also important; if households are
uncertain about some part of their production function, the social comparisons may
6

facilitate social learning about their optimal level of energy use, as documented in othercontexts by Cai et al. (2009) and Munshi and Myaux (2006). Comparative feedback
demonstrates relevant social norms in favor of energy conservation—that is, it becomes
clear that others are actively engaged in energy conservation as well. Some academic
work has shown that providing social norm information induces people to conserve
energy (Schultz et al., 2007; Nolan et al., 2008; Allcott, 2011). For example, Allcott(2011) examined non-price energy conservation programs using data from randomized
natural field experiments in 600,000 households across the United States, run by acompany called OPOWER. This company mails Home Energy Report letters thatcompare a household’s energy use to that of similar neighbors and provide energy
conservation tips. Allcott showed that average energy reduction was 2.0%. In ourempirical study, we deal with comparative feedback as evoking a sense of competition.
At the same time, separate from a sense of competition, we employ another variable that
relates to a social norm (see sections 3 and 4).
Environmental concern is also considered as one of the psychological motivating
factors on pro-environmental behavior. For assessing the degree of environmental
concern, the New Ecological Paradigm (NEP) scale has been developed as an
instrument in the social and behavioral sciences for measuring attitude about theenvironment (Dunlap et al., 2000). The NEP scale has also been used in the economic
literature on households’ green electricity participation program (Kotchen and Moore,2007).
Goal setting, which is sometimes used to promote energy saving, sets energy-saving
targets that households can strive for, such as saving 10% (Abrahamse et al., 2007). The
efficacy of goal setting is strengthened if combined with a promise to save (Katzev and7

Johnson, 1983) or with feedback (McCalley and Midden, 2002). Moreover, in the casewherein goal setting was combined with feedback, a high-level goal appeared to be
more effective in reducing energy demand than a low-level goal (Becker, 1978).
Other variables typically included in previous studies are household income, age,and education level of household members; house size; a measure of outside
temperature; and ownership of certain appliances (e.g., electric appliances). The
statistical significance of these variables is not completely consistent with some review
studies (Espey and Espey, 2004; Abrahamse et al., 2005), but it is appropriate toconsider that these variables also influence household energy-saving behavior.
This study integrates factors from economics and psychology to identify key
internal and external variables that determine household electricity-saving behavior.
Moreover, we focus on two energy-saving behaviors, whether to save and how much tosave, and analyze households’ specific motives for these two behaviors. Our analysis
uses data from 236 randomly selected households in a field experiment. Economic
variables consist of reward, household income, and standard socio-demographic
elements. Psychological variables consist of comparative feedback, social norm, amodified version of the New Ecological Paradigm (NEP) scale to measureenvironmental concerns, and goal setting. In the next section, we present our researchstrategy in greater detail.

3. Field Experiment3.1. Design
Our field experiment lasted for 8 weeks, from October to November 2011. Each8

enrolled household was entitled to receive a reward for reducing its electricity
consumption to a level below their previous year’s consumption for the same period.
Electricity consumption was measured in kWh to ensure comparability acrosshouseholds, regardless of the energy fuels used. The reduction of electricity
consumption (Reduction) was calculated as follows:
Reduction(%) ＝×100 ,	
  	
  	
  	
  	
  	
  	
 

(1)

where Cx and Cp represent the electricity consumption during the experiment and during
the same period of the previous year. If Reduction was larger than 1% (Reduction

1),households could receive a reward (economic incentive). The amount of the reward was
200 yen (about $2.50) per 1% of electricity consumption reduction (We have assumed
the following exchange rate: $1 = 80 yen). For example, if actual Reduction was 10%,households were given 2,000 yen (200 yen × 10).
On the other hand, we also examined the effectiveness of comparative feedback,in which each household received information about the average amount of electricity
saving by other households (e.g., the value of Reduction). We conducted thecomparative feedback twice: the first one informed households of the target levels ofelectricity saving by means of pre-experiment questionnaires in late September, and thesecond one provided information on the values of Reduction from the first month (i.e.,
October) in early November.4
Households were assigned randomly to three groups—(1) Reward, (2) Rewardwith Comparative Feedback, and (3) Control—to examine the effectiveness of
4

