Corporate responses to public pressures and price increases:
Evidence from Japan’s electricity crisis
Hiroko Okajima
Shigeharu Okajima
Kenji Takeuchi
May 2019
Discussion Paper No.1908

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANCorporate responses to public pressures and price increases:
Evidence from Japan’s electricity crisis
Hiroko Okajima ∗Shigeharu Okajima†

Kenji Takeuchi ‡Towson University

Osaka University

Kobe Universityof Economics
May 5, 2019

Abstract
Behaviors of non-residential electricity customers during power crises have been underinvestigated.
This study adds to the literature by providing some empirical evidence from the analysis of Japan’s
electricity crisis after the 2011 earthquake using firm-level micro data. The earthquake created a naturalexperiment to examine the effect of public pressures and price increases on firms’ electricity consumptions
during a power crisis. Using a DID model, we show that firms’ electricity consumption as a whole was
elastic to public pressures in a short term but inelastic to price. On closer look, however, the power
conservation during public pressures was made by only 30% of the firms. The remaining 70% of the firms
were free-riders, but their electricity consumption was actually elastic to price. The result indicates that
it is important for policy makers to implement both economic and non-economic incentive interventions
to promptly induce conservation from as many firms as possible during power crises as firms tend torespond to either economic or non-economic incentives.

∗ Email: hokajima@towson.edu† Email: shigeharu.okajima@gmail.com‡ Email: takeuchi@econ.kobe-u.ac.jp1

1

Introduction

Power outages can occur anywhere at any time and are not uncommon even in well-developed countries. As
electricity plays a vital role in the functioning of the society and economy, the economic loss due to electric
power outages can be significant. For example, the cost of weather-related power outages in the United States
is between $18 billion and $33 billion a year (Executive Office of the U.S. President, 2013). What’s worse,the frequency of catastrophic power outages is likely to increase worldwide due to aging infrastructures,increasing numbers of natural disasters and extreme weather conditions, and the growing threat of terror
attacks.
Non-residential customers account for about 80% of the world electricity use (U.S. Energy Information
Administration, 2017). Therefore, it is very important for policy makers to understand how firms respondto different types of conservation strategies in order to prevent a catastrophic power outage and promptly
restore power supply if it happens. There is a large body of literature dedicated to the study of electricity
demand (for review, see, e.g., Taylor, 1975; Newsham and Bowker, 2010; Torriti, 2014). However, very few
studies have investigated non-residential electricity customers at the firm level. As far as we know, Bjørner,
Togeby and Jensen (2001) is the only study that focuses entirely on the analysis of non-residential electricity
demand using firm-level micro data. They investigate the influence of various characteristics, such as size
and industrial sub-sector, on price and production elasticities using a panel of 2,949 Danish companies from1983 to 1996. Several other studies, such as Woodland (1993), Arnberg and Bjørner (2007) and Bardazzi,
Oropallo and Pazienza (2015), use micro data but analyze aggregate industrial energy demand, including
electricity. The other studies that use micro data include Kahn, Kok and Quigley (2014), which analyzes
electricity consumptions of commercial buildings but aggregates data at the building level.
Moreover, to the best of our knowledge, there has been no research that investigates the behavior of nonresidential customers during electricity crises. Yet, previous research on voluntary agreements and corporatesocial responsibility (CSR) provide some insights. According to Potier (1994), a voluntary agreement is acontract between industry (e.g., trade association, industrial branch and firm) and authorities (e.g., environmental ministry, municipality and local residents’ association). Under a contract, industry undertakes
to achieve environmental objectives, whereas government agrees not to adopt regulations while the contract
remains in force. Segerson and Miceli (1998) and Schmelzer (1999) employ game theoretic approach andshow that a voluntary agreement occurs if there is a credible legislative threat to introduce a standard or tax
if it is not reached. A legislative threat is also one of the factors that are driving CSR, as Lyon and Maxwell(2008) state in the survey of literature on CSR. To reduce regulatory threats and enforcement pressures,
many firms make environmental claims and enhance their corporate images in their CSR activities. For2

instance, Fortune Global 500 firms invest over $15 billions a year on CSR (Davidson, Dey and Smith, 2019).
Given the prevalence of voluntary agreements and CSR, it is predicted that firms are likely to reduce electricity consumption voluntarily during an electricity crisis as they face tangible threats of extended blackouts,
involuntary rolling blackouts, or stringent legislative penalties or restrictions on electricity use.
Given the gap in the literature, this study is to investigate firms’ power conservation behaviors during
an electricity crisis, in particular, their responses to public pressures (non-economic instruments) and priceincreases (economic instruments). Public pressures and price increases are commonly used as means to
reduce electricity demand. However, it is difficult to distinguish the two effects in a natural experiment asthey are often put into practice simultaneously during an electricity crisis. As far as we know, Reiss andWhite (2008) is the only study that investigates both effects during an electricity crisis. They find that
residential electricity consumption was very elastic to price and fairly responsive to voluntary conservation
requests as well during California’s energy crisis in 2000 and 2001. To overcome the difficulty in a naturalexperiment, Ito, Ida and Tanaka (2018) design a field experiment to compare the effects of public pressures(called “moral suasion” in their study) and price increases using residential customers. They show that priceincreases induced a larger and more persistent residential electricity conservation than did public pressures.
Other studies investigate the effects of either public pressures (e.g., Allcott, 2011) or price increases (e.g.,
Jessoe and Rapson, 2014; Ito, 2015). Overall, residential electricity consumption tends to be more elastic toprice than to public pressures.
In order to investigate firm responses to public pressures and price increases, we exploit Japan’s devastating earthquake in March 2011 as a natural experiment. In the wake of the earthquake, tsunami, andnuclear crisis, Japan began experiencing serious electricity shortage. In a comprehensive electricity conservation campaign by the government, non-residential customers were first exposed to public pressures to reduce
peak demand and then price increases. Importantly for our analysis, public pressures and price increases
were implemented separately and some regions were exposed to neither public pressures nor price increases.
These together provide us with a unique opportunity to compare the effects of public pressures and priceincreases on non-residential customers using a difference in difference (DID) model.
The natural experiment also provides us with a rare opportunity to investigate free-rider problem thatis common in public goods provision. During Japan’s electricity shortage, the government first used publicpressures and then price increases to reduce electricity consumption. This sequence of events allows us
to identify possible free-riding firms that did not respond to public pressures first but responded to priceincreases later. Free-riding problems have been extensively studied in experimental economics (see, forexample, Andreoni, 1988; Ledyard, 1995). Some empirical studies have estimated the proportion of freeriders to be as high as 80% to 90% in energy conservation subsidy programs and tax credit systems (e.g.,
3

