Question:
You are given the full text of a scientific research paper.

    Summarize the study’s **policy implications**—how the research findings can inform policy or decision-making. Your response should:
    - Interpret what the results suggest for governments, regulators, or institutions
    - Highlight potential actions, strategies, or decisions informed by the study
    - Use policy-appropriate language without speculation beyond the study’s conclusions
    - Be clear, practical, and informative

    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41560-020-00719-z
1RFF-CMCC European Institute on Economics and the Environment (EIEE), Centro Euro-Mediterraneo sui Cambiamenti Climatici, Milan, Italy. 2Department
of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy. 3Department of Economics, Management and Quantitative
Methods (DEMM), University of Milan, Milan, Italy. 4Fondazione Pesenti, Milan, Italy. ✉e-mail: giovanna.dadda@unimi.it
S
ocial information programmes that provide information on
the actions or beliefs of others are widely used interventions
to foster behavioural change in several domains1–4, which
include residential resource conservation5–12. These programmes
typically feature two forms of feedback: descriptive and injunc-
tive. In the case of residential energy, descriptive feedback generally
takes the form of information on other households’ average energy
consumption, whereas injunctive feedback provides social approval
for energy savings. The combination of descriptive and injunctive
feedback within the standard design of social information pro-
grammes was inspired by the finding that descriptive information
alone leads those who use less energy (low energy users) to increase
their consumption, and that the addition of injunctive feedback in
support of an energy conservation norm prevents this boomerang
effect13. Injunctive information thus counterbalances descriptive
information. However, the experimental evidence on the impact of
descriptive and injunctive information when they exert opposing
influences on behaviour is mixed and mainly focuses on short-term
or self-reported outcomes14,15.
Given the wide adoption of communication campaigns that
rely on social information to promote behavioural change among
both policymakers and private firms, it is important to understand
how different programme features interact in real-world settings16.
Indeed, impact evaluations of similar programmes find that they are
effective in fostering energy savings, but that effect sizes vary widely
across contexts and individuals17. Prominent explanations for such
differential responses rely on the heterogeneity of consumers’ traits,
such as beliefs18,19, misperceptions of one’s compliance with the
social norm20 or personal values21,22.
Here we focus on the varying effect of specific features of these
messages, and particularly on how the salience, strength and consis-
tency of the feedback they contain differ, and thus affect behaviour
differently, across users. This could inform a more effective design
and targeting of messages and provide more specific and nuanced
guidance to prevent similar information campaigns from back-
firing16. First, we exploited the features of the standard design of
home energy reports and isolate the impact of changes in injunctive
feedback. Specifically, we examined whether reinforcing the injunc-
tive feedback has different effects on electricity use if it is accompa-
nied by consistent descriptive feedback—as is the case for those who
use more energy (high energy users), for whom both the injunc-
tive and descriptive information encourage energy conservation—
or contrasting descriptive feedback—as is the case for low energy
users, for whom conforming with the descriptive feedback entails
consumption increases, at odds with the injunctive feedback that
praises energy saving. Second, we randomized descriptive or injunc-
tive information that primes a social norm of energy conservation,
and evaluated the effect of strengthening the injunctive feedback in
the presence of either the descriptive or the injunctive prime.
We propose a conceptual framework for understanding how dif-
ferent features of social information programmes impact energy
conservation that can be articulated in a set of hypotheses, illus-
trated in Fig. 1. First, the effectiveness of a normative message is
maximized by the inclusion of consistent feedback of different types
(that is, injunctive and descriptive; Fig. 1a). Second, when injunc-
tive and descriptive feedbacks are in contrast (Fig. 1b), the strength
of each single piece of information matters. The strength of the nor-
mative feedback may depend on several factors highlighted in the
literature, from the recipient’s beliefs on what relevant others think
is socially approved of18,23 to the degree of consensus or ambigu-
ity around the norm conveyed by the information16,24. In our set-
ting, we hypothesize that the effect of the descriptive information
increases according to the difference between an individual’s elec-
tricity consumption and the average consumption of the reference
group. The effect of injunctive information instead varies accord-
ing to the strength of social approval conveyed through visual cues
and encouragement messages. Third, additional pieces of consistent
feedback of the same type produce smaller savings (Fig. 1c).
Our results are in line with these hypotheses. First, we found
suggestive evidence that the standard social information message
induces larger savings among high electricity users who are exposed
to consistent descriptive and injunctive feedback, compared with
low electricity users who are exposed to contrasting descriptive and
injunctive feedback. More importantly, reinforcing the injunctive
The interaction of descriptive and injunctive social
norms in promoting energy conservation
Jacopo Bonan1,2, Cristina Cattaneo   1, Giovanna d’Adda   1,3,4 ✉ and Massimo Tavoni1,2
Behavioural interventions that leverage social norms are widely used to foster energy conservation. For instance, home energy
reports combine information on others’ behaviour (descriptive feedback) and approval for norm compliant behaviour (injunc-
tive feedback). In a randomized controlled trial, we investigated how descriptive and injunctive feedbacks interact to affect
electricity use, and evaluate the effects of additional normative feedback presented in the form of descriptive or injunctive
energy conservation norm primes. We found that consistent descriptive and injunctive feedback boosts the effectiveness of
social information in inducing energy conservation. When descriptive and injunctive feedback are in conflict, conservation
behaviour is a function of the relative strength of the two types of feedback. Additional normative feedback produces smaller
gains when it reinforces existing information of the same type. These results suggest complementarities between different
types of normative messages rather than superiority of any one kind of feedback.
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
900

Articles
NATure Energy
feedback has the largest effect among low electricity users exposed
to a consistent descriptive prime. These findings are in line with
the notion that injunctive and descriptive feedbacks have a larger
impact on electricity conservation when they pull behaviour in the
same rather than in opposite directions. Second, reinforcing the
injunctive feedback led to a reduction in consumption, but only
among customers with low electricity usage. This shows that the
relative strengths of the different types of feedback matters when
they are contrasting. Such a reinforcement has no effect on custom-
ers with high consumption. This demonstrates the limited effect of
reinforcing one type of normative feedback within a message that
already contains two consistent pieces of normative information of
different types. This is further confirmed by the finding that rein-
forcing the injunctive feedback has no effect among users exposed
to a consistent injunctive prime. Together these findings suggest
that additional pieces of feedback have a larger impact when they
pull behaviour in the same direction and are of different types.
Overall, our results support the presence of synergies between dif-
ferent types of feedback rather than the primacy of any one type of
feedback.
Field experiment
Our setting consists of a randomized controlled trial implemented
by an Italian energy company that provides almost half-a-million
households with information on their electricity use relative to
that of their neighbours6,17. The social information is included in
a Home Energy Report distributed to customers via email (eHER).
The programme was rolled out in 2016 and involved 464,523 cus-
tomers (n = 418,178 treatment, n = 46,345 control). The core fea-
ture of the eHER is the neighbour comparison, which combines
descriptive and injunctive normative information. The descriptive
norm graphically compares the customer’s electricity use over the
previous month with the average use in two reference groups: 100
similar customers who live nearby (that is, neighbours) and the
15% most-efficient neighbours. The injunctive norm takes the form
of thumbs-up symbols next to the descriptive norm graph: three
thumbs up (‘excellent’) for users who consume within the top 15%
most-efficient neighbours, two thumbs up (‘good’) for those more
efficient than the average neighbour and one thumbs up (‘you can
do better’) for the others. Figures 2a,b shows the eHER for users
receiving three and two thumbs up, respectively.
We collaborated with the energy company to augment this set-up
with a message displayed at the bottom of the eHER delivered in
April–May 2018. The utility randomly allocated half of the treated
sample at that time (n = 256,487) to receive either the descriptive
(n = 127,899) or the injunctive (n = 128,588) message priming an
energy conservation norm (Fig. 2c, Supplementary Methods and
Supplementary Fig. 1). The descriptive norm prime emphasizes that
a large majority of customers try to save energy, that is, adopt behav-
iours consistent with a social norm of saving electricity. The injunc-
tive norm prime claims that a majority of customers hold electricity
saving as a personal value, which thus supports the belief that elec-
tricity saving is approved by relevant others. The two primes use
fellow customers of the same utility as the reference group. The
information on energy saving behaviours and values featured in the
primes was taken from an online survey that we conducted with
about 3,000 utility customers (Methods).
We have access to data on monthly electricity consumption from
July 2015 to December 2019. The daily average electricity usage, nor-
malized with respect to the control group consumption in the inter-
vention period, was our main outcome variable. Pre-intervention
daily electricity usage in a month was calculated over the period
July 2015 to June 2016. Our data also include information on the
contents of customers’ reports and on whether customers open or
click on them. We provide details on the programme implementa-
tion and data in the Methods, and descriptive statistics and balance
tests in Supplementary Tables 1 and 2 and Supplementary Note 1.
Samples are balanced across all available dimensions.
Impact of the social information programme
The impact evaluation of the standard programme indicates a statis-
tically significant reduction of normalized electricity usage in its first
year (coefficient = −0.353, standard error (s.e.) = 0.113, P = 0.002;
equation (1) in Methods and Supplementary Table 3, column 1).
The impact of the treatment increases with baseline consumption,
although this result is not robust to the measure of electricity con-
sumption used, that is, discrete or continuous (Supplementary Table 3,
columns 2 and 5); its statistical significance varies with how the
sample is defined (Supplementary Table 4) and with the time frame
considered (Supplementary Tables 5–7) and it does not always hold
after multiple hypotheses corrections. Exploiting data on engage-
ment with the reports and on changes in feedback over time, we
found that the impact of social information is magnified among
users who actually read it and who experience upgrades in feedback
(see Supplementary Tables 8 and 9 and Supplementary Note 2 for
further details).
a
b
c
Hypothesis: combined effect is
maximized
Hypothesis: combined effect
depends on relative strength
Hypothesis: smaller combined
effect
Energy conservation
Sign, strength and
amount of feedback
Energy conservation
Energy conservation
Sign, strength and
amount of feedback
Sign, strength and
amount of feedback
Fig. 1 | Hypothesized impact of injunctive and descriptive feedbacks in social information messages. a–c, Hypothesized effects of consistent injunctive
and descriptive (blue) feedbacks (a), contrasting injunctive (red) and descriptive feedbacks (b) and additional consistent feedbacks of the same type
(c). The black curve represents the overall impact of the normative message. Injunctive feedback is shown in red, descriptive feedback is shown in blue
and their combined effect is shown in purple. The horizontal axes indicate the sign, strength and amount of normative feedback, where positive feedback
values imply messages that encourage electricity savings. The vertical axes represent the behavioural outcome where positive values are associated with
behaviour in compliance with the norm, which in our case is energy conservation.
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
901