The first comparative feedback is not a real value of Reduction, and there may be a difference
between the value of the target and real values. However, participants can compare others’
target reduction levels with their own. Therefore, in terms of comparison with other participants,
we deal with it as the first comparative feedback.
9

economic incentives and of comparative feedback. We use a non-parametric approachto compare the three groups in section 3.3. Because we wanted to avoid a small sample
problem like the previous studies, we set the sample size of every group as larger than
50. Moreover, we also use a parametric approach—i.e., a regression analysis—taking
Reduction as the dependent variable to examine the influences of economic factors andpsychological factors on electricity conservation behavior (see section 4 for details).
We administered two questionnaires, one before and one after the field
experiment. The responses from the subjects offer valuable information on the factorsthat play a key role in reducing energy use. In the pre-experiment questionnaire, we
asked participants about the following aspects: their attitude toward environmental
issues, their level of environmental concern,5 attitudes toward electricity conservation,
their intended target level of electricity saving as well as their level of confidence inmeeting this target, and the types of home electrical appliances in the household. Finally,
we elicited social norm, socio-economic, and demographic details. Here, in our study,
the degree of social norm is measured by the question “Do you think consider how often
people close to you are attempting electricity-saving behavior?” Comparison withneighbors or other close persons will construct people’s social norms and will induce
people to conserve energy (Nolan et al., 2008 and Allcott, 2011). Responses were madeon a 5-point Likert scale (always = 5, usually = 4, sometimes = 3, rarely = 2, never = 1),
and high score responses mean a high level of attention to social norms (and vice versa).

Respondents were asked to what extent they agreed or disagreed with 15 statements known as
the modified new ecological paradigm (NEP) scale (Dunlap and Liere, 1978; Dunlap et al.,2000). The modified NEP scale is commonly used in the psychology literature; it aims tocapture the following five facets of environmental concern: limits to growth,anti-anthropocentrism, the fragility of the balance of nature, rejection of the idea that humans
are exempt from the constraints of nature, and the possibility of an eco-crisis or ecologicalcatastrophe.
510

In the post-experiment questionnaire, we asked participants, inter alia, about changes intheir attitudes toward electricity conservation, the challenges in conserving electricity,and the appropriateness of economic incentives. Table 1 represents the schedule of ourfield experiment for three groups.
[Insert Table 1 here]

3.2. Respondents and Procedure
We conducted the study in Matsuyama, a city in Ehime Prefecture that has
approximately 516,000 inhabitants in western Japan. In August 2011, 1,800 households
in Matsuyama city were randomly selected and were sent letters informing them about
the study and inviting them to participate in our field experiment. Aside from a broad
description—i.e., that the study was about an experiment in energy saving—we did not
divulge any details about the field experiment in the initial invitation letter. Of thehouseholds invited to participate in this study, 236 (13.1%) returned a completed
pre-experiment questionnaire. All respondents were older than 20 years (81 was theoldest). Compared to a national sample (Population Statistics, 2009), male respondents
were overrepresented,6 and respondents in the category of 55 through 69 years wereslightly underrepresented. In field-experiment or questionnaire survey studies about
environmental problems, a tendency exists toward overrepresentation from high-income
households and underrepresentation from low-income households—i.e., the studies arebiased for high incomes (Poortinga et al., 2003). Figure 1 represents a comparison of the6

Most respondents were the householders. This might be the reason for male
overrepresentation.
11

income distributions in our study and a national sample.7 Figure 1 shows that both highincomes (over 6 million yen) and low incomes (under 4 million yen) are slightly
underrepresented, and that incomes of 4-6 million yen are overrepresented. The average
income of Japan is about 5.5 million yen. Therefore, the households in our study are notbiased toward high incomes, but have a slight tendency toward a higher concentrationof average incomes.
[Insert Figure 1 here]
As described earlier, participating households were entitled to receive subsidies
based on their Reduction, which was calculated according to equation (1). Shikoku
Electric Power Co. was the electricity supplier for all participating households.
Participants could access their historical data on electricity consumption per month bylogging onto the Shikoku Electric Power website, using their unique identification
numbers. We collected these consumption data for the previous year from thehouseholds (printed Web page) and used them as the measurement criteria. Additionally,
for tracking ongoing consumption, every household was asked to send a copy of its
monthly electricity consumption record from Shikoku Electric Power Co.

