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

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    Nature Energy | Volume 8 | August 2023 | 850–858
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nature energy
Article
https://doi.org/10.1038/s41560-023-01298-5
Increasing the reach of low-income energy
programmes through behaviourally
informed peer referral
Kimberly S. Wolske
  1
, Annika Todd-Blick
  2 & Emma Tome
  3
Subsidized energy assistance programmes are a popular policy tool for
promoting energy justice, but, like other social benefits programmes, are
often undersubscribed. To improve uptake, some programmes have turned
to social influence strategies, such as asking programme participants
to refer their peers. Here, through a field experiment with California’s
low-income solar programme (N = 7,676), we show that referral behaviour
depends on how existing participants are approached. Adding behavioural
science strategies to a referral reward increases peer referral rates, referral
quality and ultimately solar adoption. Compared with only reminding
existing adopters of a potential US$200 reward for referrals that result in
adoption, adding an appeal to reciprocity through a non-contingent US$1
gift—and further combining this gift with a simplified referral process—
leads to 2.6–5.2 times as many solar contracts. These results highlight the
potential of behaviourally informed peer referral programmes to accelerate
equitable access to clean energy.
To promote an equitable energy transition1, many governments have
enacted policies to provide energy technologies at reduced or zero
cost to low-income households. In Western countries, these policies
often take the form of subsidies for home retrofits2–6, heating-system
and efficiency upgrades3,4,7,8, and solar panels3,7,9—with some policies,
such as the US Inflation Reduction Act, covering 100% of costs for house-
holds below certain income thresholds. However, even when goods
and services are ‘free’, attracting qualified households can be difficult.
Studies show that low-income individuals often fail to take advantage
of the social benefits programmes for which they qualify10, includ-
ing cash11 and food assistance programmes12, and subsidized health-
care13,14. Indeed, research on low-income energy programmes finds
that participation rates can be quite low1,15, owing to non-economic
barriers such as lack of information, high transaction costs1,15, the need
for home repair, and distrust of programme providers1,16,17. Eligible
households may also question the need for unfamiliar technologies
(for example, heat pumps) when their homes already provide the same
services (that is, heat)18. Further complicating matters, administrators
of social benefits programmes often have incomplete information
about which individuals qualify for services10,19, leading budgets to be
spent inefficiently on outreach.
One strategy to address these challenges is peer referral. We define
peer referral narrowly to mean when existing programme participants
provide names to the programme provider of others who might enrol;
we exclude informal word-of-mouth. Compared with other outreach
strategies, peer referral can more efficiently find programme partici-
pants20,21: referrers know eligibility requirements and can more readily
identify qualified peers. Peer referral also leverages social influence,
a widely recognized driver of energy technology adoption22,23 that
may be especially influential among low-to-moderate income (LMI)
households16,24. Since individuals often know who referred them, a
peer’s nomination may signal endorsement for the programme, reduce
distrust of the provider and ultimately increase participation. And in
contrast to other peer-based strategies (for example, ambassador
programmes25), peer referral programmes may be easier to scale: there
is no need to identify and train individuals willing to become ongoing
Received: 20 January 2023
Accepted: 7 June 2023
Published online: 6 July 2023
 Check for updates
1Harris School of Public Policy, University of Chicago, Chicago, IL, USA. 2Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
3National Renewable Energy Laboratory, Golden, CO, USA.
 e-mail: wolske@uchicago.edu

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to overcome these challenges by streamlining the process for par-
ticipation and making pertinent details more salient and easier to
comprehend. Examples include simplifying the application process
for college financial aid55, presenting pre-selected retirement savings
plans to reduce choice overload37, and reformatting notices about tax
bills56 and student absenteeism57 to focus attention on the most critical
information. Making peer referral easier may be particularly important
for subsidized energy programmes since existing participants may
be time-constrained and the benefits of referral accrue primarily to
another person.
Our findings show that adding behavioural interventions to a
referral programme multiplies the effect of providing financial rewards
alone. Incorporating an appeal to reciprocity and further simplifying
the referral process generated 2.0 and 7.5 times as many referrals. The
control condition, on average, generated one referral from every 106
existing clients, while the reciprocity treatment led to one referral
from every 52 clients, and reciprocity + simplification produced one
referral for every 14 clients. By virtue of generating so many referrals,
the reciprocity + simplification treatment led to 5.2 times as many solar
contracts at a lower cost per contract than the baseline rewards condi-
tion. The reciprocity condition generated 2.6 as many contracts as the
control, while also increasing the average quality of nominations. Taken
together, our results demonstrate that administrators of low-income
energy programmes have low-cost, easy-to-implement tools available
to boost programme outcomes by increasing engagement of existing
programme participants.
Referral programme design
We test reciprocity and simplification in a large, pre-registered ran-
domized controlled field experiment. All California clients of the
non-profit (N = 7,680) were randomized to receive one of three mailers
highlighting the US$200 referral rewards programme (final N = 7,676;
Methods). The control condition mimicked the non-profit’s standard
approach: a postcard describing the US$200 contingent reward, with a
toll-free number and website to provide referrals (Fig. 1). The reciprocity
condition was a letter with the same information and a US$1 bill framed
as a gift. The reciprocity + simplification condition added a referral
slip with space for three nominations and a stamped return envelope
(though recipients could still call or go online). The slip, thus, simpli-
fied the referral process by reducing the steps involved and making
salient what information referrers would need to provide. Eleven days
after the initial mailing, all conditions received a reminder postcard,
with treatment postcards reminding recipients of the gift they had
received. Given the total population size, we were underpowered to
include a fourth simplification-only condition (that is, referral slip and
envelope without US$1).