Articles
NATure Energy
Although the magnitude of the average savings from the pro-
gramme (−0.353%) is outside the range of those generated by similar
ones in the United States (minimum = 0.88%, maximum = 2.55%)
(ref. 18), they are in line with the existing evidence from Europe19.
Various factors, such as lower average consumption in Europe than
that in the United States, the specific features of the programme we
studied or differences in beliefs across contexts, may be responsible
for these differences. The heterogeneous effects, although not robust
and only marginally statistically significant, are qualitatively in line
with the existing evidence on the larger impact of social informa-
tion on high electricity users17,20,25 and on the absence of boomerang
effects among low users13.
These results provide initial, albeit weak, support for our concep-
tual framework. For high users, normative and injunctive feedbacks
pull behaviour in the same direction, which results in a reduction
in electricity almost twice as large as that in the average treatment
effect. For low electricity users, conforming to the reference groups’
behaviour motivates a consumption increase (’boomerang’), but
the injunctive feedback included in the eHER counterbalances the
negative effect of the descriptive feedback. The injunctive feedback
therefore induces stronger behavioural reactions among high elec-
tricity users, who are also exposed to the supporting descriptive
feedback, than that among low electricity users, for whom the two
types of feedback are at odds. Although such an interpretation is
only suggestive based on the evidence presented so far, it shows how
established findings are consistent with our conceptual framework.
Impact of strengthening the injunctive feedback
Our conceptualization can guide the analysis and interpreta-
tion of the effect of intensifying the injunctive feedback, with the
descriptive feedback kept unchanged, for low and high users. We
isolated the causal impact of the strength of the injunctive feed-
back via a regression discontinuity (RD) estimation (Methods). We
exploited the fact that the injunctive feedback (number of thumbs
up) changes discretely as a customer’s consumption crosses the two
thresholds given by the average consumption of the efficient neigh-
bours (three versus two thumbs-up cutoff), and the neighbours’
average consumption (two versus one thumbs-up cutoff), whereas
the descriptive feedback (bars of electricity use) changes continu-
ously across the thresholds. We focused on customers around the
thresholds, whose assignment to a given injunctive feedback cat-
egory was plausibly random. Indeed, although customers that
belong to the three normative feedback categories differ on average
along various baseline characteristics (Supplementary Note 3 and
Supplementary Table 10), individuals close to the thresholds are
similar (Supplementary Tables 11 and 12, columns 1–3).
We conducted two separate RD estimations, one for each cutoff,
on the sample of treated customers who received the eHER sent in
April–May 2018 (n = 256,487) to allow a direct comparison with the
analysis presented below. In each estimation, we compared users
in the two feedback categories adjacent to the cutoff and estimated
the marginal effect of receiving one additional thumbs up. Figure 3
presents the results in terms of level changes in electricity usage.
Although there are no statistically significant changes in the effect
of the eHER when crossing the threshold between the one and
two thumbs up (Fig. 3a), the discrete shift in the injunctive norm
reduces electricity use when moving from the two to three thumbs
up (Fig. 3b). The corresponding empirical estimates are presented
in Table 1 (columns 1 and 2).
We can attribute these effects to the social information contained
in the report rather than to other content, namely electricity sav-
ing tips—tips can only be accessed through a clickable link on the
b
Injunctive feedback
Descriptive feedback
Link to tips
Descriptive prime: Are you reducing energy consumption in your house? More than 80% of [name of utility]
customers take actions to save energy.* Even little deeds can have a large impact. Discover our tips to
consume less and better.
Injunctive prime: Is saving energy important to you? For more than 80% of [name of utility] customers saving
energy is an important value.* Even little deeds can have a large impact. Discover our tips to consume less and better.
*Survey conducted with a representative sample of [name of utility] customers.
Link to tips
Descriptive or injunctive
norm prime (randomized)
Descriptive or injunctive
norm prime (randomized)
FIND WAYS TO CONSUME LESS AND BETTER
FIND WAYS TO CONSUME LESS AND BETTER
a
c
Fig. 2 | Home Energy Report. a,b, Layout and content of a Home Energy Report for a user receiving three thumbs up (a) and a user receiving two
thumbs up (b). Both versions of the report contain the injunctive feedback, that is, the thumbs up (top), and the descriptive feedback, that is, the energy
consumption bars (bottom). c, Text of the randomized norm primes. Credit: Copyright 2016-2020 Oracle. All rights reserved.
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
902