3.3. Results and non-parametric analysis
Figure 2 shows the results of electricity conservation, in terms of Reduction, inthe sampled households at the end of 8 weeks. 8 About 70% of the households
successfully reduced their electricity consumption. There are some possibilities ascauses for this result: the households showed a high response to economic incentives or
7

Ministry of Health, Labor and Welfare: National Livelihood Survey 2009.
http://www.mhlw.go.jp/index.shtml
8
Because of an inadequacy or a lack of electricity consumption data, 28 participants dropped
out of the field experiment, reducing the total number of households to 208.
12

comparative feedback; the economic incentives offered in this experiment were perhaps
higher than the marginal cost of the electricity-saving behavior; or the temperature of
the target year may have been cooler than that of the previous year.9 As Figure 2 shows,in particular, the greatest percentage of households (19.2%) was in the 10%–15%
Reduction bracket, followed by the 5%–10% bracket (17.8%), and the 1%–5% (13.9%).
However, the percentage of households that saved more than 15% was small, and it
decreased as the values of Reduction increased. Additionally, about 29.8% of thehouseholds did not conserve electricity, so we can confirm that a certain percentage ofhouseholds did not respond well to economic incentives and comparative feedback.
[Insert Figure 2 here]
The average Reduction of the households in this experiment is 5.4%, indicating adecrease in energy consumption compared to the previous year. Here, we investigate theeffectsofinterventions—i.e.,

economic

incentive

andcomparative

feedback—statistically.
[Insert Table 2 about here.]
Table 2 shows the average electricity saving rate (i.e., average Reduction) of the threegroups: (i) Reward, (ii) Reward with Feedback, and (iii) Control. The average savingrate is highest in the Reward with Feedback group (8.2%), followed by the Reward
group (5.9%), and the Control group (1.6%). Our study investigates the effects ofintervention, so we test the following null hypothesis.
9

One probable reason is a rise in the awareness of the need to save electricity after the Great
East Japan Earthquake in March 2011. Because most of the nuclear power reactors have been
shut down in the interest of safety (51 of Japan’s 54 by January 2012), massive
electricity-saving measures have been implemented in Japan. From the pre-experimentquestionnaire survey, 63.9% of households perceived a change in their awareness of interest inelectricity saving (high interest + interest). From this result, we can conclude that the awarenessfor electricity saving has become high in general and that it may slightly affect theelectricity-saving behavior of our experimental households.
13

(i), (ii), (iii),
whereindicates the average Reduction of ith groups. Table 2 shows the t-statistics ofthree hypotheses—that is, Reward vs. Control, Reward with Feedback vs. Control, andReward vs. Reward with Feedback. From the t-statistics of the first hypothesis, Reward
vs. Control, the null hypothesis was rejected at the 10% significance level (1.74 > 1.65,
p = 0.1), so the effect of the economic incentive could be found statistically. Moreover,from the t-statistics of the second hypothesis, Reward with Feedback vs. Control, thenull hypothesis was also rejected at the 5% significance level (2.59 > 1.98, p = 0.05), so
this result enhances the effect of the economic incentive. However, the intrinsic effectof comparative feedback was obscure, because the null hypothesis was not rejected(1.10 < 1.66, p = 0.1) for the third hypothesis, Reward vs. Reward with Feedback. This
result is inconsistent with those of Midden et al. (1983) and Brandon and Lewis (1999),
which supported the effectiveness of only comparative feedback, but is consistent withthose of McClelland and Cook (1980) and Petersen et al. (2007), which supported theeffectiveness of comparative feedback with reward. In the next section, we use theparametric approach to investigate the economic and psychological factors in saving
electricity consumption, and we also examine the effect of comparative feedback.

4. Parametric Analysis (regression analysis)
This section presents the results of a parametric analysis (i.e., regression
analysis) undertaken to examine whether economic and psychological factors affect thedecisions about electricity-saving behavior within households.

14

4.1. Model and Data
We begin with a Tobit model for analyzing the variables with Reduction as thedependent variable because there are a large number of households that achieve noelectricity-saving:(2)
The dependent variable Yi indicates the Reduction of the ith household and is censoredat zero. Households with a negative value of Reduction in our experiment are treated ashouseholds “not saving electricity right from the beginning” or “having abandoned
electricity conservation sometime during the experiment.” X is a vector of the
independent variables: a comparative feedback dummy, a reward (i.e., economic
incentive) dummy, an anticipation of the electricity-saving behavior of persons close to
the household (i.e., social norm), the NEP score, the target level of Reduction shown in
the pre-questionnaire, the gender and age of the household representative, the income
level, the family size, type of home ownership, size of home (area), and cooling degree
days10 (previous year – target year). Data on the independent variables were obtained
from the pre- and post-study questionnaires. Table 3 presents a statistical summary of
the above and dependent variables.
[Insert Table 3 about here.]
4.2. Estimation results
Eviews (version 7.0), which is an econometric software package, was used to10