We evaluate each condition on three outcomes: referral behaviour
(response rate and number of names provided), the quality of refer-
rals (do individuals nominate people who meet qualification criteria)
and how many referred individuals sign solar contracts. Moreover, to
determine whether treatments merely shifted the timing of intended
referrals sooner, without yielding better outcomes in the aggregate58,
we compare results at two timepoints post-intervention: 17 weeks
(when the referral programme was next marketed) and 9 months.
Although the non-profit sent additional reminders about the refer-
ral programme to subsets of clientele during the 9 month follow-up,
our randomization procedure ensured that the impact of additional
campaigns would be evenly distributed across treatments (Methods).
Referral behaviour
Figure 2a shows the response rates by condition. During the first
17 weeks, 74% of clients in the reciprocity + simplification condition
referred by slip rather than by phone or webform, affirming the slip’s
convenience. The odds of referring were five times as high in the reci-
procity + simplification condition compared with the control (response
programme advocates. Asking existing clients for nominations instead
of word-of-mouth referrals also removes the onus of convincing peers
to contact the programme provider.
Despite its potential benefits, methods to increase peer referral
for social benefit programmes have received little attention. The few
studies so far test the efficacy of referral incentives for subsidized
health programmes, with mixed results and sometimes limited sam-
ple sizes19,26. Most studies aimed at improving programme uptake
instead focus on directly recruiting new participants—overlooking
the potential benefits of tapping existing programme beneficiaries
to reach their peers.
In this Article, we test strategies to improve take up of low-income
solar programmes by increasing referrals from LMI solar adopters. We
do so in partnership with GRID Alternatives, the non-profit organiza-
tion that administers the California Solar Initiative’s Single-family
Affordable Solar Homes (SASH) Program. SASH provides rooftop
photovoltaic (PV) systems at no cost to qualified LMI homeowners
(Methods). Though there are debates about how government funding
should be spent on solar deployment27,28, programmes like SASH exist
to address inequities in access to this technology. Most US PV instal-
lations have been concentrated among moderate-to-high-income
households29–33, despite analyses showing that LMI housing stock offers
substantial suitable roof space30,34.
Recognizing that referred households were more likely to go solar
than cold contacts35, GRID Alternatives created a referral rewards
programme: existing LMI solar adopters are offered a conditional
US$200 incentive for each referred household that installs solar. Such
a programme is in line with the for-profit solar industry, though the
latter typically offers higher rewards (US$500–2,000).
Rather than offer more expensive rewards—which are often mon-
etarily infeasible for programme administrators—we test whether
incorporating behavioural insights into the design of referral rewards
programmes can increase peer referral. Behavioural science interven-
tions—or ‘nudges’—use insights from psychology and behavioural
economics to increase engagement in desirable behaviours. Interven-
tions often involve simplifying application processes and communica-
tions11,36,37, altering the motivation to act (without incentives)38,39, or
employing strategies that make following through on intentions more
likely40,41. Such interventions often produce small to medium changes in
behaviour42,43 at low incremental costs, making them potentially more
cost-effective than traditional financial inducements44. Two types of
behavioural intervention may be especially effective at increasing peer
referral: appealing to reciprocity and simplification.
Reciprocity is the idea that people feel obligated to act in kind to
others’ generosity45–49. If someone does us a favour or gives us a gift,
we feel the need to return the kindness. Because this norm, which is
deeply embedded in social life, feels uncomfortable to violate, it can
be leveraged in interventions to increase compliance with a request49.
In our case, by including a small unconditional gift with the request
for referrals, we sought to invoke a sense of indebtedness that par-
ticipants could repay by referring. While this strategy is commonly
used to increase charitable giving50–52 or pro-social behaviour47, peer
referral for energy assistance programmes presents a unique context:
the intervention target has already received a large gift (in our study,
a PV system with an estimated installed value of US$16,400–27,500).
Reciprocity appeals may consequently remind recipients of what they
have already received, thereby increasing the psychological cost of
non-compliance—and, thus, referrals.
The second strategy we test is simplification, which involves mak-
ing the target behaviour easier11,53. Aspects of a programme that are
complex or make participation cumbersome can create friction that
impedes action. Such factors have deterred participation in a variety
of public benefits programmes11,15 and can be especially detrimental
to low-income individuals11 who may have scarce mental bandwidth54
to decipher programme details. Simplification interventions seek

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rates of 4.22% versus 0.86%, odds ratio (OR) 95% confidence interval
(CI) 3.16–7.88, P < 0.0001) and three times as high compared with reci-
procity alone (4.22% versus 1.45%, OR 95% CI 2.04–4.33, P < 0.0001;
Supplementary Table 2). Clients in the reciprocity condition were
1.7 times as likely to refer than those in the control, though the com-
parison was less precise (OR 95% CI 0.99–2.84, P = 0.054). Nine months
after the campaign, the differences in response rates across conditions
narrowed but were significantly different at P < 0.05 (Fig. 2a and Sup-
plementary Table 3), indicating that the treatments had lasting effects
and did not simply shift intended referrals sooner.