Articles
NATure Energy
report and we see no difference in click shares across the cutoffs
(Supplementary Table 12). The impact of the shift in injunctive
feedback is persistent even after 6 and 12 months (Supplementary
Table 13). The results are robust to different specifications of the RD
estimate (Table 1).
These findings are consistent with our second hypothesis.
Strengthening the approval for electricity savings delivered through
the injunctive feedback affects customers around the three versus
two thumbs-up cutoff more because, for them, it reinforces the rela-
tive strength of the injunctive information in the presence of con-
flicting injunctive and descriptive feedback. On the contrary, and in
agreement with our third hypothesis, for consumers around the two
versus one thumbs-up cutoff, the reinforcement of the injunctive
feedback has a smaller marginal effect, as it adds strength to already
consistent feedback types.
Impact of additional injunctive and descriptive feedback
To further test the impact of an additional piece of normative infor-
mation, we combined the features of the standard eHER with the
randomized addition of descriptive and injunctive information
through the primes. To exploit the interaction between the discon-
tinuities in the eHER’s injunctive feedback and the randomly deliv-
ered primes, we repeated the RD analysis across the two cutoffs (two
versus one and three versus two thumbs up) separately for the sub-
samples of customers who received the two types of prime.
The results are reported in Fig. 4 and Table 1 (columns 3–6).
Across the cutoff between one and two thumbs up, we observe
no statistically significant changes in consumption, regardless of
whether the descriptive (Fig. 4a) or the injunctive (Fig. 4b) prime
is present. Conversely, a discrete shift in the injunctive feedback
across the three versus two cutoff causes electricity reduction, but
only when combined with the descriptive prime that nudges energy
efficiency (Fig. 4c). The results are robust to adjustments for mul-
tiple hypothesis testing (Table 1) and are persistent over longer time
horizons (Supplementary Table 13).
This evidence, consistent with our first hypothesis, suggests
synergies between different types of feedback: adding support-
ive descriptive information increases the impact of the shift in
injunctive feedback. The marginal contribution of additional
feedback of the same type instead decreases: customers exposed
to the injunctive prime do not react to the reinforcement of the
injunctive feedback across the three versus two thumbs-up cutoffs
(Fig. 4d). Similarly, strengthening the injunctive feedback across the
two versus one thumbs-up threshold makes no difference, regard-
less of whether the descriptive (Fig. 4a) or injunctive information
(Fig. 4b) is added. In this case, the descriptive and injunctive feed-
back within the standard neighbour comparison already pull behav-
iour in the same direction. Further priming either type of normative
feedback does not generate incremental electricity conservation.
To determine whether the overall effect of crossing the three
versus two thumbs-up threshold is exclusively due to the presence
within the eHER of the descriptive prime, we performed the RD
estimation on a standard eHER (February–March 2018). We found
statistically significant effects (coefficient = −0.855, s.e. = 0.368,
P = 0.02; Table 2, column 1). Therefore, the effect of reinforcing the
injunctive feedback for low users does not depend on the presence
of the descriptive prime within the report. In addition, we observe
that the overall effect of crossing the three versus two thumbs-up
5.5
6
6.5
7
Average daily electricity usage (kWh)
–30
–20
–10
0
10
20
30
Electricity usage with respect to cutoff (kWh)
a
3
3.5
4
4.5
5
Average daily electricity usage (kWh)
–30
–20
–10
0
10
20
30
Electricity usage with respect to cutoff (kWh)
b
Fig. 3 | Impact of the injunctive feedback on electricity usage. a,b, Each dot represents the average daily electricity usage in the 3 months after the receipt
of the April–May 2018 eHER around the two versus one thumbs-up cutoff (n = 216,328) (a) and three versus two thumbs-up cutoff (n = 130,466) (b)
within evenly spaced bins. The solid line represents the local linear fit, estimated separately on either side of the cutoff and the shaded area shows 95%
confidence intervals. The number of bins was selected through the integrated mean squared error (m.s.e). The running variable (horizontal axis) reports
the individual difference between each customer’s monthly consumption in the period reported in the eHER and the relevant cutoff. The cutoff is then
represented by the vertical line, which is set to zero. For positive values of the score, customers get an extra thumbs up with respect to those with negative
values of the score. In a, customers on the right of the cutoff consume less than the average neighbour and more than the top 15% most efficient and get
two thumbs up. In b, customers on the right of the cutoff consume within the 15% most efficient neighbours and get three thumbs up. Bandwidths (BWs)
and kernel are set following the data-driven process described for formal RD estimations of impacts reported in Table 1.
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
903

Articles
NATure Energy
threshold within the standard report is smaller than the same effect
when the report is augmented with the descriptive prime (coeffi-
cient = −2.426, s.e. = 0.619, P < 0.01; Table 1, column 3). This con-
firms that the combination of consistent descriptive and injunctive
information boosts the effectiveness of social information in induc-
ing energy conservation.
The RD estimation provides a robust identification of the causal
effects, which are, however, only local. We complemented it with an
estimation of the heterogeneous impact of the primes by the num-
ber of thumbs up that customers receive (equation (3) in Methods).
We obtained similar results: although the descriptive prime does not
influence consumption on average (coefficient = 0.088, s.e. = 0.149,
P = 0.554; Table 3, column 1), it led to a negative and statistically sig-
nificant decrease in consumption among customers who received
three thumbs up (coefficient = −0.959, s.e. = 0.284, P < 0.001;
Table 3, column 4). This negative effect was persistent over 6, 12 and
18 months (Supplementary Note 4 and Supplementary Table 14).
Interestingly, the effect of the descriptive prime on the entire group
of customers who received three thumbs up is smaller than the RD
estimates for the three versus two thumbs-up threshold combined
with the descriptive prime. Although these simple heterogeneity
results should be taken with caution, as thumbs up are correlated
with customers’ characteristics (Supplementary Table 10), they can
be interpreted in light of our conceptual framework and suggest a
potential determinant of the strength of descriptive information.
The sample of customers who received three thumbs up includes
the most efficient users, who are far from the three versus two
thumbs-up threshold. The further customers are from the thresh-
old, the larger the deviation between their own consumption and
the average electricity use, and therefore, we argue, the larger the
strength of the descriptive norm contained in the standard eHER.
In other words, we suggest that in our setting conformity motives
become more influential the further away individuals are from the
descriptive norm.
This interpretation was confirmed by analysing specifically the
effect of the descriptive prime among customers who experienced
an upgrade in the injunctive feedback (from two to three thumbs up)
relative to the previous report. These customers are likely to overlap
with the customers included in the RD estimation, as being close
to the three versus two thumbs-up threshold may result in down-
grades and upgrades between reports. The effect of the descriptive
prime on these customers is in line with the RD estimates for the
Table 1 | Regression discontinuity estimates of the impact of the injunctive norm and normative prime message on electricity usage
All
Descriptive prime
Injunctive prime
1
2
3
4
5
6
Three versus two thumbs up
Conventional
−1.217***
−1.165**
−2.426***
−2.388***
−0.0550
0.209
(0.461)
(0.456)
(0.619)
(0.611)
(0.686)
(0.676)
[0.001]
[0.001]
[1]
[0.66]
Robust bias-corrected
−1.130**
−1.077**
−2.264***
−2.349***
−0.0470
0.310
(0.461)
(0.456)
(0.619)
(0.611)
(0.686)
(0.676)
[0.002]
[0.001]
[1]
[0.68]
Observations
130,466
130,466
65,091
65,091
65,305
65,305
BW select method
1 m.s.e.
2 m.s.e.
1 m.s.e.
2 m.s.e.
1 m.s.e.
2 m.s.e.
BW above
30.59
35.67
34.50
40.27
27.94
39.15
BW below
30.59
26.95
34.50
28.89
27.94
23.14
Effective number of observations above
31,226
36,752
17,852
21,096
10,698
20,632
Effective number of observations below
23,216
20,908
12,669
11,051
13,900
9,392
Two versus one thumbs up
Conventional
0.254
−0.564
−0.426
−1.046*
0.491
−0.161
(0.531)
(0.434)
(0.704)
(0.601)
(0.691)
(0.619)
[1]
[0.14]
[1]
[0.66]
Robust bias-corrected
0.488
−0.409
−0.118
−0.883
0.731
−0.150
(0.531)
(0.434)
(0.704)
(0.601)
(0.691)
(0.619)
[1]
[0.27]
[0.771]
[0.68]
Observations
216,328
216,328
107,729
107,729
108,477
108,477
BW select method
1 m.s.e.
2 m.s.e.
1 m.s.e.
2 m.s.e.
1 m.s.e.
2 m.s.e.
BW above
30.10
76.51
35.93
84.88
33.80
67.97
BW below
30.10
33.90
35.93
37.67
33.80
30.86
Effective number of observations above
32,148
40,038
18,765
22,115
20,015
18,306
Effective number of observations below
35,704
69,226
21,124
36,967
17,843
31,980
The table shows the impact of the injunctive norm, that is, the number of thumbs up, on electricity usage, overall and by normative prime message. The outcome variable of the RD estimations is the
average daily energy usage (kWh) in the 3 months after the receipt of the eHER augmented with the normative prime, normalized by the average control group consumption in the post period, around the
three versus two thumbs up and two versus one thumbs up. The estimations in columns 1 and 2 are on the whole sample, in columns 3 and 4 are on the sample of customers who received the descriptive
norm prime and in columns 5 and 6 are on the sample of those who received the injunctive norm prime. The first row shows the conventional RD estimate for the thumbs-up comparison, whereas the
second corrects for bias48. BWs were selected to minimize the m.s.e.48,49. Odd columns use the same BW on either side of the cutoff, whereas even columns estimate separate BWs. Standard errors are
clustered at the customer level in parentheses. FDR (false discovery rate)-adjusted q values are given in square brackets. ***P < 0.01, **P < 0.05, *P < 0.1.
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
904