The difference between an average temperature higher than 24 °C and 22 °C. The day ofmeter reading by the electric company is different for each household. Therefore, we summed
up the differences for the experimental term and set an independent variable.
15

estimate equation (2). The test of heteroskedasticity was conducted based on a Lagrange
multiplier statistic. The statistic was 32.55, and this statistic has a limiting chi-squared
distribution with 12 degrees of freedom. The sample value exceeds the critical value of
21.03 (p = 0.95, d = 12), so the null hypothesis of homoscedasticity could be rejected.
To adjust for heteroskedasticity, we use White’s covariance matrix.
[Insert Table 4 about here.]

The estimation results from the Tobit model are reported in the first column ofTable 4. The comparative feedback and reward have a positive effect onelectricity-saving behavior; both variables have coefficients that are positive andstatistically significant. In the previous section, we showed the efficacy of economic
incentives and comparative feedback against the control group by a non-parametricapproach. Here, both the values of the estimation coefficient were statisticallysignificant; therefore, we confirmed also by the parametric approach that reward andcomparative feedback (with reward) have an effect by way of encouraging
electricity-saving behavior. However, the comparison of estimated coefficients between
the comparative feedback and reward is not statistically significant. Thus, similar to theresult in section 3.3, the efficacy of comparative feedback (without reward) is obscure.
The coefficient of the social norm is positive and statistically significant, thereby
showing that others’ behavior does have an impact on one’s own electricity-saving
behavior. This impact could stem from two sources. First, overall household behavior inelectricity saving may influence people’s views concerning personal responsibility.
Specifically, if others participate in electricity saving, the individual may experience aloss in his or her self-image as a morally responsible person if he or she does not do so.
16Second, there is also the presence of explicit prescriptive social norms stemming from,for instance, family members and close friends. This influence may well constitute acomplement to the above perceptions of others’ contributions. If close friends andfamily stress the importance of saving electricity, the individual may deduce that others
also believe it is important. Nyborg et al. (2006) presented a theoretical framework inwhich individual responsibility for pro-environmental behavior depends on beliefs
about others’ behavior. Using household survey data, Ek and Soderholm (2008) showed
the positive influence of social norms on participating in the Swedish green-electricity
market, and Nolan et al. (2008) and Allcott (2011) also showed the positive influence ofit on U.S. household energy-saving behavior.
Two other psychological factors, such as NEP score and target, have no influence
on electricity conservation behavior; both coefficients are statistically insignificant. The
positive effect of the NEP score has manifested in other pro-environmental behaviors,such as participation in the green-electricity market, in some previous studies (Clark etal., 2003; Kotchen and Moore, 2007; Ek and Soderholm, 2008). However,electricity-saving behavior has not only an aspect of public good—i.e., being
pro-environmental—but also has a private good aspect, such as saving one’s own
money. This consideration may be the cause of the insignificant effect of the NEP score.
Another possible reason is the Motivation Crowding Theory, or the crowding-out effect,
which suggests that external intervention via monetary incentives may undermine
intrinsic motivation (Deci, 1971). This effect is presented widely in the economy andsociety (Deci et al., 1999; Frey and Jegen, 2001). If the individuals affected perceive
external interventions to be controlling, both self-determination and self-esteem suffer,and such individuals react by reducing their intrinsic motivation in the activity
17controlled. From this effect, participants who save electricity because of motivationfrom intrinsically high environmental concern might experience reduced motivation tosave electricity if monetary rewards are introduced, and this influence might bepresented as statistically insignificant regarding the NEP parameter.
The coefficient on income is statistically insignificant, and this result is a little
different than was expected. In the theoretical model of public goods, the coefficient ofhousehold income will become positive, and this outcome has been supported
empirically (Kotchen and Moore, 2007), as pro-environmental behaviors do, in fact,increase with income. However, like the result of the NEP score above,
electricity-saving behavior does not have the nature of a pure public good, thus perhaps
causing this result. Family size and homeownership have a negative and statisticallysignificant effect on electricity saving, suggesting the importance of considering
disposable income. The size of home is positive and statistically significant. This result
may indicate the possibility that there is large electricity-saving potential in a house thathas many rooms. Finally, the coefficient of the cooling degree day (previous year –target year) is statistically significant, and the positive sign indicates that if the target
year becomes cooler than the previous year, the amount of time the air-conditioner isused decreases, a situation conducive to electricity savings. Actually, the averagetemperature within the experimental term was lower by 0.9 °C than it was the previous
year.11
A feature of the Tobit model is the restriction that explanatory variables are assumed
11