The reciprocity + simplification condition yielded 181 referred
names in the first 17 weeks, 7.5 times more than the control (24
names, incidence rate ratio (IRR) 95% CI 4.66–12.19, P < 0.0001) and
3.7 times more than reciprocity alone (49 names, IRR 95% CI 2.41–5.65,
P < 0.0001), while reciprocity yielded 2 times more than the control
(IRR 95% CI 1.16–3.59, P = 0.013; Supplementary Table 8). Among indi-
viduals who responded to the campaign, clients in the reciprocity +
simplification condition nominated more individuals (1.68 names per
referring client) than either the reciprocity or control conditions (1.32
names in reciprocity, IRR 1.27, 95% CI 1.02–1.56, P = 0.029; 1.09 names in
control, IRR 1.54, 95% CI 1.31–1.80, P < 0.0001; Supplementary Table 8).
One explanation for the number of names generated in the reci-
procity + simplification condition is that the slip provided space for
three referrals, perhaps unintentionally signalling that nominating
three people was the default expectation59,60. By contrast, the webform
allowed one referral at a time (though it could easily be completed
again). Similarly, individuals who phoned in referrals may have done
so as soon as one name came to mind.
At 9 months, the total number of nominations remained signifi-
cantly different across conditions (Fig. 2b and Supplementary Table
9), providing additional evidence that the treatments did not merely
shift nominations sooner. Between 17 weeks and 9 months, reciproc-
ity + simplification continued to outperform the control, yielding 2.2
as many additional names (55 versus 25 names, IRR 95% CI 1.30–3.71,
P = 0.003; Supplementary Table 10), only 11 of which came by slip. The
reciprocity-only condition yielded 37 additional names but could not be
statistically distinguished from either group (versus control, P = 0.157;
versus reciprocity + simplification, P = 0.113).
Quality of nominations
One potential drawback of the slip’s format is that it could lead to
lower-quality referrals if it encouraged clients to list multiple names
Refer by:
Share
GRID with
your
friends and
family and
earn $200
Share
GRID with
your
friends and
family and
earn $200
Share
GRID with
your
friends and
family and
earn $200
Reminder of conditional
US$200 reward
Reminder of conditional
US$200 reward
+
US$1 non-contingent gift
“We have enclosed a small $1 gift to thank
you for being part of the GRID community.”
Reminder of conditional
US$200 reward
+
US$1 non-contingent gift
“We have enclosed a small $1 gift to thank
you for being part of the GRID community.”
+
Referral slip and stamped return envelope
Control
N = 2,558
Reciprocity
N = 2,558
Reciprocity + simplification
N = 2,560
Referrals:
Your Name:
Refer by:
Refer by:
Phone
Web
Phone
Web
Phone
Web
Mail
Fig. 1 | Illustration of experimental conditions. Clients of the non-profit
were randomized to receive one of three mailers about the referral reward
programme. All mailers reminded clients that they could receive a US$200
reward for each referral that resulted in a solar installation. The control group
received the standard postcard reminder, which included a toll-free phone
number and website for providing referrals. The reciprocity condition provided
the same information in a letter along with a US$1 that was described as a gift.
The reciprocity + simplification condition was the same as the reciprocity
treatment but added a referral slip and stamped return envelope. Eleven days
after the initial mailing, all clients received a postcard reminder about the referral
programme. For reciprocity and reciprocity + simplification, the postcard
reminded clients of the gift they had received: ‘We hope you liked the small gift
we sent in the mail’.

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without considering who would qualify. To evaluate this, we compared
the proportion of referrals per client in each condition who lived in geo-
graphic areas eligible for solar subsidies (Methods and Supplementary
Tables 8 and 9). As shown in Fig. 2b, reciprocity + simplification had a
statistically equivalent proportion of referrals living in eligible areas
as the control (69% versus 71% at 17 weeks, P = 0.793; 73% versus 67%
at 9 months, P = 0.691). In contrast, the reciprocity condition yielded
a significantly higher proportion of eligible nominations at both time-
points, representing a 23–27% improvement over the control at 17 weeks
and 9 months, respectively, and a 28% to 22% improvement over reci-
procity + simplification. We note, however, that because reciproc-
ity + simplification generated far more nominations in total, it led to
7.3 times more qualified nominations than the control at 17 weeks
(124 versus 17; IRR 95% CI 4.17–12.74, P < 0.0001) and three times as many
than reciprocity (124 versus 42; IRR 95% CI 1.89–4.61, P < 0.0001). Simi-
larly, reciprocity alone yielded 2.5 times as many qualified nominations
as the control (95% CI 1.32–4.61, P = 0.005). The differences between
conditions largely persisted at 9 months (Supplementary Table 9).
New solar contracts
We also examined the number of signed solar contracts resulting from
each condition. For referrals generated within 17 weeks (Fig. 2d), reci-
procity + simplification led to 26 contracts: 5.2 times the control (IRR
95% CI 1.95–13.8, P < 0.001) and 2.0 times reciprocity alone (IRR 95%
CI 0.94–4.27, P = 0.074; Supplementary Table 8). The reciprocity con-
dition resulted in 2.6 times more contracts than the control, though
the comparison was less precise (IRR 95% CI 0.89–7.60, P = 0.081). At
9 months, the differences between conditions were all different at
P < 0.05 (Supplementary Table 9).