Articles
NATure Energy
three versus two thumbs-up threshold combined with the descrip-
tive prime (Fig. 5) and larger than the average impact of the descrip-
tive prime on the three thumbs-up subsample. Other factors may
contribute to these results, but we note that they are consistent with
our argument that, when descriptive and normative expectations
diverge, the resulting behaviour is a function of the relative strength
of the two types of feedback.
Conclusions
Our findings have implications for the design of social information
programmes that rely on the combination of different types of norms
to maximize behavioural change. Similar programmes are used in
several domains, such as tax compliance26,27, charitable giving28 or
water conservation29. According to our conceptual framework and
empirical results, no single type of normative information is more
effective in absolute terms. Policymakers should, instead, pay atten-
tion to the type of normative feedback they include in their commu-
nication, strive to diversify them, avoid conflicting information when
it mitigates the desirable effects and exploit it otherwise, be aware of
the diminishing returns from additional pieces of social information
and of the varying strength of conformity motives across individuals.
Of course, our results may be specific to the context that we stud-
ied, and particularly to the formulation of injunctive and descrip-
tive feedbacks that characterize the energy efficiency programme
we evaluated and the normative primes we designed. For example,
the wording and graphical representation of the injunctive feedback
in the eHER of this study differ from those of widely evaluated stan-
dard social information programmes6,17,18,21,30. Further investigations
5.5
6
6.5
7
Average daily
electricity usage (kWh)
–30
–20
–10
0
10
20
30
5.5
6
6.5
7
–30
–20
–10
0
10
20
30
3
3.5
4
4.5
5
Average daily
electricity usage (kWh)
–30
–20
–10
0
10
20
30
Electricity usage with respect to cutoff (kWh)
3
3.5
4
4.5
5
–30
–20
–10
0
10
20
30
Electricity usage with respect to cutoff (kWh)
a
b
c
d
Fig. 4 | Heterogeneous impact of the normative primes at different injunctive feedback cutoffs. Each dot represents the average daily electricity
usage (kWh) in the 3 months after the receipt of the eHER augmented with the normative prime, within evenly spaced bins. The solid line represents
local linear fit, estimated separately on either side of the cutoff with the shaded area showing 95% confidence intervals. The number of bins is
selected through the integrated m.s.e. The running variable (horizontal axis) reports the individual difference between each customer’s monthly
consumption (kWh) in the period reported in the eHER and the relevant cutoff. The running variable (horizontal axis) reports the individual difference
between each customer’s monthly consumption (kWh) in the period reported in the eHER and the relevant cutoff. For positive values of the score,
customers get an extra thumbs up with respect to those with negative values of the score. a, Customers who received the descriptive norm prime around
the two versus one thumbs-up cutoff (n = 107,729). b, Injunctive norm prime around the two versus one thumbs-up cutoff (n = 108,477). c, Descriptive
norm prime around the three versus two thumbs-up cutoff (n = 65,091). d, Injunctive norm prime around the three versus two thumbs-up cutoff
(n = 65,305).
Table 2 | Regression discontinuity estimates of the impact of
the injunctive norm on electricity usage
Three versus two
thumbs up
Two versus one
thumbs up
1
2
3
4
Conventional
−0.855**
−0.834**
0.0321
0.00172
(0.368)
(0.342)
(0.400)
(0.345)
Robust bias-corrected
−0.683*
−0.701**
0.167
0.131
(0.368)
(0.342)
(0.400)
(0.345)
Observations
134,970
134,970
224,212
224,212
BW select method
1 m.s.e.
2 m.s.e.
1 m.s.e.
2 m.s.e.
BW above
25.07
35.54
26.49
77.43
BW below
25.07
21.87
26.49
24.06
Effective number of
observations above
25,013
35,922
28,920
71,838
Effective number of
observations below
20,674
17,915
30,963
28,136
This table shows RD estimation of average daily energy usage in the 3 months after the receipt
of the eHER in February–March 2018 (that is, the one preceding the eHER augmented with the
normative prime), normalized by the average control group consumption in the post period, around
the three versus two thumbs-up cutoff (columns 1 and 2) and two versus one thumbs-up cutoff
(columns 3 and 4). The first row shows the conventional RD estimate, whereas the second corrects
for bias48. BWs were selected to minimize the m.s.e.48,49. Odd columns use the same BW on either
side of the cutoff, whereas even columns estimate separate BWs. Standard errors are clustered at
the customer level in parentheses. ***P < 0.01, **P < 0.05, *P < 0.1.
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
905

Articles
NATure Energy
in other behavioural domains and of alternative formulations of
descriptive and injunctive feedback are needed to verify the gener-
alizability of our findings.
Finally, although we identify potential determinants of the
strength of the social information messages, we are far from for-
mulating a comprehensive theoretical model. Such a model should
incorporate other important insights from the social information
literature, for instance, on the role of individual descriptive and nor-
mative second-order beliefs, from the perceived consensus around
the norm or from misperceptions that concern one’s own compli-
ance with the norm. Similarly, within our setting, due to lack of data,
we can only test a few implications of our conceptual framework
and cannot control for the influence of other important determi-
nants of the impact of social information identified in the literature.
The predictions from a more comprehensive model should, instead,
be subject to a systematic experimental investigation.
Methods
Ethics statement. Ethical approval for the use of the data that support the findings
of this study was granted by the Institutional Review Board at Politecnico di
Milano (approval number 04/2017). Consent for their administrative data to
be used for the research was given by the users as part of the privacy consent
statements that they signed with the utility.
Programme details. We evaluated a social information programme designed and
implemented by the utility and Opower (acquired by Oracle in 2016). The eHER
that constitutes the core feature of the programme differed from the standard
Opower HER, evaluated in other works6,17,18,21,30, under three respects: first, it was
delivered by email rather than by post, hence the notation eHER; second, it did
not feature a section with energy saving tips—tips could be consulted by interested
customers within their personal area on the utility’s website, accessible through
a clickable link on the eHER; third, the normative feedback was given through
thumbs-up symbols accompanied by the expressions ‘excellent’, ‘good’ and ‘you can
do better’, rather than through the standard smiley faces coupled with ‘great’, ‘good’
and ‘above average’. The first two differences are consistent with the objective to
foster customers’ digital engagement, which the utility primarily wanted to achieve
through the programme, whereas the third is the result of focus groups conducted
by the utility and Opower to define the design of the eHER.
Our augmented eHER added a simple treatment to this basic set-up in the
form of a message at the bottom of the report. We proposed a formulation of the
descriptive and injunctive normative messages based on previously collected survey
data and collaborated with Opower and the utility to finalize the wording, layout
and graphical aspects of the messages. Opower and the utility were responsible for
the randomization of the normative messages and the implementation of the test.
In addition to the experimental test discussed in the present study, we
manipulated in a similar way the November–December 2017 eHER. The experiment
aimed to test the impact of environmental identity on energy conservation. It
featured a treatment message that primed individual environmental self-identity
and a control message that encouraged energy conservation22. Given that both the
November–December 2017 and the April–May 2018 primes were randomized,
participation in the environmental prime test should not affect the results presented
here. Nevertheless, we support this claim through further tests reported below.
Sample. Our sample of analysis is represented by the entire eligible customer base
at the time of the start of the programme (n = 464,523). Eligibility criteria were
established and verified by the utility and Opower, and included availability of a
contact email address and a set of technical requirements, such as living in single
family homes, having one year of pretreatment consumption data without missing,
negative or abnormally high usage, and having a sufficient number of neighbours—
defined as customers who lived within a 10 km radius and were similar in terms
of type of housing and any other characteristic available to the utility) for the
neighbour comparison. The utility is present over the full national territory and
the programme was targeted to all eligible customers regardless of their area of
residence (Supplementary Fig. 2 shows the study sample distribution across Italian
municipalities). Moreover, to foster energy conservation was not the main goal of
the programme. These considerations alleviate concerns of site selection bias31.
Eligible customers were randomly assigned to the treatment (n = 418,178) and
control (n = 46,345) groups by the utility through the minmax t-statistic algorithm,
depending on baseline consumption and geographical location32. The small relative
size of the control group was determined by the utility in collaboration with
Opower, with the goal to minimize the number of customers who did not receive
the programme, but avoid issues of statistical power in the evaluation of its impact.
The experimental design could eventually capture a minimum detectable effect with
90% power and 5% significance of about 0.36%. As for heterogeneous effects by
pretreatment usage, the minimum detectable effects ranged from 0.8 to 1.2%. These
effect sizes are relatively small with respect to those found in the literature19,33–35.
Table 3 | Impact of the descriptive versus injunctive messages
on electricity usage
All
One
thumbs up
Two
thumbs up
Three
thumbs up
1
2
3
4
Post
−0.300***
−0.081
−0.459***
−1.190***
(0.113)
(0.194)
(0.128)
(0.213)
Descriptive
prime × post
0.088
0.225
0.296*
−0.959***
(0.149)
(0.252)
(0.170)
(0.284)
[0.142]
[0.09
[0.003]
Constant
102.302*** 133.685***
81.618***
50.329***
(0.044)
(0.076)
(0.052)
(0.080)
Observations
2,783,190
1,358,951
1,002,787
421,452
R-squared
0.109
0.171
0.093
0.044
Number of
customers
256,487
125,249
92,407
38,831
The dependent variable is the average daily electricity usage (kWh), main and heterogeneous
effects, normalized by average control group consumption in the post period. The reference
period for the analysis is October 2017 to August 2018 (3 months impact). All the specifications
include customer fixed effects and month by year fixed effects. Standard errors are clustered
at the customer level in parentheses and FDR-adjusted q values in square brackets. ***P < 0.01,
**P < 0.05, *P < 0.1.
2
0
–2
–4
Regression specifications
95% Cl
99% Cl
90% Cl
Point estimate
AII
NS
NS 1T
NS 2T
NS 3T
SU 2T
SU 3T
SD 1T
SD 2T
SU
SD
Treatment effect
Fig. 5 | Prime impact by up- and downgrades in the injunctive feedback
category. The figure plots the coefficient of the descriptive norm × post
period on average daily electricity usage (kWh), normalized by the average
control group consumption in the post period, estimating equation (3) for
the following subsamples. All, whole sample of customers included in the
normative prime trial (n = 2,783,190); NS, customers who experienced no
switch in the number of thumbs in the eHER augmented with the normative
prime with respect to the previous one (n = 2,334,191); SU, switch up,
that is, an improvement in the number of thumbs in the eHER augmented
with the normative prime with respect to the previous one (n = 239,501);
SD, switch down, that is, a decrease in the number of thumbs in the
eHER augmented with the normative prime with respect to the previous
one (n = 209,498); NS 1T, no switch and one thumbs up in the eHER
augmented with the normative prime (n = 1,232,602); NS 2T, no switch
and two thumbs up (n = 773,165); NS 3T, no switch and three thumbs up
(n = 328,424); SU 2T, switch up and two thumbs up (n = 146,473); SU
3T, switch up and three thumbs up (n = 93,028); SD 1T, switch down and
one thumbs up (n = 126,349); SD 2T, switch down and two thumbs up
(n = 83,149). CI, confidence interval.
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
906