In the post-study questionnaire, we asked participants to indicate a factor that was importantfor electricity saving. Thirty-five percent of households felt that cooperation from family
members was the most important factor. Willingness to act was identified as an importantfactor by 31%, while 15% of households believed that it was temperature. Only a small
percentage of respondents identified information on energy saving (4%) as an importantfactor.
18

to influence the extensive and intensive margins of electricity-saving behavior in thesame way. That is, an implicit assumption is made that the decision of whether to saveelectricity is the same as the decision of how much to save. However, it is possible thatthe explanatory variables influence electricity-saving on the extensive and intensive
margins in different ways. Smith et al. (1995) and Kotchen and Moore (2007) make thisobservation and find empirical support for it in a study of charitable contributions to arural health care facility and a green-electricity program. We explore the same
possibility here by decomposing the Tobit model into a probit model for the decision ofwhether to save electricity, and a truncated regression model for the decision of how
much to conserve.12
We report the results of these two models in the second and third columns ofTable 4. The qualitative results of the probit model are similar to those of the Tobit
model; most of the coefficients have the same sign except for NEP score and target.
However, the levels of statistical significance are substantially different. Thus, thevariables that influence only the extensive margin of electricity-saving (whether to saveelectricity) are different from those that jointly influence the extensive and intensive
margins (how much to save). Comparative feedback, social norm, and size of homehave a positive and statistically significant effect on the decision of whether to saveelectricity, whereas home ownership has a negative and statistically significant effect.
Here, the marginal effect of comparative feedback is statistically larger than that ofreward. Thus, under the existing economic incentives, if households know the amountof other households’ electricity saving and can compare energy-saving levels under the
12

Similar to Kotchen and Moore (2007), our sample includes all households that participated in
our field experiment; therefore, the analysis of the intensive margin needs no correcting forsample-selection bias. For such cases, Greene (2008) notes that the appropriate decompositionof a Tobit model is into a probit model and a truncated regression model.
19

economic incentives, they may be motivated to compete and may conserve electricity
consumption more than without the comparative feedback (McClelland and Cook,
1980; Petersen et al., 2007).
The results also differ substantially when we focus on the intensive margin (howmuch to save). In the truncated regression model, two psychological factors, one
socio-economic factor, and the weather variable are statistically significant explanatory
variables in the decision of how much to save, and economic incentive factors have noeffect. In particular, the coefficient of target, which was not statistically significant inboth the Tobit and probit models, is statistically significant. Becker (1978) showed thatsetting a relatively difficult goal appeared to be more effective in reducing energy use
than setting a relatively easy goal. Our result of the truncated regression model isconsistent with his result.
These results provide evidence that the decision of whether to save electricity is notdetermined in the same way as the decision of how much to save. Specifically, we foundthat comparative feedback, social norm, homeownership, and size of home influence thedecision about whether to save electricity, and social norm, target setting, family size,and cooling degree day influence the decision of how much to save.

4.3. The effects of economic and psychological factors
Which is more important in terms of electricity conservation behavior:economic factors or psychological factors? Our results show that both economic andpsychological factors are important. As we showed in subsection 4.2, however, theeffectiveness of these two factors differs based on the timing of the decisions that eachfactor influences in electricity-saving behavior—i.e., whether to save electricity and20how much to save. From the estimation result of the probit model in the second columnof Table 4, the coefficients of both comparative feedback (with reward) and social norm
are positive with a statistically significant effect. Therefore, in the decision of whether
to save electricity, both economic and psychological factors would affectelectricity-saving behavior. Here, the marginal effect of comparative feedback (with areward) is larger than that of the social norm. Therefore, in the decision of whether tosave electricity, we might conclude that comparative feedback with a reward will
become the most positive decisive factor. From the estimation result of the truncatedregression model in the third column of Table 4, on the other hand, only psychologicalfactors—i.e., social norm and target—are statistically significant. Thus, in decisions
concerning

how

much

to

save,

psychologicalfactors

would

most

affectelectricity-saving behavior. Here, only social norm has a positive and statisticallysignificant effect on both decisions of whether and how much, which imply that social
norms do have a consistent positive impact on one’s own electricity-saving behavior.