Using back-of-the-envelope calculations (Supplementary
Table 6), we estimate that the reciprocity + simplification condi-
tion, though more expensive in terms of material costs and staff
time needed to screen referrals, was less expensive per resulting
contract than the control, saving 17.8% at 17 weeks (US$522 ver-
sus $635). Reciprocity alone cost 2.5% more per contract than the
control (US$651 versus US$635), but generated more than twice as
many contracts.
0.86%
1.45%
4.22%
P < 0.0001
P < 0.0001
P = 0.054
1.72%
2.54%
5.74%
P < 0.0001
P = 0.044
P < 0.0001
Reciprocity
Reciprocity +
simplification
17 weeks
Reciprocity
Reciprocity +
simplification
9 months
Control
Control
1
2
3
4
5
6
7
Response rate (%)
(71%)
(86%)t
(69%)t
24
49
181
P = 0.013
P < 0.0001
P < 0.0001
(67%)
(90%)c,t
(73%)t
49
86
236
P < 0.0001
P = 0.008
P < 0.0001
Reciprocity
Reciprocity +
simplification
17 weeks
Reciprocity
Reciprocity +
simplification
9 months
Control
Control
0
100
200
300
Number of nominations
(% in qualifying area)
0
50
100
150
200
250
1 October 2018
1 January 2019
1 April 2019
1 July 2019
Referral date
Control
Reciprocity
Reciprocity +
simplification
Subset of
R+S that
used slip
Reciprocity
Reciprocity +
simplification
17 weeks
Reciprocity
Reciprocity +
simplification
9 months
Control
Control
P = 0.081
P = 0.074
P < 0.001
P = 0.024
P = 0.034
P < 0.0001
5
13
26
12
26
49
Number of solar contracts
Cumulative number of nominations
a
b
c
d
60
40
20
0
Control
Reciprocity
Reciprocity + simplification
Fig. 2 | Effects of mailer campaigns on response rate, number and quality of
referrals made, and the number of resulting solar contracts. Comparison of
experimental conditions (control N = 2,558; reciprocity N = 2,558; reciprocity
+ simplification N = 2,560) at 17 weeks and 9 months after mailers were sent.
a, The response rate per condition, which is the percentage of people who
responded to the mailer by providing at least one nomination. b, The overall
number of nominations per condition and, within that, the percentage of
those nominations that lived in qualified areas for subsidized solar (in darker
shading). c, The cumulative number of nominations in each treatment over
time, and, for reciprocity + simplification, which referrals came in by slip.
d, The number of signed solar contracts generated from referrals during each
period. Statistical specifications and test statistics were: logistic regression
with Firth’s penalization to correct for rare events (‘Firth’s logistic’) and pairwise
comparisons with z test statistics (a); Poisson count models to test (i) differences
between the number of nominations, and (ii) differences between the proportion
of those nominations that lived in a qualified area, with z tests for pairwise
comparisons (b); Poisson count models to test the difference in resulting solar
contracts, and pairwise comparisons using z statistics (d). For Poisson models,
standard errors were estimated using the robust Huber/White/sandwich method.
All tests were two-sided, and no adjustment for multiple comparisons was made.
For b, brackets show whether the count of nominations differed by treatment; for
the percentage of nominations living in qualified areas, superscript c indicates
reciprocity is significantly different from the control at P < 0.05 and superscript
t indicates reciprocity is significantly different from reciprocity + simplification
at P < 0.05 during the specified time periods. Supplementary Tables 2 and
3 report test statistics, CIs and effect sizes for comparisons shown in a, and
Supplementary Tables 8 and 9 report the same for b and d.

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Percentage of first-time referrers
In a non-pre-registered analysis, we examined heterogeneity with
respect to whether clients had previously referred (N = 796) or not
(N = 6,875). This type of heterogeneity may be of interest to programme
managers looking to engage more clients in peer referral or to trim costs
by selectively targeting treatments. In all conditions, previous referrers
were more likely to refer than clients who had never referred (Fig. 3 and
Supplementary Table 7). Among previous referrers, only reciprocity
+ simplification led to a significantly greater percentage of responses
than the control condition (OR 2.76, 95% CI 1.14–6.71, P = 0.025). Among
individuals who had never referred before, the odds of referring were
nearly sixfold higher in the reciprocity + simplification treatment
compared with the control (OR 5.92, 95% CI 3.41–10.28, P < 0.0001),
and 1.9 times higher in reciprocity than the control, though the latter
comparison was less precise (OR 1.87, 95% CI 0.99–3.51, P = 0.052).
Discussion and conclusions
We demonstrate a cost-effective strategy to increase take up of energy
assistance programmes through behaviourally informed peer referral
programmes. Using low-income rooftop solar as our study context, we
show that adding low-touch interventions to a referral rewards pro-
gramme increases its effectiveness several fold within a short time span,
ultimately leading to more LMI solar installations. Relative to simply
reminding clients of referral rewards, adding an appeal to reciprocity
and combining that appeal with simplification generated, respectively,
1.7 and 5 times the response rate (P = 0.054 and P < 0.0001); 2.0 and
7.5 times as many referrals (P = 0.013 and P < 0.0001); and 2.6 and 5.2
times as many solar contracts (P = 0.081 and P < 0.001) during the first
17 weeks following the campaign. Furthermore, reciprocity + simpli-
fication generated nearly six times as many referrals from individuals
who had never referred before compared to the non-profit’s standard
practice. The reciprocity + simplification treatment achieved the above
results at less cost per solar contract than the control, while reciprocity
alone cost only marginally more.