Articles
NATure Energy
The programme was rolled-out in three waves: July (39% of treated
customers), October (33%) and December 2016 (28%). After that, customers
received reports bimonthly. Conditional on being assigned to the treated group,
91% of customers received at least one eHER, on average 8.8 over the first
24 months of the programme. In April and May 2018, the report augmented with
the experimental prime was sent to 256,487 programme participants, randomly
assigned to receive the descriptive (n = 127,899) or injunctive (n = 128,588)
message. The sample of customers who received the augmented eHER was smaller
than the entire sample of programme participants at that time (n = 348,131)—
as the implementation of the programme was not under the research team’s
control, we cannot document the reasons for such a discrepancy. We did,
however, examine individual correlates of the receipt of the augmented eHER
(Supplementary Note 5 and Supplementary Table 15).
About 21.5% of customers left the dataset between the launch of the
programme and August 2018, primarily due to termination of the contract with
the utility. We checked that the attrition was non-differential by treatment status
using a two-sided t-test (Supplementary Table 1). In Supplementary Table 1, we
also report a similar test as to whether attrition in the 3 months after the delivery of
the augmented eHER, equal to about 5.8%, correlated with the experimental prime
treatment. We explored individual correlates of attrition at different points in time
using regression analysis (Supplementary Table 16). These allowed us to assess the
extent to which attrition was a threat to the internal and external validity.
Data. We had access to historical electricity consumption data from July 2015
to December 2019 for all the customers. These data were provided by the utility,
after being verified by the electricity distributor, and constituted our main
outcome variable. Similarly to other works, we computed the average daily
electricity usage in a month from the total monthly consumption and normalized
it by dividing by the average post-period control group consumption and then
multiplying by 100 (ref. 17). We also computed the average daily pretreatment
electricity usage as the mean of the average daily consumption in a month
between July 2015 and June 2016.
We also had access to information on the treated customers’ engagement with
the reports and on the reports’ contents. These data were provided by Opower. We
knew when a eHER was sent and when a customer opened or clicked on a report,
although we did not know which one. By opening the reports, customers were
able to view the neighbour comparison; by clicking on it, customers were directed
to their personal page on the utility’s website, where further information, such as
energy saving tips and bills, was available. On average, 64 and 30% of the customers
opened and clicked on an eHER, respectively, at least once over the two years after
the programme launch. As for the eHER augmented with the normative prime, 55
and 9.7% of customers opened and clicked at least once in the two months after its
receipt, respectively.
The reports’ content data include customers’ relative performance within
the reference group and the type of feedback they received within each eHER,
in terms of number of thumbs up. In general, considering all the reports in the
first 24 months, customers received 20% of reports with three thumbs up, 34%
with two thumbs up and 45% with one thumbs up, consistent with the definition
of the three feedback categories. Overall, 38% of customers received the same
feedback throughout the period, and the remaining experienced some change.
In the majority of cases, customers experienced both upgrades and downgrades.
Specifically to the eHER augmented with the randomized prime, 15.1% of
customers received three thumbs up, 36.1% received two thumbs up and 48.8%
one thumbs up. As time-invariant controls, we used dummies for the main
geographical areas in Italy, that is, the northeast, northwest, central, south and
the islands, and the population of the municipality in which the customers lived,
obtained by matching the contract municipality data with data on municipalities’
characteristics36. We missed information on the geographical location of 5,675
customers (about 1.2% of the sample), equally distributed by treatment status
(P = 0.273). This reduced the sample size to 459,088 customers whenever the
analysis featured geographical controls.
Finally, we used data from an online survey conducted with a representative
subsample of about 3,000 utility customers in April 2017 to inform the design
of the norm primes. In particular, questions on actual pro-environmental
behaviour—such as turning off the lights when leaving a room and hanging the
clothes to dry instead of using dryer—were used to design the descriptive prime,
whereas questions on personal values related to energy conservation—such as
whether the customer feels personally responsible to try to save energy, whether
the customer would act according to the customer’s principles if energy was saved
and whether the customer feels morally obliged to save energy—informed the
design of the injunctive prime. The survey questions used to elicit personal values
and norms were taken from established survey instruments, such as the World
Values Survey, and from published studies in environmental psychology37,38. More
details on the survey can be found in Bonan et al.22.
Balance. We checked that the Opower treatment and control group, and the
samples of customers assigned to the descriptive or injunctive prime, were
balanced across the observable characteristics through two-sided t-tests
(Supplementary Table 1). In addition, we tested for the randomization balance
across the subgroups identified by the combination of the April–May 2018
normative prime treatments and the November–December 2017 environmental
prime treatments, mentioned above (Supplementary Table 2). This was done
through an F-test of joint significance of subtreatment dummies regressed on the
observable characteristics. We confirmed that balance generally holds.
Impact of the programme. We evaluated the impact of the programme by
estimating the following model:
yit ¼ β1Postit þ β2Programi ´ Postit þ ht þ gi þ εit
ð1Þ
where yit is the customer’s i normalized average daily consumption in month t.
Program is a treatment indicator, Post is a dummy variable which becomes 1 when
customers receive the first eHER. The coefficient β1 captures the effect of any time
variant factors affecting consumption after the start of the programme, while β2
isolates the impact of the programme on treated customers. Ɛit is the error term.
Given the staggered phase-in of the programme, to allow the identification through
difference-in-differences, we randomly assigned control customers to the three
programme start waves, with the same proportions as treated customers. This
implies that Post becomes 1 at the beginning of each wave for the same share of
treated and control customers. The regression also included month-by-year fixed
effects, ht, and household fixed effects, gi. Standard errors were clustered at the level
of household to allow for the presence of within-customer correlation over time in
the error term39. The average treatment effects can be interpreted as the percentage
change (Supplementary Table 3).
We examined the differential response to the eHER depending on pretreatment
electricity use by interacting it with the Program × Post dummy. We expressed
electricity usage with a continuous variable and with dummies for the quartiles.
To allow for differential post-treatment trends, we also interacted the Post dummy
with pretreatment usage. To adjust for multiple hypothesis testing, in the subgroup
analysis we computed the sharpened two-stage q values (FDR-adjusted q values)40.
We analysed ex post power by calculating the minimum detectable effects with a
90% power and 5% significance41. As a robustness check, we estimated the main
and heterogeneous effects of the programme on the subsample of customers for
whom information on geographical location was available (Supplementary Table 4).
We evaluated the main and heterogeneous impacts of social information
over the first and the first two years of the programme. In a further analysis, we
extended the treatment period until December 2019 and explored in greater detail
the persistence of the treatment effects (Supplementary Tables 5–7).
As in other works6,17, our treatment effects are intent-to-treat estimates
computed on the full sample of eligible customers, regardless of whether they opted
out of the programme or did not open the reports. We kept customers who did not
receive or read reports in the analysis to maintain the balance between the treatment
and control group and avoid selection issues affecting our results. By doing this,
we were likely to underestimate the effect of the programme on the group of
customers initially assigned to receive the eHER and who actually saw the treatment
communication. In an additional analysis, we examined the role of the engagement
with the programme on treatment impacts by instrumenting opening the eHER and
clicking on it with the treatment status, and reporting the local average treatment
effect (Supplementary Table 8). We did this in cross-section. The outcome variable
was the normalized average electricity usage in the 24 months after the launch of the
programme. The specifications also included pretreatment electricity usage.
We further exploited our data on the content of the reports, specifically on
the number of thumbs up received within each eHER, to examine heterogeneous
effects of the treatment depending on whether the customers were upgraded
or downgraded with respect to the previous report. We did it by restricting the
analysis to the months when the eHERs were sent and by focusing on normalized
daily electricity usage in the 3 months after each report. Specifications include
customer and month-by-year fixed effects (Supplementary Table 9).
Regression discontinuity estimation. We used RD to estimate the impact of
changes in the injunctive feedback included in the neighbour comparison. We
justified this approach by showing that customers who received one, two or three
thumbs up within the April–May 2018 eHER were different in many respects,
which may also correlate with the impact of the treatment (Supplementary Table 10).
The RD approach allowed us to eliminate the influence of confounding factors on
the estimated effect of changing the feedback category, as it focused on customers
for whom the number of thumbs up is random. The price we paid for the improved
identification of the effects is that the impacts estimated through RD are local,
specific to a neighbourhood of the thresholds.
In our RD framework, the running variables, Xi1 and Xi2, are the customer’s i
monthly electricity usage (kWh). The cutoffs, ci1 and ci2, are the electricity usage of
the 15th percentile and the overall average electricity usage among the neighbours,
respectively. The assignment variable Ti1 takes the value of 1 when the customer’s
usage lies above ci1 (Xi1 > ci1) and 0 otherwise. Similarly, T21 takes the value of 1
when the customer’s usage lies between ci1 and ci2 and 0 otherwise. We estimated
the equations:
Yi ¼ λ1
0 þ η1Ti1 þ f Xi1  ci1
ð
Þ þ ε1
1
ð2AÞ
Yi ¼ λ2
0 þ η2Ti2 þ f Xi2  ci2
ð
Þ þ ε2
1
ð2BÞ
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
907