5. Discussion and Limitations
This section discusses the two major reasons for the 29.8% of participants who
demonstrated negative Reduction in this study: (1) lack of understanding of the
difficulties in electricity conservation and (2) the marginal cost of electricity saving.
Figure 3 shows the participants’ evaluation of the difficulties associated with electricity
conservation before and after the experiment. Before the experiment, the majority of the
respondents felt that the level of electricity conservation was “neither easy nor
difficult.” However, at the end of the experiment, 55% of the participants felt thatsaving electricity was “difficult” or “very difficult.” This change in response indicates a21

limited initial understanding of the challenges associated with the conservation behavior
beforehand. This result may also suggest that pre-experiment responses concerning
electricity saving behavior have an upward bias, as participants expect to save more
electricity before actually acting on it. In other words, using only a stated intention toevaluate household energy saving may not be appropriate, given the likelihood foroverestimation.
[Insert Figure 3 about here.]
The second reason is that the marginal cost of energy saving is greater than the
incentive amounts. As described earlier, most of the households underestimated the
difficulties in conservation before the experiment; that is, they underestimated themarginal cost of energy saving. However, actually making efforts to reduce
consumption may have helped them realize the greater marginal cost of conservation,
thus resulting in cessation of conservation efforts. To the question “Would you conserve
electricity if the incentive amounts were doubled?” in the post-experiment survey,
nearly 71% of households that failed in reducing their consumption returned negative
responses. This result also supports the notion that the marginal cost of saving
electricity is quite high.
These results carry important implications for policymakers targeting energy
conservation in the household sector: before introducing incentives to promoteconservation, it is necessary to study the marginal cost of electricity saving and toensure that the incentives exceed this cost. Moreover, it would be worthwhile tocombine economic incentives with other interventions aimed at lowering the marginalcost. For example, as we showed in section 4, the comparative feedback under
economic incentives is very effective in the decision of whether to save electricity.
22

Although these findings offer important insights, a few limitations to this study
must be acknowledged and possibly overcome in future works. The first consists ofpossible sample selection bias. Most of the previous studies recruited participants forfield experiments and surveys by mailing randomly selected households. Even if theselection of the households is random, however, it is highly likely that participants who
expressed a desire to enroll in the experiment or survey had high levels ofenvironmental awareness and were inherently motivated to save energy. They may haveviewed the experiments and/or the survey as encouragement to conserve energy. Ek andSoderholm (2010) confirm that participants who voluntarily enroll in energy-saving
experiments typically have high levels of environmental awareness and are inherently
motivated. On the other hand, households that are not interested in saving energy or thathave low levels of awareness may not participate in the experiment or survey. In otherwords, it is likely that a number of previous studies have a sample selection bias.
Sardianou (2007) surveyed the energy consumption patterns of Greek households andshowed that individuals with a thorough knowledge of environmental problems andhigh environmental awareness tend to reduce their energy consumption greatly. Whenmost of the subjects in an experiment have a high awareness of the environment, thegeneralization of the results becomes questionable. In fact, the bias is greater when thesample size is small. Randomization is essential in an experimental study, but we could
not exclude the possibility that participants had a bias toward high motivation in ourfield experiment.
Second, the sample size of our study (236 participants) is obviously larger thanthat of previous studies, which enhances the statistical reliability of the results. However,even this sample size is not sufficient for deriving policy implications from these23findings. One of the chief reasons for limitation in the sample size was the use of anobservable measure (i.e., a master meter) for validating the effects of an energy-saving
intervention. While an observable measure can ensure objectivity in measurement, it
poses considerable difficulties if the sample size is large or if the experimental period islong. However, if an electric company is willing to cooperate strongly in an experiment
for saving electricity, it is possible to obtain a large enough sample size (Allcott, 2011).
In fact, very few field experiments that use observable measures have a sample size ofmore than 300 (Abrahamse et al., 2005).
Third, because our study implemented comparative feedback that was collected
twice for two months, the frequency of the comparative feedback might be a little low.
This might be a reason why we were unable to obtain a result showing that comparative
feedback is effective in both a non-parametric and a parametric approach. In previous
studies, the effectiveness of the feedback rises if the frequency of the feedback increases(Abrahamse et al., 2005). Thus, verification and comparison are needed when thefrequency changes. These limitations should be addressed in future work.