The effects of reciprocity + simplification over time shed light on
what impedes peer referral. The large, immediate effect of the referral
slip suggests that having to phone in referrals or visit a website cre-
ates friction. The effect of reciprocity + simplification was also long
lasting, with referrals continuing to come in at a higher rate than the
control, even months after receiving the dollar and slip. As fewer slips
were returned over time, this finding may indicate that the slips, by
listing what information was needed to nominate someone, reduced
uncertainty about what referring entails. This could explain why the
reciprocity + simplification treatment was most effective at getting
first-time referrers.
Our results further reveal potential trade-offs in terms of the quan-
tity and quality of referrals generated. Compared with only sending
reminders of the referral rewards programme, adding an appeal to
reciprocity increased the number of referrals but also the propor-
tion who were likely to qualify for solar subsidies. The salience of the
dollar along with language thanking recipients for being part of the
non-profit’s solar community may have encouraged recipients to think
more deeply about who among their peers might live in qualified areas.
Further adding a referral slip and stamped return envelope counter-
acted this effect, as the proportion of quality referrals was lower but
equivalent to the control. The ease of referring by slip, however, more
than compensated for this dip in quality by generating considerably
more nominations and ultimately more installations.
Additional research is needed to understand the difference in
referral quality between treatments. By providing three spaces to list
nominations, the slips may have focused clients on quantity over qual-
ity. The return stamped envelope may have also encouraged clients to
submit referrals quickly, without fully considering who among their
peers lived in qualifying areas. Future work could examine whether
2.59%
3.49%
6.72%
0.66%
1.22%
3.76%
P = 0.052 P < 0.0001
P < 0.0001
P < 0.0001
P < 0.0001
P = 0.020
P = 0.105
P = 0.025
5.56%
5.81%
11.19%
1.27%
2.17%
4.94%
P = 0.032
P = 0.018
17 weeks
9 months
15
10
5
0
5
10
Response rate among those
who had referred before
(dark shading)
Response rate among those
who had never referred before
(light shading)
Control
Reciprocity
Reciprocity +
simplification
P = 0.837
P = 0.483
Fig. 3 | Response rates, by condition, among clients who had never
referred before and among those who had referred at least once before the
experiment. This figure shows the efficacy of each experimental condition on
response rate among existing solar adopters who had never referred before
(‘never referred’, in lighter shading; N = 6,875) and among those who had referred
at least once before the experiment (‘referred before’, in darker shading; N = 796).
Five referrers in the reciprocity + simplification treatment were excluded from
this analysis as they returned their referrals slips anonymously and could not be
categorized. The response rate is calculated within each group (that is, among
individuals who had never referred before, what percentage responded to the
mailer campaign). Logistic regressions with Firth’s penalization to correct for
bias in estimating rare events were used to test the differences between response
rates with z test statistics (two-tailed tests). No adjustments were made for
multiple comparisons. For test statistics and ORs, see Supplementary Table 7.

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making eligibility criteria salient could offset these effects. This may
be particularly beneficial when programmes, like this one, have com-
plex geographical restrictions that are probably unfamiliar to most
people (for example, disadvantaged community designations and
utility service areas).
The quality versus quantity trade-offs we observed have practical
implications for programme managers depending on their goals and
resource constraints. If the goal is to attract as many referrals as pos-
sible or to attract referrals quickly (for example, because funding will
expire), reciprocity + simplification offers the most cost-effective way
to do so. The drawback is that it requires a higher initial outlay of funds.
If the goal is to provide the highest impact within a constrained budget,
reciprocity alone leads to the greatest proportion of high-quality refer-
rals at lower upfront costs. Another consideration is target audience.
If programme managers seek to attract new referrers, reciprocity +
simplification could be used to selectively target these clients. Since
the slip seems to improve knowledge of what referring entails, this may
be a wise investment to increase the efficacy of less expensive referral
campaigns in the future.
Anecdotal evidence suggests that the US$1 gift was effective at
generating referrals, in part because it reminded recipients of the
much larger gift they had already received: a solar PV system. A hand-
ful of clients returned the dollar with their completed referral slips, a
gesture that prior work suggests is guilt motivated61. If having received
a prior gift augments the effect of a reciprocity appeal, then research is
needed to understand to what extent the value of that prior gift matters.
Although other efficiency improvements may have a smaller economic
value than a PV system, the quality-of-life improvements they bring
may have similar or greater psychological value. For these reasons,
we expect our results to generalize to energy subsidy programmes
seen globally that provide low-income homeowners subsidies for
high-value goods like home retrofits2–6, heating equipment3–5,7–9 and
efficiency upgrades3,5,7,9. Like the solar programme we studied, such
programmes often involve high interaction between the programme
provider and participating households, which might strengthen the
feeling of obligation to comply with the reciprocity appeal.
Our findings invite future research about whether the interven-
tions can be used repeatedly. Both strategies may work best as one-off
or infrequent interventions, particularly to attract first-time referrers.