Joskow and Marron, 1992; Grösche and Vance, 2009; Nauleau, 2014; Boomhower and Davis, 2014). Reiss andWhite (2008) state that despite the free-riding problem, the collective conservation of residential customers
during California’s energy crisis was significant, owing to the tangibility and high public awareness of thecosts of collective-action failures (i.e., involuntary blackouts). Nevertheless, research on free-ridership among
non-residential customers during electricity crises is premature.
Our analysis provides some empirical evidence on the behaviors of non-residential customers. We findthat non-residential customers respond to public pressures and price increases differently from residential
customers reported in Ito, Ida and Tanaka (2018). As a whole, public pressures work better than priceincreases for non-residential customers. Firms in Tokyo and Kansai regions reduced electricity consumption
by 4.6% and 3.1%, respectively, in response to public pressures, whereas they did not reduce electricity
consumption when electricity price increased. However, detailed analyses find that only 32% of the firms
contributed to the reduction in electricity consumption during public pressures. In other words, 68% of
the firms were free-riders. Moreover, these free-riders did reduce electricity consumption when electricity
price increased, whereas non-free-riders did not. This may imply that different policy instruments should be
combined to achieve the target of electricity saving.
The remainder of this paper is organized as follows. We explain the background of Japan’s electricity
shortage and corresponding governmental conservation campaign in Section 2. Section 3 describes ourdataset, treatments in our natural experiment, and hypothesis tested in our analysis. Section 4 reports our
findings. Finally, Section 5 concludes the paper.

2

Background

2.1

The Great East Japan Earthquake

The Great East Japan Earthquake on March 11, 2011, became the largest earthquake ever recorded inJapan. The magnitude 9.0 earthquake struck off the northeast Pacific coast of Japan and triggered tsunami
waves as high as 40 meters (131 feet). About 19,500 people were dead, 2,500 people were missing, and morethan 400,000 buildings were destroyed or partially collapsed. The direct financial damage of the disaster isestimated to be about 16.9 trillion Japanese yen (about 150 billion US dollars) (Cabinet Office, 2013). The
situation was compounded by the crisis of the Fukushima Daiichi nuclear power station when the coolingsystem failure caused the meltdown of fuel rods and the corresponding leakage of radioactive materials.

4

2.2

Electricity Shortage

Electricity shortage became a serious problem after the earthquake. Historically, Japan is divided into ten
regions, each of which is served by a single electric company 1 . Figure 1 shows the supply capacity in eachregion before and after the earthquake. Two regions had direct damages of the earthquake: Tokyo region
whose electric company operates the Fukushima Daiichi nuclear power station and Tohoku region that is theclosest to the epicenter. Immediately after the earthquake, the two regions lost about 40% of power supply(Soda, 2015). Power outages reached a peak of 4.05 million (14%) customers in Tokyo (Tokyo Electric Power
Company, 2013) and 4.86 million (66%) customers in Tohoku (Tohoku Electric Power Company, 2011).
Tokyo had to implement involuntary rolling blackouts for 10 weekdays following the earthquake to prevent
further major power outages.
300

Total Supply Capacity (GWh)Earthquake-hit regions

2010

2011

2012

2013

2014

250
200
Nuclear-dependent regions

Litttle-affected regions

150
100
50
0
Tokyo

TohokuKansaiKyushuShikoku

Hokkaido

Chubu

Hokuriku

Chugoku

RegionFigure 1: Supply capacity by regions before and after the earthquake
Source: Federation of Electric Power Companies of Japan (2016)

Even worse, all nuclear power stations in the nation were suspended due to safety and public concerns.
Japan has 54 nuclear reactors, accounting for 26% of the power sources before the earthquake (Agency
for Natural Resources and Energy, 2017). When the earthquake occurred, 36 reactors were operating, of
which 10 were stopped immediately, and the remaining 18 reactors were offline for regular inspections (Japan
Nuclear Safety Institute, 2017). Due to the public mistrust after the Fukushima Daiichi nuclear crisis, nuclear
reactors that remained intact in the earthquake also went offline indefinitely after regular inspections. Only
1 Okinawa, one of the ten regions, is excluded from any analyses in this study due to its peculiarity. Okinawa is much smaller
than other regions and its electricity usage patterns are very different from other regions because it is located at the far southof Japan.

5

six reactors were in operation at the end of 2011 and all reactors went offline by September 2013 (Japan
Nuclear Safety Institute, 2017) 2 . As shown in Figure 1, some regions that did not have direct damages of theearthquake but relied heavily on nuclear power, such as Kansai region, suffered declines in supply capacity
after the earthquake.