6．Conclusion
Through an 8-week field experiment, this study investigated the influences of
economic and psychological factors on electricity-conservation behavior among
Japanese households. A few studies have examined the influence of two factors on
pro-environmental behavior (Clark et al., 2003; van den Bergh, 2008; Kotchen and
Moore, 2007), but no studies focus in particular on electricity-saving behavior withrevealed data. From the results detailed in sections 3 and 4, we have shown the24effectiveness of both economic and psychological factors for encouraging electricity
conservation. However, the effectiveness of these two factors is divided, based on thetiming of the decisions, in terms of the influence of each factor on electricity-saving
behavior—i.e., whether to save electricity and how much to save. Economic incentives
are more effective than such psychological factors as social norms, environmental
concerns, and target setting regarding decisions about whether to save electricity,
especially under comparative feedback. On the other hand, in decisions about how muchto save electricity, such psychological factors as social norms and target setting are
effective. Only social norm has a consistent positive effect on both whether to save andhow much to save. The effectiveness of economic incentives on electricity saving is inagreement with that of previous studies (Midden et al., 1983; Petersen et al., 2007;
Winett et al., 1978). The effects of psychological factors—such as comparative
feedback, social norm, and target setting—are also consistent with results from previous
studies (Becker, 1978; Midden et al., 1983; Brandon and Lewis, 1999; Ek andSoderholm, 2008; Nolan et al., 2008; Allcott, 2011). Nor does this study suffer from the
drawback identified in some earlier works: sampling problem and small sample size.
Our experimental households were randomly selected, and the sample size of 236
households is higher than that of previous studies, ensuring high statistical reliability.
Underestimation of the marginal cost of saving electricity is one of the reasonsthat led about 30% of the participants to cease their energy-saving efforts. This
discovery presents a valuable insight for policymakers who target the household sector
in reducing carbon dioxide emissions. Before offering economic incentives to promoteenergy conservation, the marginal costs of electricity conservation should be evaluated.
If the costs are substantially high, economic incentives should be combined with other25

interventions, such as tailored information and goal setting, to lower the marginal costs.
Comparative feedback as examined in our study might be a potentially usefulintervention.

Acknowledgement
This research is supported by Japan Society for the Promotion of Science (Grant-in-Aid
for Young Scientists (B) #22730217) which is acknowledged by K. Mizobuchi. K.
Takeuchi would like to thank the Department of Economics at the University ofGothenburg for giving him the opportunity of visiting research.

26

References
Abrahamse W, Steg L, Vlek C, Rothengatter T (2005) A review of intervention studies
aimed at household energy conservation. J Environ Psychology 25:273–291.

Abrahamse W, Steg L, Vlek C, Rothengatter T (2007) The effect of tailored information,
goal setting and tailored feedback on household energy use, energy-related behaviors
and behavioral antecedents. J Environ Psychology 27:265–276.

Allcott H (2011) Social norms and energy conservation. J Public Econ 95:1082–1095.

Andreoni J (1990) Impure altruism and donations to public goods: A theory ofwarm-glow giving. Econ J 100:464-477.
Becker L (1978) Joint effect of feedback and goal setting on performance: A field studyof residential energy conservation. J Applied Psychology 63:428–433.

Brandon G., Lewis A (1999) Reducing household energy consumption: A qualitativeand quantitative field study. J Environ Psychology 19:75–85.

Cai H, Chen Y, Fang H (2009) Observational learning: Evidence from a randomized
natural field experiment. Am Econ Rev 99:864–882.
Clark CF, Kotchen MJ, Moore MR (2003) Internal and external influences on
pro-environmental behavior: Participation in a green electricity program. J Environ
Psychology 23:237–246.

27

Deci EL (1971) Effects of externally mediated rewards on intrinsic motivation. Journalof Personality and Social Psychology 18:105-115.

Deci EL, Koestner R, Ryan RM (1999) A meta-analytic review of experiments
examining the effects of extrinsic rewards on intrinsic motivation. Psychological
Bulletin 125:627-668.

Espey J, Espey M (2004) Turning on the lights: A meta-analysis of residential
electricity demand elasticities. J Agric and Applied Econ 36:65–81.
Dunlap RE, Van Liere KD (1978) The “new environmental paradigm”: A proposed
measuring instrument and preliminary results. J Environ Education 9:10–19.

Dunlap RE, Van Liere KD, Mertig MG, Jones RE (2000) Measuring Endorsement of
the New Ecological Paradigm: A Revised NEP Scale. J Social Issues 56 (3):425–442.

Dwyer WO, Leeming FC, Cobern MK, Porter BE, Jackson JM (1993) Critical review of
behavioral interventions to preserve the environment. Research since 1980. Environ and
Behavior 25:275–321.
Ek K, Soderholm P (2008) Norms and economic motivation in the Swedish green
electricity market. Ecological Econ 68:169-182.