Providing referrals slips too often may diminish the salience of the
request or degrade the quality of nominations. Likewise, repeated
use of the US$1 gift may provoke resentment if clients start to perceive
the gesture as manipulative62. Both hypotheses could be tested in future
referral programmes. Research is also needed to understand the effect
of the slip, absent the gift, and how much of the observed effects are
due to the referral reward, which was common to all conditions.
We see these questions as opportunities to continue exploring
more efficacious ways of expanding the reach of low-income energy
programmes. As more policies like the US Inflation Reduction Act, UK
Help to Heat scheme and others come online to address inequities
in access to clean energy technologies, administrators will face the
challenge of efficiently implementing these programmes to maximize
their benefits. Growing evidence points to the value of engaging social
networks16,63 to persuade hard-to-reach populations of a programme’s
benefits. Our findings demonstrate that how existing programme
participants are engaged in these efforts matters. Relying on financial
rewards alone leaves many peer referrals—and subsequently LMI solar
installations—unrealized. Complementing rewards with programme
simplification and an appeal to reciprocity seems to motivate existing
LMI adopters to pay it forward—resulting in five times as many solar
installations. The results further show that reciprocity and simplifica-
tion have trade-offs in terms of the timing, quality and relative cost of
the referrals generated—suggesting that managers of energy assistance
programmes have flexibility for improving peer referral depending on
their objectives and constraints.
Methods
Design and sample
The experiment was implemented in September 2018. Participants
included the entire population of California LMI homeowners who
had adopted solar with the non-profit GRID Alternatives, had inter-
connected their solar system between 2004 and 1 June 2018, and for
whom GRID had a valid mailing address, N = 7,680. The vast majority
of these customers qualified for California’s Single-family Affordable
Solar Homes (SASH) programme, of which GRID Alternatives is the
programme administrator. SASH provides incentives at US$3.00 W−1
to homeowners with household incomes below 80% of the area median
income (AMI), who live in a home defined as affordable housing by
California Public Utilities Code 2852, and who receive electrical service
from Pacific Gas & Electric, Southern California Edison or San Diego Gas
& Electric. Applicant roofs are also screened for solar suitability, and
solar installations must meet a minimum performance requirement.
For our study population, 32% of the population had a household
income below 30% AMI, 27.4% were between 30% and 50% AMI, 38.5%
were between 50% and 80% AMI, and 2.1% were missing income data.
Where SASH incentives are insufficient to cover the entire cost of a
PV system and installation, GRID sources supplementary funding to
provide the PV system at no cost.
Intervention
We randomly assigned the 7,680 clients to three groups: control
(N = 2,559), reciprocity (N = 2,560) and reciprocity + simplification
(N = 2,561). The control condition mimicked GRID’s standard proce-
dure of sending clients occasional postcards to remind them that they
could earn US$200 for successfully referring someone else to GRID. As
is standard, the postcard included a toll-free number as well as a web
address where clients could fill out referral information. The reciprocity
condition received the same information but in letter form along with
a US$1 bill. The dollar was framed as a gift: ‘We have enclosed a small
$1 gift to thank you for being part of the GRID community. Thanks in
advance for your referrals!’ The reciprocity + simplification group
received the US$1 gift along with a referral slip and first class-stamped
return envelope. Eleven days later, each group received a reminder
postcard; for the treatment conditions, the postcard reminded recipi-
ents of the gift they had received: ‘We hope you liked the small gift we
sent in the mail. Thank you for your referrals!’ Materials were provided
in English or Spanish depending on the known language preference of
the household. The study was approved for exemption by the Institu-
tional Review Board at Lawrence Berkeley National Laboratory (Human
Subjects Committee number 381H25JE23). As the experimental treat-
ments were in line with standard operating practices of the non-profit
partner, informed consent was not required. Our methods and analysis
plan were pre-registered with the American Economic Association’s
RCT Registry (https://www.socialscienceregistry.org/trials/4114).
Randomization procedure
We used a stratified complete random assignment method that ensures
a balanced panel: that is, each condition had an equal number of house-
holds, and there were no statistically significant differences between
the groups on pre-treatment variables that might influence referral
behaviour and quality. We used the R package randomizr along with
a ‘max–min’ technique. Rather than stratifying the randomization
on the basis of all pre-treatment variables, this technique selects a
random seed to perform an unstratified randomization on the study
population, calculates the P value for each pairwise comparison of the
three conditions for each pre-treatment stratification variable, and
then determines the minimum of these P values (that is, the one with
the lowest P value, showing the biggest statistical difference). This is
then repeated 100,000 times (where each of the 100,000 randomiza-
tions results in one minimum P value). Then, the seed that generated
the randomization with the highest minimum P value (that is, the

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maximum of the minimum P values) is chosen as the final seed for
creating random groups64.
Pre-treatment stratification variables included the client’s geo-
graphic region; race/ethnicity; age (whether clients were above 62 years
old); primary spoken language (English and/or Spanish, or some other
language); whether they had given previous referrals; the date their
PV system was interconnected; local population density; qualifying
income; and the CalEnviroScreen percentile for the tract where they
lived. This latter score is a cumulative impacts indicator of the extent
to which a community is socio-economically and environmentally
disadvantaged65. Tracts with scores in the top 25% are termed ‘disadvan-
taged’ and qualify for a new solar incentive through the Disadvantaged
Communities Single-family Affordable Solar Homes (DAC-SASH) pro-
gramme (a more geographically restricted version of the original SASH
programme). A higher CalEnviroScreen score served as a proxy for how
likely nearby friends and neighbours were to qualify for DAC-SASH.