2.3

Energy Saving Campaigns

The Japanese government undertook comprehensive campaigns to make electricity saving as a national
movement. While rolling blackouts were cancelled when the cold weather started to ease up at the endof March 2011, the possibility of major power outage returned in some regions during summer and wintermonths. Accordingly, the government set peak clipping targets region by region.
Table 1 summarizes the peak-clipping targets. Tokyo and Tohoku, which are the earthquake-hit regions,were requested to reduce peak demand by 15% from the 2010 levels during the three-month period between
July and September of 2011. The compliance was voluntary, except for large businesses with contract
demand of 500 kW or more. These large businesses were subject to penalties of up to one million Japaneseyen (about 9,000 US dollars) per hour for violation under Article 27 of Japan’s Electric Business Act.
During the summer of 2011, the government also set a voluntary peak clipping target of 10% reduction
for Kansai, which is a nuclear-dependent region. The other regions did not have numerical targets but
were requested to reduce electricity usage “within the range that would not hinder people’s daily lives andeconomic activities (e.g., adjusting lighting and air conditioners).” The government sent licensed electricians
to individual firms and held local information events on how to save electricity to encourage them to formulate
and publish voluntary action plans (Japanese Ministry of Economy, Industry and Trade, 2012). In addition,
an information campaign was conducted using a variety of media, including newspapers, TV, and the Internet,
to raise awareness among people about electricity saving.
Owing to these conservation efforts, power outage was avoided and the numerical targets were achieved.
Similar energy saving campaigns were undertaken in the following winters and summers. While some regions
continued to have numerical targets until the winter of 2013-2014, such targets were removed in Tokyo andTohoku regions after the summer of 2011.

2.4

Electricity Price Increases

As ten electric companies dominate electricity supplies in each region, price increases are regulated by thegovernment. Due to concerns about negative impacts on the nation’s disaster recovery efforts, electricity
2 As of April 2019, nine nuclear reactors resumed operation.

6

(Earthquake-hit)Target(Nuclear-dependent)(Little-affected)

Tokyo

TohokuKansaiKyushuShikoku

Hokkaido

Chubu

Hokuriku

Chugoku2011

Summer

-15%*

-15%*

-10%

-

-

-

-

-

-

Winter

-

-

-10%

-5%

-

-

-

-

-

2012

Summer

-

-

-10%

-10%

-5%

-7%

-

-

-

Winter

-

-

-

-

-

-7%

-

-

-

2013

Sumer

-

-

-

-

-

-

-

-

-

Winter

-

-

-

-

-

-6%

-

-

-

9/20129/20135/20135/20139/20139/2013---

Price Increase

Note: Numbers show percentage changes in peak demand from the 2010 levels. * indicates a mandatory target. - indicatesno target or price increase. Source: Institute for Sustainable Energy Policies (2012); Japanese Ministry of Economy, Industry
and Trade (2017); Agency for Natural Resources and Energy (2013).

Table 1: Numerical targets and electricity price increases during three years after the earthquake
price was not raised during the first one and half years after the earthquake. However, electric companies
experienced soaring operating costs after the earthquake owing to damaged power plants, nuclear reactor
shutdowns, and restarting inefficient old power plants to make up for lost supplies. At last, the electriccompany in Tokyo filed an application for a price increase in May 2012. After ten meetings of the Expert
Committee on Reviewing Electricity Rates, the application was approved in July 2012 and implemented on
September 1, 2012 (Agency for Natural Resources and Energy, 2013). Following Tokyo, electricity price wasraised in other earthquake-hit region and nuclear-dependent regions (Table 1). Note that Chubu, Hokuriku
and Chugoku regions, which had little impact of the earthquake, did not have price increases for three years
after the earthquake.
Electricity price for non-residential customers is not publicly available as different firms receive different
prices depending on contracts. Yet, we can still estimate the average electricity price for non-residential
customers using other publicly available data (the details are described later in Section 3.1). Figure 2 shows
the trend of the average electricity price for industrial customers based on our calculation. It shows that theestimated price sharply increased in Tokyo in September 2012 and in Kansai in May 2013.

3

Data, Treatments and Hypotheses

3.1

Data

The primary data for this study come from the panel data of monthly electricity consumptions of 620
offices of manufacturing firms in Japan from 2008 to 2013 provided by the Agency for Natural Resources
and Energy (ANRE) under a confidentiality agreement. ANRE has been conducting the Current Surveyof Energy Consumption every month since 1981 and keeps the complete electricity consumption history of7

Rate of Change in price
.2
.3
.4

.5

Tokyo
Chubu
Kansai
May 20130.1

September 2012

Jul-10Jul-11

Jul-12
Time

Jul-13

Jul-14

Figure 2: Electricity price changes for non-residential customers
Note: Electricity price for non-residential customers was calculated by dividing electricity sales by electricity consumption in
the industrial sector, which were obtained from the Federation of Electric Power Companies of Japan (Federation of ElectricPower Companies of Japan, 2016).

nine manufacturing industries surveyed: (1) pulp, paper and paperboards, (2) chemicals, (3) chemical fibers,(4) petroleum products, (5) ceramic, clay and stone products, (6) glass products, (7) iron and steel, (8)non-ferrous metals, and (9) machinery. Since regional electricity price for non-residential customers is not
publicly available, we estimated it by dividing electricity sales by electricity consumptions in the industrial
sector obtained from the Federation of Electric Power Companies of Japan (Federation of Electric Power
Companies of Japan, 2016) and then converted them to real values, using prefectural consumer price indicesobtained from the Statistics Bureau of Japan (Statistics Bureau, 2014). To control for the level of economic
activity, prefectural industrial production indexes were obtained from Ministry of Economy, Trade andIndustry (Japanese Ministry of Economy, Industry and Trade, 2017). Heating degree days (hereafter HDD)
and cooling degree days (hereafter CDD) were calculated from the historical weather data provided by theJapan Meteorological Agency (Japan Meteorological Agency, 2013). Descriptive statistics are provided inTable 2.