Articles
NATure Energy
where Yi is the customer’s i-normalized average daily electricity usage in the
3 months after the receipt of the report that contained the normative prime.
Model (2A) is estimated from customers who received either three or two thumbs
up (for example, with Xi1 > ci1 or ci1 <Xi1 <ci2), whereas model (2B) includes those
who received either two or one thumbs up (for example, Xi1 < ci1). The parameter
λ0 is a constant, while η captures the effect of obtaining an extra thumbs up.
ε1
1
I
 and ε2
1
I
 are the error terms. Our main specification used a non-parametric
approach (local polynomial point estimation), which amounts to fitting two
linear regressions on customers respectively close to the left and to the right
of the cutoff42,43. This approach used only observations within a BW from the
cutoff. The selection of the BW was data driven and aims to minimize the m.s.e.
of the local polynomial RD estimator. We computed the point estimates using
both one common BW on both sides of the cutoff and two distinct BWs. We
used a linear polynomial for f(.), which represents a good trade-off between
simplicity, precision and stability44. Observations within the BW were weighted
through a triangular kernel function, which assigned zero weight to observations
outside the BW and positive weights within it, which decreased symmetrically
and linearly as the distance from the cutoff increased. The triangular kernel had
desirable asymptotic optimality properties44. We show point estimates under
conventional (from a parametric least-squares estimation) and bias-corrected
robust standard errors, which deliver a valid inference under the m.s.e.-optimal
BW selection43 (Table 1).
Identification within RD design relies on the assumption that customers have
no full control over the running variable and hence are unable to manipulate
their position in the distribution of electricity usage within their reference group.
Manipulation should not be a threat to our identification for several reasons. First,
customers do not know the algorithm for the selection of the reference group and
assignment of thumbs up. Second, although people may be able to control their
own electricity usage, they have no control on their neighbours’ consumption,
which determines the unpredictability of the consumption level associated with
the different cutoffs every round. Third, people do not gain any direct benefit from
an extra thumbs up, apart from individual satisfaction, and hence the incentives to
game the system should be minimal. We tested this identification assumption by
checking for discontinuities in the density of the running variable at the thresholds,
using a non-parametric density estimator based on local polynomial techniques45.
We did the same with the pretreatment covariates, following a procedure suggested
in the literature46 (Supplementary Table 11).
We also performed an RD estimation on the probability that customers opened
or clicked in the April–May 2018 eHER. This analysis aimed to support the claim
that the effects we found on consumption were due to the neighbour comparison,
rather than to discontinuous changes in unobserved determinants of open rates or
the likelihood of viewing electricity saving tips at the thresholds (Supplementary
Table 12).
We repeated the test of the RD identifying assumption and the RD estimation
using a standard eHER, delivered in February–March 2018, to rule out that our
results are due to the presence of the normative primes (Supplementary Note 6 and
Supplementary Tables 17 and 2) and RD effects over the following 6 and 12 months
(Supplementary Table 13).
Impact of the normative primes. We repeated the RD estimation separately on
the subsamples of customers randomly allocated to the descriptive and injunctive
primes to obtain local marginal effects of moving across the injunctive feedback
categories by prime treatment. We adjusted for multiple comparisons40.
We also assessed the impact of receiving the eHER augmented by the
descriptive versus injunctive prime, by estimating the following model:
yit ¼ α1Postit þ α2Descriptive normi ´ Postit þ ht þ gi þ μit
ð3Þ
Descriptive_norm takes the value of 1 for the descriptive prime and 0 for
the injunctive prime. Post becomes one when customers receive the eHER that
contains the prime (roughly half in April and half in May 2018). The coefficient
α1 captures the change in consumption after receiving the eHER for customers
assigned to receive the injunctive prime, while α2 reveals any differential effect of
receiving the descriptive prime. Standard errors are clustered at the household level
(Table 3). The regression was run on the sample of treated customers at that time
(n = 256,487) and considered the period that spanned from October 2017 to August
2018, but we also explored the persistence of the effects until December 2019, that
is, 18 months after the prime (Supplementary Table 14). We also estimated equation
(3) for the subsamples of customers who received one, two or three thumbs up,
overall and separately for customers upgraded or downgraded with respect to the
previous report, to examine the heterogeneous impact of the augmented eHER.
We tested that these results were not due to interaction effects with the
November–December 2017 environmental identity test by adding to equation (3)
an interaction term between Post, Descriptive_norm and a dummy equal to 1 if
a customer was assigned to the environmental prime treatment (Supplementary
Table 18).
Preregistration. We analysed the impact of the standard and augmented eHER
using non-experimental RD techniques. In terms of individual level data, our
analysis relied only on customers’ consumption, location and content of the eHER.
For these reasons, the analysis we present was not prespecified in a pre-analysis
plan: detailed prespecification is warranted when, within the impact evaluation of
field experiments, subgroup analysis is expected to be important and there is the
possibility to cherry-pick the dimensions of heterogeneity to focus on or that a
party to the study has a vested interest47. These conditions did not apply to our case.
Reporting summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The data that support the findings of this study are proprietary data of the energy
company and cannot be shared publicly. To inquire about access to the proprietary
data, please contact M.T.
Code availability
The replication code is available on Open Science Framework at https://osf.io/
wz8gb/.
Received: 30 April 2020; Accepted: 28 September 2020;
Published online: 2 November 2020
References
	1.	 Frey, B. S. & Meier, S. Social comparisons and pro-social behavior: testing
‘conditional coop-eration’ in a field experiment. Am. Econ. Rev. 94,
1717–1722 (2004).
	2.	 Shang, J. & Croson, R. A field experiment in charitable contribution: the
impact of social information on the voluntary provision of public goods.
Econ. J. 119, 1422–1439 (2009).
	3.	 Gerber, A. S. & Rogers, T. Descriptive social norms and motivation to vote:
everybody’s voting and so should you. J. Polit. 71, 178–191 (2009).
	4.	 Beshears, J., Choi, J. J., Laibson, D., Madrian, B. C. & Milkman, K. L. The
effect of providing peer information on retirement savings decisions.
J. Finance 70, 1161–1201 (2015).
	5.	 Allcott, H., Mullainathan, S. & Taubinsky, D. Externalizing the Internality
(New York Univ., 2011).
	6.	 Allcott, H. & Rogers, T. The short-run and long-run effects of behavioral
interventions: experimental evidence from energy conservation. Am. Econ.
Rev. 104, 3003–3037 (2014).
	7.	 Ayres, I., Raseman, S. & Shih, A. Evidence from two large field experiments
that peer comparison feedback can reduce residential energy usage. J. Law
Econ. Organ. 29, 992–1022 (2013).
	8.	 Brent, D. A., Cook, J. H. & Olsen, S. Social comparisons, household water
use, and participation in utility conservation programs: evidence from three
randomized trials. J. Assoc. Environ. Resour. Econ. 2, 597–627 (2015).
	9.	 Ferraro, P. J. & Price, M. K. Using nonpecuniary strategies to influence
behavior: evidence from a large-scale field experiment. Rev. Econ. Stat. 95,
64–73 (2013).
	10.	Ferraro, P. J., Miranda, J. J. & Price, M. K. The persistence of treatment effects
with norm-based policy instruments: evidence from a randomized
environmental policy experiment. Am. Econ. Rev. 101, 318–322 (2011).
	11.	Ferraro, P. J. & Miranda, J. J. Heterogeneous treatment effects and
mechanisms in information-based environmental policies: evidence from a
large-scale field experiment. Resour. Energy Econ. 35, 356–379 (2013).
	12.	Jaime Torres, M. M. & Carlsson, F. Direct and spillover effects of a social
information campaign on residential water-savings. J. Environ. Econ. Manag.
92, 222–243 (2018).
	13.	Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J. & Griskevicius, V.
The constructive, destructive, and reconstructive power of social norms.
Psychol. Sci. 18, 429–434 (2007).
	14.	Bicchieri, C. & Xiao, E. Do the right thing: but only if others do so. J. Behav.
Decis. Mak. 22, 191–208 (2009).
	15.	Meisel, M. K. & Goodie, A. S. Descriptive and injunctive social norms’
interactive role in gambling behavior. Psychol. Addict. Behav. 28,
592–598 (2014).
	16.	Biccheri, C. & Dimant, E. Nudging with care: the risks and benefits of social
information. Public Choice https://doi.org/10.1007/s11127-019-00684-6
(2019).
	17.	Allcott, H. Social norms and energy conservation. J. Public Econ. 95,
1082–1095 (2011).
	18.	Jachimowicz, J. M., Hauser, O. P., O’Brien, J. D., Sherman, E. & Galinsky, A. D.
The critical role of second-order normative beliefs in predicting energy
conservation. Nat. Hum. Behav. 2, 757–764 (2018).
	19.	Andor, M. A., Gerster, A., Peters, J. & Schmidt, C. M. Social norms and energy
conservation beyond the US. J. Environ. Econ. Manag. 103, 102351 (2010).
	20.	Byrne, D. P., Nauze, A. L. & Martin, L. A. Tell me something I don’t already
know: informedness and the impact of information programs. Rev. Econ. Stat.
100, 510–527 (2018).
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
908