We also stratified on whether clients had an email address to account
for their comfort using online technology in the referral process. As
shown in Supplementary Table 1, there were no statistically signifi-
cant differences between the groups on any pairwise comparison of
pre-treatment stratification variables.
This randomization method enabled us to continue comparing
treatment conditions after 17 weeks, when the non-profit resumed
standard marketing of its referral programme with subsets of its cli-
entele. Geographic areas were targeted on the basis of whether they
qualified for the DAC-SASH programme. Because our experiment
randomized at the level of households, and stratified at a geographic
region level, the regions had a roughly equal mix of households exposed
to either the control, reciprocity, or reciprocity + simplification treat-
ments. As a result, any additional marketing material sent by the
non-profit would have been equally distributed to the three condi-
tions. Moreover, treated households were geographically dispersed,
minimizing risk of between-treatment contamination (for further
discussion, see Supplementary Note 3).
Data and exclusion criteria
GRID Alternatives tracks every point of contact with existing and poten-
tial clients (including referrals) in a Salesforce database. We analysed
a de-identified version of data extracted from this database. Typically,
once referred to the non-profit, individuals are contacted to determine
whether their household meets the qualification criteria for subsi-
dized solar (for example, they own their home, meet household income
requirements, have a good roof and live in a qualifying area). Unfortu-
nately for our study, some individuals were never screened; the dra-
matic increase in referred names from the reciprocity + simplification
condition outpaced the non-profit’s ability to process them as well as
available funding for subsidized PV. Of the nominations that were not
screened (94 during the first 17 weeks; 122 by 9 months), most came
from the reciprocity + simplification condition (86.2% of non-screened
at 17 weeks, 77.9% at 9 months; Supplementary Table 5). As this issue was
only discovered at the start of the coronavirus pandemic, the non-profit
was unable to recontact these individuals. This forced us to deviate from
our analysis plan and proxy for quality by examining whether nominated
individuals lived in areas that qualify for low-income solar subsidies
through SASH and DAC-SASH. See Supplementary Note 1.
After consultation with GRID Alternatives, we excluded
super-referrers from analysis. These were individuals known to the
non-profit to be well connected in the community with a history of
making many referrals at once. We defined super-referrers as those who
had previously given 20 or more referrals (regardless of whether they
referred in the present study). This led to excluding three clients, one
in each condition. Supplementary Note 2 provides further detail and
shows that the results are robust even if the definition of super-referrer
is lowered to 8 or more previous referrals (the next highest number of
past referrals given by any person in our experiment).
There were also two returned slips (with six nominations total)
from the reciprocity + simplification treatment that, through clerical
error, were not entered into Salesforce when they were received. As
we are unable to pinpoint when these slips came in, we have excluded
them from our analyses to avoid distorting conclusions we might draw
about the temporal effects of the treatment. With these slips included,
the total response rate for the reciprocity + simplification treatment,
during the 9 months for which we have data, was 5.82%, resulting in
242 nominated individuals; the total number of signed solar contracts
remained unchanged.
Finally, we excluded one outlier from the reciprocity condi-
tion. This individual provided one referral during the first 17 weeks,
which resulted in a solar contract, and an additional 19 nominations
4 months later over the course of 3 days. Of these 19 nominations,
18 resulted in solar contracts. We considered it improbable that such
a large within-subject effect would be due to the reciprocity treat-
ment, 7 months after exposure. For comparison, the median number
of nominations over the 9 month period (conditional on referring)
was one.
Analysis
Data were analysed with Stata 17 according to our pre-registration plan
using basic ordinary least squares (OLS) specifications (for analyses of
the number of referrals) and logit specifications (for the response rate
analysis of whether a client made any referrals), with two-tailed tests.
Full regression results are in Supplementary Tables 2 and 3. The only
predictors in the models are treatment conditions, except for the het-
erogeneity analysis which also accounts for whether a client had given
referrals before the experiment (Supplementary Table 7). The dataset
contained one observation per treated client; each client had variables
indicating the number of nominations, and the regressions therefore
represent standard errors clustered by client. For OLS specifications,
standard errors were estimated using the robust Huber/White/sand-
wich method. For the logit specification, we used Firth’s penalization
to correct for bias in estimating rare events.
As a robustness check, we re-specified our analyses using Poisson
count models with robust standard errors (Supplementary Tables
8–10). There were no substantive changes in results, with P values
rarely changing before the third decimal. As we find IRR a more intui-
tive and practically meaningful measure of effect size, we report the
Poisson results in the paper instead of the OLS results, but the reader
can find both in Supplementary Information. For more details, see
Supplementary Note 1.
Reporting summary
Further information on research design is available in the Nature Port-
folio Reporting Summary linked to this article.
Data availability
Exported data from the Salesforce database are not publicly available
due to ethical and privacy concerns. An anonymized, public version
of the dataset with referral behaviour and resulting solar contracts,
without any personally identifiable information, is available on the
Open Science Framework (https://osf.io/x4sqp/). The time-stamped
data needed to generate Fig. 2c are proprietary to the non-profit part-
ner. Zip-code-level data used in Supplementary Note 3 are not publicly
available to protect the privacy of participants.