8

(Earthquake-hit)(Nuclear-dependent)(Little-affected)

Variable(1) Tokyo(2) Kansai(3) Chubu(4) Hokurikuln(EC)

7.76(1.833)3.25(0.120)99.50(11.72)72.90(101.9)147.18(151.7)

7.52(2.055)3.31(0.0841)102.50(8.799)85.44(115.5)143.02(151.3)

7.78(1.766)3.19(0.0607)102.85(13.24)76.60(105.8)155.68(160.7)7.75(1.69)
3.15(0.035)103.38(10.77)66.81(100.06)
183.71(179.11)

8.35(1.915)3.38(0.041)100.40(10.54)80.19(111.4)154.22(160.54)

29,37018,69025,8104,183

12,727ln(EP )EI
HDD
CDD
Observations(5) Chugoku

Note: All columns show the sample means and standard deviations of observables. Standard deviations are in parentheses
in all columns. EC is the electricity consumption in gigajoule. EP is the average real electricity price in Japanese yen perkilowatt hour. EI is the prefectural industrial production indices. HDD and CDD are the sum of the daily heating degreedays and cooling degree days of a year with a base temperature of 65 F (18.3 C), respectively.

Table 2: Descriptive statistics

3.2

Treatments

The treatments in our natural experiment are public pressures and price increases. For public pressures,
we focus on the government imposition of numerical targets for peak clipping after the earthquake. While
the government requested voluntary electricity conservation in all regions, it took a further step and set
numerical targets for peak clipping in some regions where electricity supply was especially tight (see Table1). Focusing on these targets allows us to clearly specify the periods of public pressures as these targets
were effective only for a limited time. One potential drawback is that although these targets were for peakclipping, we only have data on total electricity consumption. We admit that our data cannot isolate theconservation efforts towards peak clipping. For instance, if production was simply shifted from peak hours
to off-peak hours, our data do not show any change in the total electricity consumption. However, it islikely that firms also adopted energy efficient behaviors, such as reducing the usage of electricity consuming
equipments. Indeed, previous studies have shown that residential electricity customers tend to adopt energy
efficient lifestyles in face of high peak hour electricity prices, which leads to the reduction of total electricity
consumption (Holland and Mansur, 2008; Wolak, 2011). Therefore, we expect that our data can reasonably
capture firms’ conservation efforts in response to peak clipping requests.
We use a DID model to investigate the treatment effects, with Tokyo and Kansai as a treated region
and Chubu, as a control region. As shown in Table 1, the government set numerical targets in Tokyo (fromJuly 1, 2011 to September 9, 2011) and Kansai (from July 1, 2011 to September 22, 2011) during the firstsummer after the earthquake. Tohoku also had a numerical target in summer 2011, but it is excluded fromthe treated region as the earthquake so severely damaged the region that the firm activities and electricity

9consumption considerably dropped. Chubu was selected as a control region because it had neither numericaltargets nor price increases during our analysis period. In addition, unlike the other little-affected regions by
the earthquake (i.e., Hokuriku and Chugoku), Chubu is reasonably comparable to Tokyo and Kansai in themagnitude of electricity demand (see Figure 1) 3 .
In order to test the parallel trend assumption for a DID model, Figures 3 and 4 compare electricity
consumption in summer (July) and winter (January), respectively, in the treated and control regions. The
parallel trend assumption is satisfied because both figures show similar trends in both treated and control

Log Electricity Usage: ln(kwh) per month
14.6
14.8
15

15.2

regions before the earthquake in March 2011.

14.4

Tokyo
Chubu
KansaiJul-08

Jul-09Jul-10Jul-11
Time

Jul-12

Jul-13Figure 3: Summer electricity consumption in the treated and control regions

3.3

Hypothesis

3.3.1

Non-economic Incentive: Public Pressures

Although it is difficult to speculate how firms’ responses will change with the intervention, findings on
residential customers offer an insight that helps our hypothesis development. Previous studies have shownthat public pressures are effective in reducing residential electricity consumption (Reiss and White, 2008;
Allcott, 2011; Ito, Ida and Tanaka, 2018). Specifically, Ito, Ida and Tanaka (2018) find three characteristics.
3 In Section 4, Hokuriku and Chugoku are excluded from the control region in the original model. Then, they are added tothe control region to test the robustness of the original model.

10

15.2
Log Electricity Usage: ln(kwh) per month
14.4
14.6
14.8
15
Jan-09Tokyo
Chubu
KansaiJan-10Jan-11
Time

Jan-12

Jan-13Figure 4: Winter electricity consumption in the treated and control regionsFirst, while residential customers reduce electricity consumption in response to public pressures, the effectdiminishes very quickly week by week (known as habituation in psychology). Second, when residential
customers are exposed again to public pressures after several months of a long interval, they restore theoriginal levels of electricity savings (known as dishabituation in psychology). Third, residential customers
are not likely to adopt energy-efficient lifestyle due to public pressures and thus, there is no electricity saving
after public pressures are withdrawn (i.e., no habit-formation often studied in economics). Based on theirfindings, we examine whether firms possess similar characteristics, in particular, dishabituation and habitformation, toward public pressures. We do not examine habituation as our electricity consumption data are
only monthly and cannot trace short-term (daily or weekly) changes.
Before examining dishabituation and habit-formation, we must check that firms do respond to publicpressures to begin with. Therefore, we first estimate the changes in monthly electricity consumption in thetreated region (Tokyo and Kansai) in the months of July and August in 2011, in which numerical targets
for peak clipping were set for the first time in the region 4 .
Confirming that firms respond to public pressures, we then investigate the possibility of dishabituation. If
4 Numerical targets were set from the beginning of July to the middle of September in 2011 in the treated region. However,
we exclude September from the analysis because our electricity consumption data are monthly and cannot capture the effectsof target setting in September correctly. We apply the same rule when dealing with numerical targets set during winter months
and limit our analysis to the months of January and February.