Articles
NATure Energy
	21.	Costa, D. L. & Kahn, M. E. Energy conservation ‘nudges’ and environmentalist
ideology: evidence from a randomized residential electricity field experiment.
J. Eur. Econ. Assoc. 11, 680–702 (2013).
	22.	Bonan, J., Cattaneo, C., D’Adda, G. & Tavoni, M. Can We Make Social
Information Programs More Effective? The Role of Identity and Values Working
Paper 19-21 (Resources for the Future, 2019).
	23.	Bursztyn, L., González, A. L. & Yanagizawa-Drott, D. Misperceived Social
Norms: Female Labor Force Participation in Saudi Arabia Working Paper
24736 (National Bureau of Economic Research, 2018); https://doi.
org/10.3386/w24736
	24.	D’Adda, G., Dufwenberg, M., Passarelli, F. & Tabellini, G. Partial Norms SSRN
Scholarly Paper ID 3362021 (Social Science Research Network, 2019).
	25.	List, J. A., Metcalfe, R. D., Price, M. K. & Rundhammer, F. Harnessing Policy
Complementarities to Conserve Energy: Evidence from a Natural Field
Experiment Working Paper 23355 (National Bureau of Economic Research,
2017); https://doi.org/10.3386/w23355.00000
	26.	Hallsworth, M., List, J. A., Metcalfe, R. D. & Vlaev, I. The behavioralist as tax
collector: using natural field experiments to enhance tax compliance. J. Public
Econ. 148, 14–31 (2017).
	27.	Bobek, D. D., Hageman, A. M. & Kelliher, C. F. Analyzing the role of social
norms in tax compliance behavior. J. Bus. Ethics 115, 451–468 (2013).
	28.	Krupka, E. L. & Croson, R. T. A. The differential impact of social norms cues
on charitable contributions. J. Econ. Behav. Organ. 128, 149–158 (2016).
	29.	Bahnot, S. Isolating the effect of injunctive norms on conservation behavior:
new evidence from a field experiment in California. Organ. Behav. Hum.
Decis. Process https://doi.org/10.1016/j.obhdp.2018.11.002 (2018).
	30.	Allcott, H. & Taubinsky, D. Evaluating behaviorally motivated policy:
experimental evidence from the lightbulb market. Am. Econ. Rev. 105,
2501–2538 (2015).
	31.	Allcott, H. Site selection bias in program evaluation. Q. J. Econ. 130,
1117–1165 (2015).
	32.	Bruhn, M. & McKenzie, D. In pursuit of balance: randomization in practice
in development field experiments. Am. Econ. J. Appl. Econ. 1, 200–232 (2009).
	33.	Andor, M. A. & Fels, K. M. Behavioral economics and energy
conservation—a systematic review of non-price interventions and their causal
effects. Ecol. Econ. 148, 178–210 (2018).
	34.	Farrow, K., Grolleau, G. & Ibanez, L. Social norms and pro-environmental
behavior: a review of the evidence. Ecol. Econ. 140, 1–13 (2017).
	35.	Delmas, M. A., Fischlein, M. & Asensio, O. I. Information strategies and
energy conservation behavior: a meta-analysis of experimental studies from
1975 to 2012. Energy Policy 61, 729–739 (2013).
	36.	Guiso, L., Sapienza, P. & Zingales, L. Long-term persistence. J. Eur. Econ.
Assoc. 14, 1401–1436 (2016).
	37.	Steg, L., Perlaviciute, G., van der Werff, E. & Lurvink, J. The significance of
hedonic values for environmentally relevant attitudes, preferences, and
actions. Environ. Behav. 46, 163–192 (2014).
	38.	van der Werff, E., Steg, L. & Keizer, K. The value of environmental
self-identity: the relationship between biospheric values, environmental
self-identity and environmental preferences, intentions and behaviour.
J. Environ. Psychol. 34, 55–63 (2013).
	39.	Bertrand, M., Duflo, E. & Mullainathan, S. How much should we trust
differences-in-differences estimates? Q. J. Econ. 119, 249–275 (2004).
	40.	Benjamini, Y., Krieger, A. M. & Yekutieli, D. Adaptive linear step-up
procedures that control the false discovery rate. Biometrika 93,
491–507 (2006).
	41.	Haushofer, J. & Shapiro, J. The short-term impact of unconditional cash
transfers to the poor: experimental evidence from Kenya. Q. J. Econ. 131,
1973–2042 (2016).
	42.	Imbens, G. W. & Lemieux, T. Regression discontinuity designs: a guide to
practice. J. Econom. 142, 615–635 (2008).
	43.	Calonico, S., Cattaneo, M. D. & Titiunik, R. Robust nonparametric
confidence intervals for regression- discontinuity designs. Econometrica 82,
2295–2326 (2014).
	44.	Cattaneo, M. D., Idrobo, N. & Titiunik, R. A Practical Introduction to
Regression Discontinuity Designs: Foundations (Cambridge
University Press, 2019).
	45.	Cattaneo, M. D., Jansson, M. & Ma, X. Simple local polynomial density
estimators. J. Am. Stat. Assoc. 0, 1–7 (2019).
	46.	Canay, I. A. & Kamat, V. Approximate permutation tests and induced order
statistics in the regression discontinuity design. Rev. Econ. Stud. 85,
1577–1608 (2018).
	47.	Banerjee, A. et al. In Praise of Moderation: Suggestions for the Scope and Use
of Pre-analysis Plans for RCTs in Economics Working Paper w26993 (National
Bureau of Economic Research, 2020).
	48.	Calonico, S., Cattaneo, M. D. & Titiunik, R. Robust nonparametric
confidence intervals for regression–discontinuity designs. Econometrica 82,
2295–2326 (2014).
	49.	Calonico, S., Cattaneo, M. D., Farrell, M. H. & Titiunik, R. Regression
discontinuity designs using covariates. Rev. Econ. Stat. 101, 442–451 (2019).
Acknowledgements
The research leading to these results received funding from the European Research
Council under the European Union’s Seventh Framework Program (FP7/2007-2013)/
ERC grant agreement no. 336155—project COBHAM, ‘The role of consumer behaviour
and heterogeneity in the integrated assessment of energy and climate policies’.
Author contributions
J.B., C.C., G.D. and M.T. conceived and designed the experiments. J.B. analysed the data.
J.B. and G.D. contributed the analysis tools. J.B., C.C., G.D. and M.T. wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-020-00719-z.
Correspondence and requests for materials should be addressed to G.d.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2020
Nature Energy | VOL 5 | November 2020 | 900–909 | www.nature.com/natureenergy
909