Ek K, Soderholm P (2010) The devil is in the details: Household electricity saving
behavior and the role of information. Energy Policy 38:1578–1587.

28

Fransson M, Garling T (1999) Environmental concern: Conceptual definitions,
measurement methods, and research findings. J Environ Psychology 19:369–382.
Frey BS, Jegen R (2001) Motivation crowding theory. J Econ Surveys 15:589-611.

Greene WH (2008) Econometric Analysis, sixth ed. Prentice Hall, New Jersey.

Guagnano GA, Stern PC, Dietz T (1995) Influences on attitude-behavior relationships:
A natural experiment with curbside recycling. Environ and Behavior 27 (5):699–718.

Halvorsen B, Larsen BM (2001) The flexibility of household electricity demand overtime. Res Energy Econ 23:1–18.

Katzev RD, Johnson TR (1983) A social-psychological analysis of residential electricity
consumption: The impact of minimal justification techniques. J Econ Psychology
3:267–284.
Kotchen MJ, Moore MR (2007) Private provision of environmental public goods:
Household participation in green-electricity programs. J Environ Econ and Management
53:1–16.

McCalley LT, Midden CJH (2002) Energy conservation through product-integrated
feedback: The roles of goal-setting and social-orientation. J Econ Psychology
23:589–603.
McClelland L, Cook SW (1980) Promoting energy conservation in master-meteredapartment through group financial incentive. J Applied Social Psychology 10 (1):20–31.

29

McConnell KE (1997) Does altruism undermine existence value? J Environ Econ andManagement 32:22–37.
Midden CJH, Meter JE, Weenig MH, Zieverink HJA (1983) Using feedback,reinforcement and information to reduce energy consumption in households: A
field-experiment. J Econ Psychology 3 (1):65–86.

Munshi K, Myaux J (2006) Social norms and the fertility transition. J Dev Econ
80:1–38.

Nolan MJ, Schultz PW, Cialdini BR, Goldstein NJ, Griskevicius V (2008) Normativesocial influence is underdetected. Personality and Social Psychology Bulletin
34:913–923.
Nyborg K, Howarth RB, Brekke KA (2006) Green consumers and public policy: On
socially contingent moral motivation. Res Energy Econ 28:351-366.

Petersen JE, Shunturov V, Janda K, Platt G, Weinberger K (2007) Dormitory residents
reduce electricity consumption when exposed to real-time visual feedback andincentives. Inter J Sustainability in Higher Education 8:16–33.

Poortinga W, Steg L, Vlek C, Wiersma G (2003) Household preferences forenergy-saving measures: A conjoint analysis. J Econ Psychology 24:49–64.

Population Statistics (2009) National Institute of Population and Social Security

30

Research. http://www.ipss.go.jp/index-e.asp. Cited 29 Mar 2009.

Sardianou E (2007) Estimating energy conservation patterns of Greek households.
Energy Policy 25:3778–2791.

Schultz PW, Nolan MJ, Cialdini BR, Goldstein NJ, Griskevicius V (2007) The
constructive, destructive, and reconstructive power of social norms. Psychological
Science 18:429–434.

Siero RW, Bakker AB, Dekker GB, van den Burg MTC (1996) Changing organizationalenergy consumption behavior through comparative feedback. J Environ Psychology
16:235–246.

Silk JI, Joutz FL (1997) Short- and long-run elasticities in US residential electricity
demand: A cointegration approach. Energy Econ 19:493–513.
Smith VH, Kehoe MR, Cremer ME (1995) The private provision of public goods:
Altruism and voluntary giving. J Pub Econ 58:107–126.

Stern PC (1992) What psychology knows about energy conservation. Am Psychologist
47:1224–1232.
Tokyo Electric Power Company (2011) The supply and demand result for this summer.
http://www.tepco.co.jp/en/press/corp-com/release/11092610-e.html. Cited 26 Sep 2011.

Vaage K (2000) Heating technology and energy use: A discrete/continuous choice
approach to Norwegian household energy demand. Energy Econ 22:649–666.

31

van den Bergh JCJM (2008) Environmental regulation of households: An empiricalreview of economic and psychological factors. Ecological Econ 66:559–574.

Winett RA, Kagel JH, Battalio RC, Winkler RC (1978) Effects of monetary rebates,
feedback, and information on residential electricity conservation. J Applied Psychology
63:73–80.

32

33

34

35

36

37

38

39