11

firms possess the characteristic of dishabituation toward public pressures, the magnitude of electricity saving
during the first and second periods of public pressures should be similar. As shown in Table 1, Kansai had
a numerical target of 10% in the winter of 2011-2012 as well as in the summer of 2011, while Tokyo did not.
Thus, we limit the treated region to Kansai and compare the changes in monthly electricity consumption
between July-August, 2011, and January-February, 2012.
Finally, we investigate whether firms form energy-saving habits when exposed to public pressures. For
this purpose, we limit the treated region to Tokyo as Tokyo had a numerical target only once. That is, if
firms in Tokyo formed energy-saving habits when they were exposed to public pressures in the summer of
2011, they must have kept reducing electricity consumption in the winter of 2011-2012 without any publicpressures. Thus, we compare the changes in monthly electricity consumption in Tokyo between July-August,
2011, and January-February, 2012.
3.3.2

Economic Incentive: Price Increases

It has been a common assumption that price elasticity of electricity demand is very low as electricity isa necessity good. However, previous studies have shown that residential electricity consumption becomes
elastic to price during electricity crises (Reiss and White, 2008) as well as under some conditions during
non-crisis periods (Jessoe and Rapson, 2014; Ito, 2015; Ito, Ida and Tanaka, 2018). Since little has been
investigated on whether this also applies to non-residential customers, we estimate the changes in monthly
electricity consumptions in the treated region (Tokyo and Kansai) when electricity price sharply increasedin September 2012 in Tokyo and in May 2013 in Kansai.
3.3.3

Free Ridership

Requests for voluntary conservation during electricity crises create a situation of public goods game. Customers are required to reduce electricity consumption to stabilize power supply, which often accompanies
a significant reduction in utility. They receive rewards (e.g., averted further blackouts, involuntary rolling
blackouts, and price hikes) only if aggregate conservation efforts become greater than a threshold. Because
the rewards are enjoyed by all customers regardless of individual contribution levels, it causes a free-riding
problem.
It is challenging to investigate free-ridership during electricity crises. Firstly, data availability is limited
as large-scale electricity crises occur only occasionally. Secondly, electricity price increases often precede
voluntary conservation requests during electricity crises. Some energy-conservation efforts, such as adoptingenergy-saving behaviors and installing energy-efficient equipments, have prolonged effects. Therefore, if priceincreases occur before public pressures, it is difficult to identify free-riders that made conservation efforts in
12response to the price increases, but not to the conservation requests. Indeed, Reiss and White (2008) mention
the possibility of free-ridership during the electricity crisis in California, but the order of the intervention,
public pressures coming after price increases, prevents them from investigating the issue further.
Japan’s electricity crisis after the earthquake provides us with a unique opportunity to examine freeridership during electricity crises as conservation requests preceded price increases. First, we divide firms
in the treated region (Tokyo and Kansai) into two groups: the firms that reduced electricity consumption
in the months of July and August in 2011 when numerical targets were set, and the firms that did not
reduce electricity consumption during the same period. Then, we examine whether two groups respond to a
price increase differently. If firms are free-riders, they will not respond to the target setting, but will reduce
electricity consumption after the price increase. Table 3 summarizes the hypotheses tested in this study.
Hypothesis

MethodNon-economic Incentive (Public Pressures)(Treated)(Control) (Period)(1) Overall Effect of Public Pressures

DID

Tokyo, Kansai

Chubu

Jul-Aug 2011

(2) Dishabituation

DID

KansaiChubu(3) Habit-formation

DID

Tokyo

Chubu

Jul-Aug 2011 (1st public pressures) vs.
Jan-Feb 2012 (2nd public pressures)
Jul-Aug 2011 (public pressures) vs.
Jan-Feb 2012 (no public pressures)

Economic Incentive(4) Price Increase

DID(5) Free Ridership

OLS(Treatment)(Control) (Period)

Tokyo, Kansai

Chubu

Sep 2012 for Tokyo
May 2013 for Kansai
Free-riders∗ vs. non-free-riders∗∗ in Sep 2012 for Tokyo andMay 2013 for Kansai∗ Firms in Tokyo and Kansai that did not reduce electricity
consumption during public pressures in Jul-Aug 2011∗∗ Firms in Tokyo and Kansai that reduced electricity consumption in Jul-Aug 2011

Table 3: Hypotheses tested in the study

4

Empirical Analysis and Results

4.1

Identification Strategy

In order to examine firm responses to public pressures and price increases, we estimate the effects of thesepolicy instruments on electricity consumption in the Tokyo and Kansai regions. First, we estimate a simple
model that allows possible level shifts in electricity consumption in the treated region by using ordinary leastsquares (OLS):
0ln ECirt = γr + λt + φi + λt Dt + Xirt
β + uirt .

(4.1)

The dependent variable, ln ECirt , is the natural log of electricity consumption (ln EC) at office i in region r
in month t. γr is a regional fixed effect, λt is a time fixed effect, and φi is an individual fixed effect. Dt equals
13one if a numerical target was set in month t and zero otherwise. Xirt denotes three exogenous variables andone endogenous variable. The exogenous variables are prefectural industrial production index ( EI), HDD
and CDD for office i in region r in month t. The endogenous variable is the natural log of lagged electricity
price (ln EP ) for office i in region r in month t as an instrumental variable for electricity price. uirt is an error
term. The standard errors are clustered at the office level to adjust for serial correlation. While the inclusionof time dummy variables (Dt ) allows us to see the change in electricity consumption during the treatments,
as a limitation of this model, the change can also include other time trends in electricity consumption.
Therefore, we next estimate a DID model that computes the difference in electricity consumption between
the treated and control regions pre- and post- treatment. We estimate the following equation:0ln ECirt = γr + λt + φi + δDrt + Xirt
β + uirt .