1
nature research  |  reporting summary
April 2020
Corresponding author(s):
Giovanna d'Adda
Last updated by author(s): May 5, 2020
Reporting Summary
Nature Research wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency
in reporting. For further information on Nature Research policies, see our Editorial Policies and the Editorial Policy Checklist.
Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement
A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly
The statistical test(s) used AND whether they are one- or two-sided
Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested
A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons
A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient)
AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals)
For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted
Give P values as exact values whenever suitable.
For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes
Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated
Our web collection on statistics for biologists contains articles on many of the points above.
Software and code
Policy information about availability of computer code
Data collection
No software was used for data collection.
Data analysis
Stata 15 was used for all statistical analysis and for the production of graphs.
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and
reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.
Data
Policy information about availability of data
All manuscripts must include a data availability statement. This statement should provide the following information, where applicable:
- Accession codes, unique identifiers, or web links for publicly available datasets
- A list of figures that have associated raw data
- A description of any restrictions on data availability
The data that support the findings of this study were used under license for the current study. Because of the privacy rules in Europe and the non-disclosure
agreement with company, the data are not publicly available. Data are however available from the authors upon reasonable request and with permission of the
company. In addition, to ensure that the results can be replicated, the code is available upon request.

2
nature research  |  reporting summary
April 2020
Field-specific reporting
Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.
Life sciences
Behavioural & social sciences
 Ecological, evolutionary & environmental sciences
For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf
Behavioural & social sciences study design
All studies must disclose on these points even when the disclosure is negative.
Study description
The study uses quantitative data generated by a field experiment (Randomized Control Trial): the dataset is a panel including monthly
electricity consumption and details on the content of email communication sent to a sample of utility customers.
Research sample
Our sample of analysis is represented by 464,763 customers eligible for the the social information program and with non-missing
electricity consumption over the entire period of analysis. We have access to this sample thanks to an existing agreement with a large
Italian energy utility. Since eligibility requirements include availability of an email contact, we imagine that the sample is not fully
representative of the entire customer base of the utility nor of the entire population of energy users in Italy. The administrative data
we use is restricted to the RCT sample, and only contains information on customers' consumption and area of residence: they thus
do not allow us to conduct formal tests of representativeness. However, the size of the sample guarantees that our results are
generalizable to important, and growing, segments of the population.
Sampling strategy
The sample used in the study is the entire population of customers targeted by the utility's social information program at the time
the program was launched. The size of the sample, eligibility criteria and randomization into treatments were all determined by the
utility and the private consultants that designed and implemented the social information program. Where the research team had any
input on the sampling, i.e., in the design of the normative prime experiment, we requested that treatment and control groups be of
the same sizes, in order to maximise statistical power.
Data collection
The data used in the study are exclusively administrative data collected directly by the partner utility, and transferred to the research
team through secure data sharing protocols.
Timing
Consumption data are available from July 2015 to August 2018. Data on the content of program communication are available for the
duration of the program: July 2016 to August 2018.
Data exclusions
No data were excluded from the analysis.
Non-participation
Non-participation in the study occurrs when users terminate their contract with the utility. Even though attrition is non-differential
by treatment, we keep attriters in the sample in order to avoid any selection bias affecting the results.
Randomization
Randomization was implemented by the utility through an algorithm (minmax t-statistic), which conducts 1000 randomizations and
selects the most balanced draw, along baseline consumption and geographic location (Bruhn and McKenzie 2009).
Reporting for specific materials, systems and methods
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material,
system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
Materials & experimental systems
n/a Involved in the study
Antibodies
Eukaryotic cell lines
Palaeontology and archaeology
Animals and other organisms
Human research participants
Clinical data
Dual use research of concern
Methods
n/a Involved in the study
ChIP-seq
Flow cytometry
MRI-based neuroimaging
Human research participants
Policy information about studies involving human research participants
Population characteristics
We only have access to information on the location of utility customers, at the regional level, and on their consumption
before the start of the program. The geographical distribution of users is as follows: 29 per cent from North-West Italy, 12.2

3
nature research  |  reporting summary
April 2020
per cent from the North East, 31.2 per cent from the Center, and the remaining from the South and Islands. Average baseline
consumption is 6.3 kWh.
Recruitment
Inclusion in the sample is based exclusively on technical eligibility criteria set by the utility: households must have a valid
name and email address as of June 2016, live in single-family homes, have at least one year of valid pre-experiment energy
consumption data, have no negative electricity meter reads, at least one meter read in the previous three months, no
significant gaps nor extreme peaks in usage history, and exactly one account per customer per location. While these criteria
may result in the study sample not being perfectly representative of the full sample of utility customers, no self-selection in
the study is present.
Ethics oversight
We obtained ethics approval for the study from Milan Polytechnic's Ethics Committee, with approval number 4/2017.
Note that full information on the approval of the study protocol must also be provided in the manuscript.

    Policy Implications:

Answer:
• The content of social information messages determines their impact on energy conservation.
• Combining descriptive information on neighbours’ efficient energy usage and injunctive social approval for energy efficiency maximizes the effectiveness of social information.
• Delivering inconsistent descriptive and injunctive information reduces the impact of each piece of feedback.
• Simply adding more pieces of feedback of the same type has a limited effect.