Code availability
Replication code to generate results from the anonymized, publicly
available data is at https://osf.io/x4sqp/.
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Acknowledgements
We thank GRID Alternatives (particularly J. Coleman, Z. Franklin,
A. Kim and L. Nobel) and B. Sigrin at the National Renewable Energy
Laboratory for their collaboration. We also thank K. Aves and
R. Walatka for their assistance in creating figures. This work was
supported by the National Renewable Energy Laboratory, operated
by Alliance for Sustainable Energy, LLC, for the US Department of
Energy (DOE) under contract no. DE-AC36-08GO28308 (K.S.W. and
E.T.). Funding was provided by the US Department of Energy Office
of Energy Efficiency and Renewable Energy Solar Energy Technology
Office. This manuscript was also authored by an employee of
Lawrence Berkeley National Laboratory under contract no. DE-AC36-
08GO28308 with the US Department of Energy (A.T.-B.). The views
expressed in the article do not necessarily represent the views of
the DOE or the US Government. The US Government retains and the
publisher, by accepting the article for publication, acknowledges
that the US Government retains a non-exclusive, paid-up, irrevocable,
worldwide licence to publish or reproduce the published form of this
work, or allow others to do so, for US Government purposes.
Author contributions
K.S.W. and A.T.-B. conceived and implemented the study, with E.T.
handling randomization of postal mail recipients. A.T.-B. and E.T.
cleaned the data, which A.T.-B. analysed. K.S.W. drafted the manuscript
with revisions from A.T.-B. E.T. contributed to the methods section.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41560-023-01298-5.
Correspondence and requests for materials should be addressed to
Kimberly S. Wolske.
Peer review information Nature Energy thanks Rohan Best,
Mahelet G. Fikru and Stefan Lamp for their contribution to the peer
review of this work.
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.
Springer Nature or its licensor (e.g. a society or other partner) holds
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© The Author(s), under exclusive licence to Springer Nature Limited
2023

    Policy Brief:

Answer:
**Behaviourally‑informed peer referral programmes can increase the reach of low‑income energy policies**

**Policy Problem**Low‑income households spend a disproportionate amount of their income on energy bills. To address this burden and promote an equitable clean energy transition, many countries have enacted policies that provide efficient and renewable energy technologies at low or no cost to lower‑income households. The US Inflation Reduction Act is one recent example. But offering such technologies free of charge may not be enough to attract eligible households. Like other social benefits programmes, many subsidized energy programmes go undersubscribed, owing to distrust of programme providers, high hassle costs for programme enrolment, and lack of information. Low participation rates translate to inefficient use of programme funds, as programme administrators must allocate additional resources for outreach, especially to capture vulnerable, hard‑to‑reach populations. Emerging research suggests there is promise in tapping existing programme beneficiaries to find new participants, but evidence on how to best do this is scant.

**Scientific Research Findings**We identify cost‑effective, scalable strategies to improve the efficacy of a peer referral programme for fully‑subsidized low‑income solar in California. The baseline programme (‘control’) offered existing programme participants a US$200 reward for every referral that resulted in a solar installation. Adding a token gift upfront with a reminder about the programme (to evoke a sense of ‘reciprocity’) led to 1.7 times the response rate, 2 times the number of referrals and 2.6 times as many solar contracts. A third ‘reciprocity and simplification’ group in which the gift was combined with a mailable referral slip (instead of just phone or web referrals) led to nearly 5 times the response rate, 7.5 times as many referrals, and 5.2 times as many solar contracts — making it more cost‑effective than the baseline programme. It was also more effective at eliciting referrals from participants who had not previously referred. The results highlight strategies that could be adapted to other energy assistance programmes for electrification measures, heating and insulation upgrades, and electric vehicles.

**Scientific Research Study Methods**The data are from a field experiment with all 7,680 homeowners who received fully subsidized solar in California from 2004 to June 2018. Households were sent one of three mailers reminding them that they could receive US$200 for each nomination which resulted in a solar installation. The ‘control’ group received the standard postcard, with a website and toll‑free number for making referrals. The ‘reciprocity’ group provided the same information in a letter along with a US$1 gift to thank clients for being part of the solar community. The ‘reciprocity + simplification’ group further provided a referral slip and stamped return envelope. We tested for differences in response rates to the mailers, the number of nominations provided, whether nominations lived in subsidy‑eligible areas, and the number of resulting solar contracts. As the referral reward was in all conditions, further research is needed to know how important the reward itself was.

**Policy Implications**• Relying on financial rewards alone to encourage referrals for a low‑income solar programme leaves many peer referrals — and subsequently solar installations — unrealized.
• Complementing referral rewards with programme simplification and an appeal to reciprocity can increase the number of referrals and resulting solar contracts by several‑fold, at less cost per contract.
• Combining referral rewards, reciprocity, and programme simplification is more effective at attracting first‑time referrers than either rewards alone or rewards combined with reciprocity.
• Reciprocity and simplification have trade‑offs in terms of the timing, quality, and relative cost of the referrals generated, giving administrators flexibility depending on their programme objectives.