(4.2)

Drt is defined as Dr × Dt , where Dr equals one for data points from the treated region and zero otherwise.
Other variables are defined as before. The standard errors are clustered at the office level to adjust for serial
correlation.
Three robustness tests are conducted to investigate the sensitivity of the results of Eq.(4.2) to changes inthe DID specifications. Firstly, we relax the common trend assumption in Eq.(4.2) by adding regional-specifictime trends, such as interaction terms between regional dummies and time trends (Besley and Burgess, 2004).
Then, the DID model with regional-specific time trends is given by0ln ECirt = γ0r + γ1r t + λt + φi + δDrt + Xirt
β + uirt ,(4.3)

where γ0r is a regional-specific intercept, γ1r is a regional-specific trend coefficient multiplied by a timetrend variable t, and other variables are defined as before. Secondly, the common trend assumptions canbe investigated as our dataset has several years of data before the treatments. To test the common trend
assumptions, we produce DID model estimates for the years before the treatments, called anticipatory
effects. If the leads of the treatments are economically or statistically insignificant, there is no evidence for
anticipatory effects. The DID model with anticipatory effects is given byln ECirt = γr + λt + φi +mX0δτ Drt−τ + Xirt
β + uirt ,

(4.4)

τ =0

where the sum on the right-hand side allows for m lead effects. Finally, another important robustness test is
to produce a DID model with other control regions. In addition to Chubu (the original control region), we
include Hokuriku and Chugoku into the control region and re-estimate Eq.(4.2). Columns (1)-(4) correspond14

to Eqs.(4.1)-(4.4) and Column (5), to the DID model with other control regions, respectively, in Tables 4
and 5 in the following sections.

4.2

Model Validation and Overall Effect of Public Pressures

Tables 4 and 5 show firm responses to public pressures (i.e., target settings) in the moths of July and August
in 2011 in Tokyo and Kansai, respectively. It is found that the imposition of numerical targets indeed reduced
electricity consumption during this period. The estimated electricity saving effects in Column (2) are 0.46
log points (4.6%) in Tokyo and 0.31 log points (3.1%) in Kansai. Note that these results are slightly different
from the result in Column (1). Column (2) provides more accurate estimates as it separates the effects ofnumerical targets from the other time trends.
Variables(1) OLS(2) DID(3) DID
Robust test 1(4) DID
Robust test 2(5) DID
Robust test 3

ln EP-0.206**(0.047)-0.168**(0.050)-0.001(0.144)0.006(0.008)-0.046**(0.013)-0.171**(0.050)-0.582**(0.221)0.006(0.008)-0.046**(0.013)-0.156**(0.053)-0.003(0.144)0.009(0.008)-0.048**(0.014)-0.000(0.000)0.006**(0.000)0.000(0.000)0.005**(0.000)7.866**(0.143)8,677

7.873**(0.188)
8,677

0.000
(0.000)0.005**(0.000)0.000**(0.000)7.878**(0.188)8,677-0.162**(0.055)0.006(0.144)0.010(0.007)-0.053**(0.015)0.008(0.023)-0.013(0.021)-0.036(0.024)0.000(0.000)0.005**(0.000)7.831**(0.197)8,677

7.879**(0.215)
11,335

620

620

620

620

810

D(T okyo)
D(Summer2011)-0.037**(0.007)D(T okyo) ∗ D(Summer2011)
D(T okyo) ∗ D(Summer2009)
D(T okyo) ∗ D(Summer2008)
D(T okyo) ∗ D(Summer2007)
HDD
EI
D(T okyo) ∗ Y ear
Constant
Observations
Number of companies-0.000(0.000)0.004**(0.001)

Note: Columns (1)-(4) show the estimation results for Eqs.(4.1)-(4.4) and Column (5) shows the estimation result to
the DID model with other control regions, respectively. The dependent variable is the natural log of the office-levelmonthly electricity consumption. D(T okyo) is a dummy for the firms in Tokyo. D(Summer2007), D(Summer2008),
D(Summer2009) and D(Summer2011) are dummies for the months of July and August in 2007, 2008, 2009 and 2011,
respectively. The other independent variables are defined in Section 3.1. Fixed effects are included. The standard errors
are clustered at the office level to adjust for serial correlation. Standard deviations are in parentheses in all columns.
The individual coefficient is statically significant at 5% level (**) or 10% level (*).

Table 4: Effects of target setting in Tokyo in July-August 2011
Columns (3)-(5) in Tables 4 and 5 test the sensitivity of the DID model for Column (2) to various changes
in the DID specifications. The results show that the model for Column (2) is robust because the estimated15

Variables(1) OLS(2) DID(3) DID
Robust test 1(4) DID
Robust test 2(5) DID
Robust test 3

ln EP-0.420**(0.060)-0.269**(0.058)-0.222(0.176)0.006(0.008)-0.031**(0.015)-0.273**(0.058)-0.927**(0.343)0.007(0.008)-0.031**(0.015)-0.219**(0.066)-0.226(0.176)0.010(0.008)-0.030*(0.015)0.000*(0.000)0.007**(0.000)0.000(0.000)0.005**(0.000)8.343**(0.197)7,000

8.162**(0.211)
7,000

0.000
(0.000)0.005**(0.000)0.000**(0.000)8.172**(0.211)7,000-0.275**(0.056)-0.225(0.177)0.006(0.007)-0.029**(0.013)0.014(0.026)0.013(0.022)-0.012(0.023)0.000(0.000)0.005**(0.000)8.160**(0.211)7,000

8.078**(0.256)
9,658

500

500

500

500

690

D(Kansai)
D(Summer2011)-0.034**(0.007)

D(Kansai) ∗ D(Summer2011)
D(Kansai) ∗ D(Summer2009)
D(Kansai) ∗ D(Summer2008)
D(Kansai) ∗ D(Summer2007)
HDD
EI
D(Kansai) ∗ Y ear
Constant
Observations
Number of companies

0.000(0.000)0.004**(0.001)

Note: Columns (1)-(4) show the estimation results for Eqs.(4.1)-(4.4) and Column (5) shows the estimation result to
the DID model with other control regions, respectively. The dependent variable is the natural log of the office-levelmonthly electricity consumption. D(Kansai) is a dummy for the firms in Kansai. D(Summer2007), D(Summer2008),
D(Summer2009) and D(Summer2011) are dummies for the months of July and August in 2007, 2008, 2009 and 2011,
respectively. The other independent variables are defined in Section 3.1. Fixed effects are included. The standard errors
are clustered at the office level to adjust for serial correlation. Standard deviations are in parentheses in all columns.
The individual coefficient is statically significant at 5% level (**) or 10% level (*).

Table 5: Effects of target setting in Kansai in July-August 2011
electricity savings in Column (2) are essentially the same as those in Columns (3)-(5) in both Tokyo andKansai. The savings in Columns (2)-(5) are 4.6%, 4.6%, 5.3%, and 4.8%, respectively, in Tokyo and 3.1%,
3.1%, 2.9%, and 3.0%, respectively, in Kansai. Note that no anticipatory effect is found in Column (4)because all the interaction terms are statistically insignificant except for D(T okyo) ∗ D(Summer2011) in
Table 4 and D(Kansai) ∗ D(Summer2011) in Table 5, which are the interaction terms between the treatedregions and the the months of July and August in 2011. That is, electricity consumption in the months ofJuly and August in 2007, 2008 and 2009 were not affected by the future policy to impose numerical targets
on electricity consumption in summer 2011.
Based on the results of the robustness tests, we use the DID model in Eq.(4.2) to examine firm responses
in Sections 4.3 and 4.4.

16

4.3

Dishabituation

Table 6 summarizes the changes in electricity consumption in the treated regions in the months of January
and February in 2012. During this period, the government set a numerical target in Kansai second time but
removed a target from Tokyo.
We first look at the result in Column (1) to examine the possibility of dishabituation. It shows that
imposing a numerical target second time in Kansai had no significant effects on electricity savings. Although Ito, Ida and Tanaka (2018) find dishabituation toward public pressures among residential customers,
our result finds no such effect on non-residential customers. Unlike residential customers, non-residential
customers must maintain their production while saving electricity. Because of the hurdle, non-residential
customers may be reluctant to take conservation measures unless they are at an imminent risk. They might
have felt that an imminent crisis had lessened by the time a numerical target was set second time and thus
made little conservation efforts.
Another implication of the result is that electricity conservation efforts made during the first period oftarget setting did not last for more than a several months. As the conservation request in the summer of2011 was on relatively short notice, firms may have had little time to fundamentally change their operations
or equipments and relied on only tentative measures.
Variables(1) Dishabituation
Treated Region: Kansai

D(Kansai)-0.207(0.173)-0.023(0.014)

D(Kansai) ∗ D(W inter2012)
D(T okyo)(2) Habit-formation
Treated Region: Tokyo

0.012(0.008)7.885**(0.246)0.023(0.143)0.014(0.013)0.005(0.008)7.633**(0.204)

Other controls

Yes

Yes

Observations

7,986

9,906

Number of companies

500

620

D(T okyo) ∗ D(W inter2012)
D(W inter2012)
Constant

Note: Two columns show the estimation results for Eq.(4.2) in each region. The dependent variable is the log of officelevel monthly electricity consumption. D(Kansai) and D(T okyo) are dummies for the firms in Kansai and Tokyo,
respectively. D(W inter2012) is a dummy for the months of January and February in 2012. The other independent
variables are ln EP , EI and CDD defined in Section 3.1. Fixed effects are included. The standard errors are clusteredat the office level to adjust for serial correlation. Standard deviations are in parentheses in all columns. The individual
coefficient is statically significant at 5% level (**) or 10% level (*).

Table 6: Dishabituation and habit-formation: Electricity consumption in Kansai and Tokyo in JanuaryFebruary 2012

17

4.4

Habit-formation

Next, we look at the result in Column (2) in Table 6 to examine the possibility of habit-formation. It isfound that the change in electricity consumption was not statistically significant in the months of January andFebruary in 2012 in Tokyo when a numerical target was removed from the region. As Ito, Ida and Tanaka(2018) show for residential customers, non-residential customers are also not likely to adopt long-lasting
conservation measures, such as installing energy efficient equipments and changing energy consumption
behaviors, after exposed to public pressures. The result is consistent with the finding in Section 4.3 thatfirms took only temporal measures to reduce electricity consumption in face of public pressures. It is likely
firms were able to implement temporal conservation measures on short notice in the summer of 2011 as therisk of large-scale blackouts was tangible. However, due to the burden in terms of time and money to cut
down electricity use, they lost incentives to maintain conservation efforts.

4.5

Effects of Price Increases

In order to investigate the effect of price increases on electricity consumption, we use the DID model inEq.(4.2) to compare electricity consumption in the treated (Tokyo and Kansai) and control (Chubu) regions.
Electricity price went up on September 1, 2012, in Tokyo and on May 1, 2013, in Kansai. Accordingly, theDID model compares electricity consumption between Tokyo and Chubu in September 2012 and between
Kansai and Chubu in May 2013.
The result is shown in Table 7. It is found that there were no significant changes in electricity consumption
in both regions when electricity price went up. That is, although residential electricity consumption is very
elastic to price during power crises in previous research (Reiss and White, 2008), we find that non-residential
electricity consumption was inelastic to price during the power crisis in Japan. The finding is largely
consistent with the empirical evidence in Hosoe and Akiyama (2009) that price elasticities in Japaneseindustrial and commercial sectors during a non-crisis period are very small (less than 0.1) in Tokyo, Kansai
and Chubu. As firms need to maintain high productivities and remain competitive in markets, they may
keep the same operation levels for the time being even if electricity price goes up.
The result in this section shows that non-residential electricity consumptions are price inelastic. However,more detailed analysis in Section 4.6 provides a new insight into price elasticities of non-residential electricity
consumption.

18

Variables(1) Tokyo

D(T okyo)-0.593**(0.223)-0.028**(0.013)-0.004(0.020)D(September2012)
D(T okyo) ∗ D(September2012)
D(Kansai)D(M ay2013)
D(Kansai) ∗ D(M ay2013)
Constant

7.618**(0.145)(2) Kansai-0.935**(0.344)-0.060**(0.016)-0.028(0.026)
7.526**(0.162)Other controls

Yes

Yes

Observations

6,198

4,999

Number of companies

620

500

Note: Two columns show the estimation results for Eq.(4.2) in each region. The dependent variable is the log of
office-level monthly electricity consumption. D(T okyo) and D(Kansai) are dummies for firms in Tokyo and Kansai,
respectively. D(September2012) is a dummy for the month of September in 2012. D(M ay2013) is a dummy for themonth of May in 2013. The other independent variables are ln EP , HDD, CDD and EI defined in Section 3.1. Fixed
effects are included and the standard errors are clustered at the office level to adjust for serial correlation. Standard
deviations are in parentheses in all columns. The individual coefficient is statically significant at 5% level (**) or 10%
level (*)

Table 7: Effects of price increases in Tokyo in September 2012 and Kansai in May 2013

4.6

Free Ridership

In Sections 4.2 and 4.5, we find that non-residential customers reduced electricity consumption when they
were exposed to public pressures (non-economic incentives) first time but did not reduce electricity consumption when price increased (economic incentives). On closer look, 68% of the firms in the treated region
actually did not reduce electricity consumption in face of public pressures in the summer of 2011. Given
that a majority of the firms free-rode on others’ conservation efforts, it is necessary to further investigate
how these firms responded to economic incentives so that we can find a better way to control their electricity
consumption. Although firms, on average, did not respond to price increases, we may find different behaviors
between free-riders and non-free-riders. Thus, we divide the firms in Tokyo into (i) free-riders that did not
reduce electricity consumption in face of the target setting in July-August 2011 and (ii) non-free-riders that
reduced electricity consumption during the same period. Then, we examine how each group reacted to the
sharp price increase that took place in Tokyo in September 2012. Similarly, we divide the firms in Kansai
into free-riders and non-free-riders and then examine their reactions to the sharp price increase that took
place in Kansai in May 2013. We use the OLS model in Eq.(4.1).
Table 8 summarizes the estimation result of the changes in electricity consumption in response to priceincreases, showing a clear difference between two groups. The changes in electricity consumption are sig-

19

nificant for free-riders with estimated elasticities of 0.1 for Tokyo and 0.2 for Kansai, whereas they are notsignificant for non-free-riders. That is, free-riders are likely to take conservation actions in response to economic incentives, whereas non-free-riders are not. Interestingly, firms tend to respond to either economic
incentives or non-economic incentives. The result implies the importance of using various incentives to induce
conservation efforts from as many firms as possible because firms selectively respond to different incentives.
Tokyo

Kansai

Variables

Free-riders

Non-free-riders

September2012-0.101**(0.022)0.079(0.085)

M ay2013

Free-riders

Non-free-riders-0.005(0.026)7.520**(0.353)7.689**(0.132)8.074**(0.242)-0.225**(0.037)6.733**(0.254)

Other controls

Yes

Yes

Yes

Yes

Observations

932

386

536

304

Number of companies

233

97

134

76

Constant

Note: This table shows the estimation results for Eq.(4.1). Free-riders are the firms that reduced electricity consumption
during public pressures in the months of July and August in 2011, and non-free-riders are the firms that did not reduce
electricity consumption during the same period. The dependent variable is the log of office-level electricity consumption.
September2012 is a dummy for the month of September in 2012. M ay2013 is a dummy for the month of May in 2013.
The other independent variables are ln EP , HDD, CDD and EI defined in Section 3.1. Fixed effects are included andthe standard errors are clustered at the office level to adjust for serial correlation. Standard deviations are in parentheses
in all columns. The individual coefficient is statically significant at 5% level (**) or 10% level (*)

Table 8: Free-riders’ Reaction to Price Increases

5

Conclusion

This study provides some empirical evidence on the behaviors of non-residential electricity customers during
electricity crises. As a whole, non-residential customers responded to public pressures better than priceincreases under a crisis situation. The effect of public pressures, however, was tentative and disappeared
when the imminent crisis lessened. On closer look, the aggregate conservation during public pressures wasmade by about 30% of non-residential customers. In other words, about 70% of non-residential customers
free-rode on others’ efforts during public pressures. Interestingly, while non-residential customers were all
together inelastic to price, free-riders were actually elastic to price. This also implies that non-residential
customers selectively respond to either public pressures or price increases.
Policy implications from this study are summarized as follows. First, public pressures can help nonresidential customers reduce electricity consumption under a crisis situation. Supported by the prevalence ofvoluntary agreements and the CSR movement, some firms respond well to public pressures. Policy makers
can rely on their conservation efforts to avoid the worst case scenario. However, the effect of public pressures20

diminishes quickly. Thus, policy makers should make effort to maintain firms’ conservation behavior to
avoid blackout. For example, policy makers may send specialists to individual firms to help them develop
practical and customized conservation plans as did the Japanese government. Second, along with publicpressures, other incentives, including economic incentives, should be used to stimulate as many firms as
possible. Imposing public pressures is a relatively cost-efficient conservation strategy that policy makers canrely on. However, some firms respond to only economic incentives. Therefore, it is important to use a varietyof conservation strategies to address a crisis situation. Finally, policy makers should encourage firms to makelong-lasting conservation efforts during non-crisis periods. Firms make only temporal conservation efforts
under a crisis situation. As serious electricity crises can occur any time due to unexpected incidences, policy
makers should support firms to purchase energy-efficient equipments and adopt energy-saving behaviors
during non-crisis periods in preparation for future electricity crises.

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